From 9d773c7a83a82816b247a0f6eae414bf851eac28 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Wed, 20 Sep 2023 15:16:14 +1000 Subject: [PATCH 01/69] First commit for VPRTempo-quant --- VPRTempo.py => VPRTempo-quant.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename VPRTempo.py => VPRTempo-quant.py (99%) diff --git a/VPRTempo.py b/VPRTempo-quant.py similarity index 99% rename from VPRTempo.py rename to VPRTempo-quant.py index f3896ad..bedccab 100644 --- a/VPRTempo.py +++ b/VPRTempo-quant.py @@ -57,9 +57,9 @@ def __init__(self): USER SETTINGS ''' self.dataset = 'nordland' # set which dataset to run network on - self.trainingPath = '' # training datapath - self.testPath = '' # testing datapath - self.number_modules = 1 # number of module networks + self.trainingPath = '/home/adam/data/nordland/' # training datapath + self.testPath = '/home/adam/data/nordland/' # testing datapath + self.number_modules = 5 # number of module networks self.number_training_images = 500 # Alter number of training images self.number_testing_images = 500 # Alter number of testing images self.locations = ["spring","fall"] # Define the datasets used in the training From 0541dcc8a9ac1967498b5fecac04116d9dd74830 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 25 Sep 2023 16:49:33 +1000 Subject: [PATCH 02/69] Initial conversions of torch network into an nn.Module --- VPRTempo-quant.py | 743 ++++++++++++---------------------------------- src/blitnet.py | 48 ++- src/utils.py | 402 +++++++++++++++++++++++++ 3 files changed, 635 insertions(+), 558 deletions(-) diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index bedccab..cd20f4a 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -41,580 +41,209 @@ import utils as ut import validation as val import numpy as np +import torch.nn as nn +import torch.nn.functional as F from os import path from datetime import datetime -''' -Spiking network model class -''' -class snn_model(): - def __init__(self): - super().__init__() - - ''' - USER SETTINGS - ''' - self.dataset = 'nordland' # set which dataset to run network on - self.trainingPath = '/home/adam/data/nordland/' # training datapath - self.testPath = '/home/adam/data/nordland/' # testing datapath - self.number_modules = 5 # number of module networks - self.number_training_images = 500 # Alter number of training images - self.number_testing_images = 500 # Alter number of testing images - self.locations = ["spring","fall"] # Define the datasets used in the training - self.test_location = "summer" # Define the dataset is used for testing - self.filter = 8 # Set to number of images to filter - self.validation = True # Set to True to calculate PR metrics - - assert (len(self.dataset) != 0),"Dataset not defined, see README.md for details on setting up images" - assert (os.path.isdir(self.trainingPath)),"Training path not set or path does not exist, specify for self.trainingPath" - assert (os.path.isdir(self.testPath)),"Test path not set or path does not exist, specify for self.testPath" - assert (os.path.isdir(self.trainingPath+self.locations[0])),"Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(self.testPath+self.test_location)),"Images must be organized into folders based on locations, see README.md for details" - - ''' - NETWORK SETTINGS - ''' - # Image and patch normalization settings - self.imWidth = 28 # image width for resize - self.imHeight = 28 # image height for resize - self.num_patches = 7 # number of patches - self.intensity = 255 # divide pixel values to get spikes in range [0,1] - self.location_repeat = len(self.locations) # Number of training locations that are the same - - # Network and training settings - self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons - self.feature_layer = int(self.input_layer*2) # number of feature layer neurons - self.output_layer = int(self.number_training_images/self.number_modules) # number of output layer neurons - self.epoch = 4 # number of training iterations - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - if self.device.type == "cuda": # clear cuda cache, initialize, and syncgronize gpu - os.environ["CUDA_LAUNCH_BLOCKING"] = "1" - torch.cuda.set_device(self.device) - gc.collect() - torch.cuda.empty_cache() - torch.cuda.init() - torch.cuda.synchronize(device=self.device) - self.T = int((self.number_training_images/self.number_modules) - *self.location_repeat) # number of training steps - self.annl_pow = 2 # learning rate anneal power - self.imgs = {'training':[],'testing':[]} - self.ids = {'training':[],'testing':[]} - self.spike_rates = {'training':[],'testing':[]} - - # Hyperparamters - self.theta_max = 0.5 # maximum threshold value - self.n_init = 0.005 # initial learning rate value - self.n_itp = 0.15 # initial intrinsic threshold plasticity rate[[0.9999]] - self.f_rate = [0.2,0.9] # firing rate range - self.p_exc = 0.1 # probability of excitatory connection - self.p_inh = 0.5 # probability of inhibitory connection - self.c= 0.1 # constant input - - # Test settings - self.test_true = False # leave default to False - - - ''' - DATA SETTINGS - ''' - # Select training images from list - with open('./'+self.dataset+'_imageNames.txt') as file: - self.imageNames = [line.rstrip() for line in file] - - # Filter the loading images based on self.filter - self.filteredNames = [] - for n in range(0,len(self.imageNames),self.filter): - self.filteredNames.append(self.imageNames[n]) - del self.filteredNames[self.number_training_images:len(self.filteredNames)] +class SNNLayer(nn.Module): + def __init__(self, dims, thr_range, fire_rate, ip_rate, stdp_rate, const_inp, + assign_weight): + super(SNNLayer, self).__init__() - # Get the full training and testing data paths - self.fullTrainPaths = [] - for n in self.locations: - self.fullTrainPaths.append(self.trainingPath+n+'/') - - # create output folder - now = datetime.now() - self.output_folder = './output/'+now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(self.output_folder) - - # setup logger - self.logger = logging.getLogger("VPRTempo") - self.logger.setLevel(logging.DEBUG) - logging.basicConfig(filename=self.output_folder+"/logfile.log", - filemode="a+", - format="%(asctime)-15s %(levelname)-8s %(message)s") - self.logger.addHandler(logging.StreamHandler()) - - # Print network details - self.logger.info('////////////') - self.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha') - self.logger.info('Queensland University of Technology, Centre for Robotics') - self.logger.info('') - self.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') - self.logger.info('MIT license - https://github.com/QVPR/VPRTempo') - self.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') - self.logger.info('') - self.logger.info('CUDA available: '+str(torch.cuda.is_available())) - if torch.cuda.is_available() == True: - current_device = torch.cuda.current_device() - self.logger.info('Current device is: '+str(torch.cuda.get_device_name(current_device))) - else: - self.logger.info('Current device is: CPU') - self.logger.info('') - self.logger.info("~~ Hyperparameters ~~") - self.logger.info('') - self.logger.info('Firing threshold max: '+str(self.theta_max)) - self.logger.info('Initial STDP learning rate: '+str(self.n_init)) - self.logger.info('Intrinsic threshold plasticity learning rate: '+str(self.n_itp)) - self.logger.info('Firing rate range: ['+str(self.f_rate[0])+', '+str(self.f_rate[1])+']') - self.logger.info('Excitatory connection probability: '+str(self.p_exc)) - self.logger.info('Inhibitory connection probability: '+str(self.p_inh)) - self.logger.info('Constant input: '+str(self.c)) - self.logger.info('') - self.logger.info("~~ Training and testing conditions ~~") - self.logger.info('') - self.logger.info('Number of training images: '+str(self.number_training_images)) - self.logger.info('Number of testing images: '+str(self.number_testing_images)) - self.logger.info('Number of training epochs: '+str(self.epoch)) - self.logger.info('Number of modules: '+str(self.number_modules)) - self.logger.info('Dataset used: '+str(self.dataset)) - self.logger.info('Training locations: '+str(self.locations)) - self.logger.info('Testing location: '+str(self.test_location)) - - # Network weights name - self.training_out = './weights/'+str(self.input_layer)+'i'+\ - str(self.feature_layer)+\ - 'f'+str(self.output_layer)+\ - 'o'+str(self.epoch)+'/' - - - # Check if pre-trained network exists, prompt if retrain or run - def checkTrainTest(self): - prompt = "A network with these parameters exists, re-train network? (y/n):\n" - self.logger.info('') - if path.isdir(self.training_out): - retrain = input(prompt) - else: - retrain = 'y' - return retrain - - def initialize(self,condition): - - ''' - Network startup and initialization - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info(condition+' startup and initialization') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - self.logger.info('Loading '+condition+' images') + # Device + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - if condition == 'testing': - self.test_true = True - del self.filteredNames[self.number_testing_images:len(self.filteredNames)] - self.epoch = 1 # Only run the network once - self.location_repeat = 1 # One location repeat for testing - imgNum = self.number_testing_images - else: - imgNum = self.number_training_images - - # load the training images - self.imgs[condition], self.ids[condition] = ut.loadImages(self.test_true, - self.fullTrainPaths, - self.filteredNames, - [self.imWidth,self.imHeight], - self.num_patches, - self.testPath, - self.test_location) - - self.spike_rates[condition] = ut.setSpikeRates(self.imgs[condition], - self.ids[condition], - self.device, - [self.imWidth,self.imHeight], - self.test_true, - imgNum, - self.number_modules, - self.intensity, - self.location_repeat) + # Check constraints etc + if np.isscalar(thr_range): thr_range = [thr_range, thr_range] + if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate] + if np.isscalar(const_inp): const_inp = [const_inp, const_inp] + + # Initialize Tensors + self.dim = torch.tensor(dims, dtype=torch.int) + self.x = torch.zeros(dims, device=self.device) + self.x_prev = torch.zeros(dims, device=self.device) + self.x_calc = torch.zeros(dims, device=self.device) + self.x_input = torch.zeros(dims, device=self.device) + self.x_fastinp = torch.zeros(dims, device=self.device) + self.eta_ip = torch.tensor(ip_rate, device=self.device) + self.eta_stdp = torch.tensor(stdp_rate, device=self.device) + + # Initialize Parameters + self.thr = nn.Parameter(torch.zeros(dims, device=self.device).uniform_(thr_range[0], thr_range[1])) + self.fire_rate = torch.zeros(dims, device=self.device).uniform_(fire_rate[0], fire_rate[1]) + self.have_rate = torch.any(self.fire_rate[:,:,0] > 0.0).to(self.device) + self.const_inp = torch.zeros(dims, device=self.device).uniform_(const_inp[0], const_inp[1]) + + # Additional State Variables + self.set_spks = [] + self.sspk_idx = 0 + self.spikes = torch.empty([], dtype=torch.float64) + + # Store the layer numbers + self.layers.append(len(self.layers)) + + # Weights (if applicable) + if assign_weight: + self.excW, self.inhW = bn.addWeights(self.layers) + +class SNNModel(nn.Module): + def __init__(self): + super(SNNModel, self).__init__() + # define layer parameters + self.number_modules = 1 # set the numnber of expert modules + self.module_max = 100 # set the maximum number of places per module + self.imWidth = 28 # set the pixel width (after pre-processing) + self.imHeight = 28 # set the pixel height (after pre-processing) + self.dim = int(self.imWidth*self.imHeight) # calculate the input layer size + self.layers = [] + + # initialize new net + self.net = bn.newNet(self.number_modules,self.dim) + + # add the layers + # input layer + self.input_layer = SNNLayer( + [self.number_modules,1,self.dim], + 0,0,0,0,0,False) + + # feature layer + self.feature_layer = SNNLayer( + [self.number_modules,1,int(self.dim*2)], + [0,0.5], + [0.2,0.9], + 0.15, + 0.005, + [0,0.1], + True) + + # output layer + self.output_layer = SNNLayer( + [self.number_modules,1,self.module_max], + 0,0,0,0,[0,0],True) # output layer + + + def forward(self): + # run the network to get the output here + bn.testSim() + +def configure_model(model): + model.dataset = 'nordland' + model.trainingPath = '/home/adam/data/nordland/' + model.testPath = '/home/adam/data/nordland/' + model.number_modules = 1 + model.number_training_images = 100 + model.number_testing_images = 100 + model.locations = ["spring", "fall"] + model.test_location = "summer" + model.filter = 8 + model.validation = True + model.log = False + + assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" + assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" + assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" + assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" + assert (os.path.isdir(model.testPath + model.test_location)), "Images must be organized into folders based on locations, see README.md for details" ''' - Run the training network + NETWORK SETTINGS ''' - def train(self): - - # remove contents of the weights folder - ut.clear_weights(self.training_out) - - # create a new blitnet netowrk - self.logger.info('Creating network and setting weights') - net = bn.newNet(self.number_modules,self.imWidth*self.imHeight) - - # add the input layer - bn.addLayer(net,[self.number_modules,1,self.input_layer], - 0.0,0.0,0.0,0.0,0.0,False) - - # add the feature layer - bn.addLayer(net,[self.number_modules,1,self.feature_layer], - [0,self.theta_max], - [self.f_rate[0],self.f_rate[1]], - self.n_itp, - [0,self.c], - 0, - False) - - # sequentially set the feature firing rates for the feature layer - fstep = (self.f_rate[1]-self.f_rate[0])/self.feature_layer - - # loop through all modules and feature layer neurons - for x in range(self.number_modules): - for i in range(self.feature_layer): - net['fire_rate'][1][x][:,i] = self.f_rate[0]+fstep*(i+1) - - # add excitatory inhibitory connections for input and feature layer - bn.addWeights(net,0,1,[-1,0,1],[self.p_exc,self.p_inh],self.n_init) - self.init_weights = [net['W'][0].clone().detach(),net['W'][1].clone().detach()] - - ''' - Feature layer training - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Training the input to feature layer') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - self.logger.info('Setting spike rates from loaded images') - - # begin timer for network training - start = timeit.default_timer() - - # Set the spikes times for the input images - net['set_spks'][0] = torch.clone(self.spike_rates['training']) - self.spike_rates['training'] = [] - if self.device == 'cuda': - torch.cuda.empty_cache() - gc.collect() - layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] - - # Train the input to feature layer for specified amount of epochs - for epoch in range(self.epoch): - net['step_num'] = 0 - epochStart = timeit.default_timer() - - # loop through each image and train the network - for t in range(int(self.T)): - bn.runSim(net,1,self.device,layers) - # anneal learning rates - if np.mod(t,10)==0: - pt = pow(float(self.T-t)/self.T,self.annl_pow) - net['eta_ip'][1] = self.n_itp*pt - net['eta_stdp'][0] = self.n_init*pt - net['eta_stdp'][1] = -1*self.n_init*pt - - # print training details - self.logger.info('Epoch '+str(epoch+1)+' trained in: ' - +str(round(timeit.default_timer()-epochStart,2))+'s') - self.logger.info('') - - self.logger.info('Finished training input to feature layer') - - ''' - Preparations for feature to output layer training - ''' - - # Turn off learning between input and feature layer - net['eta_ip'][1] = 0.0 - if self.p_exc > 0.0: net['eta_stdp'][0] = 0.0 - if self.p_inh > 0.0: net['eta_stdp'][1] = 0.0 - - self.logger.info('Getting feature layer spikes for output layer training') - - # get the feature spikes for training the output layer - net['x_feat'] = [] - net['step_num'] = 0 - for t in range(int(self.T)): # run blitnet without learning to get feature spikes - bn.runSim(net,1,self.device,layers) - net['x_feat'].append(net['x'][1]) # dictionary output of feature spikes - - # delete input spikes - net['set_spks'][0] = [] - if self.device == 'cuda': - torch.cuda.empty_cache() - gc.collect() - - self.logger.info('Creating output layer') - # Create and train the output layer with the feature layer - bn.addLayer(net,[self.number_modules,1,self.output_layer], - 0.0,0.0,0.0,0.0,0.0,False) - - # Add excitatory and inhibitory connections - bn.addWeights(net,1,2,[-1.0,0.0,1.0],[1.0,1.0],self.n_init) - self.init_weights.append(net['W'][2].clone().detach()) - self.init_weights.append(net['W'][3].clone().detach()) - - # Output spikes for spike forcing (final layer) - out_spks = torch.tensor([0],device=self.device,dtype=float) - - ''' - Output layer training - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Training the feature to output layer') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - net['spike_dims'] = 1 # change spike dims for output spike indexing - net['set_spks'][0] = [] # remove input spikes - layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] - - # Train the feature to output layer for specified number of epochs - for epoch in range(self.epoch): - net['step_num'] = 0 - epochStart = timeit.default_timer() - - # Loop through all the spikes generated in the feature layer to train output - for t in range(self.T): - out_spks = torch.tensor([0],device=self.device,dtype=float) - - net['set_spks'][-1] = torch.tile(out_spks, - (self.number_modules, - 1, - self.output_layer)) - net['x'][1] = net['x_feat'][t] - bn.runSim(net,1,self.device,layers) - # Anneal learning rates - if np.mod(t,10)==0: - pt = pow(float(self.T-t)/(self.T),self.annl_pow) - net['eta_ip'][2] = self.n_itp*pt - net['eta_stdp'][2] = self.n_init*pt - net['eta_stdp'][3] = -1*self.n_init*pt - if np.mod((t+1),(int(self.T/self.location_repeat))) == 0: - net['step_num'] = 0 - - # print training details - self.logger.info('Epoch '+str(epoch+1)+' trained in: ' - +str(round(timeit.default_timer()-epochStart,2))+'s') - self.logger.info('') - - self.logger.info('Finished training feature to output layer') - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Network trained in '+str(round(timeit.default_timer()-start,2)) - +'s') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - # Turn off learning - net['eta_ip'][2] = 0.0 - net['eta_stdp'][2] = 0.0 - net['eta_stdp'][3] = 0.0 - - # Clear the network output spikes - net['set_spks'][-1] = [] - net['spike_dims'] = self.input_layer - - # Reset network details - net['sspk_idx'] = [0,0,0] - net['step_num'] = 0 - net['spikes'] = [[],[],[]] - net['x'] = [[],[],[]] - net['x_feat'] = [] - - - self.logger.info('Network formatting and saving...') - - # Output the trained network - outputPkl = self.training_out + 'net.pkl' - with open(outputPkl, 'wb') as f: - pickle.dump(net, f) - - # output the ground truth image names for later testing - outputPkl = self.training_out + 'GT_imgnames.pkl' - with open(outputPkl, 'wb') as f: - pickle.dump(self.filteredNames, f) - - self.logger.info('Network succesfully saved!') - - # if using cuda, clear and dump the memory usage - if self.device =='cuda': - del net - del self.spike_rates - del self.imgs - torch.cuda.empty_cache() - gc.collect() + model.num_patches = 7 + model.intensity = 255 + model.location_repeat = len(model.locations) + + model.epoch = 4 + model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + if model.device.type == "cuda": + os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + torch.cuda.set_device(model.device) + gc.collect() + torch.cuda.empty_cache() + torch.cuda.init() + torch.cuda.synchronize(device=model.device) + model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) + model.annl_pow = 2 + model.imgs = {'training': [], 'testing': []} + model.ids = {'training': [], 'testing': []} + model.spike_rates = {'training': [], 'testing': []} + + model.n_itp = 0.15 + + model.test_true = False ''' - Run the testing network + DATA SETTINGS ''' - def networktester(self): - - ''' - Network startup and initialization - ''' - - # unpickle the network - self.logger.info('Unpickling the network') - with open(self.training_out+'net.pkl', 'rb') as f: - net = pickle.load(f) - - self.logger.info('Setting spike rates from loaded images') - # calculate input spikes from training images + with open('./' + model.dataset + '_imageNames.txt') as file: + model.imageNames = [line.rstrip() for line in file] - net['set_spks'][0] = self.spike_rates['testing'] - - # unpickle the ground truth image names - with open(self.training_out+'GT_imgnames.pkl', 'rb') as f: - GT_imgnames = pickle.load(f) - - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Running test network') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - # set number of correct places to 0 - numcorrect = 0 - net['spike_dims'] = self.input_layer - numpconc = [] - start = timeit.default_timer() - for t in range(self.number_testing_images): - tonump = np.array([]) - bn.testSim(net,device=self.device) - # output the index of highest amplitude spike - - tonump = np.append(tonump,np.reshape(net['x'][-1].cpu().numpy(), - [1,1,int(self.number_training_images)])) - - # detect if no output - if np.all(tonump == 0): - # find the strongest sub-threshold input index - nidx = np.argmax(torch.sub(net['x_input'][-1][0,0,:], - net['thr'][-1][0,0,:]).cpu().numpy()) - tonump[nidx] = 0.5 - else: - # find the highest output spike - nidx = np.argmax(tonump) - - gt_ind = GT_imgnames.index(self.filteredNames[t]) - - if gt_ind == nidx: - numcorrect += 1 - - if self.validation: # get similarity matrix for PR curve generation - numpconc.append(tonump.tolist()) - - self.p100r = round((numcorrect/self.number_testing_images)*100,2) - self.logger.info('Number of correct matches P@100R - '+str(self.p100r)+'%') - - end = timeit.default_timer() - queryHertz = self.number_testing_images/(end-start) - self.logger.info('System queried at '+str(round(queryHertz,2))+'Hz') - - # if self.validation = True, get PR information and plot similarity matrix - if self.validation: - val.match_metrics(numpconc, - self.output_folder, - self.number_testing_images, - self.number_training_images, - self.logger) - - # if using cuda, clear and dump the memory usage - if self.device =='cuda': - gc.collect() - torch.cuda.empty_cache() - - def sad(self): - - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('Setting up Sum of Absolute Differences (SAD) calculations') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - sadcorrect = 0 - - # load the training images - self.location_repeat = 1 # switch to only do SAD on one dataset traversal - self.fullTrainPaths = self.fullTrainPaths[1] - self.test_true = False # testing images preloaded, load the training ones - # load the training images - self.imgs['training'], self.ids['training'] = ut.loadImages(self.test_true, - self.fullTrainPaths, - self.filteredNames, - [self.imWidth,self.imHeight], - self.num_patches, - self.testPath, - self.test_location) - - # create database tensor - for ndx, n in enumerate(self.imgs['training']): - if ndx == 0: - db = torch.unsqueeze(n,0) - else: - db = torch.concat((db,torch.unsqueeze(n,0)),0) - - def calc_sad(query, database, const): - - SAD = torch.sum(torch.abs(torch.sub((database * const), (query * const))), - (1,2),keepdim=True) - for n in range(2): - SAD = torch.squeeze(SAD,-1) - return SAD - - # calculate SAD for each image to database and count correct number - imgred = 1/(self.imWidth*self.imHeight) - sad_concat = [] - print('Running SAD') - - start = timeit.default_timer() - for n, q in enumerate(self.imgs['testing']): - pixels = torch.empty([]) - # create 3D tensor of query images - for o in range(self.number_testing_images): - if o == 0: - pixels = torch.unsqueeze(q,0) - else: - pixels = torch.concat((pixels,torch.unsqueeze(q,0)),0) - - sad_score = calc_sad(pixels, db, imgred) - - best_match = np.argmin(sad_score.cpu().numpy()) - if n == best_match: - sadcorrect += 1 + model.filteredNames = [] + for n in range(0, len(model.imageNames), model.filter): + model.filteredNames.append(model.imageNames[n]) + del model.filteredNames[model.number_training_images:len(model.filteredNames)] + + model.fullTrainPaths = [] + for n in model.locations: + model.fullTrainPaths.append(model.trainingPath + n + '/') + + now = datetime.now() + model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + os.mkdir(model.output_folder) + + model.logger = logging.getLogger("VPRTempo") + model.logger.setLevel(logging.DEBUG) + logging.basicConfig(filename=model.output_folder + "/logfile.log", + filemode="a+", + format="%(asctime)-15s %(levelname)-8s %(message)s") + if model.log: + model.logger.addHandler(logging.StreamHandler()) + + model.logger.info('////////////') + model.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha') + model.logger.info('Queensland University of Technology, Centre for Robotics') + model.logger.info('') + model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') + model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + model.logger.info('') + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + if torch.cuda.is_available(): + current_device = torch.cuda.current_device() + model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) + else: + model.logger.info('Current device is: CPU') + model.logger.info('') + model.logger.info("~~ Hyperparameters ~~") + model.logger.info('') + model.logger.info('Firing threshold max: ' + str(model.theta_max)) + model.logger.info('Initial STDP learning rate: ' + str(model.n_init)) + model.logger.info('Intrinsic threshold plasticity learning rate: ' + str(model.n_itp)) + model.logger.info('Firing rate range: [' + str(model.f_rate[0]) + ', ' + str(model.f_rate[1]) + ']') + model.logger.info('Excitatory connection probability: ' + str(model.p_exc)) + model.logger.info('Inhibitory connection probability: ' + str(model.p_inh)) + model.logger.info('Constant input: ' + str(model.c)) + model.logger.info('') + model.logger.info("~~ Training and testing conditions ~~") + model.logger.info('') + model.logger.info('Number of training images: ' + str(model.number_training_images)) + model.logger.info('Number of testing images: ' + str(model.number_testing_images)) + model.logger.info('Number of training epochs: ' + str(model.epoch)) + model.logger.info('Number of modules: ' + str(model.number_modules)) + model.logger.info('Dataset used: ' + str(model.dataset)) + model.logger.info('Training locations: ' + str(model.locations)) + model.logger.info('Testing location: ' + str(model.test_location)) - end = timeit.default_timer() - p100r = round((sadcorrect/self.number_testing_images)*100,2) - print('') - print('Sum of absolute differences P@1: '+ - str(p100r)+'%') - print('Sum of absolute differences queried at ' - +str(round(self.number_testing_images/(end-start),2))+'Hz') + model.training_out = './weights/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' -''' -Run the network -''' + if __name__ == "__main__": # Instantiate model - model = snn_model() - - # check if the network has already been trained previously - flg = model.checkTrainTest() + model = SNNModel() + configure_model(model) - # if user inputs 'y' to retrain network if network doesn't exist - if flg == 'y': - model.initialize('training') # Initializes the training network - model.train() # Run network training (will check if already trained) - val.validate(model) # Validates that the network trained properly - - # Tests the network - model.initialize('testing') - model.networktester() # Test the network - #model.sad() - model.logger.info('') - model.logger.info('VPRTempo run completed') - model.logger.removeHandler(logging.StreamHandler()) # shut down the logger + model.forward() + \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index a0d84fb..850471c 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -28,6 +28,8 @@ import torch import matplotlib.pyplot as plt +import torch.quantization as tq +import torch.nn as nn ################################## @@ -71,7 +73,7 @@ def newNet(modules, dims): fire_rate=[],have_rate=[],mean_rate=[],eta_ip=[],const_inp=[],nois=[], set_spks=[],sspk_idx=[],spikes=[],rec_spks=[], W=[],I=[],is_inhib=[],W_lyr=[],eta_stdp=[], - step_num=0, num_modules = modules, spike_dims = dims) + step_num=0) return net @@ -194,6 +196,8 @@ def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate): nrmInh[nrmInh==0.0] = 1.0 net['W'][excIndex][n] = net['W'][excIndex][n,:,:]/nrmExc net['W'][inhIndex][n] = net['W'][inhIndex][n,:,:]/nrmInh + + return net['W'][excIndex], net['W'][] ################################## # Normalise all the firing rates @@ -442,4 +446,46 @@ def plotSpikes(net,cutoff): subplotSpikes(net,cutoff) plt.show(block=False) +# apply QAT once training is done +def QAT(net): + + # Initialize an empty dictionary to hold the CPU tensors + cpu_dict = {} + + + # Iterate over the items in the original dictionary + for key, value in net.items(): + # If the value associated with the key is a list + if isinstance(value, list): + # Initialize an empty list to hold the converted tensors + converted_list = [] + # Iterate over the list elements + for elem in value: + # Check if the element is a tensor + if isinstance(elem, torch.Tensor): + # If it is a tensor, move it to CPU + converted_list.append(elem.cpu()) + else: + # If it is not a tensor, keep it as it is + converted_list.append(elem) + cpu_dict[key] = converted_list + # If the value is a single tensor + elif isinstance(value, torch.Tensor): + cpu_dict[key] = value.cpu() + # If the value is neither a list nor a tensor, keep it as it is + else: + cpu_dict[key] = value + weight_qconfig = tq.QConfig( + activation=tq.FakeQuantize.with_args(observer=tq.MinMaxObserver, + dtype=torch.qint8), + weight=tq.FakeQuantize.with_args(observer=tq.MinMaxObserver, + dtype=torch.quint8)) + + fake_quant_weight = tq.FakeQuantize(observer=tq.MinMaxObserver, + quant_min=0, + quant_max=255, + dtype=torch.quint8) + fake_quant_weight(cpu_dict['W'][0]) + + ################################## diff --git a/src/utils.py b/src/utils.py index 2a739a5..92b3f07 100644 --- a/src/utils.py +++ b/src/utils.py @@ -37,6 +37,7 @@ from metrics import recallAtK, createPR from timeit import default_timer +from os import path def get_patches2D(patch_size,image_pad): @@ -185,6 +186,407 @@ def setSpikeRates(data,ids,device,dims,test_true,numImgs,numMods,intensity,locRe return spike_rates +# Check if pre-trained network exists, prompt if retrain or run +def checkTrainTest(self): + prompt = "A network with these parameters exists, re-train network? (y/n):\n" + self.logger.info('') + if path.isdir(self.training_out): + retrain = input(prompt) + else: + retrain = 'y' + return retrain + +def initialize(self,condition): + + ''' + Network startup and initialization + ''' + self.logger.info('') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info(condition+' startup and initialization') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('') + self.logger.info('Loading '+condition+' images') + + if condition == 'testing': + self.test_true = True + del self.filteredNames[self.number_testing_images:len(self.filteredNames)] + self.epoch = 1 # Only run the network once + self.location_repeat = 1 # One location repeat for testing + imgNum = self.number_testing_images + else: + imgNum = self.number_training_images + + # load the training images + self.imgs[condition], self.ids[condition] = ut.loadImages(self.test_true, + self.fullTrainPaths, + self.filteredNames, + [self.imWidth,self.imHeight], + self.num_patches, + self.testPath, + self.test_location) + + self.spike_rates[condition] = ut.setSpikeRates(self.imgs[condition], + self.ids[condition], + self.device, + [self.imWidth,self.imHeight], + self.test_true, + imgNum, + self.number_modules, + self.intensity, + self.location_repeat) +def train(self): + + # remove contents of the weights folder + ut.clear_weights(self.training_out) + + # create a new blitnet netowrk + self.logger.info('Creating network and setting weights') + net = bn.newNet(self.number_modules,self.imWidth*self.imHeight) + + # add the input layer + bn.addLayer(net,[self.number_modules,1,self.input_layer], + 0.0,0.0,0.0,0.0,0.0,False) + + # add the feature layer + bn.addLayer(net,[self.number_modules,1,self.feature_layer], + [0,self.theta_max], + [self.f_rate[0],self.f_rate[1]], + self.n_itp, + [0,self.c], + 0, + False) + + # sequentially set the feature firing rates for the feature layer + fstep = (self.f_rate[1]-self.f_rate[0])/self.feature_layer + + # loop through all modules and feature layer neurons + for x in range(self.number_modules): + for i in range(self.feature_layer): + net['fire_rate'][1][x][:,i] = self.f_rate[0]+fstep*(i+1) + + # add excitatory inhibitory connections for input and feature layer + bn.addWeights(net,0,1,[-1,0,1],[self.p_exc,self.p_inh],self.n_init) + self.init_weights = [net['W'][0].clone().detach(),net['W'][1].clone().detach()] + + ''' + Feature layer training + ''' + self.logger.info('') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('Training the input to feature layer') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('') + + self.logger.info('Setting spike rates from loaded images') + + # begin timer for network training + start = timeit.default_timer() + + # Set the spikes times for the input images + net['set_spks'][0] = torch.clone(self.spike_rates['training']) + self.spike_rates['training'] = [] + if self.device == 'cuda': + torch.cuda.empty_cache() + gc.collect() + layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] + + # Train the input to feature layer for specified amount of epochs + for epoch in range(self.epoch): + net['step_num'] = 0 + epochStart = timeit.default_timer() + + # loop through each image and train the network + for t in range(int(self.T)): + bn.runSim(net,1,self.device,layers) + # anneal learning rates + if np.mod(t,10)==0: + pt = pow(float(self.T-t)/self.T,self.annl_pow) + net['eta_ip'][1] = self.n_itp*pt + net['eta_stdp'][0] = self.n_init*pt + net['eta_stdp'][1] = -1*self.n_init*pt + + # print training details + self.logger.info('Epoch '+str(epoch+1)+' trained in: ' + +str(round(timeit.default_timer()-epochStart,2))+'s') + self.logger.info('') + + self.logger.info('Finished training input to feature layer') + + ''' + Preparations for feature to output layer training + ''' + + # Turn off learning between input and feature layer + net['eta_ip'][1] = 0.0 + if self.p_exc > 0.0: net['eta_stdp'][0] = 0.0 + if self.p_inh > 0.0: net['eta_stdp'][1] = 0.0 + + self.logger.info('Getting feature layer spikes for output layer training') + + # get the feature spikes for training the output layer + net['x_feat'] = [] + net['step_num'] = 0 + for t in range(int(self.T)): # run blitnet without learning to get feature spikes + bn.runSim(net,1,self.device,layers) + net['x_feat'].append(net['x'][1]) # dictionary output of feature spikes + + # delete input spikes + net['set_spks'][0] = [] + if self.device == 'cuda': + torch.cuda.empty_cache() + gc.collect() + + self.logger.info('Creating output layer') + # Create and train the output layer with the feature layer + bn.addLayer(net,[self.number_modules,1,self.output_layer], + 0.0,0.0,0.0,0.0,0.0,False) + + # Add excitatory and inhibitory connections + bn.addWeights(net,1,2,[-1.0,0.0,1.0],[1.0,1.0],self.n_init) + self.init_weights.append(net['W'][2].clone().detach()) + self.init_weights.append(net['W'][3].clone().detach()) + + # Output spikes for spike forcing (final layer) + out_spks = torch.tensor([0],device=self.device,dtype=float) + + ''' + Output layer training + ''' + self.logger.info('') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('Training the feature to output layer') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('') + + net['spike_dims'] = 1 # change spike dims for output spike indexing + net['set_spks'][0] = [] # remove input spikes + layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] + + # Train the feature to output layer for specified number of epochs + for epoch in range(self.epoch): + net['step_num'] = 0 + epochStart = timeit.default_timer() + + # Loop through all the spikes generated in the feature layer to train output + for t in range(self.T): + out_spks = torch.tensor([0],device=self.device,dtype=float) + + net['set_spks'][-1] = torch.tile(out_spks, + (self.number_modules, + 1, + self.output_layer)) + net['x'][1] = net['x_feat'][t] + bn.runSim(net,1,self.device,layers) + # Anneal learning rates + if np.mod(t,10)==0: + pt = pow(float(self.T-t)/(self.T),self.annl_pow) + net['eta_ip'][2] = self.n_itp*pt + net['eta_stdp'][2] = self.n_init*pt + net['eta_stdp'][3] = -1*self.n_init*pt + if np.mod((t+1),(int(self.T/self.location_repeat))) == 0: + net['step_num'] = 0 + + # print training details + self.logger.info('Epoch '+str(epoch+1)+' trained in: ' + +str(round(timeit.default_timer()-epochStart,2))+'s') + self.logger.info('') + + self.logger.info('Finished training feature to output layer') + self.logger.info('') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('Network trained in '+str(round(timeit.default_timer()-start,2)) + +'s') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('') + + # Turn off learning + net['eta_ip'][2] = 0.0 + net['eta_stdp'][2] = 0.0 + net['eta_stdp'][3] = 0.0 + + # Clear the network output spikes + net['set_spks'][-1] = [] + net['spike_dims'] = self.input_layer + + # Reset network details + net['sspk_idx'] = [0,0,0] + net['step_num'] = 0 + net['spikes'] = [[],[],[]] + net['x'] = [[],[],[]] + net['x_feat'] = [] + + + self.logger.info('Network formatting and saving...') + + # Output the trained network + outputPkl = self.training_out + 'net.pkl' + with open(outputPkl, 'wb') as f: + pickle.dump(net, f) + + # output the ground truth image names for later testing + outputPkl = self.training_out + 'GT_imgnames.pkl' + with open(outputPkl, 'wb') as f: + pickle.dump(self.filteredNames, f) + + self.logger.info('Network succesfully saved!') + + # if using cuda, clear and dump the memory usage + if self.device =='cuda': + del net + del self.spike_rates + del self.imgs + torch.cuda.empty_cache() + gc.collect() + +''' + Run the testing network +''' +def networktester(self): + + ''' + Network startup and initialization + ''' + + # unpickle the network + self.logger.info('Unpickling the network') + with open(self.training_out+'net.pkl', 'rb') as f: + net = pickle.load(f) + + self.logger.info('Setting spike rates from loaded images') + # calculate input spikes from training images + + net['set_spks'][0] = self.spike_rates['testing'] + + # unpickle the ground truth image names + with open(self.training_out+'GT_imgnames.pkl', 'rb') as f: + GT_imgnames = pickle.load(f) + + self.logger.info('') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('Running test network') + self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + self.logger.info('') + + # set number of correct places to 0 + numcorrect = 0 + net['spike_dims'] = self.input_layer + numpconc = [] + start = timeit.default_timer() + for t in range(self.number_testing_images): + tonump = np.array([]) + bn.testSim(net,device=self.device) + # output the index of highest amplitude spike + + tonump = np.append(tonump,np.reshape(net['x'][-1].cpu().numpy(), + [1,1,int(self.number_training_images)])) + + # detect if no output + if np.all(tonump == 0): + # find the strongest sub-threshold input index + nidx = np.argmax(torch.sub(net['x_input'][-1][0,0,:], + net['thr'][-1][0,0,:]).cpu().numpy()) + tonump[nidx] = 0.5 + else: + # find the highest output spike + nidx = np.argmax(tonump) + + gt_ind = GT_imgnames.index(self.filteredNames[t]) + + if gt_ind == nidx: + numcorrect += 1 + + if self.validation: # get similarity matrix for PR curve generation + numpconc.append(tonump.tolist()) + + self.p100r = round((numcorrect/self.number_testing_images)*100,2) + self.logger.info('Number of correct matches P@100R - '+str(self.p100r)+'%') + + end = timeit.default_timer() + queryHertz = self.number_testing_images/(end-start) + self.logger.info('System queried at '+str(round(queryHertz,2))+'Hz') + + # if self.validation = True, get PR information and plot similarity matrix + if self.validation: + val.match_metrics(numpconc, + self.output_folder, + self.number_testing_images, + self.number_training_images, + self.logger) + + # if using cuda, clear and dump the memory usage + if self.device =='cuda': + gc.collect() + torch.cuda.empty_cache() + +def sad(self): + + print('') + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('Setting up Sum of Absolute Differences (SAD) calculations') + print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') + print('') + + sadcorrect = 0 + + # load the training images + self.location_repeat = 1 # switch to only do SAD on one dataset traversal + self.fullTrainPaths = self.fullTrainPaths[1] + self.test_true = False # testing images preloaded, load the training ones + # load the training images + self.imgs['training'], self.ids['training'] = ut.loadImages(self.test_true, + self.fullTrainPaths, + self.filteredNames, + [self.imWidth,self.imHeight], + self.num_patches, + self.testPath, + self.test_location) + + # create database tensor + for ndx, n in enumerate(self.imgs['training']): + if ndx == 0: + db = torch.unsqueeze(n,0) + else: + db = torch.concat((db,torch.unsqueeze(n,0)),0) + + def calc_sad(query, database, const): + + SAD = torch.sum(torch.abs(torch.sub((database * const), (query * const))), + (1,2),keepdim=True) + for n in range(2): + SAD = torch.squeeze(SAD,-1) + return SAD + + # calculate SAD for each image to database and count correct number + imgred = 1/(self.imWidth*self.imHeight) + sad_concat = [] + print('Running SAD') + + start = timeit.default_timer() + for n, q in enumerate(self.imgs['testing']): + pixels = torch.empty([]) + # create 3D tensor of query images + for o in range(self.number_testing_images): + if o == 0: + pixels = torch.unsqueeze(q,0) + else: + pixels = torch.concat((pixels,torch.unsqueeze(q,0)),0) + + sad_score = calc_sad(pixels, db, imgred) + + best_match = np.argmin(sad_score.cpu().numpy()) + if n == best_match: + sadcorrect += 1 + + end = timeit.default_timer() + p100r = round((sadcorrect/self.number_testing_images)*100,2) + print('') + print('Sum of absolute differences P@1: '+ + str(p100r)+'%') + print('Sum of absolute differences queried at ' + +str(round(self.number_testing_images/(end-start),2))+'Hz') + + # plot similarity matrices def plot_similarity(mat, name, cmap, ax=None, dpi=600): From d95db16a4172d134b5870ba3fb18a728edc9a630 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 26 Sep 2023 15:47:10 +1000 Subject: [PATCH 03/69] Added a trainer class, getting things set up for nn.Module deployment --- VPRTempo-quant.py | 243 ++++++++++++++++------------------------------ 1 file changed, 83 insertions(+), 160 deletions(-) diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index cd20f4a..9b6fd9b 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -24,13 +24,9 @@ Imports ''' -import pickle import os import torch -import gc -import timeit -import logging import sys sys.path.append('./src') sys.path.append('./weights') @@ -38,19 +34,16 @@ sys.path.append('./output') import blitnet as bn -import utils as ut -import validation as val import numpy as np import torch.nn as nn import torch.nn.functional as F -from os import path -from datetime import datetime +from config import configure class SNNLayer(nn.Module): - def __init__(self, dims, thr_range, fire_rate, ip_rate, stdp_rate, const_inp, - assign_weight): + def __init__(self, dims=[0,0,0], thr_range=[0,0], fire_rate=[0,0], + ip_rate=0, stdp_rate=0, const_inp=[0,0],assign_weight=False): super(SNNLayer, self).__init__() # Device @@ -89,161 +82,91 @@ def __init__(self, dims, thr_range, fire_rate, ip_rate, stdp_rate, const_inp, if assign_weight: self.excW, self.inhW = bn.addWeights(self.layers) +class SNNTrainer: + def __init__(self, train_dataset, val_dataset, model_path): + + # Configure the network + configure(self) + + self.train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) + self.val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) + self.model_path = model_path + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + self.model = SNNModel().to(self.device) + self.criterion = torch.nn.CrossEntropyLoss() # Replace with appropriate loss function + self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001) # Adjust learning rate + + # Set up the input layer + self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) + + # Set up the feature layer + self.feature_layer = SNNLayer(dims=[self.number_modules,1,self.feature], + thr_range=[0,0.5], + fire_rate=[0.2,0.9], + ip_rate=0.15, + stdp_rate=0.005, + const_inp=[0,0.1], + assign_weight=True) + + # Set up the output layer + self.output_layer = SNNLayer(dims=[self.number_modules,1,self.output], + assign_weight=True) + + def train_model(self, num_epochs=50): + for epoch in range(num_epochs): + self.model.train() + running_loss = 0.0 + for inputs, targets in self.train_loader: + inputs, targets = inputs.to(self.device), targets.to(self.device) + + self.optimizer.zero_grad() + + outputs = self.model(inputs) + loss = self.criterion(outputs, targets) + + loss.backward() + self.optimizer.step() + + running_loss += loss.item() * inputs.size(0) + + epoch_loss = running_loss / len(self.train_loader.dataset) + print(f"Epoch {epoch}/{num_epochs}, Loss: {epoch_loss}") + + # TODO: Add validation loop here + + torch.save(self.model.state_dict(), self.model_path) + print(f"Model saved at {self.model_path}") + + def load_or_train_model(self): + if os.path.exists(self.model_path): + print(f"Loading model from {self.model_path}") + self.model.load_state_dict(torch.load(self.model_path)) + else: + print("Model not found, starting training.") + self.train_model() + class SNNModel(nn.Module): def __init__(self): super(SNNModel, self).__init__() - # define layer parameters - self.number_modules = 1 # set the numnber of expert modules - self.module_max = 100 # set the maximum number of places per module - self.imWidth = 28 # set the pixel width (after pre-processing) - self.imHeight = 28 # set the pixel height (after pre-processing) - self.dim = int(self.imWidth*self.imHeight) # calculate the input layer size - self.layers = [] - - # initialize new net - self.net = bn.newNet(self.number_modules,self.dim) - - # add the layers - # input layer - self.input_layer = SNNLayer( - [self.number_modules,1,self.dim], - 0,0,0,0,0,False) - - # feature layer - self.feature_layer = SNNLayer( - [self.number_modules,1,int(self.dim*2)], - [0,0.5], - [0.2,0.9], - 0.15, - 0.005, - [0,0.1], - True) - - # output layer - self.output_layer = SNNLayer( - [self.number_modules,1,self.module_max], - 0,0,0,0,[0,0],True) # output layer - - - def forward(self): - # run the network to get the output here - bn.testSim() - -def configure_model(model): - model.dataset = 'nordland' - model.trainingPath = '/home/adam/data/nordland/' - model.testPath = '/home/adam/data/nordland/' - model.number_modules = 1 - model.number_training_images = 100 - model.number_testing_images = 100 - model.locations = ["spring", "fall"] - model.test_location = "summer" - model.filter = 8 - model.validation = True - model.log = False - - assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" - assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" - assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" - assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(model.testPath + model.test_location)), "Images must be organized into folders based on locations, see README.md for details" - - ''' - NETWORK SETTINGS - ''' - model.num_patches = 7 - model.intensity = 255 - model.location_repeat = len(model.locations) - - model.epoch = 4 - model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - if model.device.type == "cuda": - os.environ["CUDA_LAUNCH_BLOCKING"] = "1" - torch.cuda.set_device(model.device) - gc.collect() - torch.cuda.empty_cache() - torch.cuda.init() - torch.cuda.synchronize(device=model.device) - model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) - model.annl_pow = 2 - model.imgs = {'training': [], 'testing': []} - model.ids = {'training': [], 'testing': []} - model.spike_rates = {'training': [], 'testing': []} - - model.n_itp = 0.15 - - model.test_true = False - - ''' - DATA SETTINGS - ''' - with open('./' + model.dataset + '_imageNames.txt') as file: - model.imageNames = [line.rstrip() for line in file] - - model.filteredNames = [] - for n in range(0, len(model.imageNames), model.filter): - model.filteredNames.append(model.imageNames[n]) - del model.filteredNames[model.number_training_images:len(model.filteredNames)] - - model.fullTrainPaths = [] - for n in model.locations: - model.fullTrainPaths.append(model.trainingPath + n + '/') - - now = datetime.now() - model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) - - model.logger = logging.getLogger("VPRTempo") - model.logger.setLevel(logging.DEBUG) - logging.basicConfig(filename=model.output_folder + "/logfile.log", - filemode="a+", - format="%(asctime)-15s %(levelname)-8s %(message)s") - if model.log: - model.logger.addHandler(logging.StreamHandler()) - - model.logger.info('////////////') - model.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha') - model.logger.info('Queensland University of Technology, Centre for Robotics') - model.logger.info('') - model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') - model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') - model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') - model.logger.info('') - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - if torch.cuda.is_available(): - current_device = torch.cuda.current_device() - model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) - else: - model.logger.info('Current device is: CPU') - model.logger.info('') - model.logger.info("~~ Hyperparameters ~~") - model.logger.info('') - model.logger.info('Firing threshold max: ' + str(model.theta_max)) - model.logger.info('Initial STDP learning rate: ' + str(model.n_init)) - model.logger.info('Intrinsic threshold plasticity learning rate: ' + str(model.n_itp)) - model.logger.info('Firing rate range: [' + str(model.f_rate[0]) + ', ' + str(model.f_rate[1]) + ']') - model.logger.info('Excitatory connection probability: ' + str(model.p_exc)) - model.logger.info('Inhibitory connection probability: ' + str(model.p_inh)) - model.logger.info('Constant input: ' + str(model.c)) - model.logger.info('') - model.logger.info("~~ Training and testing conditions ~~") - model.logger.info('') - model.logger.info('Number of training images: ' + str(model.number_training_images)) - model.logger.info('Number of testing images: ' + str(model.number_testing_images)) - model.logger.info('Number of training epochs: ' + str(model.epoch)) - model.logger.info('Number of modules: ' + str(model.number_modules)) - model.logger.info('Dataset used: ' + str(model.dataset)) - model.logger.info('Training locations: ' + str(model.locations)) - model.logger.info('Testing location: ' + str(model.test_location)) + + # Configure the network + configure(self) + + def forward(self, x): + # Define the forward pass to transform the input x to an output + # TODO: Replace with actual forward pass code + out = bn.testSim(self) # Assuming testSim is a function that can perform the forward pass + return out - model.training_out = './weights/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' - +# Testing the model: if __name__ == "__main__": - - # Instantiate model model = SNNModel() - configure_model(model) + test_dataset = ... # TODO: Load your test dataset here - model.forward() - \ No newline at end of file + model.eval() # Set the model to evaluation mode + with torch.no_grad(): # Disable gradient computation during testing + for inputs, targets in test_dataset: + outputs = model(inputs) # This calls the forward method and gets the model’s outputs + # TODO: Compute your evaluation metric(s) by comparing outputs to targets From 34cfa9fcb29734845200f02895640b46338af6b9 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Wed, 27 Sep 2023 16:24:59 +1000 Subject: [PATCH 04/69] Fixed all the image loading by creating a CustomDataset class, now processes everthing and loads data through torch. Fixed up the weight additions, each layer with a True flag for weight additions is responsible for storing excitatory and inhibitory weights --- VPRTempo-quant.py | 109 +- config/config.py | 115 + dataset/nordland.csv | 27593 +++++++++++++++++++++++++++++++++++++++++ src/blitnet.py | 72 +- src/dataset.py | 142 + 5 files changed, 27937 insertions(+), 94 deletions(-) create mode 100644 config/config.py create mode 100644 dataset/nordland.csv create mode 100644 src/dataset.py diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index 9b6fd9b..f23d8bf 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -32,6 +32,8 @@ sys.path.append('./weights') sys.path.append('./settings') sys.path.append('./output') +sys.path.append('./dataset') +sys.path.append('./config') import blitnet as bn import numpy as np @@ -39,13 +41,17 @@ import torch.nn.functional as F from config import configure +from dataset import CustomImageDataset, ProcessImage +from torch.utils.data import DataLoader +from timeit import default_timer class SNNLayer(nn.Module): - def __init__(self, dims=[0,0,0], thr_range=[0,0], fire_rate=[0,0], - ip_rate=0, stdp_rate=0, const_inp=[0,0],assign_weight=False): + def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], + fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[0,0], + assign_weight=False): super(SNNLayer, self).__init__() - + configure(self) # Device self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -69,89 +75,69 @@ def __init__(self, dims=[0,0,0], thr_range=[0,0], fire_rate=[0,0], self.fire_rate = torch.zeros(dims, device=self.device).uniform_(fire_rate[0], fire_rate[1]) self.have_rate = torch.any(self.fire_rate[:,:,0] > 0.0).to(self.device) self.const_inp = torch.zeros(dims, device=self.device).uniform_(const_inp[0], const_inp[1]) + self.p = p + self.dims = dims # Additional State Variables self.set_spks = [] self.sspk_idx = 0 self.spikes = torch.empty([], dtype=torch.float64) - # Store the layer numbers - self.layers.append(len(self.layers)) - # Weights (if applicable) if assign_weight: - self.excW, self.inhW = bn.addWeights(self.layers) - -class SNNTrainer: - def __init__(self, train_dataset, val_dataset, model_path): - - # Configure the network - configure(self) + self.excW, self.inhW, self.I = bn.addWeights(p=self.p, + stdp_rate=self.eta_stdp, + dims=[previous_layer.dims[2], + dims[2]], + num_modules=self.number_modules) - self.train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) - self.val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) - self.model_path = model_path +class SNNTrainer(nn.Module): + def __init__(self): + super(SNNTrainer, self).__init__() + configure(self) + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.model = SNNModel().to(self.device) - self.criterion = torch.nn.CrossEntropyLoss() # Replace with appropriate loss function - self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001) # Adjust learning rate + self.model = self.to(self.device) # Set up the input layer self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) # Set up the feature layer - self.feature_layer = SNNLayer(dims=[self.number_modules,1,self.feature], + self.feature_layer = SNNLayer(previous_layer=self.input_layer, + dims=[self.number_modules,1,self.feature], thr_range=[0,0.5], fire_rate=[0.2,0.9], ip_rate=0.15, stdp_rate=0.005, const_inp=[0,0.1], + p=[0.1,0.5], assign_weight=True) # Set up the output layer - self.output_layer = SNNLayer(dims=[self.number_modules,1,self.output], - assign_weight=True) + self.output_layer = SNNLayer(previous_layer=self.feature_layer, + dims=[self.number_modules,1,self.output], + assign_weight=True) - def train_model(self, num_epochs=50): - for epoch in range(num_epochs): - self.model.train() - running_loss = 0.0 - for inputs, targets in self.train_loader: - inputs, targets = inputs.to(self.device), targets.to(self.device) - - self.optimizer.zero_grad() - - outputs = self.model(inputs) - loss = self.criterion(outputs, targets) + def train_model(self, train_loader): + # run the training for the input to feature layer + for n in range(self.epoch): + for images, labels in train_loader: + start = default_timer() + images = images.to(self.device) + print('It took '+str(default_timer()-start)+'s to load and process an image') + labels = labels.to(self.device) + bn.runSim(self.input_layer, self.feature_layer, images) - loss.backward() - self.optimizer.step() - - running_loss += loss.item() * inputs.size(0) - - epoch_loss = running_loss / len(self.train_loader.dataset) - print(f"Epoch {epoch}/{num_epochs}, Loss: {epoch_loss}") - - # TODO: Add validation loop here - torch.save(self.model.state_dict(), self.model_path) print(f"Model saved at {self.model_path}") - - def load_or_train_model(self): - if os.path.exists(self.model_path): - print(f"Loading model from {self.model_path}") - self.model.load_state_dict(torch.load(self.model_path)) - else: - print("Model not found, starting training.") - self.train_model() class SNNModel(nn.Module): def __init__(self): super(SNNModel, self).__init__() - # Configure the network configure(self) + self.model = self.to(self.device) def forward(self, x): # Define the forward pass to transform the input x to an output @@ -159,11 +145,24 @@ def forward(self, x): out = bn.testSim(self) # Assuming testSim is a function that can perform the forward pass return out - -# Testing the model: if __name__ == "__main__": + # initialize the model and image transforms model = SNNModel() - test_dataset = ... # TODO: Load your test dataset here + image_transform = ProcessImage(model.dims,model.patches) + + # TODO: check for existence of pre-existing model, if not then run training + # just run training and testing in tandem for now + train_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.training_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_training_images) + + train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) + + # initialize the training model + trainer = SNNTrainer() + trainer.train_model(train_loader) model.eval() # Set the model to evaluation mode with torch.no_grad(): # Disable gradient computation during testing diff --git a/config/config.py b/config/config.py new file mode 100644 index 0000000..722e253 --- /dev/null +++ b/config/config.py @@ -0,0 +1,115 @@ +import os +import gc +import torch +import logging +from datetime import datetime + +def configure(model): + model.dataset = 'nordland' + model.dataset_file = './dataset/'+model.dataset+'.csv' + model.trainingPath = '/home/adam/data/nordland/' + model.testPath = '/home/adam/data/nordland/' + model.number_modules = 1 + model.number_training_images = 100 + model.number_testing_images = 100 + model.locations = ["spring", "fall"] + model.test_location = "summer" + model.filter = 8 + model.validation = True + model.log = False + + model.training_dirs = [] + for n in model.locations: + model.training_dirs.append(os.path.join(model.trainingPath,n)) + + assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" + assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" + assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" + assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" + assert (os.path.isdir(model.testPath + model.test_location)), "Images must be organized into folders based on locations, see README.md for details" + + model.patches = 7 + model.dims = [28,28] + model.input = int(model.dims[0]*model.dims[1]) + model.feature = int(model.input*2) + model.output = int(model.number_training_images/model.number_modules) + model.intensity = 255 + model.location_repeat = len(model.locations) + model.layers =[] + + model.epoch = 4 + model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + if model.device.type == "cuda": + os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + torch.cuda.set_device(model.device) + gc.collect() + torch.cuda.empty_cache() + torch.cuda.init() + torch.cuda.synchronize(device=model.device) + model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) + model.annl_pow = 2 + model.imgs = {'training': [], 'testing': []} + model.ids = {'training': [], 'testing': []} + model.spike_rates = {'training': [], 'testing': []} + + model.test_true = False + + with open('./' + model.dataset + '_imageNames.txt') as file: + model.imageNames = [line.rstrip() for line in file] + + model.filteredNames = [] + for n in range(0, len(model.imageNames), model.filter): + model.filteredNames.append(model.imageNames[n]) + del model.filteredNames[model.number_training_images:len(model.filteredNames)] + + model.fullTrainPaths = [] + for n in model.locations: + model.fullTrainPaths.append(model.trainingPath + n + '/') + + #now = datetime.now() + #model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + #os.mkdir(model.output_folder) + + #model.logger = logging.getLogger("VPRTempo") + #model.logger.setLevel(logging.DEBUG) + #logging.basicConfig(filename=model.output_folder + "/logfile.log", + # filemode="a+", + # format="%(asctime)-15s %(levelname)-8s %(message)s") + #if model.log: + # model.logger.addHandler(logging.StreamHandler()) + + #model.logger.info('////////////') + #model.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha') + #model.logger.info('Queensland University of Technology, Centre for Robotics') + #model.logger.info('') + #model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + #model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') + #model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + #model.logger.info('') + #model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + #if torch.cuda.is_available(): + # current_device = torch.cuda.current_device() + # model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) + #else: + # model.logger.info('Current device is: CPU') + #model.logger.info('') + #model.logger.info("~~ Hyperparameters ~~") + #model.logger.info('') + #model.logger.info('Firing threshold max: ' + str(model.thr) + #model.logger.info('Initial STDP learning rate: ' + str(model.n_init)) + #model.logger.info('Intrinsic threshold plasticity learning rate: ' + str(model.n_itp)) + #model.logger.info('Firing rate range: [' + str(model.f_rate[0]) + ', ' + str(model.f_rate[1]) + ']') + #model.logger.info('Excitatory connection probability: ' + str(model.p_exc)) + #model.logger.info('Inhibitory connection probability: ' + str(model.p_inh)) + #model.logger.info('Constant input: ' + str(model.c)) + #model.logger.info('') + #model.logger.info("~~ Training and testing conditions ~~") + #model.logger.info('') + #model.logger.info('Number of training images: ' + str(model.number_training_images)) + #model.logger.info('Number of testing images: ' + str(model.number_testing_images)) + #model.logger.info('Number of training epochs: ' + str(model.epoch)) + #model.logger.info('Number of modules: ' + str(model.number_modules)) + #model.logger.info('Dataset used: ' + str(model.dataset)) + #model.logger.info('Training locations: ' + str(model.locations)) + #model.logger.info('Testing location: ' + str(model.test_location)) + #model.training_out = './weights/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' diff --git a/dataset/nordland.csv b/dataset/nordland.csv new file mode 100644 index 0000000..5fa0648 --- /dev/null +++ b/dataset/nordland.csv 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probability # stdp_rate: STDP rate (0=no STDP) -def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate): +def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, + num_modules=1): # get torch device device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -139,8 +137,8 @@ def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate): if np.isscalar(W_range): W_range = [W_range,W_range] # determine dimensions of the weight matrices - nrow =net['x'][layer_pre].size(dim=2) - ncol = net['x'][layer_post].size(dim=2) + nrow = dims[0] + ncol = dims[1] # calculate mean and std for normal distributions inWmn = (W_range[0]+W_range[1])/2.0 @@ -148,56 +146,52 @@ def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate): exWmn = (W_range[1]+W_range[2])/2.0 exWsd = (W_range[2]-W_range[1])/6.0 - # loop through modules and add excitatory and inhibitory weights - for n in range(net['num_modules']): - if n == 0: # first weights to be appended - net['W'].append(torch.empty(nrow,ncol,device=device).normal_(mean=exWmn,std=exWsd)) # excitatory weights - excIndex = len(net['W']) - 1 - net['W'][excIndex] = torch.unsqueeze(net['W'][excIndex],0) - net['W'].append(torch.empty(nrow,ncol,device=device).normal_(mean=inWmn,std=inWsd)) # inhibitory weights - inhIndex = len(net['W']) - 1 - net['W'][inhIndex] = torch.unsqueeze(net['W'][inhIndex],0) - else: # stack new weights onto appended weight - net['W'][excIndex] = torch.concat((net['W'][excIndex], - torch.unsqueeze(torch.empty(nrow,ncol,device=device).normal_(mean=exWmn,std=exWsd),0)),0) - net['W'][inhIndex] = torch.concat((net['W'][inhIndex], - torch.unsqueeze(torch.empty(nrow,ncol,device=device).normal_(mean=inWmn,std=inWsd),0)),0) # inhibitory weights - net['W'][excIndex][n][net['W'][excIndex][n] < 0] = 0.0 # remove -ve excitatory weights - net['W'][inhIndex][n][net['W'][inhIndex][n] > 0] = 0.0 # remove +ve inhibitory weights + # Initialize excW and inhW as empty tensors + excW = torch.empty((0, nrow, ncol), device=device) + inhW = torch.empty((0, nrow, ncol), device=device) + + # Loop through modules and add excitatory and inhibitory weights + for n in range(num_modules): + if n == 0: # first weights to be appended + # excitatory weights + excW = torch.cat((excW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), 0)), 0) + # inhibitory weights + inhW = torch.cat((inhW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), 0)), 0) + else: # stack new weights onto appended weight + excW = torch.cat((excW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), 0)), 0) + inhW = torch.cat((inhW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), 0)), 0) + # Remove negative excitatory weights + excW[n][excW[n] < 0] = 0.0 + # Remove positive inhibitory weights + inhW[n][inhW[n] > 0] = 0.0 + # remove connections based on exc and inh probabilities setzeroExc = np.random.rand(nrow,ncol) > p[0] setzeroInh = np.random.rand(nrow,ncol) > p[1] # add current if n == 0: - Iindex = len(net['I']) - net['I'].append(torch.zeros(nrow, device=device)) - net['I'][Iindex] = torch.unsqueeze(net['I'][Iindex],0) - # append single reference arguments - net['W_lyr'].append([layer_pre,layer_post]) - net['eta_stdp'].append(stdp_rate) - net['eta_stdp'].append(-stdp_rate) - net['is_inhib'].append(W_range[0]<0.0 and W_range[1]<=0.0) + I = torch.zeros(nrow, device=device) + I = torch.unsqueeze(I,0) else: - net['I'][Iindex] = torch.concat((net['I'][Iindex], - torch.unsqueeze(torch.zeros(nrow, device=device),0)),0) + I = torch.concat((I,torch.unsqueeze(torch.zeros(nrow, device=device),0)),0) # remove connections based on calculated indexes if setzeroExc.any(): - net['W'][excIndex][n,:,:][setzeroExc] = 0.0 # excitatory connections + excW[n,:,:][setzeroExc] = 0.0 # excitatory connections if setzeroInh.any(): - net['W'][inhIndex][n,:,:][setzeroInh] = 0.0 # inhibitory connections + inhW[n,:,:][setzeroInh] = 0.0 # inhibitory connections # Normalise the weights (except fast inhib weights) - nrmExc = torch.linalg.norm(net['W'][excIndex][len(net['W'][excIndex])-1],ord=1,axis=0) - nrmInh = torch.linalg.norm(net['W'][inhIndex][len(net['W'][inhIndex])-1],ord=1,axis=0) + nrmExc = torch.linalg.norm(excW[len(excW)-1],ord=1,axis=0) + nrmInh = torch.linalg.norm(inhW[len(inhW)-1],ord=1,axis=0) nrmExc[nrmExc==0.0] = 1.0 nrmInh[nrmInh==0.0] = 1.0 - net['W'][excIndex][n] = net['W'][excIndex][n,:,:]/nrmExc - net['W'][inhIndex][n] = net['W'][inhIndex][n,:,:]/nrmInh + excW[n] = excW[n,:,:]/nrmExc + inhW[n] = inhW[n,:,:]/nrmInh - return net['W'][excIndex], net['W'][] + return excW, inhW, I ################################## # Normalise all the firing rates diff --git a/src/dataset.py b/src/dataset.py new file mode 100644 index 0000000..06133d4 --- /dev/null +++ b/src/dataset.py @@ -0,0 +1,142 @@ +import os +import math +import cv2 +import torch + +import pandas as pd +import numpy as np +import torch.nn.functional as F + +from torchvision.io import read_image +from torch.utils.data import Dataset + +class GetPatches2D: + def __init__(self, patch_size): + self.patch_size = (patch_size,patch_size) + + def __call__(self, img): + # Calculating the padding + padding = (self.patch_size[1] // 2, self.patch_size[1] // 2, + self.patch_size[0] // 2, self.patch_size[0] // 2) + + # Apply zero padding + image_pad = F.pad(img, padding, value=float('nan')) + + # Unfolding the image to get the patches + patches = image_pad.unfold(0, self.patch_size[0], 1).unfold(1, self.patch_size[1], 1) + + # Reshaping the patches + patches = patches.contiguous().view(self.patch_size[0] * self.patch_size[1], -1) + + return patches + + +class PatchNormalisePad: + def __init__(self, patches): + self.patches = patches + + def __call__(self, img): + img = img.squeeze(0) + nrows, ncols = img.shape[:2] + patcher = GetPatches2D(self.patches) + patches = patcher(img) + mus = torch.nanmean(patches, dim=0) + # Subtracting the mean from the original tensor + diff = patches - mus + + # Replacing NaN values with zeros in the difference tensor + diff[torch.isnan(diff)] = 0 + + # Calculating the unbiased estimator of the variance, ignoring NaN values + var = torch.nansum(diff**2, dim=0) / (torch.sum(~torch.isnan(patches), dim=0) - 1) + + # Taking the square root to get the standard deviation + stds = torch.sqrt(var) + + # Reshape mus and stds + mus_reshaped = mus.reshape(nrows, ncols) + stds_reshaped = stds.reshape(nrows, ncols) + + # Perform the normalization, handling division by zero + # Note: PyTorch, by default, does not raise an error or warning for NaN or Inf, it will propagate them in the computation + im_norm = (img - mus_reshaped) / stds_reshaped + + # Replace NaN values with 0.0 + im_norm[torch.isnan(im_norm)] = 0.0 + + # Clamp values to the range [-1.0, 1.0] + im_norm = torch.clamp(im_norm, min=-1.0, max=1.0) + + return im_norm + + +class ProcessImage: + def __init__(self, dims, patches): + self.dims = dims + self.patches = patches + + def __call__(self, img): + # Convert the image to grayscale using the standard weights for RGB channels + if img.shape[0] == 3: + img = 0.299 * img[0] + 0.587 * img[1] + 0.114 * img[2] + # Add a channel dimension to the resulting grayscale image + img= img.unsqueeze(0) + + # gamma correction + mid = 0.5 + mean = torch.mean(img) + gamma = math.log(mid * 255) / math.log(mean) + img = torch.pow(img, gamma).clip(0, 255) + + # resize and patch normalize + if len(img.shape) == 3: + img = img.unsqueeze(0) + img = F.interpolate(img, size=self.dims, mode='bilinear', align_corners=False) + img = img.squeeze(0) + patch_normaliser = PatchNormalisePad(self.patches) + im_norm = patch_normaliser(img) + img = (255.0 * (1 + im_norm) / 2.0).to(dtype=torch.uint8) + img = torch.squeeze(img,0) + + return img + +class CustomImageDataset(Dataset): + def __init__(self, annotations_file, img_dirs, transform=None, target_transform=None, + skip=1, max_samples=None): + self.transform = transform + self.target_transform = target_transform + self.skip = skip + + # Load image labels from each directory, apply the skip and max_samples, and concatenate + self.img_labels = [] + for img_dir in img_dirs: + img_labels = pd.read_csv(annotations_file) + img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(img_dir, row[0]), axis=1) + + # Select specific rows based on the skip parameter + img_labels = img_labels.iloc[::skip] + + # Limit the number of samples to max_samples if specified + if max_samples is not None: + img_labels = img_labels.iloc[:max_samples] + + self.img_labels.append(img_labels) + self.img_labels = pd.concat(self.img_labels, ignore_index=True) + + def __len__(self): + return len(self.img_labels) + + def __getitem__(self, idx): + img_path = self.img_labels.iloc[idx]['file_path'] + if not os.path.exists(img_path): + raise FileNotFoundError(f"No file found for index {idx} at {img_path}.") + + image = read_image(img_path) + label = self.img_labels.iloc[idx, 1] # Assuming label is the second column + + if self.transform: + image = self.transform(image) + if self.target_transform: + label = self.target_transform(label) + + return image, label From fc635f11babfd2608ec5224b344fb76ce37673ce Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 28 Sep 2023 17:46:53 +1000 Subject: [PATCH 05/69] Rewrote blitnet calculations for nn.Module, fixed up memory leakage problems, benched the system for speed compared to vanilla VPRTempo and is comparable when comparing image import times --- VPRTempo-quant.py | 34 +++-- config/config.py | 16 +- src/blitnet.py | 362 ++++++++++++---------------------------------- src/dataset.py | 44 +++++- 4 files changed, 164 insertions(+), 292 deletions(-) diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index f23d8bf..5256dbd 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -26,7 +26,8 @@ import os import torch - +import gc +gc.disable() import sys sys.path.append('./src') sys.path.append('./weights') @@ -41,7 +42,7 @@ import torch.nn.functional as F from config import configure -from dataset import CustomImageDataset, ProcessImage +from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader from timeit import default_timer @@ -49,7 +50,7 @@ class SNNLayer(nn.Module): def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[0,0], - assign_weight=False): + assign_weight=False,spk_force=False): super(SNNLayer, self).__init__() configure(self) # Device @@ -82,6 +83,7 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], self.set_spks = [] self.sspk_idx = 0 self.spikes = torch.empty([], dtype=torch.float64) + self.spk_force = spk_force # Weights (if applicable) if assign_weight: @@ -117,18 +119,26 @@ def __init__(self): # Set up the output layer self.output_layer = SNNLayer(previous_layer=self.feature_layer, dims=[self.number_modules,1,self.output], - assign_weight=True) + assign_weight=True,spk_force=True) def train_model(self, train_loader): + + # Create some dummy tensors on CUDA + dummy_a = torch.randn(10, 10, device='cuda:0') + dummy_b = torch.randn(10, 10, device='cuda:0') + + # Perform a dummy bmm operation + torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) + # run the training for the input to feature layer for n in range(self.epoch): for images, labels in train_loader: - start = default_timer() images = images.to(self.device) - print('It took '+str(default_timer()-start)+'s to load and process an image') + make_spikes = SetImageAsSpikes(self.intensity) + spikes = make_spikes(images) labels = labels.to(self.device) - bn.runSim(self.input_layer, self.feature_layer, images) - + bn.runSim(self.input_layer, self.feature_layer, spikes) + torch.save(self.model.state_dict(), self.model_path) print(f"Model saved at {self.model_path}") @@ -156,9 +166,13 @@ def forward(self, x): img_dirs=model.training_dirs, transform=image_transform, skip=model.filter, - max_samples=model.number_training_images) + max_samples=model.number_training_images, + modules=model.number_modules) - train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) + train_loader = DataLoader(train_dataset, + batch_size=model.number_modules, + shuffle=False, + num_workers=4) # initialize the training model trainer = SNNTrainer() diff --git a/config/config.py b/config/config.py index 722e253..10186e4 100644 --- a/config/config.py +++ b/config/config.py @@ -9,7 +9,7 @@ def configure(model): model.dataset_file = './dataset/'+model.dataset+'.csv' model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' - model.number_modules = 1 + model.number_modules = 5 model.number_training_images = 100 model.number_testing_images = 100 model.locations = ["spring", "fall"] @@ -39,13 +39,13 @@ def configure(model): model.epoch = 4 model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - if model.device.type == "cuda": - os.environ["CUDA_LAUNCH_BLOCKING"] = "1" - torch.cuda.set_device(model.device) - gc.collect() - torch.cuda.empty_cache() - torch.cuda.init() - torch.cuda.synchronize(device=model.device) + #if model.device.type == "cuda": + # os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + # torch.cuda.set_device(model.device) + # gc.collect() + #torch.cuda.empty_cache() + #torch.cuda.init() + #torch.cuda.synchronize(device=model.device) model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) model.annl_pow = 2 model.imgs = {'training': [], 'testing': []} diff --git a/src/blitnet.py b/src/blitnet.py index bcb96d7..c1c3307 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -26,100 +26,11 @@ import numpy as np import pdb import torch +import gc -import matplotlib.pyplot as plt -import torch.quantization as tq import torch.nn as nn - -################################## -# Return a new empty BITnet instance - -def newNet(modules, dims): - - np.random.seed() # new random seed - - # ** NEURON FIELDS ** - # x = activations - # x_input = total inputs - # x_prev = previous activations - # x_calc = calculated activations - # x_fastinp = total inputs including fast inhib - # dim = dimensions - # thr = thresholds for each neuron - # fire_rate = target firing rate for each neuron - # have_rate = have a target firing rate - # mean_rate = running avg firing rate for each neuron - # eta_ip = IP (threshold) learning rate - # const_inp = constant input to each neuron - # nois = noise st.dev. - # set_spks = pre-defined spike times (if any) - # sspk_idx = current index into set_spks - # spikes = spike events - # rec_spks = record spikes? - # - # ** CONNECTION FIELDS ** - # W = weights (-ve for inhib synapses) - # I = synaptic currents - # is_inhib = inhib weights flag - # W_lyr = pre and post layer numbers - # eta_stdp = STDP learning rate (-ve for inhib synapses) - # - # ** SIMULATION FIELDS ** - # step_num = current step - - #pdb.set_trace() - net = dict(x=[],x_input=[],x_prev=[],x_calc=[],x_fastinp=[],dim=[],thr=[], - fire_rate=[],have_rate=[],mean_rate=[],eta_ip=[],const_inp=[],nois=[], - set_spks=[],sspk_idx=[],spikes=[],rec_spks=[], - W=[],I=[],is_inhib=[],W_lyr=[],eta_stdp=[], - step_num=0) - - return net - -################################## -# Add a neuron layer (ie a neuron population) -# net: BITnet instance -# dim: layer dimensions [x,y,...] -# thr_range: initial threshold range -# fire_rate: target firing rate (0=no target) -# ip_rate: intrinsic threshold plasticity (IP) rate (0=no IP) -# const_inp: constant input to each neuron (0=none) -# nois: noise variance (0=no noise) -# rec_spks: record spikes? - -def addLayer(net,dims,thr_range,fire_rate,ip_rate,const_inp,nois,rec_spks): - - # get torch device - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - # Check constraints etc - if np.isscalar(thr_range): thr_range = [thr_range,thr_range] - if np.isscalar(fire_rate): fire_rate = [fire_rate,fire_rate] - if np.isscalar(const_inp): const_inp = [const_inp,const_inp] - - # create layer tensors - net['dim'].append(np.array(dims,int)) - net['x'].append(torch.zeros(dims[0],dims[1],dims[2],device=device)) - net['x_prev'].append(torch.zeros(dims[0],dims[1],dims[2],device=device)) - net['x_calc'].append(torch.zeros(dims[0],dims[1],dims[2],device=device)) - net['x_input'].append(torch.zeros(dims[0],dims[1],dims[2],device=device)) - net['x_fastinp'].append(torch.zeros(dims[0],dims[1],dims[2],device=device)) - net['eta_ip'].append(ip_rate) - temp_thr = torch.zeros(dims[0],dims[1],dims[2], device=device) - net['thr'].append(temp_thr.uniform_(thr_range[0],thr_range[1])) - temp_fire = torch.zeros(dims[0],dims[1],dims[2], device=device) - net['fire_rate'].append(temp_fire.uniform_(fire_rate[0],fire_rate[1])) - net['have_rate'].append(any(net['fire_rate'][-1][:,:,0]>0.0)) - - temp_const = torch.zeros(dims[0],dims[1],dims[2], device=device) - net['const_inp'].append(temp_const.uniform_(const_inp[0],const_inp[1])) - - net['nois'].append(nois) - net['set_spks'].append([]) - net['sspk_idx'].append(0) - net['spikes'].append(torch.empty([],dtype=torch.float64)) - net['rec_spks'].append(rec_spks) +from timeit import default_timer ################################## # Add a set of random connections between layers @@ -191,35 +102,37 @@ def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, excW[n] = excW[n,:,:]/nrmExc inhW[n] = inhW[n,:,:]/nrmInh - return excW, inhW, I + return excW, inhW, I ################################## # Normalise all the firing rates # net: BITnet instance -def norm_rates(net): - - for i,rate in enumerate(net['fire_rate']): - if rate.any() and net['eta_ip'][i] > 0.0: - net['thr'][i] = net['thr'][i] + net['eta_ip'][i]*(net['x'][i]-rate) - net['thr'][i][net['thr'][i]<0.0] = 0.0 +def norm_rates(pre_layer,post_layer): + # put layers into a list + layers = [pre_layer,post_layer] + + for layer in layers: + if layer.have_rate and layer.eta_ip > 0.0: + # adjust the firing threshold based on ideal firing rates and ITP + update = torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate)) + update[update<0.0] = 0.0 + + # Replace the original layer.thr with the updated one + layer.thr.detach().add_(update) + + torch.cuda.empty_cache() ################################## # Normalise inhib weights to balance input currents # net: BITnet instance -def norm_inhib(net): - - for i,W in enumerate(net['W']): - if net['eta_stdp'][i] < 0: - lyr = net['W_lyr'][len(net['W_lyr'])-1] - try: - wadj = (net['x_input'][lyr[1]]*W)*-net['eta_stdp'][i]*50 - net['W'][i] += wadj - net['W'][i][W>0.0] = -0.000001 - except RuntimeWarning: - print("norm_inhib err") - pdb.set_trace() +def norm_inhib(layer): + if torch.any(layer.inhW): + if layer.eta_ip != 0: + layer.inhW.add_(torch.mul(torch.mul(layer.x_input, layer.inhW), -layer.eta_ip*50), alpha=1) + layer.inhW[layer.inhW > 0.0] = -0.000001 + ################################## # Propagate spikes thru the network @@ -250,83 +163,51 @@ def add_spikesTest(net,device,dims): -def add_spikes(net,device,dims): +def const_thr(layer_pre,layer_post): + + # put layers into a list + layers = [layer_pre, layer_post] # Start with the constant input in the neurons of each layer - for i,const in enumerate(net['const_inp']): - if len(net['W_lyr']) > 1 and i == 1: - net['x_input'][i] = net['x_feat'][net['step_num']-1] - else: - net['x_input'][i] = torch.full_like(net['x_input'][i],0.0) - net['x_input'][i] += net['const_inp'][i] + for layer in layers: + if torch.any(layer.const_inp): + layer.x_input.fill_(0.0) # In-place fill + layer.x_input.add_(layer.const_inp) # In-place addition + # Find the threshold crossings (overwritten later if needed) - - net['x'][i] = torch.clamp((net['x_input'][i]-net['thr'][i]),0.0,0.9) - - # insert any predefined spikes - start = dims * (net['step_num'] - 1) - end = dims * net['step_num'] - for i in range(len(net['set_spks'])): - if len(net['set_spks'][i]): # detect if any set_spks tensors - start = dims * (net['step_num'] - 1) - end = dims * net['step_num'] - index = torch.arange(start,end,device=device,dtype=int) - if i == len(net['set_spks'])-1: # spike forcing - net['x'][i] = net['set_spks'][i].index_fill_(-1,index,0.5) - else: - net['x'][i] = torch.index_select(net['set_spks'][i],-1,index) + layer.x = torch.clamp(torch.sub(layer.x_input, layer.thr), 0.0, 0.9) + + del layers -def calc_spikes(net,layersInfo): - # get layer index information - excW = layersInfo[0] - inhW = layersInfo[1] - layers = net['W_lyr'][layersInfo[2]] +def calc_spikes(post_layer,spikes): + + # Batch multiply the input spikes with the excitatory and inhibitory weights + post_layer.x_input.add_(torch.bmm(spikes,post_layer.excW)) + post_layer.x_input.add_(torch.bmm(spikes,post_layer.inhW)) - # Synaptic currents last for 1 timestep - # excitatory weights - net['I'][layers[0]] = torch.bmm(net['x'][layers[0]],net['W'][excW]) - net['x_input'][layers[1]] += net['I'][layers[0]] - #inhibitory weights - net['I'][layers[0]] = torch.bmm(net['x'][layers[0]],net['W'][inhW]) - net['x_input'][layers[1]] += net['I'][layers[0]] - # adjust the spikes based on threshold - if not len(net['set_spks'][layers[1]]): # No predefined spikes for this layer - net['x'][layers[1]] = torch.clamp((net['x_input'][layers[1]]-net['thr'][layers[1]]), - min=0.0,max=0.9) + if post_layer.spk_force: # This layer has spike forcing, remember calculated spikes + post_layer.x_calc = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), + min=0.0,max=0.9) + else: # Predefined spikes exist for this layer, remember the calculated ones - net['x_calc'][layers[1]] = torch.clamp((net['x_input'][layers[1]]-net['thr'][layers[1]]), + post_layer.x = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), min=0.0,max=0.9) # update the x previous variable with calculated spikes - net['x_prev'][layers[1]] = net['x'][layers[1]] - - # Finally, update mean firing rates and record all spikes if needed - for i,eta in enumerate(net['eta_ip']): - - if net['rec_spks'][i]: - outspk = (net['x'][i][0,0,:]).detach().cpu().numpy() # detach to numpy for easy plotting - n_idx = np.nonzero(outspk) - net['spikes'][i].extend([net['step_num']+net['x'][i][0,:,:].detach().cpu().numpy(),n] - for n in n_idx) + post_layer.x_prev = post_layer.x.detach() ################################## # Calculate STDP # net: BITnet instance -def calc_stdp(net,layerInfo): - - # get weight layer information - excW = layerInfo[0] - inhW = layerInfo[1] - layers = net['W_lyr'][layerInfo[2]] - shape = list(net['W'][excW].size()) - +def calc_stdp(pre_layer,post_layer,spikes): + layers = [pre_layer,post_layer] # # Spike Forcing has special rules to make calculated and forced spikes match # - if len(net['set_spks'][layers[1]]): # will run for the output layer + if layers[-1].spk_force: # will run for the output layer # Diff between forced and calculated spikes xdiff = net['x'][layers[1]] - net['x_calc'][layers[1]] @@ -363,123 +244,60 @@ def calc_stdp(net,layerInfo): # else: - # tile out pre- and post-spikes - pre = torch.tile(torch.reshape(net['x'][layers[0]],(shape[0],shape[1],1)),(1,shape[2])) - post = torch.tile(net['x'][layers[1]],(shape[1],1)) - - # Excitatory synapses - havconnExc = net['W'][excW]>0 - inc_stdpExc = (0.5-post)*(pre>0)*(post>0)*havconnExc - # Inhibitory synapses - havconnInh = net['W'][inhW]<0 - inc_stdpInh = (0.5-post)*(pre>0)*(post>0)*havconnInh - - # Apply the weight changes - net['W'][excW] += inc_stdpExc*net['eta_stdp'][excW] - net['W'][inhW] += inc_stdpInh*net['eta_stdp'][inhW] + # Reshape spikes tensor and get post activation tensor + pre = spikes.reshape(layers[-1].inhW.size(0), layers[-1].inhW.size(1), 1) + post = layers[-1].x - # fix connection weights if too +ve or -ve - # Excitation - pruning and synaptogenesis (structural plasticity) - net['W'][excW][net['W'][excW]<0.0] = 0.000001 - net['W'][excW][net['W'][excW]>10.0] = 10.0 - # Inhibition - must not go +ve - net['W'][inhW][net['W'][inhW]>0.0] = -0.000001 - net['W'][inhW][net['W'][inhW]<-10.0] = -10.0 + # Calculate the masks for excitatory and inhibitory connections + havconnExc = layers[-1].excW > 0 + havconnInh = layers[-1].inhW < 0 + + # Compute the increment for excW directly and apply the update + layers[-1].excW.add_(((0.5 - post) * (pre > 0) * (post > 0) * havconnExc).mul_(layers[-1].eta_stdp)) + torch.cuda.empty_cache() + # Compute the increment for inhW directly and apply the update + layers[-1].inhW.add_(((0.5 - post) * (pre > 0) * (post > 0) * havconnInh).mul_(layers[-1].eta_stdp * -1)) + torch.cuda.empty_cache() + + # In-place clamp for excitatory and inhibitory weights + layers[-1].excW = layers[-1].excW.clamp(min=0, max=10.0).detach() + torch.cuda.empty_cache() + layers[-1].inhW = layers[-1].inhW.clamp_(min=-10.0, max=0).detach() + torch.cuda.empty_cache() + ################################## # Run the simulation # net: BITnet instance # n_steps: number of steps -def runSim(net,n_steps,device,layers): - - # Inc step count - net['step_num'] += 1 +def runSim(pre_layer,post_layer,spikes): + torch.cuda.synchronize(torch.device('cuda:0')) # Propagate spikes from pre to post neurons - add_spikes(net,device,net['spike_dims']) - calc_spikes(net,layers) - + #start = default_timer() + const_thr(pre_layer,post_layer) + #print("add_spikes time: ", default_timer() - start) + #start = default_timer() + calc_spikes(post_layer,spikes) + #print("calc_spikes time: ", default_timer() - start) # Calculate STDP weight changes - calc_stdp(net,layers) - + #start = default_timer() + calc_stdp(pre_layer,post_layer,spikes) + #print("calc_stdp time: ", default_timer() - start) # Normalise firing rates and inhibitory balance - norm_rates(net) - norm_inhib(net) - + #start = default_timer() + norm_rates(pre_layer,post_layer) + #print("norm_rates time: ", default_timer() - start) + #start = default_timer() + norm_inhib(post_layer) + #print("norm_inhib time: ", default_timer() - start) + #start = default_timer() + torch.cuda.empty_cache() + #print("clear cache time: ", default_timer() - start) def testSim(net,device): net['step_num'] += 1 add_spikesTest(net,device,net['spike_dims']) for n in range(int(len(net['W'])/2)): # run loop for number of layers layers = [int(0 + (n*2)), int(1 + (n*2)), int(0 + n)] - calc_spikes(net,layers) - -################################## -# Plot recorded spikes in current subplot -# net: BITnet instance - -def subplotSpikes(net,cutoff): - - n_tot = 0 - for i,sp in enumerate(net['spikes']): - x=[]; y=[] - for n in sp: - x.extend(list(n[0])) - y.extend(list(n[1]+n_tot)) - - plt.plot(x,y,'.',ms=1) - n_tot += np.size(net['x'][i].detach().cpu().numpy()) - -################################## -# Plot recorded spikes in new figure -# net: BITnet instance - -def plotSpikes(net,cutoff): - - plt.figure() - subplotSpikes(net,cutoff) - plt.show(block=False) - -# apply QAT once training is done -def QAT(net): - - # Initialize an empty dictionary to hold the CPU tensors - cpu_dict = {} - - - # Iterate over the items in the original dictionary - for key, value in net.items(): - # If the value associated with the key is a list - if isinstance(value, list): - # Initialize an empty list to hold the converted tensors - converted_list = [] - # Iterate over the list elements - for elem in value: - # Check if the element is a tensor - if isinstance(elem, torch.Tensor): - # If it is a tensor, move it to CPU - converted_list.append(elem.cpu()) - else: - # If it is not a tensor, keep it as it is - converted_list.append(elem) - cpu_dict[key] = converted_list - # If the value is a single tensor - elif isinstance(value, torch.Tensor): - cpu_dict[key] = value.cpu() - # If the value is neither a list nor a tensor, keep it as it is - else: - cpu_dict[key] = value - weight_qconfig = tq.QConfig( - activation=tq.FakeQuantize.with_args(observer=tq.MinMaxObserver, - dtype=torch.qint8), - weight=tq.FakeQuantize.with_args(observer=tq.MinMaxObserver, - dtype=torch.quint8)) - - fake_quant_weight = tq.FakeQuantize(observer=tq.MinMaxObserver, - quant_min=0, - quant_max=255, - dtype=torch.quint8) - fake_quant_weight(cpu_dict['W'][0]) - - -################################## + calc_spikes(net,layers) \ No newline at end of file diff --git a/src/dataset.py b/src/dataset.py index 06133d4..7019ded 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -69,6 +69,22 @@ def __call__(self, img): return im_norm +class SetImageAsSpikes: + def __init__(self,intensity): + self.intensity = intensity + + def __call__(self, img_tensor): + # Ensure the input is a 4D tensor (N x C x W x H) + if len(img_tensor.shape) == 3: + img_tensor = img_tensor.unsqueeze(1) + + N, C, W, H = img_tensor.shape + reshaped_batch = img_tensor.view(N, 1, -1) + + # Divide all pixel values by 255 + normalized_batch = reshaped_batch / self.intensity + + return normalized_batch class ProcessImage: def __init__(self, dims, patches): @@ -102,7 +118,7 @@ def __call__(self, img): class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dirs, transform=None, target_transform=None, - skip=1, max_samples=None): + skip=1, max_samples=None, modules=1): self.transform = transform self.target_transform = target_transform self.skip = skip @@ -120,9 +136,33 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= if max_samples is not None: img_labels = img_labels.iloc[:max_samples] - self.img_labels.append(img_labels) + # Reorder images in the DataFrame + reordered_img_labels = self.reorder_images(img_labels, modules) + self.img_labels.append(reordered_img_labels) + + # Concatenate all the reordered DataFrames self.img_labels = pd.concat(self.img_labels, ignore_index=True) + def reorder_images(self, img_labels, modules): + # Calculate the number of batches + num_batches = len(img_labels) // modules + remainder = len(img_labels) % modules + + reordered_list = [] + for i in range(num_batches): + for j in range(modules): + idx = i + j * num_batches + if idx < len(img_labels): + reordered_list.append(img_labels.iloc[idx]) + + # If there are remaining images, append them to the reordered list + for i in range(remainder): + idx = num_batches * modules + i + reordered_list.append(img_labels.iloc[idx]) + + # Convert reordered list of Series back to DataFrame + return pd.concat(reordered_list, axis=1).transpose().reset_index(drop=True) + def __len__(self): return len(self.img_labels) From 89e35ad36e02615156fe9bb0dc9d9f19c4d1bbf8 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Sat, 30 Sep 2023 16:27:15 +1000 Subject: [PATCH 06/69] Weird error with torch.bmm, need to investigate further. Adding NaNs and inf values, can't debug --- .DS_Store | Bin 6148 -> 8196 bytes VPRTempo-quant.py | 19 ++++- config/config.py | 4 +- src/.DS_Store | Bin 6148 -> 6148 bytes src/blitnet.py | 197 +++++++++++++++++++++++++++++----------------- 5 files changed, 142 insertions(+), 78 deletions(-) diff --git a/.DS_Store b/.DS_Store index da2f6b8cd3154b767d053f1085f2f46392266d00..01380ea6f2ab5d205e976628e93a0d479ef94108 100644 GIT binary patch delta 536 zcmaKpO-lk%7=_Pm#_@ti8DVZBTeK@HEy7hFP~pa;B4`zUWr5C^j!~P4aMwyMZQb+> zWHw3B--@EBGlPpJ!aKb4zKe6teeS*OrS?V)fY@r4mH~H^EVqw`I{9cPF)O0I+_UDy z<)`)-l(JLTMO20LSlK`xO@3wAXlANbu~4-Nu93CLN_=~Z6JVeS3nor+j*4F^riW?O zv2iO?Yb*2do1lD(aQI`h|vuc?YMhI3AB|_OA7_s~tc~IQ}l#1)g#VHb5XwX!p z#biGrUyXBxr^>R|*qcZ&@(Q;43e2P@JV$Z3(!a5|Ct6qS>UVQ;a9=IdnOev7ALYBJ zeASkev)n5c@$+V%QwdGlv-f2s|EH diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index 5256dbd..d803179 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -45,6 +45,7 @@ from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader from timeit import default_timer +from tqdm import tqdm class SNNLayer(nn.Module): @@ -124,20 +125,29 @@ def __init__(self): def train_model(self, train_loader): # Create some dummy tensors on CUDA - dummy_a = torch.randn(10, 10, device='cuda:0') - dummy_b = torch.randn(10, 10, device='cuda:0') + dummy_a = torch.randn(10, 10, device=self.device) + dummy_b = torch.randn(10, 10, device=self.device) # Perform a dummy bmm operation torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) + n_initstdp = self.feature_layer.eta_stdp.detach() + n_initip = self.feature_layer.eta_ip.detach() + # run the training for the input to feature layer - for n in range(self.epoch): + for n in tqdm(range(self.epoch)): + mod = 0 for images, labels in train_loader: images = images.to(self.device) make_spikes = SetImageAsSpikes(self.intensity) spikes = make_spikes(images) labels = labels.to(self.device) bn.runSim(self.input_layer, self.feature_layer, spikes) + if np.mod(mod,10)==0: + pt = pow(float(self.T-mod)/self.T,self.annl_pow) + self.feature_layer.eta_ip = torch.mul(n_initip,pt) + self.feature_layer.eta_stdp = torch.mul(n_initstdp,pt) + mod += 1 torch.save(self.model.state_dict(), self.model_path) print(f"Model saved at {self.model_path}") @@ -172,7 +182,8 @@ def forward(self, x): train_loader = DataLoader(train_dataset, batch_size=model.number_modules, shuffle=False, - num_workers=4) + num_workers=4, + persistent_workers=True) # initialize the training model trainer = SNNTrainer() diff --git a/config/config.py b/config/config.py index 10186e4..002aa1f 100644 --- a/config/config.py +++ b/config/config.py @@ -7,8 +7,8 @@ def configure(model): model.dataset = 'nordland' model.dataset_file = './dataset/'+model.dataset+'.csv' - model.trainingPath = '/home/adam/data/nordland/' - model.testPath = '/home/adam/data/nordland/' + model.trainingPath = '/Users/adam/data/nordland/' + model.testPath = '/Users/adam/data/nordland/' model.number_modules = 5 model.number_training_images = 100 model.number_testing_images = 100 diff --git a/src/.DS_Store b/src/.DS_Store index d12193098a49b11174d8218937beac0eeeb4ff5e..1e5c5df882259da67adfc0a7320b1a77ff4a54d5 100644 GIT binary patch delta 252 zcmZoMXfc=|#>B)qu~2NHo}wr_0|Nsi1A_nqLo!1m5N9x?GQ@A*$he%*93;if5D$a} z43)^z4Dp^h`N>H+`AI+(K%ESZK&X+D^DtT}rvMEr0penyaU~3TAQMpaFv?Cg zVRW|VX2@g6XDDJwVaNfh&I8ie)c!;^AIRolCB`mu~2NHo}wr-0|Nsi1A_nqLmopaLkWW(LjgnO#KPr_%#)p%EGMsL sGTQ9Ue1K*11m+8jo7p+|Ie=O>H!^=`p3E;|$pO?1(#^CvKx7Lu0IYWufdBvi diff --git a/src/blitnet.py b/src/blitnet.py index c1c3307..5fe6ae0 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -114,13 +114,13 @@ def norm_rates(pre_layer,post_layer): for layer in layers: if layer.have_rate and layer.eta_ip > 0.0: - # adjust the firing threshold based on ideal firing rates and ITP - update = torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate)) - update[update<0.0] = 0.0 - + # update = torch.add(layer.thr, + # torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate))) # Replace the original layer.thr with the updated one - layer.thr.detach().add_(update) - + layer.thr.detach().add_(torch.add(layer.thr, + torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate)))) + layer.thr = nn.Parameter(torch.where(layer.thr < 0, torch.zeros_like(layer.thr), layer.thr)) + torch.cuda.empty_cache() ################################## @@ -131,36 +131,13 @@ def norm_inhib(layer): if torch.any(layer.inhW): if layer.eta_ip != 0: layer.inhW.add_(torch.mul(torch.mul(layer.x_input, layer.inhW), -layer.eta_ip*50), alpha=1) + if torch.isnan(layer.inhW).any(): + raise ValueError("NaN value detected in layer.inhW after addition operation") + layer.inhW[layer.inhW > 0.0] = -0.000001 - - -################################## -# Propagate spikes thru the network -# net: SORN instance -def add_spikesTest(net,device,dims): - - # Start with the constant input in the neurons of each layer - for i,const in enumerate(net['const_inp']): - - net['x_input'][i] = torch.full_like(net['x_input'][i],0.0) - net['x_input'][i] += net['const_inp'][i] - # Find the threshold crossings (overwritten later if needed) - net['x'][i] = torch.clamp((net['x_input'][i]-net['thr'][i]),0.0,0.9) - - # insert any predefined spikes - start = dims * (net['step_num'] - 1) - end = dims * net['step_num'] - for i in range(len(net['set_spks'])): - if len(net['set_spks'][i]): # detect if any set_spks tensors - start = dims * (net['step_num'] - 1) - end = dims * net['step_num'] - index = torch.arange(start,end,device=device,dtype=int) - if i == len(net['set_spks'])-1: # spike forcing - net['x'][i] = net['set_spks'][i].index_fill_(-1,index,0.5) - else: - net['x'][i] = torch.index_select(net['set_spks'][i],-1,index) - + if torch.isnan(layer.inhW).any(): + raise ValueError("NaN value detected in layer.inhW after setting positive values to -0.000001") def const_thr(layer_pre,layer_post): @@ -172,31 +149,70 @@ def const_thr(layer_pre,layer_post): for layer in layers: if torch.any(layer.const_inp): layer.x_input.fill_(0.0) # In-place fill + if torch.isnan(layer.x_input).any(): + raise ValueError("NaN value detected in layer.x_input after filling with zeros") + layer.x_input.add_(layer.const_inp) # In-place addition + if torch.isnan(layer.x_input).any(): + raise ValueError("NaN value detected in layer.x_input after adding const_inp") # Find the threshold crossings (overwritten later if needed) layer.x = torch.clamp(torch.sub(layer.x_input, layer.thr), 0.0, 0.9) - - del layers + if torch.isnan(layer.x).any(): + raise ValueError("NaN value detected in layer.x after clamping") def calc_spikes(post_layer,spikes): - - # Batch multiply the input spikes with the excitatory and inhibitory weights + + # Check for NaN in spikes and post_layer.excW before the operation + if torch.isnan(spikes).any(): + raise ValueError("NaN value detected in spikes before excitatory weight multiplication") + if torch.isnan(post_layer.excW).any(): + raise ValueError("NaN value detected in post_layer.excW before excitatory weight multiplication") + + # Batch multiply the input spikes with the excitatory weights post_layer.x_input.add_(torch.bmm(spikes,post_layer.excW)) + + # Check for NaN in post_layer.x_input after the operation + if torch.isnan(post_layer.x_input).any(): + raise ValueError("NaN value detected in post_layer.x_input after excitatory weight multiplication") + + # Check for NaN in spikes and post_layer.inhW before the next operation + if torch.isnan(spikes).any(): + raise ValueError("NaN value detected in spikes before inhibitory weight multiplication") + if torch.isnan(post_layer.inhW).any(): + raise ValueError("NaN value detected in post_layer.inhW before inhibitory weight multiplication") + + # Batch multiply the input spikes with the inhibitory weights post_layer.x_input.add_(torch.bmm(spikes,post_layer.inhW)) + + # Check for NaN in post_layer.x_input after the operation + if torch.isnan(post_layer.x_input).any(): + raise ValueError("NaN value detected in post_layer.x_input after inhibitory weight multiplication") + # adjust the spikes based on threshold - if post_layer.spk_force: # This layer has spike forcing, remember calculated spikes + if post_layer.spk_force: + # This layer has spike forcing, remember calculated spikes post_layer.x_calc = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), - min=0.0,max=0.9) - - else: # Predefined spikes exist for this layer, remember the calculated ones - post_layer.x = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), min=0.0,max=0.9) + if torch.isnan(post_layer.x_calc).any(): + raise ValueError("NaN value detected in post_layer.x_calc") + + else: + # Predefined spikes exist for this layer, remember the calculated ones + post_layer.x = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), + min=0.0,max=0.9) + if torch.isnan(post_layer.x).any(): + raise ValueError("NaN value detected in post_layer.x") # update the x previous variable with calculated spikes - post_layer.x_prev = post_layer.x.detach() + post_layer.x_prev = post_layer.x.detach() + if torch.isnan(post_layer.x_prev).any(): + raise ValueError("NaN value detected in post_layer.x_prev") + + + ################################## # Calculate STDP @@ -244,26 +260,71 @@ def calc_stdp(pre_layer,post_layer,spikes): # else: - # Reshape spikes tensor and get post activation tensor - pre = spikes.reshape(layers[-1].inhW.size(0), layers[-1].inhW.size(1), 1) - post = layers[-1].x + + + # Assuming layers is a predefined list containing your layer objects + + shape = [len(layers[-1].excW[:, 0, 0]), + len(layers[-1].excW[0, :, 0]), + len(layers[-1].excW[0, 0, :])] + + # Tile out pre- and post-spikes + pre = torch.tile(torch.reshape(spikes, (shape[0], shape[1], 1)), (1, shape[2])) + + if torch.isnan(pre).any(): + raise ValueError("NaN value detected in pre") - # Calculate the masks for excitatory and inhibitory connections + post = torch.tile(layers[-1].x, (shape[1], 1)) + + if torch.isnan(post).any(): + raise ValueError("NaN value detected in post") + + # Excitatory synapses havconnExc = layers[-1].excW > 0 + inc_stdpExc = (0.5 - post) * (pre > 0) * (post > 0) * havconnExc + + if torch.isnan(inc_stdpExc).any(): + raise ValueError("NaN value detected in inc_stdpExc") + + # Inhibitory synapses havconnInh = layers[-1].inhW < 0 - - # Compute the increment for excW directly and apply the update - layers[-1].excW.add_(((0.5 - post) * (pre > 0) * (post > 0) * havconnExc).mul_(layers[-1].eta_stdp)) + inc_stdpInh = (0.5 - post) * (pre > 0) * (post > 0) * havconnInh + + if torch.isnan(inc_stdpInh).any(): + raise ValueError("NaN value detected in inc_stdpInh") + + # Apply the weight changes + layers[-1].excW += inc_stdpExc * layers[-1].eta_stdp + + if torch.isnan(layers[-1].excW).any(): + raise ValueError("NaN value detected in layers[-1].excW after weight changes") + + layers[-1].inhW += inc_stdpExc * (layers[-1].eta_stdp * -1) + + if torch.isnan(layers[-1].inhW).any(): + raise ValueError("NaN value detected in layers[-1].inhW after weight changes") + + # In-place clamp for excitatory and inhibitory weights + # Apply clamping only where the mask is True (non-zero elements) + layers[-1].excW = torch.where(havconnExc, + layers[-1].excW.clamp(min=0.000001, max=10.0), + layers[-1].excW).detach() + + if torch.isnan(layers[-1].excW).any(): + raise ValueError("NaN value detected in layers[-1].excW after clamping") + torch.cuda.empty_cache() - # Compute the increment for inhW directly and apply the update - layers[-1].inhW.add_(((0.5 - post) * (pre > 0) * (post > 0) * havconnInh).mul_(layers[-1].eta_stdp * -1)) + + # Apply clamping only where the mask is True (non-zero elements) + layers[-1].inhW = torch.where(havconnInh, + layers[-1].inhW.clamp(min=-10.0, max=-0.000001), + layers[-1].inhW).detach() + + if torch.isnan(layers[-1].inhW).any(): + raise ValueError("NaN value detected in layers[-1].inhW after clamping") + torch.cuda.empty_cache() - - # In-place clamp for excitatory and inhibitory weights - layers[-1].excW = layers[-1].excW.clamp(min=0, max=10.0).detach() - torch.cuda.empty_cache() - layers[-1].inhW = layers[-1].inhW.clamp_(min=-10.0, max=0).detach() - torch.cuda.empty_cache() + ################################## @@ -272,28 +333,20 @@ def calc_stdp(pre_layer,post_layer,spikes): # n_steps: number of steps def runSim(pre_layer,post_layer,spikes): - torch.cuda.synchronize(torch.device('cuda:0')) + #torch.cuda.synchronize(torch.device('cuda:0')) # Propagate spikes from pre to post neurons - #start = default_timer() const_thr(pre_layer,post_layer) - #print("add_spikes time: ", default_timer() - start) - #start = default_timer() calc_spikes(post_layer,spikes) - #print("calc_spikes time: ", default_timer() - start) + # Calculate STDP weight changes - #start = default_timer() calc_stdp(pre_layer,post_layer,spikes) - #print("calc_stdp time: ", default_timer() - start) + # Normalise firing rates and inhibitory balance - #start = default_timer() norm_rates(pre_layer,post_layer) - #print("norm_rates time: ", default_timer() - start) - #start = default_timer() norm_inhib(post_layer) - #print("norm_inhib time: ", default_timer() - start) - #start = default_timer() + torch.cuda.empty_cache() - #print("clear cache time: ", default_timer() - start) + def testSim(net,device): net['step_num'] += 1 From 5e1d980b7df6df2025afdc355b5aa5d9704ba458 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 3 Oct 2023 15:23:55 +1000 Subject: [PATCH 07/69] Fixed issue with inf and NaNs being thrown, fixed exploding weight values, finalized the output layer spike force training, adapted single training function to handle training of all layers --- VPRTempo-quant.py | 99 +++++++++---- config/config.py | 12 +- dataset/.~lock.nordland.csv# | 1 + src/blitnet.py | 266 +++++++++++++---------------------- 4 files changed, 172 insertions(+), 206 deletions(-) create mode 100644 dataset/.~lock.nordland.csv# diff --git a/VPRTempo-quant.py b/VPRTempo-quant.py index d803179..9e23b5e 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo-quant.py @@ -27,7 +27,7 @@ import os import torch import gc -gc.disable() +#gc.disable() import sys sys.path.append('./src') sys.path.append('./weights') @@ -50,7 +50,7 @@ class SNNLayer(nn.Module): def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], - fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[0,0], + fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[1,1], assign_weight=False,spk_force=False): super(SNNLayer, self).__init__() configure(self) @@ -64,7 +64,7 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Initialize Tensors self.dim = torch.tensor(dims, dtype=torch.int) - self.x = torch.zeros(dims, device=self.device) + self.x = torch.zeros(dims, device=self.device,requires_grad=False) self.x_prev = torch.zeros(dims, device=self.device) self.x_calc = torch.zeros(dims, device=self.device) self.x_input = torch.zeros(dims, device=self.device) @@ -88,7 +88,7 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Weights (if applicable) if assign_weight: - self.excW, self.inhW, self.I = bn.addWeights(p=self.p, + self.excW, self.inhW, self.I, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, stdp_rate=self.eta_stdp, dims=[previous_layer.dims[2], dims[2]], @@ -98,9 +98,9 @@ class SNNTrainer(nn.Module): def __init__(self): super(SNNTrainer, self).__init__() configure(self) - - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + # Set the device and model on defined device + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.model = self.to(self.device) # Set up the input layer @@ -120,35 +120,74 @@ def __init__(self): # Set up the output layer self.output_layer = SNNLayer(previous_layer=self.feature_layer, dims=[self.number_modules,1,self.output], - assign_weight=True,spk_force=True) + stdp_rate=0.005, + assign_weight=True, + spk_force=True) + + # Define number of layers (will run training on all layers) + self.layers = {'layer0':self.input_layer, + 'layer1':self.feature_layer, + 'layer2':self.output_layer} def train_model(self, train_loader): - # Create some dummy tensors on CUDA - dummy_a = torch.randn(10, 10, device=self.device) - dummy_b = torch.randn(10, 10, device=self.device) + # If using CUDA, run a dummy torch.bmm to 'spool-up' operations + if self.device.type == "cuda": + # Create some dummy tensors on CUDA + dummy_a = torch.randn(10, 10, device=self.device) + dummy_b = torch.randn(10, 10, device=self.device) + + # Perform a dummy bmm operation + torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) - # Perform a dummy bmm operation - torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) - - n_initstdp = self.feature_layer.eta_stdp.detach() - n_initip = self.feature_layer.eta_ip.detach() - - # run the training for the input to feature layer - for n in tqdm(range(self.epoch)): - mod = 0 - for images, labels in train_loader: - images = images.to(self.device) - make_spikes = SetImageAsSpikes(self.intensity) - spikes = make_spikes(images) - labels = labels.to(self.device) - bn.runSim(self.input_layer, self.feature_layer, spikes) - if np.mod(mod,10)==0: - pt = pow(float(self.T-mod)/self.T,self.annl_pow) - self.feature_layer.eta_ip = torch.mul(n_initip,pt) - self.feature_layer.eta_stdp = torch.mul(n_initstdp,pt) - mod += 1 + # Run the training for each layer, using defined parameters + for layer in range(len(self.layers)-1): + + # Define in and out layer + in_layer = self.layers['layer'+str(layer)] + out_layer = self.layers['layer'+str(layer+1)] + + # Output the learning rates for annealment during training + n_initstdp = out_layer.eta_stdp.detach() # STDP + n_initip = out_layer.eta_ip.detach() # ITP + + # Run the training for the input to feature layer for specified epochs + for n in tqdm(range(self.epoch)): + mod = 0 # Used to determine the learning rate annealment + for images, labels in train_loader: + # Put input images on device (CPU, CUDA) + images = images.to(self.device) + + # Set spikes from input images + make_spikes = SetImageAsSpikes(self.intensity) + spikes = make_spikes(images) + + # Put labels on device (CPU, CUDA) + labels = labels.to(self.device) + idx = labels/self.filter + # Layers don't include loaded input, calculate network spikes for up to training layers + if layer != 0: + spikes = bn.testSim(self.layers,layer,spikes,idx) + + # Run one timestep of the training input to feature layer + bn.runSim(in_layer, out_layer, spikes, idx) + + # Anneal the learning rate + if np.mod(mod,10)==0: + pt = pow(float(self.T-mod)/self.T,self.annl_pow) + out_layer.eta_ip = torch.mul(n_initip,pt) + out_layer.eta_stdp = torch.mul(n_initstdp,pt) + mod += 1 + + # Once layers have finished training, turn off any learning + out_layer.eta_ip = 0 + out_layer.eta_stdp = 0 + out_layer.excW.requires_grad_(False) + out_layer.inhW.requires_grad_(False) + out_layer.thr.requires_grad_(False) + + def save_model(self): torch.save(self.model.state_dict(), self.model_path) print(f"Model saved at {self.model_path}") diff --git a/config/config.py b/config/config.py index 002aa1f..98549e9 100644 --- a/config/config.py +++ b/config/config.py @@ -7,12 +7,12 @@ def configure(model): model.dataset = 'nordland' model.dataset_file = './dataset/'+model.dataset+'.csv' - model.trainingPath = '/Users/adam/data/nordland/' - model.testPath = '/Users/adam/data/nordland/' - model.number_modules = 5 - model.number_training_images = 100 - model.number_testing_images = 100 - model.locations = ["spring", "fall"] + model.trainingPath = '/home/adam/data/nordland/' + model.testPath = '/home/adam/data/nordland/' + model.number_modules = 1 + model.number_training_images = 500 + model.number_testing_images = 500 + model.locations = ["spring","fall"] model.test_location = "summer" model.filter = 8 model.validation = True diff --git a/dataset/.~lock.nordland.csv# b/dataset/.~lock.nordland.csv# new file mode 100644 index 0000000..a1b88c9 --- /dev/null +++ b/dataset/.~lock.nordland.csv# @@ -0,0 +1 @@ +,adam,QUT-PA00146192L,03.10.2023 09:45,file:///home/adam/.config/libreoffice/4; \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index 5fe6ae0..0774efb 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -65,12 +65,24 @@ def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, for n in range(num_modules): if n == 0: # first weights to be appended # excitatory weights - excW = torch.cat((excW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), 0)), 0) + excW = torch.cat((excW, + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), + 0)), + 0) # inhibitory weights - inhW = torch.cat((inhW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), 0)), 0) + inhW = torch.cat((inhW, + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), + 0)), + 0) else: # stack new weights onto appended weight - excW = torch.cat((excW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), 0)), 0) - inhW = torch.cat((inhW, torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), 0)), 0) + excW = torch.cat((excW, + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), + 0)), + 0) + inhW = torch.cat((inhW, + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), + 0)), + 0) # Remove negative excitatory weights excW[n][excW[n] < 0] = 0.0 @@ -101,8 +113,11 @@ def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, nrmInh[nrmInh==0.0] = 1.0 excW[n] = excW[n,:,:]/nrmExc inhW[n] = inhW[n,:,:]/nrmInh + + havconnExc = excW > 0 + havconnInh = inhW < 0 - return excW, inhW, I + return excW, inhW, I, havconnExc, havconnInh ################################## # Normalise all the firing rates @@ -117,10 +132,11 @@ def norm_rates(pre_layer,post_layer): # update = torch.add(layer.thr, # torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate))) # Replace the original layer.thr with the updated one - layer.thr.detach().add_(torch.add(layer.thr, - torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate)))) - layer.thr = nn.Parameter(torch.where(layer.thr < 0, torch.zeros_like(layer.thr), layer.thr)) + layer.thr = nn.Parameter(torch.where(layer.thr + layer.eta_ip * (layer.x - layer.fire_rate) < 0, + torch.zeros_like(layer.thr), + layer.thr + layer.eta_ip * (layer.x - layer.fire_rate))) + torch.cuda.empty_cache() ################################## @@ -130,138 +146,84 @@ def norm_rates(pre_layer,post_layer): def norm_inhib(layer): if torch.any(layer.inhW): if layer.eta_ip != 0: - layer.inhW.add_(torch.mul(torch.mul(layer.x_input, layer.inhW), -layer.eta_ip*50), alpha=1) - if torch.isnan(layer.inhW).any(): - raise ValueError("NaN value detected in layer.inhW after addition operation") - - layer.inhW[layer.inhW > 0.0] = -0.000001 - - if torch.isnan(layer.inhW).any(): - raise ValueError("NaN value detected in layer.inhW after setting positive values to -0.000001") + updated_inhW = layer.inhW + torch.mul(torch.mul(layer.x_input, layer.inhW), + layer.eta_stdp*50) + layer.inhW = nn.Parameter(torch.where(updated_inhW > 0.0, + torch.tensor(-0.000001, device=updated_inhW.device), + updated_inhW)) - -def const_thr(layer_pre,layer_post): - - # put layers into a list + +def const_thr(layer_pre, layer_post): layers = [layer_pre, layer_post] - - # Start with the constant input in the neurons of each layer for layer in layers: if torch.any(layer.const_inp): - layer.x_input.fill_(0.0) # In-place fill - if torch.isnan(layer.x_input).any(): - raise ValueError("NaN value detected in layer.x_input after filling with zeros") - - layer.x_input.add_(layer.const_inp) # In-place addition - if torch.isnan(layer.x_input).any(): - raise ValueError("NaN value detected in layer.x_input after adding const_inp") + layer.x_input.fill_(0.0) + layer.x_input.add_(layer.const_inp) - # Find the threshold crossings (overwritten later if needed) - layer.x = torch.clamp(torch.sub(layer.x_input, layer.thr), 0.0, 0.9) - if torch.isnan(layer.x).any(): - raise ValueError("NaN value detected in layer.x after clamping") + layer.x.detach().add_(torch.clamp(torch.sub(layer.x_input, layer.thr), + 0.0, 0.9)) -def calc_spikes(post_layer,spikes): - - # Check for NaN in spikes and post_layer.excW before the operation - if torch.isnan(spikes).any(): - raise ValueError("NaN value detected in spikes before excitatory weight multiplication") - if torch.isnan(post_layer.excW).any(): - raise ValueError("NaN value detected in post_layer.excW before excitatory weight multiplication") - - # Batch multiply the input spikes with the excitatory weights - post_layer.x_input.add_(torch.bmm(spikes,post_layer.excW)) - - # Check for NaN in post_layer.x_input after the operation - if torch.isnan(post_layer.x_input).any(): - raise ValueError("NaN value detected in post_layer.x_input after excitatory weight multiplication") - - # Check for NaN in spikes and post_layer.inhW before the next operation - if torch.isnan(spikes).any(): - raise ValueError("NaN value detected in spikes before inhibitory weight multiplication") - if torch.isnan(post_layer.inhW).any(): - raise ValueError("NaN value detected in post_layer.inhW before inhibitory weight multiplication") - - # Batch multiply the input spikes with the inhibitory weights - post_layer.x_input.add_(torch.bmm(spikes,post_layer.inhW)) - - # Check for NaN in post_layer.x_input after the operation - if torch.isnan(post_layer.x_input).any(): - raise ValueError("NaN value detected in post_layer.x_input after inhibitory weight multiplication") +def calc_spikes(post_layer, spikes): - # adjust the spikes based on threshold + post_layer.x_input.detach().add_(torch.bmm(spikes, post_layer.excW)) + post_layer.x_input.detach().add_(torch.bmm(spikes, post_layer.inhW)) + if post_layer.spk_force: - # This layer has spike forcing, remember calculated spikes - post_layer.x_calc = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), - min=0.0,max=0.9) - if torch.isnan(post_layer.x_calc).any(): - raise ValueError("NaN value detected in post_layer.x_calc") - + post_layer.x_calc.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr), + min=0.0, max=0.9)) else: - # Predefined spikes exist for this layer, remember the calculated ones - post_layer.x = torch.clamp(torch.sub(post_layer.x_input,post_layer.thr), - min=0.0,max=0.9) - if torch.isnan(post_layer.x).any(): - raise ValueError("NaN value detected in post_layer.x") - - # update the x previous variable with calculated spikes - post_layer.x_prev = post_layer.x.detach() - if torch.isnan(post_layer.x_prev).any(): - raise ValueError("NaN value detected in post_layer.x_prev") - - - + post_layer.x = torch.full_like(post_layer.x,0) + post_layer.x.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr), + min=0.0, max=0.9)) + ################################## # Calculate STDP # net: BITnet instance -def calc_stdp(pre_layer,post_layer,spikes): +def calc_stdp(pre_layer,post_layer,spikes,idx=0): layers = [pre_layer,post_layer] - # + # Spike Forcing has special rules to make calculated and forced spikes match - # if layers[-1].spk_force: # will run for the output layer - - # Diff between forced and calculated spikes - xdiff = net['x'][layers[1]] - net['x_calc'][layers[1]] + shape = [len(layers[-1].excW[:, 0, 0]), + len(layers[-1].excW[0, :, 0]), + len(layers[-1].excW[0, 0, :])] + # Get the output neuron index + idx_sel = torch.arange(int(idx),int(idx)+1,device=layers[-1].device,dtype=int) + + # Difference between forced and calculated spikes + layers[-1].x = torch.full_like(layers[-1].x,0) + xdiff = torch.clamp(layers[-1].x.index_fill_(-1,idx_sel,0.5) - layers[-1].x_calc, + min=0,max=1) # Threshold rules - lower it if calced spike is smaller (and vice versa) - net['thr'][layers[1]] -= torch.sign(xdiff)*torch.abs(torch.tensor(net['eta_stdp'][excW]))/10 - net['thr'][layers[1]] -= torch.sign(xdiff)*torch.abs(torch.tensor(net['eta_stdp'][inhW]))/10 - net['thr'][layers[1]][net['thr'][layers[1]]<0.0] = 0.0 # don't go -ve + layers[-1].thr = nn.Parameter(layers[-1].thr - + torch.sign(xdiff)*torch.abs(layers[-1].eta_stdp)/10) + layers[-1].thr = nn.Parameter(layers[-1].thr - + torch.sign(xdiff)*torch.abs((layers[-1].eta_stdp*-1))/10) + layers[-1].thr = nn.Parameter(layers[-1].thr.clamp(min=0, max=1)) # Pre and Post spikes tiled across and down for all synapses - if net['have_rate'][layers[0]]: + if layers[0].have_rate: # Modulate learning rate by firing rate (low firing rate = high learning rate) - mpre = net['x'][layers[0]]/net['fire_rate'][layers[0]] + mpre = spikes/layers[0].fire_rate else: - mpre = net['x'][layers[0]] + mpre = spikes pre = torch.tile(torch.reshape(mpre,(shape[0],shape[1],1)),(1,shape[2])) post = torch.tile(xdiff,(shape[1],1)) - # Excitatory connections - if net['eta_stdp'][excW] > 0: - havconn = net['W'][excW]>0 - inc_stdp_exc = pre*post*havconn - # Inhibitory connections - if net['eta_stdp'][inhW] < 0: - havconn = net['W'][inhW]<0 - inc_stdp_inh = -pre*post*havconn - # Apply the weight changes - net['W'][excW] += inc_stdp_exc*net['eta_stdp'][excW] - net['W'][inhW] += inc_stdp_inh*net['eta_stdp'][inhW] + layers[-1].excW = nn.Parameter(layers[-1].excW + + (pre*post*layers[-1].havconnExc)*layers[-1].eta_stdp) + layers[-1].inhW = nn.Parameter(layers[-1].inhW + + (-pre*post*layers[-1].havconnInh)*(layers[-1].eta_stdp*-1)) - # # Normal STDP - # else: - - - + # Assuming layers is a predefined list containing your layer objects shape = [len(layers[-1].excW[:, 0, 0]), @@ -270,87 +232,51 @@ def calc_stdp(pre_layer,post_layer,spikes): # Tile out pre- and post-spikes pre = torch.tile(torch.reshape(spikes, (shape[0], shape[1], 1)), (1, shape[2])) - - if torch.isnan(pre).any(): - raise ValueError("NaN value detected in pre") - post = torch.tile(layers[-1].x, (shape[1], 1)) - - if torch.isnan(post).any(): - raise ValueError("NaN value detected in post") - - # Excitatory synapses - havconnExc = layers[-1].excW > 0 - inc_stdpExc = (0.5 - post) * (pre > 0) * (post > 0) * havconnExc - - if torch.isnan(inc_stdpExc).any(): - raise ValueError("NaN value detected in inc_stdpExc") - - # Inhibitory synapses - havconnInh = layers[-1].inhW < 0 - inc_stdpInh = (0.5 - post) * (pre > 0) * (post > 0) * havconnInh - - if torch.isnan(inc_stdpInh).any(): - raise ValueError("NaN value detected in inc_stdpInh") - + # Apply the weight changes - layers[-1].excW += inc_stdpExc * layers[-1].eta_stdp - - if torch.isnan(layers[-1].excW).any(): - raise ValueError("NaN value detected in layers[-1].excW after weight changes") - - layers[-1].inhW += inc_stdpExc * (layers[-1].eta_stdp * -1) - - if torch.isnan(layers[-1].inhW).any(): - raise ValueError("NaN value detected in layers[-1].inhW after weight changes") + layers[-1].excW = nn.Parameter(layers[-1].excW + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnExc) * layers[-1].eta_stdp) + layers[-1].inhW = nn.Parameter(layers[-1].inhW + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnInh) * (layers[-1].eta_stdp * -1)) - # In-place clamp for excitatory and inhibitory weights - # Apply clamping only where the mask is True (non-zero elements) - layers[-1].excW = torch.where(havconnExc, - layers[-1].excW.clamp(min=0.000001, max=10.0), - layers[-1].excW).detach() - - if torch.isnan(layers[-1].excW).any(): - raise ValueError("NaN value detected in layers[-1].excW after clamping") - - torch.cuda.empty_cache() - - # Apply clamping only where the mask is True (non-zero elements) - layers[-1].inhW = torch.where(havconnInh, - layers[-1].inhW.clamp(min=-10.0, max=-0.000001), - layers[-1].inhW).detach() - - if torch.isnan(layers[-1].inhW).any(): - raise ValueError("NaN value detected in layers[-1].inhW after clamping") - - torch.cuda.empty_cache() - - + # In-place clamp for excitatory and inhibitory weights + # Apply clamping only where the mask is True (non-zero elements) + layers[-1].excW = nn.Parameter(torch.where(layers[-1].havconnExc, + layers[-1].excW.clamp(min=0.000001, max=10.0), + layers[-1].excW)) + + # Apply clamping only where the mask is True (non-zero elements) + layers[-1].inhW = nn.Parameter(torch.where(layers[-1].havconnInh, + layers[-1].inhW.clamp(min=-10.0, max=-0.000001), + layers[-1].inhW)) + + torch.cuda.empty_cache() ################################## # Run the simulation # net: BITnet instance # n_steps: number of steps -def runSim(pre_layer,post_layer,spikes): - #torch.cuda.synchronize(torch.device('cuda:0')) +def runSim(pre_layer,post_layer,spikes,idx): + # Propagate spikes from pre to post neurons const_thr(pre_layer,post_layer) calc_spikes(post_layer,spikes) # Calculate STDP weight changes - calc_stdp(pre_layer,post_layer,spikes) + calc_stdp(pre_layer,post_layer,spikes,idx) # Normalise firing rates and inhibitory balance norm_rates(pre_layer,post_layer) norm_inhib(post_layer) torch.cuda.empty_cache() + + del spikes -def testSim(net,device): +def testSim(layers,layer_num,spikes,idx): - net['step_num'] += 1 - add_spikesTest(net,device,net['spike_dims']) - for n in range(int(len(net['W'])/2)): # run loop for number of layers - layers = [int(0 + (n*2)), int(1 + (n*2)), int(0 + n)] - calc_spikes(net,layers) \ No newline at end of file + # run the test system through all specified layers to get an output + for n in range(layer_num): + calc_spikes(layers['layer'+str(n+1)],spikes) + + return layers['layer'+str(n+1)].x \ No newline at end of file From a3eb1839bced2509e7d9096cf9551be169305280 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 3 Oct 2023 17:08:57 +1000 Subject: [PATCH 08/69] Working on the data testing segment, now outputs model.pth files post-training, need to configure the test input tiling across modules --- .gitignore | 3 +- VPRTempo-quant.py => VPRTempo.py | 109 +- config/config.py | 25 +- nordland_imageNames.txt | 27592 ----------------------------- orc_imageNames.txt | 3369 ---- output/.output.txt | 1 - src/blitnet.py | 4 +- src/dataset.py | 6 +- weights/weights_note | 8 - 9 files changed, 111 insertions(+), 31006 deletions(-) rename VPRTempo-quant.py => VPRTempo.py (71%) delete mode 100644 nordland_imageNames.txt delete mode 100644 orc_imageNames.txt delete mode 100644 output/.output.txt delete mode 100644 weights/weights_note diff --git a/.gitignore b/.gitignore index 77305c4..ffbfd5c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ -weights/ +models/ output/ __pycache__/ src/__pycache__/ +config/__pycache__/ diff --git a/VPRTempo-quant.py b/VPRTempo.py similarity index 71% rename from VPRTempo-quant.py rename to VPRTempo.py index 9e23b5e..ee7834b 100644 --- a/VPRTempo-quant.py +++ b/VPRTempo.py @@ -27,10 +27,9 @@ import os import torch import gc -#gc.disable() import sys sys.path.append('./src') -sys.path.append('./weights') +sys.path.append('./models') sys.path.append('./settings') sys.path.append('./output') sys.path.append('./dataset') @@ -44,7 +43,6 @@ from config import configure from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader -from timeit import default_timer from tqdm import tqdm @@ -88,20 +86,22 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Weights (if applicable) if assign_weight: - self.excW, self.inhW, self.I, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, + excW, inhW, self.I, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, stdp_rate=self.eta_stdp, dims=[previous_layer.dims[2], dims[2]], num_modules=self.number_modules) + + self.excW = nn.Parameter(excW) + self.inhW = nn.Parameter(inhW) class SNNTrainer(nn.Module): def __init__(self): super(SNNTrainer, self).__init__() configure(self) - # Set the device and model on defined device + # Set the device self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.model = self.to(self.device) # Set up the input layer self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) @@ -186,18 +186,43 @@ def train_model(self, train_loader): out_layer.excW.requires_grad_(False) out_layer.inhW.requires_grad_(False) out_layer.thr.requires_grad_(False) - - def save_model(self): - torch.save(self.model.state_dict(), self.model_path) - print(f"Model saved at {self.model_path}") + + torch.cuda.empty_cache() + gc.collect() + + def save_model(self, model_out): + # save model + torch.save(self.state_dict(), model_out) class SNNModel(nn.Module): def __init__(self): super(SNNModel, self).__init__() # Configure the network configure(self) - self.model = self.to(self.device) + # Set up the input layer + self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) + + # Set up the feature layer + self.feature_layer = SNNLayer(previous_layer=self.input_layer, + dims=[self.number_modules,1,self.feature], + assign_weight=True) + + # Set up the output layer + self.output_layer = SNNLayer(previous_layer=self.feature_layer, + dims=[self.number_modules,1,self.output], + assign_weight=True,) + + # Define number of layers (will run training on all layers) + self.layers = {'layer0':self.input_layer, + 'layer1':self.feature_layer, + 'layer2':self.output_layer} + + def load_model(self,model_path): + state_dict = torch.load(model_path, map_location=self.device) + self.load_state_dict(state_dict) + self.eval() + def forward(self, x): # Define the forward pass to transform the input x to an output # TODO: Replace with actual forward pass code @@ -205,30 +230,68 @@ def forward(self, x): return out if __name__ == "__main__": - # initialize the model and image transforms + # Initialize the model and image transforms model = SNNModel() + + # Generate model name, check if pre-trained model exists + model_name = "VPRTempo"+(str(model.input)+ + str(model.feature)+ + str(model.output)+ + str(model.number_modules)+'-'+ + str(model.device.type)+'.pth') + if os.path.exists(os.path.join('./models',model_name)): + pretrain_flg = True + prompt = "A network with these parameters exists, re-train network? (y/n):\n" + retrain = input(prompt) + if retrain == 'y': + pretrain_flg = False + else: + pretrain_flg = False + + # Define the image transform class image_transform = ProcessImage(model.dims,model.patches) - # TODO: check for existence of pre-existing model, if not then run training - # just run training and testing in tandem for now - train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.training_dirs, + # If no pre-existing model, train new model with set configuration + if not pretrain_flg: + # Define the custom training image dataset class + train_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.training_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_training_images, + modules=model.number_modules, + test=False) + + # Define the training dataloader class + train_loader = DataLoader(train_dataset, + batch_size=model.number_modules, + shuffle=False, + num_workers=4, + persistent_workers=True) + + # Initialize, run, and save the training model + trainer = SNNTrainer() + trainer.train_model(train_loader) + trainer.save_model(os.path.join('./models',model_name)) + + # Load the trained model into SNNModel() + model.load_model(os.path.join('./models',model_name)) + + # Define the custom testing image dataset class + test_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.testing_dirs, transform=image_transform, skip=model.filter, - max_samples=model.number_training_images, + max_samples=model.number_testing_images, modules=model.number_modules) - train_loader = DataLoader(train_dataset, + # Define the testing dataloader class + test_loader = DataLoader(test_dataset, batch_size=model.number_modules, shuffle=False, num_workers=4, persistent_workers=True) - # initialize the training model - trainer = SNNTrainer() - trainer.train_model(train_loader) - - model.eval() # Set the model to evaluation mode with torch.no_grad(): # Disable gradient computation during testing for inputs, targets in test_dataset: outputs = model(inputs) # This calls the forward method and gets the model’s outputs diff --git a/config/config.py b/config/config.py index 98549e9..dd150c7 100644 --- a/config/config.py +++ b/config/config.py @@ -1,7 +1,8 @@ import os -import gc import torch import logging +import csv + from datetime import datetime def configure(model): @@ -9,11 +10,11 @@ def configure(model): model.dataset_file = './dataset/'+model.dataset+'.csv' model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' - model.number_modules = 1 + model.number_modules = 5 model.number_training_images = 500 model.number_testing_images = 500 model.locations = ["spring","fall"] - model.test_location = "summer" + model.test_locations = "summer" model.filter = 8 model.validation = True model.log = False @@ -21,12 +22,16 @@ def configure(model): model.training_dirs = [] for n in model.locations: model.training_dirs.append(os.path.join(model.trainingPath,n)) - + + model.testing_dirs = [] + for n in model.test_locations: + model.testing_dirs.append(os.path.join(model.testPath,n)) + assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(model.testPath + model.test_location)), "Images must be organized into folders based on locations, see README.md for details" + assert (os.path.isdir(model.testPath + model.test_locations)), "Images must be organized into folders based on locations, see README.md for details" model.patches = 7 model.dims = [28,28] @@ -53,9 +58,11 @@ def configure(model): model.spike_rates = {'training': [], 'testing': []} model.test_true = False - - with open('./' + model.dataset + '_imageNames.txt') as file: - model.imageNames = [line.rstrip() for line in file] + + with open(os.path.join('./dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: + reader = csv.reader(file) + model.imageNames = [row[0] for row in reader] + del model.imageNames[0] model.filteredNames = [] for n in range(0, len(model.imageNames), model.filter): @@ -112,4 +119,4 @@ def configure(model): #model.logger.info('Dataset used: ' + str(model.dataset)) #model.logger.info('Training locations: ' + str(model.locations)) #model.logger.info('Testing location: ' + str(model.test_location)) - #model.training_out = './weights/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' + #model.training_out = './models/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' diff --git a/nordland_imageNames.txt b/nordland_imageNames.txt deleted file mode 100644 index 01543f2..0000000 --- a/nordland_imageNames.txt +++ /dev/null @@ -1,27592 +0,0 @@ -images-00202.png -images-00203.png -images-00204.png -images-00205.png -images-00206.png -images-00207.png -images-00208.png -images-00209.png -images-00210.png -images-00211.png -images-00212.png -images-00213.png -images-00214.png -images-00215.png -images-00216.png -images-00217.png -images-00218.png -images-00219.png -images-00220.png -images-00221.png -images-00222.png -images-00223.png -images-00224.png -images-00225.png -images-00226.png -images-00227.png -images-00228.png -images-00229.png -images-00230.png -images-00231.png -images-00232.png -images-00233.png -images-00234.png -images-00235.png -images-00236.png -images-00237.png -images-00238.png -images-00239.png -images-00240.png 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-003309.png -003310.png -003311.png -003312.png -003313.png -003314.png -003315.png -003316.png -003317.png -003318.png -003319.png -003320.png -003321.png -003322.png -003323.png -003324.png -003325.png -003326.png -003327.png -003328.png -003329.png -003330.png -003331.png -003332.png -003333.png -003334.png -003335.png -003336.png -003337.png -003338.png -003339.png -003340.png -003341.png -003342.png -003343.png -003344.png -003345.png -003346.png -003347.png -003348.png -003349.png -003350.png -003351.png -003352.png -003353.png -003354.png -003355.png -003356.png -003357.png -003358.png -003359.png -003360.png -003361.png -003362.png -003363.png -003364.png -003365.png -003366.png -003367.png -003368.png diff --git a/output/.output.txt b/output/.output.txt deleted file mode 100644 index 6d575ae..0000000 --- a/output/.output.txt +++ /dev/null @@ -1 +0,0 @@ -# Hidden file for output folder diff --git a/src/blitnet.py b/src/blitnet.py index 0774efb..abd52b0 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -134,7 +134,7 @@ def norm_rates(pre_layer,post_layer): # Replace the original layer.thr with the updated one layer.thr = nn.Parameter(torch.where(layer.thr + layer.eta_ip * (layer.x - layer.fire_rate) < 0, torch.zeros_like(layer.thr), - layer.thr + layer.eta_ip * (layer.x - layer.fire_rate))) + layer.thr + (layer.eta_ip * (layer.x - layer.fire_rate)))) torch.cuda.empty_cache() @@ -192,7 +192,7 @@ def calc_stdp(pre_layer,post_layer,spikes,idx=0): len(layers[-1].excW[0, :, 0]), len(layers[-1].excW[0, 0, :])] # Get the output neuron index - idx_sel = torch.arange(int(idx),int(idx)+1,device=layers[-1].device,dtype=int) + idx_sel = torch.arange(int(idx[0]),int(idx[0])+1,device=layers[-1].device,dtype=int) # Difference between forced and calculated spikes layers[-1].x = torch.full_like(layers[-1].x,0) diff --git a/src/dataset.py b/src/dataset.py index 7019ded..ca0484a 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -118,7 +118,7 @@ def __call__(self, img): class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dirs, transform=None, target_transform=None, - skip=1, max_samples=None, modules=1): + skip=1, max_samples=None, modules=1, test=True): self.transform = transform self.target_transform = target_transform self.skip = skip @@ -136,6 +136,10 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= if max_samples is not None: img_labels = img_labels.iloc[:max_samples] + # Determine if the images being fed are training or testing + if test: + poopy=1 + # Reorder images in the DataFrame reordered_img_labels = self.reorder_images(img_labels, modules) self.img_labels.append(reordered_img_labels) diff --git a/weights/weights_note b/weights/weights_note deleted file mode 100644 index ca01bad..0000000 --- a/weights/weights_note +++ /dev/null @@ -1,8 +0,0 @@ -Trained networks will appear in this folder as a pickled Python dictionary (.pkl). The naming takes the following format: -# xxxi - number of neurons in the input (i) layer -# xxxxf - number of neurons in the feature (f) layer -# xxxo - number of neurons in the output (o) layer -# X - number of training epochs - -Which will look something like - xxxixxxfxxxoX.pkl -An example trained network would be - 784i5488f400o4.pkl From 7d0f26e5191872495e3329e399d5443b247c5484 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Wed, 4 Oct 2023 18:30:20 +1000 Subject: [PATCH 09/69] Started on the testing network, weird problem with output weight nans. Investigate further --- .DS_Store | Bin 8196 -> 8196 bytes VPRTempo.py | 130 ++++++++++++++++++++++++++++++----------------- config/config.py | 16 +++--- src/blitnet.py | 55 +++++++++----------- src/dataset.py | 27 ++++++---- 5 files changed, 132 insertions(+), 96 deletions(-) diff --git a/.DS_Store b/.DS_Store index 01380ea6f2ab5d205e976628e93a0d479ef94108..559b89382d81662d615286ec286ece32aa8c2cfa 100644 GIT binary patch delta 473 zcmZp1XmOa}&nUeyU^hRb^kg0ZxB5heVjxUqC}AkhNjD5m&d)7i00Gt~bATip1Co4h zzKcszPJR+loa02<1Y_sKqmGzrQV6Ok$UxY`%)t6#vyVUxqa_eL1Q-|?J%QG&{|^R07RXshYA5RnH*lC58R;k(nww1)6m+adl0@+nSTCw(WIx@x zc7!254eBQ(Gjj70e!`^&*-r`(dr&PZ3ogpb$5w2!VAclH8h$mD{O3rW^_t%a!yiy neh$z`bX&`Ui}G^v^FVH6+?bflw3%Jv8_VQEK?Uj?p2Y+JFm`tY diff --git a/VPRTempo.py b/VPRTempo.py index ee7834b..57fbcb3 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -45,11 +45,10 @@ from torch.utils.data import DataLoader from tqdm import tqdm - class SNNLayer(nn.Module): def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[1,1], - assign_weight=False,spk_force=False): + assign_weight=False,spk_force=False,requires_grad=False): super(SNNLayer, self).__init__() configure(self) # Device @@ -62,7 +61,7 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Initialize Tensors self.dim = torch.tensor(dims, dtype=torch.int) - self.x = torch.zeros(dims, device=self.device,requires_grad=False) + self.x = torch.zeros(dims, device=self.device) self.x_prev = torch.zeros(dims, device=self.device) self.x_calc = torch.zeros(dims, device=self.device) self.x_input = torch.zeros(dims, device=self.device) @@ -86,14 +85,13 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Weights (if applicable) if assign_weight: - excW, inhW, self.I, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, - stdp_rate=self.eta_stdp, + excW, inhW, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, dims=[previous_layer.dims[2], dims[2]], num_modules=self.number_modules) - self.excW = nn.Parameter(excW) - self.inhW = nn.Parameter(inhW) + self.excW = nn.Parameter(excW, requires_grad=requires_grad) + self.inhW = nn.Parameter(inhW, requires_grad=requires_grad) class SNNTrainer(nn.Module): def __init__(self): @@ -115,14 +113,17 @@ def __init__(self): stdp_rate=0.005, const_inp=[0,0.1], p=[0.1,0.5], - assign_weight=True) + assign_weight=True, + requires_grad=True) # Set up the output layer self.output_layer = SNNLayer(previous_layer=self.feature_layer, dims=[self.number_modules,1,self.output], + ip_rate=0.15, stdp_rate=0.005, assign_weight=True, - spk_force=True) + spk_force=True, + requires_grad=True) # Define number of layers (will run training on all layers) self.layers = {'layer0':self.input_layer, @@ -159,7 +160,7 @@ def train_model(self, train_loader): images = images.to(self.device) # Set spikes from input images - make_spikes = SetImageAsSpikes(self.intensity) + make_spikes = SetImageAsSpikes(self.intensity, test=False) spikes = make_spikes(images) # Put labels on device (CPU, CUDA) @@ -168,17 +169,21 @@ def train_model(self, train_loader): # Layers don't include loaded input, calculate network spikes for up to training layers if layer != 0: - spikes = bn.testSim(self.layers,layer,spikes,idx) - + spikes = bn.testSim(self.layers,layer,spikes) + # Run one timestep of the training input to feature layer bn.runSim(in_layer, out_layer, spikes, idx) - + # Anneal the learning rate if np.mod(mod,10)==0: pt = pow(float(self.T-mod)/self.T,self.annl_pow) out_layer.eta_ip = torch.mul(n_initip,pt) out_layer.eta_stdp = torch.mul(n_initstdp,pt) mod += 1 + + # Reset x and x_input for next iteration + out_layer.x.fill_(0.0) + out_layer.x_input.fill_(0.0) # Once layers have finished training, turn off any learning out_layer.eta_ip = 0 @@ -187,9 +192,9 @@ def train_model(self, train_loader): out_layer.inhW.requires_grad_(False) out_layer.thr.requires_grad_(False) - torch.cuda.empty_cache() - gc.collect() - + torch.cuda.empty_cache() + gc.collect() + def save_model(self, model_out): # save model torch.save(self.state_dict(), model_out) @@ -211,7 +216,7 @@ def __init__(self): # Set up the output layer self.output_layer = SNNLayer(previous_layer=self.feature_layer, dims=[self.number_modules,1,self.output], - assign_weight=True,) + assign_weight=True) # Define number of layers (will run training on all layers) self.layers = {'layer0':self.input_layer, @@ -223,29 +228,61 @@ def load_model(self,model_path): self.load_state_dict(state_dict) self.eval() - def forward(self, x): - # Define the forward pass to transform the input x to an output - # TODO: Replace with actual forward pass code - out = bn.testSim(self) # Assuming testSim is a function that can perform the forward pass - return out + def forward(self, test_loader): + + # If using CUDA, run a dummy torch.bmm to 'spool-up' operations + if self.device.type == "cuda": + # Create some dummy tensors on CUDA + dummy_a = torch.randn(10, 10, device=self.device) + dummy_b = torch.randn(10, 10, device=self.device) + + # Perform a dummy bmm operation + torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) + idx=0 + numcorr = 0 + # Run test network for each individual input + for images, labels in test_loader: + # Set images to the specified device + images = images.to(self.device) + labels = labels.to(self.device) + # Set the spikes for the input, tiling across modules if applicable + make_spikes = SetImageAsSpikes(intensity=self.intensity, + modules=self.number_modules) + spikes = make_spikes(images) + + # Run the test sim + out = bn.testSim(self.layers,len(self.layers)-1,spikes) + tonump = np.array([]) + tonump = np.append(tonump,np.reshape(out.cpu().numpy(), + [1,1,int(self.number_training_images)])) + if np.argmax(tonump) == idx: + numcorr += 1 + idx+=1 + pause=1 if __name__ == "__main__": # Initialize the model and image transforms model = SNNModel() # Generate model name, check if pre-trained model exists - model_name = "VPRTempo"+(str(model.input)+ - str(model.feature)+ - str(model.output)+ - str(model.number_modules)+'-'+ - str(model.device.type)+'.pth') + model_name = ("VPRTempo"+ # main name + str(model.input)+ # number input neurons + str(model.feature)+ # number feature neurons + str(model.output)+ # number output neurons + str(model.number_modules)+ # number of modules + '.pth') if os.path.exists(os.path.join('./models',model_name)): pretrain_flg = True + + # Prompt user to retrain network if desired prompt = "A network with these parameters exists, re-train network? (y/n):\n" retrain = input(prompt) + + # Retrain network, set flag to False if retrain == 'y': pretrain_flg = False else: + # No pretrained model exists pretrain_flg = False # Define the image transform class @@ -274,25 +311,22 @@ def forward(self, x): trainer.train_model(train_loader) trainer.save_model(os.path.join('./models',model_name)) - # Load the trained model into SNNModel() - model.load_model(os.path.join('./models',model_name)) - - # Define the custom testing image dataset class - test_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.testing_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_testing_images, - modules=model.number_modules) - - # Define the testing dataloader class - test_loader = DataLoader(test_dataset, - batch_size=model.number_modules, - shuffle=False, - num_workers=4, - persistent_workers=True) - with torch.no_grad(): # Disable gradient computation during testing - for inputs, targets in test_dataset: - outputs = model(inputs) # This calls the forward method and gets the model’s outputs - # TODO: Compute your evaluation metric(s) by comparing outputs to targets + # Load the trained model into SNNModel() + model.load_model(os.path.join('./models',model_name)) + + # Define the custom testing image dataset class + test_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.testing_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_testing_images, + modules=model.number_modules) + + # Define the testing dataloader class + test_loader = DataLoader(test_dataset, + batch_size=1, + shuffle=False, + num_workers=4, + persistent_workers=True) + model.forward(test_loader) \ No newline at end of file diff --git a/config/config.py b/config/config.py index dd150c7..c0e3d2b 100644 --- a/config/config.py +++ b/config/config.py @@ -8,13 +8,13 @@ def configure(model): model.dataset = 'nordland' model.dataset_file = './dataset/'+model.dataset+'.csv' - model.trainingPath = '/home/adam/data/nordland/' - model.testPath = '/home/adam/data/nordland/' - model.number_modules = 5 - model.number_training_images = 500 - model.number_testing_images = 500 + model.trainingPath = '/Users/adam/data/nordland/' + model.testPath = '/Users/adam/data/nordland/' + model.number_modules = 1 + model.number_training_images = 100 + model.number_testing_images = 100 model.locations = ["spring","fall"] - model.test_locations = "summer" + model.test_locations = ["spring"] model.filter = 8 model.validation = True model.log = False @@ -31,7 +31,7 @@ def configure(model): assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(model.testPath + model.test_locations)), "Images must be organized into folders based on locations, see README.md for details" + assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" model.patches = 7 model.dims = [28,28] @@ -42,7 +42,7 @@ def configure(model): model.location_repeat = len(model.locations) model.layers =[] - model.epoch = 4 + model.epoch = 1 model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #if model.device.type == "cuda": # os.environ["CUDA_LAUNCH_BLOCKING"] = "1" diff --git a/src/blitnet.py b/src/blitnet.py index abd52b0..661d7e4 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -38,7 +38,7 @@ # p: initial connection probability # stdp_rate: STDP rate (0=no STDP) -def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, +def addWeights(W_range=[-1,0,1],p=[1,1],dims=None, num_modules=1): # get torch device @@ -93,13 +93,6 @@ def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, setzeroExc = np.random.rand(nrow,ncol) > p[0] setzeroInh = np.random.rand(nrow,ncol) > p[1] - # add current - if n == 0: - I = torch.zeros(nrow, device=device) - I = torch.unsqueeze(I,0) - else: - I = torch.concat((I,torch.unsqueeze(torch.zeros(nrow, device=device),0)),0) - # remove connections based on calculated indexes if setzeroExc.any(): excW[n,:,:][setzeroExc] = 0.0 # excitatory connections @@ -117,7 +110,7 @@ def addWeights(W_range=[-1,0,1],p=[1,1],stdp_rate=0.001,dims=None, havconnExc = excW > 0 havconnInh = inhW < 0 - return excW, inhW, I, havconnExc, havconnInh + return excW, inhW, havconnExc, havconnInh ################################## # Normalise all the firing rates @@ -129,22 +122,18 @@ def norm_rates(pre_layer,post_layer): for layer in layers: if layer.have_rate and layer.eta_ip > 0.0: - # update = torch.add(layer.thr, - # torch.mul(layer.eta_ip, torch.sub(layer.x, layer.fire_rate))) # Replace the original layer.thr with the updated one layer.thr = nn.Parameter(torch.where(layer.thr + layer.eta_ip * (layer.x - layer.fire_rate) < 0, torch.zeros_like(layer.thr), layer.thr + (layer.eta_ip * (layer.x - layer.fire_rate)))) - torch.cuda.empty_cache() - ################################## # Normalise inhib weights to balance input currents # net: BITnet instance def norm_inhib(layer): - if torch.any(layer.inhW): + if torch.any(layer.inhW).item(): if layer.eta_ip != 0: updated_inhW = layer.inhW + torch.mul(torch.mul(layer.x_input, layer.inhW), layer.eta_stdp*50) @@ -156,28 +145,27 @@ def norm_inhib(layer): def const_thr(layer_pre, layer_post): layers = [layer_pre, layer_post] for layer in layers: - if torch.any(layer.const_inp): - layer.x_input.fill_(0.0) - layer.x_input.add_(layer.const_inp) - - layer.x.detach().add_(torch.clamp(torch.sub(layer.x_input, layer.thr), - 0.0, 0.9)) + layer.x_input.fill_(0.0) + if torch.any(layer.const_inp).item(): + layer.x_input.add_(layer.const_inp) # No need to detach const_inp + # Detach thr to prevent gradients + layer.x.detach().add_(torch.clamp(torch.sub(layer.x_input, layer.thr.detach()), + 0.0, 0.9)) def calc_spikes(post_layer, spikes): - post_layer.x_input.detach().add_(torch.bmm(spikes, post_layer.excW)) - post_layer.x_input.detach().add_(torch.bmm(spikes, post_layer.inhW)) + # Use detached versions of excW, inhW, and thr to prevent gradients from flowing + post_layer.x_input.add_(torch.bmm(spikes, post_layer.excW.detach())) + post_layer.x_input.add_(torch.bmm(spikes, post_layer.inhW.detach())) if post_layer.spk_force: - post_layer.x_calc.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr), + post_layer.x_calc.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), min=0.0, max=0.9)) else: - post_layer.x = torch.full_like(post_layer.x,0) - post_layer.x.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr), + post_layer.x.add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), min=0.0, max=0.9)) - ################################## # Calculate STDP @@ -235,8 +223,12 @@ def calc_stdp(pre_layer,post_layer,spikes,idx=0): post = torch.tile(layers[-1].x, (shape[1], 1)) # Apply the weight changes - layers[-1].excW = nn.Parameter(layers[-1].excW + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnExc) * layers[-1].eta_stdp) - layers[-1].inhW = nn.Parameter(layers[-1].inhW + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnInh) * (layers[-1].eta_stdp * -1)) + layers[-1].excW = nn.Parameter(layers[-1].excW + + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnExc) + * layers[-1].eta_stdp) + layers[-1].inhW = nn.Parameter(layers[-1].inhW + + ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnInh) + * (layers[-1].eta_stdp * -1)) # In-place clamp for excitatory and inhibitory weights # Apply clamping only where the mask is True (non-zero elements) @@ -273,10 +265,13 @@ def runSim(pre_layer,post_layer,spikes,idx): del spikes -def testSim(layers,layer_num,spikes,idx): +def testSim(layers,layer_num,spikes): # run the test system through all specified layers to get an output for n in range(layer_num): + layers['layer'+str(n+1)].x.fill_(0.0) + layers['layer'+str(n+1)].x_input.fill_(0.0) calc_spikes(layers['layer'+str(n+1)],spikes) + spikes=layers['layer'+str(n+1)].x - return layers['layer'+str(n+1)].x \ No newline at end of file + return spikes \ No newline at end of file diff --git a/src/dataset.py b/src/dataset.py index ca0484a..1f3ddc4 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -70,8 +70,10 @@ def __call__(self, img): return im_norm class SetImageAsSpikes: - def __init__(self,intensity): + def __init__(self,intensity=255,test=True,modules=1): self.intensity = intensity + self.test = test + self.modules = modules def __call__(self, img_tensor): # Ensure the input is a 4D tensor (N x C x W x H) @@ -83,6 +85,10 @@ def __call__(self, img_tensor): # Divide all pixel values by 255 normalized_batch = reshaped_batch / self.intensity + + # If running test, repeat input over all the modules + if self.test: + normalized_batch = normalized_batch.repeat(self.modules, 1, 1) return normalized_batch @@ -127,7 +133,7 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= self.img_labels = [] for img_dir in img_dirs: img_labels = pd.read_csv(annotations_file) - img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(img_dir, row[0]), axis=1) + img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(img_dir, row.iloc[0]), axis=1) # Select specific rows based on the skip parameter img_labels = img_labels.iloc[::skip] @@ -138,14 +144,15 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= # Determine if the images being fed are training or testing if test: - poopy=1 - - # Reorder images in the DataFrame - reordered_img_labels = self.reorder_images(img_labels, modules) - self.img_labels.append(reordered_img_labels) + self.img_labels = img_labels + else: + # Reorder images in the DataFrame + reordered_img_labels = self.reorder_images(img_labels, modules) + self.img_labels.append(reordered_img_labels) - # Concatenate all the reordered DataFrames - self.img_labels = pd.concat(self.img_labels, ignore_index=True) + if isinstance(self.img_labels,list): + # Concatenate all the reordered DataFrames + self.img_labels = pd.concat(self.img_labels, ignore_index=True) def reorder_images(self, img_labels, modules): # Calculate the number of batches @@ -176,7 +183,7 @@ def __getitem__(self, idx): raise FileNotFoundError(f"No file found for index {idx} at {img_path}.") image = read_image(img_path) - label = self.img_labels.iloc[idx, 1] # Assuming label is the second column + label = self.img_labels.iloc[idx, 1] if self.transform: image = self.transform(image) From aae3507cee1315fedb95c23e83dd3c37311ca67b Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 5 Oct 2023 17:36:44 +1000 Subject: [PATCH 10/69] Narrowing down issue with weights, seems like the amount that each weight update is adding is very high - investigate further --- VPRTempo.py | 47 ++++++----- config/config.py | 12 +-- dataset/.~lock.nordland.csv# | 1 - src/blitnet.py | 151 +++++++++++++++-------------------- src/dataset.py | 96 +++++++++++++--------- 5 files changed, 155 insertions(+), 152 deletions(-) delete mode 100644 dataset/.~lock.nordland.csv# diff --git a/VPRTempo.py b/VPRTempo.py index 57fbcb3..e38983f 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -39,6 +39,7 @@ import numpy as np import torch.nn as nn import torch.nn.functional as F +import torch.optim as optim from config import configure from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage @@ -72,6 +73,16 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], # Initialize Parameters self.thr = nn.Parameter(torch.zeros(dims, device=self.device).uniform_(thr_range[0], thr_range[1])) self.fire_rate = torch.zeros(dims, device=self.device).uniform_(fire_rate[0], fire_rate[1]) + + # Sequentially set the feature firing rates (if any) + if not torch.all(self.fire_rate==0).item(): + fstep = (fire_rate[1]-fire_rate[0])/dims[-1] + + # loop through all modules and feature layer neurons + for x in range(self.number_modules): + for i in range(dims[-1]): + self.fire_rate[x][:,i] = fire_rate[0]+fstep*(i+1) + self.have_rate = torch.any(self.fire_rate[:,:,0] > 0.0).to(self.device) self.const_inp = torch.zeros(dims, device=self.device).uniform_(const_inp[0], const_inp[1]) self.p = p @@ -113,8 +124,8 @@ def __init__(self): stdp_rate=0.005, const_inp=[0,0.1], p=[0.1,0.5], - assign_weight=True, - requires_grad=True) + requires_grad=True, + assign_weight=True) # Set up the output layer self.output_layer = SNNLayer(previous_layer=self.feature_layer, @@ -122,13 +133,14 @@ def __init__(self): ip_rate=0.15, stdp_rate=0.005, assign_weight=True, - spk_force=True, - requires_grad=True) + requires_grad=True, + spk_force=True) # Define number of layers (will run training on all layers) self.layers = {'layer0':self.input_layer, 'layer1':self.feature_layer, 'layer2':self.output_layer} + def train_model(self, train_loader): @@ -143,7 +155,7 @@ def train_model(self, train_loader): # Run the training for each layer, using defined parameters for layer in range(len(self.layers)-1): - + # Define in and out layer in_layer = self.layers['layer'+str(layer)] out_layer = self.layers['layer'+str(layer+1)] @@ -156,6 +168,7 @@ def train_model(self, train_loader): for n in tqdm(range(self.epoch)): mod = 0 # Used to determine the learning rate annealment for images, labels in train_loader: + # Put input images on device (CPU, CUDA) images = images.to(self.device) @@ -166,14 +179,16 @@ def train_model(self, train_loader): # Put labels on device (CPU, CUDA) labels = labels.to(self.device) idx = labels/self.filter - + # Layers don't include loaded input, calculate network spikes for up to training layers if layer != 0: spikes = bn.testSim(self.layers,layer,spikes) # Run one timestep of the training input to feature layer bn.runSim(in_layer, out_layer, spikes, idx) - + + #self.optimizer.step() + # Anneal the learning rate if np.mod(mod,10)==0: pt = pow(float(self.T-mod)/self.T,self.annl_pow) @@ -185,16 +200,8 @@ def train_model(self, train_loader): out_layer.x.fill_(0.0) out_layer.x_input.fill_(0.0) - # Once layers have finished training, turn off any learning - out_layer.eta_ip = 0 - out_layer.eta_stdp = 0 - out_layer.excW.requires_grad_(False) - out_layer.inhW.requires_grad_(False) - out_layer.thr.requires_grad_(False) - - torch.cuda.empty_cache() - gc.collect() - + torch.cuda.empty_cache() + def save_model(self, model_out): # save model torch.save(self.state_dict(), model_out) @@ -259,7 +266,9 @@ def forward(self, test_loader): if np.argmax(tonump) == idx: numcorr += 1 idx+=1 - pause=1 + print(str(round((numcorr/self.number_testing_images)*100,2))+'%') + torch.cuda.empty_cache() + gc.collect() if __name__ == "__main__": # Initialize the model and image transforms model = SNNModel() @@ -329,4 +338,4 @@ def forward(self, test_loader): shuffle=False, num_workers=4, persistent_workers=True) - model.forward(test_loader) \ No newline at end of file + model.forward(test_loader) diff --git a/config/config.py b/config/config.py index c0e3d2b..d1e85df 100644 --- a/config/config.py +++ b/config/config.py @@ -8,11 +8,11 @@ def configure(model): model.dataset = 'nordland' model.dataset_file = './dataset/'+model.dataset+'.csv' - model.trainingPath = '/Users/adam/data/nordland/' - model.testPath = '/Users/adam/data/nordland/' - model.number_modules = 1 - model.number_training_images = 100 - model.number_testing_images = 100 + model.trainingPath = '/home/adam/data/nordland/' + model.testPath = '/home/adam/data/nordland/' + model.number_modules = 5 + model.number_training_images = 500 + model.number_testing_images = 500 model.locations = ["spring","fall"] model.test_locations = ["spring"] model.filter = 8 @@ -42,7 +42,7 @@ def configure(model): model.location_repeat = len(model.locations) model.layers =[] - model.epoch = 1 + model.epoch = 4 model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #if model.device.type == "cuda": # os.environ["CUDA_LAUNCH_BLOCKING"] = "1" diff --git a/dataset/.~lock.nordland.csv# b/dataset/.~lock.nordland.csv# deleted file mode 100644 index a1b88c9..0000000 --- a/dataset/.~lock.nordland.csv# +++ /dev/null @@ -1 +0,0 @@ -,adam,QUT-PA00146192L,03.10.2023 09:45,file:///home/adam/.config/libreoffice/4; \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index 661d7e4..9e2bdde 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -116,131 +116,110 @@ def addWeights(W_range=[-1,0,1],p=[1,1],dims=None, # Normalise all the firing rates # net: BITnet instance -def norm_rates(pre_layer,post_layer): - # put layers into a list - layers = [pre_layer,post_layer] +def norm_rates(post_layer): - for layer in layers: - if layer.have_rate and layer.eta_ip > 0.0: - # Replace the original layer.thr with the updated one - layer.thr = nn.Parameter(torch.where(layer.thr + layer.eta_ip * (layer.x - layer.fire_rate) < 0, - torch.zeros_like(layer.thr), - layer.thr + (layer.eta_ip * (layer.x - layer.fire_rate)))) - + if post_layer.have_rate and post_layer.eta_ip > 0.0: + # Replace the original layer.thr with the updated one + post_layer.thr.data += post_layer.eta_ip * (post_layer.x - post_layer.fire_rate) + post_layer.thr.data[post_layer.thr.data < 0] = 0 ################################## # Normalise inhib weights to balance input currents # net: BITnet instance -def norm_inhib(layer): - if torch.any(layer.inhW).item(): - if layer.eta_ip != 0: - updated_inhW = layer.inhW + torch.mul(torch.mul(layer.x_input, layer.inhW), - layer.eta_stdp*50) - layer.inhW = nn.Parameter(torch.where(updated_inhW > 0.0, - torch.tensor(-0.000001, device=updated_inhW.device), - updated_inhW)) +def norm_inhib(post_layer): + if torch.any(post_layer.inhW).item(): + if post_layer.eta_stdp != 0: + post_layer.inhW.data += torch.mul(torch.mul(post_layer.x_input, post_layer.inhW.data), + post_layer.eta_stdp*50) + post_layer.inhW.data[post_layer.inhW.data > 0.0] = -1e-06 + -def const_thr(layer_pre, layer_post): - layers = [layer_pre, layer_post] - for layer in layers: - layer.x_input.fill_(0.0) - if torch.any(layer.const_inp).item(): - layer.x_input.add_(layer.const_inp) # No need to detach const_inp - # Detach thr to prevent gradients - layer.x.detach().add_(torch.clamp(torch.sub(layer.x_input, layer.thr.detach()), - 0.0, 0.9)) +def const_thr(post_layer): + + if torch.any(post_layer.const_inp).item(): + post_layer.x_input += post_layer.const_inp # No need to detach const_inp + # Detach thr to prevent gradients + post_layer.x = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), + 0.0, 0.9) def calc_spikes(post_layer, spikes): # Use detached versions of excW, inhW, and thr to prevent gradients from flowing - post_layer.x_input.add_(torch.bmm(spikes, post_layer.excW.detach())) - post_layer.x_input.add_(torch.bmm(spikes, post_layer.inhW.detach())) + post_layer.x_input += torch.bmm(spikes, post_layer.excW) + post_layer.x_input += torch.bmm(spikes, post_layer.inhW) if post_layer.spk_force: - post_layer.x_calc.detach().add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), - min=0.0, max=0.9)) + post_layer.x_calc += torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), + min=0.0, max=0.9) else: - post_layer.x.add_(torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), - min=0.0, max=0.9)) + post_layer.x += torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), + min=0.0, max=0.9) ################################## # Calculate STDP # net: BITnet instance -def calc_stdp(pre_layer,post_layer,spikes,idx=0): - layers = [pre_layer,post_layer] - +def calc_stdp(pre_layer, post_layer, spikes, idx=0): + # Spike Forcing has special rules to make calculated and forced spikes match - if layers[-1].spk_force: # will run for the output layer - shape = [len(layers[-1].excW[:, 0, 0]), - len(layers[-1].excW[0, :, 0]), - len(layers[-1].excW[0, 0, :])] + if post_layer.spk_force: # will run for the output layer + shape = [len(post_layer.excW[:, 0, 0]), + len(post_layer.excW[0, :, 0]), + len(post_layer.excW[0, 0, :])] # Get the output neuron index - idx_sel = torch.arange(int(idx[0]),int(idx[0])+1,device=layers[-1].device,dtype=int) - + idx_sel = torch.arange(int(idx[0]), int(idx[0]) + 1, device=post_layer.device, dtype=int) + # Difference between forced and calculated spikes - layers[-1].x = torch.full_like(layers[-1].x,0) - xdiff = torch.clamp(layers[-1].x.index_fill_(-1,idx_sel,0.5) - layers[-1].x_calc, - min=0,max=1) + post_layer.x = torch.full_like(post_layer.x, 0) + xdiff = torch.clamp(post_layer.x.index_fill_(-1, idx_sel, 0.5) - post_layer.x_calc, + min=0, max=1) # Threshold rules - lower it if calced spike is smaller (and vice versa) - layers[-1].thr = nn.Parameter(layers[-1].thr - - torch.sign(xdiff)*torch.abs(layers[-1].eta_stdp)/10) - layers[-1].thr = nn.Parameter(layers[-1].thr - - torch.sign(xdiff)*torch.abs((layers[-1].eta_stdp*-1))/10) - layers[-1].thr = nn.Parameter(layers[-1].thr.clamp(min=0, max=1)) + post_layer.thr.data -= torch.sign(xdiff) * torch.abs(post_layer.eta_stdp) / 10 + post_layer.thr.data -= torch.sign(xdiff) * torch.abs((post_layer.eta_stdp * -1)) / 10 + post_layer.thr = nn.Parameter(post_layer.thr.clamp(min=0, max=1)) # Pre and Post spikes tiled across and down for all synapses - if layers[0].have_rate: + if pre_layer.have_rate: # Modulate learning rate by firing rate (low firing rate = high learning rate) - mpre = spikes/layers[0].fire_rate + mpre = spikes / pre_layer.fire_rate else: mpre = spikes - pre = torch.tile(torch.reshape(mpre,(shape[0],shape[1],1)),(1,shape[2])) - post = torch.tile(xdiff,(shape[1],1)) + pre = torch.tile(torch.reshape(mpre, (shape[0], shape[1], 1)), (1, shape[2])) + post = torch.tile(xdiff, (shape[1], 1)) # Apply the weight changes - layers[-1].excW = nn.Parameter(layers[-1].excW + - (pre*post*layers[-1].havconnExc)*layers[-1].eta_stdp) - layers[-1].inhW = nn.Parameter(layers[-1].inhW + - (-pre*post*layers[-1].havconnInh)*(layers[-1].eta_stdp*-1)) - + post_layer.excW.data += (pre * post * post_layer.havconnExc) * post_layer.eta_stdp + + post_layer.inhW.data += (-pre * post * post_layer.havconnInh) * (post_layer.eta_stdp * -1) + # Normal STDP else: - - # Assuming layers is a predefined list containing your layer objects - - shape = [len(layers[-1].excW[:, 0, 0]), - len(layers[-1].excW[0, :, 0]), - len(layers[-1].excW[0, 0, :])] - + shape = [len(post_layer.excW[:, 0, 0]), + len(post_layer.excW[0, :, 0]), + len(post_layer.excW[0, 0, :])] + # Tile out pre- and post-spikes pre = torch.tile(torch.reshape(spikes, (shape[0], shape[1], 1)), (1, shape[2])) - post = torch.tile(layers[-1].x, (shape[1], 1)) - - # Apply the weight changes - layers[-1].excW = nn.Parameter(layers[-1].excW + - ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnExc) - * layers[-1].eta_stdp) - layers[-1].inhW = nn.Parameter(layers[-1].inhW + - ((0.5 - post) * (pre > 0) * (post > 0) * layers[-1].havconnInh) - * (layers[-1].eta_stdp * -1)) + post = torch.tile(post_layer.x, (shape[1], 1)) + # Apply the weight changes + post_layer.excW.data += ((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnExc) * post_layer.eta_stdp + post_layer.inhW.data += ((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnInh) * (post_layer.eta_stdp * -1) + print(torch.mean(((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnExc) * post_layer.eta_stdp)) # In-place clamp for excitatory and inhibitory weights # Apply clamping only where the mask is True (non-zero elements) - layers[-1].excW = nn.Parameter(torch.where(layers[-1].havconnExc, - layers[-1].excW.clamp(min=0.000001, max=10.0), - layers[-1].excW)) - + post_layer.excW.data[post_layer.excW.data < 0] = 1e-06 + post_layer.inhW.data[post_layer.inhW.data > 0] = -1e-06 + + post_layer.excW.data[post_layer.havconnExc] = post_layer.excW.data[post_layer.havconnExc].clamp(min=1e-06, max=10) # Apply clamping only where the mask is True (non-zero elements) - layers[-1].inhW = nn.Parameter(torch.where(layers[-1].havconnInh, - layers[-1].inhW.clamp(min=-10.0, max=-0.000001), - layers[-1].inhW)) - + post_layer.inhW.data[post_layer.havconnInh] = post_layer.inhW.data[post_layer.havconnInh].clamp(min=-10, max=-1e-06) + torch.cuda.empty_cache() ################################## @@ -251,19 +230,17 @@ def calc_stdp(pre_layer,post_layer,spikes,idx=0): def runSim(pre_layer,post_layer,spikes,idx): # Propagate spikes from pre to post neurons - const_thr(pre_layer,post_layer) + const_thr(post_layer) calc_spikes(post_layer,spikes) # Calculate STDP weight changes calc_stdp(pre_layer,post_layer,spikes,idx) # Normalise firing rates and inhibitory balance - norm_rates(pre_layer,post_layer) + norm_rates(post_layer) norm_inhib(post_layer) torch.cuda.empty_cache() - - del spikes def testSim(layers,layer_num,spikes): diff --git a/src/dataset.py b/src/dataset.py index 1f3ddc4..0b9d7c1 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -11,22 +11,21 @@ from torch.utils.data import Dataset class GetPatches2D: - def __init__(self, patch_size): - self.patch_size = (patch_size,patch_size) + def __init__(self, patch_size, image_pad): + self.patch_size = patch_size + self.image_pad = image_pad def __call__(self, img): - # Calculating the padding - padding = (self.patch_size[1] // 2, self.patch_size[1] // 2, - self.patch_size[0] // 2, self.patch_size[0] // 2) - # Apply zero padding - image_pad = F.pad(img, padding, value=float('nan')) + # Assuming image_pad is already a PyTorch tensor. If not, you can convert it: + # image_pad = torch.tensor(image_pad).to(torch.float64) - # Unfolding the image to get the patches - patches = image_pad.unfold(0, self.patch_size[0], 1).unfold(1, self.patch_size[1], 1) - - # Reshaping the patches - patches = patches.contiguous().view(self.patch_size[0] * self.patch_size[1], -1) + # Using unfold to get 2D sliding windows. + unfolded = self.image_pad.unfold(0, self.patch_size[0], 1).unfold(1, self.patch_size[1], 1) + # The size of unfolded will be [nrows, ncols, patch_size[0], patch_size[1]] + + # Reshaping the tensor to the desired shape + patches = unfolded.permute(2, 3, 0, 1).contiguous().view(self.patch_size[0]*self.patch_size[1], -1) return patches @@ -34,38 +33,57 @@ def __call__(self, img): class PatchNormalisePad: def __init__(self, patches): self.patches = patches + + + def nanstd(self,input_tensor, dim=None, unbiased=True): + if dim is not None: + valid_count = torch.sum(~torch.isnan(input_tensor), dim=dim, dtype=torch.float) + mean = torch.nansum(input_tensor, dim=dim) / valid_count + diff = input_tensor - mean.unsqueeze(dim) + variance = torch.nansum(diff * diff, dim=dim) / valid_count + + # Bessel's correction for unbiased estimation + if unbiased: + variance = variance * (valid_count / (valid_count - 1)) + else: + valid_count = torch.sum(~torch.isnan(input_tensor), dtype=torch.float) + mean = torch.nansum(input_tensor) / valid_count + diff = input_tensor - mean + variance = torch.nansum(diff * diff) / valid_count + + # Bessel's correction for unbiased estimation + if unbiased: + variance = variance * (valid_count / (valid_count - 1)) + + return torch.sqrt(variance) def __call__(self, img): - img = img.squeeze(0) - nrows, ncols = img.shape[:2] - patcher = GetPatches2D(self.patches) + img = torch.squeeze(img,0) + patch_size = (self.patches, self.patches) + patch_half_size = [int((p-1)/2) for p in patch_size ] + + # Compute the padding. If patch_half_size is a scalar, the same value will be used for all sides. + if isinstance(patch_half_size, int): + pad = (patch_half_size, patch_half_size, patch_half_size, patch_half_size) # left, right, top, bottom + else: + # If patch_half_size is a tuple, then we'll assume it's in the format (height, width) + pad = (patch_half_size[1], patch_half_size[1], patch_half_size[0], patch_half_size[0]) # left, right, top, bottom + + # Apply padding + image_pad = F.pad(img, pad, mode='constant', value=float('nan')) + + nrows = img.shape[0] + ncols = img.shape[1] + patcher = GetPatches2D(patch_size,image_pad) patches = patcher(img) mus = torch.nanmean(patches, dim=0) - # Subtracting the mean from the original tensor - diff = patches - mus - - # Replacing NaN values with zeros in the difference tensor - diff[torch.isnan(diff)] = 0 + stds = self.nanstd(patches, dim=0) + with np.errstate(divide='ignore', invalid='ignore'): + im_norm = (img - mus.reshape(nrows, ncols)) / stds.reshape(nrows, ncols) - # Calculating the unbiased estimator of the variance, ignoring NaN values - var = torch.nansum(diff**2, dim=0) / (torch.sum(~torch.isnan(patches), dim=0) - 1) - - # Taking the square root to get the standard deviation - stds = torch.sqrt(var) - - # Reshape mus and stds - mus_reshaped = mus.reshape(nrows, ncols) - stds_reshaped = stds.reshape(nrows, ncols) - - # Perform the normalization, handling division by zero - # Note: PyTorch, by default, does not raise an error or warning for NaN or Inf, it will propagate them in the computation - im_norm = (img - mus_reshaped) / stds_reshaped - - # Replace NaN values with 0.0 im_norm[torch.isnan(im_norm)] = 0.0 - - # Clamp values to the range [-1.0, 1.0] - im_norm = torch.clamp(im_norm, min=-1.0, max=1.0) + im_norm[im_norm < -1.0] = -1.0 + im_norm[im_norm > 1.0] = 1.0 return im_norm @@ -89,7 +107,7 @@ def __call__(self, img_tensor): # If running test, repeat input over all the modules if self.test: normalized_batch = normalized_batch.repeat(self.modules, 1, 1) - + return normalized_batch class ProcessImage: From bf6609a3b856ab0cf6da8f3fe03d18bc58a33da8 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 6 Oct 2023 13:36:36 +1000 Subject: [PATCH 11/69] Fixed all issues, now runs as expected. Merge to main --- VPRTempo.py | 111 +++++++---- config/config.py | 116 +++++------- src/blitnet.py | 140 +++++--------- src/utils.py | 484 +---------------------------------------------- 4 files changed, 181 insertions(+), 670 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index e38983f..ab851bb 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -36,22 +36,24 @@ sys.path.append('./config') import blitnet as bn +import utils as ut import numpy as np import torch.nn as nn import torch.nn.functional as F -import torch.optim as optim -from config import configure +from config import configure, image_csv, model_logger from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader from tqdm import tqdm +from timeit import default_timer class SNNLayer(nn.Module): def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[1,1], - assign_weight=False,spk_force=False,requires_grad=False): + assign_weight=False,spk_force=False): super(SNNLayer, self).__init__() - configure(self) + # Configure the network + configure(self) # Sets the testing configuration # Device self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -101,13 +103,15 @@ def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], dims[2]], num_modules=self.number_modules) - self.excW = nn.Parameter(excW, requires_grad=requires_grad) - self.inhW = nn.Parameter(inhW, requires_grad=requires_grad) + self.excW = nn.Parameter(excW) + self.inhW = nn.Parameter(inhW) class SNNTrainer(nn.Module): def __init__(self): super(SNNTrainer, self).__init__() - configure(self) + # Configure the network + configure(self) # Sets the testing configuration + image_csv(self) # Defines images to load # Set the device self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -124,7 +128,6 @@ def __init__(self): stdp_rate=0.005, const_inp=[0,0.1], p=[0.1,0.5], - requires_grad=True, assign_weight=True) # Set up the output layer @@ -133,7 +136,6 @@ def __init__(self): ip_rate=0.15, stdp_rate=0.005, assign_weight=True, - requires_grad=True, spk_force=True) # Define number of layers (will run training on all layers) @@ -142,21 +144,24 @@ def __init__(self): 'layer2':self.output_layer} - def train_model(self, train_loader): + def train_model(self, train_loader, logger): # If using CUDA, run a dummy torch.bmm to 'spool-up' operations if self.device.type == "cuda": - # Create some dummy tensors on CUDA - dummy_a = torch.randn(10, 10, device=self.device) - dummy_b = torch.randn(10, 10, device=self.device) - - # Perform a dummy bmm operation - torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) - + ut.dummy_bmm(self.device) + # Run the training for each layer, using defined parameters for layer in range(len(self.layers)-1): - - # Define in and out layer + + # Start timer for layer trainins + start_lyr = default_timer() + + # Initialize the tqdm progress bar + pbar = tqdm(total=int(self.T * self.epoch), + desc="Training layer "+str(layer)+" with layer "+str(layer+1), + position=0) + + # Define in and out layer (2 layers trained at a time) in_layer = self.layers['layer'+str(layer)] out_layer = self.layers['layer'+str(layer+1)] @@ -165,7 +170,7 @@ def train_model(self, train_loader): n_initip = out_layer.eta_ip.detach() # ITP # Run the training for the input to feature layer for specified epochs - for n in tqdm(range(self.epoch)): + for epoch in range(self.epoch): mod = 0 # Used to determine the learning rate annealment for images, labels in train_loader: @@ -187,8 +192,6 @@ def train_model(self, train_loader): # Run one timestep of the training input to feature layer bn.runSim(in_layer, out_layer, spikes, idx) - #self.optimizer.step() - # Anneal the learning rate if np.mod(mod,10)==0: pt = pow(float(self.T-mod)/self.T,self.annl_pow) @@ -199,9 +202,25 @@ def train_model(self, train_loader): # Reset x and x_input for next iteration out_layer.x.fill_(0.0) out_layer.x_input.fill_(0.0) + pbar.update(1) - torch.cuda.empty_cache() - + # Record training time for the layer + end_lyr = default_timer()-start_lyr + + # Close the tqdm progress bar + pbar.close() + + # Record the training time and training frequency + logger.info('') + logger.info("Training layer "+str(layer)+" with layer "+ + str(layer+1)+" took "+str(round(end_lyr,2))+"s") + logger.info('') + + # Clear the cache and garbage collect (if using cuda) + if self.device.type == "cuda": + torch.cuda.empty_cache() + gc.collect() + def save_model(self, model_out): # save model torch.save(self.state_dict(), model_out) @@ -209,8 +228,11 @@ def save_model(self, model_out): class SNNModel(nn.Module): def __init__(self): super(SNNModel, self).__init__() + # Configure the network - configure(self) + configure(self) # Sets the testing configuration + image_csv(self) # Defines images to load + model_logger(self) # Sets the logger # Set up the input layer self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) @@ -239,20 +261,23 @@ def forward(self, test_loader): # If using CUDA, run a dummy torch.bmm to 'spool-up' operations if self.device.type == "cuda": - # Create some dummy tensors on CUDA - dummy_a = torch.randn(10, 10, device=self.device) - dummy_b = torch.randn(10, 10, device=self.device) + ut.dummy_bmm(self.device) - # Perform a dummy bmm operation - torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) idx=0 numcorr = 0 - + + # Initialize the tqdm progress bar + pbar = tqdm(total=self.number_testing_images, + desc="Running the test network", + position=0) + # Run test network for each individual input for images, labels in test_loader: + # Set images to the specified device images = images.to(self.device) labels = labels.to(self.device) + # Set the spikes for the input, tiling across modules if applicable make_spikes = SetImageAsSpikes(intensity=self.intensity, modules=self.number_modules) @@ -260,15 +285,28 @@ def forward(self, test_loader): # Run the test sim out = bn.testSim(self.layers,len(self.layers)-1,spikes) + + # Store output and determine if output matches GT tonump = np.array([]) tonump = np.append(tonump,np.reshape(out.cpu().numpy(), [1,1,int(self.number_training_images)])) if np.argmax(tonump) == idx: numcorr += 1 idx+=1 - print(str(round((numcorr/self.number_testing_images)*100,2))+'%') - torch.cuda.empty_cache() - gc.collect() + + # Update the progress bar + pbar.update(1) + + pbar.close() + model.logger.info('') + model.logger.info("P@100R: "+ + str(round((numcorr/self.number_testing_images)*100,2))+'%') + + # Clear cache and empty garbage (if applicable) + if self.device.type == "cuda": + torch.cuda.empty_cache() + gc.collect() + if __name__ == "__main__": # Initialize the model and image transforms model = SNNModel() @@ -286,6 +324,7 @@ def forward(self, test_loader): # Prompt user to retrain network if desired prompt = "A network with these parameters exists, re-train network? (y/n):\n" retrain = input(prompt) + print('') # Retrain network, set flag to False if retrain == 'y': @@ -317,7 +356,7 @@ def forward(self, test_loader): # Initialize, run, and save the training model trainer = SNNTrainer() - trainer.train_model(train_loader) + trainer.train_model(train_loader,model.logger) trainer.save_model(os.path.join('./models',model_name)) with torch.no_grad(): # Disable gradient computation during testing @@ -338,4 +377,4 @@ def forward(self, test_loader): shuffle=False, num_workers=4, persistent_workers=True) - model.forward(test_loader) + model.forward(test_loader) \ No newline at end of file diff --git a/config/config.py b/config/config.py index d1e85df..ac21482 100644 --- a/config/config.py +++ b/config/config.py @@ -10,14 +10,14 @@ def configure(model): model.dataset_file = './dataset/'+model.dataset+'.csv' model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' - model.number_modules = 5 - model.number_training_images = 500 - model.number_testing_images = 500 - model.locations = ["spring","fall"] - model.test_locations = ["spring"] + model.number_modules = 10 + model.number_training_images = 2700 + model.number_testing_images = 2700 + model.locations = ["spring","fall","winter"] + model.test_locations = ["summer"] model.filter = 8 model.validation = True - model.log = False + model.log = True model.training_dirs = [] for n in model.locations: @@ -32,7 +32,8 @@ def configure(model): assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" - + + model.epoch = 4 model.patches = 7 model.dims = [28,28] model.input = int(model.dims[0]*model.dims[1]) @@ -40,25 +41,16 @@ def configure(model): model.output = int(model.number_training_images/model.number_modules) model.intensity = 255 model.location_repeat = len(model.locations) - model.layers =[] + model.layers = [] - model.epoch = 4 model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - #if model.device.type == "cuda": - # os.environ["CUDA_LAUNCH_BLOCKING"] = "1" - # torch.cuda.set_device(model.device) - # gc.collect() - #torch.cuda.empty_cache() - #torch.cuda.init() - #torch.cuda.synchronize(device=model.device) + if model.device.type == "cuda": + torch.cuda.init() + torch.cuda.synchronize(device=model.device) model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) model.annl_pow = 2 - model.imgs = {'training': [], 'testing': []} - model.ids = {'training': [], 'testing': []} - model.spike_rates = {'training': [], 'testing': []} - - model.test_true = False +def image_csv(model): with open(os.path.join('./dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: reader = csv.reader(file) model.imageNames = [row[0] for row in reader] @@ -68,55 +60,43 @@ def configure(model): for n in range(0, len(model.imageNames), model.filter): model.filteredNames.append(model.imageNames[n]) del model.filteredNames[model.number_training_images:len(model.filteredNames)] - + +def model_logger(model): model.fullTrainPaths = [] for n in model.locations: model.fullTrainPaths.append(model.trainingPath + n + '/') - #now = datetime.now() - #model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - #os.mkdir(model.output_folder) + now = datetime.now() + model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + os.mkdir(model.output_folder) - #model.logger = logging.getLogger("VPRTempo") - #model.logger.setLevel(logging.DEBUG) - #logging.basicConfig(filename=model.output_folder + "/logfile.log", - # filemode="a+", - # format="%(asctime)-15s %(levelname)-8s %(message)s") - #if model.log: - # model.logger.addHandler(logging.StreamHandler()) - - #model.logger.info('////////////') - #model.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha') - #model.logger.info('Queensland University of Technology, Centre for Robotics') - #model.logger.info('') - #model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') - #model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') - #model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') - #model.logger.info('') - #model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - #if torch.cuda.is_available(): - # current_device = torch.cuda.current_device() - # model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) - #else: - # model.logger.info('Current device is: CPU') - #model.logger.info('') - #model.logger.info("~~ Hyperparameters ~~") - #model.logger.info('') - #model.logger.info('Firing threshold max: ' + str(model.thr) - #model.logger.info('Initial STDP learning rate: ' + str(model.n_init)) - #model.logger.info('Intrinsic threshold plasticity learning rate: ' + str(model.n_itp)) - #model.logger.info('Firing rate range: [' + str(model.f_rate[0]) + ', ' + str(model.f_rate[1]) + ']') - #model.logger.info('Excitatory connection probability: ' + str(model.p_exc)) - #model.logger.info('Inhibitory connection probability: ' + str(model.p_inh)) - #model.logger.info('Constant input: ' + str(model.c)) - #model.logger.info('') - #model.logger.info("~~ Training and testing conditions ~~") - #model.logger.info('') - #model.logger.info('Number of training images: ' + str(model.number_training_images)) - #model.logger.info('Number of testing images: ' + str(model.number_testing_images)) - #model.logger.info('Number of training epochs: ' + str(model.epoch)) - #model.logger.info('Number of modules: ' + str(model.number_modules)) - #model.logger.info('Dataset used: ' + str(model.dataset)) - #model.logger.info('Training locations: ' + str(model.locations)) - #model.logger.info('Testing location: ' + str(model.test_location)) - #model.training_out = './models/' + str(model.input_layer) + 'i' + str(model.feature_layer) + 'f' + str(model.output_layer) + 'o' + str(model.epoch) + '/' + model.logger = logging.getLogger("VPRTempo") + model.logger.setLevel(logging.DEBUG) + logging.basicConfig(filename=model.output_folder + "/logfile.log", + filemode="a+", + format="%(asctime)-15s %(levelname)-8s %(message)s") + if model.log: + model.logger.addHandler(logging.StreamHandler()) + + model.logger.info('') + model.logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗') + model.logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗') + model.logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║') + model.logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║') + model.logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝') + model.logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ') + model.logger.info('-----------------------------------------------------------------------') + model.logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') + model.logger.info('Queensland University of Technology, Centre for Robotics') + model.logger.info('') + model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') + model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + model.logger.info('') + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + if torch.cuda.is_available(): + current_device = torch.cuda.current_device() + model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) + else: + model.logger.info('Current device is: CPU') + model.logger.info('') \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index 9e2bdde..7c6c369 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -24,22 +24,10 @@ Imports ''' import numpy as np -import pdb import torch -import gc -import torch.nn as nn -from timeit import default_timer - -################################## -# Add a set of random connections between layers -# W_range: weight range [lo,hi] -# p: initial connection probability -# stdp_rate: STDP rate (0=no STDP) - -def addWeights(W_range=[-1,0,1],p=[1,1],dims=None, - num_modules=1): +def addWeights(W_range=[-1,0,1],p=[1,1],dims=None,num_modules=1): # get torch device device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -66,23 +54,19 @@ def addWeights(W_range=[-1,0,1],p=[1,1],dims=None, if n == 0: # first weights to be appended # excitatory weights excW = torch.cat((excW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), - 0)), - 0) + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), + 0)), 0) # inhibitory weights inhW = torch.cat((inhW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), - 0)), - 0) + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), + 0)), 0) else: # stack new weights onto appended weight excW = torch.cat((excW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), - 0)), - 0) + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), + 0)), 0) inhW = torch.cat((inhW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), - 0)), - 0) + torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), + 0)), 0) # Remove negative excitatory weights excW[n][excW[n] < 0] = 0.0 @@ -111,62 +95,30 @@ def addWeights(W_range=[-1,0,1],p=[1,1],dims=None, havconnInh = inhW < 0 return excW, inhW, havconnExc, havconnInh - -################################## -# Normalise all the firing rates -# net: BITnet instance - -def norm_rates(post_layer): - - if post_layer.have_rate and post_layer.eta_ip > 0.0: - # Replace the original layer.thr with the updated one - post_layer.thr.data += post_layer.eta_ip * (post_layer.x - post_layer.fire_rate) - post_layer.thr.data[post_layer.thr.data < 0] = 0 - -################################## -# Normalise inhib weights to balance input currents -# net: BITnet instance - -def norm_inhib(post_layer): - if torch.any(post_layer.inhW).item(): - if post_layer.eta_stdp != 0: - post_layer.inhW.data += torch.mul(torch.mul(post_layer.x_input, post_layer.inhW.data), - post_layer.eta_stdp*50) - post_layer.inhW.data[post_layer.inhW.data > 0.0] = -1e-06 - - - -def const_thr(post_layer): - - if torch.any(post_layer.const_inp).item(): - post_layer.x_input += post_layer.const_inp # No need to detach const_inp - # Detach thr to prevent gradients - post_layer.x = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), - 0.0, 0.9) - - def calc_spikes(post_layer, spikes): - - # Use detached versions of excW, inhW, and thr to prevent gradients from flowing - post_layer.x_input += torch.bmm(spikes, post_layer.excW) - post_layer.x_input += torch.bmm(spikes, post_layer.inhW) + # Add the constant input + post_layer.x_input += post_layer.const_inp + + # Multiply input spikes by positive and negative weights + post_layer.x_input += torch.bmm(spikes, post_layer.excW.detach()) + post_layer.x_input += torch.bmm(spikes, post_layer.inhW.detach()) + + # Clamp outputs between 0 and 0.9 after subtracting thresholds from input if post_layer.spk_force: - post_layer.x_calc += torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), + post_layer.x_calc = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), min=0.0, max=0.9) else: - post_layer.x += torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), + post_layer.x = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), min=0.0, max=0.9) -################################## -# Calculate STDP -# net: BITnet instance - def calc_stdp(pre_layer, post_layer, spikes, idx=0): # Spike Forcing has special rules to make calculated and forced spikes match - if post_layer.spk_force: # will run for the output layer + if post_layer.spk_force: + + # Get layer dimensions shape = [len(post_layer.excW[:, 0, 0]), len(post_layer.excW[0, :, 0]), len(post_layer.excW[0, 0, :])] @@ -181,7 +133,7 @@ def calc_stdp(pre_layer, post_layer, spikes, idx=0): # Threshold rules - lower it if calced spike is smaller (and vice versa) post_layer.thr.data -= torch.sign(xdiff) * torch.abs(post_layer.eta_stdp) / 10 post_layer.thr.data -= torch.sign(xdiff) * torch.abs((post_layer.eta_stdp * -1)) / 10 - post_layer.thr = nn.Parameter(post_layer.thr.clamp(min=0, max=1)) + post_layer.thr.data = post_layer.thr.data.clamp(min=0, max=1) # Pre and Post spikes tiled across and down for all synapses if pre_layer.have_rate: @@ -199,6 +151,8 @@ def calc_stdp(pre_layer, post_layer, spikes, idx=0): # Normal STDP else: + + # Get layer dimensions shape = [len(post_layer.excW[:, 0, 0]), len(post_layer.excW[0, :, 0]), len(post_layer.excW[0, 0, :])] @@ -207,30 +161,42 @@ def calc_stdp(pre_layer, post_layer, spikes, idx=0): pre = torch.tile(torch.reshape(spikes, (shape[0], shape[1], 1)), (1, shape[2])) post = torch.tile(post_layer.x, (shape[1], 1)) - # Apply the weight changes - post_layer.excW.data += ((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnExc) * post_layer.eta_stdp - post_layer.inhW.data += ((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnInh) * (post_layer.eta_stdp * -1) - print(torch.mean(((0.5 - post) * (pre > 0) * (post > 0) * post_layer.havconnExc) * post_layer.eta_stdp)) + # Apply positive and negative weight changes + post_layer.excW.data += ((0.5 - post) * (pre > 0) * (post > 0) * + post_layer.havconnExc) * post_layer.eta_stdp + post_layer.inhW.data += ((0.5 - post) * (pre > 0) * + (post > 0) * post_layer.havconnInh) * (post_layer.eta_stdp * -1) + # In-place clamp for excitatory and inhibitory weights - # Apply clamping only where the mask is True (non-zero elements) post_layer.excW.data[post_layer.excW.data < 0] = 1e-06 post_layer.inhW.data[post_layer.inhW.data > 0] = -1e-06 - + + # Remove negative weights for excW and positive for inhW post_layer.excW.data[post_layer.havconnExc] = post_layer.excW.data[post_layer.havconnExc].clamp(min=1e-06, max=10) - # Apply clamping only where the mask is True (non-zero elements) post_layer.inhW.data[post_layer.havconnInh] = post_layer.inhW.data[post_layer.havconnInh].clamp(min=-10, max=-1e-06) + +def norm_rates(post_layer): + + # Check if layer has target firing rate and an ITP learning rate + if post_layer.have_rate and post_layer.eta_ip > 0.0: + + # Replace the original layer.thr with the updated one + post_layer.thr.data += post_layer.eta_ip * (post_layer.x - post_layer.fire_rate) + post_layer.thr.data[post_layer.thr.data < 0] = 0 - torch.cuda.empty_cache() - -################################## -# Run the simulation -# net: BITnet instance -# n_steps: number of steps - +def norm_inhib(post_layer): + + # Check if layer has inhibitory weights and an stdp learning rate + if torch.any(post_layer.inhW).item() and post_layer.eta_stdp != 0: + + # Normalize the inhibitory weights using homeostasis + post_layer.inhW.data += torch.mul(torch.mul(post_layer.x_input, post_layer.inhW.data), + post_layer.eta_stdp*50) + post_layer.inhW.data[post_layer.inhW.data > 0.0] = -1e-06 + def runSim(pre_layer,post_layer,spikes,idx): # Propagate spikes from pre to post neurons - const_thr(post_layer) calc_spikes(post_layer,spikes) # Calculate STDP weight changes @@ -240,8 +206,6 @@ def runSim(pre_layer,post_layer,spikes,idx): norm_rates(post_layer) norm_inhib(post_layer) - torch.cuda.empty_cache() - def testSim(layers,layer_num,spikes): # run the test system through all specified layers to get an output diff --git a/src/utils.py b/src/utils.py index 92b3f07..9887328 100644 --- a/src/utils.py +++ b/src/utils.py @@ -39,485 +39,13 @@ from timeit import default_timer from os import path - -def get_patches2D(patch_size,image_pad): - - if patch_size[0] % 2 == 0: - nrows = image_pad.shape[0] - patch_size[0] + 2 - ncols = image_pad.shape[1] - patch_size[1] + 2 - else: - nrows = image_pad.shape[0] - patch_size[0] + 1 - ncols = image_pad.shape[1] - patch_size[1] + 1 - patches = np.lib.stride_tricks.as_strided(image_pad , patch_size + (nrows, ncols), - image_pad.strides + image_pad.strides).reshape(patch_size[0]*patch_size[1],-1) - - return patches - -# Run patch normalization on imported RGB images -def patch_normalise_pad(img,patches): - - patch_size = (patches, patches) - patch_half_size = [int((p-1)/2) for p in patch_size ] - - image_pad = np.pad(np.float64(img), patch_half_size, 'constant', - constant_values=np.nan) - - nrows = img.shape[0] - ncols = img.shape[1] - patches = get_patches2D(patch_size,image_pad) - mus = np.nanmean(patches, 0) - stds = np.nanstd(patches, 0) - - with np.errstate(divide='ignore', invalid='ignore'): - im_norm = (img - mus.reshape(nrows, ncols)) / stds.reshape(nrows, ncols) - - im_norm[np.isnan(im_norm)] = 0.0 - im_norm[im_norm < -1.0] = -1.0 - im_norm[im_norm > 1.0] = 1.0 - - return im_norm - -# Process the loaded images - resize, normalize color, & patch normalize -def processImage(img,dims,patches): - # gamma correct images - mid = 0.5 - mean = np.mean(img) - gamma = math.log(mid*255)/math.log(mean) - img = np.power(img,gamma).clip(0,255).astype(np.uint8) - - # resize image to 28x28 and patch normalize - img = cv2.resize(img,(dims[0], dims[1])) - im_norm = patch_normalise_pad(img,patches) - img = np.uint8(255.0 * (1 + im_norm) / 2.0) - - return img - -# Image loader function - runs all image import functions -def loadImages(test_true,train_paths,img_names,dims,patches,testPath,testLoc): - - # get torch device - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - imgs = [] - ids = [] - - if test_true: - train_paths = [testPath+testLoc+'/'] - - if isinstance(train_paths,list): - for paths in train_paths: - for m in img_names: - fullpath = paths+m - # read and convert image from BGR to RGB - img = cv2.imread(fullpath)[:,:,::-1] - - # convert image - img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) - imgProc = processImage(img,dims,patches) - imgs.append(torch.tensor(imgProc,device=device)) - ids.append(m) - else: - for m in img_names: - fullpath = train_paths+m - # read and convert image from BGR to RGB - img = cv2.imread(fullpath)[:,:,::-1] - # convert image - img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) - imgProc = processImage(img,dims,patches) - imgs.append(torch.tensor(imgProc,device=device)) - ids.append(m) - - return imgs, ids - -# sets the spike rates from imported images - convert pixel range [0,255] to [0,1] -# spike rates are set as a 3D tensor with dimensions (m x r x s) -# m = module -# r = location repeat -# s = spikes for number of training images -def setSpikeRates(data,ids,device,dims,test_true,numImgs,numMods,intensity,locRep): - - # output tensor dimensions - n_input = dims[0] * dims[1] - - # Set the spike rates based on the number of example training images - - # loop through data and append spike rates for each image - # organise into 3D tensor based on number of expert modules - if test_true: # if loading testing data (repeat input across modules) - init_rates = torch.empty(0,device=device) - for m in range(numImgs): - - init_rates = torch.cat((init_rates, torch.reshape(data[m]/intensity,(n_input,)))) - - init_rates = torch.unsqueeze(init_rates,0) - # define the initial spike rates - for o in range(numMods): - if o == 0: - spike_rates = torch.unsqueeze(init_rates,0) - else: - spike_rates = torch.concat((spike_rates,torch.unsqueeze(init_rates,0)),0) - - - else: # if loading training data, have separate inputs across modules - for o in range(numMods): - start = [] - end = [] - for j in range(locRep): - mod = j * numImgs - start.append(int(numImgs/numMods)*(o) + mod) - end.append(int(numImgs/numMods)*(o + 1) + mod) - - # define the initial spike rates for location repeats - for m in range(locRep): - rates = torch.empty(0,device=device) - for jdx, j in enumerate(range(start[m],end[m])): - rates = torch.cat((rates, - torch.reshape(data[j]/intensity,(n_input,))),0) - if m == 0: - init_rates = torch.unsqueeze(rates,0) - else: - init_rates = torch.cat((init_rates,torch.unsqueeze(rates,0)),-1) - - # output spike rates into modules - if o == 0: # append the location repeat initial spikes to a new module - spike_rates = torch.unsqueeze(init_rates,0) - else: - spike_rates = torch.concat((spike_rates,torch.unsqueeze(init_rates,0)),0) - - return spike_rates - -# Check if pre-trained network exists, prompt if retrain or run -def checkTrainTest(self): - prompt = "A network with these parameters exists, re-train network? (y/n):\n" - self.logger.info('') - if path.isdir(self.training_out): - retrain = input(prompt) - else: - retrain = 'y' - return retrain - -def initialize(self,condition): - - ''' - Network startup and initialization - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info(condition+' startup and initialization') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - self.logger.info('Loading '+condition+' images') - - if condition == 'testing': - self.test_true = True - del self.filteredNames[self.number_testing_images:len(self.filteredNames)] - self.epoch = 1 # Only run the network once - self.location_repeat = 1 # One location repeat for testing - imgNum = self.number_testing_images - else: - imgNum = self.number_training_images - - # load the training images - self.imgs[condition], self.ids[condition] = ut.loadImages(self.test_true, - self.fullTrainPaths, - self.filteredNames, - [self.imWidth,self.imHeight], - self.num_patches, - self.testPath, - self.test_location) - - self.spike_rates[condition] = ut.setSpikeRates(self.imgs[condition], - self.ids[condition], - self.device, - [self.imWidth,self.imHeight], - self.test_true, - imgNum, - self.number_modules, - self.intensity, - self.location_repeat) -def train(self): - - # remove contents of the weights folder - ut.clear_weights(self.training_out) - - # create a new blitnet netowrk - self.logger.info('Creating network and setting weights') - net = bn.newNet(self.number_modules,self.imWidth*self.imHeight) - - # add the input layer - bn.addLayer(net,[self.number_modules,1,self.input_layer], - 0.0,0.0,0.0,0.0,0.0,False) - - # add the feature layer - bn.addLayer(net,[self.number_modules,1,self.feature_layer], - [0,self.theta_max], - [self.f_rate[0],self.f_rate[1]], - self.n_itp, - [0,self.c], - 0, - False) - - # sequentially set the feature firing rates for the feature layer - fstep = (self.f_rate[1]-self.f_rate[0])/self.feature_layer - - # loop through all modules and feature layer neurons - for x in range(self.number_modules): - for i in range(self.feature_layer): - net['fire_rate'][1][x][:,i] = self.f_rate[0]+fstep*(i+1) - - # add excitatory inhibitory connections for input and feature layer - bn.addWeights(net,0,1,[-1,0,1],[self.p_exc,self.p_inh],self.n_init) - self.init_weights = [net['W'][0].clone().detach(),net['W'][1].clone().detach()] - - ''' - Feature layer training - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Training the input to feature layer') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - self.logger.info('Setting spike rates from loaded images') - - # begin timer for network training - start = timeit.default_timer() - - # Set the spikes times for the input images - net['set_spks'][0] = torch.clone(self.spike_rates['training']) - self.spike_rates['training'] = [] - if self.device == 'cuda': - torch.cuda.empty_cache() - gc.collect() - layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] - - # Train the input to feature layer for specified amount of epochs - for epoch in range(self.epoch): - net['step_num'] = 0 - epochStart = timeit.default_timer() - - # loop through each image and train the network - for t in range(int(self.T)): - bn.runSim(net,1,self.device,layers) - # anneal learning rates - if np.mod(t,10)==0: - pt = pow(float(self.T-t)/self.T,self.annl_pow) - net['eta_ip'][1] = self.n_itp*pt - net['eta_stdp'][0] = self.n_init*pt - net['eta_stdp'][1] = -1*self.n_init*pt - - # print training details - self.logger.info('Epoch '+str(epoch+1)+' trained in: ' - +str(round(timeit.default_timer()-epochStart,2))+'s') - self.logger.info('') - - self.logger.info('Finished training input to feature layer') - - ''' - Preparations for feature to output layer training - ''' - - # Turn off learning between input and feature layer - net['eta_ip'][1] = 0.0 - if self.p_exc > 0.0: net['eta_stdp'][0] = 0.0 - if self.p_inh > 0.0: net['eta_stdp'][1] = 0.0 - - self.logger.info('Getting feature layer spikes for output layer training') - - # get the feature spikes for training the output layer - net['x_feat'] = [] - net['step_num'] = 0 - for t in range(int(self.T)): # run blitnet without learning to get feature spikes - bn.runSim(net,1,self.device,layers) - net['x_feat'].append(net['x'][1]) # dictionary output of feature spikes - - # delete input spikes - net['set_spks'][0] = [] - if self.device == 'cuda': - torch.cuda.empty_cache() - gc.collect() - - self.logger.info('Creating output layer') - # Create and train the output layer with the feature layer - bn.addLayer(net,[self.number_modules,1,self.output_layer], - 0.0,0.0,0.0,0.0,0.0,False) - - # Add excitatory and inhibitory connections - bn.addWeights(net,1,2,[-1.0,0.0,1.0],[1.0,1.0],self.n_init) - self.init_weights.append(net['W'][2].clone().detach()) - self.init_weights.append(net['W'][3].clone().detach()) - - # Output spikes for spike forcing (final layer) - out_spks = torch.tensor([0],device=self.device,dtype=float) - - ''' - Output layer training - ''' - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Training the feature to output layer') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - net['spike_dims'] = 1 # change spike dims for output spike indexing - net['set_spks'][0] = [] # remove input spikes - layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1] - - # Train the feature to output layer for specified number of epochs - for epoch in range(self.epoch): - net['step_num'] = 0 - epochStart = timeit.default_timer() - - # Loop through all the spikes generated in the feature layer to train output - for t in range(self.T): - out_spks = torch.tensor([0],device=self.device,dtype=float) - - net['set_spks'][-1] = torch.tile(out_spks, - (self.number_modules, - 1, - self.output_layer)) - net['x'][1] = net['x_feat'][t] - bn.runSim(net,1,self.device,layers) - # Anneal learning rates - if np.mod(t,10)==0: - pt = pow(float(self.T-t)/(self.T),self.annl_pow) - net['eta_ip'][2] = self.n_itp*pt - net['eta_stdp'][2] = self.n_init*pt - net['eta_stdp'][3] = -1*self.n_init*pt - if np.mod((t+1),(int(self.T/self.location_repeat))) == 0: - net['step_num'] = 0 - - # print training details - self.logger.info('Epoch '+str(epoch+1)+' trained in: ' - +str(round(timeit.default_timer()-epochStart,2))+'s') - self.logger.info('') - - self.logger.info('Finished training feature to output layer') - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Network trained in '+str(round(timeit.default_timer()-start,2)) - +'s') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - # Turn off learning - net['eta_ip'][2] = 0.0 - net['eta_stdp'][2] = 0.0 - net['eta_stdp'][3] = 0.0 - - # Clear the network output spikes - net['set_spks'][-1] = [] - net['spike_dims'] = self.input_layer - - # Reset network details - net['sspk_idx'] = [0,0,0] - net['step_num'] = 0 - net['spikes'] = [[],[],[]] - net['x'] = [[],[],[]] - net['x_feat'] = [] - - - self.logger.info('Network formatting and saving...') - - # Output the trained network - outputPkl = self.training_out + 'net.pkl' - with open(outputPkl, 'wb') as f: - pickle.dump(net, f) - - # output the ground truth image names for later testing - outputPkl = self.training_out + 'GT_imgnames.pkl' - with open(outputPkl, 'wb') as f: - pickle.dump(self.filteredNames, f) - - self.logger.info('Network succesfully saved!') - - # if using cuda, clear and dump the memory usage - if self.device =='cuda': - del net - del self.spike_rates - del self.imgs - torch.cuda.empty_cache() - gc.collect() - -''' - Run the testing network -''' -def networktester(self): - - ''' - Network startup and initialization - ''' - - # unpickle the network - self.logger.info('Unpickling the network') - with open(self.training_out+'net.pkl', 'rb') as f: - net = pickle.load(f) - - self.logger.info('Setting spike rates from loaded images') - # calculate input spikes from training images - - net['set_spks'][0] = self.spike_rates['testing'] - - # unpickle the ground truth image names - with open(self.training_out+'GT_imgnames.pkl', 'rb') as f: - GT_imgnames = pickle.load(f) - - self.logger.info('') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('Running test network') - self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - self.logger.info('') - - # set number of correct places to 0 - numcorrect = 0 - net['spike_dims'] = self.input_layer - numpconc = [] - start = timeit.default_timer() - for t in range(self.number_testing_images): - tonump = np.array([]) - bn.testSim(net,device=self.device) - # output the index of highest amplitude spike - - tonump = np.append(tonump,np.reshape(net['x'][-1].cpu().numpy(), - [1,1,int(self.number_training_images)])) - - # detect if no output - if np.all(tonump == 0): - # find the strongest sub-threshold input index - nidx = np.argmax(torch.sub(net['x_input'][-1][0,0,:], - net['thr'][-1][0,0,:]).cpu().numpy()) - tonump[nidx] = 0.5 - else: - # find the highest output spike - nidx = np.argmax(tonump) - - gt_ind = GT_imgnames.index(self.filteredNames[t]) - - if gt_ind == nidx: - numcorrect += 1 - - if self.validation: # get similarity matrix for PR curve generation - numpconc.append(tonump.tolist()) - - self.p100r = round((numcorrect/self.number_testing_images)*100,2) - self.logger.info('Number of correct matches P@100R - '+str(self.p100r)+'%') - - end = timeit.default_timer() - queryHertz = self.number_testing_images/(end-start) - self.logger.info('System queried at '+str(round(queryHertz,2))+'Hz') +def dummy_bmm(device): + # Create some dummy tensors on CUDA + dummy_a = torch.randn(10, 10, device=device) + dummy_b = torch.randn(10, 10, device=device) - # if self.validation = True, get PR information and plot similarity matrix - if self.validation: - val.match_metrics(numpconc, - self.output_folder, - self.number_testing_images, - self.number_training_images, - self.logger) - - # if using cuda, clear and dump the memory usage - if self.device =='cuda': - gc.collect() - torch.cuda.empty_cache() + # Perform a dummy bmm operation + torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) def sad(self): From 146ca7606917e27287b7dfcdd9c6d4a694a9796f Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 6 Oct 2023 16:56:16 +1000 Subject: [PATCH 12/69] Started on some quantization now that network works, but fatal flaw that bmm not supported for quantized tensors so need to explore a bit further --- VPRTempo.py | 117 +++++++++++++++++++++++++++++++++++------------ config/config.py | 8 ++-- src/blitnet.py | 7 ++- 3 files changed, 96 insertions(+), 36 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index ab851bb..ad8d46a 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -40,12 +40,12 @@ import numpy as np import torch.nn as nn import torch.nn.functional as F +import torch.quantization as quantization from config import configure, image_csv, model_logger from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader from tqdm import tqdm -from timeit import default_timer class SNNLayer(nn.Module): def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], @@ -150,17 +150,13 @@ def train_model(self, train_loader, logger): if self.device.type == "cuda": ut.dummy_bmm(self.device) + warmup_iters = 10 + for _ in range(warmup_iters): + _ = next(iter(train_loader)) + # Run the training for each layer, using defined parameters for layer in range(len(self.layers)-1): - - # Start timer for layer trainins - start_lyr = default_timer() - - # Initialize the tqdm progress bar - pbar = tqdm(total=int(self.T * self.epoch), - desc="Training layer "+str(layer)+" with layer "+str(layer+1), - position=0) - + # Define in and out layer (2 layers trained at a time) in_layer = self.layers['layer'+str(layer)] out_layer = self.layers['layer'+str(layer+1)] @@ -168,6 +164,11 @@ def train_model(self, train_loader, logger): # Output the learning rates for annealment during training n_initstdp = out_layer.eta_stdp.detach() # STDP n_initip = out_layer.eta_ip.detach() # ITP + + # Initialize the tqdm progress bar + pbar = tqdm(total=int(self.T * self.epoch), + desc="Training layer "+str(layer)+" with layer "+str(layer+1), + position=0) # Run the training for the input to feature layer for specified epochs for epoch in range(self.epoch): @@ -203,18 +204,10 @@ def train_model(self, train_loader, logger): out_layer.x.fill_(0.0) out_layer.x_input.fill_(0.0) pbar.update(1) - - # Record training time for the layer - end_lyr = default_timer()-start_lyr - + # Close the tqdm progress bar pbar.close() - - # Record the training time and training frequency - logger.info('') - logger.info("Training layer "+str(layer)+" with layer "+ - str(layer+1)+" took "+str(round(end_lyr,2))+"s") - logger.info('') + print('') # Clear the cache and garbage collect (if using cuda) if self.device.type == "cuda": @@ -228,7 +221,7 @@ def save_model(self, model_out): class SNNModel(nn.Module): def __init__(self): super(SNNModel, self).__init__() - + # Configure the network configure(self) # Sets the testing configuration image_csv(self) # Defines images to load @@ -252,11 +245,51 @@ def __init__(self): 'layer1':self.feature_layer, 'layer2':self.output_layer} + self.is_quantized = False + def load_model(self,model_path): state_dict = torch.load(model_path, map_location=self.device) self.load_state_dict(state_dict) self.eval() - + + def quantize_tensors(self): + if not self.is_quantized: + tensors_to_quantize = ["excW", "inhW", "thr"] + + # Iterate over the layers + for layer_name, layer in self.layers.items(): + for tensor_name in tensors_to_quantize: + # Check if the tensor exists in the current layer + if hasattr(layer, tensor_name): + tensor = getattr(layer, tensor_name) + + # Convert nn.Parameter to regular tensor if necessary + if isinstance(tensor, nn.Parameter): + tensor = tensor.data + # Explicitly delete the attribute + delattr(layer, tensor_name) + + # If the tensor is all zeros, skip quantization + if (tensor == 0).all(): + continue + + # Check if all values in the tensor are negative, and if so, multiply by -1 + if (tensor < 0).all(): + tensor = tensor * -1 + + # Calculate the non-zero max and min of this tensor + non_zero_max = tensor[tensor.nonzero(as_tuple=True)].max() + non_zero_min = tensor[tensor.nonzero(as_tuple=True)].min() + + # Calculate scale based on non-zero max and min + scale = (non_zero_max - non_zero_min) / 255.0 + + # Now, quantize the tensor + quantized_tensor = nn.quantized.Quantize(scale=scale, zero_point=0, dtype=torch.quint8)(tensor) + setattr(layer, tensor_name, quantized_tensor) + + + def forward(self, test_loader): # If using CUDA, run a dummy torch.bmm to 'spool-up' operations @@ -265,18 +298,26 @@ def forward(self, test_loader): idx=0 numcorr = 0 + + # If using CUDA, run a dummy torch.bmm to 'spool-up' operations + if self.device.type == "cuda": + ut.dummy_bmm(self.device) + + warmup_iters = 10 + for _ in range(warmup_iters): + _ = next(iter(test_loader)) # Initialize the tqdm progress bar pbar = tqdm(total=self.number_testing_images, desc="Running the test network", position=0) - + # Run test network for each individual input for images, labels in test_loader: # Set images to the specified device - images = images.to(self.device) - labels = labels.to(self.device) + #images = images.to(self.device) + #labels = labels.to(self.device) # Set the spikes for the input, tiling across modules if applicable make_spikes = SetImageAsSpikes(intensity=self.intensity, @@ -310,7 +351,13 @@ def forward(self, test_loader): if __name__ == "__main__": # Initialize the model and image transforms model = SNNModel() - + qconfig = torch.quantization.QConfig( + activation=torch.quantization.FakeQuantize.with_args(observer=torch.quantization.MinMaxObserver), + weight=torch.quantization.default_weight_fake_quant) + + model.qconfig = qconfig + model = quantization.prepare_qat(model) + # Generate model name, check if pre-trained model exists model_name = ("VPRTempo"+ # main name str(model.input)+ # number input neurons @@ -351,7 +398,7 @@ def forward(self, test_loader): train_loader = DataLoader(train_dataset, batch_size=model.number_modules, shuffle=False, - num_workers=4, + num_workers=8, persistent_workers=True) # Initialize, run, and save the training model @@ -360,9 +407,14 @@ def forward(self, test_loader): trainer.save_model(os.path.join('./models',model_name)) with torch.no_grad(): # Disable gradient computation during testing + + model = quantization.convert(model.eval()) + # Load the trained model into SNNModel() model.load_model(os.path.join('./models',model_name)) - + model = model.cpu() + model.quantize_tensors() + # Define the custom testing image dataset class test_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.testing_dirs, @@ -375,6 +427,11 @@ def forward(self, test_loader): test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, - num_workers=4, + num_workers=8, persistent_workers=True) - model.forward(test_loader) \ No newline at end of file + model.forward(test_loader) + + + for name, module in model.named_modules(): + if 'quantized' in str(type(module)): + print(f"{name}: {type(module)} is quantized!") diff --git a/config/config.py b/config/config.py index ac21482..8caed52 100644 --- a/config/config.py +++ b/config/config.py @@ -10,10 +10,10 @@ def configure(model): model.dataset_file = './dataset/'+model.dataset+'.csv' model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' - model.number_modules = 10 - model.number_training_images = 2700 - model.number_testing_images = 2700 - model.locations = ["spring","fall","winter"] + model.number_modules = 5 + model.number_training_images = 500 + model.number_testing_images = 500 + model.locations = ["spring","fall"] model.test_locations = ["summer"] model.filter = 8 model.validation = True diff --git a/src/blitnet.py b/src/blitnet.py index 7c6c369..5f9ddb8 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -96,10 +96,12 @@ def addWeights(W_range=[-1,0,1],p=[1,1],dims=None,num_modules=1): return excW, inhW, havconnExc, havconnInh -def calc_spikes(post_layer, spikes): - +def add_input(post_layer): + # Add the constant input post_layer.x_input += post_layer.const_inp + +def calc_spikes(post_layer, spikes): # Multiply input spikes by positive and negative weights post_layer.x_input += torch.bmm(spikes, post_layer.excW.detach()) @@ -197,6 +199,7 @@ def norm_inhib(post_layer): def runSim(pre_layer,post_layer,spikes,idx): # Propagate spikes from pre to post neurons + add_input(post_layer) calc_spikes(post_layer,spikes) # Calculate STDP weight changes From e71172ba51c4aef9726dbf26011c4d0f1a34eaf8 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 9 Oct 2023 17:39:27 +1000 Subject: [PATCH 13/69] Complete nn.Module integration, replaced torch.bmm weight operations with nn.Linear - removed modularity for now. Working on QAT, can train but cannot evaluate due to x and x_input tensors not quantized. --- VPRTempo.py | 286 ++++++++++++---------------------- config/config.py | 11 +- src/blitnet.py | 398 +++++++++++++++++++++++++++-------------------- src/dataset.py | 61 +++----- 4 files changed, 354 insertions(+), 402 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index ad8d46a..05e9ff8 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -34,7 +34,7 @@ sys.path.append('./output') sys.path.append('./dataset') sys.path.append('./config') - +torch.multiprocessing.set_sharing_strategy("file_system") import blitnet as bn import utils as ut import numpy as np @@ -47,65 +47,6 @@ from torch.utils.data import DataLoader from tqdm import tqdm -class SNNLayer(nn.Module): - def __init__(self, previous_layer=None,dims=[0,0,0],thr_range=[0,0], - fire_rate=[0,0],ip_rate=0,stdp_rate=0,const_inp=[0,0],p=[1,1], - assign_weight=False,spk_force=False): - super(SNNLayer, self).__init__() - # Configure the network - configure(self) # Sets the testing configuration - # Device - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - # Check constraints etc - if np.isscalar(thr_range): thr_range = [thr_range, thr_range] - if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate] - if np.isscalar(const_inp): const_inp = [const_inp, const_inp] - - # Initialize Tensors - self.dim = torch.tensor(dims, dtype=torch.int) - self.x = torch.zeros(dims, device=self.device) - self.x_prev = torch.zeros(dims, device=self.device) - self.x_calc = torch.zeros(dims, device=self.device) - self.x_input = torch.zeros(dims, device=self.device) - self.x_fastinp = torch.zeros(dims, device=self.device) - self.eta_ip = torch.tensor(ip_rate, device=self.device) - self.eta_stdp = torch.tensor(stdp_rate, device=self.device) - - # Initialize Parameters - self.thr = nn.Parameter(torch.zeros(dims, device=self.device).uniform_(thr_range[0], thr_range[1])) - self.fire_rate = torch.zeros(dims, device=self.device).uniform_(fire_rate[0], fire_rate[1]) - - # Sequentially set the feature firing rates (if any) - if not torch.all(self.fire_rate==0).item(): - fstep = (fire_rate[1]-fire_rate[0])/dims[-1] - - # loop through all modules and feature layer neurons - for x in range(self.number_modules): - for i in range(dims[-1]): - self.fire_rate[x][:,i] = fire_rate[0]+fstep*(i+1) - - self.have_rate = torch.any(self.fire_rate[:,:,0] > 0.0).to(self.device) - self.const_inp = torch.zeros(dims, device=self.device).uniform_(const_inp[0], const_inp[1]) - self.p = p - self.dims = dims - - # Additional State Variables - self.set_spks = [] - self.sspk_idx = 0 - self.spikes = torch.empty([], dtype=torch.float64) - self.spk_force = spk_force - - # Weights (if applicable) - if assign_weight: - excW, inhW, self.havconnExc, self.havconnInh = bn.addWeights(p=self.p, - dims=[previous_layer.dims[2], - dims[2]], - num_modules=self.number_modules) - - self.excW = nn.Parameter(excW) - self.inhW = nn.Parameter(inhW) - class SNNTrainer(nn.Module): def __init__(self): super(SNNTrainer, self).__init__() @@ -114,34 +55,33 @@ def __init__(self): image_csv(self) # Defines images to load # Set the device - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - # Set up the input layer - self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) - + #self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.device = torch.device("cpu") + # Set up the feature layer - self.feature_layer = SNNLayer(previous_layer=self.input_layer, - dims=[self.number_modules,1,self.feature], + self.feature_layer = bn.SNNLayer( + dims=[self.input,self.feature], thr_range=[0,0.5], fire_rate=[0.2,0.9], ip_rate=0.15, stdp_rate=0.005, const_inp=[0,0.1], - p=[0.1,0.5], - assign_weight=True) + p=[0.1,0.5] + ) # Set up the output layer - self.output_layer = SNNLayer(previous_layer=self.feature_layer, - dims=[self.number_modules,1,self.output], + self.output_layer = bn.SNNLayer( + dims=[self.feature,self.output], ip_rate=0.15, stdp_rate=0.005, - assign_weight=True, - spk_force=True) + spk_force=True + ) # Define number of layers (will run training on all layers) - self.layers = {'layer0':self.input_layer, - 'layer1':self.feature_layer, - 'layer2':self.output_layer} + self.layers = { + 'layer0':self.feature_layer, + 'layer1':self.output_layer + } def train_model(self, train_loader, logger): @@ -149,32 +89,38 @@ def train_model(self, train_loader, logger): # If using CUDA, run a dummy torch.bmm to 'spool-up' operations if self.device.type == "cuda": ut.dummy_bmm(self.device) - + + # Warm up the DataLoader warmup_iters = 10 for _ in range(warmup_iters): _ = next(iter(train_loader)) - + # Run the training for each layer, using defined parameters - for layer in range(len(self.layers)-1): - - # Define in and out layer (2 layers trained at a time) - in_layer = self.layers['layer'+str(layer)] - out_layer = self.layers['layer'+str(layer+1)] + for lyrCount, lyr in enumerate(self.layers): + + # Get the training layer + layer = self.layers[lyr] # Output the learning rates for annealment during training - n_initstdp = out_layer.eta_stdp.detach() # STDP - n_initip = out_layer.eta_ip.detach() # ITP - + n_initstdp = layer.eta_stdp.detach() # STDP + n_initip = layer.eta_ip.detach() # ITP + # Initialize the tqdm progress bar pbar = tqdm(total=int(self.T * self.epoch), - desc="Training layer "+str(layer)+" with layer "+str(layer+1), + desc="Training "+str(lyr), position=0) # Run the training for the input to feature layer for specified epochs for epoch in range(self.epoch): - mod = 0 # Used to determine the learning rate annealment + # Used to determine the learning rate annealment, resets at each epoch + mod = 0 + + # Load and run images through BliTNET for images, labels in train_loader: - + + # Reset fire rate to None + fr = None + # Put input images on device (CPU, CUDA) images = images.to(self.device) @@ -187,22 +133,35 @@ def train_model(self, train_loader, logger): idx = labels/self.filter # Layers don't include loaded input, calculate network spikes for up to training layers - if layer != 0: - spikes = bn.testSim(self.layers,layer,spikes) - + if list(lyr[-1]) != ['0']: + blitnet = bn.BLiTNET(layer_lst=self.layers, + spikes=spikes, + idx=idx, + testCount=lyrCount) + spikes = blitnet.testSim() + fr = self.layers['layer'+str(lyrCount-1)].fire_rate + # Run one timestep of the training input to feature layer - bn.runSim(in_layer, out_layer, spikes, idx) + blitnet = bn.BLiTNET( + layer=layer, + spikes=spikes, + idx=idx, + fr=fr + ) + blitnet.runSim() # Anneal the learning rate if np.mod(mod,10)==0: pt = pow(float(self.T-mod)/self.T,self.annl_pow) - out_layer.eta_ip = torch.mul(n_initip,pt) - out_layer.eta_stdp = torch.mul(n_initstdp,pt) + layer.eta_ip = torch.mul(n_initip,pt) + layer.eta_stdp = torch.mul(n_initstdp,pt) + + # Update the learning rate annealment modifier mod += 1 # Reset x and x_input for next iteration - out_layer.x.fill_(0.0) - out_layer.x_input.fill_(0.0) + layer.x.fill_(0.0) + layer.x_input.fill_(0.0) pbar.update(1) # Close the tqdm progress bar @@ -227,74 +186,25 @@ def __init__(self): image_csv(self) # Defines images to load model_logger(self) # Sets the logger - # Set up the input layer - self.input_layer = SNNLayer(dims=[self.number_modules,1,self.input]) # Set up the feature layer - self.feature_layer = SNNLayer(previous_layer=self.input_layer, - dims=[self.number_modules,1,self.feature], - assign_weight=True) + self.feature_layer = bn.SNNLayer(dims=[self.input,self.feature]) # Set up the output layer - self.output_layer = SNNLayer(previous_layer=self.feature_layer, - dims=[self.number_modules,1,self.output], - assign_weight=True) + self.output_layer = bn.SNNLayer(dims=[self.feature,self.output]) # Define number of layers (will run training on all layers) - self.layers = {'layer0':self.input_layer, - 'layer1':self.feature_layer, - 'layer2':self.output_layer} - - self.is_quantized = False + self.layers = { + 'layer0':self.feature_layer, + 'layer1':self.output_layer + } def load_model(self,model_path): state_dict = torch.load(model_path, map_location=self.device) self.load_state_dict(state_dict) self.eval() - def quantize_tensors(self): - if not self.is_quantized: - tensors_to_quantize = ["excW", "inhW", "thr"] - - # Iterate over the layers - for layer_name, layer in self.layers.items(): - for tensor_name in tensors_to_quantize: - # Check if the tensor exists in the current layer - if hasattr(layer, tensor_name): - tensor = getattr(layer, tensor_name) - - # Convert nn.Parameter to regular tensor if necessary - if isinstance(tensor, nn.Parameter): - tensor = tensor.data - # Explicitly delete the attribute - delattr(layer, tensor_name) - - # If the tensor is all zeros, skip quantization - if (tensor == 0).all(): - continue - - # Check if all values in the tensor are negative, and if so, multiply by -1 - if (tensor < 0).all(): - tensor = tensor * -1 - - # Calculate the non-zero max and min of this tensor - non_zero_max = tensor[tensor.nonzero(as_tuple=True)].max() - non_zero_min = tensor[tensor.nonzero(as_tuple=True)].min() - - # Calculate scale based on non-zero max and min - scale = (non_zero_max - non_zero_min) / 255.0 - - # Now, quantize the tensor - quantized_tensor = nn.quantized.Quantize(scale=scale, zero_point=0, dtype=torch.quint8)(tensor) - setattr(layer, tensor_name, quantized_tensor) - - - def forward(self, test_loader): - - # If using CUDA, run a dummy torch.bmm to 'spool-up' operations - if self.device.type == "cuda": - ut.dummy_bmm(self.device) idx=0 numcorr = 0 @@ -316,23 +226,23 @@ def forward(self, test_loader): for images, labels in test_loader: # Set images to the specified device - #images = images.to(self.device) - #labels = labels.to(self.device) + images = images.to(self.device) + labels = labels.to(self.device) # Set the spikes for the input, tiling across modules if applicable - make_spikes = SetImageAsSpikes(intensity=self.intensity, - modules=self.number_modules) + make_spikes = SetImageAsSpikes(intensity=self.intensity) spikes = make_spikes(images) # Run the test sim - out = bn.testSim(self.layers,len(self.layers)-1,spikes) - - # Store output and determine if output matches GT - tonump = np.array([]) - tonump = np.append(tonump,np.reshape(out.cpu().numpy(), - [1,1,int(self.number_training_images)])) - if np.argmax(tonump) == idx: + blitnet = bn.BLiTNET(layer_lst=self.layers, + spikes=spikes, + testCount=len(self.layers) + ) + out = blitnet.testSim() + + if torch.argmax(out.reshape(1, self.number_training_images)) == idx: numcorr += 1 + idx+=1 # Update the progress bar @@ -351,13 +261,7 @@ def forward(self, test_loader): if __name__ == "__main__": # Initialize the model and image transforms model = SNNModel() - qconfig = torch.quantization.QConfig( - activation=torch.quantization.FakeQuantize.with_args(observer=torch.quantization.MinMaxObserver), - weight=torch.quantization.default_weight_fake_quant) - - model.qconfig = qconfig - model = quantization.prepare_qat(model) - + qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') # Generate model name, check if pre-trained model exists model_name = ("VPRTempo"+ # main name str(model.input)+ # number input neurons @@ -365,6 +269,8 @@ def forward(self, test_loader): str(model.output)+ # number output neurons str(model.number_modules)+ # number of modules '.pth') + + # Check if a pre-trained model exists if os.path.exists(os.path.join('./models',model_name)): pretrain_flg = True @@ -391,47 +297,51 @@ def forward(self, test_loader): transform=image_transform, skip=model.filter, max_samples=model.number_training_images, - modules=model.number_modules, test=False) # Define the training dataloader class train_loader = DataLoader(train_dataset, - batch_size=model.number_modules, + batch_size=1, shuffle=False, - num_workers=8, + num_workers=1, persistent_workers=True) # Initialize, run, and save the training model trainer = SNNTrainer() + trainer.to('cpu') + + trainer.feature_layer.qconfig = qconfig + trainer.output_layer.qconfig = qconfig + trainer = torch.quantization.prepare_qat(trainer, inplace=True) + trainer.train_model(train_loader,model.logger) + + trainer.eval() + trainer = torch.quantization.convert(trainer, inplace=True) + trainer.save_model(os.path.join('./models',model_name)) with torch.no_grad(): # Disable gradient computation during testing - - model = quantization.convert(model.eval()) - + model.to('cpu') + model.feature_layer.qconfig = qconfig + model.output_layer.qconfig = qconfig + model = torch.quantization.prepare(model, inplace=False) + model = torch.quantization.convert(model, inplace=False) # Load the trained model into SNNModel() model.load_model(os.path.join('./models',model_name)) - model = model.cpu() - model.quantize_tensors() - + + # Define the custom testing image dataset class test_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.testing_dirs, transform=image_transform, skip=model.filter, - max_samples=model.number_testing_images, - modules=model.number_modules) + max_samples=model.number_testing_images) # Define the testing dataloader class test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, - num_workers=8, + num_workers=1, persistent_workers=True) - model.forward(test_loader) - - - for name, module in model.named_modules(): - if 'quantized' in str(type(module)): - print(f"{name}: {type(module)} is quantized!") + model.forward(test_loader) \ No newline at end of file diff --git a/config/config.py b/config/config.py index 8caed52..c6d7fae 100644 --- a/config/config.py +++ b/config/config.py @@ -10,9 +10,9 @@ def configure(model): model.dataset_file = './dataset/'+model.dataset+'.csv' model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' - model.number_modules = 5 - model.number_training_images = 500 - model.number_testing_images = 500 + model.number_modules = 1 + model.number_training_images = 10 + model.number_testing_images = 10 model.locations = ["spring","fall"] model.test_locations = ["summer"] model.filter = 8 @@ -43,7 +43,8 @@ def configure(model): model.location_repeat = len(model.locations) model.layers = [] - model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + #model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + model.device = torch.device("cpu") if model.device.type == "cuda": torch.cuda.init() torch.cuda.synchronize(device=model.device) @@ -71,6 +72,8 @@ def model_logger(model): os.mkdir(model.output_folder) model.logger = logging.getLogger("VPRTempo") + if (model.logger.hasHandlers()): + model.logger.handlers.clear() model.logger.setLevel(logging.DEBUG) logging.basicConfig(filename=model.output_folder + "/logfile.log", filemode="a+", diff --git a/src/blitnet.py b/src/blitnet.py index 5f9ddb8..7803ea3 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -23,199 +23,251 @@ ''' Imports ''' -import numpy as np import torch +import torch.nn as nn +import numpy as np + +from config import configure -def addWeights(W_range=[-1,0,1],p=[1,1],dims=None,num_modules=1): - # get torch device - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +class SNNLayer(nn.Module): + def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, + stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False): + super(SNNLayer, self).__init__() + # Configure the network + configure(self) # Sets the testing configuration + # Device + #self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.device = torch.device("cpu") + + # Check constraints etc + if np.isscalar(thr_range): thr_range = [thr_range, thr_range] + if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate] + if np.isscalar(const_inp): const_inp = [const_inp, const_inp] + + # Initialize Tensors + self.x = torch.zeros([1, dims[-1]], device=self.device) + self.x_prev = torch.zeros([1, dims[-1]], device=self.device) + self.x_calc = torch.zeros([1, dims[-1]], device=self.device) + self.x_input = torch.zeros([1, dims[-1]], device=self.device) + self.x_fastinp = torch.zeros([1, dims[-1]], device=self.device) + + self.eta_ip = torch.tensor(ip_rate, device=self.device) + self.eta_stdp = torch.tensor(stdp_rate, device=self.device) + + # Initialize Parameters + self.thr = nn.Parameter(torch.zeros([1, dims[-1]], + device=self.device).uniform_(thr_range[0], + thr_range[1])) + self.fire_rate = torch.zeros([1,dims[-1]], device=self.device).uniform_(fire_rate[0], fire_rate[1]) + + # Sequentially set the feature firing rates (if any) + if not torch.all(self.fire_rate==0).item(): + fstep = (fire_rate[1]-fire_rate[0])/dims[-1] + + for i in range(dims[-1]): + self.fire_rate[:,i] = fire_rate[0]+fstep*(i+1) + + self.have_rate = torch.any(self.fire_rate[:,0] > 0.0).to(self.device) + self.const_inp = torch.zeros([1, dims[-1]], device=self.device).uniform_(const_inp[0], const_inp[1]) + self.p = p + self.dims = dims + + # Additional State Variables + self.set_spks = [] + self.sspk_idx = 0 + self.spikes = torch.empty([], dtype=torch.float64) + self.spk_force = spk_force + + # Create the excitatory weights + self.exc = nn.Linear(dims[0], dims[1], bias=False) + self.exc.weight = self.addWeights(dims=dims, + W_range=[0,1], + p=p[0]) + + # Create the inhibitory weights + self.inh = nn.Linear(dims[0], dims[1], bias=False) + self.inh.weight = self.addWeights(dims=dims, + W_range=[-1,0], + p=p[-1]) + + # Output boolean reference of which neurons have connection weights + self.havconnExc = self.exc.weight > 0 + self.havconnInh = self.inh.weight < 0 + + def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0]): - # Check constraints etc - if np.isscalar(W_range): W_range = [W_range,W_range] + # Get torch device + #device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + device = torch.device("cpu") - # determine dimensions of the weight matrices - nrow = dims[0] - ncol = dims[1] + # Check constraints etc + if np.isscalar(W_range): W_range = [W_range,W_range] + + # Determine dimensions of the weight matrices + nrow = dims[1] + ncol = dims[0] + + # Calculate mean and std for normal distributions + Wmn = (W_range[0]+W_range[1])/2.0 + Wsd = (W_range[1]-W_range[0])/6.0 + + # Initialize weights as empty tensors + W = torch.empty((0, nrow, ncol), device=device) + + # Normally disribute random weights + W = torch.empty(nrow, ncol, device=device).normal_(mean=Wmn, std=Wsd) + + # Remove inappropriate weights based on sign from W_range + if W_range[-1] != 0: + # For excitatory weights + W[W < 0] = 0.0 + else: + # For inhibitory weights + W[W > 0] = 0.0 - # calculate mean and std for normal distributions - inWmn = (W_range[0]+W_range[1])/2.0 - inWsd = (W_range[1]-W_range[0])/6.0 - exWmn = (W_range[1]+W_range[2])/2.0 - exWsd = (W_range[2]-W_range[1])/6.0 + # Remove weights based on connection probability + setzero = np.random.rand(nrow,ncol) > p + if setzero.any(): + W[setzero] = 0.0 - # Initialize excW and inhW as empty tensors - excW = torch.empty((0, nrow, ncol), device=device) - inhW = torch.empty((0, nrow, ncol), device=device) + # Normalise the weights + nrm = torch.linalg.norm(W[len(W)-1],ord=1,axis=0) + nrm[nrm==0.0] = 1.0 + W = nn.Parameter(W/nrm) - # Loop through modules and add excitatory and inhibitory weights - for n in range(num_modules): - if n == 0: # first weights to be appended - # excitatory weights - excW = torch.cat((excW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), - 0)), 0) - # inhibitory weights - inhW = torch.cat((inhW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), - 0)), 0) - else: # stack new weights onto appended weight - excW = torch.cat((excW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=exWmn, std=exWsd), - 0)), 0) - inhW = torch.cat((inhW, - torch.unsqueeze(torch.empty(nrow, ncol, device=device).normal_(mean=inWmn, std=inWsd), - 0)), 0) - - # Remove negative excitatory weights - excW[n][excW[n] < 0] = 0.0 - # Remove positive inhibitory weights - inhW[n][inhW[n] > 0] = 0.0 + return W - # remove connections based on exc and inh probabilities - setzeroExc = np.random.rand(nrow,ncol) > p[0] - setzeroInh = np.random.rand(nrow,ncol) > p[1] - - # remove connections based on calculated indexes - if setzeroExc.any(): - excW[n,:,:][setzeroExc] = 0.0 # excitatory connections - if setzeroInh.any(): - inhW[n,:,:][setzeroInh] = 0.0 # inhibitory connections - - # Normalise the weights (except fast inhib weights) - nrmExc = torch.linalg.norm(excW[len(excW)-1],ord=1,axis=0) - nrmInh = torch.linalg.norm(inhW[len(inhW)-1],ord=1,axis=0) - nrmExc[nrmExc==0.0] = 1.0 - nrmInh[nrmInh==0.0] = 1.0 - excW[n] = excW[n,:,:]/nrmExc - inhW[n] = inhW[n,:,:]/nrmInh +class BLiTNET(nn.Module): + def __init__(self,layer=None,spikes=None,idx=None,fr=None,testCount=None, + layer_lst=None): + super(BLiTNET, self).__init__() + + # Define the layer & spikes to be parsed through BLiTNET + self.layer = layer + self.spikes = spikes + + # For spike forcing, define the output idx and pre-layer fire rate + self.idx = idx + self.fr = fr + + # For running testing, determine number of layers to iterate through for output + self.testCount = testCount + self.layer_lst = layer_lst + + def add_input(self): + # Add the constant input + self.layer.x_input += self.layer.const_inp - havconnExc = excW > 0 - havconnInh = inhW < 0 + def calc_spikes(self): + # Use nn.Linear to perform multiplication of spikes to weights + self.layer.x_input += self.layer.exc(self.spikes) + self.layer.x_input += self.layer.inh(self.spikes) - return excW, inhW, havconnExc, havconnInh - -def add_input(post_layer): - - # Add the constant input - post_layer.x_input += post_layer.const_inp - -def calc_spikes(post_layer, spikes): + # Clamp outputs between 0 and 0.9 after subtracting thresholds from input + if self.layer.spk_force: + self.layer.x_calc = torch.clamp(torch.sub(self.layer.x_input, self.layer.thr), min=0.0, max=0.9) + else: + self.layer.x = torch.clamp(torch.sub(self.layer.x_input, self.layer.thr), min=0.0, max=0.9) - # Multiply input spikes by positive and negative weights - post_layer.x_input += torch.bmm(spikes, post_layer.excW.detach()) - post_layer.x_input += torch.bmm(spikes, post_layer.inhW.detach()) + def calc_stdp(self): + # Spike Forcing has special rules to make calculated and forced spikes match + if self.layer.spk_force: + + # Get layer dimensions + shape = self.layer.exc.weight.data.shape + + # Get the output neuron index + idx_sel = torch.arange(int(self.idx[0]), int(self.idx[0]) + 1, + device=self.layer.device, + dtype=int) - # Clamp outputs between 0 and 0.9 after subtracting thresholds from input - if post_layer.spk_force: - post_layer.x_calc = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), - min=0.0, max=0.9) - else: - post_layer.x = torch.clamp(torch.sub(post_layer.x_input, post_layer.thr.detach()), - min=0.0, max=0.9) - -def calc_stdp(pre_layer, post_layer, spikes, idx=0): - - # Spike Forcing has special rules to make calculated and forced spikes match - if post_layer.spk_force: - - # Get layer dimensions - shape = [len(post_layer.excW[:, 0, 0]), - len(post_layer.excW[0, :, 0]), - len(post_layer.excW[0, 0, :])] - # Get the output neuron index - idx_sel = torch.arange(int(idx[0]), int(idx[0]) + 1, device=post_layer.device, dtype=int) - - # Difference between forced and calculated spikes - post_layer.x = torch.full_like(post_layer.x, 0) - xdiff = torch.clamp(post_layer.x.index_fill_(-1, idx_sel, 0.5) - post_layer.x_calc, - min=0, max=1) - - # Threshold rules - lower it if calced spike is smaller (and vice versa) - post_layer.thr.data -= torch.sign(xdiff) * torch.abs(post_layer.eta_stdp) / 10 - post_layer.thr.data -= torch.sign(xdiff) * torch.abs((post_layer.eta_stdp * -1)) / 10 - post_layer.thr.data = post_layer.thr.data.clamp(min=0, max=1) - - # Pre and Post spikes tiled across and down for all synapses - if pre_layer.have_rate: - # Modulate learning rate by firing rate (low firing rate = high learning rate) - mpre = spikes / pre_layer.fire_rate - else: - mpre = spikes - pre = torch.tile(torch.reshape(mpre, (shape[0], shape[1], 1)), (1, shape[2])) - post = torch.tile(xdiff, (shape[1], 1)) - - # Apply the weight changes - post_layer.excW.data += (pre * post * post_layer.havconnExc) * post_layer.eta_stdp - - post_layer.inhW.data += (-pre * post * post_layer.havconnInh) * (post_layer.eta_stdp * -1) - - # Normal STDP - else: - - # Get layer dimensions - shape = [len(post_layer.excW[:, 0, 0]), - len(post_layer.excW[0, :, 0]), - len(post_layer.excW[0, 0, :])] - - # Tile out pre- and post-spikes - pre = torch.tile(torch.reshape(spikes, (shape[0], shape[1], 1)), (1, shape[2])) - post = torch.tile(post_layer.x, (shape[1], 1)) - - # Apply positive and negative weight changes - post_layer.excW.data += ((0.5 - post) * (pre > 0) * (post > 0) * - post_layer.havconnExc) * post_layer.eta_stdp - post_layer.inhW.data += ((0.5 - post) * (pre > 0) * - (post > 0) * post_layer.havconnInh) * (post_layer.eta_stdp * -1) - - # In-place clamp for excitatory and inhibitory weights - post_layer.excW.data[post_layer.excW.data < 0] = 1e-06 - post_layer.inhW.data[post_layer.inhW.data > 0] = -1e-06 + # Difference between forced and calculated spikes + self.layer.x = torch.full_like(self.layer.x, 0) + xdiff = torch.clamp(self.layer.x.index_fill_(-1, idx_sel, 0.5) - self.layer.x_calc, min=0, max=1) - # Remove negative weights for excW and positive for inhW - post_layer.excW.data[post_layer.havconnExc] = post_layer.excW.data[post_layer.havconnExc].clamp(min=1e-06, max=10) - post_layer.inhW.data[post_layer.havconnInh] = post_layer.inhW.data[post_layer.havconnInh].clamp(min=-10, max=-1e-06) + # Threshold rules - lower it if calced spike is smaller (and vice versa) + self.layer.thr.data -= torch.sign(xdiff) * torch.abs(self.layer.eta_stdp) / 10 + self.layer.thr.data -= torch.sign(xdiff) * torch.abs((self.layer.eta_stdp * -1)) / 10 + self.layer.thr.data = self.layer.thr.data.clamp(min=0, max=1) -def norm_rates(post_layer): + # Pre and Post spikes tiled across and down for all synapses + if self.fr == None: + mpre = self.spikes + else: + # Modulate learning rate by firing rate (low firing rate = high learning rate) + mpre = self.spikes/self.fr + + # Tile out pre- and post- spikes for STDP weight updates + pre = torch.tile(torch.reshape(mpre, (shape[1], 1)), (1, shape[0])) + post = torch.tile(xdiff, (shape[1], 1)) - # Check if layer has target firing rate and an ITP learning rate - if post_layer.have_rate and post_layer.eta_ip > 0.0: - - # Replace the original layer.thr with the updated one - post_layer.thr.data += post_layer.eta_ip * (post_layer.x - post_layer.fire_rate) - post_layer.thr.data[post_layer.thr.data < 0] = 0 - -def norm_inhib(post_layer): + # Apply the weight changes + self.layer.exc.weight.data += ((pre * post * self.layer.havconnExc.T) * + self.layer.eta_stdp).T + self.layer.inh.weight.data += ((-pre * post * self.layer.havconnInh.T) * + (self.layer.eta_stdp * -1)).T + + # Normal STDP + else: + + # Get layer dimensions + shape = self.layer.exc.weight.data.shape + + # Tile out pre- and post-spikes + pre = torch.tile(torch.reshape(self.spikes, (shape[1], 1)), (1, shape[0])) + post = torch.tile(self.layer.x, (shape[1], 1)) + + # Apply positive and negative weight changes + self.layer.exc.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * + self.layer.havconnExc.T) * self.layer.eta_stdp).T + self.layer.inh.weight.data += (((0.5 - post) * (pre > 0) * + (post > 0) * self.layer.havconnInh.T) * (self.layer.eta_stdp * -1)).T - # Check if layer has inhibitory weights and an stdp learning rate - if torch.any(post_layer.inhW).item() and post_layer.eta_stdp != 0: + # In-place clamp for excitatory and inhibitory weights + self.layer.exc.weight.data[self.layer.exc.weight.data < 0] = 1e-06 + self.layer.inh.weight.data[self.layer.inh.weight.data > 0] = -1e-06 - # Normalize the inhibitory weights using homeostasis - post_layer.inhW.data += torch.mul(torch.mul(post_layer.x_input, post_layer.inhW.data), - post_layer.eta_stdp*50) - post_layer.inhW.data[post_layer.inhW.data > 0.0] = -1e-06 + # Remove negative weights for excW and positive for inhW + self.layer.exc.weight.data[self.layer.havconnExc] = self.layer.exc.weight.data[self.layer.havconnExc].clamp(min=1e-06, max=10) + self.layer.inh.weight.data[self.layer.havconnInh] = self.layer.inh.weight.data[self.layer.havconnInh].clamp(min=-10, max=-1e-06) + + # Check if layer has target firing rate and an ITP learning rate + if self.layer.have_rate and self.layer.eta_ip > 0.0: + + # Replace the original layer.thr with the updated one + self.layer.thr.data += self.layer.eta_ip * (self.layer.x - self.layer.fire_rate) + self.layer.thr.data[self.layer.thr.data < 0] = 0 -def runSim(pre_layer,post_layer,spikes,idx): + # Check if layer has inhibitory weights and an stdp learning rate + if torch.any(self.layer.inh.weight.data).item() and self.layer.eta_stdp != 0: + + # Normalize the inhibitory weights using homeostasis + inhW = self.layer.inh.weight.data.T + self.layer.inh.weight.data += (torch.mul(self.layer.x_input,inhW) * self.layer.eta_stdp*50).T + self.layer.inh.weight.data[self.layer.inh.weight.data > 0.0] = -1e-06 - # Propagate spikes from pre to post neurons - add_input(post_layer) - calc_spikes(post_layer,spikes) - - # Calculate STDP weight changes - calc_stdp(pre_layer,post_layer,spikes,idx) + + def runSim(self): + + # Propagate spikes from pre to post neurons + self.add_input() + self.calc_spikes() + + # Calculate STDP weight changes + self.calc_stdp() - # Normalise firing rates and inhibitory balance - norm_rates(post_layer) - norm_inhib(post_layer) -def testSim(layers,layer_num,spikes): - - # run the test system through all specified layers to get an output - for n in range(layer_num): - layers['layer'+str(n+1)].x.fill_(0.0) - layers['layer'+str(n+1)].x_input.fill_(0.0) - calc_spikes(layers['layer'+str(n+1)],spikes) - spikes=layers['layer'+str(n+1)].x - - return spikes \ No newline at end of file + def testSim(self): + + # run the test system through all specified layers to get an output + for count, layer in enumerate(self.layer_lst): + if count != self.testCount: + self.layer = self.layer_lst[layer] + self.layer.x.fill_(0.0) + self.layer.x_input.fill_(0.0) + self.calc_spikes() + self.spikes = self.layer.x + + return self.layer.x \ No newline at end of file diff --git a/src/dataset.py b/src/dataset.py index 0b9d7c1..39c5824 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -6,6 +6,7 @@ import pandas as pd import numpy as np import torch.nn.functional as F +import torch.quantization as tq from torchvision.io import read_image from torch.utils.data import Dataset @@ -88,26 +89,34 @@ def __call__(self, img): return im_norm class SetImageAsSpikes: - def __init__(self,intensity=255,test=True,modules=1): + def __init__(self,intensity=255,test=True): self.intensity = intensity self.test = test - self.modules = modules - - def __call__(self, img_tensor): - # Ensure the input is a 4D tensor (N x C x W x H) - if len(img_tensor.shape) == 3: - img_tensor = img_tensor.unsqueeze(1) - N, C, W, H = img_tensor.shape + self.qconfig = tq.default_qconfig + self.observer = tq.MinMaxObserver() + + def __call__(self, img_tensor): + N, W, H = img_tensor.shape reshaped_batch = img_tensor.view(N, 1, -1) # Divide all pixel values by 255 normalized_batch = reshaped_batch / self.intensity - + normalized_batch = torch.squeeze(normalized_batch,0) # If running test, repeat input over all the modules + #if self.test: + # normalized_batch = normalized_batch.repeat(self.modules, 1, 1) if self.test: - normalized_batch = normalized_batch.repeat(self.modules, 1, 1) - + self.observer(normalized_batch) + scale, zero_point = self.observer.calculate_qparams() + scale = float(scale) + zero_point = int(zero_point) + + # Quantize the image tensor + normalized_batch = torch.quantize_per_tensor(normalized_batch, + scale=scale, + zero_point=zero_point, + dtype=torch.quint8) return normalized_batch class ProcessImage: @@ -142,7 +151,7 @@ def __call__(self, img): class CustomImageDataset(Dataset): def __init__(self, annotations_file, img_dirs, transform=None, target_transform=None, - skip=1, max_samples=None, modules=1, test=True): + skip=1, max_samples=None, test=True): self.transform = transform self.target_transform = target_transform self.skip = skip @@ -164,34 +173,12 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= if test: self.img_labels = img_labels else: - # Reorder images in the DataFrame - reordered_img_labels = self.reorder_images(img_labels, modules) - self.img_labels.append(reordered_img_labels) + self.img_labels.append(img_labels) if isinstance(self.img_labels,list): - # Concatenate all the reordered DataFrames + # Concatenate all the DataFrames self.img_labels = pd.concat(self.img_labels, ignore_index=True) - def reorder_images(self, img_labels, modules): - # Calculate the number of batches - num_batches = len(img_labels) // modules - remainder = len(img_labels) % modules - - reordered_list = [] - for i in range(num_batches): - for j in range(modules): - idx = i + j * num_batches - if idx < len(img_labels): - reordered_list.append(img_labels.iloc[idx]) - - # If there are remaining images, append them to the reordered list - for i in range(remainder): - idx = num_batches * modules + i - reordered_list.append(img_labels.iloc[idx]) - - # Convert reordered list of Series back to DataFrame - return pd.concat(reordered_list, axis=1).transpose().reset_index(drop=True) - def __len__(self): return len(self.img_labels) @@ -208,4 +195,4 @@ def __getitem__(self, idx): if self.target_transform: label = self.target_transform(label) - return image, label + return image, label \ No newline at end of file From b0ae58e3d242037ab8fad3b317df99a10cec621b Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Wed, 11 Oct 2023 17:09:12 +1000 Subject: [PATCH 14/69] Now runs through training and inference with quantization, but its garbage out. Looking like some x and x_input values are adding gradients and getting big over time, investigate further. --- VPRTempo.py | 467 +++++++++++++++++++++-------------------------- config/config.py | 4 +- src/blitnet.py | 211 +++++++++------------ src/dataset.py | 49 +++-- 4 files changed, 327 insertions(+), 404 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index 05e9ff8..d440476 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -45,303 +45,246 @@ from config import configure, image_csv, model_logger from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage from torch.utils.data import DataLoader +from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm -class SNNTrainer(nn.Module): +class VPRTempo(nn.Module): def __init__(self): - super(SNNTrainer, self).__init__() + super(VPRTempo, self).__init__() + # Configure the network - configure(self) # Sets the testing configuration - image_csv(self) # Defines images to load + configure(self) - # Set the device - #self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.device = torch.device("cpu") + # Define the images to load (both training and inference) + image_csv(self) - # Set up the feature layer + # Common model architecture self.feature_layer = bn.SNNLayer( - dims=[self.input,self.feature], - thr_range=[0,0.5], - fire_rate=[0.2,0.9], - ip_rate=0.15, - stdp_rate=0.005, - const_inp=[0,0.1], - p=[0.1,0.5] - ) - - # Set up the output layer + dims=[self.input, self.feature], + thr_range=[0, 0.5], + fire_rate=[0.2, 0.9], + ip_rate=0.15, + stdp_rate=0.005, + const_inp=[0, 0.1], + p=[0.1, 0.5] + ) self.output_layer = bn.SNNLayer( - dims=[self.feature,self.output], - ip_rate=0.15, - stdp_rate=0.005, - spk_force=True - ) - - # Define number of layers (will run training on all layers) + dims=[self.feature, self.output], + ip_rate=0.15, + stdp_rate=0.005, + spk_force=True + ) self.layers = { - 'layer0':self.feature_layer, - 'layer1':self.output_layer - } - - - def train_model(self, train_loader, logger): + 'layer0': self.feature_layer, + 'layer1': self.output_layer + } - # If using CUDA, run a dummy torch.bmm to 'spool-up' operations - if self.device.type == "cuda": - ut.dummy_bmm(self.device) + self.quant = QuantStub() + self.dequant = DeQuantStub() - # Warm up the DataLoader - warmup_iters = 10 - for _ in range(warmup_iters): - _ = next(iter(train_loader)) + self.add = nn.quantized.FloatFunctional() - # Run the training for each layer, using defined parameters - for lyrCount, lyr in enumerate(self.layers): - - # Get the training layer - layer = self.layers[lyr] - - # Output the learning rates for annealment during training - n_initstdp = layer.eta_stdp.detach() # STDP - n_initip = layer.eta_ip.detach() # ITP - - # Initialize the tqdm progress bar - pbar = tqdm(total=int(self.T * self.epoch), - desc="Training "+str(lyr), - position=0) - - # Run the training for the input to feature layer for specified epochs - for epoch in range(self.epoch): - # Used to determine the learning rate annealment, resets at each epoch - mod = 0 - - # Load and run images through BliTNET - for images, labels in train_loader: - - # Reset fire rate to None - fr = None - - # Put input images on device (CPU, CUDA) - images = images.to(self.device) - - # Set spikes from input images - make_spikes = SetImageAsSpikes(self.intensity, test=False) - spikes = make_spikes(images) - - # Put labels on device (CPU, CUDA) - labels = labels.to(self.device) - idx = labels/self.filter - - # Layers don't include loaded input, calculate network spikes for up to training layers - if list(lyr[-1]) != ['0']: - blitnet = bn.BLiTNET(layer_lst=self.layers, - spikes=spikes, - idx=idx, - testCount=lyrCount) - spikes = blitnet.testSim() - fr = self.layers['layer'+str(lyrCount-1)].fire_rate - - # Run one timestep of the training input to feature layer - blitnet = bn.BLiTNET( - layer=layer, - spikes=spikes, - idx=idx, - fr=fr - ) - blitnet.runSim() - - # Anneal the learning rate - if np.mod(mod,10)==0: - pt = pow(float(self.T-mod)/self.T,self.annl_pow) - layer.eta_ip = torch.mul(n_initip,pt) - layer.eta_stdp = torch.mul(n_initstdp,pt) - - # Update the learning rate annealment modifier - mod += 1 - - # Reset x and x_input for next iteration - layer.x.fill_(0.0) - layer.x_input.fill_(0.0) - pbar.update(1) - - # Close the tqdm progress bar - pbar.close() - print('') - - # Clear the cache and garbage collect (if using cuda) - if self.device.type == "cuda": - torch.cuda.empty_cache() - gc.collect() - - def save_model(self, model_out): - # save model - torch.save(self.state_dict(), model_out) + def model_logger(self): + # Start the model logger + model_logger(self) -class SNNModel(nn.Module): - def __init__(self): - super(SNNModel, self).__init__() + def _anneal_learning_rate(self, layer, mod, itp, stdp): + if np.mod(mod, 10) == 0: + pt = pow(float(self.T - mod) / self.T, self.annl_pow) + layer.eta_ip = torch.mul(itp, pt) + layer.eta_stdp = torch.mul(stdp, pt) + + return layer - # Configure the network - configure(self) # Sets the testing configuration - image_csv(self) # Defines images to load - model_logger(self) # Sets the logger - - - # Set up the feature layer - self.feature_layer = bn.SNNLayer(dims=[self.input,self.feature]) - - # Set up the output layer - self.output_layer = bn.SNNLayer(dims=[self.feature,self.output]) + def train_model(self,layer,train_loader,logger,prev_layer=None): + + # Initialize the tqdm progress bar + pbar = tqdm(total=int(self.T * self.epoch), + desc="Training ", + position=0) - # Define number of layers (will run training on all layers) - self.layers = { - 'layer0':self.feature_layer, - 'layer1':self.output_layer - } + init_itp = layer.eta_ip.detach() + init_stdp = layer.eta_stdp.detach() - def load_model(self,model_path): - state_dict = torch.load(model_path, map_location=self.device) - self.load_state_dict(state_dict) - self.eval() + for epoch in range(self.epoch): + mod = 0 # Used to determine the learning rate annealment, resets at each epoch - def forward(self, test_loader): - - idx=0 - numcorr = 0 + for images, labels in train_loader: + images, labels = images.to(self.device), labels.to(self.device) + idx = labels / self.filter + if not prev_layer == None: + # Get the spikes to train the next layer + prev_layer.x_input = self.forward(images,prev_layer) + out = bn.clamp_spikes(prev_layer) + images = out + # Run forward pass + layer = bn.add_input(layer) + layer.x_input = self.forward(images, layer) + out = bn.clamp_spikes(layer) + layer = bn.calc_stdp(images, out, layer, idx, prev_layer=prev_layer) + + # Adjust learning rates + layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) + + # Reset x and x_input for next iteration + layer.x.fill_(0.0) + layer.x_input.fill_(0.0) + mod += 1 + pbar.update(1) - # If using CUDA, run a dummy torch.bmm to 'spool-up' operations + # Close the tqdm progress bar + pbar.close() + if self.device.type == "cuda": - ut.dummy_bmm(self.device) + torch.cuda.empty_cache() + gc.collect() - warmup_iters = 10 - for _ in range(warmup_iters): - _ = next(iter(test_loader)) - + + def evaluate(self, test_loader, layers=None): + numcorr = 0 + idx = 0 + # Initialize the tqdm progress bar pbar = tqdm(total=self.number_testing_images, desc="Running the test network", position=0) - - # Run test network for each individual input + for images, labels in test_loader: - - # Set images to the specified device images = images.to(self.device) labels = labels.to(self.device) - - # Set the spikes for the input, tiling across modules if applicable - make_spikes = SetImageAsSpikes(intensity=self.intensity) - spikes = make_spikes(images) - - # Run the test sim - blitnet = bn.BLiTNET(layer_lst=self.layers, - spikes=spikes, - testCount=len(self.layers) - ) - out = blitnet.testSim() - if torch.argmax(out.reshape(1, self.number_training_images)) == idx: + for layer in layers: + images = self.forward(images, layer) + + if torch.argmax(images.reshape(1, self.number_training_images)) == idx: numcorr += 1 - - idx+=1 + + idx += 1 - # Update the progress bar pbar.update(1) - + pbar.close() - model.logger.info('') - model.logger.info("P@100R: "+ - str(round((numcorr/self.number_testing_images)*100,2))+'%') - - # Clear cache and empty garbage (if applicable) - if self.device.type == "cuda": - torch.cuda.empty_cache() - gc.collect() - -if __name__ == "__main__": - # Initialize the model and image transforms - model = SNNModel() - qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') - # Generate model name, check if pre-trained model exists - model_name = ("VPRTempo"+ # main name - str(model.input)+ # number input neurons - str(model.feature)+ # number feature neurons - str(model.output)+ # number output neurons - str(model.number_modules)+ # number of modules - '.pth') + accuracy = round((numcorr/self.number_testing_images)*100,2) + model.logger.info("P@100R: "+ str(accuracy) + '%') + + return accuracy + - # Check if a pre-trained model exists - if os.path.exists(os.path.join('./models',model_name)): - pretrain_flg = True - - # Prompt user to retrain network if desired - prompt = "A network with these parameters exists, re-train network? (y/n):\n" - retrain = input(prompt) - print('') - - # Retrain network, set flag to False - if retrain == 'y': - pretrain_flg = False - else: - # No pretrained model exists - pretrain_flg = False + def forward(self, spikes, layer): + """ + Compute the forward pass of the model. - # Define the image transform class - image_transform = ProcessImage(model.dims,model.patches) + Parameters: + - spikes (Tensor): Input spikes. - # If no pre-existing model, train new model with set configuration - if not pretrain_flg: - # Define the custom training image dataset class - train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.training_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_training_images, - test=False) - - # Define the training dataloader class - train_loader = DataLoader(train_dataset, - batch_size=1, - shuffle=False, - num_workers=1, - persistent_workers=True) - - # Initialize, run, and save the training model - trainer = SNNTrainer() - trainer.to('cpu') + Returns: + - Tensor: Output after processing. + """ + spikes = self.quant(spikes) + layer.x_input = self.quant(layer.x_input) + layer.x_input = bn.calc_spikes(spikes, layer, self.add) + layer.x_input = self.dequant(layer.x_input) + spikes = self.dequant(spikes) - trainer.feature_layer.qconfig = qconfig - trainer.output_layer.qconfig = qconfig - trainer = torch.quantization.prepare_qat(trainer, inplace=True) - - trainer.train_model(train_loader,model.logger) - - trainer.eval() - trainer = torch.quantization.convert(trainer, inplace=True) - - trainer.save_model(os.path.join('./models',model_name)) + return layer.x_input - with torch.no_grad(): # Disable gradient computation during testing - model.to('cpu') - model.feature_layer.qconfig = qconfig - model.output_layer.qconfig = qconfig - model = torch.quantization.prepare(model, inplace=False) - model = torch.quantization.convert(model, inplace=False) - # Load the trained model into SNNModel() - model.load_model(os.path.join('./models',model_name)) - - - # Define the custom testing image dataset class - test_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.testing_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_testing_images) + def save_model(self, model_out): + """Save the trained model to models output folder.""" + torch.save(self.state_dict(), model_out) - # Define the testing dataloader class - test_loader = DataLoader(test_dataset, - batch_size=1, - shuffle=False, - num_workers=1, - persistent_workers=True) - model.forward(test_loader) \ No newline at end of file + def load_model(self, model_path): + """Load pre-trained model and set the state dictionary keys.""" + self.load_state_dict(torch.load(model_path, map_location=self.device), + strict=False) + self.eval() + +def generate_model_name(model): + """Generate the model name based on its parameters.""" + return ("VPRTempo" + + str(model.input) + + str(model.feature) + + str(model.output) + + str(model.number_modules) + + '.pth') + +def check_pretrained_model(model_name): + """Check if a pre-trained model exists and prompt the user to retrain if desired.""" + if os.path.exists(os.path.join('./models', model_name)): + prompt = "A network with these parameters exists, re-train network? (y/n):\n" + retrain = input(prompt).strip().lower() + return retrain == 'n' + return False + +def train_new_model(model, model_name, qconfig): + set_image_as_spikes = SetImageAsSpikes(test=False) + set_image_as_spikes.eval() + set_image_as_spikes.train() + image_transform = ProcessImage(model.dims, model.patches) + train_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.training_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_training_images, + test=False) + train_loader = DataLoader(train_dataset, + batch_size=1, + shuffle=False, + num_workers=1, + persistent_workers=True) + trainer = VPRTempo() + trainer.train() + trainer.to('cpu') + trainer.feature_layer.qconfig = qconfig + trainer.output_layer.qconfig = qconfig + trainer = quantization.prepare_qat(trainer, inplace=True) + trainer.train_model(trainer.feature_layer, train_loader, model.logger) + trainer.train_model(trainer.output_layer, train_loader, model.logger, + prev_layer=trainer.feature_layer) + trainer.eval() + trainer.save_model(os.path.join('./models', model_name)) + +def run_inference(model, model_name, qconfig): + set_image_as_spikes = SetImageAsSpikes(test=True) + set_image_as_spikes.eval() + image_transform = ProcessImage(model.dims, model.patches) + test_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.testing_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_testing_images) + test_loader = DataLoader(test_dataset, + batch_size=1, + shuffle=False, + num_workers=1, + persistent_workers=True) + + model = VPRTempo() + model.to('cpu') + model.eval() + model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') + model.feature_layer.qconfig = qconfig + model.output_layer.qconfig = qconfig + model = quantization.prepare(model, inplace=True) + model.load_model(os.path.join('./models', model_name)) + model = quantization.convert(model, inplace=True) + + # Use evaluate method for inference accuracy + model.evaluate(test_loader, + layers=[model.feature_layer, model.output_layer] + ) + +if __name__ == "__main__": + # Temporary place holder for now, weird multiprocessing bug with larger models + torch.set_num_threads(1) + + # Initialize the model and image transforms + model = VPRTempo() + model.model_logger() + qconfig = quantization.get_default_qat_qconfig('fbgemm') + model_name = generate_model_name(model) + use_pretrained = check_pretrained_model(model_name) + + if not use_pretrained: + train_new_model(model, model_name, qconfig) + with torch.no_grad(): + run_inference(model, model_name, qconfig) \ No newline at end of file diff --git a/config/config.py b/config/config.py index c6d7fae..a5ff85c 100644 --- a/config/config.py +++ b/config/config.py @@ -11,8 +11,8 @@ def configure(model): model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' model.number_modules = 1 - model.number_training_images = 10 - model.number_testing_images = 10 + model.number_training_images = 100 + model.number_testing_images = 100 model.locations = ["spring","fall"] model.test_locations = ["summer"] model.filter = 8 diff --git a/src/blitnet.py b/src/blitnet.py index 7803ea3..2c5765d 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -26,6 +26,7 @@ import torch import torch.nn as nn + import numpy as np from config import configure @@ -138,136 +139,106 @@ def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0]): W = nn.Parameter(W/nrm) return W - -class BLiTNET(nn.Module): - def __init__(self,layer=None,spikes=None,idx=None,fr=None,testCount=None, - layer_lst=None): - super(BLiTNET, self).__init__() - - # Define the layer & spikes to be parsed through BLiTNET - self.layer = layer - self.spikes = spikes - - # For spike forcing, define the output idx and pre-layer fire rate - self.idx = idx - self.fr = fr - # For running testing, determine number of layers to iterate through for output - self.testCount = testCount - self.layer_lst = layer_lst - - def add_input(self): - # Add the constant input - self.layer.x_input += self.layer.const_inp - - def calc_spikes(self): - # Use nn.Linear to perform multiplication of spikes to weights - self.layer.x_input += self.layer.exc(self.spikes) - self.layer.x_input += self.layer.inh(self.spikes) - - # Clamp outputs between 0 and 0.9 after subtracting thresholds from input - if self.layer.spk_force: - self.layer.x_calc = torch.clamp(torch.sub(self.layer.x_input, self.layer.thr), min=0.0, max=0.9) - else: - self.layer.x = torch.clamp(torch.sub(self.layer.x_input, self.layer.thr), min=0.0, max=0.9) - - def calc_stdp(self): - # Spike Forcing has special rules to make calculated and forced spikes match - if self.layer.spk_force: - - # Get layer dimensions - shape = self.layer.exc.weight.data.shape - - # Get the output neuron index - idx_sel = torch.arange(int(self.idx[0]), int(self.idx[0]) + 1, - device=self.layer.device, - dtype=int) +def add_input(layer): - # Difference between forced and calculated spikes - self.layer.x = torch.full_like(self.layer.x, 0) - xdiff = torch.clamp(self.layer.x.index_fill_(-1, idx_sel, 0.5) - self.layer.x_calc, min=0, max=1) + # Add the constant input + layer.x_input += layer.const_inp - # Threshold rules - lower it if calced spike is smaller (and vice versa) - self.layer.thr.data -= torch.sign(xdiff) * torch.abs(self.layer.eta_stdp) / 10 - self.layer.thr.data -= torch.sign(xdiff) * torch.abs((self.layer.eta_stdp * -1)) / 10 - self.layer.thr.data = self.layer.thr.data.clamp(min=0, max=1) - - # Pre and Post spikes tiled across and down for all synapses - if self.fr == None: - mpre = self.spikes - else: - # Modulate learning rate by firing rate (low firing rate = high learning rate) - mpre = self.spikes/self.fr - - # Tile out pre- and post- spikes for STDP weight updates - pre = torch.tile(torch.reshape(mpre, (shape[1], 1)), (1, shape[0])) - post = torch.tile(xdiff, (shape[1], 1)) + return layer + +def calc_spikes(spikes, layer, adder): - # Apply the weight changes - self.layer.exc.weight.data += ((pre * post * self.layer.havconnExc.T) * - self.layer.eta_stdp).T - self.layer.inh.weight.data += ((-pre * post * self.layer.havconnInh.T) * - (self.layer.eta_stdp * -1)).T + # Use nn.Linear to perform multiplication of spikes to weights + layer.x_input = adder.add(layer.x_input,layer.exc(spikes)) + layer.x_input = adder.add(layer.x_input,layer.inh(spikes)) - # Normal STDP + return layer.x_input + +def clamp_spikes(layer): + # Clamp outputs between 0 and 0.9 after subtracting thresholds from input + if layer.spk_force: + layer.x_calc = torch.clamp(torch.sub(layer.x_input, layer.thr), min=0.0, max=0.9) + else: + layer.x = torch.clamp(torch.sub(layer.x_input, layer.thr), min=0.0, max=0.9) + + return layer.x + +def calc_stdp(spikes, out, layer, idx, prev_layer=None): + # Spike Forcing has special rules to make calculated and forced spikes match + if layer.spk_force: + + # Get layer dimensions + shape = layer.exc.weight.data.shape + + # Get the output neuron index + idx_sel = torch.arange(int(idx[0]), int(idx[0]) + 1, + device=layer.device, + dtype=int) + + # Difference between forced and calculated spikes + layer.x = torch.full_like(layer.x, 0) + xdiff = torch.clamp(layer.x.index_fill_(-1, idx_sel, 0.5) - layer.x_calc, min=0, max=1) + + # Threshold rules - lower it if calced spike is smaller (and vice versa) + layer.thr.data -= torch.sign(xdiff) * torch.abs(layer.eta_stdp) / 10 + layer.thr.data -= torch.sign(xdiff) * torch.abs((layer.eta_stdp * -1)) / 10 + layer.thr.data = layer.thr.data.clamp(min=0, max=1) + + # Pre and Post spikes tiled across and down for all synapses + if prev_layer.fire_rate == None: + mpre = spikes else: + # Modulate learning rate by firing rate (low firing rate = high learning rate) + mpre = spikes/prev_layer.fire_rate - # Get layer dimensions - shape = self.layer.exc.weight.data.shape - - # Tile out pre- and post-spikes - pre = torch.tile(torch.reshape(self.spikes, (shape[1], 1)), (1, shape[0])) - post = torch.tile(self.layer.x, (shape[1], 1)) - - # Apply positive and negative weight changes - self.layer.exc.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * - self.layer.havconnExc.T) * self.layer.eta_stdp).T - self.layer.inh.weight.data += (((0.5 - post) * (pre > 0) * - (post > 0) * self.layer.havconnInh.T) * (self.layer.eta_stdp * -1)).T - - # In-place clamp for excitatory and inhibitory weights - self.layer.exc.weight.data[self.layer.exc.weight.data < 0] = 1e-06 - self.layer.inh.weight.data[self.layer.inh.weight.data > 0] = -1e-06 + # Tile out pre- and post- spikes for STDP weight updates + pre = torch.tile(torch.reshape(mpre, (shape[1], 1)), (1, shape[0])) + post = torch.tile(xdiff, (shape[1], 1)) + + # Apply the weight changes + layer.exc.weight.data += ((pre * post * layer.havconnExc.T) * + layer.eta_stdp).T + layer.inh.weight.data += ((-pre * post * layer.havconnInh.T) * + (layer.eta_stdp * -1)).T + + # Normal STDP + else: - # Remove negative weights for excW and positive for inhW - self.layer.exc.weight.data[self.layer.havconnExc] = self.layer.exc.weight.data[self.layer.havconnExc].clamp(min=1e-06, max=10) - self.layer.inh.weight.data[self.layer.havconnInh] = self.layer.inh.weight.data[self.layer.havconnInh].clamp(min=-10, max=-1e-06) - - # Check if layer has target firing rate and an ITP learning rate - if self.layer.have_rate and self.layer.eta_ip > 0.0: - - # Replace the original layer.thr with the updated one - self.layer.thr.data += self.layer.eta_ip * (self.layer.x - self.layer.fire_rate) - self.layer.thr.data[self.layer.thr.data < 0] = 0 + # Get layer dimensions + shape = layer.exc.weight.data.shape - # Check if layer has inhibitory weights and an stdp learning rate - if torch.any(self.layer.inh.weight.data).item() and self.layer.eta_stdp != 0: - - # Normalize the inhibitory weights using homeostasis - inhW = self.layer.inh.weight.data.T - self.layer.inh.weight.data += (torch.mul(self.layer.x_input,inhW) * self.layer.eta_stdp*50).T - self.layer.inh.weight.data[self.layer.inh.weight.data > 0.0] = -1e-06 + # Tile out pre- and post-spikes + pre = torch.tile(torch.reshape(spikes, (shape[1], 1)), (1, shape[0])) + post = torch.tile(out, (shape[1], 1)) + + # Apply positive and negative weight changes + layer.exc.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * + layer.havconnExc.T) * layer.eta_stdp).T + layer.inh.weight.data += (((0.5 - post) * (pre > 0) * + (post > 0) * layer.havconnInh.T) * (layer.eta_stdp * -1)).T - - def runSim(self): + # In-place clamp for excitatory and inhibitory weights + layer.exc.weight.data[layer.exc.weight.data < 0] = 1e-06 + layer.inh.weight.data[layer.inh.weight.data > 0] = -1e-06 - # Propagate spikes from pre to post neurons - self.add_input() - self.calc_spikes() + # Remove negative weights for excW and positive for inhW + layer.exc.weight.data[layer.havconnExc] = layer.exc.weight.data[layer.havconnExc].clamp(min=1e-06, max=10) + layer.inh.weight.data[layer.havconnInh] = layer.inh.weight.data[layer.havconnInh].clamp(min=-10, max=-1e-06) + + # Check if layer has target firing rate and an ITP learning rate + if layer.have_rate and layer.eta_ip > 0.0: + + # Replace the original layer.thr with the updated one + layer.thr.data += layer.eta_ip * (layer.x - layer.fire_rate) + layer.thr.data[layer.thr.data < 0] = 0 - # Calculate STDP weight changes - self.calc_stdp() - - - def testSim(self): + # Check if layer has inhibitory weights and an stdp learning rate + if torch.any(layer.inh.weight.data).item() and layer.eta_stdp != 0: - # run the test system through all specified layers to get an output - for count, layer in enumerate(self.layer_lst): - if count != self.testCount: - self.layer = self.layer_lst[layer] - self.layer.x.fill_(0.0) - self.layer.x_input.fill_(0.0) - self.calc_spikes() - self.spikes = self.layer.x - - return self.layer.x \ No newline at end of file + # Normalize the inhibitory weights using homeostasis + inhW = layer.inh.weight.data.T + layer.inh.weight.data += (torch.mul(layer.x_input,inhW) * layer.eta_stdp*50).T + layer.inh.weight.data[layer.inh.weight.data > 0.0] = -1e-06 + + return layer \ No newline at end of file diff --git a/src/dataset.py b/src/dataset.py index 39c5824..c395ae2 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -89,35 +89,41 @@ def __call__(self, img): return im_norm class SetImageAsSpikes: - def __init__(self,intensity=255,test=True): + def __init__(self, intensity=255, test=True): self.intensity = intensity - self.test = test - self.qconfig = tq.default_qconfig - self.observer = tq.MinMaxObserver() + # Setup QAT FakeQuantize for the activations (your spikes) + self.fake_quantize = torch.quantization.FakeQuantize( + observer=torch.quantization.MovingAverageMinMaxObserver, + quant_min=0, + quant_max=255, + dtype=torch.quint8, + qscheme=torch.per_tensor_affine, + reduce_range=False + ) + def train(self): + self.fake_quantize.train() + + def eval(self): + self.fake_quantize.eval() + def __call__(self, img_tensor): N, W, H = img_tensor.shape reshaped_batch = img_tensor.view(N, 1, -1) # Divide all pixel values by 255 normalized_batch = reshaped_batch / self.intensity - normalized_batch = torch.squeeze(normalized_batch,0) - # If running test, repeat input over all the modules - #if self.test: - # normalized_batch = normalized_batch.repeat(self.modules, 1, 1) - if self.test: - self.observer(normalized_batch) - scale, zero_point = self.observer.calculate_qparams() - scale = float(scale) - zero_point = int(zero_point) - - # Quantize the image tensor - normalized_batch = torch.quantize_per_tensor(normalized_batch, - scale=scale, - zero_point=zero_point, - dtype=torch.quint8) - return normalized_batch + normalized_batch = torch.squeeze(normalized_batch, 0) + + # Apply FakeQuantize + spikes = self.fake_quantize(normalized_batch) + + if not self.fake_quantize.training: + scale, zero_point = self.fake_quantize.calculate_qparams() + spikes = torch.quantize_per_tensor(spikes, float(scale), int(zero_point), dtype=torch.quint8) + + return spikes class ProcessImage: def __init__(self, dims, patches): @@ -145,6 +151,9 @@ def __call__(self, img): patch_normaliser = PatchNormalisePad(self.patches) im_norm = patch_normaliser(img) img = (255.0 * (1 + im_norm) / 2.0).to(dtype=torch.uint8) + img = torch.unsqueeze(img,0) + spike_maker = SetImageAsSpikes() + img = spike_maker(img) img = torch.squeeze(img,0) return img From 97d88e0e3b8bb2545949d430e61bd50c07a94a34 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 12 Oct 2023 17:42:51 +1000 Subject: [PATCH 15/69] Now works, trains and inferences ok - problem is the scale point for quantized spikes is set to 1 so essentially all temporal information lost --- VPRTempo.py | 64 +++++++++++++++++++++++++++++--------------------- src/blitnet.py | 46 ++++++++++++------------------------ 2 files changed, 52 insertions(+), 58 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index d440476..d8ab532 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -109,19 +109,19 @@ def train_model(self,layer,train_loader,logger,prev_layer=None): for epoch in range(self.epoch): mod = 0 # Used to determine the learning rate annealment, resets at each epoch - for images, labels in train_loader: - images, labels = images.to(self.device), labels.to(self.device) + for spikes, labels in train_loader: + spikes, labels = spikes.to(self.device), labels.to(self.device) idx = labels / self.filter if not prev_layer == None: # Get the spikes to train the next layer - prev_layer.x_input = self.forward(images,prev_layer) - out = bn.clamp_spikes(prev_layer) - images = out + spikes = self.forward(spikes,prev_layer) + spikes = bn.clamp_spikes(spikes,prev_layer) # Run forward pass - layer = bn.add_input(layer) - layer.x_input = self.forward(images, layer) - out = bn.clamp_spikes(layer) - layer = bn.calc_stdp(images, out, layer, idx, prev_layer=prev_layer) + pre_spike = spikes.detach() + spikes = self.forward(spikes, layer) + spikes_noclp = spikes.detach() + spikes = bn.clamp_spikes(spikes, layer) + layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer) # Adjust learning rates layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) @@ -129,6 +129,11 @@ def train_model(self,layer,train_loader,logger,prev_layer=None): # Reset x and x_input for next iteration layer.x.fill_(0.0) layer.x_input.fill_(0.0) + layer.x_calc.fill_(0.0) + if not prev_layer == None: + prev_layer.x.fill_(0.0) + prev_layer.x_calc.fill_(0.0) + prev_layer.x_input.fill_(0.0) mod += 1 pbar.update(1) @@ -139,7 +144,6 @@ def train_model(self,layer,train_loader,logger,prev_layer=None): torch.cuda.empty_cache() gc.collect() - def evaluate(self, test_loader, layers=None): numcorr = 0 idx = 0 @@ -148,17 +152,21 @@ def evaluate(self, test_loader, layers=None): pbar = tqdm(total=self.number_testing_images, desc="Running the test network", position=0) - - for images, labels in test_loader: - images = images.to(self.device) + + for spikes, labels in test_loader: + + spikes = spikes.to(self.device) labels = labels.to(self.device) for layer in layers: - images = self.forward(images, layer) - - if torch.argmax(images.reshape(1, self.number_training_images)) == idx: + spikes = self.forward(spikes, layer) + spikes = bn.clamp_spikes(spikes, layer) + + topk_indices = torch.topk(spikes.reshape(1, self.number_training_images), 5).indices + + if idx in topk_indices: numcorr += 1 - + idx += 1 pbar.update(1) @@ -180,13 +188,12 @@ def forward(self, spikes, layer): Returns: - Tensor: Output after processing. """ + spikes = self.quant(spikes) - layer.x_input = self.quant(layer.x_input) - layer.x_input = bn.calc_spikes(spikes, layer, self.add) - layer.x_input = self.dequant(layer.x_input) + spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) spikes = self.dequant(spikes) - - return layer.x_input + + return spikes def save_model(self, model_out): """Save the trained model to models output folder.""" @@ -236,7 +243,7 @@ def train_new_model(model, model_name, qconfig): trainer.to('cpu') trainer.feature_layer.qconfig = qconfig trainer.output_layer.qconfig = qconfig - trainer = quantization.prepare_qat(trainer, inplace=True) + trainer = quantization.prepare_qat(trainer, inplace=False) trainer.train_model(trainer.feature_layer, train_loader, model.logger) trainer.train_model(trainer.output_layer, train_loader, model.logger, prev_layer=trainer.feature_layer) @@ -264,14 +271,16 @@ def run_inference(model, model_name, qconfig): model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') model.feature_layer.qconfig = qconfig model.output_layer.qconfig = qconfig - model = quantization.prepare(model, inplace=True) + model = quantization.prepare(model, inplace=False) model.load_model(os.path.join('./models', model_name)) - model = quantization.convert(model, inplace=True) + model = quantization.convert(model, inplace=False) # Use evaluate method for inference accuracy model.evaluate(test_loader, layers=[model.feature_layer, model.output_layer] ) + + if __name__ == "__main__": # Temporary place holder for now, weird multiprocessing bug with larger models @@ -285,6 +294,7 @@ def run_inference(model, model_name, qconfig): use_pretrained = check_pretrained_model(model_name) if not use_pretrained: - train_new_model(model, model_name, qconfig) + with torch.no_grad(): + train_new_model(model, model_name, qconfig) with torch.no_grad(): - run_inference(model, model_name, qconfig) \ No newline at end of file + run_inference(model, model_name, qconfig) \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index 2c5765d..9d4228b 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -140,31 +140,20 @@ def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0]): return W -def add_input(layer): +def add_input(spikes, layer): # Add the constant input - layer.x_input += layer.const_inp + spikes += layer.const_inp - return layer + return spikes -def calc_spikes(spikes, layer, adder): - - # Use nn.Linear to perform multiplication of spikes to weights - layer.x_input = adder.add(layer.x_input,layer.exc(spikes)) - layer.x_input = adder.add(layer.x_input,layer.inh(spikes)) - - return layer.x_input - -def clamp_spikes(layer): +def clamp_spikes(spikes, layer): # Clamp outputs between 0 and 0.9 after subtracting thresholds from input - if layer.spk_force: - layer.x_calc = torch.clamp(torch.sub(layer.x_input, layer.thr), min=0.0, max=0.9) - else: - layer.x = torch.clamp(torch.sub(layer.x_input, layer.thr), min=0.0, max=0.9) + spikes = torch.clamp(torch.sub(spikes, layer.thr), min=0.0, max=0.9) - return layer.x + return spikes -def calc_stdp(spikes, out, layer, idx, prev_layer=None): +def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None): # Spike Forcing has special rules to make calculated and forced spikes match if layer.spk_force: @@ -178,19 +167,14 @@ def calc_stdp(spikes, out, layer, idx, prev_layer=None): # Difference between forced and calculated spikes layer.x = torch.full_like(layer.x, 0) - xdiff = torch.clamp(layer.x.index_fill_(-1, idx_sel, 0.5) - layer.x_calc, min=0, max=1) - - # Threshold rules - lower it if calced spike is smaller (and vice versa) - layer.thr.data -= torch.sign(xdiff) * torch.abs(layer.eta_stdp) / 10 - layer.thr.data -= torch.sign(xdiff) * torch.abs((layer.eta_stdp * -1)) / 10 - layer.thr.data = layer.thr.data.clamp(min=0, max=1) + xdiff = layer.x.index_fill_(-1, idx_sel, 0.5) # Pre and Post spikes tiled across and down for all synapses if prev_layer.fire_rate == None: - mpre = spikes + mpre = prespike else: # Modulate learning rate by firing rate (low firing rate = high learning rate) - mpre = spikes/prev_layer.fire_rate + mpre = prespike/prev_layer.fire_rate # Tile out pre- and post- spikes for STDP weight updates pre = torch.tile(torch.reshape(mpre, (shape[1], 1)), (1, shape[0])) @@ -209,8 +193,8 @@ def calc_stdp(spikes, out, layer, idx, prev_layer=None): shape = layer.exc.weight.data.shape # Tile out pre- and post-spikes - pre = torch.tile(torch.reshape(spikes, (shape[1], 1)), (1, shape[0])) - post = torch.tile(out, (shape[1], 1)) + pre = torch.tile(torch.reshape(prespike, (shape[1], 1)), (1, shape[0])) + post = torch.tile(spikes, (shape[1], 1)) # Apply positive and negative weight changes layer.exc.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * @@ -225,7 +209,7 @@ def calc_stdp(spikes, out, layer, idx, prev_layer=None): # Remove negative weights for excW and positive for inhW layer.exc.weight.data[layer.havconnExc] = layer.exc.weight.data[layer.havconnExc].clamp(min=1e-06, max=10) layer.inh.weight.data[layer.havconnInh] = layer.inh.weight.data[layer.havconnInh].clamp(min=-10, max=-1e-06) - + # Check if layer has target firing rate and an ITP learning rate if layer.have_rate and layer.eta_ip > 0.0: @@ -238,7 +222,7 @@ def calc_stdp(spikes, out, layer, idx, prev_layer=None): # Normalize the inhibitory weights using homeostasis inhW = layer.inh.weight.data.T - layer.inh.weight.data += (torch.mul(layer.x_input,inhW) * layer.eta_stdp*50).T - layer.inh.weight.data[layer.inh.weight.data > 0.0] = -1e-06 + layer.inh.weight.data += (torch.mul(noclp,inhW) * layer.eta_stdp*50).T + layer.inh.weight.data[layer.inh.weight.data > 0.0] = -1e-06 return layer \ No newline at end of file From c8c08f382871a5aaf205bcabcb72ba61a791749c Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 13 Oct 2023 14:17:23 +1000 Subject: [PATCH 16/69] Quantization aware training now works perfectly. Ready to merge with main --- .../vprtempo_demo-checkpoint.ipynb | 65 ++++++++++ VPRTempo.py | 119 +++++++++++------- config/config.py | 6 +- src/blitnet.py | 3 +- vprtempo_tutorial.ipynb | 65 ++++++++++ 5 files changed, 208 insertions(+), 50 deletions(-) create mode 100644 .ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb create mode 100644 vprtempo_tutorial.ipynb diff --git a/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb b/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb new file mode 100644 index 0000000..11a9f2d --- /dev/null +++ b/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "ce79b3d3-ed51-4749-98b3-44ea2cfa45e3", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "import gc\n", + "import sys\n", + "sys.path.append('./src')\n", + "sys.path.append('./models')\n", + "sys.path.append('./settings')\n", + "sys.path.append('./output')\n", + "sys.path.append('./dataset')\n", + "sys.path.append('./config')\n", + "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n", + "import blitnet as bn\n", + "import utils as ut\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.quantization as quantization\n", + "\n", + "from config import configure, image_csv, model_logger\n", + "from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "from torch.ao.quantization import QuantStub, DeQuantStub\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f651774b-4a55-47f0-81c4-6f5fb4f7e1e9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/VPRTempo.py b/VPRTempo.py index d8ab532..9b6ff41 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -58,8 +58,20 @@ def __init__(self): # Define the images to load (both training and inference) image_csv(self) - # Common model architecture - self.feature_layer = bn.SNNLayer( + # Add quantization stubs for Quantization Aware Training (QAT) + self.quant = QuantStub() + self.dequant = DeQuantStub() + + # Define the add function for quantized addition + self.add = nn.quantized.FloatFunctional() + + # Layer dict to keep track of layer names and their order + self.layer_dict = {} + self.layer_counter = 0 + + # Define trainable layers here + self.add_layer( + 'feature_layer', dims=[self.input, self.feature], thr_range=[0, 0.5], fire_rate=[0.2, 0.9], @@ -68,36 +80,54 @@ def __init__(self): const_inp=[0, 0.1], p=[0.1, 0.5] ) - self.output_layer = bn.SNNLayer( + self.add_layer( + 'output_layer', dims=[self.feature, self.output], ip_rate=0.15, stdp_rate=0.005, spk_force=True ) - self.layers = { - 'layer0': self.feature_layer, - 'layer1': self.output_layer - } - self.quant = QuantStub() - self.dequant = DeQuantStub() + def add_layer(self, name, **kwargs): + """ + Dynamically add a layer with given name and keyword arguments. - self.add = nn.quantized.FloatFunctional() + :param name: Name of the layer to be added + :type name: str + :param kwargs: Hyperparameters for the layer + """ + # Check for layer name duplicates + if name in self.layer_dict: + raise ValueError(f"Layer with name {name} already exists.") + + # Add a new SNNLayer with provided kwargs + setattr(self, name, bn.SNNLayer(**kwargs)) + + # Add layer name and index to the layer_dict + self.layer_dict[name] = self.layer_counter + self.layer_counter += 1 def model_logger(self): - # Start the model logger + """ + Log the model configuration to the console. + """ model_logger(self) def _anneal_learning_rate(self, layer, mod, itp, stdp): - if np.mod(mod, 10) == 0: + """ + Anneal the learning rate for the current layer. + """ + if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps pt = pow(float(self.T - mod) / self.T, self.annl_pow) - layer.eta_ip = torch.mul(itp, pt) - layer.eta_stdp = torch.mul(stdp, pt) + layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate + layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate return layer - def train_model(self,layer,train_loader,logger,prev_layer=None): - + def train_model(self,layer,train_loader,prev_layer=None): + """ + Train a new network model, iterating through all defined layers + """ # Initialize the tqdm progress bar pbar = tqdm(total=int(self.T * self.epoch), desc="Training ", @@ -162,9 +192,11 @@ def evaluate(self, test_loader, layers=None): spikes = self.forward(spikes, layer) spikes = bn.clamp_spikes(spikes, layer) - topk_indices = torch.topk(spikes.reshape(1, self.number_training_images), 5).indices - - if idx in topk_indices: + #topk_indices = torch.topk(spikes.reshape(1, self.number_training_images), 5).indices + + #print(topk_indices) + #if idx in topk_indices: + if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx: numcorr += 1 idx += 1 @@ -202,8 +234,7 @@ def save_model(self, model_out): def load_model(self, model_path): """Load pre-trained model and set the state dictionary keys.""" self.load_state_dict(torch.load(model_path, map_location=self.device), - strict=False) - self.eval() + strict=True) def generate_model_name(model): """Generate the model name based on its parameters.""" @@ -223,9 +254,7 @@ def check_pretrained_model(model_name): return False def train_new_model(model, model_name, qconfig): - set_image_as_spikes = SetImageAsSpikes(test=False) - set_image_as_spikes.eval() - set_image_as_spikes.train() + image_transform = ProcessImage(model.dims, model.patches) train_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.training_dirs, @@ -238,21 +267,23 @@ def train_new_model(model, model_name, qconfig): shuffle=False, num_workers=1, persistent_workers=True) - trainer = VPRTempo() - trainer.train() - trainer.to('cpu') - trainer.feature_layer.qconfig = qconfig - trainer.output_layer.qconfig = qconfig - trainer = quantization.prepare_qat(trainer, inplace=False) - trainer.train_model(trainer.feature_layer, train_loader, model.logger) - trainer.train_model(trainer.output_layer, train_loader, model.logger, - prev_layer=trainer.feature_layer) - trainer.eval() - trainer.save_model(os.path.join('./models', model_name)) + model.train() + model.to('cpu') + model.qconfig = qconfig + model.feature_layer.qconfig = qconfig + model.output_layer.qconfig = qconfig + model = quantization.prepare_qat(model, inplace=False) + model.train_model(model.feature_layer, train_loader) + model.train_model(model.output_layer, train_loader, + prev_layer=model.feature_layer) + model = quantization.convert(model, inplace=False) + model.eval() + model.save_model(os.path.join('./models', model_name)) + + return model def run_inference(model, model_name, qconfig): - set_image_as_spikes = SetImageAsSpikes(test=True) - set_image_as_spikes.eval() + image_transform = ProcessImage(model.dims, model.patches) test_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.testing_dirs, @@ -264,27 +295,23 @@ def run_inference(model, model_name, qconfig): shuffle=False, num_workers=1, persistent_workers=True) - model = VPRTempo() - model.to('cpu') model.eval() - model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') + model.qconfig = qconfig model.feature_layer.qconfig = qconfig model.output_layer.qconfig = qconfig model = quantization.prepare(model, inplace=False) - model.load_model(os.path.join('./models', model_name)) model = quantization.convert(model, inplace=False) + model.load_model(os.path.join('./models', model_name)) # Use evaluate method for inference accuracy model.evaluate(test_loader, layers=[model.feature_layer, model.output_layer] ) - - if __name__ == "__main__": # Temporary place holder for now, weird multiprocessing bug with larger models - torch.set_num_threads(1) + torch.set_num_threads(4) # Initialize the model and image transforms model = VPRTempo() @@ -295,6 +322,6 @@ def run_inference(model, model_name, qconfig): if not use_pretrained: with torch.no_grad(): - train_new_model(model, model_name, qconfig) + model = train_new_model(model, model_name, qconfig) with torch.no_grad(): - run_inference(model, model_name, qconfig) \ No newline at end of file + run_inference(model, model_name, qconfig) diff --git a/config/config.py b/config/config.py index a5ff85c..debd862 100644 --- a/config/config.py +++ b/config/config.py @@ -11,8 +11,8 @@ def configure(model): model.trainingPath = '/home/adam/data/nordland/' model.testPath = '/home/adam/data/nordland/' model.number_modules = 1 - model.number_training_images = 100 - model.number_testing_images = 100 + model.number_training_images = 250 + model.number_testing_images = 250 model.locations = ["spring","fall"] model.test_locations = ["summer"] model.filter = 8 @@ -39,7 +39,7 @@ def configure(model): model.input = int(model.dims[0]*model.dims[1]) model.feature = int(model.input*2) model.output = int(model.number_training_images/model.number_modules) - model.intensity = 255 + model.intensity = 1 model.location_repeat = len(model.locations) model.layers = [] diff --git a/src/blitnet.py b/src/blitnet.py index 9d4228b..1da7e06 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -167,7 +167,8 @@ def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None): # Difference between forced and calculated spikes layer.x = torch.full_like(layer.x, 0) - xdiff = layer.x.index_fill_(-1, idx_sel, 0.5) + xdiff = layer.x.index_fill_(-1, idx_sel, 0.5) - spikes + xdiff.clamp(min=0.0, max=0.9) # Pre and Post spikes tiled across and down for all synapses if prev_layer.fire_rate == None: diff --git a/vprtempo_tutorial.ipynb b/vprtempo_tutorial.ipynb new file mode 100644 index 0000000..606f8bd --- /dev/null +++ b/vprtempo_tutorial.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", + "metadata": {}, + "source": [ + "## VPRTempo - tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ce79b3d3-ed51-4749-98b3-44ea2cfa45e3", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "import gc\n", + "import sys\n", + "sys.path.append('./src')\n", + "sys.path.append('./models')\n", + "sys.path.append('./settings')\n", + "sys.path.append('./output')\n", + "sys.path.append('./dataset')\n", + "sys.path.append('./config')\n", + "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n", + "import blitnet as bn\n", + "import utils as ut\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.quantization as quantization\n", + "\n", + "from config import configure, image_csv, model_logger\n", + "from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "from torch.ao.quantization import QuantStub, DeQuantStub\n", + "from tqdm import tqdm" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 82b853e1ff0daed8068cf4419326ca080f214db8 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 13 Oct 2023 16:45:38 +1000 Subject: [PATCH 17/69] Fixing up comments, reorganising modules, and fixed multi-layer training and inferencing --- .gitignore | 4 +- .../vprtempo_demo-checkpoint.ipynb | 65 ------ VPRTempo.py | 204 +++++++++++------- models/.gitkeep | 0 output/.gitkeep | 0 src/blitnet.py | 19 +- config/config.py => src/settings.py | 82 ++++--- 7 files changed, 194 insertions(+), 180 deletions(-) delete mode 100644 .ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb create mode 100644 models/.gitkeep create mode 100644 output/.gitkeep rename config/config.py => src/settings.py (68%) diff --git a/.gitignore b/.gitignore index ffbfd5c..64e7ef6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,3 @@ -models/ -output/ __pycache__/ +.ipynb_checkpoints/ src/__pycache__/ -config/__pycache__/ diff --git a/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb b/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb deleted file mode 100644 index 11a9f2d..0000000 --- a/.ipynb_checkpoints/vprtempo_demo-checkpoint.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "ce79b3d3-ed51-4749-98b3-44ea2cfa45e3", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import gc\n", - "import sys\n", - "sys.path.append('./src')\n", - "sys.path.append('./models')\n", - "sys.path.append('./settings')\n", - "sys.path.append('./output')\n", - "sys.path.append('./dataset')\n", - "sys.path.append('./config')\n", - "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n", - "import blitnet as bn\n", - "import utils as ut\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.quantization as quantization\n", - "\n", - "from config import configure, image_csv, model_logger\n", - "from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "from torch.ao.quantization import QuantStub, DeQuantStub\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f651774b-4a55-47f0-81c4-6f5fb4f7e1e9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/VPRTempo.py b/VPRTempo.py index 9b6ff41..9892b86 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -30,11 +30,9 @@ import sys sys.path.append('./src') sys.path.append('./models') -sys.path.append('./settings') sys.path.append('./output') sys.path.append('./dataset') -sys.path.append('./config') -torch.multiprocessing.set_sharing_strategy("file_system") + import blitnet as bn import utils as ut import numpy as np @@ -42,8 +40,8 @@ import torch.nn.functional as F import torch.quantization as quantization -from config import configure, image_csv, model_logger -from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage +from settings import configure, image_csv, model_logger +from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm @@ -69,7 +67,9 @@ def __init__(self): self.layer_dict = {} self.layer_counter = 0 - # Define trainable layers here + """ + Define trainable layers here + """ self.add_layer( 'feature_layer', dims=[self.input, self.feature], @@ -124,57 +124,70 @@ def _anneal_learning_rate(self, layer, mod, itp, stdp): return layer - def train_model(self,layer,train_loader,prev_layer=None): + def train_model(self, train_loader, layer, prev_layers=None): """ - Train a new network model, iterating through all defined layers + Train a layer of the network model. + + :param train_loader: Training data loader + :param layer: Layer to train + :param prev_layers: Previous layers to pass data through """ + # Initialize the tqdm progress bar pbar = tqdm(total=int(self.T * self.epoch), desc="Training ", position=0) + # Initialize the learning rates for each layer (used for annealment) init_itp = layer.eta_ip.detach() init_stdp = layer.eta_stdp.detach() + # Run training for the specified number of epochs for epoch in range(self.epoch): mod = 0 # Used to determine the learning rate annealment, resets at each epoch - + # Run training for the specified number of timesteps for spikes, labels in train_loader: spikes, labels = spikes.to(self.device), labels.to(self.device) - idx = labels / self.filter - if not prev_layer == None: - # Get the spikes to train the next layer - spikes = self.forward(spikes,prev_layer) - spikes = bn.clamp_spikes(spikes,prev_layer) - # Run forward pass - pre_spike = spikes.detach() - spikes = self.forward(spikes, layer) - spikes_noclp = spikes.detach() - spikes = bn.clamp_spikes(spikes, layer) + idx = labels / self.filter # Set output index for spike forcing + # Pass through previous layers if they exist + if prev_layers: + with torch.no_grad(): + for prev_layer_name in prev_layers: + prev_layer = getattr(self, prev_layer_name) # Get the previous layer object + spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer + spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9] + else: + prev_layer = None + # Get the output spikes from the current layer + pre_spike = spikes.detach() # Previous layer spikes for STDP + spikes = self.forward(spikes, layer) # Current layer spikes + spikes_noclp = spikes.detach() # Used for inhibitory homeostasis + spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9] + # Calculate STDP layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer) - # Adjust learning rates layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) - - # Reset x and x_input for next iteration - layer.x.fill_(0.0) - layer.x_input.fill_(0.0) - layer.x_calc.fill_(0.0) - if not prev_layer == None: - prev_layer.x.fill_(0.0) - prev_layer.x_calc.fill_(0.0) - prev_layer.x_input.fill_(0.0) + # Update the annealing mod & progress bar mod += 1 pbar.update(1) # Close the tqdm progress bar pbar.close() - + + # Free up memory if self.device.type == "cuda": torch.cuda.empty_cache() gc.collect() def evaluate(self, test_loader, layers=None): + """ + Run the inferencing model and calculate the accuracy. + + :param test_loader: Testing data loader + :param layers: Layers to pass data through + """ + + # Initialize the number of correct predictions numcorr = 0 idx = 0 @@ -183,33 +196,31 @@ def evaluate(self, test_loader, layers=None): desc="Running the test network", position=0) + # Run inference for the specified number of timesteps for spikes, labels in test_loader: - - spikes = spikes.to(self.device) - labels = labels.to(self.device) - - for layer in layers: - spikes = self.forward(spikes, layer) - spikes = bn.clamp_spikes(spikes, layer) - - #topk_indices = torch.topk(spikes.reshape(1, self.number_training_images), 5).indices - - #print(topk_indices) - #if idx in topk_indices: + # Set device + spikes, labels = spikes.to(self.device), labels.to(self.device) + # Pass through previous layers if they exist + if layers: + for layer_name in layers: + layer = getattr(self, layer_name) + spikes = self.forward(spikes, layer) + spikes = bn.clamp_spikes(spikes, layer) + + # Evaluate if the prediction is correct if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx: numcorr += 1 + # Update the index and progress bar idx += 1 - pbar.update(1) - + + # Close the tqdm progress bar pbar.close() + # Calculate and record the accuracy accuracy = round((numcorr/self.number_testing_images)*100,2) model.logger.info("P@100R: "+ str(accuracy) + '%') - return accuracy - - def forward(self, spikes, layer): """ Compute the forward pass of the model. @@ -228,16 +239,22 @@ def forward(self, spikes, layer): return spikes def save_model(self, model_out): - """Save the trained model to models output folder.""" + """ + Save the trained model to models output folder. + """ torch.save(self.state_dict(), model_out) def load_model(self, model_path): - """Load pre-trained model and set the state dictionary keys.""" + """ + Load pre-trained model and set the state dictionary keys. + """ self.load_state_dict(torch.load(model_path, map_location=self.device), strict=True) def generate_model_name(model): - """Generate the model name based on its parameters.""" + """ + Generate the model name based on its parameters. + """ return ("VPRTempo" + str(model.input) + str(model.feature) + @@ -246,7 +263,9 @@ def generate_model_name(model): '.pth') def check_pretrained_model(model_name): - """Check if a pre-trained model exists and prompt the user to retrain if desired.""" + """ + Check if a pre-trained model exists and prompt the user to retrain if desired. + """ if os.path.exists(os.path.join('./models', model_name)): prompt = "A network with these parameters exists, re-train network? (y/n):\n" retrain = input(prompt).strip().lower() @@ -254,7 +273,14 @@ def check_pretrained_model(model_name): return False def train_new_model(model, model_name, qconfig): - + """ + Train a new model. + + :param model: Model to train + :param model_name: Name of the model to save after training + :param qconfig: Quantization configuration + """ + # Initialize the image transforms and datasets image_transform = ProcessImage(model.dims, model.patches) train_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.training_dirs, @@ -262,66 +288,94 @@ def train_new_model(model, model_name, qconfig): skip=model.filter, max_samples=model.number_training_images, test=False) + # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, - num_workers=1, + num_workers=8, persistent_workers=True) + # Set the model to training mode and move to device model.train() model.to('cpu') model.qconfig = qconfig - model.feature_layer.qconfig = qconfig - model.output_layer.qconfig = qconfig + + # Apply quantization configurations to the model model = quantization.prepare_qat(model, inplace=False) - model.train_model(model.feature_layer, train_loader) - model.train_model(model.output_layer, train_loader, - prev_layer=model.feature_layer) + + # Keep track of trained layers to pass data through them + trained_layers = [] + + # Training each layer + for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): + print(f"Training layer: {layer_name}") + # Retrieve the layer object + layer = getattr(model, layer_name) + # Train the layer + model.train_model(train_loader, layer, prev_layers=trained_layers) + # After training the current layer, add it to the list of trained layers + trained_layers.append(layer_name) + # Convert the model to a quantized model model = quantization.convert(model, inplace=False) model.eval() + # Save the model model.save_model(os.path.join('./models', model_name)) - - return model def run_inference(model, model_name, qconfig): - + """ + Run inference on a pre-trained model. + + :param model: Model to run inference on + :param model_name: Name of the model to load + :param qconfig: Quantization configuration + """ + # Initialize the image transforms and datasets image_transform = ProcessImage(model.dims, model.patches) test_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.testing_dirs, transform=image_transform, skip=model.filter, max_samples=model.number_testing_images) + # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, - num_workers=1, + num_workers=8, persistent_workers=True) + # Set the model to evaluation mode and set configuration model = VPRTempo() model.eval() model.qconfig = qconfig - model.feature_layer.qconfig = qconfig - model.output_layer.qconfig = qconfig + + # Apply quantization configurations to all layers in layer_dict + for layer_name, _ in model.layer_dict.items(): + getattr(model, layer_name).qconfig = qconfig + # Prepare and convert the model to a quantized model model = quantization.prepare(model, inplace=False) model = quantization.convert(model, inplace=False) + # Load the model model.load_model(os.path.join('./models', model_name)) + # Retrieve layer names for inference + layer_names = list(model.layer_dict.keys()) + # Use evaluate method for inference accuracy - model.evaluate(test_loader, - layers=[model.feature_layer, model.output_layer] - ) + model.evaluate(test_loader, layers=layer_names) if __name__ == "__main__": - # Temporary place holder for now, weird multiprocessing bug with larger models - torch.set_num_threads(4) - - # Initialize the model and image transforms + # Set the number of threads for PyTorch + torch.set_num_threads(8) + # Initialize the model model = VPRTempo() + # Initialize the logger model.model_logger() + # Set the quantization configuration qconfig = quantization.get_default_qat_qconfig('fbgemm') + # Generate the model name model_name = generate_model_name(model) + # Check if a pre-trained model exists use_pretrained = check_pretrained_model(model_name) - + # Train or run inference based on the user's input if not use_pretrained: - with torch.no_grad(): - model = train_new_model(model, model_name, qconfig) + train_new_model(model, model_name, qconfig) # Training with torch.no_grad(): - run_inference(model, model_name, qconfig) + run_inference(model, model_name, qconfig) # Inference \ No newline at end of file diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/output/.gitkeep b/output/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/blitnet.py b/src/blitnet.py index 1da7e06..b6823e3 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -26,16 +26,26 @@ import torch import torch.nn as nn - import numpy as np -from config import configure +from settings import configure class SNNLayer(nn.Module): def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False): super(SNNLayer, self).__init__() + """ + dims: [input, output] dimensions of the layer + thr_range: [min, max] range of thresholds + fire_rate: [min, max] range of firing rates + ip_rate: learning rate for input threshold plasticity + stdp_rate: learning rate for stdp + const_inp: [min, max] range of constant input + p: [min, max] range of connection probabilities + spk_force: boolean to force spikes + """ + # Configure the network configure(self) # Sets the testing configuration # Device @@ -49,11 +59,6 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, # Initialize Tensors self.x = torch.zeros([1, dims[-1]], device=self.device) - self.x_prev = torch.zeros([1, dims[-1]], device=self.device) - self.x_calc = torch.zeros([1, dims[-1]], device=self.device) - self.x_input = torch.zeros([1, dims[-1]], device=self.device) - self.x_fastinp = torch.zeros([1, dims[-1]], device=self.device) - self.eta_ip = torch.tensor(ip_rate, device=self.device) self.eta_stdp = torch.tensor(stdp_rate, device=self.device) diff --git a/config/config.py b/src/settings.py similarity index 68% rename from config/config.py rename to src/settings.py index debd862..52a7a4d 100644 --- a/config/config.py +++ b/src/settings.py @@ -6,78 +6,100 @@ from datetime import datetime def configure(model): - model.dataset = 'nordland' - model.dataset_file = './dataset/'+model.dataset+'.csv' - model.trainingPath = '/home/adam/data/nordland/' - model.testPath = '/home/adam/data/nordland/' - model.number_modules = 1 - model.number_training_images = 250 - model.number_testing_images = 250 - model.locations = ["spring","fall"] - model.test_locations = ["summer"] - model.filter = 8 - model.validation = True - model.log = True + """ + Configure the model + """ + model.dataset = 'nordland' # Dataset name + model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) + model.trainingPath = '/home/adam/data/nordland/' # Path to training images + model.testPath = '/home/adam/data/nordland/' # Path to testing images + model.number_modules = 1 # Number of expert modules (currently not implemented) + model.number_training_images = 500 # Number of training images + model.number_testing_images = 500 # Number of testing images + model.locations = ["spring","fall"] # Locations to train on (location repeats for training datasets) + model.test_locations = ["summer"] # Location to query with + model.filter = 8 # Filter for training images + model.validation = True # Validation (maybe deprecated for now?) + model.log = True # Log to console + # Output the training and testing directories model.training_dirs = [] for n in model.locations: - model.training_dirs.append(os.path.join(model.trainingPath,n)) - + model.training_dirs.append(os.path.join(model.trainingPath,n)) model.testing_dirs = [] for n in model.test_locations: model.testing_dirs.append(os.path.join(model.testPath,n)) - + + # Check that the dataset is defined properly assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" - model.epoch = 4 - model.patches = 7 - model.dims = [28,28] - model.input = int(model.dims[0]*model.dims[1]) - model.feature = int(model.input*2) - model.output = int(model.number_training_images/model.number_modules) - model.intensity = 1 - model.location_repeat = len(model.locations) - model.layers = [] + # Set the model parameters + model.epoch = 4 # Number of epochs + model.patches = 7 # Number of patches + model.dims = [28,28] # Dimensions of the input image + model.location_repeat = len(model.locations) # Number of times to repeat the locations + model.annl_pow = 2 # Power of the annealmeant function + + """ + These parameters are used to define the network architecture + """ + model.input = int(model.dims[0]*model.dims[1]) # Number of input neurons + model.feature = int(model.input*2) # Number of feature neurons + model.output = int(model.number_training_images/model.number_modules) # Number of output neurons + # Set the torch device #model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.device = torch.device("cpu") if model.device.type == "cuda": torch.cuda.init() torch.cuda.synchronize(device=model.device) + + # Determine the total number of timesteps across training images, modules, and location repeats model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) - model.annl_pow = 2 def image_csv(model): + """ + Load the image names from the CSV file and filter them + """ + # Load the image names from the CSV file with open(os.path.join('./dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: reader = csv.reader(file) model.imageNames = [row[0] for row in reader] + # Remove the header del model.imageNames[0] - + # Filter the image names model.filteredNames = [] for n in range(0, len(model.imageNames), model.filter): model.filteredNames.append(model.imageNames[n]) + # Remove the training images from the filtered names del model.filteredNames[model.number_training_images:len(model.filteredNames)] - -def model_logger(model): + # Store the full training paths model.fullTrainPaths = [] for n in model.locations: model.fullTrainPaths.append(model.trainingPath + n + '/') - + +def model_logger(model): + """ + Configure the model logger + """ + # Create the output folder now = datetime.now() model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") os.mkdir(model.output_folder) - + # Create the logger model.logger = logging.getLogger("VPRTempo") if (model.logger.hasHandlers()): model.logger.handlers.clear() + # Set the logger level model.logger.setLevel(logging.DEBUG) logging.basicConfig(filename=model.output_folder + "/logfile.log", filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s") + # Add the logger to the console (if specified) if model.log: model.logger.addHandler(logging.StreamHandler()) From 1f67585b92e0a9a05825fa9320ac57d60cead40d Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Sat, 14 Oct 2023 21:38:15 +1000 Subject: [PATCH 18/69] Working on jupyter notebook --- .DS_Store | Bin 8196 -> 10244 bytes .gitignore | 4 + VPRTempo.py | 2 +- dataset/.DS_Store | Bin 0 -> 8196 bytes models/.DS_Store | Bin 0 -> 6148 bytes src/nordland.py | 199 ++++++------- src/settings.py | 4 +- vprtempo_tutorial.ipynb | 605 +++++++++++++++++++++++++++++++++++++++- 8 files changed, 711 insertions(+), 103 deletions(-) create mode 100644 dataset/.DS_Store create mode 100644 models/.DS_Store diff --git a/.DS_Store b/.DS_Store index 559b89382d81662d615286ec286ece32aa8c2cfa..cefb3d4d18a3b7e5a728f61d2285ec21fac05280 100644 GIT binary patch literal 10244 zcmeHM&2Jk;6n~QhXH6QqNt31skXHCosZnr}eyHNW)=7lSM-#5w83JaOBE?e}D@IPUX$Vw(B*mda9~qCYt#@&%8I|_nVKG83O>R zb^T=k3jhqv9L)<@{7>TTJddO?RkMId&>sB6i(JN{5lvm88PE)91~dbj0nNbc!T`o> zo{TApy3`D41~dat8Q|xGg_)zRa3E0}9axAf0OABzi-fYt1w>1st#BX_TQCu6iYQHq zx?&J%j`fz(vlR{`N^>CU@i9FMt#hx0iI>h`Ffzv)hTnB&}BeAYr&jhIG^wy>Vtl)wW9 z6poOG)>&~k=TgDlN$_!gQ!&Rl!)L?cf>WV)tQIZ#EnBpInr60a%WqCyn11V`IWv1{ zW^QJF{_?_=`L}1^dADg^avkqcE9T2_jYTZ3MctrjUQBRN*F8qJ;@}t;^IjT!oi@U6 zuxYknupRjKpH=OgbWmsRS%v8I<2VTDK`RdZ&7-3vNcVE`J|E4CSrl!tBuqm__YcMB zY0$3IBZedLZ?M_nP04N$Cx|09d;3ltJG2$lPVPmH8$_G5#?q9v_d@i7e{(TpqvLoX zZB;({TW*wsMvQ0LN?~IDurZ+G8&-KmaM4QpQ;uEuE8B>z#)7E1NZ@c zg2(VH{0@J?Kk$T1lgs3Na*ZsK8{`(TNu7K`4#)%IkT0eEShkhlQ~fC*2?~X&9$DR= zK^@j$1McGZ8}*2J>h$9O9X!Uo^h6}lWeM82^?f|)qO)_-`~Wjg zTyG#zm|!8E;}OMkJbvKM@!}Z)>#8V=gtE;A#DPSVFunR81M+uHeg78^yZAJs@BjS0 J`Fz~}{{sbfEO-C_ delta 618 zcmZn(XmOBWU|?W$DortDU;r^WfEYvza8FDWo2aMAD7`UYH$S8FWF7(4de5BvD7#P?X5*dntFqNT%p*SbqFgQ6sw*V}|`eY8ed~Uvr3)C!*6J-;O zofD6O%tJ9FH$MegO(0f91sTW|eE`|V#*oaA&ydHE#*oR7j_PPeS)e@J(US!Pr6;cu zE9EdYH_=frGPRtnDQ-XcnV_}|sxGis=fS)>`L2ka2&w``PoS~uCo_vGgN-vaGSX2n zG&choSI@?f3v^8iFj#WXf(7JOkZY0Tk%Q&VwIdAiY0zLnQG*B;Txt-Z!UnNr@;%XM zsvOYRDP<^N&;#O1G++D#s+~7EQ)~xvfXv&NCdnko3^7K68%Vo?(#OWc@640=RRTE} bA*qI8ay-w}$pNAs+!EE*NZ|?8F2)1^Z(5fA diff --git a/.gitignore b/.gitignore index 64e7ef6..b6d574a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ __pycache__/ .ipynb_checkpoints/ src/__pycache__/ +dataset/fall +dataset/winter +dataset/summer +dataset/spring \ No newline at end of file diff --git a/VPRTempo.py b/VPRTempo.py index 9892b86..60578c6 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -363,7 +363,7 @@ def run_inference(model, model_name, qconfig): if __name__ == "__main__": # Set the number of threads for PyTorch - torch.set_num_threads(8) + #torch.set_num_threads(8) # Initialize the model model = VPRTempo() # Initialize the logger diff --git a/dataset/.DS_Store b/dataset/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..17107f0a347ac1f22aaa454ec4f2ddd9b416e122 GIT binary patch literal 8196 zcmeHM&1)1%6n`&fO^?w{M-z=J3&V~F!JtX}LPQ+Lh=yfJHXUMu#<4#tlO{b?Lw8R` z4S~UDwW$S2xm9uhXdu(&_ z+|<-~e)7Wkt?~Tk#i@z${Mj?-x3+TDXyNpw`SN;H<>Ik+6|>I;9MIR> zLx~@YD4G*Vn1+Hc-$^LWGbyXi@Kr%8ZcvP8Yf_XfvN$2^u~}M)T#rSwyd=_8RF~a2 zc6o)B9xR2v;QG#o)UKr30-f$+)-uH@kO;JbyEAr>vmaHOiyPow6%8Jd9=9l`_)c)@KJefZD;Pr zu-byQ*WGYp9tmEd`Q&}>2Mz*a2q@uPq60VuJ|wWwm3(sjQ#$vE0@zDBHL?rIrqZd4 zzmmK?Q9E?Ml4R}h$t3LmlCYk7(>`;;c0A~=*8V@HWAoO5y1XwXSzflx6V=B&dXXmPyHTs*`!`m zqcWjEmE(k}94FlVhau5L+z9Q5MU1M@^Jp%1G}f5h}r*D7{2o1KcZlO A*Z=?k literal 0 HcmV?d00001 diff --git a/models/.DS_Store b/models/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c57311ea80e8b15f67695fdfd2c7bc0a5de4731e GIT binary patch literal 6148 zcmeHKu};H441F#gidZ@U3(|c-MGyhTP?doVQN)Z2NDM_~fIT}O!zaS?*{W#LQYHk* zj(qR#VmrBaQG5mJ3#z n?P|lh3p+U#(^gaQE;S41lXi%eV{OP8N*n|-4bcPxKgz%drUOoe literal 0 HcmV?d00001 diff --git a/src/nordland.py b/src/nordland.py index b7d03d2..b8fae44 100644 --- a/src/nordland.py +++ b/src/nordland.py @@ -5,108 +5,111 @@ import re import shutil import zipfile +import sys +sys.path.append('./VPRTempo-quant/dataset') from os import walk -# load and sort the file names in order, not by how OS indexes them -def atoi(text): - return int(text) if text.isdigit() else text - -def natural_keys(text): - return [ atoi(c) for c in re.split(r'(\d+)', text) ] - -# set the base path to the location of the downloaded Nordland datasets -basePath = '' -assert(os.path.isdir(basePath)),"Please set the basePath to the location of the downloaded Nordland datasets" - -# define the subfolders of the Nordland datasets -subPath = ["spring_images_train/section1/","spring_images_train/section2/", - "fall_images_train/section1/","fall_images_train/section2/", - "winter_images_train/section1/","winter_images_train/section2/", - "summer_images_train/section1/","summer_images_train/section2/"] - -# set the desired output folder for unzipping and organization -outDir = '' -assert(os.path.isdir(outDir)),"Please set the outDir to the desired output location for unzipping the Nordland datasets" - -# define output paths for the data -outPath = [os.path.join(outDir,"spring/"),os.path.join(outDir,"fall/"), - os.path.join(outDir,"winter/"),os.path.join(outDir,"summer/")] - -# check for existence of the zip folders, throw exception if missing -zipNames = ["spring_images_train.zip","fall_images_train.zip", - "winter_images_train.zip","summer_images_train.zip"] -for n in zipNames: - if not os.path.exists(basePath+n): - raise Exception('Please ensure dataset .zip folders have been downloaded') - -# check if nordland data folders have already been unzipped -zip_flag = [] -for n, ndx in enumerate(range(0,len(subPath),2)): - print('Unzipping '+zipNames[n]) - if os.path.exists(basePath+subPath[ndx]): - # check if the folder contains any files - file_lst = os.listdir(basePath+subPath[ndx]) - # remove folder if it is empty and unzip the data folder - if len(file_lst) == 0: - shutil.rmtree(basePath+subPath[ndx].replace('section1/','')) +def nord_sort(): + # load and sort the file names in order, not by how OS indexes them + def atoi(text): + return int(text) if text.isdigit() else text + + def natural_keys(text): + return [ atoi(c) for c in re.split(r'(\d+)', text) ] + + # set the base path to the location of the downloaded Nordland datasets + basePath = './dataset/' + assert(os.path.isdir(basePath)),"Please set the basePath to the location of the downloaded Nordland datasets" + + # define the subfolders of the Nordland datasets + subPath = ["spring_images_train/section1/","spring_images_train/section2/", + "fall_images_train/section1/","fall_images_train/section2/", + "winter_images_train/section1/","winter_images_train/section2/", + "summer_images_train/section1/","summer_images_train/section2/"] + + # set the desired output folder for unzipping and organization + outDir = '' + assert(os.path.isdir(outDir)),"Please set the outDir to the desired output location for unzipping the Nordland datasets" + + # define output paths for the data + outPath = [os.path.join(outDir,"spring/"),os.path.join(outDir,"fall/"), + os.path.join(outDir,"winter/"),os.path.join(outDir,"summer/")] + + # check for existence of the zip folders, throw exception if missing + zipNames = ["spring_images_train.zip","fall_images_train.zip", + "winter_images_train.zip","summer_images_train.zip"] + for n in zipNames: + if not os.path.exists(basePath+n): + raise Exception('Please ensure dataset .zip folders have been downloaded') + + # check if nordland data folders have already been unzipped + zip_flag = [] + for n, ndx in enumerate(range(0,len(subPath),2)): + print('Unzipping '+zipNames[n]) + if os.path.exists(basePath+subPath[ndx]): + # check if the folder contains any files + file_lst = os.listdir(basePath+subPath[ndx]) + # remove folder if it is empty and unzip the data folder + if len(file_lst) == 0: + shutil.rmtree(basePath+subPath[ndx].replace('section1/','')) + with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref: + zip_ref.extractall(basePath) + else: with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref: zip_ref.extractall(basePath) - else: - with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref: - zip_ref.extractall(basePath) - -# load image paths -tempPaths = [] -imgPaths = [] -for n in range(0,len(subPath)): + + # load image paths tempPaths = [] - for (path, dir_names, file_names) in walk(basePath+subPath[n]): - tempPaths.extend(file_names) - # sort image names - tempPaths.sort(key=natural_keys) - tempPaths = [basePath+subPath[n]+s for s in tempPaths] - imgPaths = imgPaths + tempPaths - -# if output folders already exist, delete them -for n in outPath: - if os.path.exists(n): - shutil.rmtree(n) - print('Removed pre-existing output folder') + imgPaths = [] + for n in range(0,len(subPath)): + tempPaths = [] + for (path, dir_names, file_names) in walk(basePath+subPath[n]): + tempPaths.extend(file_names) + # sort image names + tempPaths.sort(key=natural_keys) + tempPaths = [basePath+subPath[n]+s for s in tempPaths] + imgPaths = imgPaths + tempPaths -# rename and move the training data to match the nordland_imageNames.txt file -for n in outPath: - os.mkdir(n) -for n, filename in enumerate(imgPaths): - base = os.path.basename(filename) - split_base = os.path.splitext(base) - if int(split_base[0]) < 10: - my_dest = "images-0000"+split_base[0] + ".png" - elif int(split_base[0]) < 100: - my_dest = "images-000"+split_base[0] + ".png" - elif int(split_base[0]) < 1000: - my_dest = "images-00"+split_base[0] + ".png" - elif int(split_base[0]) < 10000: - my_dest = "images-0"+split_base[0] + ".png" - else: - my_dest = "images-"+split_base[0] + ".png" - if "spring" in filename: - out = outPath[0] - elif "fall" in filename: - out = outPath[1] - elif "winter" in filename: - out = outPath[2] - else: - out = outPath[-1] - - fileDest = out + my_dest - os.rename(filename, fileDest) - -# remove the empty folders -for n, ndx in enumerate(subPath): - if n%2 == 0: - shutil.rmtree(basePath+ndx.replace('section1/','')) - else: - continue - -print('Finished unzipping and organizing Nordland dataset') \ No newline at end of file + # if output folders already exist, delete them + for n in outPath: + if os.path.exists(n): + shutil.rmtree(n) + print('Removed pre-existing output folder') + + # rename and move the training data to match the nordland_imageNames.txt file + for n in outPath: + os.mkdir(n) + for n, filename in enumerate(imgPaths): + base = os.path.basename(filename) + split_base = os.path.splitext(base) + if int(split_base[0]) < 10: + my_dest = "images-0000"+split_base[0] + ".png" + elif int(split_base[0]) < 100: + my_dest = "images-000"+split_base[0] + ".png" + elif int(split_base[0]) < 1000: + my_dest = "images-00"+split_base[0] + ".png" + elif int(split_base[0]) < 10000: + my_dest = "images-0"+split_base[0] + ".png" + else: + my_dest = "images-"+split_base[0] + ".png" + if "spring" in filename: + out = outPath[0] + elif "fall" in filename: + out = outPath[1] + elif "winter" in filename: + out = outPath[2] + else: + out = outPath[-1] + + fileDest = out + my_dest + os.rename(filename, fileDest) + + # remove the empty folders + for n, ndx in enumerate(subPath): + if n%2 == 0: + shutil.rmtree(basePath+ndx.replace('section1/','')) + else: + continue + + print('Finished unzipping and organizing Nordland dataset') \ No newline at end of file diff --git a/src/settings.py b/src/settings.py index 52a7a4d..b2d159f 100644 --- a/src/settings.py +++ b/src/settings.py @@ -11,8 +11,8 @@ def configure(model): """ model.dataset = 'nordland' # Dataset name model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) - model.trainingPath = '/home/adam/data/nordland/' # Path to training images - model.testPath = '/home/adam/data/nordland/' # Path to testing images + model.trainingPath = './dataset/' # Path to training images + model.testPath = './dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) model.number_training_images = 500 # Number of training images model.number_testing_images = 500 # Number of testing images diff --git a/vprtempo_tutorial.ipynb b/vprtempo_tutorial.ipynb index 606f8bd..5963c62 100644 --- a/vprtempo_tutorial.ipynb +++ b/vprtempo_tutorial.ipynb @@ -5,7 +5,402 @@ "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", "metadata": {}, "source": [ - "## VPRTempo - tutorial" + "## VPRTempoQuant - Training and Inferencing Tutorial\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", + "\n", + "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", + "\n", + "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", + "\n", + "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). \n", + "\n", + "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f846c03", + "metadata": {}, + "outputs": [], + "source": [ + "!conda activate vprtempo" + ] + }, + { + "cell_type": "markdown", + "id": "0928d7a4", + "metadata": {}, + "source": [ + "## 1. Get the Nordland dataset\n", + "\n", + "### 1.1 Download the dataset\n", + "\n", + "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", + "\n", + "Save the data in the `./VPRTempo-quant/dataset/` subfolder.\n", + "\n", + "### 1.2 Prepare the dataset for the model\n", + "\n", + "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51f350d0", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "Please set the outDir to the desired output location for unzipping the Nordland datasets", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnordland\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m nord_sort\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# unzip, re-organise, and re-name the Nordland datasets\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m nord_sort()\n", + "File \u001b[0;32m~/repos/VPRTempo-quant/./src/nordland.py:33\u001b[0m, in \u001b[0;36mnord_sort\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# set the desired output folder for unzipping and organization\u001b[39;00m\n\u001b[1;32m 32\u001b[0m outDir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(outDir)),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease set the outDir to the desired output location for unzipping the Nordland datasets\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# define output paths for the data\u001b[39;00m\n\u001b[1;32m 36\u001b[0m outPath \u001b[38;5;241m=\u001b[39m [os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspring/\u001b[39m\u001b[38;5;124m\"\u001b[39m),os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfall/\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 37\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwinter/\u001b[39m\u001b[38;5;124m\"\u001b[39m),os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummer/\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n", + "\u001b[0;31mAssertionError\u001b[0m: Please set the outDir to the desired output location for unzipping the Nordland datasets" + ] + } + ], + "source": [ + "import os\n", + "import re\n", + "import shutil\n", + "import zipfile\n", + "import sys\n", + "sys.path.append('./src')\n", + "sys.path.append('./VPRTempo-quant/dataset')\n", + "\n", + "from os import walk\n", + "from nordland import nord_sort\n", + "\n", + "# unzip, re-organise, and re-name the Nordland datasets\n", + "nord_sort()" + ] + }, + { + "cell_type": "markdown", + "id": "f0a607d1", + "metadata": {}, + "source": [ + "## Prepare the model for training\n", + "\n", + "Let's now look at preparing our network to train our first model. There are a few initial steps to take care of first.\n", + "\n", + "### 2.1 Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d9caff25", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import jdc\n", + "import torch.nn as nn\n", + "import blitnet as bn\n", + "import numpy as np\n", + "from torch.ao.quantization import QuantStub, DeQuantStub\n", + "from tqdm import tqdm\n", + "from settings import configure, image_csv, model_logger" + ] + }, + { + "cell_type": "markdown", + "id": "f4d2f885", + "metadata": {}, + "source": [ + "### 2.2 Define and initialize the VPRTempo model\n", + "\n", + "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", + "\n", + "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", + "\n", + "The other hyperparameters for each layer are set here as well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "99b5130c", + "metadata": {}, + "outputs": [], + "source": [ + "class VPRTempo(nn.Module):\n", + " def __init__(self):\n", + " super(VPRTempo, self).__init__()\n", + "\n", + " # Configure the network\n", + " configure(self)\n", + " \n", + " # Define the images to load (both training and inference)\n", + " image_csv(self)\n", + "\n", + " # Add quantization stubs for Quantization Aware Training (QAT)\n", + " self.quant = QuantStub()\n", + " self.dequant = DeQuantStub()\n", + " \n", + " # Define the add function for quantized addition\n", + " self.add = nn.quantized.FloatFunctional() \n", + "\n", + " # Layer dict to keep track of layer names and their order\n", + " self.layer_dict = {}\n", + " self.layer_counter = 0\n", + "\n", + " \"\"\"\n", + " Define trainable layers here\n", + " \"\"\"\n", + " self.add_layer(\n", + " 'feature_layer',\n", + " dims=[self.input, self.feature],\n", + " thr_range=[0, 0.5],\n", + " fire_rate=[0.2, 0.9],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " const_inp=[0, 0.1],\n", + " p=[0.1, 0.5]\n", + " )\n", + " self.add_layer(\n", + " 'output_layer',\n", + " dims=[self.feature, self.output],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " spk_force=True\n", + " )\n", + " def add_layer(self, name, **kwargs):\n", + " \"\"\"\n", + " Dynamically add a layer with given name and keyword arguments.\n", + "\n", + " :param name: Name of the layer to be added\n", + " :type name: str\n", + " :param kwargs: Hyperparameters for the layer\n", + " \"\"\"\n", + " # Check for layer name duplicates\n", + " if name in self.layer_dict:\n", + " raise ValueError(f\"Layer with name {name} already exists.\")\n", + "\n", + " # Add a new SNNLayer with provided kwargs\n", + " setattr(self, name, bn.SNNLayer(**kwargs))\n", + "\n", + " # Add layer name and index to the layer_dict\n", + " self.layer_dict[name] = self.layer_counter\n", + " self.layer_counter += 1 \n", + "\n", + " print('Succesfully added '+name)\n", + "\n", + " def train_model(self, train_loader, layer, prev_layers=None):\n", + " \"\"\"\n", + " Train a layer of the network model.\n", + "\n", + " :param train_loader: Training data loader\n", + " :param layer: Layer to train\n", + " :param prev_layers: Previous layers to pass data through\n", + " \"\"\"\n", + "\n", + " # Initialize the tqdm progress bar\n", + " pbar = tqdm(total=int(self.T * self.epoch),\n", + " desc=\"Training \",\n", + " position=0)\n", + "\n", + " # Initialize the learning rates for each layer (used for annealment)\n", + " init_itp = layer.eta_ip.detach()\n", + " init_stdp = layer.eta_stdp.detach()\n", + "\n", + " # Run training for the specified number of epochs\n", + " for epoch in range(self.epoch):\n", + " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", + " # Run training for the specified number of timesteps\n", + " for spikes, labels in train_loader:\n", + " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", + " idx = labels / self.filter # Set output index for spike forcing\n", + " # Pass through previous layers if they exist\n", + " if prev_layers:\n", + " with torch.no_grad():\n", + " for prev_layer_name in prev_layers:\n", + " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", + " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", + " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", + " else:\n", + " prev_layer = None\n", + " # Get the output spikes from the current layer\n", + " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", + " spikes = self.forward(spikes, layer) # Current layer spikes\n", + " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", + " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", + " # Calculate STDP\n", + " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", + " # Adjust learning rates\n", + " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", + " # Update the annealing mod & progress bar \n", + " mod += 1\n", + " pbar.update(1)\n", + "\n", + " # Close the tqdm progress bar\n", + " pbar.close()\n", + "\n", + " def forward(self, spikes, layer):\n", + " \"\"\"\n", + " Compute the forward pass of the model.\n", + "\n", + " Parameters:\n", + " - spikes (Tensor): Input spikes.\n", + "\n", + " Returns:\n", + " - Tensor: Output after processing.\n", + " \"\"\"\n", + "\n", + " spikes = self.quant(spikes)\n", + " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", + " spikes = self.dequant(spikes)\n", + "\n", + " return spikes\n", + "\n", + " def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", + " \"\"\"\n", + " Anneal the learning rate for the current layer.\n", + " \"\"\"\n", + " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", + " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", + " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", + " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", + "\n", + " return layer" + ] + }, + { + "cell_type": "markdown", + "id": "34bbfd06", + "metadata": {}, + "source": [ + "Layers are dynamically added, such that if you wish to add more layers you simply need to define one in the `__init__` script and the system will iterate through all the layers.\n", + "\n", + "To add the layers, we call the `add_layer()` function which will set all the hyperparameters and seed the initial weights." + ] + }, + { + "cell_type": "markdown", + "id": "dbdd4ffd", + "metadata": {}, + "source": [ + "Now, we can initialize the model and add the layers." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "68a22d64", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def forward(self, spikes, layer):\n", + " \"\"\"\n", + " Compute the forward pass of the model.\n", + "\n", + " Parameters:\n", + " - spikes (Tensor): Input spikes.\n", + "\n", + " Returns:\n", + " - Tensor: Output after processing.\n", + " \"\"\"\n", + "\n", + " spikes = self.quant(spikes)\n", + " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", + " spikes = self.dequant(spikes)\n", + "\n", + " return spikes\n", + "\n", + "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", + " \"\"\"\n", + " Anneal the learning rate for the current layer.\n", + " \"\"\"\n", + " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", + " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", + " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", + " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", + "\n", + " return layer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "55aa0e9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully added feature_layer\n", + "Succesfully added output_layer\n" + ] + } + ], + "source": [ + "model = VPRTempo()" + ] + }, + { + "cell_type": "markdown", + "id": "17640d20", + "metadata": {}, + "source": [ + "### 2.3 Set the DataLoader\n", + "\n", + "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", + "\n", + "Since we present the network with one image at a time, the `batch_size` is kept to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6714bc35", + "metadata": {}, + "outputs": [], + "source": [ + "from dataset import CustomImageDataset, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.training_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_training_images,\n", + " test=False)\n", + "# Initialize the data loader\n", + "train_loader = DataLoader(train_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "0de072c8", + "metadata": {}, + "source": [ + "### 2.4 Other network settings\n", + "\n", + "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." ] }, { @@ -39,6 +434,212 @@ "from torch.ao.quantization import QuantStub, DeQuantStub\n", "from tqdm import tqdm" ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dc786b3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VPRTempo78415685001.pth\n" + ] + } + ], + "source": [ + "def generate_model_name(model):\n", + " \"\"\"\n", + " Generate the model name based on its parameters.\n", + " \"\"\"\n", + " return (\"VPRTempo\" +\n", + " str(model.input) +\n", + " str(model.feature) +\n", + " str(model.output) +\n", + " str(model.number_modules) +\n", + " '.pth')\n", + "\n", + "model_name = generate_model_name(model)\n", + "\n", + "print(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9e923809", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def model_logger(self):\n", + " \"\"\"\n", + " Log the model configuration to the console.\n", + " \"\"\"\n", + " model_logger(self)" + ] + }, + { + "cell_type": "markdown", + "id": "a3067711", + "metadata": {}, + "source": [ + "### 2.5 Model quantization\n", + "\n", + "VPRTempoQuant makes use of Quantized Aware Training QAT and has a few simple steps to prepare the model to accomodate this. First, we will get the default quantization configuration for `fggbem`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "36244802", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.quantization as quantization\n", + "\n", + "# Set the quantization configuration\n", + "qconfig = quantization.get_default_qat_qconfig('fbgemm')" + ] + }, + { + "cell_type": "markdown", + "id": "47a292cc", + "metadata": {}, + "source": [ + "Next, we will set the model to be configured for network training and add our quantization configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a884ad96", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the model to training mode and move to device\n", + "model.train()\n", + "model.to('cpu')\n", + "model.qconfig = qconfig" + ] + }, + { + "cell_type": "markdown", + "id": "ed34b802", + "metadata": {}, + "source": [ + "Now we will convert the model over to QAT." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "087f3b36", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# Apply quantization configurations to the model\n", + "model = quantization.prepare_qat(model, inplace=False)" + ] + }, + { + "cell_type": "markdown", + "id": "07f74ad2", + "metadata": {}, + "source": [ + "At this point, we are ready to start training our network!" + ] + }, + { + "cell_type": "markdown", + "id": "2f3f4bdb", + "metadata": {}, + "source": [ + "## 3. Set up and run the training \n", + "\n", + "### 3.1 Define the training regime\n", + "\n", + "Training through the layers is dynamic, such that all you need to do to train everything is to define a new layer. We will first define the training function and explain how it operates." + ] + }, + { + "cell_type": "markdown", + "id": "4eb33796", + "metadata": {}, + "source": [ + "This training regime runs every image through the newtork for a defined number of epochs. Images are loaded and converted to input spikes using the `train_loader` we defined earlier. For the first layer, it will simply calculate network spikes for the following layer. Otherwise, it will loop through each previous layer and generate spikes through the learned connection weights until it reaches the final layer currently being trained. \n", + "\n", + "The main calculation is in the `self.forward()` function. Weights in our layers are a `nn.Linear()` class, where input spikes are multiplied by connection weights between the layers. Once spikes from the input to layer have been calculated, spike-timing dependent plasticity (STDP) learning rules are applied. The learning rate for the network is annealed after every 100 images that are propagated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0075638b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: feature_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:25<00:00, 46.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: output_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 67%|██████████████████▋ | 2671/4000 [00:54<00:26, 49.88it/s]" + ] + } + ], + "source": [ + "# Keep track of trained layers to pass data through them\n", + "trained_layers = [] \n", + "\n", + "# Training each layer\n", + "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", + " print(f\"Training layer: {layer_name}\")\n", + " # Retrieve the layer object\n", + " layer = getattr(model, layer_name)\n", + " # Train the layer\n", + " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", + " # After training the current layer, add it to the list of trained layers\n", + " trained_layers.append(layer_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51d8e6d9", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -57,7 +658,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.0" } }, "nbformat": 4, From d642c80b70dca6c2005c40cf25819742150c5b22 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Sun, 15 Oct 2023 10:10:58 +1000 Subject: [PATCH 19/69] Finished the QAT jupyter notebook --- .DS_Store | Bin 10244 -> 10244 bytes VPRTempo.py | 2 - dataset/.DS_Store | Bin 8196 -> 8196 bytes src/settings.py | 8 +- tutorials/2_Quantization.ipynb | 924 +++++++++++++++++++++++++++++++++ vprtempo_tutorial.ipynb | 666 ------------------------ 6 files changed, 928 insertions(+), 672 deletions(-) create mode 100644 tutorials/2_Quantization.ipynb delete mode 100644 vprtempo_tutorial.ipynb diff --git a/.DS_Store b/.DS_Store index cefb3d4d18a3b7e5a728f61d2285ec21fac05280..2e3e8215ef405c688d92e1b551ce3ba13b846760 100644 GIT binary patch delta 274 zcmZn(XbG6$&nUSuU^hRbxP3hqMLFq)!O8i#1q=v)MOALTi%U{YeiBd-$Ca;%cUR>ebp*1ps!YMD(gf9g Z1sTLxJ-JETbz|BRw$1DcUAVlN003L%LyrIe delta 239 zcmZn(XbG6$&nUhzU^hRb_+}n~R`$udl4iE-4CM@|44Dk+3>ge148@)~`N>H+`AG~6 z3<3-cjIuy_-hVIvvKSc9R8L+g*2-^eWL~SIP;F^sprc@FXf)YE!q^N=T}p9sPEvk; d4i+QIf{XHU^7GPxQj8lD+1NL;D|Au8WdM6hJ{bT2 diff --git a/VPRTempo.py b/VPRTempo.py index 60578c6..ebbf7c1 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -34,10 +34,8 @@ sys.path.append('./dataset') import blitnet as bn -import utils as ut import numpy as np import torch.nn as nn -import torch.nn.functional as F import torch.quantization as quantization from settings import configure, image_csv, model_logger diff --git a/dataset/.DS_Store b/dataset/.DS_Store index 17107f0a347ac1f22aaa454ec4f2ddd9b416e122..a321f0b86a27a4ebfe9a8a908a70828e9d4c93f7 100644 GIT binary patch delta 90 zcmZp1XmQx!Ex@>avX4Nans{}!iLs7?iJ?)gjzYDikpYlxY*t&#$sww&Zygk$os*lF t-#K}rh&*o>0|NsKLmEROLk>gE=7S>PjGNPi7O-w+llacE`JV_oGXRV18P)&* delta 41 xcmZp1XmQx!Ein1J@E%SUhBSsmh8%{R%?Cun88@d1EnwZuF7b_J^B)m*W&kRx4dDO) diff --git a/src/settings.py b/src/settings.py index b2d159f..b6ceea6 100644 --- a/src/settings.py +++ b/src/settings.py @@ -10,9 +10,9 @@ def configure(model): Configure the model """ model.dataset = 'nordland' # Dataset name - model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) - model.trainingPath = './dataset/' # Path to training images - model.testPath = './dataset/' # Path to testing images + model.dataset_file = '../dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) + model.trainingPath = '../dataset/' # Path to training images + model.testPath = '../dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) model.number_training_images = 500 # Number of training images model.number_testing_images = 500 # Number of testing images @@ -66,7 +66,7 @@ def image_csv(model): Load the image names from the CSV file and filter them """ # Load the image names from the CSV file - with open(os.path.join('./dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: + with open(os.path.join('../dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: reader = csv.reader(file) model.imageNames = [row[0] for row in reader] # Remove the header diff --git a/tutorials/2_Quantization.ipynb b/tutorials/2_Quantization.ipynb new file mode 100644 index 0000000..4cd08dd --- /dev/null +++ b/tutorials/2_Quantization.ipynb @@ -0,0 +1,924 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", + "metadata": {}, + "source": [ + "## VPRTempo - Quantized Aware Training and Inferencing Tutorial\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", + "\n", + "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", + "\n", + "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", + "\n", + "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). Functionally, this tutorial is similar to the previous one as we go through and define the network architecture so if you can skip to **4. Quantization** if you are already familiar with how this works.\n", + "\n", + "**Note: it does not appear that Apple Silicon is currently a supported backend for QAT**\n", + "\n", + "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f846c03", + "metadata": {}, + "outputs": [], + "source": [ + "!conda activate vprtempo" + ] + }, + { + "cell_type": "markdown", + "id": "0928d7a4", + "metadata": {}, + "source": [ + "## 1. Get the Nordland dataset\n", + "\n", + "### 1.1 Download the dataset\n", + "\n", + "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", + "\n", + "Save the data in the `./VPRTempo-quant/dataset/` subfolder." + ] + }, + { + "cell_type": "markdown", + "id": "f0a607d1", + "metadata": {}, + "source": [ + "### 1.2 Import modules\n", + "\n", + "Once we have downloaded the dataset, we'll start by importing all the necessary modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d9caff25", + "metadata": {}, + "outputs": [], + "source": [ + "import jdc\n", + "import os\n", + "import torch\n", + "import gc\n", + "import sys\n", + "sys.path.append('../src')\n", + "sys.path.append('../models')\n", + "sys.path.append('../output')\n", + "sys.path.append('../dataset')\n", + "\n", + "import blitnet as bn\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "import torch.quantization as quantization\n", + "\n", + "from settings import configure, image_csv, model_logger\n", + "from dataset import CustomImageDataset, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "from torch.ao.quantization import QuantStub, DeQuantStub\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "id": "ffac2f0e", + "metadata": {}, + "source": [ + "### 1.3 Prepare the dataset for the model (optional)\n", + "\n", + "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file.\n", + "\n", + "If you have already done this from the previous tutorial, you can skip this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51f350d0", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from os import walk\n", + "from nordland import nord_sort\n", + "\n", + "# unzip, re-organise, and re-name the Nordland datasets\n", + "nord_sort()" + ] + }, + { + "cell_type": "markdown", + "id": "f4d2f885", + "metadata": {}, + "source": [ + "## 2. Set up the network\n", + "\n", + "### 2.1 Define and initialize the VPRTempo model class\n", + "\n", + "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", + "\n", + "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", + "\n", + "The other hyperparameters for each layer are set here as well." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "99b5130c", + "metadata": {}, + "outputs": [], + "source": [ + "class VPRTempo(nn.Module):\n", + " def __init__(self):\n", + " super(VPRTempo, self).__init__()\n", + "\n", + " # Configure the network\n", + " configure(self)\n", + " \n", + " # Define the images to load (both training and inference)\n", + " image_csv(self)\n", + "\n", + " # Add quantization stubs for Quantization Aware Training (QAT)\n", + " self.quant = QuantStub()\n", + " self.dequant = DeQuantStub()\n", + " \n", + " # Define the add function for quantized addition\n", + " self.add = nn.quantized.FloatFunctional() \n", + "\n", + " # Layer dict to keep track of layer names and their order\n", + " self.layer_dict = {}\n", + " self.layer_counter = 0\n", + "\n", + " \"\"\"\n", + " Define trainable layers here\n", + " \"\"\"\n", + " self.add_layer(\n", + " 'feature_layer',\n", + " dims=[self.input, self.feature],\n", + " thr_range=[0, 0.5],\n", + " fire_rate=[0.2, 0.9],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " const_inp=[0, 0.1],\n", + " p=[0.1, 0.5]\n", + " )\n", + " self.add_layer(\n", + " 'output_layer',\n", + " dims=[self.feature, self.output],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " spk_force=True\n", + " )\n", + " \n", + " print('VPRTempo succesfully initialized')" + ] + }, + { + "cell_type": "markdown", + "id": "d9e3c15b", + "metadata": {}, + "source": [ + "### 2.2 Dynamically add layers\n", + "\n", + "As above, the only thing we need to do in order to add additional layers to our model is to include a self.add_layer(args) to the `__init__` component of the script. The actual handling of the layer generation is done by the blitnet.SNNLayer() class from `blitnet.py`. Here, hyperparameters are stored in the layer information and the initial weights are seeded and normalized for training." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dabd3c7d", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def add_layer(self, name, **kwargs):\n", + " \"\"\"\n", + " Dynamically add a layer with given name and keyword arguments.\n", + "\n", + " :param name: Name of the layer to be added\n", + " :type name: str\n", + " :param kwargs: Hyperparameters for the layer\n", + " \"\"\"\n", + " # Check for layer name duplicates\n", + " if name in self.layer_dict:\n", + " raise ValueError(f\"Layer with name {name} already exists.\")\n", + "\n", + " # Add a new SNNLayer with provided kwargs\n", + " setattr(self, name, bn.SNNLayer(**kwargs))\n", + "\n", + " # Add layer name and index to the layer_dict\n", + " self.layer_dict[name] = self.layer_counter\n", + " self.layer_counter += 1 \n", + "\n", + " print('Succesfully added '+name)" + ] + }, + { + "cell_type": "markdown", + "id": "e3b92db1", + "metadata": {}, + "source": [ + "### 2.3 Set the training regime\n", + "\n", + "Training is also handled by the `VPRTempo()` class and recursively runs until all the defined layers are trained. The initial learning rates are copied out so that they can be annealed appropriately for the defined number of time steps. Training runs for the specified number of epochs and the total number of timesteps as set in the train_loader class (more later on that, a simple [PyTorch DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)).\n", + "\n", + "Once a layer has been trained, the learning for that layer will be turned off and training deeper layers will propagate the input spikes through each trained layer until it reaches the one being currently learned. Learning involves spike-timing dependent plasticity (STDP) rules, firing threshold adjustments, and inhibitory connection normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "623595aa", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def train_model(self, train_loader, layer, prev_layers=None):\n", + " \"\"\"\n", + " Train a layer of the network model.\n", + "\n", + " :param train_loader: Training data loader\n", + " :param layer: Layer to train\n", + " :param prev_layers: Previous layers to pass data through\n", + " \"\"\"\n", + "\n", + " # Initialize the tqdm progress bar\n", + " pbar = tqdm(total=int(self.T * self.epoch),\n", + " desc=\"Training \",\n", + " position=0)\n", + "\n", + " # Initialize the learning rates for each layer (used for annealment)\n", + " init_itp = layer.eta_ip.detach()\n", + " init_stdp = layer.eta_stdp.detach()\n", + "\n", + " # Run training for the specified number of epochs\n", + " for epoch in range(self.epoch):\n", + " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", + " # Run training for the specified number of timesteps\n", + " for spikes, labels in train_loader:\n", + " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", + " idx = labels / self.filter # Set output index for spike forcing\n", + " # Pass through previous layers if they exist\n", + " if prev_layers:\n", + " with torch.no_grad():\n", + " for prev_layer_name in prev_layers:\n", + " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", + " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", + " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", + " else:\n", + " prev_layer = None\n", + " # Get the output spikes from the current layer\n", + " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", + " spikes = self.forward(spikes, layer) # Current layer spikes\n", + " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", + " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", + " # Calculate STDP\n", + " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", + " # Adjust learning rates\n", + " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", + " # Update the annealing mod & progress bar \n", + " mod += 1\n", + " pbar.update(1)\n", + "\n", + " # Close the tqdm progress bar\n", + " pbar.close()" + ] + }, + { + "cell_type": "markdown", + "id": "bc5e068e", + "metadata": {}, + "source": [ + "### 2.4 Create the forward pass\n", + "\n", + "Layers in VPRTempo are defined as an [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layer, with incoming spikes being linearly transformed with the layer weights. The forward pass simply takes incoming spikes and caluclates the transform with positive and negative weights and adds them together, returning the transformed spikes." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "68a22d64", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def forward(self, spikes, layer):\n", + " \"\"\"\n", + " Compute the forward pass of the model.\n", + "\n", + " Parameters:\n", + " - spikes (Tensor): Input spikes.\n", + "\n", + " Returns:\n", + " - Tensor: Output after processing.\n", + " \"\"\"\n", + "\n", + " spikes = self.quant(spikes)\n", + " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", + " spikes = self.dequant(spikes)\n", + "\n", + " return spikes" + ] + }, + { + "cell_type": "markdown", + "id": "9fb8e16d", + "metadata": {}, + "source": [ + "### 2.5 Learning rate annealment & model loader/saver\n", + "\n", + "Finally, the last thing we will add to the model is the learning rate annealment regime and the functions for loading and saving trained models." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e99f6d13", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", + " \"\"\"\n", + " Anneal the learning rate for the current layer.\n", + " \"\"\"\n", + " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", + " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", + " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", + " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", + "\n", + " return layer\n", + "\n", + "def save_model(self, model_out): \n", + " \"\"\"\n", + " Save the trained model to models output folder.\n", + " \"\"\"\n", + " torch.save(self.state_dict(), model_out) \n", + "\n", + "def load_model(self, model_path):\n", + " \"\"\"\n", + " Load pre-trained model and set the state dictionary keys.\n", + " \"\"\"\n", + " self.load_state_dict(torch.load(model_path, map_location=self.device),\n", + " strict=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a4d65918", + "metadata": {}, + "source": [ + "### 2.6 Initialize the model\n", + "\n", + "Now that the model has been defined, we can initialize it and start with the quantization process." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "55aa0e9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully added feature_layer\n", + "Succesfully added output_layer\n", + "VPRTempo succesfully initialized\n" + ] + } + ], + "source": [ + "model = VPRTempo()" + ] + }, + { + "cell_type": "markdown", + "id": "a88d4a18", + "metadata": {}, + "source": [ + "### 2.7 Generate unique model name\n", + "\n", + "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dc786b3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VPRTempo78415685001.pth\n" + ] + } + ], + "source": [ + "def generate_model_name(model):\n", + " \"\"\"\n", + " Generate the model name based on its parameters.\n", + " \"\"\"\n", + " return (\"VPRTempo\" +\n", + " str(model.input) +\n", + " str(model.feature) +\n", + " str(model.output) +\n", + " str(model.number_modules) +\n", + " '.pth')\n", + "\n", + "model_name = generate_model_name(model)\n", + "\n", + "print(model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "17640d20", + "metadata": {}, + "source": [ + "## 3. Define the DataLoader\n", + "\n", + "### 3.1 Set the DataLoader\n", + "\n", + "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", + "\n", + "Since we present the network with one image at a time, the `batch_size` is kept to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6714bc35", + "metadata": {}, + "outputs": [], + "source": [ + "from dataset import CustomImageDataset, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.training_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_training_images,\n", + " test=False)\n", + "# Initialize the data loader\n", + "train_loader = DataLoader(train_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a3067711", + "metadata": {}, + "source": [ + "## 4. Quantization\n", + "\n", + "### 4.1 Model quantization\n", + "\n", + "VPRTempoQuant makes use of Quantized Aware Training QAT and has a few simple steps to prepare the model to accomodate this. First, we will get the default quantization configuration for `fggbem`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "36244802", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.quantization as quantization\n", + "\n", + "# Set the quantization configuration\n", + "qconfig = quantization.get_default_qat_qconfig('fbgemm')" + ] + }, + { + "cell_type": "markdown", + "id": "47a292cc", + "metadata": {}, + "source": [ + "Next, we will set the model to be configured for network training and add our quantization configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a884ad96", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the model to training mode and move to device\n", + "model.train()\n", + "model.to('cpu')\n", + "model.qconfig = qconfig" + ] + }, + { + "cell_type": "markdown", + "id": "ed34b802", + "metadata": {}, + "source": [ + "Now we will convert the model over to QAT." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "087f3b36", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply quantization configurations to the model\n", + "model = quantization.prepare_qat(model, inplace=False)" + ] + }, + { + "cell_type": "markdown", + "id": "07f74ad2", + "metadata": {}, + "source": [ + "At this point, we are ready to start training our network!" + ] + }, + { + "cell_type": "markdown", + "id": "2f3f4bdb", + "metadata": {}, + "source": [ + "## 5. Set up and run the training \n", + "\n", + "### 5.1 Define and run the training regime\n", + "\n", + "The training will loop through each defined layer until every single one has trained. In order to propagate spikes throughout the system, trained layers are appended to a list so that they can be re-fed back into the network to calculate spikes based on learned weights.\n", + "\n", + "Run the below cell to train our `feature_layer` and `output_layer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0075638b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: feature_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:30<00:00, 44.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: output_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:23<00:00, 48.00it/s]\n" + ] + } + ], + "source": [ + "# Keep track of trained layers to pass data through them\n", + "trained_layers = [] \n", + "\n", + "# Training each layer\n", + "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", + " print(f\"Training layer: {layer_name}\")\n", + " # Retrieve the layer object\n", + " layer = getattr(model, layer_name)\n", + " # Train the layer\n", + " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", + " # After training the current layer, add it to the list of trained layers\n", + " trained_layers.append(layer_name)\n", + " \n", + "print('All layers trained succesfully')" + ] + }, + { + "cell_type": "markdown", + "id": "5e6daea3", + "metadata": {}, + "source": [ + "### 5.2 Convert and save the model\n", + "\n", + "Now that the training has been completed, we can convert the QAT model over to be fully quantized. As the layers were trained, scale and zero-point factors will learned for all the elements of the model and can now be applied to the layers. Once converted, we will save the model for use in inferencing." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "53ededaa", + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Convert the model to a quantized model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Save the model\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight_bias(wq, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:32\u001b[0m, in \u001b[0;36mLinearPackedParams.set_weight_bias\u001b[0;34m(self, weight, bias)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mexport\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_weight_bias\u001b[39m(\u001b[38;5;28mself\u001b[39m, weight: torch\u001b[38;5;241m.\u001b[39mTensor, bias: Optional[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8:\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", + "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" + ] + } + ], + "source": [ + "# Convert the model to a quantized model\n", + "model = quantization.convert(model, inplace=False)\n", + "model.eval()\n", + "# Save the model\n", + "model.save_model(os.path.join('./models', model_name)) " + ] + }, + { + "cell_type": "markdown", + "id": "c0d69843", + "metadata": {}, + "source": [ + "## 6. Inferencing\n", + "\n", + "As in the previous tutorial, inferencing with a trained model is quite simple. The only additional thing we need to do is reinitialize the VPRTempo class and convert it to quantized before loading the model. Without pre-quantizing the inference model, state dictionary keys will not match since all the layers and associated components have new parameters such as scale and zero-point.\n", + "\n", + "### 6.1 Add the inference function to the VPRTempo class\n", + "\n", + "We will start by adding in the inference function to VPRTempo. It is similar to the training regime but omits the learning components `calc_stdp` and simply runs through all the layers until it reaches the output." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a368c532", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def evaluate(self, test_loader, layers=None):\n", + " \"\"\"\n", + " Run the inferencing model and calculate the accuracy.\n", + "\n", + " :param test_loader: Testing data loader\n", + " :param layers: Layers to pass data through\n", + " \"\"\"\n", + "\n", + " # Initialize the number of correct predictions\n", + " numcorr = 0\n", + " idx = 0\n", + "\n", + " # Initialize the tqdm progress bar\n", + " pbar = tqdm(total=self.number_testing_images,\n", + " desc=\"Running the test network\",\n", + " position=0)\n", + "\n", + " # Run inference for the specified number of timesteps\n", + " for spikes, labels in test_loader:\n", + " # Set device\n", + " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", + " # Pass through previous layers if they exist\n", + " if layers:\n", + " for layer_name in layers:\n", + " layer = getattr(self, layer_name)\n", + " spikes = self.forward(spikes, layer)\n", + " spikes = bn.clamp_spikes(spikes, layer)\n", + "\n", + " # Evaluate if the prediction is correct\n", + " if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx:\n", + " numcorr += 1\n", + "\n", + " # Update the index and progress bar\n", + " idx += 1\n", + " pbar.update(1)\n", + "\n", + " # Close the tqdm progress bar\n", + " pbar.close()\n", + " # Calculate and record the accuracy\n", + " accuracy = round((numcorr/self.number_testing_images)*100,2)\n", + " model.logger.info(\"P@100R: \"+ str(accuracy) + '%')" + ] + }, + { + "cell_type": "markdown", + "id": "5e841fc7", + "metadata": {}, + "source": [ + "### 6.2 Define the inferencing DataLoader\n", + "\n", + "The only difference between the training and testing DataLoader is the directory with which it will import images from." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f4a1adc8", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the image transforms and datasets\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.testing_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_testing_images)\n", + "# Initialize the data loader\n", + "test_loader = DataLoader(test_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "018de09a", + "metadata": {}, + "source": [ + "### 6.3 Re-initialize the model class, convert to quantization, and load the model\n", + "\n", + "Now we will re-initialize the VPRTempo class model, set to eval mode, and convert it over to quantized so that we can import our newly trained model." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "30d51e96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully added feature_layer\n", + "Succesfully added output_layer\n", + "VPRTempo succesfully initialized\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", + " warnings.warn(\n", + "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Prepare and convert the model to a quantized model\u001b[39;00m\n\u001b[1;32m 10\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mprepare(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 11\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Load the model\u001b[39;00m\n\u001b[1;32m 13\u001b[0m model\u001b[38;5;241m.\u001b[39mload_model(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./models\u001b[39m\u001b[38;5;124m'\u001b[39m, model_name))\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m 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32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", + "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" + ] + } + ], + "source": [ + "# Set the model to evaluation mode and set configuration\n", + "model = VPRTempo()\n", + "model.eval()\n", + "model.qconfig = qconfig\n", + "\n", + "# Apply quantization configurations to all layers in layer_dict\n", + "for layer_name, _ in model.layer_dict.items():\n", + " getattr(model, layer_name).qconfig = qconfig\n", + "# Prepare and convert the model to a quantized model\n", + "model = quantization.prepare(model, inplace=False)\n", + "model = quantization.convert(model, inplace=False)\n", + "# Load the model\n", + "model.load_model(os.path.join('./models', model_name))\n", + "\n", + "# Retrieve layer names for inference\n", + "layer_names = list(model.layer_dict.keys())" + ] + }, + { + "cell_type": "markdown", + "id": "472d24e8", + "metadata": {}, + "source": [ + "### 6.4 Run the model inference\n", + "\n", + "Now we are ready to inference the model!" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "37aa84e1", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'layer_names' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Use evaluate method for inference accuracy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(test_loader, layers\u001b[38;5;241m=\u001b[39mlayer_names)\n", + "\u001b[0;31mNameError\u001b[0m: name 'layer_names' is not defined" + ] + } + ], + "source": [ + "# Use evaluate method for inference accuracy\n", + "model.evaluate(test_loader, layers=layer_names)" + ] + }, + { + "cell_type": "markdown", + "id": "26f46e43", + "metadata": {}, + "source": [ + "## 7. Conslusions\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "763e86c5", + "metadata": {}, + "source": [ + "This tutorial covered how we can convert the VPRTempo model to perform Quantized Aware Training (QAT) to keep the model size more lightweight. You might notice that if you compare the system between FP32 to Int8, the model works equally as well with a reduced bit-depth with the added benefit of a reduced model size.\n", + "\n", + "To read more about QAT and quantization in general, PyTorch provides many useful articles;\n", + "https://pytorch.org/docs/stable/quantization.html\n", + "https://pytorch.org/blog/quantization-in-practice/\n", + "\n", + "The key benefit to this is being able to perform fast training and inferencing on CPU architecture, which for resource limited compute scenarios is critical." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/vprtempo_tutorial.ipynb b/vprtempo_tutorial.ipynb deleted file mode 100644 index 5963c62..0000000 --- a/vprtempo_tutorial.ipynb +++ /dev/null @@ -1,666 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", - "metadata": {}, - "source": [ - "## VPRTempoQuant - Training and Inferencing Tutorial\n", - "\n", - "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", - "\n", - "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", - " \n", - "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", - "\n", - "### Introduction\n", - "\n", - "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", - "\n", - "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", - "\n", - "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", - "\n", - "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). \n", - "\n", - "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f846c03", - "metadata": {}, - "outputs": [], - "source": [ - "!conda activate vprtempo" - ] - }, - { - "cell_type": "markdown", - "id": "0928d7a4", - "metadata": {}, - "source": [ - "## 1. Get the Nordland dataset\n", - "\n", - "### 1.1 Download the dataset\n", - "\n", - "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", - "\n", - "Save the data in the `./VPRTempo-quant/dataset/` subfolder.\n", - "\n", - "### 1.2 Prepare the dataset for the model\n", - "\n", - "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "51f350d0", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "Please set the outDir to the desired output location for unzipping the Nordland datasets", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnordland\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m nord_sort\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# unzip, re-organise, and re-name the Nordland datasets\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m nord_sort()\n", - "File \u001b[0;32m~/repos/VPRTempo-quant/./src/nordland.py:33\u001b[0m, in \u001b[0;36mnord_sort\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# set the desired output folder for unzipping and organization\u001b[39;00m\n\u001b[1;32m 32\u001b[0m outDir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(outDir)),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease set the outDir to the desired output location for unzipping the Nordland datasets\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# define output paths for the data\u001b[39;00m\n\u001b[1;32m 36\u001b[0m outPath \u001b[38;5;241m=\u001b[39m [os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspring/\u001b[39m\u001b[38;5;124m\"\u001b[39m),os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfall/\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 37\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwinter/\u001b[39m\u001b[38;5;124m\"\u001b[39m),os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(outDir,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummer/\u001b[39m\u001b[38;5;124m\"\u001b[39m)]\n", - "\u001b[0;31mAssertionError\u001b[0m: Please set the outDir to the desired output location for unzipping the Nordland datasets" - ] - } - ], - "source": [ - "import os\n", - "import re\n", - "import shutil\n", - "import zipfile\n", - "import sys\n", - "sys.path.append('./src')\n", - "sys.path.append('./VPRTempo-quant/dataset')\n", - "\n", - "from os import walk\n", - "from nordland import nord_sort\n", - "\n", - "# unzip, re-organise, and re-name the Nordland datasets\n", - "nord_sort()" - ] - }, - { - "cell_type": "markdown", - "id": "f0a607d1", - "metadata": {}, - "source": [ - "## Prepare the model for training\n", - "\n", - "Let's now look at preparing our network to train our first model. There are a few initial steps to take care of first.\n", - "\n", - "### 2.1 Import modules" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d9caff25", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import jdc\n", - "import torch.nn as nn\n", - "import blitnet as bn\n", - "import numpy as np\n", - "from torch.ao.quantization import QuantStub, DeQuantStub\n", - "from tqdm import tqdm\n", - "from settings import configure, image_csv, model_logger" - ] - }, - { - "cell_type": "markdown", - "id": "f4d2f885", - "metadata": {}, - "source": [ - "### 2.2 Define and initialize the VPRTempo model\n", - "\n", - "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", - "\n", - "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", - "\n", - "The other hyperparameters for each layer are set here as well." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "99b5130c", - "metadata": {}, - "outputs": [], - "source": [ - "class VPRTempo(nn.Module):\n", - " def __init__(self):\n", - " super(VPRTempo, self).__init__()\n", - "\n", - " # Configure the network\n", - " configure(self)\n", - " \n", - " # Define the images to load (both training and inference)\n", - " image_csv(self)\n", - "\n", - " # Add quantization stubs for Quantization Aware Training (QAT)\n", - " self.quant = QuantStub()\n", - " self.dequant = DeQuantStub()\n", - " \n", - " # Define the add function for quantized addition\n", - " self.add = nn.quantized.FloatFunctional() \n", - "\n", - " # Layer dict to keep track of layer names and their order\n", - " self.layer_dict = {}\n", - " self.layer_counter = 0\n", - "\n", - " \"\"\"\n", - " Define trainable layers here\n", - " \"\"\"\n", - " self.add_layer(\n", - " 'feature_layer',\n", - " dims=[self.input, self.feature],\n", - " thr_range=[0, 0.5],\n", - " fire_rate=[0.2, 0.9],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " const_inp=[0, 0.1],\n", - " p=[0.1, 0.5]\n", - " )\n", - " self.add_layer(\n", - " 'output_layer',\n", - " dims=[self.feature, self.output],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " spk_force=True\n", - " )\n", - " def add_layer(self, name, **kwargs):\n", - " \"\"\"\n", - " Dynamically add a layer with given name and keyword arguments.\n", - "\n", - " :param name: Name of the layer to be added\n", - " :type name: str\n", - " :param kwargs: Hyperparameters for the layer\n", - " \"\"\"\n", - " # Check for layer name duplicates\n", - " if name in self.layer_dict:\n", - " raise ValueError(f\"Layer with name {name} already exists.\")\n", - "\n", - " # Add a new SNNLayer with provided kwargs\n", - " setattr(self, name, bn.SNNLayer(**kwargs))\n", - "\n", - " # Add layer name and index to the layer_dict\n", - " self.layer_dict[name] = self.layer_counter\n", - " self.layer_counter += 1 \n", - "\n", - " print('Succesfully added '+name)\n", - "\n", - " def train_model(self, train_loader, layer, prev_layers=None):\n", - " \"\"\"\n", - " Train a layer of the network model.\n", - "\n", - " :param train_loader: Training data loader\n", - " :param layer: Layer to train\n", - " :param prev_layers: Previous layers to pass data through\n", - " \"\"\"\n", - "\n", - " # Initialize the tqdm progress bar\n", - " pbar = tqdm(total=int(self.T * self.epoch),\n", - " desc=\"Training \",\n", - " position=0)\n", - "\n", - " # Initialize the learning rates for each layer (used for annealment)\n", - " init_itp = layer.eta_ip.detach()\n", - " init_stdp = layer.eta_stdp.detach()\n", - "\n", - " # Run training for the specified number of epochs\n", - " for epoch in range(self.epoch):\n", - " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", - " # Run training for the specified number of timesteps\n", - " for spikes, labels in train_loader:\n", - " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", - " idx = labels / self.filter # Set output index for spike forcing\n", - " # Pass through previous layers if they exist\n", - " if prev_layers:\n", - " with torch.no_grad():\n", - " for prev_layer_name in prev_layers:\n", - " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", - " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", - " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", - " else:\n", - " prev_layer = None\n", - " # Get the output spikes from the current layer\n", - " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", - " spikes = self.forward(spikes, layer) # Current layer spikes\n", - " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", - " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", - " # Calculate STDP\n", - " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", - " # Adjust learning rates\n", - " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", - " # Update the annealing mod & progress bar \n", - " mod += 1\n", - " pbar.update(1)\n", - "\n", - " # Close the tqdm progress bar\n", - " pbar.close()\n", - "\n", - " def forward(self, spikes, layer):\n", - " \"\"\"\n", - " Compute the forward pass of the model.\n", - "\n", - " Parameters:\n", - " - spikes (Tensor): Input spikes.\n", - "\n", - " Returns:\n", - " - Tensor: Output after processing.\n", - " \"\"\"\n", - "\n", - " spikes = self.quant(spikes)\n", - " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", - " spikes = self.dequant(spikes)\n", - "\n", - " return spikes\n", - "\n", - " def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", - " \"\"\"\n", - " Anneal the learning rate for the current layer.\n", - " \"\"\"\n", - " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", - " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", - " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", - " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", - "\n", - " return layer" - ] - }, - { - "cell_type": "markdown", - "id": "34bbfd06", - "metadata": {}, - "source": [ - "Layers are dynamically added, such that if you wish to add more layers you simply need to define one in the `__init__` script and the system will iterate through all the layers.\n", - "\n", - "To add the layers, we call the `add_layer()` function which will set all the hyperparameters and seed the initial weights." - ] - }, - { - "cell_type": "markdown", - "id": "dbdd4ffd", - "metadata": {}, - "source": [ - "Now, we can initialize the model and add the layers." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "68a22d64", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def forward(self, spikes, layer):\n", - " \"\"\"\n", - " Compute the forward pass of the model.\n", - "\n", - " Parameters:\n", - " - spikes (Tensor): Input spikes.\n", - "\n", - " Returns:\n", - " - Tensor: Output after processing.\n", - " \"\"\"\n", - "\n", - " spikes = self.quant(spikes)\n", - " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", - " spikes = self.dequant(spikes)\n", - "\n", - " return spikes\n", - "\n", - "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", - " \"\"\"\n", - " Anneal the learning rate for the current layer.\n", - " \"\"\"\n", - " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", - " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", - " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", - " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", - "\n", - " return layer" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "55aa0e9b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Succesfully added feature_layer\n", - "Succesfully added output_layer\n" - ] - } - ], - "source": [ - "model = VPRTempo()" - ] - }, - { - "cell_type": "markdown", - "id": "17640d20", - "metadata": {}, - "source": [ - "### 2.3 Set the DataLoader\n", - "\n", - "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", - "\n", - "Since we present the network with one image at a time, the `batch_size` is kept to 1." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6714bc35", - "metadata": {}, - "outputs": [], - "source": [ - "from dataset import CustomImageDataset, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.training_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_training_images,\n", - " test=False)\n", - "# Initialize the data loader\n", - "train_loader = DataLoader(train_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" - ] - }, - { - "cell_type": "markdown", - "id": "0de072c8", - "metadata": {}, - "source": [ - "### 2.4 Other network settings\n", - "\n", - "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ce79b3d3-ed51-4749-98b3-44ea2cfa45e3", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import torch\n", - "import gc\n", - "import sys\n", - "sys.path.append('./src')\n", - "sys.path.append('./models')\n", - "sys.path.append('./settings')\n", - "sys.path.append('./output')\n", - "sys.path.append('./dataset')\n", - "sys.path.append('./config')\n", - "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n", - "import blitnet as bn\n", - "import utils as ut\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.quantization as quantization\n", - "\n", - "from config import configure, image_csv, model_logger\n", - "from dataset import CustomImageDataset, SetImageAsSpikes, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "from torch.ao.quantization import QuantStub, DeQuantStub\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dc786b3d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VPRTempo78415685001.pth\n" - ] - } - ], - "source": [ - "def generate_model_name(model):\n", - " \"\"\"\n", - " Generate the model name based on its parameters.\n", - " \"\"\"\n", - " return (\"VPRTempo\" +\n", - " str(model.input) +\n", - " str(model.feature) +\n", - " str(model.output) +\n", - " str(model.number_modules) +\n", - " '.pth')\n", - "\n", - "model_name = generate_model_name(model)\n", - "\n", - "print(model_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "9e923809", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def model_logger(self):\n", - " \"\"\"\n", - " Log the model configuration to the console.\n", - " \"\"\"\n", - " model_logger(self)" - ] - }, - { - "cell_type": "markdown", - "id": "a3067711", - "metadata": {}, - "source": [ - "### 2.5 Model quantization\n", - "\n", - "VPRTempoQuant makes use of Quantized Aware Training QAT and has a few simple steps to prepare the model to accomodate this. First, we will get the default quantization configuration for `fggbem`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "36244802", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.quantization as quantization\n", - "\n", - "# Set the quantization configuration\n", - "qconfig = quantization.get_default_qat_qconfig('fbgemm')" - ] - }, - { - "cell_type": "markdown", - "id": "47a292cc", - "metadata": {}, - "source": [ - "Next, we will set the model to be configured for network training and add our quantization configuration." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a884ad96", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the model to training mode and move to device\n", - "model.train()\n", - "model.to('cpu')\n", - "model.qconfig = qconfig" - ] - }, - { - "cell_type": "markdown", - "id": "ed34b802", - "metadata": {}, - "source": [ - "Now we will convert the model over to QAT." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "087f3b36", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# Apply quantization configurations to the model\n", - "model = quantization.prepare_qat(model, inplace=False)" - ] - }, - { - "cell_type": "markdown", - "id": "07f74ad2", - "metadata": {}, - "source": [ - "At this point, we are ready to start training our network!" - ] - }, - { - "cell_type": "markdown", - "id": "2f3f4bdb", - "metadata": {}, - "source": [ - "## 3. Set up and run the training \n", - "\n", - "### 3.1 Define the training regime\n", - "\n", - "Training through the layers is dynamic, such that all you need to do to train everything is to define a new layer. We will first define the training function and explain how it operates." - ] - }, - { - "cell_type": "markdown", - "id": "4eb33796", - "metadata": {}, - "source": [ - "This training regime runs every image through the newtork for a defined number of epochs. Images are loaded and converted to input spikes using the `train_loader` we defined earlier. For the first layer, it will simply calculate network spikes for the following layer. Otherwise, it will loop through each previous layer and generate spikes through the learned connection weights until it reaches the final layer currently being trained. \n", - "\n", - "The main calculation is in the `self.forward()` function. Weights in our layers are a `nn.Linear()` class, where input spikes are multiplied by connection weights between the layers. Once spikes from the input to layer have been calculated, spike-timing dependent plasticity (STDP) learning rules are applied. The learning rate for the network is annealed after every 100 images that are propagated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0075638b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: feature_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:25<00:00, 46.94it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: output_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 67%|██████████████████▋ | 2671/4000 [00:54<00:26, 49.88it/s]" - ] - } - ], - "source": [ - "# Keep track of trained layers to pass data through them\n", - "trained_layers = [] \n", - "\n", - "# Training each layer\n", - "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", - " print(f\"Training layer: {layer_name}\")\n", - " # Retrieve the layer object\n", - " layer = getattr(model, layer_name)\n", - " # Train the layer\n", - " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", - " # After training the current layer, add it to the list of trained layers\n", - " trained_layers.append(layer_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51d8e6d9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 5c1e7aca6a65ae64e4a6f214c319bbd0714cb4a3 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Sun, 15 Oct 2023 10:13:42 +1000 Subject: [PATCH 20/69] Fixed up jupyter output --- tutorials/2_Quantization.ipynb | 168 +++++---------------------------- 1 file changed, 24 insertions(+), 144 deletions(-) diff --git a/tutorials/2_Quantization.ipynb b/tutorials/2_Quantization.ipynb index 4cd08dd..246137d 100644 --- a/tutorials/2_Quantization.ipynb +++ b/tutorials/2_Quantization.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d9caff25", "metadata": {}, "outputs": [], @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "99b5130c", "metadata": {}, "outputs": [], @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "dabd3c7d", "metadata": {}, "outputs": [], @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "623595aa", "metadata": {}, "outputs": [], @@ -310,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "68a22d64", "metadata": {}, "outputs": [], @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "e99f6d13", "metadata": {}, "outputs": [], @@ -389,20 +389,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "55aa0e9b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Succesfully added feature_layer\n", - "Succesfully added output_layer\n", - "VPRTempo succesfully initialized\n" - ] - } - ], + "outputs": [], "source": [ "model = VPRTempo()" ] @@ -419,18 +409,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "dc786b3d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VPRTempo78415685001.pth\n" - ] - } - ], + "outputs": [], "source": [ "def generate_model_name(model):\n", " \"\"\"\n", @@ -464,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "6714bc35", "metadata": {}, "outputs": [], @@ -501,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "36244802", "metadata": {}, "outputs": [], @@ -522,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "a884ad96", "metadata": {}, "outputs": [], @@ -543,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "087f3b36", "metadata": {}, "outputs": [], @@ -576,39 +558,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "0075638b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: feature_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:30<00:00, 44.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: output_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:23<00:00, 48.00it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# Keep track of trained layers to pass data through them\n", "trained_layers = [] \n", @@ -638,31 +591,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "53ededaa", "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Convert the model to a quantized model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Save the model\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight_bias(wq, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:32\u001b[0m, in \u001b[0;36mLinearPackedParams.set_weight_bias\u001b[0;34m(self, weight, bias)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mexport\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_weight_bias\u001b[39m(\u001b[38;5;28mself\u001b[39m, weight: torch\u001b[38;5;241m.\u001b[39mTensor, bias: Optional[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8:\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", - "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" - ] - } - ], + "outputs": [], "source": [ "# Convert the model to a quantized model\n", "model = quantization.convert(model, inplace=False)\n", @@ -687,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "a368c532", "metadata": {}, "outputs": [], @@ -748,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "f4a1adc8", "metadata": {}, "outputs": [], @@ -780,50 +712,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "30d51e96", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Succesfully added feature_layer\n", - "Succesfully added output_layer\n", - "VPRTempo succesfully initialized\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", - " warnings.warn(\n", - "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Prepare and convert the model to a quantized model\u001b[39;00m\n\u001b[1;32m 10\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mprepare(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 11\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Load the model\u001b[39;00m\n\u001b[1;32m 13\u001b[0m model\u001b[38;5;241m.\u001b[39mload_model(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./models\u001b[39m\u001b[38;5;124m'\u001b[39m, model_name))\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight_bias(wq, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:32\u001b[0m, in \u001b[0;36mLinearPackedParams.set_weight_bias\u001b[0;34m(self, weight, bias)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mexport\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_weight_bias\u001b[39m(\u001b[38;5;28mself\u001b[39m, weight: torch\u001b[38;5;241m.\u001b[39mTensor, bias: Optional[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8:\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", - "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" - ] - } - ], + "outputs": [], "source": [ "# Set the model to evaluation mode and set configuration\n", "model = VPRTempo()\n", @@ -855,22 +747,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "37aa84e1", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'layer_names' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Use evaluate method for inference accuracy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(test_loader, layers\u001b[38;5;241m=\u001b[39mlayer_names)\n", - "\u001b[0;31mNameError\u001b[0m: name 'layer_names' is not defined" - ] - } - ], + "outputs": [], "source": [ "# Use evaluate method for inference accuracy\n", "model.evaluate(test_loader, layers=layer_names)" From 351a46599368753d74f2448201a3babcae5b128c Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Sun, 15 Oct 2023 22:54:30 +1000 Subject: [PATCH 21/69] Started work on the intro jupyter notebook. Simplified QAT tutorial --- tutorials/1_Introduction.ipynb | 836 +++++++++++++++++++++++++++++++++ tutorials/2_Quantization.ipynb | 445 ++++-------------- 2 files changed, 937 insertions(+), 344 deletions(-) create mode 100644 tutorials/1_Introduction.ipynb diff --git a/tutorials/1_Introduction.ipynb b/tutorials/1_Introduction.ipynb new file mode 100644 index 0000000..e6a7adb --- /dev/null +++ b/tutorials/1_Introduction.ipynb @@ -0,0 +1,836 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", + "metadata": {}, + "source": [ + "## VPRTempo - Introduction\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", + "\n", + "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", + "\n", + "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", + "\n", + "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo. For more information how to install and setup the environment, please see the [README.md](https://github.com/AdamDHines/VPRTempo-quant/blob/main/README.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f846c03", + "metadata": {}, + "outputs": [], + "source": [ + "!conda activate vprtempo" + ] + }, + { + "cell_type": "markdown", + "id": "0928d7a4", + "metadata": {}, + "source": [ + "## 1. Get the Nordland dataset\n", + "\n", + "### 1.1 Download the dataset\n", + "\n", + "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", + "\n", + "Save the data in the `./VPRTempo-quant/dataset/` subfolder." + ] + }, + { + "cell_type": "markdown", + "id": "f0a607d1", + "metadata": {}, + "source": [ + "### 1.2 Import modules\n", + "\n", + "Once we have downloaded the dataset, we'll start by importing all the necessary modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d9caff25", + "metadata": {}, + "outputs": [], + "source": [ + "import jdc\n", + "import os\n", + "import torch\n", + "import gc\n", + "import sys\n", + "sys.path.append('../src')\n", + "sys.path.append('../models')\n", + "sys.path.append('../output')\n", + "sys.path.append('../dataset')\n", + "\n", + "import blitnet as bn\n", + "import numpy as np\n", + "import torch.nn as nn\n", + "import torch.quantization as quantization\n", + "\n", + "from settings import configure, image_csv, model_logger\n", + "from dataset import CustomImageDataset, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "from torch.ao.quantization import QuantStub, DeQuantStub\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "id": "ffac2f0e", + "metadata": {}, + "source": [ + "### 1.3 Prepare the dataset for the model (optional)\n", + "\n", + "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file.\n", + "\n", + "If you have already done this from the previous tutorial, you can skip this step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51f350d0", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from os import walk\n", + "from nordland import nord_sort\n", + "\n", + "# unzip, re-organise, and re-name the Nordland datasets\n", + "nord_sort()" + ] + }, + { + "cell_type": "markdown", + "id": "f4d2f885", + "metadata": {}, + "source": [ + "## 2. Set up the network\n", + "\n", + "### 2.1 Define and initialize the VPRTempo model class\n", + "\n", + "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", + "\n", + "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", + "\n", + "The other hyperparameters for each layer are set here as well." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99b5130c", + "metadata": {}, + "outputs": [], + "source": [ + "class VPRTempo(nn.Module):\n", + " def __init__(self):\n", + " super(VPRTempo, self).__init__()\n", + "\n", + " # Configure the network\n", + " configure(self)\n", + " \n", + " # Define the images to load (both training and inference)\n", + " image_csv(self)\n", + "\n", + " # Add quantization stubs for Quantization Aware Training (QAT)\n", + " self.quant = QuantStub()\n", + " self.dequant = DeQuantStub()\n", + " \n", + " # Define the add function for quantized addition\n", + " self.add = nn.quantized.FloatFunctional() \n", + "\n", + " # Layer dict to keep track of layer names and their order\n", + " self.layer_dict = {}\n", + " self.layer_counter = 0\n", + "\n", + " \"\"\"\n", + " Define trainable layers here\n", + " \"\"\"\n", + " self.add_layer(\n", + " 'feature_layer',\n", + " dims=[self.input, self.feature],\n", + " thr_range=[0, 0.5],\n", + " fire_rate=[0.2, 0.9],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " const_inp=[0, 0.1],\n", + " p=[0.1, 0.5]\n", + " )\n", + " self.add_layer(\n", + " 'output_layer',\n", + " dims=[self.feature, self.output],\n", + " ip_rate=0.15,\n", + " stdp_rate=0.005,\n", + " spk_force=True\n", + " )\n", + " \n", + " print('VPRTempo succesfully initialized')" + ] + }, + { + "cell_type": "markdown", + "id": "d9e3c15b", + "metadata": {}, + "source": [ + "### 2.2 Dynamically add layers\n", + "\n", + "As above, the only thing we need to do in order to add additional layers to our model is to include a self.add_layer(args) to the `__init__` component of the script. The actual handling of the layer generation is done by the blitnet.SNNLayer() class from `blitnet.py`. Here, hyperparameters are stored in the layer information and the initial weights are seeded and normalized for training." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dabd3c7d", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def add_layer(self, name, **kwargs):\n", + " \"\"\"\n", + " Dynamically add a layer with given name and keyword arguments.\n", + "\n", + " :param name: Name of the layer to be added\n", + " :type name: str\n", + " :param kwargs: Hyperparameters for the layer\n", + " \"\"\"\n", + " # Check for layer name duplicates\n", + " if name in self.layer_dict:\n", + " raise ValueError(f\"Layer with name {name} already exists.\")\n", + "\n", + " # Add a new SNNLayer with provided kwargs\n", + " setattr(self, name, bn.SNNLayer(**kwargs))\n", + "\n", + " # Add layer name and index to the layer_dict\n", + " self.layer_dict[name] = self.layer_counter\n", + " self.layer_counter += 1 \n", + "\n", + " print('Succesfully added '+name)" + ] + }, + { + "cell_type": "markdown", + "id": "e3b92db1", + "metadata": {}, + "source": [ + "### 2.3 Set the training regime\n", + "\n", + "Training is also handled by the `VPRTempo()` class and recursively runs until all the defined layers are trained. The initial learning rates are copied out so that they can be annealed appropriately for the defined number of time steps. Training runs for the specified number of epochs and the total number of timesteps as set in the train_loader class (more later on that, a simple [PyTorch DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)).\n", + "\n", + "Once a layer has been trained, the learning for that layer will be turned off and training deeper layers will propagate the input spikes through each trained layer until it reaches the one being currently learned. Learning involves spike-timing dependent plasticity (STDP) rules, firing threshold adjustments, and inhibitory connection normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "623595aa", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def train_model(self, train_loader, layer, prev_layers=None):\n", + " \"\"\"\n", + " Train a layer of the network model.\n", + "\n", + " :param train_loader: Training data loader\n", + " :param layer: Layer to train\n", + " :param prev_layers: Previous layers to pass data through\n", + " \"\"\"\n", + "\n", + " # Initialize the tqdm progress bar\n", + " pbar = tqdm(total=int(self.T * self.epoch),\n", + " desc=\"Training \",\n", + " position=0)\n", + "\n", + " # Initialize the learning rates for each layer (used for annealment)\n", + " init_itp = layer.eta_ip.detach()\n", + " init_stdp = layer.eta_stdp.detach()\n", + "\n", + " # Run training for the specified number of epochs\n", + " for epoch in range(self.epoch):\n", + " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", + " # Run training for the specified number of timesteps\n", + " for spikes, labels in train_loader:\n", + " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", + " idx = labels / self.filter # Set output index for spike forcing\n", + " # Pass through previous layers if they exist\n", + " if prev_layers:\n", + " with torch.no_grad():\n", + " for prev_layer_name in prev_layers:\n", + " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", + " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", + " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", + " else:\n", + " prev_layer = None\n", + " # Get the output spikes from the current layer\n", + " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", + " spikes = self.forward(spikes, layer) # Current layer spikes\n", + " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", + " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", + " # Calculate STDP\n", + " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", + " # Adjust learning rates\n", + " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", + " # Update the annealing mod & progress bar \n", + " mod += 1\n", + " pbar.update(1)\n", + "\n", + " # Close the tqdm progress bar\n", + " pbar.close()" + ] + }, + { + "cell_type": "markdown", + "id": "bc5e068e", + "metadata": {}, + "source": [ + "### 2.4 Create the forward pass\n", + "\n", + "Layers in VPRTempo are defined as an [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layer, with incoming spikes being linearly transformed with the layer weights. The forward pass simply takes incoming spikes and caluclates the transform with positive and negative weights and adds them together, returning the transformed spikes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "68a22d64", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def forward(self, spikes, layer):\n", + " \"\"\"\n", + " Compute the forward pass of the model.\n", + "\n", + " Parameters:\n", + " - spikes (Tensor): Input spikes.\n", + "\n", + " Returns:\n", + " - Tensor: Output after processing.\n", + " \"\"\"\n", + "\n", + " spikes = self.quant(spikes)\n", + " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", + " spikes = self.dequant(spikes)\n", + "\n", + " return spikes" + ] + }, + { + "cell_type": "markdown", + "id": "9fb8e16d", + "metadata": {}, + "source": [ + "### 2.5 Learning rate annealment & model loader/saver\n", + "\n", + "Finally, the last thing we will add to the model is the learning rate annealment regime and the functions for loading and saving trained models." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e99f6d13", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", + " \"\"\"\n", + " Anneal the learning rate for the current layer.\n", + " \"\"\"\n", + " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", + " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", + " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", + " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", + "\n", + " return layer\n", + "\n", + "def save_model(self, model_out): \n", + " \"\"\"\n", + " Save the trained model to models output folder.\n", + " \"\"\"\n", + " torch.save(self.state_dict(), model_out) \n", + "\n", + "def load_model(self, model_path):\n", + " \"\"\"\n", + " Load pre-trained model and set the state dictionary keys.\n", + " \"\"\"\n", + " self.load_state_dict(torch.load(model_path, map_location=self.device),\n", + " strict=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a4d65918", + "metadata": {}, + "source": [ + "### 2.6 Initialize the model\n", + "\n", + "Now that the model has been defined, we can initialize it and start with the quantization process." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "55aa0e9b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully added feature_layer\n", + "Succesfully added output_layer\n", + "VPRTempo succesfully initialized\n" + ] + }, + { + "data": { + "text/plain": [ + "VPRTempo(\n", + " (quant): QuantStub()\n", + " (dequant): DeQuantStub()\n", + " (add): FloatFunctional(\n", + " (activation_post_process): Identity()\n", + " )\n", + " (feature_layer): SNNLayer(\n", + " (exc): Linear(in_features=784, out_features=1568, bias=False)\n", + " (inh): Linear(in_features=784, out_features=1568, bias=False)\n", + " )\n", + " (output_layer): SNNLayer(\n", + " (exc): Linear(in_features=1568, out_features=500, bias=False)\n", + " (inh): Linear(in_features=1568, out_features=500, bias=False)\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = VPRTempo()\n", + "model.train()" + ] + }, + { + "cell_type": "markdown", + "id": "a88d4a18", + "metadata": {}, + "source": [ + "### 2.7 Generate unique model name\n", + "\n", + "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dc786b3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VPRTempo78415685001.pth\n" + ] + } + ], + "source": [ + "def generate_model_name(model):\n", + " \"\"\"\n", + " Generate the model name based on its parameters.\n", + " \"\"\"\n", + " return (\"VPRTempo\" +\n", + " str(model.input) +\n", + " str(model.feature) +\n", + " str(model.output) +\n", + " str(model.number_modules) +\n", + " '.pth')\n", + "\n", + "model_name = generate_model_name(model)\n", + "\n", + "print(model_name)" + ] + }, + { + "cell_type": "markdown", + "id": "17640d20", + "metadata": {}, + "source": [ + "## 3. Define the DataLoader\n", + "\n", + "### 3.1 Set the DataLoader\n", + "\n", + "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", + "\n", + "Since we present the network with one image at a time, the `batch_size` is kept to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6714bc35", + "metadata": {}, + "outputs": [], + "source": [ + "from dataset import CustomImageDataset, ProcessImage\n", + "from torch.utils.data import DataLoader\n", + "\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.training_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_training_images,\n", + " test=False)\n", + "# Initialize the data loader\n", + "train_loader = DataLoader(train_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2f3f4bdb", + "metadata": {}, + "source": [ + "## 5. Set up and run the training \n", + "\n", + "### 5.1 Define and run the training regime\n", + "\n", + "The training will loop through each defined layer until every single one has trained. In order to propagate spikes throughout the system, trained layers are appended to a list so that they can be re-fed back into the network to calculate spikes based on learned weights.\n", + "\n", + "Run the below cell to train our `feature_layer` and `output_layer`!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0075638b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: feature_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:13<00:00, 54.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: output_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [00:55<00:00, 72.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All layers trained succesfully\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Keep track of trained layers to pass data through them\n", + "trained_layers = [] \n", + "\n", + "# Training each layer\n", + "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", + " print(f\"Training layer: {layer_name}\")\n", + " # Retrieve the layer object\n", + " layer = getattr(model, layer_name)\n", + " # Train the layer\n", + " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", + " # After training the current layer, add it to the list of trained layers\n", + " trained_layers.append(layer_name)\n", + " \n", + "print('All layers trained succesfully')" + ] + }, + { + "cell_type": "markdown", + "id": "5e6daea3", + "metadata": {}, + "source": [ + "### 5.2 Convert and save the model\n", + "\n", + "Now that the training has been completed, we can convert the QAT model over to be fully quantized. As the layers were trained, scale and zero-point factors will learned for all the elements of the model and can now be applied to the layers. Once converted, we will save the model for use in inferencing." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "53ededaa", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the model to eval mode\n", + "model.eval()\n", + "# Save the model\n", + "model.save_model(os.path.join('../models', model_name)) " + ] + }, + { + "cell_type": "markdown", + "id": "c0d69843", + "metadata": {}, + "source": [ + "## 6. Inferencing\n", + "\n", + "As in the previous tutorial, inferencing with a trained model is quite simple. The only additional thing we need to do is reinitialize the VPRTempo class and convert it to quantized before loading the model. Without pre-quantizing the inference model, state dictionary keys will not match since all the layers and associated components have new parameters such as scale and zero-point.\n", + "\n", + "### 6.1 Add the inference function to the VPRTempo class\n", + "\n", + "We will start by adding in the inference function to VPRTempo. It is similar to the training regime but omits the learning components `calc_stdp` and simply runs through all the layers until it reaches the output." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a368c532", + "metadata": {}, + "outputs": [], + "source": [ + "%%add_to VPRTempo\n", + "def evaluate(self, test_loader, layers=None):\n", + " \"\"\"\n", + " Run the inferencing model and calculate the accuracy.\n", + "\n", + " :param test_loader: Testing data loader\n", + " :param layers: Layers to pass data through\n", + " \"\"\"\n", + "\n", + " # Initialize the number of correct predictions\n", + " numcorr = 0\n", + " idx = 0\n", + "\n", + " # Initialize the tqdm progress bar\n", + " pbar = tqdm(total=self.number_testing_images,\n", + " desc=\"Running the test network\",\n", + " position=0)\n", + "\n", + " # Run inference for the specified number of timesteps\n", + " for spikes, labels in test_loader:\n", + " # Set device\n", + " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", + " # Pass through previous layers if they exist\n", + " if layers:\n", + " for layer_name in layers:\n", + " layer = getattr(self, layer_name)\n", + " spikes = self.forward(spikes, layer)\n", + " spikes = bn.clamp_spikes(spikes, layer)\n", + "\n", + " # Evaluate if the prediction is correct\n", + " if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx:\n", + " numcorr += 1\n", + "\n", + " # Update the index and progress bar\n", + " idx += 1\n", + " pbar.update(1)\n", + "\n", + " # Close the tqdm progress bar\n", + " pbar.close()\n", + " # Calculate and record the accuracy\n", + " accuracy = round((numcorr/self.number_testing_images)*100,2)\n", + " model.logger.info(\"P@100R: \"+ str(accuracy) + '%')" + ] + }, + { + "cell_type": "markdown", + "id": "5e841fc7", + "metadata": {}, + "source": [ + "### 6.2 Define the inferencing DataLoader\n", + "\n", + "The only difference between the training and testing DataLoader is the directory with which it will import images from." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f4a1adc8", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the image transforms and datasets\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.testing_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_testing_images)\n", + "# Initialize the data loader\n", + "test_loader = DataLoader(test_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "018de09a", + "metadata": {}, + "source": [ + "### 6.3 Re-initialize the model class, convert to quantization, and load the model\n", + "\n", + "Now we will re-initialize the VPRTempo class model, set to eval mode, and convert it over to quantized so that we can import our newly trained model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "30d51e96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succesfully added feature_layer\n", + "Succesfully added output_layer\n", + "VPRTempo succesfully initialized\n" + ] + } + ], + "source": [ + "# Set the model to evaluation mode and set configuration\n", + "model = VPRTempo()\n", + "model.eval()\n", + "\n", + "# Load the model\n", + "model.load_model(os.path.join('../models', model_name))\n", + "\n", + "# Retrieve layer names for inference\n", + "layer_names = list(model.layer_dict.keys())" + ] + }, + { + "cell_type": "markdown", + "id": "472d24e8", + "metadata": {}, + "source": [ + "### 6.4 Run the model inference\n", + "\n", + "Now we are ready to inference the model!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "37aa84e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running the test network: 100%|██████████████| 500/500 [00:02<00:00, 196.29it/s]\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'VPRTempo' object has no attribute 'logger'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Use evaluate method for inference accuracy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(test_loader, layers\u001b[38;5;241m=\u001b[39mlayer_names)\n", + "File \u001b[0;32m:41\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(self, test_loader, layers)\u001b[0m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/nn/modules/module.py:1614\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1614\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 1615\u001b[0m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, name))\n", + "\u001b[0;31mAttributeError\u001b[0m: 'VPRTempo' object has no attribute 'logger'" + ] + } + ], + "source": [ + "# Use evaluate method for inference accuracy\n", + "model.evaluate(test_loader, layers=layer_names)" + ] + }, + { + "cell_type": "markdown", + "id": "26f46e43", + "metadata": {}, + "source": [ + "## 7. Conslusions\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "763e86c5", + "metadata": {}, + "source": [ + "This tutorial covered how we can convert the VPRTempo model to perform Quantized Aware Training (QAT) to keep the model size more lightweight. You might notice that if you compare the system between FP32 to Int8, the model works equally as well with a reduced bit-depth with the added benefit of a reduced model size.\n", + "\n", + "To read more about QAT and quantization in general, PyTorch provides many useful articles;\n", + "https://pytorch.org/docs/stable/quantization.html\n", + "https://pytorch.org/blog/quantization-in-practice/\n", + "\n", + "The key benefit to this is being able to perform fast training and inferencing on CPU architecture, which for resource limited compute scenarios is critical." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/2_Quantization.ipynb b/tutorials/2_Quantization.ipynb index 246137d..e547383 100644 --- a/tutorials/2_Quantization.ipynb +++ b/tutorials/2_Quantization.ipynb @@ -15,13 +15,7 @@ "\n", "### Introduction\n", "\n", - "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", - "\n", - "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", - "\n", - "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", - "\n", - "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). Functionally, this tutorial is similar to the previous one as we go through and define the network architecture so if you can skip to **4. Quantization** if you are already familiar with how this works.\n", + "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). Functionally, this tutorial is similar to the previous one but will be simplified. For a more detailed dive into how VPRTempo works, please see [Tutorial 1](https://github.com/AdamDHines/VPRTempo-quant/blob/main/tutorials/1_Introduction.ipynb)\n", "\n", "**Note: it does not appear that Apple Silicon is currently a supported backend for QAT**\n", "\n", @@ -64,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d9caff25", "metadata": {}, "outputs": [], @@ -74,6 +68,7 @@ "import torch\n", "import gc\n", "import sys\n", + "sys.path.append('../')\n", "sys.path.append('../src')\n", "sys.path.append('../models')\n", "sys.path.append('../output')\n", @@ -128,272 +123,17 @@ "\n", "### 2.1 Define and initialize the VPRTempo model class\n", "\n", - "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", - "\n", - "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", - "\n", - "The other hyperparameters for each layer are set here as well." + "We'll now import the main network model class `VPRTempo`. Please see [Tutorial 1](https://github.com/AdamDHines/VPRTempo-quant/blob/main/tutorials/1_Introduction.ipynb) for a more detailed look at what this includes." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "99b5130c", "metadata": {}, "outputs": [], "source": [ - "class VPRTempo(nn.Module):\n", - " def __init__(self):\n", - " super(VPRTempo, self).__init__()\n", - "\n", - " # Configure the network\n", - " configure(self)\n", - " \n", - " # Define the images to load (both training and inference)\n", - " image_csv(self)\n", - "\n", - " # Add quantization stubs for Quantization Aware Training (QAT)\n", - " self.quant = QuantStub()\n", - " self.dequant = DeQuantStub()\n", - " \n", - " # Define the add function for quantized addition\n", - " self.add = nn.quantized.FloatFunctional() \n", - "\n", - " # Layer dict to keep track of layer names and their order\n", - " self.layer_dict = {}\n", - " self.layer_counter = 0\n", - "\n", - " \"\"\"\n", - " Define trainable layers here\n", - " \"\"\"\n", - " self.add_layer(\n", - " 'feature_layer',\n", - " dims=[self.input, self.feature],\n", - " thr_range=[0, 0.5],\n", - " fire_rate=[0.2, 0.9],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " const_inp=[0, 0.1],\n", - " p=[0.1, 0.5]\n", - " )\n", - " self.add_layer(\n", - " 'output_layer',\n", - " dims=[self.feature, self.output],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " spk_force=True\n", - " )\n", - " \n", - " print('VPRTempo succesfully initialized')" - ] - }, - { - "cell_type": "markdown", - "id": "d9e3c15b", - "metadata": {}, - "source": [ - "### 2.2 Dynamically add layers\n", - "\n", - "As above, the only thing we need to do in order to add additional layers to our model is to include a self.add_layer(args) to the `__init__` component of the script. The actual handling of the layer generation is done by the blitnet.SNNLayer() class from `blitnet.py`. Here, hyperparameters are stored in the layer information and the initial weights are seeded and normalized for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dabd3c7d", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def add_layer(self, name, **kwargs):\n", - " \"\"\"\n", - " Dynamically add a layer with given name and keyword arguments.\n", - "\n", - " :param name: Name of the layer to be added\n", - " :type name: str\n", - " :param kwargs: Hyperparameters for the layer\n", - " \"\"\"\n", - " # Check for layer name duplicates\n", - " if name in self.layer_dict:\n", - " raise ValueError(f\"Layer with name {name} already exists.\")\n", - "\n", - " # Add a new SNNLayer with provided kwargs\n", - " setattr(self, name, bn.SNNLayer(**kwargs))\n", - "\n", - " # Add layer name and index to the layer_dict\n", - " self.layer_dict[name] = self.layer_counter\n", - " self.layer_counter += 1 \n", - "\n", - " print('Succesfully added '+name)" - ] - }, - { - "cell_type": "markdown", - "id": "e3b92db1", - "metadata": {}, - "source": [ - "### 2.3 Set the training regime\n", - "\n", - "Training is also handled by the `VPRTempo()` class and recursively runs until all the defined layers are trained. The initial learning rates are copied out so that they can be annealed appropriately for the defined number of time steps. Training runs for the specified number of epochs and the total number of timesteps as set in the train_loader class (more later on that, a simple [PyTorch DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)).\n", - "\n", - "Once a layer has been trained, the learning for that layer will be turned off and training deeper layers will propagate the input spikes through each trained layer until it reaches the one being currently learned. Learning involves spike-timing dependent plasticity (STDP) rules, firing threshold adjustments, and inhibitory connection normalization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "623595aa", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def train_model(self, train_loader, layer, prev_layers=None):\n", - " \"\"\"\n", - " Train a layer of the network model.\n", - "\n", - " :param train_loader: Training data loader\n", - " :param layer: Layer to train\n", - " :param prev_layers: Previous layers to pass data through\n", - " \"\"\"\n", - "\n", - " # Initialize the tqdm progress bar\n", - " pbar = tqdm(total=int(self.T * self.epoch),\n", - " desc=\"Training \",\n", - " position=0)\n", - "\n", - " # Initialize the learning rates for each layer (used for annealment)\n", - " init_itp = layer.eta_ip.detach()\n", - " init_stdp = layer.eta_stdp.detach()\n", - "\n", - " # Run training for the specified number of epochs\n", - " for epoch in range(self.epoch):\n", - " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", - " # Run training for the specified number of timesteps\n", - " for spikes, labels in train_loader:\n", - " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", - " idx = labels / self.filter # Set output index for spike forcing\n", - " # Pass through previous layers if they exist\n", - " if prev_layers:\n", - " with torch.no_grad():\n", - " for prev_layer_name in prev_layers:\n", - " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", - " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", - " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", - " else:\n", - " prev_layer = None\n", - " # Get the output spikes from the current layer\n", - " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", - " spikes = self.forward(spikes, layer) # Current layer spikes\n", - " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", - " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", - " # Calculate STDP\n", - " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", - " # Adjust learning rates\n", - " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", - " # Update the annealing mod & progress bar \n", - " mod += 1\n", - " pbar.update(1)\n", - "\n", - " # Close the tqdm progress bar\n", - " pbar.close()" - ] - }, - { - "cell_type": "markdown", - "id": "bc5e068e", - "metadata": {}, - "source": [ - "### 2.4 Create the forward pass\n", - "\n", - "Layers in VPRTempo are defined as an [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layer, with incoming spikes being linearly transformed with the layer weights. The forward pass simply takes incoming spikes and caluclates the transform with positive and negative weights and adds them together, returning the transformed spikes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68a22d64", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def forward(self, spikes, layer):\n", - " \"\"\"\n", - " Compute the forward pass of the model.\n", - "\n", - " Parameters:\n", - " - spikes (Tensor): Input spikes.\n", - "\n", - " Returns:\n", - " - Tensor: Output after processing.\n", - " \"\"\"\n", - "\n", - " spikes = self.quant(spikes)\n", - " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", - " spikes = self.dequant(spikes)\n", - "\n", - " return spikes" - ] - }, - { - "cell_type": "markdown", - "id": "9fb8e16d", - "metadata": {}, - "source": [ - "### 2.5 Learning rate annealment & model loader/saver\n", - "\n", - "Finally, the last thing we will add to the model is the learning rate annealment regime and the functions for loading and saving trained models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e99f6d13", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", - " \"\"\"\n", - " Anneal the learning rate for the current layer.\n", - " \"\"\"\n", - " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", - " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", - " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", - " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", - "\n", - " return layer\n", - "\n", - "def save_model(self, model_out): \n", - " \"\"\"\n", - " Save the trained model to models output folder.\n", - " \"\"\"\n", - " torch.save(self.state_dict(), model_out) \n", - "\n", - "def load_model(self, model_path):\n", - " \"\"\"\n", - " Load pre-trained model and set the state dictionary keys.\n", - " \"\"\"\n", - " self.load_state_dict(torch.load(model_path, map_location=self.device),\n", - " strict=True)" - ] - }, - { - "cell_type": "markdown", - "id": "a4d65918", - "metadata": {}, - "source": [ - "### 2.6 Initialize the model\n", - "\n", - "Now that the model has been defined, we can initialize it and start with the quantization process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55aa0e9b", - "metadata": {}, - "outputs": [], - "source": [ + "from VPRTempo import VPRTempo\n", "model = VPRTempo()" ] }, @@ -402,17 +142,25 @@ "id": "a88d4a18", "metadata": {}, "source": [ - "### 2.7 Generate unique model name\n", + "### 2.2 Generate unique model name\n", "\n", - "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." + "We will set up a unique model name to save and load for inferencing." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "dc786b3d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VPRTempo78415685001Quantized.pth\n" + ] + } + ], "source": [ "def generate_model_name(model):\n", " \"\"\"\n", @@ -423,6 +171,7 @@ " str(model.feature) +\n", " str(model.output) +\n", " str(model.number_modules) +\n", + " \"Quantized\"+\n", " '.pth')\n", "\n", "model_name = generate_model_name(model)\n", @@ -446,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "6714bc35", "metadata": {}, "outputs": [], @@ -483,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "36244802", "metadata": {}, "outputs": [], @@ -504,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a884ad96", "metadata": {}, "outputs": [], @@ -525,10 +274,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "087f3b36", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "# Apply quantization configurations to the model\n", "model = quantization.prepare_qat(model, inplace=False)" @@ -558,10 +316,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "0075638b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: feature_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:28<00:00, 45.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training layer: output_layer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training : 100%|████████████████████████████| 4000/4000 [01:20<00:00, 49.89it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All layers trained succesfully\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# Keep track of trained layers to pass data through them\n", "trained_layers = [] \n", @@ -591,10 +392,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "53ededaa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Convert the model to a quantized model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Save the model\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight_bias(wq, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:32\u001b[0m, in \u001b[0;36mLinearPackedParams.set_weight_bias\u001b[0;34m(self, weight, bias)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mexport\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_weight_bias\u001b[39m(\u001b[38;5;28mself\u001b[39m, weight: torch\u001b[38;5;241m.\u001b[39mTensor, bias: Optional[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8:\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", + "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", + "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" + ] + } + ], "source": [ "# Convert the model to a quantized model\n", "model = quantization.convert(model, inplace=False)\n", @@ -603,71 +425,6 @@ "model.save_model(os.path.join('./models', model_name)) " ] }, - { - "cell_type": "markdown", - "id": "c0d69843", - "metadata": {}, - "source": [ - "## 6. Inferencing\n", - "\n", - "As in the previous tutorial, inferencing with a trained model is quite simple. The only additional thing we need to do is reinitialize the VPRTempo class and convert it to quantized before loading the model. Without pre-quantizing the inference model, state dictionary keys will not match since all the layers and associated components have new parameters such as scale and zero-point.\n", - "\n", - "### 6.1 Add the inference function to the VPRTempo class\n", - "\n", - "We will start by adding in the inference function to VPRTempo. It is similar to the training regime but omits the learning components `calc_stdp` and simply runs through all the layers until it reaches the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a368c532", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def evaluate(self, test_loader, layers=None):\n", - " \"\"\"\n", - " Run the inferencing model and calculate the accuracy.\n", - "\n", - " :param test_loader: Testing data loader\n", - " :param layers: Layers to pass data through\n", - " \"\"\"\n", - "\n", - " # Initialize the number of correct predictions\n", - " numcorr = 0\n", - " idx = 0\n", - "\n", - " # Initialize the tqdm progress bar\n", - " pbar = tqdm(total=self.number_testing_images,\n", - " desc=\"Running the test network\",\n", - " position=0)\n", - "\n", - " # Run inference for the specified number of timesteps\n", - " for spikes, labels in test_loader:\n", - " # Set device\n", - " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", - " # Pass through previous layers if they exist\n", - " if layers:\n", - " for layer_name in layers:\n", - " layer = getattr(self, layer_name)\n", - " spikes = self.forward(spikes, layer)\n", - " spikes = bn.clamp_spikes(spikes, layer)\n", - "\n", - " # Evaluate if the prediction is correct\n", - " if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx:\n", - " numcorr += 1\n", - "\n", - " # Update the index and progress bar\n", - " idx += 1\n", - " pbar.update(1)\n", - "\n", - " # Close the tqdm progress bar\n", - " pbar.close()\n", - " # Calculate and record the accuracy\n", - " accuracy = round((numcorr/self.number_testing_images)*100,2)\n", - " model.logger.info(\"P@100R: \"+ str(accuracy) + '%')" - ] - }, { "cell_type": "markdown", "id": "5e841fc7", From 88bec3d8b576241f91e33b6f2d1ab10acd3b2c7e Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 16 Oct 2023 10:58:21 +1000 Subject: [PATCH 22/69] Fixed up tutorial notebooks and main code so that both work --- VPRTempo.py | 5 +- src/dataset.py | 1 + src/nordland.py | 6 +- src/settings.py | 53 ++++++--- tutorials/1_Introduction.ipynb | 178 +++++++----------------------- tutorials/2_Quantization.ipynb | 194 +++++++++++---------------------- 6 files changed, 145 insertions(+), 292 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index ebbf7c1..6a51d2e 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -177,7 +177,7 @@ def train_model(self, train_loader, layer, prev_layers=None): torch.cuda.empty_cache() gc.collect() - def evaluate(self, test_loader, layers=None): + def evaluate(self, model, test_loader, layers=None): """ Run the inferencing model and calculate the accuracy. @@ -341,6 +341,7 @@ def run_inference(model, model_name, qconfig): persistent_workers=True) # Set the model to evaluation mode and set configuration model = VPRTempo() + model.model_logger() model.eval() model.qconfig = qconfig @@ -357,7 +358,7 @@ def run_inference(model, model_name, qconfig): layer_names = list(model.layer_dict.keys()) # Use evaluate method for inference accuracy - model.evaluate(test_loader, layers=layer_names) + model.evaluate(model, test_loader, layers=layer_names) if __name__ == "__main__": # Set the number of threads for PyTorch diff --git a/src/dataset.py b/src/dataset.py index c395ae2..84a17ad 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -168,6 +168,7 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= # Load image labels from each directory, apply the skip and max_samples, and concatenate self.img_labels = [] for img_dir in img_dirs: + img_labels = pd.read_csv(annotations_file) img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(img_dir, row.iloc[0]), axis=1) diff --git a/src/nordland.py b/src/nordland.py index b8fae44..0c804ec 100644 --- a/src/nordland.py +++ b/src/nordland.py @@ -6,7 +6,7 @@ import shutil import zipfile import sys -sys.path.append('./VPRTempo-quant/dataset') +sys.path.append('..//dataset') from os import walk @@ -19,7 +19,7 @@ def natural_keys(text): return [ atoi(c) for c in re.split(r'(\d+)', text) ] # set the base path to the location of the downloaded Nordland datasets - basePath = './dataset/' + basePath = '../dataset/' assert(os.path.isdir(basePath)),"Please set the basePath to the location of the downloaded Nordland datasets" # define the subfolders of the Nordland datasets @@ -29,7 +29,7 @@ def natural_keys(text): "summer_images_train/section1/","summer_images_train/section2/"] # set the desired output folder for unzipping and organization - outDir = '' + outDir = '../dataset/' assert(os.path.isdir(outDir)),"Please set the outDir to the desired output location for unzipping the Nordland datasets" # define output paths for the data diff --git a/src/settings.py b/src/settings.py index b6ceea6..14b9912 100644 --- a/src/settings.py +++ b/src/settings.py @@ -10,9 +10,9 @@ def configure(model): Configure the model """ model.dataset = 'nordland' # Dataset name - model.dataset_file = '../dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) - model.trainingPath = '../dataset/' # Path to training images - model.testPath = '../dataset/' # Path to testing images + model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) + model.trainingPath = './dataset/' # Path to training images + model.testPath = './dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) model.number_training_images = 500 # Number of training images model.number_testing_images = 500 # Number of testing images @@ -22,6 +22,26 @@ def configure(model): model.validation = True # Validation (maybe deprecated for now?) model.log = True # Log to console + # Set default paths if the provided paths are not valid directories + if not os.path.isdir(getattr(model, 'trainingPath', '')): + model.trainingPath = '../dataset/' + + if not os.path.isdir(getattr(model, 'testPath', '')): + model.testPath = '../dataset/' + + # Now, check if the dataset_file exists based on the determined paths + if not os.path.exists(os.path.join('./dataset', model.dataset + '.csv')): + model.dataset_file = os.path.join('../dataset', model.dataset + '.csv') + else: + model.dataset_file = os.path.join('./dataset', model.dataset + '.csv') + + # Now, check the conditions using assert statements + assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" + assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" + assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" + assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" + assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" + # Output the training and testing directories model.training_dirs = [] for n in model.locations: @@ -29,14 +49,7 @@ def configure(model): model.testing_dirs = [] for n in model.test_locations: model.testing_dirs.append(os.path.join(model.testPath,n)) - - # Check that the dataset is defined properly - assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" - assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" - assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" - assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" - + # Set the model parameters model.epoch = 4 # Number of epochs model.patches = 7 # Number of patches @@ -61,12 +74,14 @@ def configure(model): # Determine the total number of timesteps across training images, modules, and location repeats model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) + def image_csv(model): """ Load the image names from the CSV file and filter them """ + # Load the image names from the CSV file - with open(os.path.join('../dataset', model.dataset + '.csv'), mode='r', newline='', encoding='utf-8') as file: + with open(model.dataset_file, mode='r', newline='', encoding='utf-8') as file: reader = csv.reader(file) model.imageNames = [row[0] for row in reader] # Remove the header @@ -86,10 +101,16 @@ def model_logger(model): """ Configure the model logger """ - # Create the output folder - now = datetime.now() - model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) + try: + # Create the output folder + now = datetime.now() + model.output_folder = '../output/' + now.strftime("%d%m%y-%H-%M-%S") + os.mkdir(model.output_folder) + except: + # Create the output folder + now = datetime.now() + model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + os.mkdir(model.output_folder) # Create the logger model.logger = logging.getLogger("VPRTempo") if (model.logger.hasHandlers()): diff --git a/tutorials/1_Introduction.ipynb b/tutorials/1_Introduction.ipynb index e6a7adb..d57c263 100644 --- a/tutorials/1_Introduction.ipynb +++ b/tutorials/1_Introduction.ipynb @@ -55,12 +55,24 @@ "source": [ "### 1.2 Import modules\n", "\n", - "Once we have downloaded the dataset, we'll start by importing all the necessary modules." + "Once we have downloaded the dataset, we'll start by importing all the necessary modules.\n", + "\n", + "For this tutorial, we use [Jupyter Dynamic Classes](https://alexhagen.github.io/jdc/) so if not already installed please install. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "id": "ca1aa5e3-4537-4e1e-8629-bb134e749707", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install jdc" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "d9caff25", "metadata": {}, "outputs": [], @@ -133,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "99b5130c", "metadata": {}, "outputs": [], @@ -195,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "dabd3c7d", "metadata": {}, "outputs": [], @@ -237,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "623595aa", "metadata": {}, "outputs": [], @@ -306,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "68a22d64", "metadata": {}, "outputs": [], @@ -342,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e99f6d13", "metadata": {}, "outputs": [], @@ -385,46 +397,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "55aa0e9b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Succesfully added feature_layer\n", - "Succesfully added output_layer\n", - "VPRTempo succesfully initialized\n" - ] - }, - { - "data": { - "text/plain": [ - "VPRTempo(\n", - " (quant): QuantStub()\n", - " (dequant): DeQuantStub()\n", - " (add): FloatFunctional(\n", - " (activation_post_process): Identity()\n", - " )\n", - " (feature_layer): SNNLayer(\n", - " (exc): Linear(in_features=784, out_features=1568, bias=False)\n", - " (inh): Linear(in_features=784, out_features=1568, bias=False)\n", - " )\n", - " (output_layer): SNNLayer(\n", - " (exc): Linear(in_features=1568, out_features=500, bias=False)\n", - " (inh): Linear(in_features=1568, out_features=500, bias=False)\n", - " )\n", - ")" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model = VPRTempo()\n", + "model_logger(model)\n", "model.train()" ] }, @@ -440,18 +419,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "dc786b3d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VPRTempo78415685001.pth\n" - ] - } - ], + "outputs": [], "source": [ "def generate_model_name(model):\n", " \"\"\"\n", @@ -485,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6714bc35", "metadata": {}, "outputs": [], @@ -524,53 +495,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "0075638b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: feature_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:13<00:00, 54.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: output_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [00:55<00:00, 72.47it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All layers trained succesfully\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "# Keep track of trained layers to pass data through them\n", "trained_layers = [] \n", @@ -600,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "53ededaa", "metadata": {}, "outputs": [], @@ -627,13 +555,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "a368c532", "metadata": {}, "outputs": [], "source": [ "%%add_to VPRTempo\n", - "def evaluate(self, test_loader, layers=None):\n", + "def evaluate(self, model, test_loader, layers=None):\n", " \"\"\"\n", " Run the inferencing model and calculate the accuracy.\n", "\n", @@ -688,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "f4a1adc8", "metadata": {}, "outputs": [], @@ -720,23 +648,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "30d51e96", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Succesfully added feature_layer\n", - "Succesfully added output_layer\n", - "VPRTempo succesfully initialized\n" - ] - } - ], + "outputs": [], "source": [ "# Set the model to evaluation mode and set configuration\n", "model = VPRTempo()\n", + "model.model_logger()\n", "model.eval()\n", "\n", "# Load the model\n", @@ -758,34 +677,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "37aa84e1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running the test network: 100%|██████████████| 500/500 [00:02<00:00, 196.29it/s]\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'VPRTempo' object has no attribute 'logger'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Use evaluate method for inference accuracy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model\u001b[38;5;241m.\u001b[39mevaluate(test_loader, layers\u001b[38;5;241m=\u001b[39mlayer_names)\n", - "File \u001b[0;32m:41\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(self, test_loader, layers)\u001b[0m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/nn/modules/module.py:1614\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[1;32m 1613\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1614\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 1615\u001b[0m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, name))\n", - "\u001b[0;31mAttributeError\u001b[0m: 'VPRTempo' object has no attribute 'logger'" - ] - } - ], + "outputs": [], "source": [ "# Use evaluate method for inference accuracy\n", - "model.evaluate(test_loader, layers=layer_names)" + "model.evaluate(model, test_loader, layers=layer_names)" ] }, { @@ -828,7 +726,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/tutorials/2_Quantization.ipynb b/tutorials/2_Quantization.ipynb index e547383..790dff6 100644 --- a/tutorials/2_Quantization.ipynb +++ b/tutorials/2_Quantization.ipynb @@ -53,12 +53,24 @@ "source": [ "### 1.2 Import modules\n", "\n", - "Once we have downloaded the dataset, we'll start by importing all the necessary modules." + "Once we have downloaded the dataset, we'll start by importing all the necessary modules.\n", + "\n", + "For this tutorial, we use [Jupyter Dynamic Classes](https://alexhagen.github.io/jdc/) so if not already installed please install. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "id": "e7bf4ad2-755b-40f1-8bd3-5b98376ed5df", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install jdc" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "d9caff25", "metadata": {}, "outputs": [], @@ -128,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "99b5130c", "metadata": {}, "outputs": [], @@ -149,18 +161,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "dc786b3d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VPRTempo78415685001Quantized.pth\n" - ] - } - ], + "outputs": [], "source": [ "def generate_model_name(model):\n", " \"\"\"\n", @@ -195,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6714bc35", "metadata": {}, "outputs": [], @@ -232,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "36244802", "metadata": {}, "outputs": [], @@ -253,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a884ad96", "metadata": {}, "outputs": [], @@ -274,19 +278,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "087f3b36", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/adam/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "# Apply quantization configurations to the model\n", "model = quantization.prepare_qat(model, inplace=False)" @@ -316,53 +311,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "0075638b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: feature_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:28<00:00, 45.19it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training layer: output_layer\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training : 100%|████████████████████████████| 4000/4000 [01:20<00:00, 49.89it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All layers trained succesfully\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "# Keep track of trained layers to pass data through them\n", "trained_layers = [] \n", @@ -392,69 +344,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "53ededaa", "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Didn't find engine for operation quantized::linear_prepack NoQEngine", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Convert the model to a quantized model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m quantization\u001b[38;5;241m.\u001b[39mconvert(model, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Save the model\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:551\u001b[0m, in \u001b[0;36mconvert\u001b[0;34m(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m inplace:\n\u001b[1;32m 550\u001b[0m module \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(module)\n\u001b[0;32m--> 551\u001b[0m _convert(\n\u001b[1;32m 552\u001b[0m module, mapping, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, is_reference\u001b[38;5;241m=\u001b[39mis_reference,\n\u001b[1;32m 553\u001b[0m convert_custom_config_dict\u001b[38;5;241m=\u001b[39mconvert_custom_config_dict)\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remove_qconfig:\n\u001b[1;32m 555\u001b[0m _remove_qconfig(module)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:589\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, mod \u001b[38;5;129;01min\u001b[39;00m module\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# both fused modules and observed custom modules are\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# swapped as one unit\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[0;32m--> 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[1;32m 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:591\u001b[0m, in \u001b[0;36m_convert\u001b[0;34m(module, mapping, inplace, is_reference, convert_custom_config_dict)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, _FusedModule) \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 588\u001b[0m type_before_parametrizations(mod) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m custom_module_class_mapping:\n\u001b[1;32m 589\u001b[0m _convert(mod, mapping, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# inplace\u001b[39;00m\n\u001b[1;32m 590\u001b[0m is_reference, convert_custom_config_dict)\n\u001b[0;32m--> 591\u001b[0m reassign[name] \u001b[38;5;241m=\u001b[39m swap_module(mod, mapping, custom_module_class_mapping)\n\u001b[1;32m 593\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m reassign\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 594\u001b[0m module\u001b[38;5;241m.\u001b[39m_modules[key] \u001b[38;5;241m=\u001b[39m value\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/quantization/quantize.py:624\u001b[0m, in \u001b[0;36mswap_module\u001b[0;34m(mod, mapping, custom_module_class_mapping)\u001b[0m\n\u001b[1;32m 622\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod, weight_qparams)\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 624\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m qmod\u001b[38;5;241m.\u001b[39mfrom_float(mod)\n\u001b[1;32m 625\u001b[0m swapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swapped:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# Preserve module's pre forward hooks. They'll be called on quantized input\u001b[39;00m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:277\u001b[0m, in \u001b[0;36mLinear.from_float\u001b[0;34m(cls, mod)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight observer must have dtype torch.qint8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 276\u001b[0m qweight \u001b[38;5;241m=\u001b[39m _quantize_weight(mod\u001b[38;5;241m.\u001b[39mweight\u001b[38;5;241m.\u001b[39mfloat(), weight_post_process)\n\u001b[0;32m--> 277\u001b[0m qlinear \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(mod\u001b[38;5;241m.\u001b[39min_features,\n\u001b[1;32m 278\u001b[0m mod\u001b[38;5;241m.\u001b[39mout_features,\n\u001b[1;32m 279\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 280\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, mod\u001b[38;5;241m.\u001b[39mbias)\n\u001b[1;32m 281\u001b[0m qlinear\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(act_scale)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:151\u001b[0m, in \u001b[0;36mLinear.__init__\u001b[0;34m(self, in_features, out_features, bias_, dtype)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnsupported dtype specified for quantized Linear!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m LinearPackedParams(dtype)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params\u001b[38;5;241m.\u001b[39mset_weight_bias(qweight, bias)\n\u001b[1;32m 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:27\u001b[0m, in \u001b[0;36mLinearPackedParams.__init__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 26\u001b[0m wq \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_weight_bias(wq, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/ao/nn/quantized/modules/linear.py:32\u001b[0m, in \u001b[0;36mLinearPackedParams.set_weight_bias\u001b[0;34m(self, weight, bias)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mexport\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_weight_bias\u001b[39m(\u001b[38;5;28mself\u001b[39m, weight: torch\u001b[38;5;241m.\u001b[39mTensor, bias: Optional[torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mqint8:\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack(weight, bias)\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mfloat16:\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_packed_params \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mops\u001b[38;5;241m.\u001b[39mquantized\u001b[38;5;241m.\u001b[39mlinear_prepack_fp16(weight, bias)\n", - "File \u001b[0;32m~/mambaforge/envs/vprtempo/lib/python3.11/site-packages/torch/_ops.py:502\u001b[0m, in \u001b[0;36mOpOverloadPacket.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;66;03m# overloading __call__ to ensure torch.ops.foo.bar()\u001b[39;00m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;66;03m# is still callable from JIT\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We save the function ptr as the `op` attribute on\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# OpOverloadPacket to access it here.\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs \u001b[38;5;129;01mor\u001b[39;00m {})\n", - "\u001b[0;31mRuntimeError\u001b[0m: Didn't find engine for operation quantized::linear_prepack NoQEngine" - ] - } - ], + "outputs": [], "source": [ "# Convert the model to a quantized model\n", "model = quantization.convert(model, inplace=False)\n", "model.eval()\n", "# Save the model\n", - "model.save_model(os.path.join('./models', model_name)) " - ] - }, - { - "cell_type": "markdown", - "id": "5e841fc7", - "metadata": {}, - "source": [ - "### 6.2 Define the inferencing DataLoader\n", - "\n", - "The only difference between the training and testing DataLoader is the directory with which it will import images from." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4a1adc8", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the image transforms and datasets\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.testing_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_testing_images)\n", - "# Initialize the data loader\n", - "test_loader = DataLoader(test_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" + "model.save_model(os.path.join('../models', model_name)) " ] }, { @@ -476,6 +375,7 @@ "source": [ "# Set the model to evaluation mode and set configuration\n", "model = VPRTempo()\n", + "model.model_logger()\n", "model.eval()\n", "model.qconfig = qconfig\n", "\n", @@ -486,12 +386,44 @@ "model = quantization.prepare(model, inplace=False)\n", "model = quantization.convert(model, inplace=False)\n", "# Load the model\n", - "model.load_model(os.path.join('./models', model_name))\n", + "model.load_model(os.path.join('../models', model_name))\n", "\n", "# Retrieve layer names for inference\n", "layer_names = list(model.layer_dict.keys())" ] }, + { + "cell_type": "markdown", + "id": "5e841fc7", + "metadata": {}, + "source": [ + "### 6.2 Define the inferencing DataLoader\n", + "\n", + "The only difference between the training and testing DataLoader is the directory with which it will import images from." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4a1adc8", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the image transforms and datasets\n", + "image_transform = ProcessImage(model.dims, model.patches)\n", + "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", + " img_dirs=model.testing_dirs,\n", + " transform=image_transform,\n", + " skip=model.filter,\n", + " max_samples=model.number_testing_images)\n", + "# Initialize the data loader\n", + "test_loader = DataLoader(test_dataset, \n", + " batch_size=1, \n", + " shuffle=False,\n", + " num_workers=8,\n", + " persistent_workers=True)" + ] + }, { "cell_type": "markdown", "id": "472d24e8", @@ -510,7 +442,7 @@ "outputs": [], "source": [ "# Use evaluate method for inference accuracy\n", - "model.evaluate(test_loader, layers=layer_names)" + "model.evaluate(model, test_loader, layers=layer_names)" ] }, { @@ -553,7 +485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.4" } }, "nbformat": 4, From 7cae5afdd8822b6670da8695d6c84fe594518a5d Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 3 Nov 2023 15:03:26 +1000 Subject: [PATCH 23/69] Added in basic tutorial, changed learning annealment, and removed constant input --- .gitignore | 6 +- VPRTempo.py | 10 +- dataset/nordland.csv | 50387 +++++++++++------------- models/.gitkeep | 0 src/settings.py | 2 +- tutorials/0_BasicDemo.ipynb | 344 + tutorials/mats/0_basicdemo/summer.png | Bin 0 -> 295003 bytes 7 files changed, 23145 insertions(+), 27604 deletions(-) delete mode 100644 models/.gitkeep create mode 100644 tutorials/0_BasicDemo.ipynb create mode 100755 tutorials/mats/0_basicdemo/summer.png diff --git a/.gitignore b/.gitignore index b6d574a..46fe2eb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,5 @@ __pycache__/ .ipynb_checkpoints/ src/__pycache__/ -dataset/fall -dataset/winter -dataset/summer -dataset/spring \ No newline at end of file +dataset/ +models/ diff --git a/VPRTempo.py b/VPRTempo.py index 6a51d2e..ab49e54 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -75,7 +75,6 @@ def __init__(self): fire_rate=[0.2, 0.9], ip_rate=0.15, stdp_rate=0.005, - const_inp=[0, 0.1], p=[0.1, 0.5] ) self.add_layer( @@ -132,17 +131,16 @@ def train_model(self, train_loader, layer, prev_layers=None): """ # Initialize the tqdm progress bar - pbar = tqdm(total=int(self.T * self.epoch), + pbar = tqdm(total=int(self.T), desc="Training ", position=0) # Initialize the learning rates for each layer (used for annealment) init_itp = layer.eta_ip.detach() init_stdp = layer.eta_stdp.detach() - + mod = 0 # Used to determine the learning rate annealment, resets at each epoch # Run training for the specified number of epochs for epoch in range(self.epoch): - mod = 0 # Used to determine the learning rate annealment, resets at each epoch # Run training for the specified number of timesteps for spikes, labels in train_loader: spikes, labels = spikes.to(self.device), labels.to(self.device) @@ -253,7 +251,7 @@ def generate_model_name(model): """ Generate the model name based on its parameters. """ - return ("VPRTempo" + + return ("VPRTempoQuant" + str(model.input) + str(model.feature) + str(model.output) + @@ -289,7 +287,7 @@ def train_new_model(model, model_name, qconfig): # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, - shuffle=False, + shuffle=True, num_workers=8, persistent_workers=True) # Set the model to training mode and move to device diff --git a/dataset/nordland.csv b/dataset/nordland.csv index 5fa0648..9e031de 100644 --- a/dataset/nordland.csv +++ b/dataset/nordland.csv @@ -1,27593 +1,22794 @@ -Image name,index -images-00202.png,0 -images-00203.png,1 -images-00204.png,2 -images-00205.png,3 -images-00206.png,4 -images-00207.png,5 -images-00208.png,6 -images-00209.png,7 -images-00210.png,8 -images-00211.png,9 -images-00212.png,10 -images-00213.png,11 -images-00214.png,12 -images-00215.png,13 -images-00216.png,14 -images-00217.png,15 -images-00218.png,16 -images-00219.png,17 -images-00220.png,18 -images-00221.png,19 -images-00222.png,20 -images-00223.png,21 -images-00224.png,22 -images-00225.png,23 -images-00226.png,24 -images-00227.png,25 -images-00228.png,26 -images-00229.png,27 -images-00230.png,28 -images-00231.png,29 -images-00232.png,30 -images-00233.png,31 -images-00234.png,32 -images-00235.png,33 -images-00236.png,34 -images-00237.png,35 -images-00238.png,36 -images-00239.png,37 -images-00240.png,38 -images-00241.png,39 -images-00242.png,40 -images-00243.png,41 -images-00244.png,42 -images-00245.png,43 -images-00246.png,44 -images-00247.png,45 -images-00248.png,46 -images-00249.png,47 -images-00250.png,48 -images-00251.png,49 -images-00252.png,50 -images-00253.png,51 -images-00254.png,52 -images-00255.png,53 -images-00256.png,54 -images-00257.png,55 -images-00258.png,56 -images-00259.png,57 -images-00260.png,58 -images-00261.png,59 -images-00262.png,60 -images-00263.png,61 -images-00264.png,62 -images-00265.png,63 -images-00266.png,64 -images-00267.png,65 -images-00268.png,66 -images-00269.png,67 -images-00270.png,68 -images-00271.png,69 -images-00272.png,70 -images-00273.png,71 -images-00274.png,72 -images-00275.png,73 -images-00276.png,74 -images-00277.png,75 -images-00278.png,76 -images-00279.png,77 -images-00280.png,78 -images-00281.png,79 -images-00282.png,80 -images-00283.png,81 -images-00284.png,82 -images-00285.png,83 -images-00286.png,84 -images-00287.png,85 -images-00288.png,86 -images-00289.png,87 -images-00290.png,88 -images-00291.png,89 -images-00292.png,90 -images-00293.png,91 -images-00294.png,92 -images-00295.png,93 -images-00296.png,94 -images-00297.png,95 -images-00298.png,96 -images-00299.png,97 -images-00300.png,98 -images-00301.png,99 -images-00302.png,100 -images-00303.png,101 -images-00304.png,102 -images-00305.png,103 -images-00306.png,104 -images-00307.png,105 -images-00308.png,106 -images-00309.png,107 -images-00310.png,108 -images-00311.png,109 -images-00312.png,110 -images-00313.png,111 -images-00314.png,112 -images-00315.png,113 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model.number_modules) * model.location_repeat) + model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) * model.epoch def image_csv(model): diff --git a/tutorials/0_BasicDemo.ipynb b/tutorials/0_BasicDemo.ipynb new file mode 100644 index 0000000..0770532 --- /dev/null +++ b/tutorials/0_BasicDemo.ipynb @@ -0,0 +1,344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37", + "metadata": {}, + "source": [ + "## VPRTempo - Basic Demo\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance. Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with a pre-trained model.\n", + "\n", + "Before starting, make sure the following packages are installed and imported:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c879cd02-82db-441d-9476-fff1925bf494", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n", + "try:\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + "except:\n", + " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "bb45df38-e333-46b2-9161-80e6ac367532", + "metadata": {}, + "source": [ + "### Image processing\n", + "\n", + "Let's have a look at how we process our images to run through VPRTempo. We utilize a technique called *patch normalization* to resize input images and normalize the pixel intensities. To start, let's see what the original image looks like before patch normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the input image\n", + "raw_img = cv2.imread('./mats/0_basicdemo/summer.png')\n", + "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n", + "\n", + "# Plot the image\n", + "plt.imshow(rgb_img)\n", + "plt.title('Nordland Summer')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42", + "metadata": {}, + "source": [ + "What we have here is a 360x640 RGB image, which for processing through neural networks is too big (230,400 total pixels). So instead, we'll use patch normalization to reduce the image size down to a grayscale 28x28 image to just 784 pixels in total." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the patch normalized image\n", + "patch_img = np.load('./mats/0_basicdemo/summer_patchnorm.npy', allow_pickle=True)\n", + "\n", + "# Plot the image\n", + "plt.matshow(patch_img)\n", + "plt.title('Nordland Summer Patch Normalized')\n", + "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d404dfd2-10bd-4981-96ef-6092a9866fc6", + "metadata": {}, + "source": [ + "The reduced image dimensions with patch normalization allows for a decent representation of the full scene, despite the smaller size.\n", + "\n", + "### Convert images to spikes\n", + "\n", + "'Spikes' in the context of VPRTempo are a little different than conventional spiking neural networks. Typically, spikes from image datasets are converted into Poisson spike trains where the pixel intensity determines the number of spikes to propagate throughout a network. VPRTempo only considers each pixel as a single spike, but considers the *amplitude* of the spike to determine the timing within a single timestep - where large amplitudes (high pixel intensity) spike early in a timestep, and vice versa for small amplitudes. \n", + "\n", + "Let's flatten the patch normalized image into a 1D-array so we can apply our network weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bd6ae95-2a79-4b45-8a60-503079339739", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 2D image to a 1D-array\n", + "patch_1d = np.reshape(patch_img, (784,))" + ] + }, + { + "cell_type": "markdown", + "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae", + "metadata": {}, + "source": [ + "### Load the pre-trained network weights\n", + "\n", + "Our network consists of the following architecture:\n", + "\n", + " - An input layer sparsely connected to a feature layer, 784 input neurons to 1568 feature neurons\n", + " - The feature layer fully connected to a one-hot-encoded output layer, 1568 feature neurons to 500 output neurons\n", + "\n", + "Each layer connection is trained separately and stored in different weight matrices for excitatory (positive) and inhibitory (negative) connections. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d98749a-8f28-477b-871c-93626e96786c", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "if_exc = np.load('./mats/0_basicdemo/if_exc.npy')\n", + "if_inh = np.load('./mats/0_basicdemo/if_inh.npy')\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", + "\n", + "# Plot the excitatory weights\n", + "exc_plot = axes[0].matshow(if_exc.T)\n", + "axes[0].set_title('Input > Feature Excitatory Weights')\n", + "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Plot the inhibitory weights\n", + "inh_plot = axes[1].matshow(if_inh.T, cmap='viridis_r')\n", + "axes[1].set_title('Input > Feature Inhibitory Weights')\n", + "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "826213d7-7721-440c-b1a8-47fb613339eb", + "metadata": {}, + "source": [ + "In this case, we have more inhibitory connections than we do excitatory for the input to feature layer. Let's load the feature to output layer spikes and visualise them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7609eae5-f584-4c98-9eb6-3c3cf1e2aa04", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "fo_exc = np.load('./mats/0_basicdemo/fo_exc.npy')\n", + "fo_inh = np.load('./mats/0_basicdemo/fo_inh.npy')\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", + "\n", + "# Plot the excitatory weights\n", + "exc_plot = axes[0].matshow(fo_exc)\n", + "axes[0].set_title('Feature > Output Excitatory Weights')\n", + "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Plot the inhibitory weights\n", + "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", + "axes[1].set_title('Feature > Output Inhibitory Weights')\n", + "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6", + "metadata": {}, + "source": [ + "### Propagate network spikes\n", + "\n", + "Now we'll propagate the input spikes across the layers to get the output. All we have to do is multiply the input spikes by the Input > Feature weights for both excitatory and inhibitory matrices and add them, then take the feature spikes and multiply them by the Feature > Output weights and do the smae thing. We'll also clamp spikes in the range of [0, 0.9] to prevent negative spikes and spike explosions.\n", + "\n", + "Let's do that and visualize the spikes as they're going through, we'll start with the Input to Feature layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6c84239-c176-48c3-8954-25da5f989d61", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate feature spikes (positive and negative weights)\n", + "feature_spikes = np.matmul(if_exc,patch_1d) + np.matmul(if_inh,patch_1d)\n", + "feature_spikes = np.clip(feature_spikes, 0, 0.9)\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(feature_spikes)), feature_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Feature Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", + "metadata": {}, + "source": [ + "Now let's propagate the feature layer spikes through to the output layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate output spikes (positive and negative weights)\n", + "output_spikes = np.matmul(fo_exc,feature_spikes) + np.matmul(fo_inh,feature_spikes)\n", + "output_spikes = np.clip(output_spikes, 0, 0.9)\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(output_spikes)), output_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f", + "metadata": {}, + "source": [ + "Success! We have propagated our input spikes across the layers to reach this output. Clearly, one of the output spikes has the highest amplitude. Our network weights were trained on 500 locations from a Fall and Spring traversal of Nordland. For this example, we passed the first location from the Summer traversal through the network to achieve this output - which clearly looks to have spikes Neuron ID '0' the highest!\n", + "\n", + "Let's prove that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23", + "metadata": {}, + "outputs": [], + "source": [ + "# Output the argmax from the output spikes\n", + "prediction = np.argmax(output_spikes)\n", + "print(f\"Neuron ID with the highest output is {prediction}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", + "metadata": {}, + "source": [ + "### Conclusions\n", + "\n", + "We have gone through a very basic demo of how VPRTempo takes input images, patch normalizes them, and propagates the spikes throughout the weights to achieve the desired matching output. Although this demonstration was performed using NumPy, the torch implementation is virtually the same except we use tensors with or without quantization. \n", + "\n", + "The purpose of splitting up excitatory and inhibitory weights is to allow for extra hometostatic normalization of inhibitory connections, which has proven to be critical in regulating overall system activity.\n", + "\n", + "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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ziXbQFQ_l(S=hp7dlNm(ac`6B9JzooYqjYI3+|Qd@4EtiQLBLTa0WXxDKr>h+Xi;Pc zF{)PpyG|e3a$)LYGE8XgQxqFZL%CU@*e20RZ!LF7`pSUM1%*)$nzE4v`gjm*<@B=Xx$#f!gcn zi&hH~RTh_PCk)12>(ZXWfU`leFNjm5|KwgZong-gUYu;|uZ3s$)DIv@%0U zlu5sG&KD;}5q-{7#P=T#I6@E70EfNXiUOxJx~ly9Qo1GHT=B0C8evII8iWkFj&)p$ ziL6vOcjretP(^-KA7WI>&Z(r`CH<{)N+Q;qQ?!Kb=l21)?L>Se7hOrUq>zlL@hx`% z9{F3b3F;xidjr~@gNs8}W6!B)9vw}|p{u%`2n6LLDF&qHAPvLh3joh_A_$wnm_C~M zjPDYAEO(>4TP&xwAI#~pO0ihc6;465O3ivQ!qkEN2ms9UtTTdy6R&Uar}dGQI}%3Q z*~$S}9$WVA82Y9Id!H|?grIHh`Gj%@q6K)+-NoB`MMaXuW{j5aD zO*N~wq@9QAi~o1r(!TJ3|7B<)RM2eWWoyZp=1G;Fjs+!_TJ#IYd=(y&N36EPz*#&a(M|)ng`A#IlsuF$E{CSWw&#vuQyr9+h1?nmWcaw@x0^67BmaRHnUOQLftcw- zw*rg~(;d(}V(CcruWp@fh%f4}uBi52?8MX1O1i3l2f#W7A4~kpF(Xi)1#Db3kKRq3u?OW#Wq8vLpvN_RXiiG*^_(ghrq1;DMA%LX^H?(Z0al4eaF(K6}$E&%&6nYa#X0tv2` zVF0irD05tG@4_}k0BnmgewuPz*?rnR?3Z6Y{QAq6r^kCT9wL!x?sRN yvH`P(TqR@sp$}Iau^t%Q0N4YMAIL7WpZ_0pvv4PZ=^U{D0000 Date: Mon, 6 Nov 2023 13:02:40 +1000 Subject: [PATCH 24/69] Split out quantized and non-quantized versions of software, split out training and inference models --- VPRTempo.py | 234 ++++++----------------------- VPRTempoQuant.py | 250 +++++++++++++++++++++++++++++++ VPRTempoQuant_Train.py | 288 ++++++++++++++++++++++++++++++++++++ VPRTempo_Train.py | 268 +++++++++++++++++++++++++++++++++ src/blitnet.py | 18 +-- src/metrics.py | 2 - src/settings.py | 64 +++----- tutorials/0_BasicDemo.ipynb | 34 +---- 8 files changed, 888 insertions(+), 270 deletions(-) create mode 100644 VPRTempoQuant.py create mode 100644 VPRTempoQuant_Train.py create mode 100644 VPRTempo_Train.py diff --git a/VPRTempo.py b/VPRTempo.py index ab49e54..97a80a1 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -36,13 +36,13 @@ import blitnet as bn import numpy as np import torch.nn as nn -import torch.quantization as quantization -from settings import configure, image_csv, model_logger +from settings import configure, model_logger from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader -from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm +from prettytable import PrettyTable +from metrics import recallAtK class VPRTempo(nn.Module): def __init__(self): @@ -50,16 +50,8 @@ def __init__(self): # Configure the network configure(self) - - # Define the images to load (both training and inference) - image_csv(self) - # Add quantization stubs for Quantization Aware Training (QAT) - self.quant = QuantStub() - self.dequant = DeQuantStub() - - # Define the add function for quantized addition - self.add = nn.quantized.FloatFunctional() + model_logger(self) # Layer dict to keep track of layer names and their order self.layer_dict = {} @@ -71,18 +63,12 @@ def __init__(self): self.add_layer( 'feature_layer', dims=[self.input, self.feature], - thr_range=[0, 0.5], - fire_rate=[0.2, 0.9], - ip_rate=0.15, - stdp_rate=0.005, - p=[0.1, 0.5] + device=self.device ) self.add_layer( 'output_layer', dims=[self.feature, self.output], - ip_rate=0.15, - stdp_rate=0.005, - spk_force=True + device=self.device ) def add_layer(self, name, **kwargs): @@ -103,77 +89,6 @@ def add_layer(self, name, **kwargs): # Add layer name and index to the layer_dict self.layer_dict[name] = self.layer_counter self.layer_counter += 1 - - def model_logger(self): - """ - Log the model configuration to the console. - """ - model_logger(self) - - def _anneal_learning_rate(self, layer, mod, itp, stdp): - """ - Anneal the learning rate for the current layer. - """ - if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps - pt = pow(float(self.T - mod) / self.T, self.annl_pow) - layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate - layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate - - return layer - - def train_model(self, train_loader, layer, prev_layers=None): - """ - Train a layer of the network model. - - :param train_loader: Training data loader - :param layer: Layer to train - :param prev_layers: Previous layers to pass data through - """ - - # Initialize the tqdm progress bar - pbar = tqdm(total=int(self.T), - desc="Training ", - position=0) - - # Initialize the learning rates for each layer (used for annealment) - init_itp = layer.eta_ip.detach() - init_stdp = layer.eta_stdp.detach() - mod = 0 # Used to determine the learning rate annealment, resets at each epoch - # Run training for the specified number of epochs - for epoch in range(self.epoch): - # Run training for the specified number of timesteps - for spikes, labels in train_loader: - spikes, labels = spikes.to(self.device), labels.to(self.device) - idx = labels / self.filter # Set output index for spike forcing - # Pass through previous layers if they exist - if prev_layers: - with torch.no_grad(): - for prev_layer_name in prev_layers: - prev_layer = getattr(self, prev_layer_name) # Get the previous layer object - spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer - spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9] - else: - prev_layer = None - # Get the output spikes from the current layer - pre_spike = spikes.detach() # Previous layer spikes for STDP - spikes = self.forward(spikes, layer) # Current layer spikes - spikes_noclp = spikes.detach() # Used for inhibitory homeostasis - spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9] - # Calculate STDP - layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer) - # Adjust learning rates - layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) - # Update the annealing mod & progress bar - mod += 1 - pbar.update(1) - - # Close the tqdm progress bar - pbar.close() - - # Free up memory - if self.device.type == "cuda": - torch.cuda.empty_cache() - gc.collect() def evaluate(self, model, test_loader, layers=None): """ @@ -182,16 +97,12 @@ def evaluate(self, model, test_loader, layers=None): :param test_loader: Testing data loader :param layers: Layers to pass data through """ - - # Initialize the number of correct predictions - numcorr = 0 - idx = 0 - # Initialize the tqdm progress bar pbar = tqdm(total=self.number_testing_images, desc="Running the test network", position=0) - + # Initiliaze the output spikes variable + out = [] # Run inference for the specified number of timesteps for spikes, labels in test_loader: # Set device @@ -203,19 +114,29 @@ def evaluate(self, model, test_loader, layers=None): spikes = self.forward(spikes, layer) spikes = bn.clamp_spikes(spikes, layer) - # Evaluate if the prediction is correct - if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx: - numcorr += 1 - - # Update the index and progress bar - idx += 1 + # Add output spikes to list + out.append(spikes.detach().cpu().tolist()) pbar.update(1) # Close the tqdm progress bar pbar.close() - # Calculate and record the accuracy - accuracy = round((numcorr/self.number_testing_images)*100,2) - model.logger.info("P@100R: "+ str(accuracy) + '%') + + # Rehsape output spikes into a similarity matrix + out = np.reshape(np.array(out),(model.number_training_images,model.number_testing_images)) + # Calculate and print the Recall@N + N = [1,5,10,15,20,25] + R = [] + # Create GT matrix + GT = np.zeros((model.number_testing_images,model.number_training_images), dtype=int) + for n in range(len(GT)): + GT[n,n] = 1 + for n in N: + R.append(round(recallAtK(out,GThard=GT,K=n),2)) + # Print the results + table = PrettyTable() + table.field_names = ["N", "1", "5", "10", "15", "20", "25"] + table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]]) + model.logger.info(table) def forward(self, spikes, layer): """ @@ -228,17 +149,9 @@ def forward(self, spikes, layer): - Tensor: Output after processing. """ - spikes = self.quant(spikes) - spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) - spikes = self.dequant(spikes) + spikes = layer.exc(spikes) + layer.inh(spikes) return spikes - - def save_model(self, model_out): - """ - Save the trained model to models output folder. - """ - torch.save(self.state_dict(), model_out) def load_model(self, model_path): """ @@ -251,7 +164,7 @@ def generate_model_name(model): """ Generate the model name based on its parameters. """ - return ("VPRTempoQuant" + + return ("VPRTempo" + str(model.input) + str(model.feature) + str(model.output) + @@ -260,63 +173,16 @@ def generate_model_name(model): def check_pretrained_model(model_name): """ - Check if a pre-trained model exists and prompt the user to retrain if desired. - """ - if os.path.exists(os.path.join('./models', model_name)): - prompt = "A network with these parameters exists, re-train network? (y/n):\n" - retrain = input(prompt).strip().lower() - return retrain == 'n' - return False - -def train_new_model(model, model_name, qconfig): + Check if a pre-trained model exists and tell user if it does not. """ - Train a new model. - - :param model: Model to train - :param model_name: Name of the model to save after training - :param qconfig: Quantization configuration - """ - # Initialize the image transforms and datasets - image_transform = ProcessImage(model.dims, model.patches) - train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.training_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_training_images, - test=False) - # Initialize the data loader - train_loader = DataLoader(train_dataset, - batch_size=1, - shuffle=True, - num_workers=8, - persistent_workers=True) - # Set the model to training mode and move to device - model.train() - model.to('cpu') - model.qconfig = qconfig - - # Apply quantization configurations to the model - model = quantization.prepare_qat(model, inplace=False) - - # Keep track of trained layers to pass data through them - trained_layers = [] - - # Training each layer - for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): - print(f"Training layer: {layer_name}") - # Retrieve the layer object - layer = getattr(model, layer_name) - # Train the layer - model.train_model(train_loader, layer, prev_layers=trained_layers) - # After training the current layer, add it to the list of trained layers - trained_layers.append(layer_name) - # Convert the model to a quantized model - model = quantization.convert(model, inplace=False) - model.eval() - # Save the model - model.save_model(os.path.join('./models', model_name)) - -def run_inference(model, model_name, qconfig): + if not os.path.exists(os.path.join('./models', model_name)): + model.logger.info("A pre-trained network does not exist: please train one using VPRTempoQuant_Trainer") + pretrain = 'n' + else: + pretrain = 'y' + return pretrain + +def run_inference(model, model_name): """ Run inference on a pre-trained model. @@ -338,17 +204,8 @@ def run_inference(model, model_name, qconfig): num_workers=8, persistent_workers=True) # Set the model to evaluation mode and set configuration - model = VPRTempo() - model.model_logger() model.eval() - model.qconfig = qconfig - # Apply quantization configurations to all layers in layer_dict - for layer_name, _ in model.layer_dict.items(): - getattr(model, layer_name).qconfig = qconfig - # Prepare and convert the model to a quantized model - model = quantization.prepare(model, inplace=False) - model = quantization.convert(model, inplace=False) # Load the model model.load_model(os.path.join('./models', model_name)) @@ -363,16 +220,13 @@ def run_inference(model, model_name, qconfig): #torch.set_num_threads(8) # Initialize the model model = VPRTempo() - # Initialize the logger - model.model_logger() - # Set the quantization configuration - qconfig = quantization.get_default_qat_qconfig('fbgemm') + if model.quantize: + raise ValueError("Please disable quantization to run inference.") # Generate the model name model_name = generate_model_name(model) # Check if a pre-trained model exists use_pretrained = check_pretrained_model(model_name) - # Train or run inference based on the user's input - if not use_pretrained: - train_new_model(model, model_name, qconfig) # Training - with torch.no_grad(): - run_inference(model, model_name, qconfig) # Inference \ No newline at end of file + if not use_pretrained == 'n': + # Run inference based on the user's input + with torch.no_grad(): + run_inference(model, model_name) # Inference \ No newline at end of file diff --git a/VPRTempoQuant.py b/VPRTempoQuant.py new file mode 100644 index 0000000..e2ef724 --- /dev/null +++ b/VPRTempoQuant.py @@ -0,0 +1,250 @@ +#MIT License + +#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer + +#Permission is hereby granted, free of charge, to any person obtaining a copy +#of this software and associated documentation files (the "Software"), to deal +#in the Software without restriction, including without limitation the rights +#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +#copies of the Software, and to permit persons to whom the Software is +#furnished to do so, subject to the following conditions: + +#The above copyright notice and this permission notice shall be included in all +#copies or substantial portions of the Software. + +#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +#SOFTWARE. + +''' +Imports +''' + +import os +import torch +import gc +import sys +sys.path.append('./src') +sys.path.append('./models') +sys.path.append('./output') +sys.path.append('./dataset') + +import blitnet as bn +import numpy as np +import torch.nn as nn +import torch.quantization as quantization + +from settings import configure, model_logger +from dataset import CustomImageDataset, ProcessImage +from torch.utils.data import DataLoader +from torch.ao.quantization import QuantStub, DeQuantStub +from tqdm import tqdm +from prettytable import PrettyTable +from metrics import recallAtK + +class VPRTempo(nn.Module): + def __init__(self): + super(VPRTempo, self).__init__() + + # Configure the network + configure(self) + + model_logger(self) + + # Add quantization stubs for Quantization Aware Training (QAT) + self.quant = QuantStub() + self.dequant = DeQuantStub() + + # Define the add function for quantized addition + self.add = nn.quantized.FloatFunctional() + + # Layer dict to keep track of layer names and their order + self.layer_dict = {} + self.layer_counter = 0 + + """ + Define trainable layers here + """ + self.add_layer( + 'feature_layer', + dims=[self.input, self.feature], + device=self.device + ) + self.add_layer( + 'output_layer', + dims=[self.feature, self.output], + device=self.device + ) + + def add_layer(self, name, **kwargs): + """ + Dynamically add a layer with given name and keyword arguments. + + :param name: Name of the layer to be added + :type name: str + :param kwargs: Hyperparameters for the layer + """ + # Check for layer name duplicates + if name in self.layer_dict: + raise ValueError(f"Layer with name {name} already exists.") + + # Add a new SNNLayer with provided kwargs + setattr(self, name, bn.SNNLayer(**kwargs)) + + # Add layer name and index to the layer_dict + self.layer_dict[name] = self.layer_counter + self.layer_counter += 1 + + def evaluate(self, model, test_loader, layers=None): + """ + Run the inferencing model and calculate the accuracy. + + :param test_loader: Testing data loader + :param layers: Layers to pass data through + """ + # Initialize the tqdm progress bar + pbar = tqdm(total=self.number_testing_images, + desc="Running the test network", + position=0) + # Initiliaze the output spikes variable + out = [] + # Run inference for the specified number of timesteps + for spikes, labels in test_loader: + # Set device + spikes, labels = spikes.to(self.device), labels.to(self.device) + # Pass through previous layers if they exist + if layers: + for layer_name in layers: + layer = getattr(self, layer_name) + spikes = self.forward(spikes, layer) + spikes = bn.clamp_spikes(spikes, layer) + + # Add output spikes to list + out.append(spikes.detach().cpu().tolist()) + pbar.update(1) + + # Close the tqdm progress bar + pbar.close() + + # Rehsape output spikes into a similarity matrix + out = np.reshape(np.array(out),(model.number_training_images,model.number_testing_images)) + # Calculate and print the Recall@N + N = [1,5,10,15,20,25] + R = [] + # Create GT matrix + GT = np.zeros((model.number_testing_images,model.number_training_images), dtype=int) + for n in range(len(GT)): + GT[n,n] = 1 + for n in N: + R.append(recallAtK(out,GThard=GT,K=n)) + # Print the results + table = PrettyTable() + table.field_names = ["N", "1", "5", "10", "15", "20", "25"] + table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]]) + model.logger.info(table) + + def forward(self, spikes, layer): + """ + Compute the forward pass of the model. + + Parameters: + - spikes (Tensor): Input spikes. + + Returns: + - Tensor: Output after processing. + """ + + spikes = self.quant(spikes) + spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) + spikes = self.dequant(spikes) + + return spikes + + def load_model(self, model_path): + """ + Load pre-trained model and set the state dictionary keys. + """ + self.load_state_dict(torch.load(model_path, map_location=self.device), + strict=True) + +def generate_model_name(model): + """ + Generate the model name based on its parameters. + """ + return ("VPRTempoQuant" + + str(model.input) + + str(model.feature) + + str(model.output) + + str(model.number_modules) + + '.pth') + +def check_pretrained_model(model_name): + """ + Check if a pre-trained model exists and tell user if it does not. + """ + if not os.path.exists(os.path.join('./models', model_name)): + model.logger.info("A pre-trained network does not exist: please train one using VPRTempoQuant_Trainer") + pretrain = 'n' + else: + pretrain = 'y' + return pretrain + +def run_inference(model, model_name, qconfig): + """ + Run inference on a pre-trained model. + + :param model: Model to run inference on + :param model_name: Name of the model to load + :param qconfig: Quantization configuration + """ + # Initialize the image transforms and datasets + image_transform = ProcessImage(model.dims, model.patches) + test_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.testing_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_testing_images) + # Initialize the data loader + test_loader = DataLoader(test_dataset, + batch_size=1, + shuffle=False, + num_workers=8, + persistent_workers=True) + # Set the model to evaluation mode and set configuration + model.eval() + model.qconfig = qconfig + + # Apply quantization configurations to all layers in layer_dict + for layer_name, _ in model.layer_dict.items(): + getattr(model, layer_name).qconfig = qconfig + # Prepare and convert the model to a quantized model + model = quantization.prepare(model, inplace=False) + model = quantization.convert(model, inplace=False) + # Load the model + model.load_model(os.path.join('./models', model_name)) + + # Retrieve layer names for inference + layer_names = list(model.layer_dict.keys()) + + # Use evaluate method for inference accuracy + model.evaluate(model, test_loader, layers=layer_names) + +if __name__ == "__main__": + # Set the number of threads for PyTorch + #torch.set_num_threads(8) + # Initialize the model + model = VPRTempo() + # Set the quantization configuration + qconfig = quantization.get_default_qat_qconfig('fbgemm') + # Generate the model name + model_name = generate_model_name(model) + # Check if a pre-trained model exists + use_pretrained = check_pretrained_model(model_name) + if not use_pretrained == 'n': + # Run inference based on the user's input + with torch.no_grad(): + run_inference(model, model_name, qconfig) # Inference \ No newline at end of file diff --git a/VPRTempoQuant_Train.py b/VPRTempoQuant_Train.py new file mode 100644 index 0000000..7e02bf2 --- /dev/null +++ b/VPRTempoQuant_Train.py @@ -0,0 +1,288 @@ +#MIT License + +#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer + +#Permission is hereby granted, free of charge, to any person obtaining a copy +#of this software and associated documentation files (the "Software"), to deal +#in the Software without restriction, including without limitation the rights +#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +#copies of the Software, and to permit persons to whom the Software is +#furnished to do so, subject to the following conditions: + +#The above copyright notice and this permission notice shall be included in all +#copies or substantial portions of the Software. + +#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +#SOFTWARE. + +''' +Imports +''' + +import os +import torch +import gc +import sys +sys.path.append('./src') +sys.path.append('./models') +sys.path.append('./output') +sys.path.append('./dataset') + +import blitnet as bn +import numpy as np +import torch.nn as nn +import torch.quantization as quantization + +from settings import configure, model_logger +from dataset import CustomImageDataset, ProcessImage +from torch.utils.data import DataLoader +from torch.ao.quantization import QuantStub, DeQuantStub +from tqdm import tqdm + +class VPRTempo(nn.Module): + def __init__(self): + super(VPRTempo, self).__init__() + + # Configure the network + configure(self) + + # Add quantization stubs for Quantization Aware Training (QAT) + self.quant = QuantStub() + self.dequant = DeQuantStub() + + # Define the add function for quantized addition + self.add = nn.quantized.FloatFunctional() + + # Layer dict to keep track of layer names and their order + self.layer_dict = {} + self.layer_counter = 0 + + """ + Define trainable layers here + """ + self.add_layer( + 'feature_layer', + dims=[self.input, self.feature], + thr_range=[0, 0.5], + fire_rate=[0.2, 0.9], + ip_rate=0.15, + stdp_rate=0.005, + p=[0.1, 0.5], + device=self.device + ) + self.add_layer( + 'output_layer', + dims=[self.feature, self.output], + ip_rate=0.15, + stdp_rate=0.005, + spk_force=True, + device=self.device + ) + + def add_layer(self, name, **kwargs): + """ + Dynamically add a layer with given name and keyword arguments. + + :param name: Name of the layer to be added + :type name: str + :param kwargs: Hyperparameters for the layer + """ + # Check for layer name duplicates + if name in self.layer_dict: + raise ValueError(f"Layer with name {name} already exists.") + + # Add a new SNNLayer with provided kwargs + setattr(self, name, bn.SNNLayer(**kwargs)) + + # Add layer name and index to the layer_dict + self.layer_dict[name] = self.layer_counter + self.layer_counter += 1 + + def model_logger(self): + """ + Log the model configuration to the console. + """ + model_logger(self) + + def _anneal_learning_rate(self, layer, mod, itp, stdp): + """ + Anneal the learning rate for the current layer. + """ + if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps + pt = pow(float(self.T - mod) / self.T, self.annl_pow) + layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate + layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate + + return layer + + def train_model(self, train_loader, layer, prev_layers=None): + """ + Train a layer of the network model. + + :param train_loader: Training data loader + :param layer: Layer to train + :param prev_layers: Previous layers to pass data through + """ + + # Initialize the tqdm progress bar + pbar = tqdm(total=int(self.T), + desc="Training ", + position=0) + + # Initialize the learning rates for each layer (used for annealment) + init_itp = layer.eta_ip.detach() + init_stdp = layer.eta_stdp.detach() + mod = 0 # Used to determine the learning rate annealment, resets at each epoch + # Run training for the specified number of epochs + for epoch in range(self.epoch): + # Run training for the specified number of timesteps + for spikes, labels in train_loader: + spikes, labels = spikes.to(self.device), labels.to(self.device) + idx = labels / self.filter # Set output index for spike forcing + # Pass through previous layers if they exist + if prev_layers: + with torch.no_grad(): + for prev_layer_name in prev_layers: + prev_layer = getattr(self, prev_layer_name) # Get the previous layer object + spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer + spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9] + else: + prev_layer = None + # Get the output spikes from the current layer + pre_spike = spikes.detach() # Previous layer spikes for STDP + spikes = self.forward(spikes, layer) # Current layer spikes + spikes_noclp = spikes.detach() # Used for inhibitory homeostasis + spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9] + # Calculate STDP + layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer) + # Adjust learning rates + layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) + # Update the annealing mod & progress bar + mod += 1 + pbar.update(1) + + # Close the tqdm progress bar + pbar.close() + + # Free up memory + if self.device.type == "cuda": + torch.cuda.empty_cache() + gc.collect() + + def forward(self, spikes, layer): + """ + Compute the forward pass of the model. + + Parameters: + - spikes (Tensor): Input spikes. + + Returns: + - Tensor: Output after processing. + """ + + spikes = self.quant(spikes) + spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) + spikes = self.dequant(spikes) + + return spikes + + def save_model(self, model_out): + """ + Save the trained model to models output folder. + """ + torch.save(self.state_dict(), model_out) + +def generate_model_name(model): + """ + Generate the model name based on its parameters. + """ + return ("VPRTempoQuant" + + str(model.input) + + str(model.feature) + + str(model.output) + + str(model.number_modules) + + '.pth') + +def check_pretrained_model(model_name): + """ + Check if a pre-trained model exists and prompt the user to retrain if desired. + """ + if os.path.exists(os.path.join('./models', model_name)): + prompt = "A network with these parameters exists, re-train network? (y/n):\n" + retrain = input(prompt).strip().lower() + return retrain == 'n' + return False + +def train_new_model(model, model_name, qconfig): + """ + Train a new model. + + :param model: Model to train + :param model_name: Name of the model to save after training + :param qconfig: Quantization configuration + """ + # Initialize the image transforms and datasets + image_transform = ProcessImage(model.dims, model.patches) + train_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.training_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_training_images, + test=False) + # Initialize the data loader + train_loader = DataLoader(train_dataset, + batch_size=1, + shuffle=True, + num_workers=8, + persistent_workers=True) + # Set the model to training mode and move to device + model.train() + model.to('cpu') + model.qconfig = qconfig + + # Apply quantization configurations to the model + model = quantization.prepare_qat(model, inplace=False) + + # Keep track of trained layers to pass data through them + trained_layers = [] + + # Training each layer + for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): + print(f"Training layer: {layer_name}") + # Retrieve the layer object + layer = getattr(model, layer_name) + # Train the layer + model.train_model(train_loader, layer, prev_layers=trained_layers) + # After training the current layer, add it to the list of trained layers + trained_layers.append(layer_name) + # Convert the model to a quantized model + model = quantization.convert(model, inplace=False) + model.eval() + # Save the model + model.save_model(os.path.join('./models', model_name)) + +if __name__ == "__main__": + # Set the number of threads for PyTorch + #torch.set_num_threads(8) + # Initialize the model + model = VPRTempo() + # Initialize the logger + model.model_logger() + # Set the quantization configuration + if model.quantize: + qconfig = quantization.get_default_qat_qconfig('fbgemm') + else: + raise ValueError("Quantization must be enabled for training.") + # Generate the model name + model_name = generate_model_name(model) + # Check if a pre-trained model exists + use_pretrained = check_pretrained_model(model_name) + # Train or run inference based on the user's input + if not use_pretrained: + train_new_model(model, model_name, qconfig) # Training + model.logger.info('Training complete.') \ No newline at end of file diff --git a/VPRTempo_Train.py b/VPRTempo_Train.py new file mode 100644 index 0000000..4eebfb9 --- /dev/null +++ b/VPRTempo_Train.py @@ -0,0 +1,268 @@ +#MIT License + +#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer + +#Permission is hereby granted, free of charge, to any person obtaining a copy +#of this software and associated documentation files (the "Software"), to deal +#in the Software without restriction, including without limitation the rights +#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +#copies of the Software, and to permit persons to whom the Software is +#furnished to do so, subject to the following conditions: + +#The above copyright notice and this permission notice shall be included in all +#copies or substantial portions of the Software. + +#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +#SOFTWARE. + +''' +Imports +''' + +import os +import torch +import gc +import sys +sys.path.append('./src') +sys.path.append('./models') +sys.path.append('./output') +sys.path.append('./dataset') + +import blitnet as bn +import numpy as np +import torch.nn as nn + +from settings import configure, model_logger +from dataset import CustomImageDataset, ProcessImage +from torch.utils.data import DataLoader +from tqdm import tqdm + +class VPRTempo(nn.Module): + def __init__(self): + super(VPRTempo, self).__init__() + + # Configure the network + configure(self) + + # Layer dict to keep track of layer names and their order + self.layer_dict = {} + self.layer_counter = 0 + + """ + Define trainable layers here + """ + self.add_layer( + 'feature_layer', + dims=[self.input, self.feature], + thr_range=[0, 0.5], + fire_rate=[0.2, 0.9], + ip_rate=0.15, + stdp_rate=0.005, + p=[0.1, 0.5], + device=self.device + ) + self.add_layer( + 'output_layer', + dims=[self.feature, self.output], + ip_rate=0.15, + stdp_rate=0.005, + spk_force=True, + device=self.device + ) + + def add_layer(self, name, **kwargs): + """ + Dynamically add a layer with given name and keyword arguments. + + :param name: Name of the layer to be added + :type name: str + :param kwargs: Hyperparameters for the layer + """ + # Check for layer name duplicates + if name in self.layer_dict: + raise ValueError(f"Layer with name {name} already exists.") + + # Add a new SNNLayer with provided kwargs + setattr(self, name, bn.SNNLayer(**kwargs)) + + # Add layer name and index to the layer_dict + self.layer_dict[name] = self.layer_counter + self.layer_counter += 1 + + def model_logger(self): + """ + Log the model configuration to the console. + """ + model_logger(self) + + def _anneal_learning_rate(self, layer, mod, itp, stdp): + """ + Anneal the learning rate for the current layer. + """ + if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps + pt = pow(float(self.T - mod) / self.T, self.annl_pow) + layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate + layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate + + return layer + + def train_model(self, train_loader, layer, prev_layers=None): + """ + Train a layer of the network model. + + :param train_loader: Training data loader + :param layer: Layer to train + :param prev_layers: Previous layers to pass data through + """ + + # Initialize the tqdm progress bar + pbar = tqdm(total=int(self.T), + desc="Training ", + position=0) + + # Initialize the learning rates for each layer (used for annealment) + init_itp = layer.eta_ip.detach() + init_stdp = layer.eta_stdp.detach() + mod = 0 # Used to determine the learning rate annealment, resets at each epoch + # Run training for the specified number of epochs + for _ in range(self.epoch): + # Run training for the specified number of timesteps + for spikes, labels in train_loader: + spikes, labels = spikes.to(self.device), labels.to(self.device) + idx = labels / self.filter # Set output index for spike forcing + # Pass through previous layers if they exist + if prev_layers: + with torch.no_grad(): + for prev_layer_name in prev_layers: + prev_layer = getattr(self, prev_layer_name) # Get the previous layer object + spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer + spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9] + else: + prev_layer = None + # Get the output spikes from the current layer + pre_spike = spikes.detach() # Previous layer spikes for STDP + spikes = self.forward(spikes, layer) # Current layer spikes + spikes_noclp = spikes.detach() # Used for inhibitory homeostasis + spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9] + # Calculate STDP + layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer) + # Adjust learning rates + layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp) + # Update the annealing mod & progress bar + mod += 1 + pbar.update(1) + + # Close the tqdm progress bar + pbar.close() + + # Free up memory + if self.device.type == "cuda": + torch.cuda.empty_cache() + gc.collect() + + def forward(self, spikes, layer): + """ + Compute the forward pass of the model. + + Parameters: + - spikes (Tensor): Input spikes. + + Returns: + - Tensor: Output after processing. + """ + + spikes = layer.exc(spikes) + layer.inh(spikes) + + return spikes + + def save_model(self, model_out): + """ + Save the trained model to models output folder. + """ + torch.save(self.state_dict(), model_out) + +def generate_model_name(model): + """ + Generate the model name based on its parameters. + """ + return ("VPRTempo" + + str(model.input) + + str(model.feature) + + str(model.output) + + str(model.number_modules) + + '.pth') + +def check_pretrained_model(model_name): + """ + Check if a pre-trained model exists and prompt the user to retrain if desired. + """ + if os.path.exists(os.path.join('./models', model_name)): + prompt = "A network with these parameters exists, re-train network? (y/n):\n" + retrain = input(prompt).strip().lower() + return retrain == 'n' + return False + +def train_new_model(model, model_name): + """ + Train a new model. + + :param model: Model to train + :param model_name: Name of the model to save after training + :param qconfig: Quantization configuration + """ + # Initialize the image transforms and datasets + image_transform = ProcessImage(model.dims, model.patches) + train_dataset = CustomImageDataset(annotations_file=model.dataset_file, + img_dirs=model.training_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.number_training_images, + test=False) + # Initialize the data loader + train_loader = DataLoader(train_dataset, + batch_size=1, + shuffle=True, + num_workers=8, + persistent_workers=True) + # Set the model to training mode and move to device + model.train() + + # Keep track of trained layers to pass data through them + trained_layers = [] + + # Training each layer + for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): + print(f"Training layer: {layer_name}") + # Retrieve the layer object + layer = getattr(model, layer_name) + # Train the layer + model.train_model(train_loader, layer, prev_layers=trained_layers) + # After training the current layer, add it to the list of trained layers + trained_layers.append(layer_name) + # Convert the model to a quantized model + model.eval() + # Save the model + model.save_model(os.path.join('./models', model_name)) + +if __name__ == "__main__": + # Set the number of threads for PyTorch + #torch.set_num_threads(8) + # Initialize the model + model = VPRTempo() + if model.quantize: + raise ValueError("Quantization enabled, please disable.") + # Initialize the logger + model.model_logger() + # Generate the model name + model_name = generate_model_name(model) + # Check if a pre-trained model exists + use_pretrained = check_pretrained_model(model_name) + # Train or run inference based on the user's input + if not use_pretrained: + train_new_model(model, model_name) # Training + model.logger.info('Training complete.') \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index b6823e3..1b83e36 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -33,7 +33,7 @@ class SNNLayer(nn.Module): def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, - stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False): + stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None): super(SNNLayer, self).__init__() """ dims: [input, output] dimensions of the layer @@ -49,8 +49,7 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, # Configure the network configure(self) # Sets the testing configuration # Device - #self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.device = torch.device("cpu") + self.device = device # Check constraints etc if np.isscalar(thr_range): thr_range = [thr_range, thr_range] @@ -90,23 +89,24 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, self.exc = nn.Linear(dims[0], dims[1], bias=False) self.exc.weight = self.addWeights(dims=dims, W_range=[0,1], - p=p[0]) + p=p[0], + device=device) # Create the inhibitory weights self.inh = nn.Linear(dims[0], dims[1], bias=False) self.inh.weight = self.addWeights(dims=dims, W_range=[-1,0], - p=p[-1]) + p=p[-1], + device=device) # Output boolean reference of which neurons have connection weights self.havconnExc = self.exc.weight > 0 self.havconnInh = self.inh.weight < 0 - def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0]): + def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0],device=None): - # Get torch device - #device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - device = torch.device("cpu") + # Get torch device + device = device # Check constraints etc if np.isscalar(W_range): W_range = [W_range,W_range] diff --git a/src/metrics.py b/src/metrics.py index 11c4ef3..9a85b84 100644 --- a/src/metrics.py +++ b/src/metrics.py @@ -149,11 +149,9 @@ def recallAtK(S_in, GThard, GTsoft=None, K=1): # ensure logical datatype in GT and GTsoft GT = GThard.astype('bool') - GTsoft = GTsoft.astype('bool') # copy S and set elements that are only true in GTsoft to min(S) to ignore them during evaluation S = S_in.copy() - S[GTsoft & ~GT] = S.min() # discard all query images without an actually matching database image j = GT.sum(0) > 0 # columns with matches diff --git a/src/settings.py b/src/settings.py index 843061c..75f7771 100644 --- a/src/settings.py +++ b/src/settings.py @@ -1,7 +1,6 @@ import os import torch import logging -import csv from datetime import datetime @@ -10,17 +9,18 @@ def configure(model): Configure the model """ model.dataset = 'nordland' # Dataset name - model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset ) + model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset) model.trainingPath = './dataset/' # Path to training images model.testPath = './dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) model.number_training_images = 500 # Number of training images model.number_testing_images = 500 # Number of testing images model.locations = ["spring","fall"] # Locations to train on (location repeats for training datasets) - model.test_locations = ["summer"] # Location to query with + model.test_locations = ["winter"] # Location to query with model.filter = 8 # Filter for training images model.validation = True # Validation (maybe deprecated for now?) model.log = True # Log to console + model.quantize = False # Quantize the network # Set default paths if the provided paths are not valid directories if not os.path.isdir(getattr(model, 'trainingPath', '')): @@ -52,8 +52,8 @@ def configure(model): # Set the model parameters model.epoch = 4 # Number of epochs - model.patches = 7 # Number of patches - model.dims = [28,28] # Dimensions of the input image + model.patches = 15 # Number of patches + model.dims = [56,56] # Dimensions of the input image model.location_repeat = len(model.locations) # Number of times to repeat the locations model.annl_pow = 2 # Power of the annealmeant function @@ -65,8 +65,10 @@ def configure(model): model.output = int(model.number_training_images/model.number_modules) # Number of output neurons # Set the torch device - #model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - model.device = torch.device("cpu") + if not model.quantize: + model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + else: + model.device = torch.device("cpu") if model.device.type == "cuda": torch.cuda.init() torch.cuda.synchronize(device=model.device) @@ -74,43 +76,18 @@ def configure(model): # Determine the total number of timesteps across training images, modules, and location repeats model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) * model.epoch - -def image_csv(model): - """ - Load the image names from the CSV file and filter them - """ - - # Load the image names from the CSV file - with open(model.dataset_file, mode='r', newline='', encoding='utf-8') as file: - reader = csv.reader(file) - model.imageNames = [row[0] for row in reader] - # Remove the header - del model.imageNames[0] - # Filter the image names - model.filteredNames = [] - for n in range(0, len(model.imageNames), model.filter): - model.filteredNames.append(model.imageNames[n]) - # Remove the training images from the filtered names - del model.filteredNames[model.number_training_images:len(model.filteredNames)] - # Store the full training paths - model.fullTrainPaths = [] - for n in model.locations: - model.fullTrainPaths.append(model.trainingPath + n + '/') - def model_logger(model): """ Configure the model logger """ - try: - # Create the output folder + if os.path.isdir('../output'): now = datetime.now() model.output_folder = '../output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) - except: - # Create the output folder + else: now = datetime.now() model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) + + os.mkdir(model.output_folder) # Create the logger model.logger = logging.getLogger("VPRTempo") if (model.logger.hasHandlers()): @@ -139,10 +116,15 @@ def model_logger(model): model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') model.logger.info('') - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - if torch.cuda.is_available(): - current_device = torch.cuda.current_device() - model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) - else: + if model.quantize: + model.logger.info('Quantization enabled') model.logger.info('Current device is: CPU') + else: + if torch.cuda.is_available(): + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + current_device = torch.cuda.current_device() + model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) + else: + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + model.logger.info('Current device is: CPU') model.logger.info('') \ No newline at end of file diff --git a/tutorials/0_BasicDemo.ipynb b/tutorials/0_BasicDemo.ipynb index 0770532..6db59e2 100644 --- a/tutorials/0_BasicDemo.ipynb +++ b/tutorials/0_BasicDemo.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c879cd02-82db-441d-9476-fff1925bf494", "metadata": {}, "outputs": [], @@ -51,21 +51,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load the input image\n", "raw_img = cv2.imread('./mats/0_basicdemo/summer.png')\n", @@ -87,21 +76,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load the patch normalized image\n", "patch_img = np.load('./mats/0_basicdemo/summer_patchnorm.npy', allow_pickle=True)\n", @@ -336,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.10" } }, "nbformat": 4, From 7e52eee3fd985f3e2a0d4b6e0decbeb4fb377f15 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 6 Nov 2023 15:47:49 +1000 Subject: [PATCH 25/69] Combined the inh and exc weights for inferencing in non-quantized version --- VPRTempo.py | 20 +++++---- VPRTempo_Train.py | 23 +++++++++- src/blitnet.py | 111 ++++++++++++++++++++++++---------------------- src/settings.py | 10 ++--- 4 files changed, 98 insertions(+), 66 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index 97a80a1..ac82f33 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -63,12 +63,14 @@ def __init__(self): self.add_layer( 'feature_layer', dims=[self.input, self.feature], - device=self.device + device=self.device, + inference=True ) self.add_layer( 'output_layer', dims=[self.feature, self.output], - device=self.device + device=self.device, + inference=True ) def add_layer(self, name, **kwargs): @@ -123,13 +125,15 @@ def evaluate(self, model, test_loader, layers=None): # Rehsape output spikes into a similarity matrix out = np.reshape(np.array(out),(model.number_training_images,model.number_testing_images)) - # Calculate and print the Recall@N - N = [1,5,10,15,20,25] - R = [] + + # Recall@N + N = [1,5,10,15,20,25] # N values to calculate + R = [] # Recall@N values # Create GT matrix GT = np.zeros((model.number_testing_images,model.number_training_images), dtype=int) for n in range(len(GT)): GT[n,n] = 1 + # Calculate Recall@N for n in N: R.append(round(recallAtK(out,GThard=GT,K=n),2)) # Print the results @@ -149,7 +153,7 @@ def forward(self, spikes, layer): - Tensor: Output after processing. """ - spikes = layer.exc(spikes) + layer.inh(spikes) + spikes = layer.w(spikes) return spikes @@ -158,7 +162,7 @@ def load_model(self, model_path): Load pre-trained model and set the state dictionary keys. """ self.load_state_dict(torch.load(model_path, map_location=self.device), - strict=True) + strict=False) def generate_model_name(model): """ @@ -176,7 +180,7 @@ def check_pretrained_model(model_name): Check if a pre-trained model exists and tell user if it does not. """ if not os.path.exists(os.path.join('./models', model_name)): - model.logger.info("A pre-trained network does not exist: please train one using VPRTempoQuant_Trainer") + model.logger.info("A pre-trained network does not exist: please train one using VPRTempo_Trainer") pretrain = 'n' else: pretrain = 'y' diff --git a/VPRTempo_Train.py b/VPRTempo_Train.py index 4eebfb9..4bdb1fc 100644 --- a/VPRTempo_Train.py +++ b/VPRTempo_Train.py @@ -36,6 +36,7 @@ import blitnet as bn import numpy as np import torch.nn as nn +import torchvision.transforms as transforms from settings import configure, model_logger from dataset import CustomImageDataset, ProcessImage @@ -179,6 +180,22 @@ def forward(self, spikes, layer): spikes = layer.exc(spikes) + layer.inh(spikes) return spikes + + def combine_weights(self, model): + for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): + # Retrieve the layer object + layer = getattr(model, layer_name) + dims = layer.dims + # Define weight variable in layer + layer.w = nn.Linear(dims[0],dims[1],bias=False) + # Send to model device + layer.x.to(model.device) + # Replace weights with combined weights + layer.w.weight = nn.Parameter(layer.exc.weight + layer.inh.weight) + # Delete original weights + del layer.exc, layer.inh + + return model def save_model(self, model_out): """ @@ -216,7 +233,9 @@ def train_new_model(model, model_name): :param qconfig: Quantization configuration """ # Initialize the image transforms and datasets - image_transform = ProcessImage(model.dims, model.patches) + image_transform = transforms.Compose([ + ProcessImage(model.dims, model.patches) + ]) train_dataset = CustomImageDataset(annotations_file=model.dataset_file, img_dirs=model.training_dirs, transform=image_transform, @@ -244,6 +263,8 @@ def train_new_model(model, model_name): model.train_model(train_loader, layer, prev_layers=trained_layers) # After training the current layer, add it to the list of trained layers trained_layers.append(layer_name) + # Combine excitatory and inhibitory weights + model = model.combine_weights(model) # Convert the model to a quantized model model.eval() # Save the model diff --git a/src/blitnet.py b/src/blitnet.py index 1b83e36..19a7531 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -33,7 +33,7 @@ class SNNLayer(nn.Module): def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, - stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None): + stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None,inference=False): super(SNNLayer, self).__init__() """ dims: [input, output] dimensions of the layer @@ -50,58 +50,65 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, configure(self) # Sets the testing configuration # Device self.device = device - - # Check constraints etc - if np.isscalar(thr_range): thr_range = [thr_range, thr_range] - if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate] - if np.isscalar(const_inp): const_inp = [const_inp, const_inp] - - # Initialize Tensors - self.x = torch.zeros([1, dims[-1]], device=self.device) - self.eta_ip = torch.tensor(ip_rate, device=self.device) - self.eta_stdp = torch.tensor(stdp_rate, device=self.device) - - # Initialize Parameters - self.thr = nn.Parameter(torch.zeros([1, dims[-1]], - device=self.device).uniform_(thr_range[0], - thr_range[1])) - self.fire_rate = torch.zeros([1,dims[-1]], device=self.device).uniform_(fire_rate[0], fire_rate[1]) - - # Sequentially set the feature firing rates (if any) - if not torch.all(self.fire_rate==0).item(): - fstep = (fire_rate[1]-fire_rate[0])/dims[-1] + # Add different parameters depending if trainnig or running inference model + if inference: # If running inference model + self.w = nn.Linear(dims[0], dims[1], bias=False) # Combined weight tensors + self.w.to(device) + self.thr = nn.Parameter(torch.zeros([1, dims[-1]], + device=self.device).uniform_(thr_range[0], + thr_range[1])) + else: # If training new model + # Check constraints etc + if np.isscalar(thr_range): thr_range = [thr_range, thr_range] + if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate] + if np.isscalar(const_inp): const_inp = [const_inp, const_inp] - for i in range(dims[-1]): - self.fire_rate[:,i] = fire_rate[0]+fstep*(i+1) - - self.have_rate = torch.any(self.fire_rate[:,0] > 0.0).to(self.device) - self.const_inp = torch.zeros([1, dims[-1]], device=self.device).uniform_(const_inp[0], const_inp[1]) - self.p = p - self.dims = dims - - # Additional State Variables - self.set_spks = [] - self.sspk_idx = 0 - self.spikes = torch.empty([], dtype=torch.float64) - self.spk_force = spk_force - - # Create the excitatory weights - self.exc = nn.Linear(dims[0], dims[1], bias=False) - self.exc.weight = self.addWeights(dims=dims, - W_range=[0,1], - p=p[0], - device=device) - - # Create the inhibitory weights - self.inh = nn.Linear(dims[0], dims[1], bias=False) - self.inh.weight = self.addWeights(dims=dims, - W_range=[-1,0], - p=p[-1], - device=device) - - # Output boolean reference of which neurons have connection weights - self.havconnExc = self.exc.weight > 0 - self.havconnInh = self.inh.weight < 0 + # Initialize Tensors + self.x = torch.zeros([1, dims[-1]], device=self.device) + self.eta_ip = torch.tensor(ip_rate, device=self.device) + self.eta_stdp = torch.tensor(stdp_rate, device=self.device) + + # Initialize Parameters + self.thr = nn.Parameter(torch.zeros([1, dims[-1]], + device=self.device).uniform_(thr_range[0], + thr_range[1])) + self.fire_rate = torch.zeros([1,dims[-1]], device=self.device).uniform_(fire_rate[0], fire_rate[1]) + + # Sequentially set the feature firing rates (if any) + if not torch.all(self.fire_rate==0).item(): + fstep = (fire_rate[1]-fire_rate[0])/dims[-1] + + for i in range(dims[-1]): + self.fire_rate[:,i] = fire_rate[0]+fstep*(i+1) + + self.have_rate = torch.any(self.fire_rate[:,0] > 0.0).to(self.device) + self.const_inp = torch.zeros([1, dims[-1]], device=self.device).uniform_(const_inp[0], const_inp[1]) + self.p = p + self.dims = dims + + # Additional State Variables + self.set_spks = [] + self.sspk_idx = 0 + self.spikes = torch.empty([], dtype=torch.float64) + self.spk_force = spk_force + + # Create the excitatory weights + self.exc = nn.Linear(dims[0], dims[1], bias=False) + self.exc.weight = self.addWeights(dims=dims, + W_range=[0,1], + p=p[0], + device=device) + + # Create the inhibitory weights + self.inh = nn.Linear(dims[0], dims[1], bias=False) + self.inh.weight = self.addWeights(dims=dims, + W_range=[-1,0], + p=p[-1], + device=device) + + # Output boolean reference of which neurons have connection weights + self.havconnExc = self.exc.weight > 0 + self.havconnInh = self.inh.weight < 0 def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0],device=None): diff --git a/src/settings.py b/src/settings.py index 75f7771..d1b648b 100644 --- a/src/settings.py +++ b/src/settings.py @@ -9,13 +9,13 @@ def configure(model): Configure the model """ model.dataset = 'nordland' # Dataset name - model.dataset_file = './dataset/'+model.dataset+'.csv' # Dataset file (must be PyTorch Dataset) + model.dataset_file = os.path.join('./dataset',model.dataset+'.csv') # Dataset file (must be PyTorch Dataset) model.trainingPath = './dataset/' # Path to training images model.testPath = './dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) - model.number_training_images = 500 # Number of training images - model.number_testing_images = 500 # Number of testing images - model.locations = ["spring","fall"] # Locations to train on (location repeats for training datasets) + model.number_training_images = 250 # Number of training images + model.number_testing_images = model.number_training_images # Number of testing images + model.locations = ["spring"] # Locations to train on (location repeats for training datasets) model.test_locations = ["winter"] # Location to query with model.filter = 8 # Filter for training images model.validation = True # Validation (maybe deprecated for now?) @@ -51,7 +51,7 @@ def configure(model): model.testing_dirs.append(os.path.join(model.testPath,n)) # Set the model parameters - model.epoch = 4 # Number of epochs + model.epoch = 8 # Number of epochs model.patches = 15 # Number of patches model.dims = [56,56] # Dimensions of the input image model.location_repeat = len(model.locations) # Number of times to repeat the locations From 3e8990315cd0433bb6a7c3f369982af66e621e3e Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 9 Nov 2023 12:26:00 +1000 Subject: [PATCH 26/69] Fixed and finalized basic quantized tutorial --- VPRTempo.py | 2 +- notebook_testing.py | 58 +++ src/settings.py | 12 +- tutorials/0_BasicDemo.ipynb | 2 +- tutorials/1_BasicDemo-Quantized.ipynb | 490 ++++++++++++++++++ ...ntroduction.ipynb => 2_Introduction.ipynb} | 0 ...uantization.ipynb => 3_Quantization.ipynb} | 0 tutorials/mats/0_basicdemo/summer.png | Bin 295003 -> 309004 bytes tutorials/mats/1_basicdemoquant/summer.png | Bin 0 -> 309004 bytes 9 files changed, 556 insertions(+), 8 deletions(-) create mode 100644 notebook_testing.py create mode 100644 tutorials/1_BasicDemo-Quantized.ipynb rename tutorials/{1_Introduction.ipynb => 2_Introduction.ipynb} (100%) rename tutorials/{2_Quantization.ipynb => 3_Quantization.ipynb} (100%) mode change 100755 => 100644 tutorials/mats/0_basicdemo/summer.png create mode 100644 tutorials/mats/1_basicdemoquant/summer.png diff --git a/VPRTempo.py b/VPRTempo.py index ac82f33..3ea1409 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -204,7 +204,7 @@ def run_inference(model, model_name): # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, - shuffle=False, + shuffle=True, num_workers=8, persistent_workers=True) # Set the model to evaluation mode and set configuration diff --git a/notebook_testing.py b/notebook_testing.py new file mode 100644 index 0000000..cf6e162 --- /dev/null +++ b/notebook_testing.py @@ -0,0 +1,58 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +# Load network scale factor +scale_factors = np.load('./tutorials/mats/1_basicdemoquant/if_scales.npy',allow_pickle=True) +# Load the patch normalized image +patch_img = np.load('./tutorials/mats/1_basicdemoquant/summer_patchnorm.npy', allow_pickle=True) +# Divide the patch image by the QAT scale factor for input spikes +spike_scale = scale_factors[4] +patch_img_int = np.round(patch_img*spike_scale).astype(np.int32) + +# Find the maximum quantized pixel intensity and print it +max_int = np.max(patch_img_int) + + +# Convert 2D image to a 1D-array +patch_1d = np.reshape(patch_img_int, (784,)) + +# Load network scale factor +scale_factors = np.load('./tutorials/mats/1_basicdemoquant/if_scales.npy',allow_pickle=True) + +# Divide the patch image by the QAT scale factor for input spikes +spike_scale = scale_factors[4] +patch_img_int = np.round(patch_img*spike_scale).astype(np.int32) + +# Find the maximum quantized pixel intensity and print it +max_int = np.max(patch_img_int) + +# Load the input to feature excitatory and inhibitory network weights +if_exc = np.load('./tutorials/mats/1_basicdemoquant/if_exc.npy') +if_inh = np.load('./tutorials/mats/1_basicdemoquant/if_inh.npy') + +if_exc = if_exc.astype(np.int32) + +zeropoint_inh = 127 + +# Calculate feature spikes for the positive weight calculation +exc_feature_spikes = (np.matmul(if_exc,patch_1d)) + +# Get the required scale factors to transform the feature spikes +perslice_scale_exc = scale_factors[0] +perchannel_scale_exc = scale_factors[2] + +# Transform the feature layer spikes based on the scale factors +scaled_exc_feature_spikes = np.round(exc_feature_spikes/(perslice_scale_exc*spike_scale))*perchannel_scale_exc +scaled_exc_feature_spikes = scaled_exc_feature_spikes.astype(np.int32) + +# Calculate feature spikes for the negative weight calculation +inh_feature_spikes = (np.matmul(if_inh,patch_1d)) + +# Get the required scale factors to transform the feature spikes +perslice_scale_inh = scale_factors[1] +perchannel_scale_inh = scale_factors[3] + +# Transform the feature layer spikes based on the scale factors +scaled_inh_feature_spikes = (np.round(inh_feature_spikes/(perslice_scale_inh*spike_scale))*perchannel_scale_inh) + zeropoint_inh +scaled_inh_feature_spikes = scaled_inh_feature_spikes.astype(np.int32) \ No newline at end of file diff --git a/src/settings.py b/src/settings.py index d1b648b..07dd23f 100644 --- a/src/settings.py +++ b/src/settings.py @@ -13,14 +13,14 @@ def configure(model): model.trainingPath = './dataset/' # Path to training images model.testPath = './dataset/' # Path to testing images model.number_modules = 1 # Number of expert modules (currently not implemented) - model.number_training_images = 250 # Number of training images + model.number_training_images = 500 # Number of training images model.number_testing_images = model.number_training_images # Number of testing images - model.locations = ["spring"] # Locations to train on (location repeats for training datasets) - model.test_locations = ["winter"] # Location to query with + model.locations = ["winter"] # Locations to train on (location repeats for training datasets) + model.test_locations = ["summer"] # Location to query with model.filter = 8 # Filter for training images model.validation = True # Validation (maybe deprecated for now?) model.log = True # Log to console - model.quantize = False # Quantize the network + model.quantize = True # Quantize the network # Set default paths if the provided paths are not valid directories if not os.path.isdir(getattr(model, 'trainingPath', '')): @@ -52,8 +52,8 @@ def configure(model): # Set the model parameters model.epoch = 8 # Number of epochs - model.patches = 15 # Number of patches - model.dims = [56,56] # Dimensions of the input image + model.patches = 7 # Number of patches + model.dims = [28,28] # Dimensions of the input image model.location_repeat = len(model.locations) # Number of times to repeat the locations model.annl_pow = 2 # Power of the annealmeant function diff --git a/tutorials/0_BasicDemo.ipynb b/tutorials/0_BasicDemo.ipynb index 6db59e2..97d7cc9 100644 --- a/tutorials/0_BasicDemo.ipynb +++ b/tutorials/0_BasicDemo.ipynb @@ -314,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/tutorials/1_BasicDemo-Quantized.ipynb b/tutorials/1_BasicDemo-Quantized.ipynb new file mode 100644 index 0000000..2d87f15 --- /dev/null +++ b/tutorials/1_BasicDemo-Quantized.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37", + "metadata": {}, + "source": [ + "## VPRTempoQuant - Basic Demo for a quantized version of VPRTempo\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using Quantized Aware Training (QAT). We will view the system through the lens of integer based weights and spikes to see how a quantized version of VPRTempo operates under the hood.\n", + "\n", + "*Note: In this example, we will lose some amount of precision because we are only using integers for all calculations. In the deployed version, PyTorch quantizes and dequantizes spikes and weights so that some calculations are performed in the floating point domain. As such, this tutorial should be taken purely for conceptual understanding of a quantized version of VPRTempo and not for implementation purposes.*\n", + "\n", + "Before starting, make sure the following packages are installed and imported:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c879cd02-82db-441d-9476-fff1925bf494", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n", + "try:\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + "except:\n", + " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "bb45df38-e333-46b2-9161-80e6ac367532", + "metadata": {}, + "source": [ + "### Image processing\n", + "\n", + "As in the previous tutorial, we will load in a 360x640 image and show the patch-normalized version. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input image\n", + "raw_img = cv2.imread('./mats/1_basicdemoquant/summer.png')\n", + "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n", + "\n", + "# Load the patch normalized image\n", + "patch_img = np.load('./mats/1_basicdemoquant/summer_patchnorm.npy', allow_pickle=True)\n", + "patch_img = patch_img.astype(np.int32)\n", + "# Create a figure to hold the subplots\n", + "plt.figure(figsize=(10, 4))\n", + "\n", + "# Plot the first image\n", + "plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", + "plt.imshow(rgb_img)\n", + "plt.title('Nordland Summer')\n", + "\n", + "# Plot the second image\n", + "plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", + "plt.matshow(patch_img, fignum=False)\n", + "plt.title('Nordland Summer Patch Normalized')\n", + "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "max_int = np.max(patch_img)\n", + "print(f\"The maximum integer pixel value is {max_int}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42", + "metadata": {}, + "source": [ + "The patch normalized image here are floating point values in the range [0, 1]. For the base VPRTempo system, this is fine because the entire system works using floating points. However, in our quantized model we will be using integers. To demonstrate the conversion from floating point to integer, we'll manually quantize our input spikes by dividing using the `scale_factor` determined from the QAT.\n", + "\n", + "Let's load in some model scale factors, we'll use some of these later for the weight calculations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Load network scale factor\n", + "scale_factors = np.load('./mats/1_basicdemoquant/if_scales.npy',allow_pickle=True)" + ] + }, + { + "cell_type": "markdown", + "id": "82691809-7b06-4f0b-aca0-e19d293355b3", + "metadata": {}, + "source": [ + "Like in the previous tutorial, we will convert this to a 1D-array to pass through the layers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bd6ae95-2a79-4b45-8a60-503079339739", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 2D image to a 1D-array\n", + "patch_1d = np.reshape(patch_img, (784,))" + ] + }, + { + "cell_type": "markdown", + "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae", + "metadata": {}, + "source": [ + "### Load the pre-trained network weights\n", + "\n", + "Our network consists of the same architecture as in the previous tutorial. The excitatory and inhibitory weights have been converted to the integer representations from the QAT and will be applied directly to the quantized input spikes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d98749a-8f28-477b-871c-93626e96786c", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "if_exc = np.load('./mats/1_basicdemoquant/if_exc.npy')\n", + "if_inh = np.load('./mats/1_basicdemoquant/if_inh.npy')\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", + "\n", + "# Plot the excitatory weights\n", + "exc_plot = axes[0].matshow(if_exc.T)\n", + "axes[0].set_title('Input > Feature Excitatory Weights')\n", + "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Plot the inhibitory weights\n", + "inh_plot = axes[1].matshow(if_inh.T, cmap='viridis_r')\n", + "axes[1].set_title('Input > Feature Inhibitory Weights')\n", + "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()\n", + "\n", + "# Print dtype\n", + "print(f\"Excitatory weights integer type is {if_exc.dtype}\")\n", + "print(f\"Inhibitory weights integer type is {if_inh.dtype}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6de0fada-374c-4289-8042-5347b6b2ba21", + "metadata": {}, + "source": [ + "In addition to this, we will set the zero points for these weights. From the QAT, the zero point for the excitatory weights was 0 and 127 for the inhibitory weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0405142-f8bd-4d83-a78e-d078f911d649", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the zero point for the inhibitory weights\n", + "zeropoint_inh = 127" + ] + }, + { + "cell_type": "markdown", + "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6", + "metadata": {}, + "source": [ + "### Propagate network spikes\n", + "\n", + "Now we'll propagate the input spikes across the feature to get the output, like in the previous tutorial. Let's start with the excitatory weights though first, since we will need to use different scaling of the output based on the zero point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6c84239-c176-48c3-8954-25da5f989d61", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate feature spikes for the positive weight calculation\n", + "exc_feature_spikes = (np.matmul(if_exc,patch_1d))\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(exc_feature_spikes)), exc_feature_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Excitatory Feature Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", + "metadata": {}, + "source": [ + "We can see here that the spike values calculated for the feature layer are huge. That is because they need to be properly re-scaled after calculation. To do this, we need to take a couple of the scaling factors we imported earlier to transform the output to a reasonable range." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bd2b7f6-c698-4a7d-9099-da9cc59ed007", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the required scale factors to transform the feature spikes\n", + "perslice_scale_exc = scale_factors[0]\n", + "perchannel_scale_exc = scale_factors[2]\n", + "\n", + "# Transform the feature layer spikes based on the scale factors\n", + "scaled_exc_feature_spikes = (exc_feature_spikes//(perslice_scale_exc*perchannel_scale_exc))//perchannel_scale_exc\n", + "scaled_exc_feature_spikes = scaled_exc_feature_spikes.astype(np.int32)\n", + "# Plot out the scaled feature layer spikes\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(scaled_exc_feature_spikes)), scaled_exc_feature_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Excitatory Feature Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4d9d5a9e-05d3-475f-beec-1c6d29923cd5", + "metadata": {}, + "source": [ + "Now let's do the same thing for our inhibitory weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1059733-2b2b-4c49-8ba2-2a52531f483c", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate feature spikes for the negative weight calculation\n", + "inh_feature_spikes = (np.matmul(if_inh, patch_1d))\n", + "\n", + "# Get the required scale factors to transform the feature spikes\n", + "perslice_scale_inh = scale_factors[1]\n", + "perchannel_scale_inh = scale_factors[3]\n", + "\n", + "# Transform the feature layer spikes based on the scale factors\n", + "scaled_inh_feature_spikes = ((inh_feature_spikes - zeropoint_inh) // (perslice_scale_inh * perchannel_scale_inh)) // perchannel_scale_inh + zeropoint_inh\n", + "scaled_inh_feature_spikes = scaled_inh_feature_spikes.astype(np.int32)\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 5)) # 'figsize' can be adjusted as needed\n", + "\n", + "# First subplot\n", + "axs[0].plot(np.arange(len(inh_feature_spikes)), inh_feature_spikes)\n", + "axs[0].set_title('Inhibitory Feature Layer Spikes')\n", + "axs[0].set_xlabel('Neuron ID')\n", + "axs[0].set_ylabel('Spike Amplitude')\n", + "\n", + "# Second subplot\n", + "axs[1].plot(np.arange(len(scaled_inh_feature_spikes)), scaled_inh_feature_spikes)\n", + "axs[1].set_title('Scaled Inhibitory Feature Layer Spikes')\n", + "axs[1].set_xlabel('Neuron ID')\n", + "axs[1].set_ylabel('Spike Amplitude')\n", + "\n", + "# Adjust the layout\n", + "plt.tight_layout()\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8e8e56a1-591c-4617-b3f0-2b41082b9a31", + "metadata": {}, + "source": [ + "One thing you may notice is that although we used negative weights, we output positive spikes from in this operation. That is because of the `zeropoint_inh` of 127, which we add to the final spike calculation.\n", + "\n", + "Now that we separately calculated our positive and negative feature layer spikes, we need to add them together to get the final feature spikes. However, we'll note that because the scales and zero points for the two operations are different they will require to undergo additional transformation to match the scales. In the VPRTempoQuant model, we derive this addition scale and zero point from the `nn.quantized.FloatFunctional.add` function which learns these values during QAT. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "274df441-3015-4bab-a3bb-1498a2a7837a", + "metadata": {}, + "outputs": [], + "source": [ + "# Combined scale factors\n", + "combined_scale = 58\n", + "combined_zeropoint = 61\n", + "\n", + "# Remove zeropoint from inhibitory spikes\n", + "scaled_inh_feature_spikes_zero = scaled_inh_feature_spikes - zeropoint_inh\n", + "\n", + "# Combine the excitiatory and inhibitory feature spikes\n", + "exc_rescaled = (scaled_exc_feature_spikes/perchannel_scale_exc) * combined_scale\n", + "inh_rescaled = (scaled_inh_feature_spikes_zero/perchannel_scale_exc) * combined_scale\n", + "print(perchannel_scale_inh.dtype)\n", + "combined = (exc_rescaled.astype(np.int32) + inh_rescaled.astype(np.int32)) + combined_zeropoint\n", + "combined = np.clip(combined,0,max_int)\n", + "\n", + "# Plot the combined spikes\n", + "plt.plot(np.arange(len(combined)), combined)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Combined Feature Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e557a0ee-5202-45ac-af53-c49ccc65b4aa", + "metadata": {}, + "source": [ + "Now we will apply the same process for the output layer to get the output spikes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "fo_exc = np.load('./mats/1_basicdemoquant/fo_exc.npy')\n", + "fo_inh = np.load('./mats/1_basicdemoquant/fo_inh.npy')\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", + "\n", + "# Plot the excitatory weights\n", + "exc_plot = axes[0].matshow(fo_exc)\n", + "axes[0].set_title('Feature > Output Excitatory Weights')\n", + "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Plot the inhibitory weights\n", + "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", + "axes[1].set_title('Feature > Output Inhibitory Weights')\n", + "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()\n", + "\n", + "# Print dtype\n", + "print(f\"Excitatory weights integer type is {if_exc.dtype}\")\n", + "print(f\"Inhibitory weights integer type is {if_inh.dtype}\")" + ] + }, + { + "cell_type": "markdown", + "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f", + "metadata": {}, + "source": [ + "We'll get our excitatory and inhibitory spikes for the output and scale them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the output layer scales\n", + "fo_scales = np.load('./mats/1_basicdemoquant/fo_scales.npy',allow_pickle=True)\n", + "\n", + "# Calculate the excitatory and inhibitory spikes and scale them\n", + "exc_output_spikes = np.round(np.matmul(fo_exc,combined))\n", + "scaled_exc_output_spikes = exc_output_spikes // (fo_scales[0]) \n", + "\n", + "inh_output_spikes = (np.matmul(fo_inh,combined.astype(np.int32)))\n", + "scaled_inh_output_spikes = (inh_output_spikes - zeropoint_inh) // fo_scales[1]\n", + "\n", + "# Combine the excitiatory and inhibitory feature spikes\n", + "exc_rescaled = (scaled_exc_output_spikes/fo_scales[2]) * combined_scale\n", + "inh_rescaled = ((scaled_inh_output_spikes - zeropoint_inh)/fo_scales[3]) * combined_scale\n", + "\n", + "output_spikes = (exc_rescaled.astype(np.int32) + inh_rescaled.astype(np.int32)) + combined_zeropoint\n", + "output_spikes = np.clip(output_spikes,0,max_int)\n", + "\n", + "# Plot the combined spikes\n", + "plt.plot(np.arange(len(output_spikes)), output_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Combined Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fcd7e8f1-d9fc-48cc-82f6-6ca17c478c37", + "metadata": {}, + "source": [ + "And now, as in the previous tutorial, we can clearly see that Neuron ID has the highest output spike amplitude corresponding to our first learned location.\n", + "\n", + "Let's quickly prove it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2cf56e4-1bb8-47fd-869f-91e96c35f480", + "metadata": {}, + "outputs": [], + "source": [ + "# Output the argmax from the output spikes\n", + "prediction = np.argmax(output_spikes)\n", + "print(f\"Neuron ID with the highest output is {prediction}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", + "metadata": {}, + "source": [ + "### Conclusions\n", + "\n", + "We have gone through a very basic demo of how VPRTempoQuant works and the operations involved for quantizing floating points spikes and weights into the integer domain. Although this is isn't exactly how PyTorch performs these tasks (a lot of them are done in the FP space, especially with regards to rescaling for addition) - it should give you a good idea as to how we can perform these kinds of operations in whole integers. This is particularly useful for implementation on hardware such as neuromorphic processors.\n", + "\n", + "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b780c62e-53da-46c4-b882-f2dac2ca75ea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff 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z4a^ftxkB8@tc%D7Q{*~&Bp3;c(c*3d`s!T4Iiwl&EB))6yY=PGLPzZbbh16^MBbb9 zGH|OS7D;qDnV0zO>gIaERvG}Ni!~F2pbT3a*0kBsVo0~lM?v;4U~*2>;4u8Gc9rCd-T`<0!PwYu%|erA2+;>S4v7YnSv>v8iGY1h*QzN!KPR#dJ|6X|I2;DEt8(gGbCr%}`*!w)$Q@JzLs1dqPqy5frazZCX< zY19hTK>`c|zyX6P9#cBWl19B67@U}ElU0bZm)IN+&M(F{w+m6DpN8WS${0GqCV^RA zPNwtf+{b~0#6sVGe^<~eW)=?$6g3Tl2Zw`U$uu`cHqoTVPUrPBSHc2}0bP84NIQeU z05B{W`$7dyg#mh*2?l@(q3?R#h5;TUfsOzG15ZgrK~&R_g_H+=O2PLR8-}1pDYzB) z4V7R3*l#R`if;hm5V~Ce+^#*dwk!u=qsUhJJ;3f8zY)_1_LW*oTf4O<+^yB0R%=fx z)n6O+r_IK*PWMT#|6BkZ4Sww;{{Voa$9@;{Nieci|_7UrDagji08GFgWVfB7gH6xr=Q8V0}3Sw1%~#_L0_&xROMN zX|HA`QE5}aY}oAbZQ3v5^~6eHwy|z7ng$JGypK!1()45lefjZ?c^bK zGCg+9tRV=!MTLjkQ_CcxGk+8}4~F=L?DY1S5bzwZ*+fy8f%adC8SC4ASbVy_`?Ib- zOuDEadpa5wtpcejVX!V_eQz;x=r{%=)*%443UOK=>+{tYPg@5u`}5KN0u$v-Rr^f4 QKL7v#07*qoM6N<$f{mKoHUIzs literal 0 HcmV?d00001 From f75d391e9c30fec314af38dc0d173ad053b09fe4 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 9 Nov 2023 12:27:03 +1000 Subject: [PATCH 27/69] Remove testing.py --- notebook_testing.py | 58 --------------------------------------------- 1 file changed, 58 deletions(-) delete mode 100644 notebook_testing.py diff --git a/notebook_testing.py b/notebook_testing.py deleted file mode 100644 index cf6e162..0000000 --- a/notebook_testing.py +++ /dev/null @@ -1,58 +0,0 @@ -import cv2 -import numpy as np -import matplotlib.pyplot as plt - -# Load network scale factor -scale_factors = np.load('./tutorials/mats/1_basicdemoquant/if_scales.npy',allow_pickle=True) -# Load the patch normalized image -patch_img = np.load('./tutorials/mats/1_basicdemoquant/summer_patchnorm.npy', allow_pickle=True) -# Divide the patch image by the QAT scale factor for input spikes -spike_scale = scale_factors[4] -patch_img_int = np.round(patch_img*spike_scale).astype(np.int32) - -# Find the maximum quantized pixel intensity and print it -max_int = np.max(patch_img_int) - - -# Convert 2D image to a 1D-array -patch_1d = np.reshape(patch_img_int, (784,)) - -# Load network scale factor -scale_factors = np.load('./tutorials/mats/1_basicdemoquant/if_scales.npy',allow_pickle=True) - -# Divide the patch image by the QAT scale factor for input spikes -spike_scale = scale_factors[4] -patch_img_int = np.round(patch_img*spike_scale).astype(np.int32) - -# Find the maximum quantized pixel intensity and print it -max_int = np.max(patch_img_int) - -# Load the input to feature excitatory and inhibitory network weights -if_exc = np.load('./tutorials/mats/1_basicdemoquant/if_exc.npy') -if_inh = np.load('./tutorials/mats/1_basicdemoquant/if_inh.npy') - -if_exc = if_exc.astype(np.int32) - -zeropoint_inh = 127 - -# Calculate feature spikes for the positive weight calculation -exc_feature_spikes = (np.matmul(if_exc,patch_1d)) - -# Get the required scale factors to transform the feature spikes -perslice_scale_exc = scale_factors[0] -perchannel_scale_exc = scale_factors[2] - -# Transform the feature layer spikes based on the scale factors -scaled_exc_feature_spikes = np.round(exc_feature_spikes/(perslice_scale_exc*spike_scale))*perchannel_scale_exc -scaled_exc_feature_spikes = scaled_exc_feature_spikes.astype(np.int32) - -# Calculate feature spikes for the negative weight calculation -inh_feature_spikes = (np.matmul(if_inh,patch_1d)) - -# Get the required scale factors to transform the feature spikes -perslice_scale_inh = scale_factors[1] -perchannel_scale_inh = scale_factors[3] - -# Transform the feature layer spikes based on the scale factors -scaled_inh_feature_spikes = (np.round(inh_feature_spikes/(perslice_scale_inh*spike_scale))*perchannel_scale_inh) + zeropoint_inh -scaled_inh_feature_spikes = scaled_inh_feature_spikes.astype(np.int32) \ No newline at end of file From f37622e99196abd977caf4dc456dd5db0d3a04ff Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 23 Nov 2023 13:57:05 +1000 Subject: [PATCH 28/69] Combined positive and negative weights in BlitNet, now only a single matrix --- .gitignore | 3 ++- VPRTempo.py | 2 +- VPRTempoQuant.py | 46 ++++++++++++++++++++++-------------------- VPRTempoQuant_Train.py | 8 +++----- VPRTempo_Train.py | 25 +++-------------------- models/.gitkeep | 0 src/blitnet.py | 40 +++++++++++++++++++----------------- src/settings.py | 8 ++++---- 8 files changed, 59 insertions(+), 73 deletions(-) create mode 100644 models/.gitkeep diff --git a/.gitignore b/.gitignore index 46fe2eb..ee89f42 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,5 @@ __pycache__/ .ipynb_checkpoints/ src/__pycache__/ dataset/ -models/ +models/VPRTempo313662725001.pth +models/VPRTempoQuant313662725001.pth diff --git a/VPRTempo.py b/VPRTempo.py index 3ea1409..ac82f33 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -204,7 +204,7 @@ def run_inference(model, model_name): # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, - shuffle=True, + shuffle=False, num_workers=8, persistent_workers=True) # Set the model to evaluation mode and set configuration diff --git a/VPRTempoQuant.py b/VPRTempoQuant.py index e2ef724..1fa4413 100644 --- a/VPRTempoQuant.py +++ b/VPRTempoQuant.py @@ -57,10 +57,7 @@ def __init__(self): # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() - self.dequant = DeQuantStub() - - # Define the add function for quantized addition - self.add = nn.quantized.FloatFunctional() + self.dequant = DeQuantStub() # Layer dict to keep track of layer names and their order self.layer_dict = {} @@ -72,12 +69,14 @@ def __init__(self): self.add_layer( 'feature_layer', dims=[self.input, self.feature], - device=self.device + device=self.device, + inference=True ) self.add_layer( 'output_layer', dims=[self.feature, self.output], - device=self.device + device=self.device, + inference=True ) def add_layer(self, name, **kwargs): @@ -99,13 +98,24 @@ def add_layer(self, name, **kwargs): self.layer_dict[name] = self.layer_counter self.layer_counter += 1 - def evaluate(self, model, test_loader, layers=None): + def evaluate(self, model, test_loader): """ Run the inferencing model and calculate the accuracy. :param test_loader: Testing data loader :param layers: Layers to pass data through """ + # Determine the Hardtahn max value + maxSpike = 1//model.quant.scale + # Define the sequential inference model + self.inference = nn.Sequential( + self.feature_layer.w, + nn.Hardtanh(0, maxSpike), + nn.ReLU(), + self.output_layer.w, + nn.Hardtanh(0, maxSpike), + nn.ReLU() + ) # Initialize the tqdm progress bar pbar = tqdm(total=self.number_testing_images, desc="Running the test network", @@ -117,12 +127,7 @@ def evaluate(self, model, test_loader, layers=None): # Set device spikes, labels = spikes.to(self.device), labels.to(self.device) # Pass through previous layers if they exist - if layers: - for layer_name in layers: - layer = getattr(self, layer_name) - spikes = self.forward(spikes, layer) - spikes = bn.clamp_spikes(spikes, layer) - + spikes = self.forward(spikes) # Add output spikes to list out.append(spikes.detach().cpu().tolist()) pbar.update(1) @@ -131,12 +136,12 @@ def evaluate(self, model, test_loader, layers=None): pbar.close() # Rehsape output spikes into a similarity matrix - out = np.reshape(np.array(out),(model.number_training_images,model.number_testing_images)) + out = np.reshape(np.array(out),(self.number_training_images,self.number_testing_images)) # Calculate and print the Recall@N N = [1,5,10,15,20,25] R = [] # Create GT matrix - GT = np.zeros((model.number_testing_images,model.number_training_images), dtype=int) + GT = np.zeros((self.number_testing_images,self.number_training_images), dtype=int) for n in range(len(GT)): GT[n,n] = 1 for n in N: @@ -147,7 +152,7 @@ def evaluate(self, model, test_loader, layers=None): table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]]) model.logger.info(table) - def forward(self, spikes, layer): + def forward(self, spikes): """ Compute the forward pass of the model. @@ -159,7 +164,7 @@ def forward(self, spikes, layer): """ spikes = self.quant(spikes) - spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) + spikes = self.inference(spikes) spikes = self.dequant(spikes) return spikes @@ -169,7 +174,7 @@ def load_model(self, model_path): Load pre-trained model and set the state dictionary keys. """ self.load_state_dict(torch.load(model_path, map_location=self.device), - strict=True) + strict=False) def generate_model_name(model): """ @@ -227,11 +232,8 @@ def run_inference(model, model_name, qconfig): # Load the model model.load_model(os.path.join('./models', model_name)) - # Retrieve layer names for inference - layer_names = list(model.layer_dict.keys()) - # Use evaluate method for inference accuracy - model.evaluate(model, test_loader, layers=layer_names) + model.evaluate(model, test_loader) if __name__ == "__main__": # Set the number of threads for PyTorch diff --git a/VPRTempoQuant_Train.py b/VPRTempoQuant_Train.py index 7e02bf2..d228128 100644 --- a/VPRTempoQuant_Train.py +++ b/VPRTempoQuant_Train.py @@ -53,10 +53,7 @@ def __init__(self): # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() - self.dequant = DeQuantStub() - - # Define the add function for quantized addition - self.add = nn.quantized.FloatFunctional() + self.dequant = DeQuantStub() # Layer dict to keep track of layer names and their order self.layer_dict = {} @@ -81,6 +78,7 @@ def __init__(self): ip_rate=0.15, stdp_rate=0.005, spk_force=True, + p=[0.25, 0.75], device=self.device ) @@ -186,7 +184,7 @@ def forward(self, spikes, layer): """ spikes = self.quant(spikes) - spikes = self.add.add(layer.exc(spikes), layer.inh(spikes)) + spikes = layer.w(spikes) spikes = self.dequant(spikes) return spikes diff --git a/VPRTempo_Train.py b/VPRTempo_Train.py index 4bdb1fc..2958cac 100644 --- a/VPRTempo_Train.py +++ b/VPRTempo_Train.py @@ -72,6 +72,7 @@ def __init__(self): dims=[self.feature, self.output], ip_rate=0.15, stdp_rate=0.005, + p=[0.25, 0.75], spk_force=True, device=self.device ) @@ -177,25 +178,9 @@ def forward(self, spikes, layer): - Tensor: Output after processing. """ - spikes = layer.exc(spikes) + layer.inh(spikes) + spikes = layer.w(spikes) - return spikes - - def combine_weights(self, model): - for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): - # Retrieve the layer object - layer = getattr(model, layer_name) - dims = layer.dims - # Define weight variable in layer - layer.w = nn.Linear(dims[0],dims[1],bias=False) - # Send to model device - layer.x.to(model.device) - # Replace weights with combined weights - layer.w.weight = nn.Parameter(layer.exc.weight + layer.inh.weight) - # Delete original weights - del layer.exc, layer.inh - - return model + return spikes def save_model(self, model_out): """ @@ -250,10 +235,8 @@ def train_new_model(model, model_name): persistent_workers=True) # Set the model to training mode and move to device model.train() - # Keep track of trained layers to pass data through them trained_layers = [] - # Training each layer for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): print(f"Training layer: {layer_name}") @@ -263,8 +246,6 @@ def train_new_model(model, model_name): model.train_model(train_loader, layer, prev_layers=trained_layers) # After training the current layer, add it to the list of trained layers trained_layers.append(layer_name) - # Combine excitatory and inhibitory weights - model = model.combine_weights(model) # Convert the model to a quantized model model.eval() # Save the model diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/src/blitnet.py b/src/blitnet.py index 19a7531..b73be01 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -110,6 +110,13 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, self.havconnExc = self.exc.weight > 0 self.havconnInh = self.inh.weight < 0 + # Combine weights into a single tensor + self.w = nn.Linear(dims[0], dims[1], bias=False) + self.w.weight = nn.Parameter(torch.add(self.exc.weight, self.inh.weight)) + + self.havconnCombinedExc = self.w.weight > 0 + self.havconnCombinedInh = self.w.weight < 0 + def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0],device=None): # Get torch device @@ -170,7 +177,7 @@ def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None): if layer.spk_force: # Get layer dimensions - shape = layer.exc.weight.data.shape + shape = layer.w.weight.data.shape # Get the output neuron index idx_sel = torch.arange(int(idx[0]), int(idx[0]) + 1, @@ -194,34 +201,30 @@ def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None): post = torch.tile(xdiff, (shape[1], 1)) # Apply the weight changes - layer.exc.weight.data += ((pre * post * layer.havconnExc.T) * + layer.w.weight.data += ((pre * post * layer.havconnCombinedExc.T) * layer.eta_stdp).T - layer.inh.weight.data += ((-pre * post * layer.havconnInh.T) * + layer.w.weight.data += ((-pre * post * layer.havconnCombinedInh.T) * (layer.eta_stdp * -1)).T # Normal STDP else: # Get layer dimensions - shape = layer.exc.weight.data.shape + shape = layer.w.weight.data.shape # Tile out pre- and post-spikes pre = torch.tile(torch.reshape(prespike, (shape[1], 1)), (1, shape[0])) post = torch.tile(spikes, (shape[1], 1)) # Apply positive and negative weight changes - layer.exc.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * - layer.havconnExc.T) * layer.eta_stdp).T - layer.inh.weight.data += (((0.5 - post) * (pre > 0) * - (post > 0) * layer.havconnInh.T) * (layer.eta_stdp * -1)).T - - # In-place clamp for excitatory and inhibitory weights - layer.exc.weight.data[layer.exc.weight.data < 0] = 1e-06 - layer.inh.weight.data[layer.inh.weight.data > 0] = -1e-06 + layer.w.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) * + layer.havconnCombinedExc.T) * layer.eta_stdp).T + layer.w.weight.data += (((0.5 - post) * (pre > 0) * + (post > 0) * layer.havconnCombinedInh.T) * (layer.eta_stdp * -1)).T # Remove negative weights for excW and positive for inhW - layer.exc.weight.data[layer.havconnExc] = layer.exc.weight.data[layer.havconnExc].clamp(min=1e-06, max=10) - layer.inh.weight.data[layer.havconnInh] = layer.inh.weight.data[layer.havconnInh].clamp(min=-10, max=-1e-06) + layer.w.weight.data[layer.havconnCombinedExc] = layer.w.weight.data[layer.havconnCombinedExc].clamp(min=1e-06, max=10) + layer.w.weight.data[layer.havconnCombinedInh] = layer.w.weight.data[layer.havconnCombinedInh].clamp(min=-10, max=-1e-06) # Check if layer has target firing rate and an ITP learning rate if layer.have_rate and layer.eta_ip > 0.0: @@ -231,11 +234,12 @@ def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None): layer.thr.data[layer.thr.data < 0] = 0 # Check if layer has inhibitory weights and an stdp learning rate - if torch.any(layer.inh.weight.data).item() and layer.eta_stdp != 0: + if torch.any(layer.w.weight.data).item() and layer.eta_stdp != 0: # Normalize the inhibitory weights using homeostasis - inhW = layer.inh.weight.data.T - layer.inh.weight.data += (torch.mul(noclp,inhW) * layer.eta_stdp*50).T - layer.inh.weight.data[layer.inh.weight.data > 0.0] = -1e-06 + inhW = layer.w.weight.data.T.clone() + inhW[inhW>0] = 0 + layer.w.weight.data += (torch.mul(noclp,inhW) * layer.eta_stdp*50).T + #layer.w.weight.data[layer.w.weight.data > 0.0] = -1e-06 return layer \ No newline at end of file diff --git a/src/settings.py b/src/settings.py index 07dd23f..a83832c 100644 --- a/src/settings.py +++ b/src/settings.py @@ -15,7 +15,7 @@ def configure(model): model.number_modules = 1 # Number of expert modules (currently not implemented) model.number_training_images = 500 # Number of training images model.number_testing_images = model.number_training_images # Number of testing images - model.locations = ["winter"] # Locations to train on (location repeats for training datasets) + model.locations = ["spring","fall"] # Locations to train on (location repeats for training datasets) model.test_locations = ["summer"] # Location to query with model.filter = 8 # Filter for training images model.validation = True # Validation (maybe deprecated for now?) @@ -51,9 +51,9 @@ def configure(model): model.testing_dirs.append(os.path.join(model.testPath,n)) # Set the model parameters - model.epoch = 8 # Number of epochs - model.patches = 7 # Number of patches - model.dims = [28,28] # Dimensions of the input image + model.epoch = 4 # Number of epochs + model.patches = 15 # Number of patches + model.dims = [56,56] # Dimensions of the input image model.location_repeat = len(model.locations) # Number of times to repeat the locations model.annl_pow = 2 # Power of the annealmeant function From 27b9b415343f3b4c1c2d98c7330c11377aa0e700 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 23 Nov 2023 16:07:06 +1000 Subject: [PATCH 29/69] Re-organising script so it is run out of main.py, adding in abilit to run networks via terminal --- VPRTempoQuant.py | 16 +- ...mpoQuant_Train.py => VPRTempoQuantTrain.py | 30 ++-- VPRTempo_Train.py => VPRTempoTrain.py | 10 +- config/base_config.json | 20 +++ main.py | 144 ++++++++++++++++++ src/settings.py | 86 ++++++++--- 6 files changed, 250 insertions(+), 56 deletions(-) rename VPRTempoQuant_Train.py => VPRTempoQuantTrain.py (93%) rename VPRTempo_Train.py => VPRTempoTrain.py (98%) create mode 100644 config/base_config.json create mode 100644 main.py diff --git a/VPRTempoQuant.py b/VPRTempoQuant.py index 1fa4413..ab7e806 100644 --- a/VPRTempoQuant.py +++ b/VPRTempoQuant.py @@ -38,7 +38,7 @@ import torch.nn as nn import torch.quantization as quantization -from settings import configure, model_logger +from settings import configure, model_logger_quant from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub @@ -46,14 +46,14 @@ from prettytable import PrettyTable from metrics import recallAtK -class VPRTempo(nn.Module): +class VPRTempoQuant(nn.Module): def __init__(self): - super(VPRTempo, self).__init__() + super(VPRTempoQuant, self).__init__() # Configure the network configure(self) - model_logger(self) + model_logger_quant(self) # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() @@ -176,7 +176,7 @@ def load_model(self, model_path): self.load_state_dict(torch.load(model_path, map_location=self.device), strict=False) -def generate_model_name(model): +def generate_model_name_quant(model): """ Generate the model name based on its parameters. """ @@ -198,7 +198,7 @@ def check_pretrained_model(model_name): pretrain = 'y' return pretrain -def run_inference(model, model_name, qconfig): +def run_inference_quant(model, model_name, qconfig): """ Run inference on a pre-trained model. @@ -236,10 +236,8 @@ def run_inference(model, model_name, qconfig): model.evaluate(model, test_loader) if __name__ == "__main__": - # Set the number of threads for PyTorch - #torch.set_num_threads(8) # Initialize the model - model = VPRTempo() + model = VPRTempoQuant() # Set the quantization configuration qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name diff --git a/VPRTempoQuant_Train.py b/VPRTempoQuantTrain.py similarity index 93% rename from VPRTempoQuant_Train.py rename to VPRTempoQuantTrain.py index d228128..9457044 100644 --- a/VPRTempoQuant_Train.py +++ b/VPRTempoQuantTrain.py @@ -38,19 +38,19 @@ import torch.nn as nn import torch.quantization as quantization -from settings import configure, model_logger +from settings import configure, model_logger_quant from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm -class VPRTempo(nn.Module): +class VPRTempoQuantTrain(nn.Module): def __init__(self): - super(VPRTempo, self).__init__() + super(VPRTempoQuantTrain, self).__init__() # Configure the network configure(self) - + model_logger_quant(self) # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() self.dequant = DeQuantStub() @@ -100,19 +100,13 @@ def add_layer(self, name, **kwargs): # Add layer name and index to the layer_dict self.layer_dict[name] = self.layer_counter self.layer_counter += 1 - - def model_logger(self): - """ - Log the model configuration to the console. - """ - model_logger(self) def _anneal_learning_rate(self, layer, mod, itp, stdp): """ Anneal the learning rate for the current layer. """ if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps - pt = pow(float(self.T - mod) / self.T, self.annl_pow) + pt = pow(float(self.T - mod) / self.T, 2) layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate @@ -195,7 +189,7 @@ def save_model(self, model_out): """ torch.save(self.state_dict(), model_out) -def generate_model_name(model): +def generate_model_name_quant(model): """ Generate the model name based on its parameters. """ @@ -216,7 +210,7 @@ def check_pretrained_model(model_name): return retrain == 'n' return False -def train_new_model(model, model_name, qconfig): +def train_new_model_quant(model, model_name, qconfig): """ Train a new model. @@ -265,22 +259,18 @@ def train_new_model(model, model_name, qconfig): model.save_model(os.path.join('./models', model_name)) if __name__ == "__main__": - # Set the number of threads for PyTorch - #torch.set_num_threads(8) # Initialize the model - model = VPRTempo() - # Initialize the logger - model.model_logger() + model = VPRTempoQuantTrain() # Set the quantization configuration if model.quantize: qconfig = quantization.get_default_qat_qconfig('fbgemm') else: raise ValueError("Quantization must be enabled for training.") # Generate the model name - model_name = generate_model_name(model) + model_name = generate_model_name_quant(model) # Check if a pre-trained model exists use_pretrained = check_pretrained_model(model_name) # Train or run inference based on the user's input if not use_pretrained: - train_new_model(model, model_name, qconfig) # Training + train_new_model_quant(model, model_name, qconfig) # Training model.logger.info('Training complete.') \ No newline at end of file diff --git a/VPRTempo_Train.py b/VPRTempoTrain.py similarity index 98% rename from VPRTempo_Train.py rename to VPRTempoTrain.py index 2958cac..1fedb2f 100644 --- a/VPRTempo_Train.py +++ b/VPRTempoTrain.py @@ -43,13 +43,13 @@ from torch.utils.data import DataLoader from tqdm import tqdm -class VPRTempo(nn.Module): +class VPRTempoTrain(nn.Module): def __init__(self): - super(VPRTempo, self).__init__() + super(VPRTempoTrain, self).__init__() # Configure the network configure(self) - + model_logger(self) # Layer dict to keep track of layer names and their order self.layer_dict = {} self.layer_counter = 0 @@ -107,7 +107,7 @@ def _anneal_learning_rate(self, layer, mod, itp, stdp): Anneal the learning rate for the current layer. """ if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps - pt = pow(float(self.T - mod) / self.T, self.annl_pow) + pt = pow(float(self.T - mod) / self.T, 2) layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate @@ -255,7 +255,7 @@ def train_new_model(model, model_name): # Set the number of threads for PyTorch #torch.set_num_threads(8) # Initialize the model - model = VPRTempo() + model = VPRTempoTrain() if model.quantize: raise ValueError("Quantization enabled, please disable.") # Initialize the logger diff --git a/config/base_config.json b/config/base_config.json new file mode 100644 index 0000000..6478384 --- /dev/null +++ b/config/base_config.json @@ -0,0 +1,20 @@ +{ + "dataset": "nordland", + "number_places": 500, + "number_modules": 1, + "database_dirs": [ + "spring", + "fall" + ], + "query_dir": [ + "summer" + ], + "filter": 8, + "epoch": 4, + "patches": 15, + "dims": [ + 56, + 56 + ], + "anneal_pow": 2 +} \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..3419ab2 --- /dev/null +++ b/main.py @@ -0,0 +1,144 @@ +#MIT License + +#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer + +#Permission is hereby granted, free of charge, to any person obtaining a copy +#of this software and associated documentation files (the "Software"), to deal +#in the Software without restriction, including without limitation the rights +#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +#copies of the Software, and to permit persons to whom the Software is +#furnished to do so, subject to the following conditions: + +#The above copyright notice and this permission notice shall be included in all +#copies or substantial portions of the Software. + +#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +#SOFTWARE. + +''' +Imports +''' +import argparse +import json + +import torch.quantization as quantization + +from VPRTempoTrain import VPRTempoTrain, generate_model_name, check_pretrained_model, train_new_model +from VPRTempo import VPRTempo, run_inference +from VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant +from VPRTempoQuant import VPRTempoQuant, run_inference_quant + +def initialize_and_run_model(train: bool, quantize: bool): + if train: + if quantize: + # Initialize the quantized model + model = VPRTempoQuantTrain() + # Get the quantization config + qconfig = quantization.get_default_qat_qconfig('fbgemm') + # Generate the model name + model_name = generate_model_name_quant(model) + # Check if the model has been trained before + check_pretrained_model(model_name) + # Train the model + train_new_model_quant(model, model_name, qconfig) + else: + # Initialize the model + model = VPRTempoTrain() + # Generate the model name + model_name = generate_model_name(model) + # Check if the model has been trained before + check_pretrained_model(model_name) + # Train the model + train_new_model(model, model_name) + else: + # Set the quantization configuration + if quantize: + # Initialize the quantized model + model = VPRTempoQuant() + # Get the quantization config + qconfig = quantization.get_default_qat_qconfig('fbgemm') + # Generate the model name + model_name = generate_model_name_quant(model) + # Run the quantized inference model + run_inference_quant(model, model_name, qconfig) + else: + # Initialize the model + model = VPRTempo() + # Generate the model name + model_name = generate_model_name(model) + # Run the inference model + run_inference(model, model_name) + +def main(): + ''' + Define the base parameter parser (configurable by the user) + ''' + base_parser = argparse.ArgumentParser(description="Args for base configuration file") + + # Define the dataset arguments + base_parser.add_argument('--dataset', type=str, default='nordland', + help="Dataset to use for training and/or inferencing") + base_parser.add_argument('--data_dir', type=str, default='./dataset/', + help="Directory where dataset files are stored") + base_parser.add_argument('--num_places', type=int, default=500, + help="Number of places to use for training and/or inferencing") + base_parser.add_argument('--num_modules', type=int, default=1, + help="Number of expert modules to use split images into") + base_parser.add_argument('--database_dirs', nargs='+', default=['spring', 'fall'], + help="Directories to use for training") + base_parser.add_argument('--query_dir', nargs='+', default=['summer'], + help="Directories to use for testing") + + # Define training parameters + base_parser.add_argument('--filter', type=int, default=8, + help="Images to skip for training and/or inferencing") + base_parser.add_argument('--epoch', type=int, default=4, + help="Number of epochs to train the model") + + # Define image transformation parameters + base_parser.add_argument('--patches', type=int, default=15, + help="Number of patches to generate for patch normalization image into") + base_parser.add_argument('--dims', nargs='+', type=int, default=[56,56], + help="Dimensions to resize the image to") + + # Output base configuration + base_args = base_parser.parse_args() + + # Write to base_config JSON file + with open('./config/base_config.json', 'w') as file: + json.dump(vars(base_args), file, indent=4) + + ''' + Define network architecture parameter parser + ''' + network_parser = argparse.ArgumentParser(description="Args for network architecture configuration file") + + # Define network architecure (number of neurons in each layer) + network_parser.add_argument('--input', type=int, default=base_args.dims[0]*base_args.dims[1], + help="Number of input neurons") + network_parser.add_argument('--feature', type=int, default=(base_args.dims[0]*base_args.dims[1])*2, + help="Number of feature neurons") + network_parser.add_argument('--output', type=int, default=int(base_args.num_places/base_args.num_modules), + help="Number of output neurons") + + # Determine total number of timesteps + network_parser.add_argument('--T', type=int, + default=(base_args.num_places / base_args.num_modules) * len(base_args.database_dirs) * base_args.epoch) + + # Determine network functionality + parser = argparse.ArgumentParser(description="Determine training or inferencing and the quantization scheme") + parser.add_argument('--train', action='store_true', + help="Flag to run the training or inferencing model") + parser.add_argument('--quantize', action='store_true', + help="Enable/disable quantization for the model") + args = parser.parse_args() + initialize_and_run_model(args.train, + args.quantize) + +if __name__ == "__main__": + main() diff --git a/src/settings.py b/src/settings.py index a83832c..d949bae 100644 --- a/src/settings.py +++ b/src/settings.py @@ -1,26 +1,31 @@ import os import torch import logging +import json +import sys +sys.path.append('./config') from datetime import datetime def configure(model): """ - Configure the model + Configure the model settings """ - model.dataset = 'nordland' # Dataset name - model.dataset_file = os.path.join('./dataset',model.dataset+'.csv') # Dataset file (must be PyTorch Dataset) - model.trainingPath = './dataset/' # Path to training images - model.testPath = './dataset/' # Path to testing images - model.number_modules = 1 # Number of expert modules (currently not implemented) - model.number_training_images = 500 # Number of training images - model.number_testing_images = model.number_training_images # Number of testing images - model.locations = ["spring","fall"] # Locations to train on (location repeats for training datasets) - model.test_locations = ["summer"] # Location to query with - model.filter = 8 # Filter for training images - model.validation = True # Validation (maybe deprecated for now?) - model.log = True # Log to console - model.quantize = True # Quantize the network + base_config = { + "dataset": "nordland", + "number_places": 500, + "number_modules": 1, + "database_dirs": ["spring", "fall"], + "query_dir": ["summer"], + "filter": 8, + "epoch": 4, + "patches": 15, + "dims": [56,56], + } + + # Write to base_config JSON file + with open('./config/base_config.json', 'w') as file: + json.dump(base_config, file, indent=4) # Set default paths if the provided paths are not valid directories if not os.path.isdir(getattr(model, 'trainingPath', '')): @@ -50,19 +55,11 @@ def configure(model): for n in model.test_locations: model.testing_dirs.append(os.path.join(model.testPath,n)) - # Set the model parameters - model.epoch = 4 # Number of epochs - model.patches = 15 # Number of patches - model.dims = [56,56] # Dimensions of the input image model.location_repeat = len(model.locations) # Number of times to repeat the locations - model.annl_pow = 2 # Power of the annealmeant function """ These parameters are used to define the network architecture """ - model.input = int(model.dims[0]*model.dims[1]) # Number of input neurons - model.feature = int(model.input*2) # Number of feature neurons - model.output = int(model.number_training_images/model.number_modules) # Number of output neurons # Set the torch device if not model.quantize: @@ -127,4 +124,49 @@ def model_logger(model): else: model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) model.logger.info('Current device is: CPU') + model.logger.info('') + +def model_logger_quant(model): + """ + Configure the model logger + """ + if os.path.isdir('../output'): + now = datetime.now() + model.output_folder = '../output/' + now.strftime("%d%m%y-%H-%M-%S") + else: + now = datetime.now() + model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + + os.mkdir(model.output_folder) + # Create the logger + model.logger = logging.getLogger("VPRTempo") + if (model.logger.hasHandlers()): + model.logger.handlers.clear() + # Set the logger level + model.logger.setLevel(logging.DEBUG) + logging.basicConfig(filename=model.output_folder + "/logfile.log", + filemode="a+", + format="%(asctime)-15s %(levelname)-8s %(message)s") + # Add the logger to the console (if specified) + if model.log: + model.logger.addHandler(logging.StreamHandler()) + + model.logger.info('') + + model.logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗ ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗████████╗') + model.logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗ ██╔═══██╗██║ ██║██╔══██╗████╗ ██║╚══██╔══╝') + model.logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║█████╗██║ ██║██║ ██║███████║██╔██╗ ██║ ██║') + model.logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║╚════╝██║▄▄ ██║██║ ██║██╔══██║██║╚██╗██║ ██║') + model.logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝ ╚██████╔╝╚██████╔╝██║ ██║██║ ╚████║ ██║') + model.logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ╚══▀▀═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝') + model.logger.info('-----------------------------------------------------------------------') + model.logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') + model.logger.info('Queensland University of Technology, Centre for Robotics') + model.logger.info('') + model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') + model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + model.logger.info('') + model.logger.info('Quantization enabled') + model.logger.info('Current device is: CPU') model.logger.info('') \ No newline at end of file From f028dba6551bfa8c258f49cab49135af5bb2864a Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 27 Nov 2023 11:48:43 +1000 Subject: [PATCH 30/69] Networks now run through main.py, user can set quantized or train_new_model to do either from terminal --- .gitignore | 15 +- config/base_config.json | 20 - dataset/event.csv | 160 + dataset/orc.csv | 2650 +++++++++++++++++ main.py | 97 +- VPRTempo.py => networks/base/VPRTempo.py | 61 +- .../base/VPRTempoTrain.py | 62 +- .../quantized/VPRTempoQuant.py | 70 +- .../quantized/VPRTempoQuantTrain.py | 62 +- src/blitnet.py | 6 +- src/dataset.py | 4 +- src/{settings.py => loggers.py} | 101 +- src/utils.py | 299 -- src/validation.py | 303 -- 14 files changed, 3002 insertions(+), 908 deletions(-) delete mode 100644 config/base_config.json create mode 100644 dataset/event.csv create mode 100644 dataset/orc.csv rename VPRTempo.py => networks/base/VPRTempo.py (81%) rename VPRTempoTrain.py => networks/base/VPRTempoTrain.py (86%) rename VPRTempoQuant.py => networks/quantized/VPRTempoQuant.py (81%) rename VPRTempoQuantTrain.py => networks/quantized/VPRTempoQuantTrain.py (86%) rename src/{settings.py => loggers.py} (64%) delete mode 100644 src/utils.py delete mode 100644 src/validation.py diff --git a/.gitignore b/.gitignore index ee89f42..4052199 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,19 @@ __pycache__/ .ipynb_checkpoints/ src/__pycache__/ -dataset/ +dataset/conv/ +dataset/Dusk/ +dataset/fall/ +dataset/model/ +dataset/output_database/ +dataset/output_query/ +dataset/Rain/ +dataset/spring/ +dataset/summer/ +dataset/Sun/ +dataset/winter/ +dataset/event.csv/ +models/VPRTempo78415685001.pth +models/VPRTempoQuant78415685001.pth models/VPRTempo313662725001.pth models/VPRTempoQuant313662725001.pth diff --git a/config/base_config.json b/config/base_config.json deleted file mode 100644 index 6478384..0000000 --- a/config/base_config.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "dataset": "nordland", - "number_places": 500, - "number_modules": 1, - "database_dirs": [ - "spring", - "fall" - ], - "query_dir": [ - "summer" - ], - "filter": 8, - "epoch": 4, - "patches": 15, - "dims": [ - 56, - 56 - ], - "anneal_pow": 2 -} \ No newline at end of file diff --git a/dataset/event.csv b/dataset/event.csv new file mode 100644 index 0000000..d39b74a --- /dev/null +++ b/dataset/event.csv @@ -0,0 +1,160 @@ +Image_names,Index +match_0245.png,0 +match_0247.png,1 +match_0248.png,2 +match_0249.png,3 +match_0250.png,4 +match_0251.png,5 +match_0253.png,6 +match_0256.png,7 +match_0329.png,8 +match_0332.png,9 +match_0333.png,10 +match_0334.png,11 +match_0337.png,12 +match_0338.png,13 +match_0345.png,14 +match_0346.png,15 +match_0347.png,16 +match_0348.png,17 +match_0349.png,18 +match_0350.png,19 +match_0351.png,20 +match_0352.png,21 +match_0354.png,22 +match_0355.png,23 +match_0356.png,24 +match_0357.png,25 +match_0358.png,26 +match_0359.png,27 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+003359.png,2639 +003360.png,2640 +003361.png,2641 +003362.png,2642 +003363.png,2643 +003364.png,2644 +003365.png,2645 +003366.png,2646 +003367.png,2647 +003368.png,2648 diff --git a/main.py b/main.py index 3419ab2..0459fbb 100644 --- a/main.py +++ b/main.py @@ -24,7 +24,9 @@ Imports ''' import argparse -import json +import sys +sys.path.append('./networks/base') +sys.path.append('./networks/quantized') import torch.quantization as quantization @@ -33,11 +35,13 @@ from VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant from VPRTempoQuant import VPRTempoQuant, run_inference_quant -def initialize_and_run_model(train: bool, quantize: bool): - if train: - if quantize: +def initialize_and_run_model(args): + # If user wants to train a new network + if args.train_new_model: + # If using quantization aware training + if args.quantize: # Initialize the quantized model - model = VPRTempoQuantTrain() + model = VPRTempoQuantTrain(args) # Get the quantization config qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name @@ -46,20 +50,21 @@ def initialize_and_run_model(train: bool, quantize: bool): check_pretrained_model(model_name) # Train the model train_new_model_quant(model, model_name, qconfig) - else: + else: # Normal model # Initialize the model - model = VPRTempoTrain() + model = VPRTempoTrain(args) # Generate the model name model_name = generate_model_name(model) # Check if the model has been trained before check_pretrained_model(model_name) # Train the model train_new_model(model, model_name) + # Run the inference network else: # Set the quantization configuration - if quantize: + if args.quantize: # Initialize the quantized model - model = VPRTempoQuant() + model = VPRTempoQuant(args) # Get the quantization config qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name @@ -68,77 +73,65 @@ def initialize_and_run_model(train: bool, quantize: bool): run_inference_quant(model, model_name, qconfig) else: # Initialize the model - model = VPRTempo() + model = VPRTempo(args) # Generate the model name model_name = generate_model_name(model) # Run the inference model run_inference(model, model_name) -def main(): +def parse_network(use_quantize=False, train_new_model=False): ''' Define the base parameter parser (configurable by the user) ''' - base_parser = argparse.ArgumentParser(description="Args for base configuration file") + parser = argparse.ArgumentParser(description="Args for base configuration file") # Define the dataset arguments - base_parser.add_argument('--dataset', type=str, default='nordland', + parser.add_argument('--dataset', type=str, default='nordland', help="Dataset to use for training and/or inferencing") - base_parser.add_argument('--data_dir', type=str, default='./dataset/', + parser.add_argument('--data_dir', type=str, default='./dataset/', help="Directory where dataset files are stored") - base_parser.add_argument('--num_places', type=int, default=500, + parser.add_argument('--num_places', type=int, default=500, help="Number of places to use for training and/or inferencing") - base_parser.add_argument('--num_modules', type=int, default=1, + parser.add_argument('--num_modules', type=int, default=1, help="Number of expert modules to use split images into") - base_parser.add_argument('--database_dirs', nargs='+', default=['spring', 'fall'], + parser.add_argument('--database_dirs', nargs='+', default=['spring', 'fall'], help="Directories to use for training") - base_parser.add_argument('--query_dir', nargs='+', default=['summer'], + parser.add_argument('--query_dir', nargs='+', default=['summer'], help="Directories to use for testing") # Define training parameters - base_parser.add_argument('--filter', type=int, default=8, + parser.add_argument('--filter', type=int, default=8, help="Images to skip for training and/or inferencing") - base_parser.add_argument('--epoch', type=int, default=4, + parser.add_argument('--epoch', type=int, default=4, help="Number of epochs to train the model") # Define image transformation parameters - base_parser.add_argument('--patches', type=int, default=15, + parser.add_argument('--patches', type=int, default=15, help="Number of patches to generate for patch normalization image into") - base_parser.add_argument('--dims', nargs='+', type=int, default=[56,56], + parser.add_argument('--dims', nargs='+', type=int, default=[56,56], help="Dimensions to resize the image to") - - # Output base configuration - base_args = base_parser.parse_args() - - # Write to base_config JSON file - with open('./config/base_config.json', 'w') as file: - json.dump(vars(base_args), file, indent=4) - - ''' - Define network architecture parameter parser - ''' - network_parser = argparse.ArgumentParser(description="Args for network architecture configuration file") - - # Define network architecure (number of neurons in each layer) - network_parser.add_argument('--input', type=int, default=base_args.dims[0]*base_args.dims[1], - help="Number of input neurons") - network_parser.add_argument('--feature', type=int, default=(base_args.dims[0]*base_args.dims[1])*2, - help="Number of feature neurons") - network_parser.add_argument('--output', type=int, default=int(base_args.num_places/base_args.num_modules), - help="Number of output neurons") - - # Determine total number of timesteps - network_parser.add_argument('--T', type=int, - default=(base_args.num_places / base_args.num_modules) * len(base_args.database_dirs) * base_args.epoch) - # Determine network functionality - parser = argparse.ArgumentParser(description="Determine training or inferencing and the quantization scheme") - parser.add_argument('--train', action='store_true', + # Define the network functionality + parser.add_argument('--train_new_model', action='store_true', help="Flag to run the training or inferencing model") parser.add_argument('--quantize', action='store_true', help="Enable/disable quantization for the model") + + # If the function is called with specific arguments, override sys.argv + if use_quantize or train_new_model: + sys.argv = [''] + if use_quantize: + sys.argv.append('--quantize') + if train_new_model: + sys.argv.append('--train_new_model') + + # Output base configuration args = parser.parse_args() - initialize_and_run_model(args.train, - args.quantize) + + # Run the network with the desired settings + initialize_and_run_model(args) if __name__ == "__main__": - main() + # User input to determine if using quantized network or to train new model + parse_network(use_quantize=False, + train_new_model=False) \ No newline at end of file diff --git a/VPRTempo.py b/networks/base/VPRTempo.py similarity index 81% rename from VPRTempo.py rename to networks/base/VPRTempo.py index ac82f33..252e052 100644 --- a/VPRTempo.py +++ b/networks/base/VPRTempo.py @@ -37,7 +37,7 @@ import numpy as np import torch.nn as nn -from settings import configure, model_logger +from loggers import model_logger from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from tqdm import tqdm @@ -45,18 +45,29 @@ from metrics import recallAtK class VPRTempo(nn.Module): - def __init__(self): + def __init__(self, args): super(VPRTempo, self).__init__() - # Configure the network - configure(self) + # Set the arguments + self.args = args + for arg in vars(args): + setattr(self, arg, getattr(args, arg)) - model_logger(self) + # Set the dataset file + self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') + + # Set the model logger and return the device + self.device = model_logger(self) # Layer dict to keep track of layer names and their order self.layer_dict = {} self.layer_counter = 0 + # Define layer architecture + self.input = int(args.dims[0]*args.dims[1]) + self.feature = int(self.input * 2) + self.output = int(args.num_places / args.num_modules) + """ Define trainable layers here """ @@ -100,7 +111,7 @@ def evaluate(self, model, test_loader, layers=None): :param layers: Layers to pass data through """ # Initialize the tqdm progress bar - pbar = tqdm(total=self.number_testing_images, + pbar = tqdm(total=self.num_places, desc="Running the test network", position=0) # Initiliaze the output spikes variable @@ -124,13 +135,13 @@ def evaluate(self, model, test_loader, layers=None): pbar.close() # Rehsape output spikes into a similarity matrix - out = np.reshape(np.array(out),(model.number_training_images,model.number_testing_images)) + out = np.reshape(np.array(out),(model.num_places,model.num_places)) # Recall@N N = [1,5,10,15,20,25] # N values to calculate R = [] # Recall@N values # Create GT matrix - GT = np.zeros((model.number_testing_images,model.number_training_images), dtype=int) + GT = np.zeros((model.num_places,model.num_places), dtype=int) for n in range(len(GT)): GT[n,n] = 1 # Calculate Recall@N @@ -163,17 +174,6 @@ def load_model(self, model_path): """ self.load_state_dict(torch.load(model_path, map_location=self.device), strict=False) - -def generate_model_name(model): - """ - Generate the model name based on its parameters. - """ - return ("VPRTempo" + - str(model.input) + - str(model.feature) + - str(model.output) + - str(model.number_modules) + - '.pth') def check_pretrained_model(model_name): """ @@ -197,10 +197,11 @@ def run_inference(model, model_name): # Initialize the image transforms and datasets image_transform = ProcessImage(model.dims, model.patches) test_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.testing_dirs, + base_dir=model.data_dir, + img_dirs=model.query_dir, transform=image_transform, skip=model.filter, - max_samples=model.number_testing_images) + max_samples=model.num_places) # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, @@ -217,20 +218,4 @@ def run_inference(model, model_name): layer_names = list(model.layer_dict.keys()) # Use evaluate method for inference accuracy - model.evaluate(model, test_loader, layers=layer_names) - -if __name__ == "__main__": - # Set the number of threads for PyTorch - #torch.set_num_threads(8) - # Initialize the model - model = VPRTempo() - if model.quantize: - raise ValueError("Please disable quantization to run inference.") - # Generate the model name - model_name = generate_model_name(model) - # Check if a pre-trained model exists - use_pretrained = check_pretrained_model(model_name) - if not use_pretrained == 'n': - # Run inference based on the user's input - with torch.no_grad(): - run_inference(model, model_name) # Inference \ No newline at end of file + model.evaluate(model, test_loader, layers=layer_names) \ No newline at end of file diff --git a/VPRTempoTrain.py b/networks/base/VPRTempoTrain.py similarity index 86% rename from VPRTempoTrain.py rename to networks/base/VPRTempoTrain.py index 1fedb2f..07f4c49 100644 --- a/VPRTempoTrain.py +++ b/networks/base/VPRTempoTrain.py @@ -38,22 +38,39 @@ import torch.nn as nn import torchvision.transforms as transforms -from settings import configure, model_logger +from loggers import model_logger from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from tqdm import tqdm class VPRTempoTrain(nn.Module): - def __init__(self): + def __init__(self, args): super(VPRTempoTrain, self).__init__() - # Configure the network - configure(self) - model_logger(self) + # Set the arguments + self.args = args + for arg in vars(args): + setattr(self, arg, getattr(args, arg)) + + # Set the dataset file + self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') + + # Configure the model logger and get the device + self.device = model_logger(self) + # Layer dict to keep track of layer names and their order self.layer_dict = {} self.layer_counter = 0 + # Define layer architecture + self.input = int(args.dims[0]*args.dims[1]) + self.feature = int(self.input * 2) + self.output = int(args.num_places / args.num_modules) + + # Set the total timestep count + self.location_repeat = len(args.database_dirs) # Number of times to repeat the locations + self.T = int((self.num_places / self.num_modules) * self.location_repeat * self.epoch) + """ Define trainable layers here """ @@ -163,7 +180,7 @@ def train_model(self, train_loader, layer, prev_layers=None): pbar.close() # Free up memory - if self.device.type == "cuda": + if self.device == "cuda:0": torch.cuda.empty_cache() gc.collect() @@ -196,7 +213,7 @@ def generate_model_name(model): str(model.input) + str(model.feature) + str(model.output) + - str(model.number_modules) + + str(model.num_modules) + '.pth') def check_pretrained_model(model_name): @@ -222,11 +239,12 @@ def train_new_model(model, model_name): ProcessImage(model.dims, model.patches) ]) train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.training_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_training_images, - test=False) + base_dir=model.data_dir, + img_dirs=model.database_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.num_places, + test=False) # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, @@ -249,22 +267,4 @@ def train_new_model(model, model_name): # Convert the model to a quantized model model.eval() # Save the model - model.save_model(os.path.join('./models', model_name)) - -if __name__ == "__main__": - # Set the number of threads for PyTorch - #torch.set_num_threads(8) - # Initialize the model - model = VPRTempoTrain() - if model.quantize: - raise ValueError("Quantization enabled, please disable.") - # Initialize the logger - model.model_logger() - # Generate the model name - model_name = generate_model_name(model) - # Check if a pre-trained model exists - use_pretrained = check_pretrained_model(model_name) - # Train or run inference based on the user's input - if not use_pretrained: - train_new_model(model, model_name) # Training - model.logger.info('Training complete.') \ No newline at end of file + model.save_model(os.path.join('./models', model_name)) \ No newline at end of file diff --git a/VPRTempoQuant.py b/networks/quantized/VPRTempoQuant.py similarity index 81% rename from VPRTempoQuant.py rename to networks/quantized/VPRTempoQuant.py index ab7e806..eb819d1 100644 --- a/VPRTempoQuant.py +++ b/networks/quantized/VPRTempoQuant.py @@ -26,7 +26,7 @@ import os import torch -import gc +import subprocess import sys sys.path.append('./src') sys.path.append('./models') @@ -38,7 +38,8 @@ import torch.nn as nn import torch.quantization as quantization -from settings import configure, model_logger_quant +from loggers import model_logger_quant +from VPRTempoQuantTrain import generate_model_name_quant from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub @@ -46,14 +47,22 @@ from prettytable import PrettyTable from metrics import recallAtK +#from main import parse_network + class VPRTempoQuant(nn.Module): - def __init__(self): + def __init__(self, args): super(VPRTempoQuant, self).__init__() - # Configure the network - configure(self) + # Set the arguments + self.args = args + for arg in vars(args): + setattr(self, arg, getattr(args, arg)) + + # Set the dataset file + self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') - model_logger_quant(self) + # Set the model logger and return the device + self.device = model_logger_quant(self) # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() @@ -63,6 +72,11 @@ def __init__(self): self.layer_dict = {} self.layer_counter = 0 + # Define layer architecture + self.input = int(args.dims[0]*args.dims[1]) + self.feature = int(self.input * 2) + self.output = int(args.num_places / args.num_modules) + """ Define trainable layers here """ @@ -106,7 +120,7 @@ def evaluate(self, model, test_loader): :param layers: Layers to pass data through """ # Determine the Hardtahn max value - maxSpike = 1//model.quant.scale + maxSpike = (1//model.quant.scale).item() # Define the sequential inference model self.inference = nn.Sequential( self.feature_layer.w, @@ -117,7 +131,7 @@ def evaluate(self, model, test_loader): nn.ReLU() ) # Initialize the tqdm progress bar - pbar = tqdm(total=self.number_testing_images, + pbar = tqdm(total=self.num_places, desc="Running the test network", position=0) # Initiliaze the output spikes variable @@ -136,12 +150,12 @@ def evaluate(self, model, test_loader): pbar.close() # Rehsape output spikes into a similarity matrix - out = np.reshape(np.array(out),(self.number_training_images,self.number_testing_images)) + out = np.reshape(np.array(out),(self.num_places,self.num_places)) # Calculate and print the Recall@N N = [1,5,10,15,20,25] R = [] # Create GT matrix - GT = np.zeros((self.number_testing_images,self.number_training_images), dtype=int) + GT = np.zeros((self.num_places,self.num_places), dtype=int) for n in range(len(GT)): GT[n,n] = 1 for n in N: @@ -176,17 +190,6 @@ def load_model(self, model_path): self.load_state_dict(torch.load(model_path, map_location=self.device), strict=False) -def generate_model_name_quant(model): - """ - Generate the model name based on its parameters. - """ - return ("VPRTempoQuant" + - str(model.input) + - str(model.feature) + - str(model.output) + - str(model.number_modules) + - '.pth') - def check_pretrained_model(model_name): """ Check if a pre-trained model exists and tell user if it does not. @@ -209,10 +212,11 @@ def run_inference_quant(model, model_name, qconfig): # Initialize the image transforms and datasets image_transform = ProcessImage(model.dims, model.patches) test_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.testing_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_testing_images) + base_dir=model.data_dir, + img_dirs=model.query_dir, + transform=image_transform, + skip=model.filter, + max_samples=model.num_places) # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, @@ -233,18 +237,4 @@ def run_inference_quant(model, model_name, qconfig): model.load_model(os.path.join('./models', model_name)) # Use evaluate method for inference accuracy - model.evaluate(model, test_loader) - -if __name__ == "__main__": - # Initialize the model - model = VPRTempoQuant() - # Set the quantization configuration - qconfig = quantization.get_default_qat_qconfig('fbgemm') - # Generate the model name - model_name = generate_model_name(model) - # Check if a pre-trained model exists - use_pretrained = check_pretrained_model(model_name) - if not use_pretrained == 'n': - # Run inference based on the user's input - with torch.no_grad(): - run_inference(model, model_name, qconfig) # Inference \ No newline at end of file + model.evaluate(model, test_loader) \ No newline at end of file diff --git a/VPRTempoQuantTrain.py b/networks/quantized/VPRTempoQuantTrain.py similarity index 86% rename from VPRTempoQuantTrain.py rename to networks/quantized/VPRTempoQuantTrain.py index 9457044..60b8a21 100644 --- a/VPRTempoQuantTrain.py +++ b/networks/quantized/VPRTempoQuantTrain.py @@ -38,19 +38,27 @@ import torch.nn as nn import torch.quantization as quantization -from settings import configure, model_logger_quant +from loggers import model_logger_quant from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm class VPRTempoQuantTrain(nn.Module): - def __init__(self): + def __init__(self, args): super(VPRTempoQuantTrain, self).__init__() + # Set the arguments + self.args = args + for arg in vars(args): + setattr(self, arg, getattr(args, arg)) + # Configure the network - configure(self) - model_logger_quant(self) + self.device = model_logger_quant(self) + + # Set the dataset file + self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') + # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() self.dequant = DeQuantStub() @@ -59,6 +67,15 @@ def __init__(self): self.layer_dict = {} self.layer_counter = 0 + # Define layer architecture + self.input = int(args.dims[0]*args.dims[1]) + self.feature = int(self.input * 2) + self.output = int(args.num_places / args.num_modules) + + # Set the total timestep count + self.location_repeat = len(args.database_dirs) # Number of times to repeat the locations + self.T = int((self.num_places / self.num_modules) * self.location_repeat * self.epoch) + """ Define trainable layers here """ @@ -161,11 +178,6 @@ def train_model(self, train_loader, layer, prev_layers=None): # Close the tqdm progress bar pbar.close() - # Free up memory - if self.device.type == "cuda": - torch.cuda.empty_cache() - gc.collect() - def forward(self, spikes, layer): """ Compute the forward pass of the model. @@ -197,7 +209,7 @@ def generate_model_name_quant(model): str(model.input) + str(model.feature) + str(model.output) + - str(model.number_modules) + + str(model.num_modules) + '.pth') def check_pretrained_model(model_name): @@ -221,11 +233,12 @@ def train_new_model_quant(model, model_name, qconfig): # Initialize the image transforms and datasets image_transform = ProcessImage(model.dims, model.patches) train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - img_dirs=model.training_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.number_training_images, - test=False) + base_dir=model.data_dir, + img_dirs=model.database_dirs, + transform=image_transform, + skip=model.filter, + max_samples=model.num_places, + test=False) # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, @@ -256,21 +269,4 @@ def train_new_model_quant(model, model_name, qconfig): model = quantization.convert(model, inplace=False) model.eval() # Save the model - model.save_model(os.path.join('./models', model_name)) - -if __name__ == "__main__": - # Initialize the model - model = VPRTempoQuantTrain() - # Set the quantization configuration - if model.quantize: - qconfig = quantization.get_default_qat_qconfig('fbgemm') - else: - raise ValueError("Quantization must be enabled for training.") - # Generate the model name - model_name = generate_model_name_quant(model) - # Check if a pre-trained model exists - use_pretrained = check_pretrained_model(model_name) - # Train or run inference based on the user's input - if not use_pretrained: - train_new_model_quant(model, model_name, qconfig) # Training - model.logger.info('Training complete.') \ No newline at end of file + model.save_model(os.path.join('./models', model_name)) \ No newline at end of file diff --git a/src/blitnet.py b/src/blitnet.py index b73be01..a7aa6ce 100644 --- a/src/blitnet.py +++ b/src/blitnet.py @@ -28,12 +28,10 @@ import torch.nn as nn import numpy as np -from settings import configure - class SNNLayer(nn.Module): def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, - stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None,inference=False): + stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None,inference=False,args=None): super(SNNLayer, self).__init__() """ dims: [input, output] dimensions of the layer @@ -46,8 +44,6 @@ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0, spk_force: boolean to force spikes """ - # Configure the network - configure(self) # Sets the testing configuration # Device self.device = device # Add different parameters depending if trainnig or running inference model diff --git a/src/dataset.py b/src/dataset.py index 84a17ad..bfc2a98 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -159,7 +159,7 @@ def __call__(self, img): return img class CustomImageDataset(Dataset): - def __init__(self, annotations_file, img_dirs, transform=None, target_transform=None, + def __init__(self, annotations_file, base_dir, img_dirs, transform=None, target_transform=None, skip=1, max_samples=None, test=True): self.transform = transform self.target_transform = target_transform @@ -170,7 +170,7 @@ def __init__(self, annotations_file, img_dirs, transform=None, target_transform= for img_dir in img_dirs: img_labels = pd.read_csv(annotations_file) - img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(img_dir, row.iloc[0]), axis=1) + img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(base_dir,img_dir, row.iloc[0]), axis=1) # Select specific rows based on the skip parameter img_labels = img_labels.iloc[::skip] diff --git a/src/settings.py b/src/loggers.py similarity index 64% rename from src/settings.py rename to src/loggers.py index d949bae..326eee7 100644 --- a/src/settings.py +++ b/src/loggers.py @@ -1,78 +1,9 @@ import os import torch import logging -import json -import sys -sys.path.append('./config') from datetime import datetime -def configure(model): - """ - Configure the model settings - """ - base_config = { - "dataset": "nordland", - "number_places": 500, - "number_modules": 1, - "database_dirs": ["spring", "fall"], - "query_dir": ["summer"], - "filter": 8, - "epoch": 4, - "patches": 15, - "dims": [56,56], - } - - # Write to base_config JSON file - with open('./config/base_config.json', 'w') as file: - json.dump(base_config, file, indent=4) - - # Set default paths if the provided paths are not valid directories - if not os.path.isdir(getattr(model, 'trainingPath', '')): - model.trainingPath = '../dataset/' - - if not os.path.isdir(getattr(model, 'testPath', '')): - model.testPath = '../dataset/' - - # Now, check if the dataset_file exists based on the determined paths - if not os.path.exists(os.path.join('./dataset', model.dataset + '.csv')): - model.dataset_file = os.path.join('../dataset', model.dataset + '.csv') - else: - model.dataset_file = os.path.join('./dataset', model.dataset + '.csv') - - # Now, check the conditions using assert statements - assert (len(model.dataset) != 0), "Dataset not defined, see README.md for details on setting up images" - assert (os.path.isdir(model.trainingPath)), "Training path not set or path does not exist, specify for model.trainingPath" - assert (os.path.isdir(model.testPath)), "Test path not set or path does not exist, specify for model.testPath" - assert (os.path.isdir(model.trainingPath + model.locations[0])), "Images must be organized into folders based on locations, see README.md for details" - assert (os.path.isdir(model.testPath + model.test_locations[0])), "Images must be organized into folders based on locations, see README.md for details" - - # Output the training and testing directories - model.training_dirs = [] - for n in model.locations: - model.training_dirs.append(os.path.join(model.trainingPath,n)) - model.testing_dirs = [] - for n in model.test_locations: - model.testing_dirs.append(os.path.join(model.testPath,n)) - - model.location_repeat = len(model.locations) # Number of times to repeat the locations - - """ - These parameters are used to define the network architecture - """ - - # Set the torch device - if not model.quantize: - model.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - else: - model.device = torch.device("cpu") - if model.device.type == "cuda": - torch.cuda.init() - torch.cuda.synchronize(device=model.device) - - # Determine the total number of timesteps across training images, modules, and location repeats - model.T = int((model.number_training_images / model.number_modules) * model.location_repeat) * model.epoch - def model_logger(model): """ Configure the model logger @@ -95,8 +26,7 @@ def model_logger(model): filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s") # Add the logger to the console (if specified) - if model.log: - model.logger.addHandler(logging.StreamHandler()) + model.logger.addHandler(logging.StreamHandler()) model.logger.info('') model.logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗') @@ -113,19 +43,19 @@ def model_logger(model): model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') model.logger.info('') - if model.quantize: - model.logger.info('Quantization enabled') - model.logger.info('Current device is: CPU') + if torch.cuda.is_available(): + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + device = "cuda:0" + current_device = torch.cuda.current_device() + model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) else: - if torch.cuda.is_available(): - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - current_device = torch.cuda.current_device() - model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) - else: - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - model.logger.info('Current device is: CPU') + model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) + model.logger.info('Current device is: CPU') + device = "cpu" model.logger.info('') + return device + def model_logger_quant(model): """ Configure the model logger @@ -148,8 +78,9 @@ def model_logger_quant(model): filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s") # Add the logger to the console (if specified) - if model.log: - model.logger.addHandler(logging.StreamHandler()) + model.logger.addHandler(logging.StreamHandler()) + + device = "cpu" model.logger.info('') @@ -169,4 +100,6 @@ def model_logger_quant(model): model.logger.info('') model.logger.info('Quantization enabled') model.logger.info('Current device is: CPU') - model.logger.info('') \ No newline at end of file + model.logger.info('') + + return device \ No newline at end of file diff --git a/src/utils.py b/src/utils.py deleted file mode 100644 index 9887328..0000000 --- a/src/utils.py +++ /dev/null @@ -1,299 +0,0 @@ -#MIT License - -#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer - -#Permission is hereby granted, free of charge, to any person obtaining a copy -#of this software and associated documentation files (the "Software"), to deal -#in the Software without restriction, including without limitation the rights -#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -#copies of the Software, and to permit persons to whom the Software is -#furnished to do so, subject to the following conditions: - -#The above copyright notice and this permission notice shall be included in all -#copies or substantial portions of the Software. - -#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -#SOFTWARE. - -# Get the 2D patches or the patch normalization - -''' -Imports -''' -import cv2 -import os -import math -import torch - -import numpy as np -import matplotlib.pyplot as plt -import utils as ut -import blitnet as bn - -from metrics import recallAtK, createPR -from timeit import default_timer -from os import path - -def dummy_bmm(device): - # Create some dummy tensors on CUDA - dummy_a = torch.randn(10, 10, device=device) - dummy_b = torch.randn(10, 10, device=device) - - # Perform a dummy bmm operation - torch.bmm(dummy_a.unsqueeze(0), dummy_b.unsqueeze(0)) - -def sad(self): - - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('Setting up Sum of Absolute Differences (SAD) calculations') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - sadcorrect = 0 - - # load the training images - self.location_repeat = 1 # switch to only do SAD on one dataset traversal - self.fullTrainPaths = self.fullTrainPaths[1] - self.test_true = False # testing images preloaded, load the training ones - # load the training images - self.imgs['training'], self.ids['training'] = ut.loadImages(self.test_true, - self.fullTrainPaths, - self.filteredNames, - [self.imWidth,self.imHeight], - self.num_patches, - self.testPath, - self.test_location) - - # create database tensor - for ndx, n in enumerate(self.imgs['training']): - if ndx == 0: - db = torch.unsqueeze(n,0) - else: - db = torch.concat((db,torch.unsqueeze(n,0)),0) - - def calc_sad(query, database, const): - - SAD = torch.sum(torch.abs(torch.sub((database * const), (query * const))), - (1,2),keepdim=True) - for n in range(2): - SAD = torch.squeeze(SAD,-1) - return SAD - - # calculate SAD for each image to database and count correct number - imgred = 1/(self.imWidth*self.imHeight) - sad_concat = [] - print('Running SAD') - - start = timeit.default_timer() - for n, q in enumerate(self.imgs['testing']): - pixels = torch.empty([]) - # create 3D tensor of query images - for o in range(self.number_testing_images): - if o == 0: - pixels = torch.unsqueeze(q,0) - else: - pixels = torch.concat((pixels,torch.unsqueeze(q,0)),0) - - sad_score = calc_sad(pixels, db, imgred) - - best_match = np.argmin(sad_score.cpu().numpy()) - if n == best_match: - sadcorrect += 1 - - end = timeit.default_timer() - p100r = round((sadcorrect/self.number_testing_images)*100,2) - print('') - print('Sum of absolute differences P@1: '+ - str(p100r)+'%') - print('Sum of absolute differences queried at ' - +str(round(self.number_testing_images/(end-start),2))+'Hz') - - -# plot similarity matrices -def plot_similarity(mat, name, cmap, ax=None, dpi=600): - - if ax is None: - fig, ax = plt.subplots(dpi=dpi,figsize=(8, 6)) - else: - fig = ax.get_figure() - - cax = ax.matshow(mat, cmap=cmap, aspect='equal') - fig.colorbar(cax, ax=ax, label="Spike amplitude") - ax.set_title(name,fontsize = 12) - ax.set_xlabel("Query",fontsize = 12) - ax.set_ylabel("Database",fontsize = 12) - - - -# plot weight matrices -def plot_weights(W, name, cmap, vmax, dims, ax=None): - newx = dims[0] - newy = dims[1] - - # loop through expert modules and output weights - init_weight = np.array([]) - for n in range(len(W[:,0,0])): - init_weight = np.append(init_weight, np.reshape(W[n,:,:].cpu().numpy(), (dims[2],))) - - # reshape the weight matrices - reshape_weight = np.reshape(init_weight, (newx, newy)) - - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.get_figure() - - # plot the weight matrix to specified subplot - cax = ax.matshow(reshape_weight, cmap=cmap, vmin=0, vmax=vmax) - fig.colorbar(cax, ax=ax, label="Weight strength",shrink=0.5) - - # set figure titles and labels - ax.set_title(name, fontsize = 12) - ax.set_xlabel("x-weights", fontsize = 12) - ax.set_ylabel("y-weights", fontsize = 12) - -# plot PR curves -def plot_PR(P, R, name, ax=None): - - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.get_figure() - - ax.plot(R, P) - ax.set_title(name, fontsize=12) - ax.set_xlabel("Recall", fontsize=12) - ax.set_ylabel("Precision", fontsize=12) - -# plot the recall@N -def plot_recallN(recallN, N_vals, name, ax=None): - - if ax is None: - fig, ax = plt.subplots() - else: - fig = ax.get_figure() - - ax.plot(N_vals, recallN) - ax.set_title(name, fontsize=12) - ax.set_xlabel("N", fontsize=12) - ax.set_ylabel("Recall", fontsize=12) - -# run recallAtK() function from VPR Tutorial -def recallAtN(S_in, GThard, GTsoft, N): - - # run recall at N over each value of N - recall_list = [] - for n in N: - recall_list.append(recallAtK(S_in, GThard, GTsoft, K=n)) - - return recall_list - -def sad(fullTrainPaths, filteredNames, imWidth, imHeight, num_patches, testPath, - test_location, imgs, ids, number_testing_images, number_training_images, - validation): - - print('') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('Setting up Sum of Absolute Differences (SAD) calculations') - print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') - print('') - - sadcorrect = 0 - # load the training images - imgs['training'], ids['training'] = ut.loadImages(False, - fullTrainPaths, - filteredNames, - [imWidth,imHeight], - num_patches, - testPath, - test_location) - del imgs['training'][number_training_images:len(imgs['training'])] - - # create database tensor - for ndx, n in enumerate(imgs['training']): - if ndx == 0: - db = torch.unsqueeze(n,0) - else: - db = torch.cat((db, torch.unsqueeze(n,0)), 0) - - def calc_sad(query, database, const): - SAD = torch.sum(torch.abs((database * const) - (query * const)), (1,2), keepdim=True) - for n in range(2): - SAD = torch.squeeze(SAD,-1) - return SAD - - # calculate SAD for each image to database and count correct number - imgred = 1/(imWidth * imHeight) - sad_concat = [] - - print('Running SAD') - correctidx = [] - incorrectidx = [] - - start = default_timer() - for n, q in enumerate(imgs['testing']): - pixels = torch.empty([]) - - # create 3D tensor of query images - for o in range(number_testing_images): - if o == 0: - pixels = torch.unsqueeze(q,0) - else: - pixels = torch.cat((pixels,torch.unsqueeze(q,0)),0) - - sad_score = calc_sad(pixels, db, imgred) - best_match = np.argmin(sad_score.cpu().numpy()) - - if n == best_match: - sadcorrect += 1 - correctidx.append(n) - else: - incorrectidx.append(n) - - if validation: - sad_concat.append(sad_score.cpu().numpy()) - - end = default_timer() - - p100r_local = round((sadcorrect/number_testing_images)*100,2) - print('') - print('Sum of absolute differences P@1: '+ str(p100r_local) + '%') - print('Sum of absolute differences queried at ' + str(round(number_testing_images/(end-start),2)) + 'Hz') - - GT = np.zeros((number_testing_images,number_training_images), dtype=int) - for n in range(len(GT)): - GT[n,n] = 1 - sad_concat = (1-np.reshape(np.array(sad_concat),(number_training_images,number_testing_images))) - P,R = createPR(sad_concat,GT,GT,matching="single") - for n, ndx in enumerate(P): - P[n] = round(ndx,2) - R[n] = round(R[n],2) - - # make the PR curve - fig = plt.figure() - plt.plot(R,P) - fig.suptitle("Precision Recall curve",fontsize = 12) - plt.xlabel("Recall",fontsize = 12) - plt.ylabel("Precision",fontsize = 12) - plt.show() - - # calculate the recall at N - N_vals = [1,5,10,15,20,25] - recallN = ut.recallAtN(sad_concat, GT, GT, N_vals) - - return P,R,recallN,N_vals - -# clear the contents of the weights folder if retraining with same settings -def clear_weights(training_out): - if os.path.isfile(training_out + 'net.pkl'): - os.remove(training_out+'net.pkl') - if os.path.isfile(training_out + 'GT_imgnames.pkl'): - os.remove(training_out+'GT_imgnames.pkl') - if not os.path.isdir(training_out): - os.mkdir(training_out) \ No newline at end of file diff --git a/src/validation.py b/src/validation.py deleted file mode 100644 index 372dafa..0000000 --- a/src/validation.py +++ /dev/null @@ -1,303 +0,0 @@ -#MIT License - -#Copyright (c) 2023 Adam Hines, Michael Milford, Tobias Fischer - -#Permission is hereby granted, free of charge, to any person obtaining a copy -#of this software and associated documentation files (the "Software"), to deal -#in the Software without restriction, including without limitation the rights -#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -#copies of the Software, and to permit persons to whom the Software is -#furnished to do so, subject to the following conditions: - -#The above copyright notice and this permission notice shall be included in all -#copies or substantial portions of the Software. - -#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -#SOFTWARE. - -''' -Imports -''' -import os -import pickle -import timeit -import torch -import math - -import utils as ut -import numpy as np -import blitnet as bn -import matplotlib.pyplot as plt - -from metrics import createPR - -# used to validate the network training, reject network training if threshold not met -def validate(model): - - # unpickle the network - model.logger.info('Unpickling the network') - with open(model.training_out+'net.pkl', 'rb') as f: - net = pickle.load(f) - - model.logger.info('Validating network training') - # set input spikes for training data from one location - net['set_spks'][0] = ut.setSpikeRates( - model.imgs['training'][0:model.number_training_images], - model.ids['training'][0:model.number_training_images], - model.device, - [model.imWidth,model.imHeight], - True, - model.number_training_images, - model.number_modules, - model.intensity, - model.location_repeat) - - # correct place matches variable - numcorrect = 0 - - if model.validation: - outconcat = [] - - # run the test network on training data to evaluate network performance - start = timeit.default_timer() - for t in range(model.number_training_images): - tonump = np.array([]) - bn.testSim(net, device=model.device) - # output the index of highest amplitude spike - tonump = np.append(tonump, - np.reshape(net['x'][-1].cpu().numpy(), - [1,1,int(model.number_training_images)])) - nidx = np.argmax(tonump) - - if model.validation: - outconcat.append(tonump.tolist()) - - gt_ind = model.filteredNames.index(model.filteredNames[t]) - - # adjust number of correct matches if GT matches peak output - if gt_ind == nidx: - numcorrect += 1 - - end = timeit.default_timer() - - # network must match >75% of training places to be successful - p100r = (numcorrect/model.number_training_images)*100 - testFlag = (p100r>75) - if testFlag: # network training was successful - - model.logger.info('') - model.logger.info('Network training successful!') - model.logger.info('') - - model.logger.info('Performance details:') - model.logger.info("-------------------------------------------") - model.logger.info('P@100R: '+str(p100r)+ - '% | Query frequency: '+ - str(round(model.number_training_images/(end-start),2))+'Hz') - model.logger.info('') - - # create output folder (if it does not already exist) - if not os.path.isdir(model.training_out): - os.mkdir(model.training_out) - if not os.path.isdir(model.training_out+'images/'): - os.mkdir(model.training_out+'images/') - if not os.path.isdir(model.training_out+'images/training/'): - os.mkdir(model.training_out+'images/training/') - - # plot the similarity matrix for network validation - if model.validation: - outconcat = np.array(outconcat) - concatReshape = np.reshape(outconcat, - (model.number_training_images, - model.number_training_images)) - folderName = model.output_folder+'/similarity/' - os.mkdir(folderName) - - fig, axes = plt.subplots(1, 1, figsize=(10, 10)) - fig.suptitle('Training similarity', fontsize = 18) - cmap = plt.cm.gist_yarg - plot_name = "Similarity: network training validation" - ut.plot_similarity(concatReshape, - plot_name, - cmap, - ax=axes) - - # Reset network details - net['sspk_idx'] = [0,0,0] - net['step_num'] = 0 - net['spikes'] = [[],[],[]] - net['x'] = [[],[],[]] - net['set_spks'][0] = 0 - - # plot the weight matrices - cmap = plt.cm.magma - cmap_reverse = plt.cm.magma_r - - model.logger.info('Plotting weight matrices') - # make weights folder - weights_folder = model.output_folder+'/weights/' - os.mkdir(weights_folder) - - # get the maximum weight value for each set - IF_Exc = torch.max(net['W'][0]).cpu().numpy() - IF_Inh = torch.min(net['W'][1]).cpu().numpy() - FO_Exc = torch.max(net['W'][2]).cpu().numpy() - FO_Inh = torch.min(net['W'][3]).cpu().numpy() - - # find highest divisors of weight matrices for plotting - def closestDivisors(n): - factor1 = round(math.sqrt(n)) - while n%factor1 > 0: factor1 -= 1 - return factor1, n//factor1 - - factor1IF, factor2IF = closestDivisors(len(net['W'][0][0][0])*len(net['W'][0][0])*model.number_modules) - factor1FO, factor2FO = closestDivisors(len(net['W'][2][0][0])*len(net['W'][2][0])*model.number_modules) - - # initial weights - fig, axes = plt.subplots(2, 2, figsize=(10, 10)) - fig.suptitle('Initial weights', fontsize = 18) - - ut.plot_weights(W=model.init_weights[0], - name='I->F Excitatory Weights', - cmap=cmap, - vmax=IF_Exc, - dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)], - ax=axes[0, 0]) - - ut.plot_weights(W=model.init_weights[1], - name='I->F Inhibitory Weights', - cmap=cmap_reverse, - vmax=IF_Inh, - dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)], - ax=axes[0, 1]) - - ut.plot_weights(W=model.init_weights[2], - name='F->O Excitatory Weights', - cmap=cmap, - vmax=FO_Exc, - dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)], - ax=axes[1, 0]) - - ut.plot_weights(W=model.init_weights[3], - name='F->O Inhibitory Weights', - cmap=cmap_reverse, - vmax=FO_Inh, - dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)], - ax=axes[1, 1]) - - - plt.tight_layout() - plt.show() - - fig.savefig(weights_folder + 'Combined_Weights.pdf', dpi=600) - - # calculated weights - fig, axes = plt.subplots(2, 2, figsize=(10, 10)) - fig.suptitle('Calculated weights', fontsize = 18) - - ut.plot_weights(W=net['W'][0], - name='I->F Excitatory Weights', - cmap=cmap, - vmax=IF_Exc, - dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)], - ax=axes[0, 0]) - - ut.plot_weights(W=net['W'][1], - name='I->F Inhibitory Weights', - cmap=cmap_reverse, - vmax=IF_Inh, - dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)], - ax=axes[0, 1]) - - ut.plot_weights(W=net['W'][2], - name='F->O Excitatory Weights', - cmap=cmap, - vmax=FO_Exc, - dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)], - ax=axes[1, 0]) - - ut.plot_weights(W=net['W'][3], - name='F->O Inhibitory Weights', - cmap=cmap_reverse, - vmax=FO_Inh, - dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)], - ax=axes[1, 1]) - - - plt.tight_layout() - plt.show() - - fig.savefig(weights_folder + 'Combined_Weights.pdf', dpi=600) - - - else: - # log unsuccessful training details - model.logger.info('') - model.logger.info('Network training unsuccessful.') - model.logger.info('') - model.logger.info('Performance details:') - model.logger.info("-------------------------------------------") - model.logger.info('P@100R: '+str(p100r)+ - '% | Query frequency: '+ - str(round(model.number_training_images/(end-start),2))+'Hz') - model.logger.info('') - return testFlag - -# run PR and recall at N -def match_metrics(numpconc, output_folder, number_testing_images, number_training_images, logger): - - numpconc = np.array(numpconc).T - - # make the output folder - folderName = output_folder+'/similarity/' - if not os.path.isdir(folderName): - os.mkdir(folderName) - - # reshape similarity matrix - sim_mat = np.reshape(numpconc,(number_testing_images, number_training_images)) - - # generate the ground truth matrix - GT = np.zeros((number_testing_images, number_training_images), dtype=int) - for n in range(len(GT)): - GT[n,n] = 1 - - # create the main figure - fig, axes = plt.subplots(2, 2, figsize=(10, 10)) - fig.suptitle('Network metrics', fontsize = 18) - cmap = plt.cm.tab20c - - # plot the similarity matrices - ut.plot_similarity(sim_mat, 'VPRTempo similarity', cmap, ax=axes[0,0]) - ut.plot_similarity(GT, 'Ground truth', cmap, ax=axes[0,1]) - - # get the P & R - P, R = createPR(sim_mat, GT, GT, matching="single") - for n, ndx in enumerate(P): - P[n] = round(ndx,2) - R[n] = round(R[n],2) - - logger.info('Precision values: '+str(P)) - logger.info('Recall values: '+str(R)) - - # plot the PR curve - ut.plot_PR(P, R, 'Precision-recall curve',ax=axes[1,0]) - - # calculate the recall at N - N_vals = [1, 5, 10, 15, 20, 25] - recallN = ut.recallAtN(sim_mat, GT, GT, N_vals) - - # plot the recall at N - ut.plot_recallN(recallN, N_vals, 'Recall@N',ax=axes[1,1]) - - # show the full plot - plt.tight_layout() - plt.show() - - logger.info('') - for n, ndx in enumerate(recallN): - logger.info('Recall at N='+str(N_vals[n])+': '+str(round(ndx,2))) From c0a8c50d40f8720c263631a0bce9c600a0700dea Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 27 Nov 2023 11:56:39 +1000 Subject: [PATCH 31/69] Fixed up forward pass for base inference model to be an nn.Sequential --- networks/base/VPRTempo.py | 32 +++++++++++++------------------- 1 file changed, 13 insertions(+), 19 deletions(-) diff --git a/networks/base/VPRTempo.py b/networks/base/VPRTempo.py index 252e052..46f4e42 100644 --- a/networks/base/VPRTempo.py +++ b/networks/base/VPRTempo.py @@ -114,18 +114,23 @@ def evaluate(self, model, test_loader, layers=None): pbar = tqdm(total=self.num_places, desc="Running the test network", position=0) + + self.inference = nn.Sequential( + self.feature_layer.w, + nn.Hardtanh(0, 0.9), + nn.ReLU(), + self.output_layer.w, + nn.Hardtanh(0, 0.9), + nn.ReLU() + ) # Initiliaze the output spikes variable out = [] # Run inference for the specified number of timesteps for spikes, labels in test_loader: # Set device spikes, labels = spikes.to(self.device), labels.to(self.device) - # Pass through previous layers if they exist - if layers: - for layer_name in layers: - layer = getattr(self, layer_name) - spikes = self.forward(spikes, layer) - spikes = bn.clamp_spikes(spikes, layer) + # Forward pass + spikes = self.forward(spikes) # Add output spikes to list out.append(spikes.detach().cpu().tolist()) @@ -153,7 +158,7 @@ def evaluate(self, model, test_loader, layers=None): table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]]) model.logger.info(table) - def forward(self, spikes, layer): + def forward(self, spikes): """ Compute the forward pass of the model. @@ -164,7 +169,7 @@ def forward(self, spikes, layer): - Tensor: Output after processing. """ - spikes = layer.w(spikes) + spikes = self.inference(spikes) return spikes @@ -175,17 +180,6 @@ def load_model(self, model_path): self.load_state_dict(torch.load(model_path, map_location=self.device), strict=False) -def check_pretrained_model(model_name): - """ - Check if a pre-trained model exists and tell user if it does not. - """ - if not os.path.exists(os.path.join('./models', model_name)): - model.logger.info("A pre-trained network does not exist: please train one using VPRTempo_Trainer") - pretrain = 'n' - else: - pretrain = 'y' - return pretrain - def run_inference(model, model_name): """ Run inference on a pre-trained model. From 31f045b388d64715c61d154998388af34d781d2f Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 27 Nov 2023 15:58:31 +1000 Subject: [PATCH 32/69] Update README.md --- README.md | 68 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 36 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 8a862ea..200a4b9 100644 --- a/README.md +++ b/README.md @@ -25,35 +25,49 @@ If you use our code, please cite the following [paper](https://arxiv.org/abs/230 } ``` ## Installation and setup -We recommend installing dependencies for VPRTempo with [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html), however `conda` may also be used. VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) GPU acceleration. Follow the installation instructions based on your operating system and hardware specifications. - -### Windows & Linux -#### CUDA enabled installation -Use conda/mamba to create a new environment and install Python, CUDA tools, and dependencies. - +VPRTempo uses [PyTorch](https://pytorch.org/) with [CUDA](https://developer.nvidia.com/cuda-toolkit) GPU acceleration. Follow the installation instructions based on your operating system and hardware specifications. MacOS has no compatibly with CUDA. +### Get the repository +Download the Github repository. ```console -conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit opencv matplotlib +git clone https://github.com/QVPR/VPRTempo.git +cd ~/VPRTempo ``` -> **Note** -> Install the version of PyTorch-CUDA that is compatible with your graphics card, see [Start Locally | PyTorch](https://pytorch.org/get-started/locally/) for more details. +Once downloaded, please install the required dependencies to run the network through one of the following options: + +### Option 1: Pip install +Dependencies for VPRTempo can downloaded from our PyPi package. -#### CPU only -To install using the CPU only, simply install Python + dependencies. ```console -conda create -n vprtempo python pytorch torchvision torchaudio cpuonly opencv matplotlib -c pytorch +# For Windows/Linux systems +!pip install vprtempo + +# For MacOS +!pip install vprtempomacos ``` -### MacOS -CUDA acceleration is not available on MacOS and the network will only use the CPU, so simply just need to install Python + dependencies. + +### Option 2: Local requirements install +Dependencies can be installed either through our provided `requirements.txt` files. + ```console -conda create -n vprtempo -c conda-forge python opencv matplotlib -c pytorch pytorch::pytorch torchvision torchaudio +# For Windows/Linux +!pip install -r requirements.txt + +# For MacOS +!pip instal -r requirements_macos.txt ``` +### Option 3: Conda install +>**:heavy_exclamation_mark: Recommended:** +> Use [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html) instead of conda. -### Get the repository -Activate the environment & download the Github repository ```console -conda activate vprtempo -git clone https://github.com/QVPR/VPRTempo.git -cd ~/VPRTempo +# Windows/Linux - CUDA enabled +conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit prettytable tqdm numpy pandas scikit-learn + +# Windows/Linux - CPU only +conda create -n vprtempo python pytorch torchvision torchaudio cpuonly prettytable tqdm numpy pandas scikit-learn -c pytorch + +# MacOS +conda create -n vprtempo -c conda-forge python prettytable tqdm numpy pandas scikit-learn -c pytorch pytorch::pytorch torchvision torchaudio ``` ## Datasets @@ -65,7 +79,7 @@ VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure. ### Custom datasets -In general, data should be organised in the following way in order to train the network on multiple traversals of the same location. +In general, data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location. ``` --dataset @@ -76,17 +90,7 @@ In general, data should be organised in the following way in order to train the |--testing | |--test_traversal ``` -Speicfy the datapaths by altering `self.trainingPath` and `self.testPath` in `VPRTempo.py`. You can specify which traversals you want to train and test on by also altering `self.locations` and `self.test_location`. In the case above it would be the following; - -```python -self.trainingPath = '/training/' -self.testPath = '/testing/' - -self.locations = ["traversal_1","traversal_2"] -self.test_location = "test_traversal" -``` - -Image names for the same locations across traversals (training and testing) must be the same as they are imported based on a `.txt` file. +If you wish to specify a different directory where data is stored, modify the `--data_dir` default argument in `main.py`. Similarly, if you wish to train/query different traversals modify `--database_dirs` and `--query_dir` in `main.py` accordingly. ## Usage Both the training and testing is handled by the `VPRTempo.py` script. Initial installs do not contain any pre-defined networks and will need to be trained prior to use. From c5ca1f91d87008f098ba27c5e48caabfea425e25 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 27 Nov 2023 16:00:37 +1000 Subject: [PATCH 33/69] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 200a4b9..d11d767 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ # VPRTempo - Temporally encoded spiking neural network for visual place recognition +![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://creativecommons.org/licenses/by-nc-sa/4.0/) [![stars](https://img.shields.io/github/stars/QVPR/VPRTempo.svg?style=flat-square)](https://github.com/QVPR/VPRTempo/stargazers) [![QUT Centre for Robotics](https://img.shields.io/badge/collection-QUT%20Robotics-%23043d71?style=flat-square)](https://qcr.ai) From 2188bcb9d8dd260545c574a998c1120f44ff80ce Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 27 Nov 2023 16:27:57 +1000 Subject: [PATCH 34/69] Update README.md --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index d11d767..fbe824d 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,14 @@ This repository contains code for VPRTempo, a spiking neural network that uses t VPRTempo method diagram

+VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) network that employs custom learning rules based on the temporal codes of spikes in order to train layer weights. + +In this repository, we provide two networks: + - `VPRTempo`: Our base network architecture to perform visual place recognition (fp32) + - `VPRTempoQuant`: A modified base network with [Quantization Aware Training (QAT)](https://pytorch.org/docs/stable/quantization.html) enabled (int8) + +To use VPRTempo, please follow the instructions below for installation and usage. + ## License & Citation This repository is licensed under the [MIT License](./LICENSE) From 6096c210060d6a2967789d860a4c2a8afd7a14f5 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:02:55 +1000 Subject: [PATCH 35/69] Update setup --- setup.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/setup.py b/setup.py index ecb2740..85cc2ab 100644 --- a/setup.py +++ b/setup.py @@ -9,22 +9,20 @@ # define the base requires needed for the repo requirements = [ - 'matplotlib', 'torch', 'torchvision', 'torchaudio', + 'numpy', + 'pandas', + 'tqdm', + 'prettytable', + 'scikit-learn' ] -# workaround as opencv-python does not show up in "pip list" within a conda environment -# we do not care as conda recipe has py-opencv requirement anyhow -is_conda = os.path.exists(os.path.join(sys.prefix, 'conda-meta')) -if not is_conda: - requirements.append('opencv-python') - # define the setup setup( name="VPRTempo", - version="1.0.1", + version="0.2.0", description='VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition', long_description=long_description, long_description_content_type='text/markdown', @@ -33,7 +31,7 @@ url='https://github.com/QVPR/VPRTempo', license='MIT', install_requires=requirements, - python_requires='>=3.8', + python_requires='>=3.6', classifiers=[ # 3 - Alpha # 4 - Beta @@ -55,6 +53,5 @@ packages=find_packages(), keywords=['python', 'place recognition', 'spiking neural networks', 'computer vision', 'robotics'], - scripts=['VPRTempo.py'], - package_data={'':['nordland_imageNames.txt','orc_imageNames.txt']} + scripts=['main.py'], ) From cf0701d8011d843c18cc77e152587c642bc22bf0 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:03:15 +1000 Subject: [PATCH 36/69] Create main.yml --- .github/workflows/main.yml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 .github/workflows/main.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..bf2bc34 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,29 @@ +name: Update PyPi Package + +on: + push: + branches: [ main ] # Trigger on push to main branch. Adjust as needed. + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Check out code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' # Specify the Python version. + + - name: Install dependencies + run: | + pip install setuptools wheel twine + + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* From 656c6421ddcf0d1127cdc4380ec49ff42086acb0 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:04:49 +1000 Subject: [PATCH 37/69] Remove old workflow --- .github/workflows/python-publish.yml | 29 ---------------------------- 1 file changed, 29 deletions(-) delete mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml deleted file mode 100644 index 0a80fdb..0000000 --- a/.github/workflows/python-publish.yml +++ /dev/null @@ -1,29 +0,0 @@ -# This workflows will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries - -name: Upload Python Package - -on: - release: - types: [created] - -jobs: - deploy: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} - run: | - python setup.py sdist bdist_wheel - twine upload dist/* From cea563e458e3bf5df199b87d988ef64340cc9b22 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:09:25 +1000 Subject: [PATCH 38/69] Update main.yml --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index bf2bc34..1ea5172 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -23,7 +23,7 @@ jobs: - name: Build and publish env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} run: | python setup.py sdist bdist_wheel twine upload dist/* From 8167e441fe7d60c3487d477f30e03332935047c4 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:14:09 +1000 Subject: [PATCH 39/69] Test --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index fbe824d..6b8319a 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ In this repository, we provide two networks: To use VPRTempo, please follow the instructions below for installation and usage. ## License & Citation -This repository is licensed under the [MIT License](./LICENSE) +This repository is licensed under the [MIT License](./LICENSE) a If you use our code, please cite the following [paper](https://arxiv.org/abs/2309.10225): ``` From 56adcb000b787e79f2024fa5a55072e12a0dea2d Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:20:26 +1000 Subject: [PATCH 40/69] Test --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6b8319a..85cd2f2 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ In this repository, we provide two networks: To use VPRTempo, please follow the instructions below for installation and usage. ## License & Citation -This repository is licensed under the [MIT License](./LICENSE) a +This repository is licensed under the [MIT License](./LICENSE) If you use our code, please cite the following [paper](https://arxiv.org/abs/2309.10225): ``` From 1aeeeb0501c5bd8ece3c5d39afc4b0619ef1c44f Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:49:40 +1000 Subject: [PATCH 41/69] Update README.md --- README.md | 49 +++++++++++++++++++++---------------------------- 1 file changed, 21 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 85cd2f2..17e04b9 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,10 @@ -# VPRTempo - Temporally encoded spiking neural network for visual place recognition +# VPRTempo - A Temporally Encoded Spiking Neural Network for Visual Place Recognition ![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://creativecommons.org/licenses/by-nc-sa/4.0/) [![stars](https://img.shields.io/github/stars/QVPR/VPRTempo.svg?style=flat-square)](https://github.com/QVPR/VPRTempo/stargazers) [![QUT Centre for Robotics](https://img.shields.io/badge/collection-QUT%20Robotics-%23043d71?style=flat-square)](https://qcr.ai) ![GitHub repo size](https://img.shields.io/github/repo-size/QVPR/VPRTempo.svg?style=flat-square) +[![PyPI downloads](https://img.shields.io/pypi/dw/VPRTempo.svg)](https://pypistats.org/packages/VPRTempo) This repository contains code for VPRTempo, a spiking neural network that uses temporally encoding to perform visual place recognition tasks. The network is based off of [BLiTNet](https://arxiv.org/pdf/2208.01204.pdf) and adapted to the [VPRSNN](https://github.com/QVPR/VPRSNN) framework. @@ -34,7 +35,7 @@ If you use our code, please cite the following [paper](https://arxiv.org/abs/230 } ``` ## Installation and setup -VPRTempo uses [PyTorch](https://pytorch.org/) with [CUDA](https://developer.nvidia.com/cuda-toolkit) GPU acceleration. Follow the installation instructions based on your operating system and hardware specifications. MacOS has no compatibly with CUDA. +VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) acceleration. Please use one of the following options below to install the required dependencies, and if desired follow the instructions to install CUDA for your hardware and operating system. ### Get the repository Download the Github repository. ```console @@ -44,26 +45,20 @@ cd ~/VPRTempo Once downloaded, please install the required dependencies to run the network through one of the following options: ### Option 1: Pip install -Dependencies for VPRTempo can downloaded from our PyPi package. +Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/). -```console -# For Windows/Linux systems -!pip install vprtempo - -# For MacOS -!pip install vprtempomacos +```python +pip3 install vprtempo ``` +If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system. ### Option 2: Local requirements install Dependencies can be installed either through our provided `requirements.txt` files. -```console -# For Windows/Linux -!pip install -r requirements.txt - -# For MacOS -!pip instal -r requirements_macos.txt +```python +pip3 install -r requirements.txt ``` +As above, if you wish to install CUDA please visit [PyTorch - Get Started](https://pytorch.org/get-started/locally/). ### Option 3: Conda install >**:heavy_exclamation_mark: Recommended:** > Use [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html) instead of conda. @@ -88,26 +83,24 @@ VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure. ### Custom datasets -In general, data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location. +For convenience, all data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location. ``` --dataset - |--training - | |--traversal_1 - | |--traversal_2 - | - |--testing - | |--test_traversal + |--traversal_1 + |--traversal_2 + |-- ... + |--test_traversal ``` -If you wish to specify a different directory where data is stored, modify the `--data_dir` default argument in `main.py`. Similarly, if you wish to train/query different traversals modify `--database_dirs` and `--query_dir` in `main.py` accordingly. - +Running `nordland.py` script will automatically do this for you. ## Usage -Both the training and testing is handled by the `VPRTempo.py` script. Initial installs do not contain any pre-defined networks and will need to be trained prior to use. +Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operated either through the command terminal or directly running the script. See below for more details. ### Pre-requisites -* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland or custom datasets) -* The VPRTempo `conda` environment has been activated +* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland dataset) +* The VPRTempo dependencies have been installed and/or the conda environment has been activated + +### Pre-trained model -Once these two things have been setup, run `VPRTempo.py` to train and test your first network with the default settings. ## Issues, bugs, and feature requests If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). From 06ac16a55206bfc0f0c75d1c3fa639e7ed4b6286 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:51:43 +1000 Subject: [PATCH 42/69] Added base pre-trained model to git lfs From a73a3c50e73a8f63edadccc7f3b3acbeb454c8d1 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 10:55:47 +1000 Subject: [PATCH 43/69] Upload quantized model to lfs From 2e1a196afcc3d94da1db91c6163db1859fe92067 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 11:09:20 +1000 Subject: [PATCH 44/69] Update workflow to push on release not on push --- .github/workflows/main.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 1ea5172..0be658e 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,8 +1,8 @@ -name: Update PyPi Package +name: Publish to PyPi on: - push: - branches: [ main ] # Trigger on push to main branch. Adjust as needed. + release: + types: [created] jobs: deploy: @@ -14,7 +14,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v2 with: - python-version: '3.x' # Specify the Python version. + python-version: '3.x' - name: Install dependencies run: | @@ -26,4 +26,4 @@ jobs: TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} run: | python setup.py sdist bdist_wheel - twine upload dist/* + twine upload dist/* \ No newline at end of file From 0f2af1eef9beb5d3897a869f86de9fc2d8b62cc0 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 11:31:21 +1000 Subject: [PATCH 45/69] Update README.md --- README.md | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 17e04b9..30c5b70 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This repository contains code for VPRTempo, a spiking neural network that uses t VPRTempo method diagram

-VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) network that employs custom learning rules based on the temporal codes of spikes in order to train layer weights. +VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) framework and employs custom learning rules based on the temporal codes of spikes in order to train layer weights. In this repository, we provide two networks: - `VPRTempo`: Our base network architecture to perform visual place recognition (fp32) @@ -48,7 +48,7 @@ Once downloaded, please install the required dependencies to run the network thr Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/). ```python -pip3 install vprtempo +pip3 install VPRTempo ``` If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system. @@ -95,12 +95,30 @@ For convenience, all data should be organised in the `./dataset` folder in the f Running `nordland.py` script will automatically do this for you. ## Usage Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operated either through the command terminal or directly running the script. See below for more details. -### Pre-requisites +### Prerequisites * Training and testing data is organized as above (see **Datasets** on how to set up the Nordland dataset) * The VPRTempo dependencies have been installed and/or the conda environment has been activated -### Pre-trained model +### Pretrained models +We provide two pretrained models, for `VPRTempo` and `VPRTempoQuant`, that have learned a 500 place sequence from two Nordland traversals (Spring & Fall) which can be used to inference with Summer or Winter. To get the pretrained models, please download them: +```console +# Ensure your directory is set to VPRTempo +cd ~/VPRTempo + +# If not already installed, install Git lfs +git lfs install + +# Download the pretrained models +git lfs pull +``` +### Run the inference network +To run the inference network, there are two options: + +#### Command terminal +```console +python main.py +``` ## Issues, bugs, and feature requests If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). 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+ Example of the base VPRTempo networking running +

+ +To run the quantized network, parse the `--quantize` argument. +```console +python main.py --quantize +``` +

+ Example of the quantized VPRTempo networking running +

+ +### Run `main.py` in IDE ## Issues, bugs, and feature requests If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). 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-### Run `main.py` in IDE +#### IDE +You can also run VPRTempo through your IDE by running `main.py`. Change the `bool` flag for `use_quantize` to `True` if you wish to run VPRTempoQuant. + +### Train new network +If you do not wish to use the pretrained models or you would like to train your own, we can parse the `--train_new_model` flag to `main.py`. Note, if a pretrained model already exists you will be prompted if you would like to retrain it. +```console +# For VPRTempo +python main.py --train_new_model + +# For VPRTempoQuant +python main.py --train_new_model --quantize +``` +

+ Example of the training VPRTempo networking running +

+ +Similarly above, if you wish to run the training through an IDE then change the `bool` flag for `train_new_model` to `True`. ## Issues, bugs, and feature requests If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). 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z4a^ftxkB8@tc%D7Q{*~&Bp3;c(c*3d`s!T4Iiwl&EB))6yY=PGLPzZbbh16^MBbb9 zGH|OS7D;qDnV0zO>gIaERvG}Ni!~F2pbT3a*0kBsVo0~lM?v;4U~*2>;4u8Gc9rCd-T`<0!PwYu%|erA2+;>S4v7YnSv>v8iGY1h*QzN!KPR#dJ|6X|I2;DEt8(gGbCr%}`*!w)$Q@JzLs1dqPqy5frazZCX< zY19hTK>`c|zyX6P9#cBWl19B67@U}ElU0bZm)IN+&M(F{w+m6DpN8WS${0GqCV^RA zPNwtf+{b~0#6sVGe^<~eW)=?$6g3Tl2Zw`U$uu`cHqoTVPUrPBSHc2}0bP84NIQeU z05B{W`$7dyg#mh*2?l@(q3?R#h5;TUfsOzG15ZgrK~&R_g_H+=O2PLR8-}1pDYzB) z4V7R3*l#R`if;hm5V~Ce+^#*dwk!u=qsUhJJ;3f8zY)_1_LW*oTf4O<+^yB0R%=fx z)n6O+r_IK*PWMT#|6BkZ4Sww;{{Voa$9@;{Nieci|_7UrDagji08GFgWVfB7gH6xr=Q8V0}3Sw1%~#_L0_&xROMN zX|HA`QE5}aY}oAbZQ3v5^~6eHwy|z7ng$JGypK!1()45lefjZ?c^bK zGCg+9tRV=!MTLjkQ_CcxGk+8}4~F=L?DY1S5bzwZ*+fy8f%adC8SC4ASbVy_`?Ib- zOuDEadpa5wtpcejVX!V_eQz;x=r{%=)*%443UOK=>+{tYPg@5u`}5KN0u$v-Rr^f4 QKL7v#07*qoM6N<$f{mKoHUIzs From c1ceb730b06c524494511f809575183ca19b1b33 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:38:34 +1000 Subject: [PATCH 59/69] Update README.md --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index bef9aa9..3ef4a4f 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,14 @@ In this repository, we provide two networks: To use VPRTempo, please follow the instructions below for installation and usage. +## :start: Update v1.1.0: What's new? + - Full integration of VPRTempo into torch.nn architecture + - Quantization Aware Training (QAT) enabled to train weights in int8 space + - Addition of tutorials in Jupyter Notebooks to learn how to use VPRTempo as well as explain the computational logic + - Simplification of weight operations, reducing to a single weight tensor - allowing positive and negative connections to change sign during training + - Easier dependency installation with PyPi/pip + - And more! + ## License & Citation This repository is licensed under the [MIT License](./LICENSE) From 210778f989084e749eaedc89eb26b1eb51727d10 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:38:53 +1000 Subject: [PATCH 60/69] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3ef4a4f..3a4b5ab 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ In this repository, we provide two networks: To use VPRTempo, please follow the instructions below for installation and usage. -## :start: Update v1.1.0: What's new? +## :star: Update v1.1.0: What's new? - Full integration of VPRTempo into torch.nn architecture - Quantization Aware Training (QAT) enabled to train weights in int8 space - Addition of tutorials in Jupyter Notebooks to learn how to use VPRTempo as well as explain the computational logic From 65044584925161aee5927a84ed068dc4e9078df2 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:41:59 +1000 Subject: [PATCH 61/69] Adding in scale files, will go to lfs though --- tutorials/mats/1_BasicDemo/summer.png | Bin 0 -> 309004 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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.gitattributes | 3 + .gitignore | 2 - VPRTempo.egg-info/PKG-INFO | 126 +++++++ VPRTempo.egg-info/SOURCES.txt | 9 + VPRTempo.egg-info/dependency_links.txt | 1 + VPRTempo.egg-info/requires.txt | 8 + VPRTempo.egg-info/top_level.txt | 1 + dist/VPRTempo-0.2.0.tar.gz | Bin 0 -> 6831 bytes src/dataset.py | 1 - tutorials/0_BasicDemo.ipynb | 322 ---------------- ...o-Quantized.ipynb => 1111_BasicDemo.ipynb} | 8 +- tutorials/1_BasicDemo.ipynb | 354 ++++++++++++++++++ 12 files changed, 505 insertions(+), 330 deletions(-) create mode 100644 .gitattributes create mode 100644 VPRTempo.egg-info/PKG-INFO create mode 100644 VPRTempo.egg-info/SOURCES.txt create mode 100644 VPRTempo.egg-info/dependency_links.txt create mode 100644 VPRTempo.egg-info/requires.txt create mode 100644 VPRTempo.egg-info/top_level.txt create mode 100644 dist/VPRTempo-0.2.0.tar.gz delete mode 100644 tutorials/0_BasicDemo.ipynb rename tutorials/{1_BasicDemo-Quantized.ipynb => 1111_BasicDemo.ipynb} (96%) create mode 100644 tutorials/1_BasicDemo.ipynb diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5aeef22 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +*.zip filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 4052199..f7b6871 100644 --- a/.gitignore +++ b/.gitignore @@ -15,5 +15,3 @@ dataset/winter/ dataset/event.csv/ models/VPRTempo78415685001.pth models/VPRTempoQuant78415685001.pth -models/VPRTempo313662725001.pth -models/VPRTempoQuant313662725001.pth diff --git a/VPRTempo.egg-info/PKG-INFO b/VPRTempo.egg-info/PKG-INFO new file mode 100644 index 0000000..2d362b3 --- /dev/null +++ b/VPRTempo.egg-info/PKG-INFO @@ -0,0 +1,126 @@ +Metadata-Version: 2.1 +Name: VPRTempo +Version: 0.2.0 +Summary: VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition +Home-page: https://github.com/QVPR/VPRTempo +Author: Adam D Hines, Peter G Stratton, Michael Milford and Tobias Fischer +Author-email: adam.hines@qut.edu.au +License: MIT +Keywords: python,place recognition,spiking neural networks,computer vision,robotics +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Requires-Python: >=3.6 +Description-Content-Type: text/markdown +License-File: LICENSE + +# VPRTempo - A Temporally Encoded Spiking Neural Network for Visual Place Recognition +![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://creativecommons.org/licenses/by-nc-sa/4.0/) +[![stars](https://img.shields.io/github/stars/QVPR/VPRTempo.svg?style=flat-square)](https://github.com/QVPR/VPRTempo/stargazers) +[![QUT Centre for Robotics](https://img.shields.io/badge/collection-QUT%20Robotics-%23043d71?style=flat-square)](https://qcr.ai) +![GitHub repo size](https://img.shields.io/github/repo-size/QVPR/VPRTempo.svg?style=flat-square) +[![PyPI downloads](https://img.shields.io/pypi/dw/VPRTempo.svg)](https://pypistats.org/packages/VPRTempo) + +This repository contains code for VPRTempo, a spiking neural network that uses temporally encoding to perform visual place recognition tasks. The network is based off of [BLiTNet](https://arxiv.org/pdf/2208.01204.pdf) and adapted to the [VPRSNN](https://github.com/QVPR/VPRSNN) framework. + +

+ VPRTempo method diagram +

+ +VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) network that employs custom learning rules based on the temporal codes of spikes in order to train layer weights. + +In this repository, we provide two networks: + - `VPRTempo`: Our base network architecture to perform visual place recognition (fp32) + - `VPRTempoQuant`: A modified base network with [Quantization Aware Training (QAT)](https://pytorch.org/docs/stable/quantization.html) enabled (int8) + +To use VPRTempo, please follow the instructions below for installation and usage. + +## License & Citation +This repository is licensed under the [MIT License](./LICENSE) + +If you use our code, please cite the following [paper](https://arxiv.org/abs/2309.10225): +``` +@misc{hines2023vprtempo, + title={VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition}, + author={Adam D. Hines and Peter G. Stratton and Michael Milford and Tobias Fischer}, + year={2023}, + eprint={2309.10225}, + archivePrefix={arXiv}, + primaryClass={cs.RO} +} +``` +## Installation and setup +VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) acceleration. Please use one of the following options below to install the required dependencies, and if desired follow the instructions to install CUDA for your hardware and operating system. +### Get the repository +Download the Github repository. +```console +git clone https://github.com/QVPR/VPRTempo.git +cd ~/VPRTempo +``` +Once downloaded, please install the required dependencies to run the network through one of the following options: + +### Option 1: Pip install +Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/). + +```python +pip3 install vprtempo +``` +If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system. + +### Option 2: Local requirements install +Dependencies can be installed either through our provided `requirements.txt` files. + +```python +pip3 install -r requirements.txt +``` +As above, if you wish to install CUDA please visit [PyTorch - Get Started](https://pytorch.org/get-started/locally/). +### Option 3: Conda install +>**:heavy_exclamation_mark: Recommended:** +> Use [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html) instead of conda. + +```console +# Windows/Linux - CUDA enabled +conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit prettytable tqdm numpy pandas scikit-learn + +# Windows/Linux - CPU only +conda create -n vprtempo python pytorch torchvision torchaudio cpuonly prettytable tqdm numpy pandas scikit-learn -c pytorch + +# MacOS +conda create -n vprtempo -c conda-forge python prettytable tqdm numpy pandas scikit-learn -c pytorch pytorch::pytorch torchvision torchaudio +``` + +## Datasets +VPRTempo was developed to be simple to train and test a variety of datasets. Please see the information below about running a test with the Nordland traversal dataset and how to organize custom datasets. + +### Nordland +VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) traversal dataset. This software will work for either the full-resolution or down-sampled datasets, however our paper details the full-resolution datasets. + +To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure. + +### Custom datasets +For convenience, all data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location. + +``` +--dataset + |--traversal_1 + |--traversal_2 + |-- ... + |--test_traversal +``` +Running `nordland.py` script will automatically do this for you. +## Usage +Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operated either through the command terminal or directly running the script. See below for more details. +### Pre-requisites +* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland dataset) +* The VPRTempo dependencies have been installed and/or the conda environment has been activated + +### Pre-trained model + + +## Issues, bugs, and feature requests +If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). diff --git a/VPRTempo.egg-info/SOURCES.txt b/VPRTempo.egg-info/SOURCES.txt new file mode 100644 index 0000000..5b53485 --- /dev/null +++ b/VPRTempo.egg-info/SOURCES.txt @@ -0,0 +1,9 @@ +LICENSE +README.md +main.py +setup.py +VPRTempo.egg-info/PKG-INFO +VPRTempo.egg-info/SOURCES.txt +VPRTempo.egg-info/dependency_links.txt +VPRTempo.egg-info/requires.txt +VPRTempo.egg-info/top_level.txt \ No newline at end of file diff --git a/VPRTempo.egg-info/dependency_links.txt b/VPRTempo.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/VPRTempo.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/VPRTempo.egg-info/requires.txt b/VPRTempo.egg-info/requires.txt new file mode 100644 index 0000000..60165d6 --- /dev/null +++ b/VPRTempo.egg-info/requires.txt @@ -0,0 +1,8 @@ +torch +torchvision +torchaudio +numpy +pandas +tqdm +prettytable +scikit-learn diff --git a/VPRTempo.egg-info/top_level.txt b/VPRTempo.egg-info/top_level.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/VPRTempo.egg-info/top_level.txt @@ -0,0 +1 @@ + diff --git a/dist/VPRTempo-0.2.0.tar.gz b/dist/VPRTempo-0.2.0.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..ce1fe3ee229e80e2c151d7af42e607dab54ca6a1 GIT binary patch literal 6831 zcma)(u0R zbc#%ZOTKIP;OQrM*(+7hGE7_@YcS$`;oN2c+sU7K2fu8fhTzeS)hO(&+l8d*(8ml9 z4hF#z7U*-a07QEElV$tUM;$d^D!AA046FI?b)lBQ)dn^5Jv8~<0a59fOK}A+7u^$| zpbDK?evSGf6$|!>Z{eBI2k+Zsneum;Lq5W9$Fn(ouF74UjJgx;`qhDa@8ZJ3y_+jD zl59ZERiG14piz0DODgZR1+18NJ(COhs=l|7jdj$lNnZfcX-|OU`y!^x7!dUpn4$*$ zmA{I=ZWr8NUzQp0RT^AFE3bjh)$?1qJdPyPtr$Ni_*b7793T!s!FsNG5Z_}A89b~!|WWn@}E2`uu0pw3amOqY z9sRQhQbxJ7DC;72%fQ&%b6)7Sp)hL}RV+s`#9%R*h#?eNG7o2h(3TH4Ly7u0Pt$bx zUb!H3djD63Cd=`@orYeoa8j~t@<)t*>?_NDqJ9g0EVwcpaBhezU*!qD zsopQ8(oa-q&+xYfZQC+SVwZ^!o-sOetk`${Xgew3GdcH3e$r-h6!~BB)V^AvG~llg zl}I(V=tcU->iJ_8RES>PV(BDtGxeRx?rtMBzr8CB=lGSI$Xkx)FN9=0zR#}(&Trl1 z<@LH`H9*BgvdaW=)Pnj6Eivf@Eb>XkON7Spw1~$szt8fY_Cu#gur9Z1$1X*)-wNal z0dZs0uJg-vCm(J2*mi;ViNpiLv}4Vx2)PtTUc9P{qf||bys*nwXu_ny;o(xoOgs1} z8VO?efOrwLzWCYFeL-COSmK}(Lt??-Ya`XTuVCe{`dl_Pw~^hTMx;mwVn9ryrz|DE z@!g9P^~IsD=e~9IVw-d2ZNnESnT}}RHCH=mmE23Qgzq#7i|S|pyX>fhr&MCME%@Y4 zxE^1IPd^8nF>ETcSapF?=(usRuo_!uh^jPF#R>0~IIVmUdi$Jg0?gd^Gy;g?UF_jI z|HO;mOhuM9T^y@`x#W|TcPPb6{t##V5_1m>4PI5NrKW&`+|>0Emx5jY$ObYsmt5?j z{*1f!77<#xR{XGxsH_=Tkzik;EEeB33}#p}GV=f)vj*tB zaAp<~`B|`be`xzDm{oNBY z)qY)tbCp1F5t7XGZ6Ilo9Hdj^eeVIO+#l{D61Riu7*VzLEL31*Aj?lLPyWyPl*;!qg% zO*C%>Z`Rv&j1C~Um#=Y^-ls8P&>(c_ThUAG3Ce8U^vZFTS?lGNv9*fR@DuFb0?q>; zDXK^bSv&(mILzN}nXg!h6V;m`@?em7_{|)GW=vUDS)s)kB9>+Ni^?FgG%?9zw~5NX zZLszO>7>S)$&Ty$7q^D#KcD ztop^*e6W?MKa`0Fp?JhhPmjG&LW?3!W3S8*Hfw+Y=v4`0p|UFmEza%q7WO$t#S`e( zOfC~GdGHCdJP-WZAIa7KVN%o0+s9abo^z>}&Z3=!KjbcpuqgV=jJ4S0!o)xPL6#fN zPZk>`jc907n)=Z8FiPlZCyVS;4?xP!+Q%u;rMlkxeklO^#dh+wUg}>A%#bjJT;%dm;n05GYwAc+sW=<%eXWj~BV*)@Ybyiqmev_8X zmfd2C?U$N>=RX9c0$k1s*yPr^xPjm1>02ZP*+Ok*nK+RJOm(10PcW1#Kg0szF75^w zW*NJeQFKGSZXkN(;@SPpLAHp_S4EwWn^GObIJ+I$Ns zPr}6_Po^60(qAegTqJ!Ls(XDD%hs}(Pwo4-DBS0zb)Bj&Hy4V$hxrYz#QFMsO5fBXHbAyXaQ7|y(e58BcubPAZiQe7gYZLZUz zVQDC|e&Lgv-fRxJmN7~f4dJmjEabDMH2De}se3k-wovgUZKb;u^L3n+D(-Bc?vEQ* zD?|^?eXY4%iDncW5+KPL78jV!Zc{Ttk96aSLH29J=$z~$DK3}^eFo#fDasisDRqJ} zEk4tyPW2ZTSp?A}j)I@LtZK7RSU8Lx3|{x1L^yqcXJ%( z!g$JIv#r0|JW>Sw^>w91l9Y1DKS+lN1wSzv58i%YRLF2wB8I^Ot8xp5Om{Zqu^r$! 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Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with a pre-trained model.\n", - "\n", - "Before starting, make sure the following packages are installed and imported:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c879cd02-82db-441d-9476-fff1925bf494", - "metadata": {}, - "outputs": [], - "source": [ - "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n", - "try:\n", - " import cv2\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - "except:\n", - " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", - " import cv2\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "bb45df38-e333-46b2-9161-80e6ac367532", - "metadata": {}, - "source": [ - "### Image processing\n", - "\n", - "Let's have a look at how we process our images to run through VPRTempo. We utilize a technique called *patch normalization* to resize input images and normalize the pixel intensities. To start, let's see what the original image looks like before patch normalization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input image\n", - "raw_img = cv2.imread('./mats/0_basicdemo/summer.png')\n", - "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n", - "\n", - "# Plot the image\n", - "plt.imshow(rgb_img)\n", - "plt.title('Nordland Summer')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42", - "metadata": {}, - "source": [ - "What we have here is a 360x640 RGB image, which for processing through neural networks is too big (230,400 total pixels). So instead, we'll use patch normalization to reduce the image size down to a grayscale 28x28 image to just 784 pixels in total." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the patch normalized image\n", - "patch_img = np.load('./mats/0_basicdemo/summer_patchnorm.npy', allow_pickle=True)\n", - "\n", - "# Plot the image\n", - "plt.matshow(patch_img)\n", - "plt.title('Nordland Summer Patch Normalized')\n", - "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d404dfd2-10bd-4981-96ef-6092a9866fc6", - "metadata": {}, - "source": [ - "The reduced image dimensions with patch normalization allows for a decent representation of the full scene, despite the smaller size.\n", - "\n", - "### Convert images to spikes\n", - "\n", - "'Spikes' in the context of VPRTempo are a little different than conventional spiking neural networks. Typically, spikes from image datasets are converted into Poisson spike trains where the pixel intensity determines the number of spikes to propagate throughout a network. VPRTempo only considers each pixel as a single spike, but considers the *amplitude* of the spike to determine the timing within a single timestep - where large amplitudes (high pixel intensity) spike early in a timestep, and vice versa for small amplitudes. \n", - "\n", - "Let's flatten the patch normalized image into a 1D-array so we can apply our network weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bd6ae95-2a79-4b45-8a60-503079339739", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert 2D image to a 1D-array\n", - "patch_1d = np.reshape(patch_img, (784,))" - ] - }, - { - "cell_type": "markdown", - "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae", - "metadata": {}, - "source": [ - "### Load the pre-trained network weights\n", - "\n", - "Our network consists of the following architecture:\n", - "\n", - " - An input layer sparsely connected to a feature layer, 784 input neurons to 1568 feature neurons\n", - " - The feature layer fully connected to a one-hot-encoded output layer, 1568 feature neurons to 500 output neurons\n", - "\n", - "Each layer connection is trained separately and stored in different weight matrices for excitatory (positive) and inhibitory (negative) connections. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d98749a-8f28-477b-871c-93626e96786c", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input to feature excitatory and inhibitory network weights\n", - "if_exc = np.load('./mats/0_basicdemo/if_exc.npy')\n", - "if_inh = np.load('./mats/0_basicdemo/if_inh.npy')\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", - "\n", - "# Plot the excitatory weights\n", - "exc_plot = axes[0].matshow(if_exc.T)\n", - "axes[0].set_title('Input > Feature Excitatory Weights')\n", - "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Plot the inhibitory weights\n", - "inh_plot = axes[1].matshow(if_inh.T, cmap='viridis_r')\n", - "axes[1].set_title('Input > Feature Inhibitory Weights')\n", - "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Display the plots\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "826213d7-7721-440c-b1a8-47fb613339eb", - "metadata": {}, - "source": [ - "In this case, we have more inhibitory connections than we do excitatory for the input to feature layer. Let's load the feature to output layer spikes and visualise them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7609eae5-f584-4c98-9eb6-3c3cf1e2aa04", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input to feature excitatory and inhibitory network weights\n", - "fo_exc = np.load('./mats/0_basicdemo/fo_exc.npy')\n", - "fo_inh = np.load('./mats/0_basicdemo/fo_inh.npy')\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", - "\n", - "# Plot the excitatory weights\n", - "exc_plot = axes[0].matshow(fo_exc)\n", - "axes[0].set_title('Feature > Output Excitatory Weights')\n", - "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Plot the inhibitory weights\n", - "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", - "axes[1].set_title('Feature > Output Inhibitory Weights')\n", - "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Display the plots\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6", - "metadata": {}, - "source": [ - "### Propagate network spikes\n", - "\n", - "Now we'll propagate the input spikes across the layers to get the output. All we have to do is multiply the input spikes by the Input > Feature weights for both excitatory and inhibitory matrices and add them, then take the feature spikes and multiply them by the Feature > Output weights and do the smae thing. We'll also clamp spikes in the range of [0, 0.9] to prevent negative spikes and spike explosions.\n", - "\n", - "Let's do that and visualize the spikes as they're going through, we'll start with the Input to Feature layer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6c84239-c176-48c3-8954-25da5f989d61", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate feature spikes (positive and negative weights)\n", - "feature_spikes = np.matmul(if_exc,patch_1d) + np.matmul(if_inh,patch_1d)\n", - "feature_spikes = np.clip(feature_spikes, 0, 0.9)\n", - "\n", - "# Now create the line plot\n", - "plt.plot(np.arange(len(feature_spikes)), feature_spikes)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Feature Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", - "metadata": {}, - "source": [ - "Now let's propagate the feature layer spikes through to the output layer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate output spikes (positive and negative weights)\n", - "output_spikes = np.matmul(fo_exc,feature_spikes) + np.matmul(fo_inh,feature_spikes)\n", - "output_spikes = np.clip(output_spikes, 0, 0.9)\n", - "\n", - "# Now create the line plot\n", - "plt.plot(np.arange(len(output_spikes)), output_spikes)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Output Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f", - "metadata": {}, - "source": [ - "Success! We have propagated our input spikes across the layers to reach this output. Clearly, one of the output spikes has the highest amplitude. Our network weights were trained on 500 locations from a Fall and Spring traversal of Nordland. For this example, we passed the first location from the Summer traversal through the network to achieve this output - which clearly looks to have spikes Neuron ID '0' the highest!\n", - "\n", - "Let's prove that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23", - "metadata": {}, - "outputs": [], - "source": [ - "# Output the argmax from the output spikes\n", - "prediction = np.argmax(output_spikes)\n", - "print(f\"Neuron ID with the highest output is {prediction}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", - "metadata": {}, - "source": [ - "### Conclusions\n", - "\n", - "We have gone through a very basic demo of how VPRTempo takes input images, patch normalizes them, and propagates the spikes throughout the weights to achieve the desired matching output. Although this demonstration was performed using NumPy, the torch implementation is virtually the same except we use tensors with or without quantization. \n", - "\n", - "The purpose of splitting up excitatory and inhibitory weights is to allow for extra hometostatic normalization of inhibitory connections, which has proven to be critical in regulating overall system activity.\n", - "\n", - "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/1_BasicDemo-Quantized.ipynb b/tutorials/1111_BasicDemo.ipynb similarity index 96% rename from tutorials/1_BasicDemo-Quantized.ipynb rename to tutorials/1111_BasicDemo.ipynb index 2d87f15..3150d68 100644 --- a/tutorials/1_BasicDemo-Quantized.ipynb +++ b/tutorials/1111_BasicDemo.ipynb @@ -5,7 +5,7 @@ "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37", "metadata": {}, "source": [ - "## VPRTempoQuant - Basic Demo for a quantized version of VPRTempo\n", + "## VPRTempo & VPRTempoQuant - Basic Demo\n", "\n", "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", "\n", @@ -15,16 +15,14 @@ "\n", "### Introduction\n", "\n", - "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using Quantized Aware Training (QAT). We will view the system through the lens of integer based weights and spikes to see how a quantized version of VPRTempo operates under the hood.\n", - "\n", - "*Note: In this example, we will lose some amount of precision because we are only using integers for all calculations. In the deployed version, PyTorch quantizes and dequantizes spikes and weights so that some calculations are performed in the floating point domain. As such, this tutorial should be taken purely for conceptual understanding of a quantized version of VPRTempo and not for implementation purposes.*\n", + "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using our base system and the Quantized Aware Training (QAT) version. This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance. Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with pre-trained set of weights.\n", "\n", "Before starting, make sure the following packages are installed and imported:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c879cd02-82db-441d-9476-fff1925bf494", "metadata": {}, "outputs": [], diff --git a/tutorials/1_BasicDemo.ipynb b/tutorials/1_BasicDemo.ipynb new file mode 100644 index 0000000..b245d5e --- /dev/null +++ b/tutorials/1_BasicDemo.ipynb @@ -0,0 +1,354 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37", + "metadata": {}, + "source": [ + "## VPRTempo & VPRTempoQuant - Basic Demo\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using our base system and the Quantized Aware Training (QAT) version. This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance. Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with pre-trained set of weights.\n", + "\n", + "Before starting, make sure the following packages are installed and imported:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c879cd02-82db-441d-9476-fff1925bf494", + "metadata": {}, + "outputs": [], + "source": [ + "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n", + "try:\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + "except:\n", + " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", + " import cv2\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "66b11853-6e17-4884-ac92-d35d814add42", + "metadata": {}, + "source": [ + "Next, we will need to get the pretrained weights for the model. To get them and the other materials for the , we will use [Git Large File Storage](https://git-lfs.com/) to download " + ] + }, + { + "cell_type": "markdown", + "id": "bb45df38-e333-46b2-9161-80e6ac367532", + "metadata": {}, + "source": [ + "### Image processing\n", + "\n", + "Let's have a look at how we process our images to run through VPRTempo. We utilize a technique called *patch normalization* to resize input images and normalize the pixel intensities. To start, let's see what the original image looks like before patch normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the input image\n", + "raw_img = cv2.imread('./mats/1_BasicDemo/summer.png')\n", + "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n", + "\n", + "# Plot the image\n", + "plt.imshow(rgb_img)\n", + "plt.title('Nordland Summer')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42", + "metadata": {}, + "source": [ + "What we have here is a 360x640 RGB image, which for processing through neural networks is too big (230,400 total pixels). So instead, we'll use patch normalization to reduce the image size down to a grayscale 56x56 image to just 3136 pixels in total." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the patch normalized image\n", + "patch_img = np.load('./mats/1_BasicDemo/summer_patchnorm.npy', allow_pickle=True)\n", + "\n", + "# Plot the image\n", + "plt.matshow(patch_img)\n", + "plt.title('Nordland Summer Patch Normalized')\n", + "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d404dfd2-10bd-4981-96ef-6092a9866fc6", + "metadata": {}, + "source": [ + "The reduced image dimensions with patch normalization allows for a decent representation of the full scene, despite the smaller size.\n", + "\n", + "### Convert images to spikes\n", + "\n", + "'Spikes' in the context of VPRTempo are a little different than conventional spiking neural networks. Typically, spikes from image datasets are converted into Poisson spike trains where the pixel intensity determines the number of spikes to propagate throughout a network. VPRTempo only considers each pixel as a single spike, but considers the *amplitude* of the spike to determine the timing within a single timestep - where large amplitudes (high pixel intensity) spike early in a timestep, and vice versa for small amplitudes. \n", + "\n", + "Let's flatten the patch normalized image into a 1D-array so we can apply our network weights." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2bd6ae95-2a79-4b45-8a60-503079339739", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert 2D image to a 1D-array\n", + "patch_1d = np.reshape(patch_img, (3136,))" + ] + }, + { + "cell_type": "markdown", + "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae", + "metadata": {}, + "source": [ + "### Load the pre-trained network weights\n", + "\n", + "Our network consists of the following architecture:\n", + "\n", + " - An input layer sparsely connected to a feature layer, 784 input neurons to 1568 feature neurons\n", + " - The feature layer fully connected to a one-hot-encoded output layer, 1568 feature neurons to 500 output neurons\n", + "\n", + "Each layer connection is trained separately and stored in different weight matrices for excitatory (positive) and inhibitory (negative) connections. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6d98749a-8f28-477b-871c-93626e96786c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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88MEHHz4Z+G5FPvjggw8+nHL45S9/CQAYPXo0otEoNm/ejF/84he44YYbfMHABx988OFvCL5w4IMPPvjgwymHjIwM/PznP8fBgwfR39+PSy65BHfddRe+//3vn+qh+eCDDz58rsB3K/LBBx988MEHH3zwwQcfAAB+hWQffPDBBx988MEHH3zwAYAvHPjggw8++OCDDz744IMPCnzhwAcffPDBBx988MEHH3wA4AsHPvjggw8++OCDDz744IMCXzjwwQcffPDBBx988MEHHwB8hoWDxx57DMOHD0daWhry8/Oxbdu2Uz2k/1VQXV2N2bNnY+jQobAsC88995zxvRAC99xzD4YOHYr09HRMmTIFu3btMp7p7+/H0qVLcc455yAcDuOaa67BW2+9ZTzz8ccf42tf+xoGDx6MwYMH42tf+xqOHDlykmd3+sIDDzyAwsJCZGVl4bzzzsN1112Hffv2Gc/4uD858Otf/xo5OTkYNGgQBg0ahNLSUqxfv56/9/H+t4MHHngAlmXhjjvu4M98/J8cuOeee2BZlvEzZMgQ/t7H+8mFP/3pT7jhhhtw9tlnIyMjAxMmTEBLSwt/7+Pfh1MCp6o088mE3/72tyIUCoknnnhC7N69W9x+++0iHA6LQ4cOneqh/a+BF198UXzve98Tv/vd7wQA8fvf/974/t///d9FVlaW+N3vfic6OjrEvHnzxAUXXCCOHTvGzyxZskRceOGF4qWXXhI7duwQU6dOFbm5uSIWi/EzX/ziF0V2draora0VtbW1Ijs7W1x99dV/q2mednDVVVeJJ598UnR2doq2tjYxa9Yscckll4ju7m5+xsf9yYEXXnhBrFu3Tuzbt0/s27dPfPe73xWhUEh0dnYKIXy8/62gsbFRXHrppSInJ0fcfvvt/LmP/5MDd999txg3bpx45513+Oe9997j7328nzz46KOPxLBhw8SiRYtEQ0ODeP3118WmTZvEK6+8ws/4+PfhVMBnUjgoKioSS5YsMT4bPXq0+Pa3v32KRvS/G7zCgeM4YsiQIeLf//3f+bO+vj4xePBgsWzZMiGEEEeOHBGhUEj89re/5Wf+9Kc/Cdu2xR//+EchhBC7d+8WAER9fT0/U1dXJwCIvXv3nuRZ/e+A9957TwAQVVVVQggf939rOPPMM8V//Md/+Hj/G0FXV5cYMWKEeOmll0RlZSULBz7+Tx7cfffdIjc3N+l3Pt5PLtx1111i0qRJx/3ex78Ppwo+c25FAwMDaGlpwYwZM4zPZ8yYgdra2lM0qs8WvP766zh8+LCB49TUVFRWVjKOW1paEI1GjWeGDh2K7Oxsfqaurg6DBw9GcXExP1NSUoLBgwf7a6Xg6NGjAICzzjoLgI/7vxXE43H89re/RSQSQWlpqY/3vxHcdtttmDVrFqZPn2587uP/5MKBAwcwdOhQDB8+HF/96lfx2muvAfDxfrLhhRdeQEFBAf7P//k/OO+885CXl4cnnniCv/fx78Opgs+ccPDBBx8gHo/j/PPPNz4///zzcfjw4VM0qs8WEB5PhOPDhw8jJSUFZ5555gmfOe+88xLaP++88/y1gvQ1vfPOOzFp0iRkZ2cD8HF/sqGjowOZmZlITU3FkiVL8Pvf/x5jx4718f43gN/+9rfYsWMHHnjggYTvfPyfPCguLsaKFSuwYcMGPPHEEzh8+DDKysrw4Ycf+ng/yfDaa6/h17/+NUaMGIENGzZgyZIl+Kd/+iesWLECgL/vfTh1EDzVAzhZYFmW8b8QIuEzH/46+DQ49j6T7Hl/rSR84xvfwM6dO7F9+/aE73zcnxwYNWoU2tracOTIEfzud7/DjTfeiKqqKv7ex/vJgTfffBO33347Nm7ciLS0tOM+5+P/fx5mzpzJf48fPx6lpaW4/PLL8Zvf/AYlJSUAfLyfLHAcBwUFBbj//vsBAHl5edi1axd+/etfY+HChfycj38f/tbwmbMcnHPOOQgEAgnS8HvvvZcgffvw6YAyWZwIx0OGDMHAwAA+/vjjEz7z7rvvJrT//vvvf+7XaunSpXjhhRewZcsWXHTRRfy5j/uTCykpKbjiiitQUFCABx54ALm5uXjkkUd8vJ9kaGlpwXvvvYf8/HwEg0EEg0FUVVXhF7/4BYLBIOPGx//Jh3A4jPHjx+PAgQP+vj/JcMEFF2Ds2LHGZ2PGjMEbb7wBwKf3Ppw6+MwJBykpKcjPz8dLL71kfP7SSy+hrKzsFI3qswXDhw/HkCFDDBwPDAygqqqKcZyfn49QKGQ8884776Czs5OfKS0txdGjR9HY2MjPNDQ04OjRo5/btRJC4Bvf+AbWrFmDzZs3Y/jw4cb3Pu7/tiCEQH9/v4/3kwxXXnklOjo60NbWxj8FBQVYsGAB2tracNlll/n4/xtBf38/9uzZgwsuuMDf9ycZysvLE1JV79+/H8OGDQPg03sfTiH8LaOf/1ZAqUyXL18udu/eLe644w4RDofFwYMHT/XQ/tdAV1eXaG1tFa2trQKAeOihh0Rrayung/33f/93MXjwYLFmzRrR0dEh5s+fnzS92kUXXSQ2bdokduzYIaZNm5Y0vVpOTo6oq6sTdXV1Yvz48Z/r9Gr/+I//KAYPHiy2bt1qpBbs6enhZ3zcnxz4zne+I6qrq8Xrr78udu7cKb773e8K27bFxo0bhRA+3v/WoGcrEsLH/8mCb37zm2Lr1q3itddeE/X19eLqq68WWVlZfF/6eD950NjYKILBoPjxj38sDhw4IFauXCkyMjLE008/zc/4+PfhVMBnUjgQQohf/epXYtiwYSIlJUVMnDiRU0H68JfBli1bBICEnxtvvFEIIVOs3X333WLIkCEiNTVVVFRUiI6ODqON3t5e8Y1vfEOcddZZIj09XVx99dXijTfeMJ758MMPxYIFC0RWVpbIysoSCxYsEB9//PHfaJanHyTDOQDx5JNP8jM+7k8O/P3f/z3TjHPPPVdceeWVLBgI4eP9bw1e4cDH/8kBypsfCoXE0KFDxZw5c8SuXbv4ex/vJxfWrl0rsrOzRWpqqhg9erR4/PHHje99/PtwKsASQohTY7PwwQcffPDBBx988MEHH04n+MzFHPjggw8++OCDDz744IMPnw584cAHH3zwwQcffPDBBx98AOALBz744IMPPvjggw8++OCDAl848MEHH3zwwQcffPDBBx8A+MKBDz744IMPPvjggw8++KDAFw588MEHH3zwwQcffPDBBwC+cOCDDz744IMPPvjggw8+KPjMCgf9/f2455570N/ff6qH8rkDH/enDnzcnzrwcX/qwMf9qQMf96cOfNz7cLLgtC+C9thjj+GnP/0p3nnnHYwbNw4PP/wwJk+e/GffO3bsGAYPHoyjR49i0KBBf4OR+kDg4/7UgY/7Uwc+7k8d+Lg/deDj/tSBj3sfThac1paDZ555BnfccQe+973vobW1FZMnT8bMmTPxxhtvnOqh+eCDDz744IMPPvjgw2cOTmvh4KGHHsLixYvxD//wDxgzZgwefvhhXHzxxfj1r399qofmgw8++OCDDz744IMPnzkInuoBHA8GBgbQ0tKCb3/728bnM2bMQG1tbcLz/f39ht/dkSNHAABHjx49qeP0IRGOHTtm/Pbhbwc+7k8d+Lg/deDj/tSBj/tTB38L3Ash0NXVhaFDh8K2Ty99cl9fHwYGBj7VuykpKUhLS/sfHtFnB05b4eCDDz5APB7H+eefb3x+/vnn4/DhwwnPP/DAA7j33nsTPr/kkktO2hh9ODFcfPHFp3oIn1vwcX/qwMf9qQMf96cOfNyfOvhb4P7NN9/ERRdddNL7+Uuhr68Pw4dl4vB78U/1/pAhQ/D666/7AsJx4LQVDggsyzL+F0IkfAYA3/nOd3DnnXfy/0ePHsUll1yCix75V5xzYQwfvD0YsADELcAWgGNhflE9jkQzkBaI4vndOfyu0x/EFZcexps1FyN6Wa/7eSwACGDa2D3Y3DEWCAggasPOiMrve4OABdhpMTgDAdgpcTj9QSBuwc6IwokGYIfkZ3ZqDE5vEHa69mw0AMQs2OkxiGMpuOiK93D5oA9Q/coVsv2+IGbmdmDDvjFyLANSirdiFqxBA7hkyEc4ePA85Ix8A51vDOV+8FEqcFY/zjmrGwPrz8GRiXK8dijO7cKCfFbBBecexTvvD3bH3BPifi5eZaPwRztgWwLPthTKttLcd2leOj4J73ZaDP+avwH/vu1q2L02cHY/Pz9z9G7EnABe2j/KfbcnJHHn+Q0AOcP+hLZXLgH6bSAoGO8Fow6iec9w2GkxFF12CPV7L5Nrr3DLc+sLGuOmsdqpMTiRkNwnQcFr9uzUZbh+2y1yvgEHTl8QXy+uwuOtk2HZTsLcnZ4QEHLk+9EA4FiAJTA7px1rWycAQu0L6lPNreKKV7B1zyheHx0XCDoIHAsinhU33uF1FOB2ec9G5bydvqCcTzCOsstfQ+2rl+HSCz7EwXfOlnsXkHiyINcqFIf4OBXWmWYmDCcaQMXo/dj+6uXc/7NlT2DuplslztR+uDm3Bv+3qULu05Dg/eX0BfGDSS/gx81fMtvtC6Js3AF82BfGgT+dh8dKVuLW+gUY+cN38cW1u/CL1mncvx2K87nRzx4AWP02rMEDmDuuFc82F8q5a3gy9tVAAFYkyHPU2+R2QwJ3Fm3EwzuuNPE9EJC41s6NgZtICHZYPiuOpCA393W07b4USHEAR75Hc9H3nzVgw8oawNiLD6Nz/0WABayc+ji+9uI/4oEZq3DX1nmww1G5nraA3R0Ezurn9bf6ArDO7MePC5/D95quY9zaaTH8uPA5fKd+DuyAw31ePvR9vPr2uai44hVUv3KFbEftBatP4lI/GxOHv4mg5aB+z+VyrVOT4JdoD8HHqQDhmHCv00tFX0RGHHbQXVM6S2fUpuFIWR+cviAmjDqEnYcuhBMLwA7GjfOi090p4/Yy7QQApzsEOzMKcTQFIiOOgpGSThSMeR07Xr+Y18sZkGfVToslzGPkRe9hf8fFPBcdt17Q99Lcca14tq1Ark9PANYZA7xfiC453SHp6GsJ2Okx3DqhGr+smybvg64UWFkD7vx6g0BAyL9V/05/ELCEe88JwIrasKKWpLNqf9jpMThxm/cA70F1Txh3lzBpuziWAmtQoqaWx6CfD0WvELdcmtgfRMmYV9H42jCJ54DgcUy6/FVsf/Vyt6+uFIigY94l2lyN/aXh/PwLjuDdd87AhBFvoG3PpbzX9HaKLjuE+v2XwQ7GE/Yu9W1lDUAIC6I36NLxHnkv3FhYgxXbJ0OEFL2jOWtn/rILPsArb52n1sKC1WvDOmNAtqHW2OkJwYpbcm2PQ6MYl4DkOWIW96HTj8su+ACvvXOOiQ8NT040YPIr6jxMHP4mdrwuhQs6U05vEBDyvnLivXj7zn9HVlZWAr5PJQwMDODwe3G83jIMg7I+mUXjWJeD4fmHMDAw4AsHx4HTNlvRwMAAMjIy8P/+3//Dl7/8Zf789ttvR1tbG6qqqk74PkXxX/TLe2BnpMlDEdYOXiSEKRP2oPqAvDzKr3gVNa9cjlljOjEx8xDu3X4N7NS4JNgW+NIYc9nb2PfW+SYTqBNsKOLbEwRsl7A6XSHYWVH3AupVF3uqJO6IW5JQpsX4mXGXvIOOPZcAlsDknH2oeeVyHjtCDhCzAAu4Nr8VzzdMlO9nuO8zoXo/FU5WHHZaDHOzW1H33nC8+dbZBhM0fdRebNo3Gk5/AHZq3OxHQF6Wal7n1gbx/pQB2EF1ufQEZb/qN+NYzZn/7w3KsTZNlB9Yarx9QUwZvxdbd47mC0+HmaN3Y11LrsQXMblq7gg6sPoCECHHGIPTH3DH3CcvTZ4XPdMXBByYY1brKj5OgXWmvIz1veP0yUvZ6g3wJenEbCBmy4s5pj1L66Bw6kRCWFxWjeU1FbAz5R6YPbENH/RnouG1S93nu0Moz92PPU+NwZHJfcaFw+NUDBzvr+MwKLnD3kL7oYsQeCsN0bNjzEAT4yDS4/LS0/YMQpJ5RdCBdSwEBEUCQzBnXBvW7Jpgjona6AvC6rchUuTeuW/KGmz6eCyqD1zB4xRHUgDHgnVWP783beR+bNo5VjEZ8nKyw1GM+rej2PPNc3j9AeC2vCr8qrUSAOR7reNk3z1BWDFbMhVqXZ1ISK5zllyLhYV1mJm1E/M3/COQ4sizGwlh6aRN+FVrpbHvac98t2A97qu52t1DHnx7mQ8viGMpEKlxwLFQOPY1tLyeaNGkNbX6bIhUB1Ny9mJrx2hjnY1n4xqT0B+QX8TsxL2iaJG+1wcN6sWRd7Pc80FKk5gl8ZRMcCZhJCURB05EMjnCFkBAYNil7+NrF9fj/uaZcv5HUwAHEKlqbwGwMzXaQPSxPyCFGxJIFF4zm9Mxb/HLWN5ehmkj92Pz/pHuu4qZhwNmaHQ6+JPi3+Fb1dfLc6z6vPj8j3Ho1fNwV+U6PFg9C3Y4ynta31tOr8KzNlZxJAWPzvwN/qluvrFPWdnTE8T8ogY805kv95Il3Og+S8DqV/Qq3T27105sxQv1+fjpF1bhzehZqD9yGRp2Xy7PQq88T9YZA3B6g5iVtxPr946Vax6TDU+bsBtbD4xIoMGEW6RKJn1azh5sPTDC2EMISBbA6gnAOit5SkwnEuK/vfvreMBzF9o+1QRCunPpWe+9uGrS45i34TYg6GDQrhQcGx3lO7X4soOobx0Ja/AARl30Lva8OlTSWFoDRXPvKtgAAHiw+Sq5VoBxTyzJq8ZjNdMYZ1cMfR/7X71A3nkWEs50MuGIPnN6g5KmBgTTIjsjhumj9mJja3bCuogjKXJNvfdkj7xj4FgYcvFHOHzwbHk2hWXQQG7no1ReNycSQun4A6jbc7nEB/EmkRCmTdiNzW1jXYXFsRRMzd/l7oeBANM8fb2daA/e+sY9p11GJOLx3tv36YSD80YdOu3mdDrB6eVApkFKSgry8/Px0ksvGZ+/9NJLKCsr+2SNKfGHNnzhZYcAS6D6wBWSSQGwbecoON0hrN0xAfduvY6ZAEAjhgLYs/9C+RkR9t4ga29IKwNIAmSnyYvXiYRgCUsSC8UsSs1hXBKGtJi8uD2X8a43LpB9Cws1r1zOxA0CmDZur+wjPYa1u8fDzopKohcNYG5RE8THKUxsRUjIixPA6s48/Om9M/iCdvoDcHqC2NiarZgo9bliVEpHvyqJkmPJS8YGui+S/y/MaQAA3Fq+WRIhdSE7PUHJOGRF3TErWLt7vJyTA8zOb5PfO0CKLRnXRQW1yLv0TTiREAovOwSnO4T1e6WVhrRrdloMFjEYqXHJSFhqfYX7OV3UEr8WHi9bAScSwpyCFh7PnZM2yvFGQkpbolAcEvJ/x2OlciT+v1pW5+4PpR2bPn4PYAtXSAyb85+ZvxPL6yYzo2Gnx7C2OQ91na52c/74ZkAAda8Ox5HJfcgfLjNz6e04XXTpwv1Ora9B1LtCaN13KQAgen7UvVQEML+wQV5ycYvHyr8t+czM7F1JBQMAWN2aD3EkBfPHNzP+ACk0QEhrlp0eAwICP9g4VwoG2tisMwYgUhyeFzP4JBxnxHhM+344WJ0Dt39m3iIhbGodBwSEOoPSukUMAABYccnwiqMpsFPjWFFbjgU1/yAvfxJEHdmm0x9Q+9MCYrbENYD7tl7DgrwTCfHZJpiWs0d+12fud6cniJmjd/Ola6fHDMHAGQjw33aapBkiVTKO1QeukHtXrZE4muLurbQY7qpYJ7WBkG1fn99sKkC6Q5g9tgN3TX5R7g9F7Z3+AI58kOkqMWgPpMf4zM6Z2AInEoL4KNWdjAAzGjQG/buvlDZiQWkd7PQYDh08F/c3z4TTG0ThZYdgDR7AF0vb3blmRo19w4qT1DhEwF1o2rPdBb1Y3l4GpzeITW1j5Tz6XHpop8Uwv6iB6SUALC6rBgB8a/NXJe4zXabo0KFzYWdG8WD1LN5XqxsL4fQH8GjdlXC6Q4qeAA9e+QyKLzsIJxJC8FAarDMG8I1NCxUjayy3XOuAwKrGYknbaTxxy11HdXadgQDTp7W7x8MaNIBvbZmHR2q+gKbXhhntiqDA3OxW2OkxKRj0BOWcwlHY4Sg2K5xAWLhq9B73rH2YCljAmMvehp0ew+bWseZ+C0el8JMWcxlMdW5or+m0Z3ZBa8IeF0dT+DOnS+Lttrwq14oBuRf5jkyLYdaYTnmnRd3973SF+F4EgPnbb4EdjmJWbge6hylhX61Vw2uXwho8gH8regF7Xh3q0n26f1PjEHEbDzZfhQebr8JPin+nLKqyb0DSqmWtFUDcYny98va5Ln7SY+b4eoKuQqhb3XMKN+JYCuz0mPt9nyukbdo3OlFgi4RY2LOzopib3aoh1GJacfiNs3Dv1DW4a/KLWFxWrVml3b6ts/ox5uLDEEfknd/w2qXyPuwOMb7scFQKAZbg9/9pygYpUKq55488yIKBHY5idoEck1dZd7qBA/Gpfnw4MZy2lgNApjL92te+hmXLlqG0tBSPP/44nnjiCezatQvDhg074bskVV7y+A8w5JIBvPfBIBRfdhB1u69gBgRI1ILoWuVkWlnXfOpK92TG1bXL08bv4YM3OXcva0iOB9NG7semjjFSE0Smw0zP2DzWCsQsQytzPNCtAZlZfTh2OEsSiaBgqwACAnkjDqH90EWutmEgAPTbmFHQgY3t2ax5FEdSINLjsu80VwuzMKcBTzWXuZpFDcdOr9L2BzyapL4gCsdITSprj9iVSbp0kNkdQKIbRiQk27QE8kcdROvBi1n7RJaDwssOoem1Ycgf/gYuTv8Yz+3ONfCTP/wNZth07R+CArNyOrCuJRf5416TbZMVgLR2KQ4wkFxbm3Ah9AVRPPZVvviPp20eceF72HdgqKv51i1SSoAhQRBRy9CWMk4EpJZJt4p49mzeqINo3T3ctUI5Fspz96Pu1eEJY6oY8Qpb2ZihDAnDTe941g12ZaD17ZFCF+3vZJYRY30F5F6NJ+nD4wZE78zKb8e6HTkJa2A81x1CfvZraNl/KXKveBMdbwx1z38ybR4gXX7OGJDjCpJEprtNqP3RE8Sikhrs6z6faY6BF2LWNYuWPn5D0+poVkW1/tNyFX2J2a4FT7f+BITbh6a0SIYDYtQREq6rISsiJM6t91IhzuvnPWSnxjFlxAFX69gjNcL5Y15Hy57hss/eAJzMGOzUOCpGvIKtbWPc8WkaUDprZKFLZnFLtj/oczrfOtyQ04gVDWVuH10hTJu4m2nyFSPewWtvn+Ouh9pbusXRiyMASDmQjr6Lo4bFxLAaqDGXXv46aptHwTrTFazJ8sLaWa/1R6313KImtsoRnTHw0aXWV1mDEBTIveJNZIb6UbNrhLnXuqTlm5Urqg+71wbOca12ZDmeMuIANneMMdugfpXFls8tfa7RJJqH16rObam15vvFFrx/kz6v7lv7nTQ4F/S5eAo5QH/A3Ee61ZvuaXUWkllVRdyG6AuYArW25wAAIQdzJuzAc7tzTTdGZY2WLsOua7A4lgKR4iT0Z9wxmiKx+LKDqOsYkbC/x13yDna9cYFcf9WHF/ie077XcaCPxVgTnZ/RP/f0ReN0+vrw1tK7TzstO/F4b++76FNZDoaOeuu0m9PpBKet5QAA5s2bh4cffhj/9m//hgkTJqC6uhovvvjinxUMdHB6gzj8pzPhdIekRK20NxCuYOBEQlLjCc3FxBYQH0urAl/K9DcxvUq6t9NiUmNMBMWBvISU5mhb+2ipmdZ+ACkQkOVi8/6RSkMPl+FSY3MiIakF0CFqm0y2apPcC8SxFPfzmA2nNwinP4DurjT5jg3AsTAzt5M1gu2HZLDRzJxOtoggIKTmIy3GloHQEVsSoLjFZncAWLGzGF7LA1/2SivJJnjNCnJOagRXjd7jChFKk2NnxGCH4pieu9vV2CoXGNLowlKEX1hoPSj9JmdPbDMsBw27L4fTH0DL65dgTVOBHJ/ScN2Q0+gSbTVO2gd2ShzrWnNgh6No2Tuc8e70alq7UJzH7XTLcY266F3WZAFSoCFtc9Nrw9i6AqGtr6ZF3ffKULZuBN5MU3EEiklJj8FSrgTlo19hlzdDMCAGJEPuW4PRU64tdloMrZ3D2ScYQl76NR0j4AwEMP6St419tbVtjLvvM2Iu46wEA68i5geF62CnxbCgsB5wLNfFgCAoeO8z/jQcOJEQbsqtAyyBcWPedJk3jyUKUZOEkRVlXUsu7IwYbshpdL8jbV9vkIWOlj3D5d7fM4zPmNMT5Pk5kRBbxSAs9hW3w1G5B1LjmD2xjfuYPn4P/71iZzHqdms+7x5XMDstlmDClw+6zLmdLrW7bFFJl+MgJtcOOnIvK4sHzYHOK53N2WM7DBrhDKg9SQoIYblnqzeIabl7AGFhTmGzfOfcAZeJV7FOpK12uqTFzk6PoWXXZS6jeHY/Mypb28YALg/o7gm4Zw2OxNHk3L1sRSi9/HV2VSM66PQqy6TaDw2dlydos5/eWcRzJ19+xlk4itfePgdOV0ha6nShM2a751LtFUuzHg6M6HUZq36XsVxSupXnNXP0btS2jIJIEbzXyEJgp8Rd9xeluadnaK1XNxRyf9fnN+P6YrmHDWu2skzbGdIq2r73EtR0jnDvNyh6lhWFNWBjzIg/sWXYDkeBc5T7kDqTm/aNdtfUkffHVaP3cL9Ev2ALiGMprksS7VGdeRUu3YBj4ba8KiOOBY6F2WM7YKfEkZ7lusPMy25hy5QTCeHuwrVyT0RCcC7oc7XrAu6aOZb0BFBrN33UXgBA8dhX5djo7Gj3J1m8hfLd53Ov8MNuiMqKuqapQO43EgyUtd9OjbvzJquQRxAh2t/y+iVshSOaKI6moOG1S/mOdHqCmDZyPwCg44AW+OtY7hjJmqOsLBAWxoz8k9xDXSHDCkJWGvFRqvy8R95RfC76gsZazcjZBQgLU0YcUPiUCqMp4/bidIa4EJ/qx4cTw2ktHADArbfeioMHD6K/vx8tLS2oqKj4RO9Pyt4viWpAGIwFm5iV+43Xf3r8FW/BHrCYCS0d90qiy0Cvy8wQg0BMrc5gLCipYyJMZmCnJ4jN+0eqACXVri1cpouYSzKxK42vro2fXdCaaAFxNNM1IJnnXpvN2mm706UAoQjs+tbxrhk+V6aI3bB3DAsnOtGfNnK/HL/uikJaXTX3WRN2SkafLj7N3YTcqmh+4qg0xa5vGy/HAbA/KuGr+LKDUnBSAXbOQACwBOYUN/PzsnOXoVq3J9sNsIXJhLEvaFYUsARW1JUzQ0TfzRnXhtljO+S481vYRE7MlKFVjIQkA5Ou/PltYM+BC12CHwm5jC25Lim8Em6JuV9SvoW1cGt25Mv2U5RWPiMGOzMqLzbFI9TsvsIVxiIheXmqQDdysyJmkvphporGkRrH3IJmw0fWTomj/ZWLzX0FGAIACXFLy16GHY7ikelPG4LvvZuvgxMJYWV9qXTrIXcltfb3Tv49u484PUF2QQKAKSMOwA5HsbxuMsrHH8ChdcN5HFaqpmWMhLCwtMb4f35RA+ysKO6cvAF3F67F0zuL3DPiscTZWe5a2uGoXNuMGOYWNrNrxpzCZjeeyHIVAPrvD/oz+e9NHWMMlwJyFaK/nV7JcBKNuPC8I/JdjbZYar3ZvYOsFjGJwEWl2wEAt0/eKC1pWVG2VtiZUWPNiIEKWXFe49Ixr0om1fuc+r207GVsbh8DAGxls8gypVy05IeSps4taXKRKpBUywlI9y7qY1Hp9gTGzFJC67bW0fh+5QtweoOoe3U4bshpRPAj1+WPFTJQfVnArAk7jb6cPokLp0sJQI45T/kPkGZHMW3CbiyduAUAkDf2dfdr5bYJoYSrPsmAsVCrxWY93jYJd0zczG4/FNSvuyWOv+RtzBnXxnSP3iWhAACv4bSR++H0BLG6Mw+rO/MgjqXwGBeU1eG6se2oGPEKC7k01pn5Lh44Buesfux763x5vxAdov1G94VSJvH6xSxs2DuG917x+FcYJyLosEIGUPEkCu4uXIuZEyXtpDPz6PbpxtrBEnihPh9OfwD9vSFMH7UXdjiKVXUlKj5GPpxiKeZb/f9scyHHeJSPfQVOTxALS2ukwkXRtQ/7wywwpu1Od+em3Gyc3iBW7Cxmtyy5MfWNI2mVnSX3Jil/2B1HJe/gs0mCn8cyR4oOVh6pdRDHUrCqo8CwnJP7j50RY5c5Gtulw96X32Wqs+JYEMdSMLekidvYc+BC5I0+yMo3QN6T1uABTBlxANZZ/fIMCAvzS+phh6Mov+JVthJQOxvbswFLYOuBEbgzfxPTEj2o34fPD5zWbkV/DZDJ6YbN81H9ei4Qs7GgpA6rOgoSA2fVRW/Fpb8yS94qQA82TOKdoTQRylRLgVWsnVDEJsEFgkzPui+7Mgsz8SQNsGbKJm3w9NzdRuAlafL1vpy+IBYXb8Pymgp2K9GDqq8b2441zfnczw05jVhRX2a6sDiaRiXkJLg9pO5Nx/QvN2FtUx63y3NU4y6+7CDqdo7gz3kumrl3YWmNZN400+YdEzfj4R3TEvAGmK4R3sCwMRcfxq69FzMDTAz5LRO24/G2SdLlqb48AWesNZnYgY07xpuWGAe4tngHnm+amLCW4lgKhArcNVwhPC4huquBMSd6vlsyRdagAWNN7XAUmTvS0T2xN+EdwrOOe8M8rK+hd68n+Y5+j7/kbelaE7MxZcx+bO0cxcGhCftMH4u6sAFwACL/Vu9MGXFAaiVt15VN398zJnaw9tLbV+CtNETPjGN63i4jEBWQMRohK84XPoQUIpdO3oQ3+s/C2t3juU0r6mr+k7mq/Ev+Rvy0ZQauGr1HCsga3nVGzw5LH+Fn64uYBvCzniB3L57ImoO45bp7aEkIyOXKWFuvG4569rsF63Ff3dVS46/Wf864NqxuLDTcd7416Y/42bYvyrFre2HpxC14dMdUY2+cKKj6eHSTx0kuGXpWoJCTICzcVbABDzZfZbaj3OBojN710YNveW8R7hTOF5duw5PtpW6b5FblQLpMKYFibnYrumJpxhonw/PssR1Yu3u8Mc/gwTQMnBN3mX8BQFhYOmkTHq25MgHHXrpguL5oLowJdKk7xBnYAPM+oP/nl9RjVUMJ75ul5S/j0e3TE2iY3a9ciKg/rZ/vFqznoPGEsapx3ZRbJ2OlaIwBgWsntuL5xonuOqc4mDGhEzmZb8m9JqyEM0PtXTe2XbrpaC6a1+a1MZ11+oJYVFSDp2onuZnTTrAvCfQ7wekNYuglH+LtQ2djafnLHKNkrLUGxl7TaCQAw8VQPwNXjd6DceE/IQCHg9qT3gPa37PGdGJtcx7scBSzxnSi3wliY/N4o32v2y0pxiiJBfEkNG7Cv50uXXtX7Cw2XeX0oHltjy+duAWP1HzB3RNJaLMcUx/euOVHp50LDvF4h/YO/VRuRcNGv33azel0gtPecvDXwlalAbPDUazqkC4lyTRbdjiKLxa180G2w0qrSEF8REzIbKsyGTiREAcL25lRLC6vNqwDunWBNa9EeGyB70z9g2uW1VxR7MwoZo7ezeOZmdeBzftHukRM08Lp7hZ2WgzL6yazJmLKiAPsQqVrrQme3ll0XH9GOzPq+pRq0D+6V6XiTBSAyL2qrmME7qzYgDsrNkiiRhcSmVwFsKKxDDfkNDIend4gHtp2lWuKV646MyZ0mowvgK9XbDYsQXveHCKtB1oQltMTxAfRTM333bS86JaMja3ZgC04WM1SWVu8goETCaFixCuwBg1gadnLuHXSywCAWflusCWvubL0JHMdYU1tZhQiIOQFROugtFTHxrj9Ls6tZc0rAEzL283MFOHTHYB7uSSsrQAWltYYDMm1Ba1weoLoeGOo7N4CtraPxuRx+6Vg0K006OEoMrP6XPwp16xFJTW8D+kSIo0UXUjnpHbz2MiFiaxydjiKTftGY2FOg/JdF1g6aRMAmUUrflEf7HAUm/eP5ABtglUdBXiqbpLJuAoZXExZseaPl1YmFgy6Nd98QllXCD9tmQEAzDRyUKq6THXB7NnmQqWRlmvC7ktKMNADBgH30qd9wQqENHfNyepoCAZKIzq/sIHbssPSgnRf1TUGLpxICKsbC3FzWZVy9QEQt1xmTT1D54EEA1aODNgJ7elwc2m18b/BhAKagCh/3Th5m+HaRufiga1XyyBWciXRrDZexpp+yI2Ps7YoVzvC+bTcPehzQpg2cj/mj2+WwZtZak/aMM7H6s48pNgelzNi4Ehn0xWS2m1lUaSzNnCeGxTL7njhKH7VWmmM39hb3a5LnW5tsNNjmD++GfOLGtz+bYEledUGc7Y4t9YN4CaNdGYUq5qK3XYzYnIMXmuQDdx05VbkXfqmaSVTz91XdQ1bSWfltxvuN7SPn2wvdQUDtaeeb85zUwcPHoCdHsOmfaPxUMt0dw0pVkajRU4khGOxdGlxHSNdfxCzsXb3eKalcICoCCTG3HW5LpjGHlGJAIQ6QyRgH35/MCAsPLpturGfDBDuXcDz1s96f4Czd101eo9hCVq/Yzx+VjUTD1bNMixvAHBzWZV8Pxow2v9Dw0Qey7o92dJlV3Nh1HkAwgWgWT0deS+RqxkAlOTt52QdPU6K8R4FzdvhKLtp0v59dMdUeUf1BJE/7jXAkmd8VoG7DxLcmU9DcCAQ/4Q/fkDyn4fPvOXgksd/gKHD+vH2QRl8Rkwy5dVfv3esPJSe9HdOXxChzAFEu6QJmAK2dHB6gphb2Iw1uyYkahs8WmDA1ZoDSNTwIQmjDUkQF5S5Fg9dOwZoKUj1QFXVtzia4qYEpD76bDgpjhuYqi5br0baMIl7NDeUGx8AbsqtM7R1uoaINW/UpoZjXgulEZuX3cKp/5LhgefgALdXbjQ1nkogWFS+XWpNvFptLaWsvnY6I2JYFvSgZIUjssBw8KZiUBaVbUePk4JnGwtN4Y7WlTQ+x5mXV8O0dNImPFp3JeAAV1xx2A2apIuZrExxr3YnMQDa24dxMdJaqEDS6wubsLozTzLyFx3BFy7cK9fjONrk6aP2YuOO8Zhb1ITVjYVYVKpw3x3CtUU7WKvoREK4tnAHa/CNcXWFcG3xDtbOssbUcvFnMKAq+4kVtV0LH7nPEe41fC8oqcPK+lIWZIxUwlrbc8a1wbYEz58+v25su4xREZrlS7fo6X7XauzTJ+7CpvaxoDgMOzMKEbdhUU55TUtO+F9SthXLaqbKM+kJuk5mNdPHcO7ZXXj3jbMY/wD4LAFSaHl6Z5HZd0BgzIg/Yc/+C909oLueEf1QAY2IWUBaXAbee2mafpbJIiqA6RNdKw/RKHEsBdeUtBx3vXUaQG0mgz/3/awxndK1MImF0EufARzXwjEvu0Uy4ADgyPiLrW9fgY/eGyRjXbz+5br1jgKBrcRxGlYNPZGAOou6FZn28aqOgoSzuCSvGt3xNLzVe6bMQpQkaJ+DoFXyCn0v81iIFmiWhVn57TJ9tCZ8JKw7pQf2WDHpezr33rO2ur4QlrCQk32QFRLcpjY+/f7ygp5i9qrRe7C+bbys56Ix2llDuhDpSkuYZ8JaCCTeT0RHyNJNv+kug8yGpd/F3nbtcJTTmBIOaU5zs1uxujMv6XsAGP9ey7BhZUqSCMJ75yTsL2VxS5rcQbcqR2Ta7Y+jGVjTPOq0thy8uncIsj6h5aCry8Hlow+fdnM6neAzbzlwukN4+62zDG0JMb3rWnIxL7sFcICFJbUuoQAAAcRjNhPLgO5vTEF/GTEpGETIt1symaQBZA2lFoh4W16V1PA1FLrj0TTdunbDiUiGhiweEBaW17oxF3PGtUnBoCeIKTl7AWFJ/3fV9zWlbspOOxyVwYVn90tmOSOGOUXkt+/OzQ1Q1DRM5Neuxhd35La5avQevN1/hhtQR0yXumiIIbQzNY2fGsu3KtYbhPaZznzclFvHTAbhMUHbYwOPbP+CiyPys86K4qn6cm6f8Ed4p79vyq2Tweear74TCWHFzuJElwkaL2v0FWOm/GDtzChW7CzG6s482BkxXF/c6NlncIWJ44FjMjuPbp/O/rwsGHSFMLugFXdWbMCtpZsBAczI7zDnmMTdA4CRnlU+6Grylk7cwoGkqzvzMHP0biwuq4YjgN/WlrrZMKBiRlS7t+VVSUFZyPSPEGDG1M6M4vnmPHcfWQLPN01kLfysMZ3umLPkszxWtZb6HAzcqbGSC9bismq2uBHe5xY3MS5ZMNDifxgN2sW4uqlACgbkR6xZ+3RtspyPua+M9baAM0I9LgNnS4uQpRUcoz3B882IYXCgR7bhCZCUD1kJwtmisu24IacRC3Ma8P6HMvMY4R+QZ4kCeLtjqca7JOjve+t8LCiu5/gHfR7MNBPzawGXXvRBglaT6Nv3K19waSDk/Mh3GgBeqpcFJkXIwfPNeTy/7xasZxw7kZBM/6ntOQLdWpSMydfpstMbxLo92e50SRmkWbHkYFyaJmxTaeH0yNiCVQ0lck6KYVrTko8jR8JuvIZuuDuWYqyTjGly8eREQka6St6PKth0XnYLBxfz2NS4V9aWMn71u+Gx7Vfi6Z1F2HpghKSBaa7Ge252q7TKqiBoVgBZ4OQblBSBGX+t/fV7xxp7wYv/mfk7mXtYMmmLQXPpmbW7x8vAY/2sNRYCAQEREGg/cDEH7NL9saC0zo2xUe6MPOf+AJwumahgYXEtn6MNe8cogdsdox2OsmAgFwhsuVuSV834XTppExaVbWerp+xYcNuz8ySu2PVPxa/cXFaF5bUVCbRWnwsAbOwcxzTK6QpBpDpwIiE821DkBhfr1hplPVrXkmvgfUFRvbEedC97GXyDXlHdD/pOnfNrJ7YeV7DW21leWyGDsXtCJ3z2VIMfkHxy4DMvHHhn6D2Iz3TmAzawor4MsAUzFzPzOjBokOvvvWHvGIMJuGPiZgDK5EsMiGIW+TnlYqMzp49um27kfwZcZsE4+NpvMpV6DzRltbAzYpxmsmbnSNlXQODC1I8N4kXBhWRipMw91C8VjLHDUc6WQxmXiOkHgDffPZNxsmHvGNxatjlhzEbwarcbdEf4/9nWmYxrABBHUvBke6kMhEqPSWZHC+I2fmuaEBKE9PE5ETfTjfdyW15bIYPPhVpDT30AQxBRGiWXodDWyqPJBSB90FUbt0zYzntvRl6nMQavEEjvLJ24JVHDDwA2MCTlGB7eMQ0BSCFLt2LR88R8GK4F6THTPK+ty6M7phoBvet25ODJ9lJ8/GGWZAzTY+wGoQuzj26XZnqYvKIhqIgjKXxG7LB0G7m+uFEybsJl+nUhWhceefza+hn7X1h4sr00Yd1WNxYmPJ/Un1sbsy78Mc7CUekX7VmjxWXVrjCfZM+s2TWBzyq5CBm4yTLn4PQEZa59wA0415hjQyuoxv1U3SSsqCvHU3WTuB1ACr7MnCiNNmfn6g5hblETPzvuknfQ75g+6IApjNlhVRsiHMUhZSmkueluB/c3z8RdFesAAEvKt7g4V+fyy+VNStNsYX5RA+PkvqprpCumoqF2RgyLimvMFKcAmjrNQlnUPwfQ61Y6clHsdi0Tdrr0m6c56bRUCvpu2/QZuyTBrZdg0LSI6zboREJucD0pNLq9dESwplinZ3amZOa8FhODbikLQ8K7Gh4A18rk9ATxbH0Rnt5Z5GYc8jDoBr30KLH09ee11HEfCWF9Sw4/t6xmqnueWaEkv8uwBxgXtAaz89v4vFPA7u2TN0oBsaNACq0RmUmOxjB4cA/IHW95bQUnGtCFQ76nkpwZK26xK86yVqlgs9NjeHT7dDxVN0laDz3vOZEQC5o0v3nZLYAFPFFbaQjU+h1H+8AASyUb0VxD3Sxu4Ex3upBN/RJeeFxdbtvXjW1P7M9xFYv8jvbe800TWUi6Y+Jm837Txm+Ho1g6aRO81aNPN3A+5Y8PJ4bPvnAg4FY6hmLuLE0bqjEpcCzOWrRh7xgcOyazHdjhKGtb5oxrw9yiJnTF0+BEQniitjJBA6BbFkhzpX9ObRIYF5MicJyyT313fZHLSDMQ4+G50O1wFNfmt+KxbVcy0Wf/aQXLayp4jNeNbYcdjuKBLVfzM2uaCiSjnmWOi8dMadEAPLZdBuLdlldlDM+JhDhbCL9LDFiWzBBFvpmUO/6hlulMuHRm3+kOuZYFjZlkbQ49pxg43cKSFM9ZUTy8YxoWltcoYU3w9wuLa9mn2dCoaoWbrhvb7gpt5DaU6ZpjH2+bxP1u2jfatM6oeS0p38LmeDscZVcpqf3RORHpg+tEQgl+xTrDQsKJjgv6233eMn4bLidwL3AvfKtyPf/NzKElXAZHG4sTkQV+Ht4xzVjDZxuKGE/MPPYEcXvFRrddbdzJtFujLnrX/CDJhcY40FOThrUYB5X5w/s8Wf8AmFpe7Znl2ysSfKGpb3dMrlXKzowinNWXODeOj7CM98mCpbd3y4TthlWFmE0A7NMMAKm2R4jWxr1k0hY3d35vELveuACDg70uY8lFvyxj34iMOG7Lq4LldS/JjBoWkAerpICzbPtUKRgDfNbXNBWYFhZym1H7KCPgprN8qm6Su7c8eIAlMC+7xT3zSnAn2kz9LSytcbO7qM/Y9bHbnSOvh2cPEU4W59ai9PLXWQjVkzYYZ1QAlC2N5kn3DGlyDcue2pfzslskndPwotNZOz3mCvxeXEDSWzpL9Dw9C0sqdjbsHWO8t6Ckzjwvah1vyq1TFsvEc+cVkGEJzC7QtM8BYQgaTrfrwvdMZz7TzeW1FYAAQpZUONhpMVwy5CMAUlFB+2GlCrK24QpER49mwE6P4c6KDe7Yw4rZtpIL0zr809QN0sKbhHm3w1FDqZBMSQHILGHPdOYbAj8LoplRYz9x2yo9sOH2GI4aeKb/F5bXmOtPApSHdtlZUb4LdYUf4Z/+53bUWfFaT6NOkJN/iGMppqItHMUdEzfLwpCnu+XgU8QcxP2Ygz8Ln33hAOB8vgR2hiybvmbXBEVIhdRy0oXrCcJxIiFEhawfsLq+EGt2TcATtZW4tnCHJGLpMSworneZYOEhPpq5nVKK0WHn57tDhuvGkklbjOwk5A9N/4tjKQmaHQrivCGnUVYiJqY1zQxynDWm00h5SNpFAJivtDbXFu7Az6pmun12u5oj6tPOjHJFX0BqlOn5OQUtfDHqwNou9dwTdRUGzqkfEjgAyHUJCENTTGOlNVtYWsMEly4hbwwAta+3saKuHBAWFpdu42dW1JUzESZ886Wn9sua5nxDw2cwkB7BhDSc1xc3utqhcNQQIJhJ6wrJlLL62LNci9TiXOn+5vQFTYYRMAO/j3MJGkIH3JzfN5dV4fqiJhYaIWBoEn+2dSb/vTi3Vvr+q+wx+iWkW5qciMwjT/OVmms1394gM03ewFjaZ8z0afPZ99b5HoYRfFFCuIHBhmAkLGZYdFzpgrj7hTAEGR3scFRqmdVZZY2yNmZ9rIB8tuvjDOMZFip1hhkwxnLn5A3cP+0T1pJqro/8Xk8QUSfI59c79sfbJrmMsMqg9WR7KTh1I9EDSxjzsHoDnOnFUGDoyhYVYD43uxV2prmv9TnOy27BqoYSHrvTE8Ti8mrGEzNMFnD7pJfcddF88L34ZcaOaHdvkAVeOhsLcxowN7sVTm8QD0z7fwmMmeHKpmXZeqK2Ej0xtY+L611FUrop2Om0gmD++GaXobdMYZ36W9VQgmWtFe53mi87aYT1FLQ8R9UOW/BoD+jJLygegFLwEn0Rlmup0RjIVDvKZ1Lvg3BE+4rckAzXLVUIk9Y/WbY+6Voj576mxbWSHHzzXHcOXaaih9xEdeXXQ9vcLFeAnKOdEUtwvSO6RmN5pGoGC1rJgmyTKRgMhYnluk5SDQSiOfp8WRj07FG2/GiKJDk+2txgawivTY9rCbq9cqNxdy2vcV2ayA2X2ifBDIBBo7x33xM1lcyHiPQ4JyEhfuShbVcpofj0thzExaf78eHE8LkQDmAJWMLiAilObxABCCaodkYMD9XMMLRiugbVDkfxfKM0xelBjWt3j08w+wGJEjq1wSBgHGASGsjdBZCX+XVj2w1Nj96OsAVKL38dTk8QpZe/DkBmaHF6g1hRW24wWs5AAHdO3sAERCfsJGTQ3J7pzDc05XY4yqZUJlYxG4WXHcINOY1YWV/Kl59kniRe1zTnY+bo3bi9YiO7JrjrAXyn8g8GobK0DE60ZgCkAKaIPxNOIrCa29DTO4uwZNIWmdM6IGS2IZ3A6rgOu5l2qK/l9ZMN1wPKc0/4sbQYBZ0xps9YW6renzWmM0FIfLahiHP5G8yVwgn9Xt5eZjDxTleIM748UasYtTRp6dLXjy0qcNfKy3zqa8Wa2oi0gD3bWAhYQGp4ALClvzllMtH36xM10nphDRrALRO2m/tT7X2Kv9DPxZpdE0y3LEtw7njjUlS4MbR5XtDcLJ5sL2VBSb9gdcvPzAI3/ztlpCKYkrfHNbs7bvwCpfZLEJAzo1jdXOAKgQLMmCVYXgRgBx0ubuR+bjH9oYwuujD90LarjP1rZDNTMUN3Vmxw87ALCxmBfsl86z77WjFFCMhMJP0Bw+rmREIsVM1VRc94mHr1Wt2FI9OkSU4khP9XU2zs97matdPpDWJVfYkriCiN95PtpXh0x1QsLq/G/JJ6bu/RHVMNYYHAcL9hJt1VVnhdvigeY3VnHiCA7zVe56aEtATXndCtvZwJRkAWhrQEB3gDrtBsRS32T7hj4ma+P5xICOeHjvFc6Lcddl13aJ94mXCnN4jldZOlS1tfECvrSrluBgsbnpoNvC/VmRLHUuR57Qpxxj1695nOfElfFMzKlxbQx2qmGWtph2X6YcIP3QvMvHsy2C0orsezTYUJzLUu8BoZdwgvIS17k34nKsWLrnXXaQTRCcp6lXfpm+76REIyDaqGI9gyS5U4miIF0mR1L+j9HhX3EhCuEA2Z6ljvQxxLMZRsXsWM/izdU3PGtbkCvOI95mW3uNXWdbAkHYUtLStLJ2/CA1P/H1squB8S6NTeImuUHY5ief1k5g9gyZTdBLpQawUdVwjPjOLm8ipNSDm9LQc+nBz47AsHFmCnxiECAoOCfarqooVftVZiyoQ9xgVDAVrTRu43tL90ic4vrjdTZdKhT+Yj7NHy6FK77h5wbeEOk1GFq5l7bncuVtaXJhAdItI1O2VV5bpX3SJR5GNuh6Nc2MZOiePhHdNcTYzGxFBeaPI9p76eb5rIVVXpYuAAxqCDpteGYUVduSzwpgP5VMYtrNuRgzQrCqFXj1bjf7D5Klkci4IfPRptOyOGxbm1uCDlKBf/oeJPxOiwH6a6hJZtnyrn4wB/X1kFpyeI+UUNmDl6t4E/0iLb4Sgo9aSdHuNL4IacRo4xIfxYgwZcFyAKiiUCH45Kv9tICFZcaqnX7cl2NUqaxpMtFGrP3JZXxf6qRPRvyGk0fY4tuOluNc3unKJmHp8uNBkXaq8nVoX2rqa5vrZwh/rQwsLSGqSEYpxt6vkW6Se9dOIWzC5QLg7UT09QZtlRQEyAnonD6QnyHtH3gAyUlkWv7KyovIhJo5dE8+hElCvXQIAZBwM8wbJzxrUZQrpe0GltywQWeuYWNcnqvRak4KaE4OvGtsOb79twj3NcxpDXJxzFf26rdJ9RRQ1F3ObMPcxUCVnEzE6P4Vvlf3RTVOraeYUj3a9c1/iSS4ATCQEBgV9svYrbZnwKjanKjMpAR4V3nlc4KosBdofYH53mMHNiB5ZO3MKZ0ggonoR9/8NRmdKSBO+eINbsmqCleLUSrIg6PNleilUNJSadSKJkIUul13pEeLi1fLPrRkZWo4jL+PN7Kg3p/MIGXmcSxp5tLHTdIXuDxt4668yI2+0ZAzzGh7Zd5cYyWMLUcKszN3P0bsY70RqD2Y1IOjC/qIHfszOjuL6gSeKfNP7e4HrPvhSWMFyq+LmwSx9IIFq/dywLlzTOuyrWwYmEsPXACEAAi4pr3HvMaz1Rn61sKIGe5IHjBYR7392Q02goJpz+AC4496jxWdKzrY3fO28SIFv2DNe+kAHFTsyWe1GLP7EGD2B5bUWiVVg7fySI2WkxWTVYwUexTNm8evfGSdvcNvpcOnvLhO0J9x09t2bXBBY0SeH1TGc+x3YZipb0GJ6tL+I986vWSnxny/9x+yTBS0i3U4o1OBqTlkoSDms6RvAeo1o+uuIBAlzkkPYGKaEW5jQYKbRPR/BjDk4OfOaFAzaJCZmV5O4tc/i76gNXuH79GTFOjeYttASAD3FS8FxWul8rvWtnyorGlK0IkMRt7e7x0Ius0ft8MQHmQdae8TL03tzqIiCYcOnaC1giIdMCm+LpUrWFdE3SCNvK+lK+AAhW1peyO5OOD5mSz8JPW2aYhFgThPQgPjvLTaFIc3iithKPt03CI9tk/vkPo+Gkmks282qfUW7uVY3FnHlDXwsGLW7jhpxGXDe2Hd2xVIijKUaAJzGTANwKqUpQcCJqLRw5pwStqlqXG3IaNbcduT9+1VrpMsPqkqeqzTrQ5XX7pJfk/31B9jfVLwq6kEmIuLVss9RM0XgpM40WC7B293jjEu86koEbchoRcwLc/6M7piZkgTneJf6s0vgTc7ayvpSzCwFICN4D4BZ7ioQMUzigghGh3N+SxNnQeOh9O+z6EJMQo+McAFsC1+yawFYOYpDmFLTgud255pn0MO+yU80KowQbofA+N7uV98Z55x3lcTBTZQEfRLMAAA+1TJd9azfW3OxWQ5Onr5cOFHOwuHgb/n6yjN9ZVFIjha7M5Gs0Z2ILV0k3gtipCJk2x/UtOXhk+xdYy08C4kPVVzEDnZApSyMROl3RhXN+vtvduwtLawzL1215VViY02C8RwoBriYOU9u9rLUi0Z3Qo3ggGmCHo4jDdY0zqqpnuWvKAltGDB+8NwiTs/e57/QE2XqWEPhOZ1Kdq/V7xzIt9iqN9P/7nSALCk5EurjZGTFD42/g27NmdpZrnXEiIczNl9Ygcr+z4hYLa8Z+UtwAFfSiOZNLI90hTrdLD88+qxt2Rgx3TX5RvqyY9RW15dKaqu2FFXWucko+a+FPb5/F50dfe8PioD4nJY8+ZqpdQri6bmy768YbdBgXelwA4TkBb0k0/hvbJc1bXLKN8U97jdJmQ4CVYk5EVstmuj8Q4OxI3jUj6xUgLfhMxyNudiuK16DMXgCwoNS1cBPOV9aX8r5fWVcqA9FtaUWg+BN6NiF7HtFU2v/qjv9uwXqs2FnM9/TpCg4sxD/hj3MiTYUPAD4HwoGX+Ol+qMSksIuCXowEgBUQCcTezoy6wZlajnUyResXD3eriN+6Pdl4tHq6wUwYY6XDSa4fOgOlXXjUx4qacm4fAGs8CEjTrqe4o+eIeSQth5fp8GYM0TVcPAb1/69aKw1GBpbUarMWUcOhnSVTH8o5aXNXF8/SiVuOqzletycb7Oqht6uZow2tOWkONa1OQsYRTbufGehDZrAfz+3Oxd9XVOHJ9lJ8p/IPsMNuMTQnEoIIuf7NhIendxbhzsoNCfihMSworpcF5+iC0uJbTnRBGWsSlu4Wui++sW7CnRMxKo9tvxK2x4pF60qMF72zOLcWK2rLYQUcPL2zSGoV0xMDIedmtyYwJfrla2fEeIy0BwEZi6ELvjrjzuMOSzchilUAgKNHNZ995YawqKQmOZI0TTnjzZvxSLgWgbnZrSzMAAAsYFCwz8CvV7PKY9HWelHZdhmQrwQGPTPNu+8OBrR0mfTeBSlHjD3EqRh7gswQ/qXwZHspa6NX7Cxmpp8KwBEu5xfXu0GMmVE8W1/E558ER4L545tlykqVVYnPIOFABUay0E+0QgkMtIZe+mJnRtkaqNMNYh7vzN8kY1G2T8eKncW4bmw7zjgj4grdtsCy7VMTqv3qwZS6tthrRdM16BSkaoejzBTZGTHXZcTjy26H4tjWMQpOdwjXFzdiSu5eI8aC3geQYD0GkJD+lu4K/swCC6Z6piGdoSPG2ekJcpYoXbjxKgtIUKb9IbR0xsa4tax6Tk+Q18h7/heW1XCa6g8/ktp0yrjFbWVG2arIli92ExOuIBpyK8frQsOdk2XxTJ1+JGPoaN4VI14BhBk/522Xfi8srQFnCKL9o6ey9ayXjjv9/N6UWyfTKWv3Pe0tShMNgONK7HAU141tl0k4HM0aDGmZJsXOzWXSpYfrIAiLq1hTQLfOj3CRP7XfFpVtx/odcn0e234lu5bR89cXNRn3JM81PYb5xfW4vrgRd07egPubZyblU043cMSn+/HhxPC5KIJmZ6QZ3y3Jq+Z0ZoDSItjC0M7pFz/B3KImrmtA2nFmrgDWvjoRVYCprhR2pgyspEwhgLx0dZcYAiYskzbhaDxdpldVJm+dsEWdAH5bWyq11EksDt45JPtf/5z77gq54qItpDaE6hOoQD3Gi14wRZk2b55UxdqVExWwMfpXhVl07V+y8emabUBqWlijQZYKDx6vL240Cs0kMLRaKlQjKJE78Vgo4GX6Zb/XFu5Ahj3AlqVZYzqxtjnPmMvc7FYZc6DtGwCMc2ZoaBza37R/jLGpQjZeSLbm+u85hc3Sn7knaOytBLx3hYCgW/yOiogR6MW19PHq406GNwIdR3T+vEWako6rPwBo8R7eczq/uB6rGouNvUrj43e0TDZefAHgwm7UPhe0QuJ+WJxb6+75Yyn4u/JaWbDKe/6oiJFab71omT7XZGfYS490muP9nGgLnc85hc1uyuIkbRzvM95zSc6nPkZyIUugoSfYXzqdYcZXW3snEjLwk2wfkFBHTB0XCutxi5sl24tOdwhzippxXkqXZF6dRKWQPkdat2kj92Pz/pE475xjeO+DQTxH6t+KWzI+Q2g0Mcn+8tKOBHqg4Vsfs3ce4lgKhC1cuueAzyu1r7+zMKfBxWePCjzWCl7JiQsWkOCo8fE9AGN9ku7tSAh3Tt6Ah3dMS0rLaFwLy2rQHUuVdCgaAAZso20d9LW4avQerG/JSTg7NAbE3HEmKz7nBWcgADslnpT+83r0B2S2Q22PAol0gNv03FejLnoXe/ZdlPRe473vueP1uVMhRnr/uwXrmWmfX1KPjMAAaj+8DPveOt/t34PLhLNOc0k2Tw99AYCZYxrwfyt+d9oVDCMer2HXEGR+wiJo3V0Oisf5RdBOBJ8bywFptu4q2IDHtl8p/ZdJ26UIJUFSrUpYBgx7/aLJpMyl39WzK+tLAVv6fhJjR9qclXWlrtZC8yGnsT66fbrUMmt50kmzNDLtMFY1lLCmgbQys8Z0uubBbpldQ2+TLnz6TM+VTJ/dNWWdq8VK19wSVKo6Ay+az7WdGcWCsjrD7CqCQma10dr3Ej/Go1ebT8/r+ciPYxKer7JEceC2pjmmwlakUVtUup2/p2wiN+Q0JrgB6SkIveOiv+l7Oyxdw0gwuGXCdqzbk40FJXWGhu/ZhiKJ18wodPcRZkySZB4iIMGS1maOChrl9VD4s2Ku2wzl7fYKn8/tzpV7ltId0oVN2T0IUhzOaqNrE2n+K+rKTetNZlT6VCszuPcdXVt7Z/4mrNWKn/E+U5c6a7I9jJUTCbFvrjF/+lsAq+pLAGG5cRS0XpluTnlac++5o7S6a3ZNMNvOcveg3uYNOY1Y3l7G7wtbsGCgt6uvEcW6kEuCt4BgMmv3gpI6E4/HcYlwIq7bjV6rgbXlGs1x+gMJ/ZCmeUleNVJtZeVIUmdCx8Oz9UUJ7kX0Hgefq/coyJ/jfSxZJE7P+kPPM34It3Hb+H91Zx7Hh1xf3Mg01Rhvpnu29H26pjkfy2pk6kwjgxWNSTuLxPQOTTsCAHjvg0EovOyQMe6FpTUc+wNI5c/c4ibYYWkh0fczF8T0WJd5DLqLlmZJJrgptw435DTK/mhPeeaxuKyaNePcFlxr6s2l1azFp+xiRPN5HFnyPE3L3eO27XVDAnDGGRHcXFLNn3OGm8wkdE0xrSvqy1zrlQqo161CerIBva0Ne8cAACdR8Gq17ayoCuBNru/0nks7Rfn5a3cL3SPctopbYNDWYs64NjhdIaNWAOHy1skvY864Nux5bWhSBQTgrq+dqdwQw5oFSQFZMWk8bEGwZLarJ9tLsWffRdx2wp5SqLhz8gYTV+EoYAvcMmG7tJKlxtmyZmRSEzDcck9H+KQuRfTjw4nhMy8c2OGolIpVyrMHqq52L031GSyBOUXNhumWU4OpwyY+lj6BC8tcv1g7HEWPI/3PH6uZlsBk2hkxzCzYyRllZuW3G0wKYJozqXotEUXKOqATwR9snIvZBa382ZJJ0g2HiksBktisaihJYBB1bSgTfPXO3KImfBDLghMJsasAz0UzN3OQs2KO6eLTLSE0hydqK3kevBY9Qc76MGdcm3Lrgut/qV0MbrYKOQ52k8p0A4UpNeKqjgKZFUkLLGQtoroon6qbxMJIb1yO29B+05wtJBTFYQFGCQ5S4+PWyiD3jcfbJmFxrtQeswChBCuOW1CnjoM1AbfoXHeI05Xq4PTKmIV/yd8oL1ZiYrS9KIKuu9Pq5oLjCp9UtIgvAAsGMybitqGdBzQhRvm463uI9iJV9ZQ1Q5TZvXQbZwlbUFIHCOljPzl3rzuurhD7wBODRuPVg8mpwBb9b+CHNLVq/dfuHm+cG6oVAMBYc2pr/vhm9IuggSujj4Aw6AM/p7mv6FpVL87JVcX4jNq3hBRWvUyECuRf1VFg+rNrjOV55xxL1HZ6UhZ630uWElT3fV7WWuHuX/WdcCwj0HJxbi3mjGvD7MJWgzGl8+r0BHntCCjI/3iuCkZ6Z8/49CrTrNxQCQ1Wd+ZhYVmNxFdPkM8ZtXP7pJeMuAM7I4ZFpdt5HAatowQJx1IgPk7h+a9oKPOkvAUL6byfLAAOsLKhhAV6o3aJhmud1tjhKLu56mPJHfWGUWHYiYTwZHsp/mubcmPyWI5JkFleWwE77GboqhjxCp7eWcRFFp+or3AZ/eNwACTEbT0wwhy/MMd/5EgYy9vL2L1JH6sTCXEQO6XPJhwnxNAZgrTU0HuLsunt2+Eo5pfIrD/lV7zKbZA7XjLBK0Gpos9VfUb32M3lUlFgRS03XWo4CivmBtav2TUBsCDruYRNWristYK/p7ldNXqPoRialrOH/17dmYfFubVM++cX12PWmE48VTcp6VwMUFt7yoQ9RmwcAFbWPFR1ldxfanzzxzfDTo/h8bZJEAGBURe9m5CiVwrAwILxjTidwRHWp/rx4cTwmRcOErQLYVezqeeUX9Ocb1zAv9k2GQDY71OkCMzM35ng5//0ziK2PjiRUEIhsA17x7D7y/q9Y5NefKTlt9NjXOvg5rIqPFYzzbiMARmApQsCy2qmGi4PV43e46a2U0WDpk1Ql7QnMK9ixCs8ltWNhRxw+GR7KRaWJmaocCIhQ0Pm1dCwdqxbMnXEJP26arrrD+9Y7J9KmVEggJtL3KCtWWM6DW0fze+p+nIu6KavJ4E12M0gBOFmQqLgTH5euH691IdejROQFYHJekHaK52ZkFmqgNXNBW7QrWrridpKtlKRtnBJXjVXptT3Do2JqvHamVGuvsl4VbExK+rKudiUnRrn+d1esZHbnV9cDzs9hoXFtZhT2Jxc8xuOGsG5DMLzjOrHHYjFRX2cniCmjdzPWj9mklnQlX66H0QzsXGHTPm7sr5UptKMhLCtY5Q7rqyo7Md2cUv9k9bKichAPxLKAPBlCriM7/zxrpCvC+td8TTYGTGjIjTv794gVnUUGAXhvJc9pcukC/aWCduNGBLdHYbf8fh2U4YrQNNmO3KfruooMGqG2GEZTA7A8OfXxw0Ah985E4A6M6ShTqKVTsaQ35DTyEXFKA5DaGea+xEWRCTIgZZ3TNwMB7Jg5NqmPEM4pv1gZ8QSNI7M7Am4Wn7dMqoLGb3JAsCFy+BRoKk67ytqymWtmYwY+8PTu4/umMpxMDSnp+omGbjmfnuCuL6oCdagAVhnKlpHlmVb4KwzI2h6bRjssJuvf252K2eGYYWTDsK0XtxcKlNpzstu4TOju8nQZ+37LmELjtMTxJLyLVg6cQsrATi7jKoxQLFVdljWQ/ljoxTWt3aOYjzIScqAZD0AnGBJ+RbcVbEugcnntbBcAYrpYTSAn7bMkEHi3aZw3BOXAtba5jxT208MaDTg3hsRLc7Ogpuk4Dh7f5UqllbzyuUGrr3g9Mh0yWta8nFXgatBd6IBF99aoLjTF2QrOAU80xi8rryGRbvXtZKYc5W/Nuwdw/FxSydtwuadYxiXC3MasLy9DMvby2CHo3ir7wysbZnAfS+ZJBUjuqKNx6GUCBemHeG9ryvVpHsYXOVJRiwhC+K+t85PSE9rZ0VhCQsrO0xlig+fD/jMxxxc9PN/gz1Ims2KLzuIus4rjMDASy/4EAffORsATP9a8icl01+6q1HSGWTS8OkFywCZP5qEAZ1p8moZy3P2o+7V4dwf+bbqcQpen1OnNwgrapuMOpQfblMhu4OwqTBdaS0dSezEkRRZkbhPauvJp9LOiEF8nMKX4rSR+7G5bjxwdr+LD+V7Tr7WTiSE6Xm7MDTtCFbUlWNWfjuGpB6VWXkCmg8sFReyhCRU5CObouW5JjyRz6Tuq6rHQADGGuogjqQANsw1Uv6oum9xgjbmw1Tg7H5+3k6PJfhmet8z4gWIGIccyfCp91kTeTQFIuTw2AFIbXnUlnNUueftNLl2iFsYdH43urtkvIzTJ5liOyMmmYA0+Rtxd79aURsiKFy/X20erK2zlSWo182cI46mQASTBynyXHVf7l7ZL0IOELWxqGw7B8E+21AEq9+GSHHk+qbFjDUlWJjTgKcays1zo/nKXl/cKGM0oGkju0JAqoM5E3ZIYUrNYf74ZqxsLEkIHvXCrDGdWLcnm+cyc/RurNuRA4SEERhJOCFhk9dfwwH5P0MAVLeE1un2spfw6I6pvE4ibsMKOG7ciQJKEWgNGjDWqHz8AdS9Ovy46zC3sBmvdp+D1j2Xumui7QV9vbz0C3DjRZxICFbMwheL27G+OQfXFu3A8zvysKioxoyH6FFtZ3niVuh8qjgQ7lvbW8liDypGvCIzxSn6V7NzJDNozBBqMS703vhL3kb7nmGYXdCKdXuyIY6kQKTHZapqRdOmjDiAze1jjPHCFtIf3TL3IM9Po8M8Zo1W6Wlf9e/tcJTptRMJIW/s67IuAnQrpHDXwbGOe8bofJdmv4K6jhGSdmrMMbXBdMnx0ELNZ3xxWTUH0AKAE7MxK7sT61pyXasnkCDEOAPK/z+gAoZjtjEOHV92OGqs0eLcWlZq8Pc9QdxcWp2QZYnG6m0TAGYXtOK8lC6ErHhCoDcg78LcsYfQvv8SLCisd934vDFYngrb0ybsxtFoGloPXmzSMsKbvvcIz31BWAPuPWsIMUnWUafxtH90em08eywFkybuQchysHnnGM6UyPPU4jjo7tTjxYhWzMjrxKZ9o82YkiTjI/oAyBi555smAkJ6HuzqvsAUsDwQey+Ot+784Wnnn088XlXnhZ8q5qAy+0+n3ZxOJ/jMWw5gASOHH4YTCUmiq2f96Qrh4Dtns/aFCMasMZ2uRF9ciyWlW5mgGto/OoQqXoGLpCg/PacniNsnb9Q0cPKXrmmlC4kIGV00qxukC5DTHWLNsDtwC8IWZtaabpV5g9K4pcZlLnJHZuuxBg1AhASGnHsUIig1DddObHXzK1PWhDNdpnrTjnHMMDOTrC4Dchmyw1Fsah0nqwqHozgj1IPlNRXKt14LeqL3M2KSUc+IsWDAz9DfnNHHxTG5H0FYsGI2u2jQ3Ak/37zyRblGupZfy9aQwOCTtihdGwsxUlpBLP1i0LXj1BYXToraEueUzpZSXIYcZS53fX8XFdbyXJcUV/EFYqfGgbjFgsGSvGr5He1dRyP2JNoLizWKnGM9ruFc/aY+FhS5lXSTBY6zBYZwovbn4ME9zOzZqXHYmVE8VTvJ3X/hKL5UJlPw8XzCUdZuAbK2w4qdxaaPczhqaJEBySgY6XptALasTk0M0tKJW6QrgMID5VJPNp+1jW72D6dL5XjPiAEDdkIMDuPEcd/XmY31LTlyrJlRTnvpREJAzMKjO6ZKS1SatDSQ+kUPjgekFpKLjJElR1io2TkyYQ5kwYSQGnsSDC4874iylGljpTgqgLXEuuXg6Z1FkikKR/H1ys1So5kpM3Ihbsn11GlcRswo/vjc7lzWwC7MaWDBgLSvnO++2409YCYHKoW0Rv9uLlO1PhS+ABipRAk63hiKm8uqpBa6OwSR6mBhoXSBpAxim9vHsFWP6aNjsQ+90x0y9gj9JgUN4ZhrsCicGtW61edOT1DS624ZkNx+6CJ5brpcGsMMOOGENNQazXIicu52RgwjMt+TNEdjJu+t/L38g5hfjZZyCtGYza6ZnFlH9TF/QpNhxZk1cWdi+lkAdkqcMzTZqXFQQngepxbr4kSkYHDu2V0AwBpv+n5xbi3sjJi8J7pDZjpP5bJ4xdD34UQDhivMuj3ZeLK9FMtqpiY9y3ZmFCOy3gPi0lrLIMw7xBtwu/XACAzL+Ijx5vQEXV972nuKD5hfqFyK02Q9G3EkJdENqtu1KjONT3FwbUGre+8RjmOmC4sTkRb4mlcul/s1PWYKBurOoj4XFNdjcVm1a11W7c4vasCmfaPh9AS56Js+Z4JZYzqxqKiG7zLKvAdLusFuaxsN4VgGvXci0jrqdIfw5aIWnM4Qh/2pfnw4MXzmMWRnRPHK2+cmuksoMzb7XpN2R0gCRUTp6Z1FUoOhn2/lIjM9bxe/70RCeLBqFpvVZd8xadJWh9LQWulBuDrRJbOsenZ+Sb00Bys/Tx5vpgyQnpctc5Yb5kxlGv351i9K7YW6aOYWNeHw+4PZ3WHt7vFc6Ex3tWIm2hNDQaZrcg/goE/yw4/IWgiGtp9A831nd4Bu6S9KhWr0i8C7XpLpkHizBg1wQB4JERRw9bMqN+XbVaP3JLgOGO1pfbHLiMYULy6XF9rc7FZmErxma2rn2QbpYjJ94i7XtUJzuZk5UfoOEyMEwNXOWoKLqNF3t1a4Ae6UWUsn+pOz9/H/Mp84BVGbePbOlS6clQ0lnKZVPiwMwWd63i5QQL2Op/5oEHdO3sBVhmmf8JpGQlIbD1NoszNiHDD96PbpRl8/KHT9lcUxWV9idWce1rZMwIqGMmPulqWq+CrN7SPbZhgX5oracsNS50S0gkxahhzYSKhdwOvfEzSYVN2Ebw1Y7Ltth90gf2JOFpXJuBhKy5kQ9+bZj9Mn7IYXri3cYbg/AcBHAxlmjIma75/eO0MyF5oAvLCkFuR/D1tqgWkv0LqRlvnxNikIkIsLu3Qk0ZASfVqcW4sHm6+SwaUqB76cq3ADXbVAVKc3iGRBorRGy9vLDPcswBVwKN1uRqZUUrDLncrUs6K+jMfNhdki0r2LFCywZSVkO0tWfn16Z5HB9Oo4lf0Kt7igwvPqxkLTB97WhO7MKL5R+ZJLmz0xGIxHVXUctsD8kvqEfepEQmzRAVxB4t6m2dwPjZf+frBqFgtedrqrQJiX3cLP6FWlnUgI61pzZPVj7Qw63W5wPK+NR1infPlzxrWxoPf+h1m8xux+2hVi10o7HMX1JY2IqtoNehzK/teHwA7FuZ4O7S9ag6d3FiVqwHuCRsIAXejQ46jscNRwGwJkmtM549q47osgy7qe2ScjZhTjc3qD0qrqjWNQiqqZ+Tt5rex05dLmyZRkZ0YhjqRgyogD7lg196p52S2uYK0B9bmqo4DrEjGONOVXafYrLr+gpYCmdMbr9mTLmLIud7/PH9/MNJ/veU2ov764UQbtZ0bx/O4cnM4gPkW8gfBjDv4sfObdii55/AdAIGy42NyUW4cn20uNNISAPOC3lm7GY9uvxMz8nVxV1dCyKFOqkY5UT+vZFcKtFS8jZMW5eFAyuC2vStYHgDzgSydtwqPbpxvuF8QQ6wXCAOC6se2sRQASNQWAZorW0u3Rc7PGdCIz0M8+m9SXV6MuGxfs/+nFhdGfRrgoCMrwTdUIZjIhwEgnp5tUBaTJ+DgmeS9zp7t4AMp9pbEccIDvV6zFfVXXuPOiDFWePnQrAbWtpxM9rmuAZz14/Ely3N+UW4c+J4SRae/g7q1uYb5k6+FNI+otVkPMGFeh9bi5cTtJ9kIy1ykrLQ5EghCpcUzJ3odzUrqxurEQpeMPoOG1Sxmvb/cPxqBgHzNhSdMc/jnXrJ4grJiN/1PW4FZVTjJmbssCu9Tp8/HiLxneeE3IxU17R3cjMtbPUvtf4XNJXjUe23alcUb1fa/3Q/Mddtl7ePPdM902cZwzS+9objk6nvQAfWZoUhzYoTi7Ci3Jq8Zj2690n9HPkjZfcqe4ZcJ2dt+gOei0LRkkS9MJKGsWBWxaZmpoOm+z8tvZvUVvz0jl6HFBA4BwVh8iXWZK6mS41hluLxB9tmIWRIpggd84W2q/LinfIgUnpTBK5h5C7nX6vqffc7NbZXVbilVKhi+dvqu9trCklq2wxr1CAoBGf+dlt8i0vYDczwHXjY/3sOaOyO5knnPjPZe0XkYVZN3NS5u3sX4e+mKsh6XoazK3rhOcHR6TcrVcXLotoeYA9/vn0sciyRrorsTHeVZ32UkYuwcv7Kp5JAVfKW/Eml0TcFteFR6tuTLh7rxq9B6sbx0POJaRdptppi2Ms8vuQGrP0HqSm54x326ZPOWN3rPYhe94969BYyMhTJuwG1sPjOB1ib0fx1v/fPq6FW3sGIbwJ3QrinQ5mDH+0Gk3p9MJPvOWAycS0vIUS0JEUjin3lSZbRC3WEu7vklJy+QGQn6aytytpyzUfX0BWfmUguDumLjZkPLpnUe3T8d1Y9s5G8WvWisNjZCrbYKRos+JhPDc7lyjSq/XvOlEpKbae0k6PUE4MRvr9mTLku1JCOH0UXtVITLlz5oeY8Fg5kTX/KubIMmdyomEYDkW7szfBCtqSw1HVyJRZg1lQGr0ZPEoreCcMql+q2I9M2bMAHvWFpbQUjSCtV2kgVqxs1he6HELu3uG4vbJG2XfmrCha+R0zZnOoFIa27lFTVI70xc0cHDHxM1GmXl5UQnOTMUWJrVGy2tlRph3omcammnS4Bn4ynKzL/EF5FF86KkjyZUMkK45LtNlJWqDM2Ic5Kl/Zw0aAGI2qg9cgdXNBbh98kbU7bqC3zsWS8OmfaNlQZ7MaMKlz77upHEjKwRl1lCaS+qr3wkyrpdO2mSclbQMze3JSvTRp7VLYLhtVewwYhbisjNihgVo+qi97EZEbVAmKTsj5gpa3bKoHFkHeK66xlvb79NH7QUsgTffPRPOQMA4a7dM0Io76QGXKvg8of0Mt7DcnCIt+DckaduKRknLlrVWuPsOMARII2OPZjmgPq4vkXPW3TWocis9QwKavpdZO64H/mfEOEgakO6ZAJAeGGCLI+NDvcOJE2xhfA8Aka40pk08DppLWBZj07NZUXuLSrfj5rIq1+3PBkSqI60+xc2SBmkuZZS9LsN2XROZkfbUFdHdQWjfLCipYxc7ypKmB4jnDnvLFIIUYzm3qAl2RswVDDy0DrbAreXSMjJnXBvEsRRO20tnjAUXtc5zs1vlZ0E5x3OC3cx4sisa3MxseoIKCLX2dDfa2vprMSRCBbI7vaZgcMfEzUmVAYB23wJwogHcXF4lteeK9ukZ62gfUMICurv1dtnqT/tSx51mqaPnrxvbbq63grlFTWxl1O+C32yfbKYB12gJWVOoL7qL/25yLccM/qq1EkvKtrpzVu+ubxvPd5vuckgFHtlFWI2b3YEU17airhxOJIStO8awFZ7pt2PhaCydhQYDLDMrIuGg8LJDsMMyQxXxS/8bIC7sT/Xjw4nh82E5sDNhp8VMbb+uqdY1Bkm0Vl5wCYNI0Ab8oHAd7t1yHRMq1uTBvFABWQyGLwMPI8/Psu8woAe/Hi+YMUEz7NECibiNL49vTbA8JCXiHhwlFELzFjPqdjWEcnBgArNk0haDEfFqjw0NsEfr6J2LztgntKU0OQnadjXWJeVbOMc5oCwLJ0gXZ6yLkHPyFlfTYV52C9c8IPwZn3m1mqrNZPgwxuHRwPPn2h45Hk687ega6OtLEgvFWWlxiH65sLwOWvC1Fz/efhOsbWrON5dVocdJ4eKA9Lw4lgKR4mBJyVYjCJG/VwG9yebh7UPfy7dM2M5rTdZCb/E277sUYHo8POqfkUZRf18P1KfPMrP60N2VliCIe9vXgzoNfGp4hbDwrYr1eKhlOm6ZsB2/fzMX7751Jmu3eZxKiJw9tsOo7r2wtEa6atmCg+H1M6Dvs2RWO2O9NY0xM4yeQGJjvpqG12tlOt5cCYi+eOmRMee/QIOdoJFW4+W1pH5Jo98bxOyJbUZlaCN4VZ0jYvTnj2+WrpXhxHEY+02j57RecFyLhfGsZiny3lNePOiCBwTMQoYejbGBs2SaZQdc4+Z4ay+EBdEXSG6d0taf9qH3fnWiAdxcsA1P1FQaCo+k2n8P3um8PVFXYe6D41kDlPWC+1EJFW4uqzI9CLT3Z43pxNrGPCPmhnHmsfAZ66KeufSCD/HaK0OMOc3LbsGq+hLD8qLTcOrHW/zSewaoz8LsV9HUcbk7L8/eAIAlZSZtNc68Z10T76M+vHHLj047LTvxeOt2XoZwVmLdlhNBpCuOWTmvnXZzOp3gcyE+5Y06CMDViJGWmXzyWAOhHQhKzcfPA64vMgU/KU3skjy3CMyPmmbxe7qJX2+HtMnkU6nHHLDPbsTUOCHgynB2OAqRxC82oeohCSKKaMwZ1wYr4BiCAY2LxqFrA2nO1CZp1fidLHe8PDalwZ5dqIq6KK0y+9RrhIi0MV5G2IrKbUn+xnowIBwrAZ/6uBP8fUmLZEst+rJtUw2NyFO1GlNE7ymtt56WT98juvsL/6j+n+nMl4F03SHcWSHjIMiHdc64NsYXaXJpjRnvXaFEwUAFN3s1Wvoe8Qqexvv68xkx3JZXxfPR5wJAxnQEHM4hzykls6KYW9RkPqvwsXTiFj4Xt+VVHdfVJyoC6I1rFytpuQYNAI4MjqMzqb9vBRx3Dv0BXivS7uuaX6oX4ERCcq1VW+SKsKKunPeLvh9pLJtaxxmf6+OgeiUJMSPaHJ+oqUwQVEgw4AJLSdp3IqGErC5kQdEZzblFTfhZ9UzclFuHuLDx/odZMmNTkcdCogR0Tm2ocpb3Oa71hjTbK3YW4/riRol7bxXYJFmsDDoG0xKjF/VLVtxQIpL6F2zZscNR9gXXcWqcPY/S5lrNKuZEpNWSxnNTbp1xZokh954RGu9TdZP4XTscxfenvsDpSfWCfQCkJbUrhOmj3Fod35/6AgDPHQMk3UdUpMygm4rJpBgQJ+LGPhnpMj2Cxncq/8Dz1dv/VuV6bpfSP5NlxQ5HZaxXRLqe0Gc63heW1jBuqAaDwfArBtiyhBvAq+4vwKWd9PfzLRKHt05+2aWFCvR9n0ypwZ/FrYTPnqitdOmUWlvSvKeHZaFQsmLIDEJgGmz1BLgNA3QFmLBhZ0U5Jkefu25RdBVbqjCqWrPXDp1nNK0/57V+3TrpZV7fG3KkS5JeHC1ZO3Y4ipbXLzHOxs3lVRxbRJalZdslLaTzxoKBtq56KlYACUkafPh8wefDcqBiDgiOp1n1+t4ZmpQkVgJ+jzQJpLXu1gioxngzkEbd45urjw9I1DDSb692U9foO5EQ7qpYh5+2zJAa2UDi5U6gl2antvV4hgRtl+Y36tVgEB6M2IKeIG4tlzEcCVYHD9PD2hMHsIRlpCKdm92KZ5sLE/xpE3BGGhMtbRunTo1rvrswx5igdYNpCTmRhlpvSxxLwdcmbUdc2Kw91N9L0H56td9JrFmBUBxxFVh3orEDwNKJW/DojqlsISOGVPcf92pIEzRHEWk5sAKOmT61S63t8TS+3SHcXF4lNYC2u9akoWKLEhkAPFaI48WzeDWbnHpQH7+urVbPGz7fnvgX0paT1t8OK+tFyMGConpj7ZZO3IJHts3gvaXHKRnr5flbD+pFahzod4Mjl+RVY1mrdI+w4hbuufJ3HHSaMP8T0IIT7QX92eP9n6w90lzfMmG7ZCjS5NiTahhhMuyzx3awMEJt31xWxcxXAj1J9hmdYc33X7f0GWNXmluvT7gu6HqDq2kf3zpZBvyTGylZ9050LvhvlYJY1/b+oHAdftQ0K0FTrVsO5mW34LWec9DQcQVbf4zxAonWEA+9MfDvnZueQlbtrS8U7pQZbTx0h9w1eY1qKjkdqDdWJdl+mz++GStrS3HWxUcw8bw/YVObzIZErrQPVV3lWrqT0DUAGHHhe9h38AJYvQGuBeTdEzTeE8UW0Z3lxRe/ryvJtFgcaUXwWF2EZK4NC153CEsmbZHnwRs754kR9AqC9P+Ckjr0xkNY05zv7i91Ty0q3W5o8fX3dKB+rxvbjjWNBaZ1QFiYW9SE1Y2FWFxWjeW1FfyenrqUztaikhqu9ZEQO6LHADmA09+Ht+44fWMOXth5+aeyHFyT8+ppN6fTCT7zlgNd46JreOlz0j5S6jUCy6OlILcd0pJyu7opm0yPyjJA77KGl340jZae2tLoLwlBpudIMNDHRpl77HAUP22ZIVO1aQSX2hJx223XShQcOI+yxnQwQVeal3PP7sKiMpnmjDUUUERGxXWQBvyx7VfCirl+sZyClMZLlyfhxZYp4VgDCqndvr6gycAn+ULra8qazDQqpCMMrSZpYfT9wBkavJkuaEzaxcGXgXYhU3vzslsgbIEVdeUGc6lbhahNtsZQhqTukKFFIosJABYMAOmWQ1WiJQ7NFJWP7piKJXnVUjBQ8/EGlnbHU429o2fbIk0Ra+rVpUOZXrxaJWIsnC45rzRbPkPVnhfmNOCp+nLZF5nfLUB3j6N5e7XAnMZQczW4bmx7gt83nTt6jy61Z+uK3GxW2hkj/D9VO8mwDlqDBnBtfqvU/FquH/oj22bI8atLc3l7GZ9/HR+kHDie9Yb3VZeMW6B5W4MGcG/TbDiREFtNqDq7d4/xmnk+nzOuLYGhLL7sYMIeJJgzrk1aYCKyZgHFP9jhKPocOYfH2ybJ/R90AEtWUNXbX1xezTghGmEIBmpfRYUULOYWNRnjkGfdzcDGNIZdI+Qfj7dNkpmz4NIwqrhN8FRDubt3yCKlmB59j0uEA6U5B/BxNMyCwZxxbZzRh+gUrX/epW+6cyJNvkrhOyvf9V2/d/N1hrVEp+1W3ILTE0RUBNCw+3LY4SgeanEzdi2dtCmp4ERz8X5HViC3sCRYk63fIyIgsLFduUMJjYYBmD2xjbXGy9vLZByNLvxrgsEtE7bj1kkvMy4p5s7OisJxbGzePxJ2Rkym8YSsGEyWWgBYOnkTvOBEQjjwp/Ngh+KwBg3gWxXrmWbTfuC9QlmhwlFWLizMaeAzRIIBP0+KHbhnXt7JQqYF75Z0n+/1iGbRDoiEIpSWY3HGQv3+uLmsyjgDSQuwDQSwoKQOK+tLudAq3/9p0hL/VM0kzCls5jEvLqs25+/ZA8/tzjVdiFSba3ZNwKLS7VheU8HZ++xwFC/U5+Pmsio5HhW/6GbKU/si6mY8NOhaVhR2RnLl4ukCfszByYHPvOXge3Uz8F+7pwExWxZWqityRSJNkqYsCMkyE+gaAZ3w0AEliZ3aM/xOvRo+j4ZxQanU7Hq11Mm0Vt4sRXMKm2FbggOfACT1g0ymiQGQ0Jde4C2ZT7eBA6+GQ88Ao/BAGiSvq8+3KtbjZ9Uzj+szzMWq6CJTRY+Sac70fr04JubUm+VJLzCXbK762vA6ev2dae7au+JYiqwwq2moKCsVjZe1Oppvqj6PpHvmBBopQ4vliUtJliGENWZA4vNqTCJuQ0TtBP/e4609j4EsZ0l8vucX17sMmDbm2ydvxCNVM0yf3uNobI310fqw4q6liTO4eKxUhhY5idWL2/WMgdfveJaNZBmR1GdeTfr88c1YWVdqKg6Ok02Ix+35jNpHQGBx6TYsb54kg5J1rXESrahegM0OR7lwWULbx8G9iNvStzzJd4yzvyAOhPe7Wo+5RU3GWTweDB7cg6NHM0y8eOKPeO4CsGKJRSL1MSTLMAQgeaxDRNFmrbAbubKcqPCeE5ExHkljXDxxBARG9qjeIFuydDzxuPWzrOJDdFcX49wniU3SIaG4pyd+Q8eNTl+Y9h0v2xdgFGWjfcia84DA4oLtbm0cfQ2OY6333gPJ6DLBdwvW4/7mmSbeaP+kOEDUMmjV7LEdGBzsdaula2tN9Qo4u5TmfufdWwAwc/RuWe9Iw/3coiZERUBq8gMC1xc0Jbh20vhvyGnEivoyYz/q33OfyYqsqfXgdimGRrMAe3GpA1mhASD2Qfy0thz8rn3kp7IcfCV3/2k3p9MJPvPi0391FLNL0erOPL7I7XCUGdGFJbVYXj8ZTkT64umXMj2r/+Yy5Or/1co1h4jEf9dItwPKu61ruxeVbXc1OBaQZmuXj1eLAVfb7XSHzFgBITUIqxsLE+ZM/R4P7IwYrzxrqbpDEHo8gYcof6tivYkDx8QLa0QEcFflOgBSg6QXiCKN+M+qZ5paQhqHJYn5uj3ZnEHDDke5jPzSSZt4rMZcFD6YuCrCxxUjlT/qc7tzsTi3ljMP8Too7Thnk+iRvpcr60t53qxhISacxkzFqwBXMFDPA+B0tXam9ONdvl0TDJSP57WFOwALrlXAMuV1Y/9RTQnlpqJbJignP4NKh2fgWJhtUv0M+aH82xv8C0iGlGIOaF1mjelkLevi3FrXR7mkFtNG7jfGvaqp2LhApWuDUDU8tH48hXi8fuvJNPM6U/NMZ35CNiI9ew9bDzzWmhtyGtmvmAQOJxLCo9umu7jUx6WAglABuRbzslsYD7pgAAAhK25oH+ePbzYKJFna31ybAQBlQdH7tDNieLK9FHYojpmjd5tMKs1djXludutxA+j1GCLC/U25dYwD+s4KOAYdI+sKWV4BeRZvmSDpG8cTeWgn002FB6rVwmMgeqRltAGAo0cz3LVX87NU0LyhaVYo/Nepf0iYl06/jf+1GCY7IyYtFaqmCmvvNfzOyOvEvOwWLCiqN3z9vXOww1EZ4+KZC+HBWGP1LsVmAdLaRgX+VjcVuGNU63BtvuuPbmdGuVo473Fl/XD6glg6eZOhgaZsZmQBFAHBNMUYn65A0s7VgpI62BkxnHt2F5y+IBdgNM6d+nt5TQW3QYUSnR6XoU21o+yDT/1IK0GildCIZVB3hnd9xVG3QOZ9W6/hvyljFd1TdijuurSq9tbuHm8Ic2y1SJeFAKk+in5/iWMpnHmPeACnK4R1zbmMS+neKrC6ucDNOhSzOEsbxTPROJxISAoGcSkk0d1gKMdojCoGht5dklfNhU7lA8Lk9LS7LKniJRJiwUCux+ltOXA+RQE057PP+v7V8JnHkNOT6MNHfzvdIazdMUEGBnsuV76kIiE3jWRP0DXpQmMSPJoma9AA5hfXS+ZYHb5bJ70MYQsMDvbwc3Y4Ki94xVzPHtuB+cX1Rtt0qRumPl1TqxOU7hBmTOjkCrkcbOQpsEJpWwnmFDbLy1oTnBbn1vL3M0fvlpp+DXSmf+nELcwUAMBPW2ZI1wVi5hS+KOiP21CXGeHcDkdZy5MRGHAL5ij8UvEsNhNr63VzuQyyvWPiZibOANxUlIpJpkwyeiAvjcWo4qzgZ9UzGY9k+razXCadfuaMa3PNsh4iT38/vbMIiydVM3GGkGlgn2+aCDs9hqfqJsmiPsKjDdUZDm/wucb0ElOn7wvv3jT+z3KFOvqO31OMqtPnFnb6XV2RMRbK4GJnRWUgrsL50zuLsKltLJyIZB5lETnZnp4iVJ8DXVR2egxUoIqYHmP86hKmOSfDEQBTmFP9sGldrT+ExUzsf22fhFVNUpicXdDKsTrJ1tSrGQ1ZcdYYZgb63fH0uEGS87JbErKDpNmK2VDtXFnYyUGJK2rLuV/dvQ1QbkFa2+t25LjnTZuvHZbWRqYhYdfNRu4Nc6+TFn55bQXscNRwSwRUoKWibxCy6rC3UBXFB+iFqo4HTk8Qi3NrjaJTOiwu3WbgmSumq7NLKUlpbuJYClca/mnLDDd5RHoMc7NbORhTF4ioXYJZYzqxqXWcOuPge2HOuDbeK5v2jcaqhhKs6ijAk+2lUrGiuUsaArcAB4UCLlN+y4TtvMaG0GtLRlBPtwkLbpYnusccKXzqwg+gAqIp8FrtLTsthke3TWeaToy5PkY7HOVqwYB8f0WNOz6vYEXW7vc/zGI3ThKUaD5eNxW+d9X+G3XRu7BDcTy2TWXzI5cuTyA2jWlOQYvxGRUKnJ5rFhIUQa14o+Uy0qs781ym2OMSSOOj8d9VsMGl3V1ugTiik7SPbp38MqxBAxIHWupROyvKwd5yUOpMqtSli3NrgYCQCgNIBYeeAAOQlZGp2J5ebNDpCuH2io28ToB5dz9W7WZHpD71hAH6mfHSzWR/LxjvKgBOR/Ddik4OfObdio4XkAyAg+W+U/kHPNh8FZyIrKx5XLci3ax6HNcE3f9dHFUBwR7z8bzsFqxqKDFf9AQ2Ae7hXTppEz6KhQ3/cXEsBSLocD7xtc1SI1N82UE0vHap0YY+BwAJRY70YGA9sJPm4XSHsHSyKtKmt6m7CNjCYDz1ttg1Q2estNSYFPD1eJtkjh+rncaMCrlbURaVWye/zL7CuhZtTmEzzgz1SM28x7WGAmpJoPEGBHvxze8KYFHZdjddZZI14vdICLIh00XWlbtaNM2NgvYXM2gU4H2ctIeEZz1jCQl8RjpFD8697+pwvKI8Ok7JzcDLDOtCtncvsLbSlmOhgmwJaQU9aVGd7hCnzOT3tK2kv+sdZ7L/jdSatH/ilunelgynnnlxkKrGaFhxC0JVHNZdAZOuG7WXGpfCeMxm9yrjOf0sqL3MAZbHC5zUca8CTL1F/Oi7pK6KmluR0VYklJDaEQCEY8GyTabXOAsOMKuwHeuac013Fs+eOe6aefeWnhLZ854RFP7n2u0KAUHT/YgDMmsnJYyVc+yf4Gw4fUFYA24hPsQt3DVlHR7c9iXYGVLhcSQqXaDW7x2bdJy0/8SRFIiQACyBJWVbsax2CmcUI6sjjdmbjtKLt6Ra5SRB8vxudwizC02XTT0A34pbECEnaXpscvVMhiM9tSv/r50TYy9HA7BD8ePPJaK5xHhwOC+7BZmBfhnwfoL9orenu9sAZoEzGiu5kwIALAErZkMEHcZ/Au1R9CshrSkAhBwsyG9w48B0d0utwB4VxdT7XVBsJke4M38TflY1k+8ZO2yma9bvFNlGIu10umU9E3LtIhCOBaFlkDNodaDrtE5l+t9t2cj4hG5FPV1x/N2EztNuTqcTfObFp1snVLNGRNewQMi0gwDwwNar+TAnq4RIjIIe+Ht7xUZDq8F/a0yNCAosmbQlgdhRAbLFZbL0+81lVVhcXs0p46hPMl/+qrUSKz3ChNCI4bo92dw+VbA1iFd/ABCu1pZrPRCxJc1JlhswPLuwFfNLpBVjRn4HHt0+nbUaF5//Mbve0Pu3T3pJahXjrl8mgVcwkA9ohCtTBi4CMnsIaVeo7bmFzfKCyIzisZppbhsqeA0OsKY5X1phNM3IrPx2DiR3q5BqQ9AC3XRtCv+2ZKpTfkZnhjyFenTt04q6crePzCiguRs9VasuF6U55/c0IcWrRaX4CUBaoACgfNyBBK0PzdGrQdfB6QlKpiNJIDy1L+I2REBI7Zaa53cL1rv9EL7IvUoxOjfl1rn4t1XfScrU0zNOxHVLYa1blhu0L9fA1V3ohcOSjZ1gYUmtqbUUFlM6Q+iNyFgIOywzCHnXlAV4zY1Q6AW61Px43TSmTC+mBEAGsYajnBHHiYRwW16VG+yt7bs1zW5NDDs9hpn5O80Jkna6WwYSXlvQmrSism5F0LXOTm+Qx6OPkcZv5Hyn/WSZTJbBdEKu2/q9Y7GgVBYBY6ufAurHG5S8OLc2QSizw9KCtKBEaqIpIJmKqj1RK+lJxYhX3HYjIbYaAm5Qt50VdYsoRkKyYKNjSSuOEspZmxxWljRNMMgd9pa0yHW7Z37K+L28D8iC8tOWGfxOvxPE+r1j2WXEC8Z+IRenjBgy7AHumxQxNLY5BS1Ytk26HM0e2wGnO4Q78zfxMwYzrcU8MQjzTpg9tgOwgLXNefz+lBEH8OiOqbh98kbcXF4Fa/CAgQsaixW32NXTOy+mk5Z5HvT1hbBkwoG+IC4c+hEnZQDgJkhQLnoADDcwXdu9qqGE9yqtsf6M0acCww8fSdIRW8DKhhI3QFplzrMzYpwa+XigJyoBpCJo2MUfYGV9qeQZJr2k0gWDff9n57cBAGe443lmxNx7WjH9D7VMx71T1xj0c9m2qZiet8uwlAMwXAKN9OOZUde1y3tP2NJV0OvC6MPnEz4XlgM7I40/JwJGhUi8GQim5+3C5v0jWTNL1gIrZuGr5XVGUGUybaHeB4ERZKZpQm7IacSKhjKuCIyASEzTqDR5VOjFG0zMfZJWQqWf1J+h0vPe4F9j7p7UdXo6TADJ56nGSZrGZFrqP6tdVW0sLK3BivoyY46z89s4xzgxEnQZeDU+yTSlx1uT462fN/iOTfmZydsWR1NczVoSfFpRm91TuKCNFsA6c/RuQ9PKhNzjVsR4gramalxcdEvTll81eg+ygn1sRp+cuxc1r1wOJxLizBmkESPrAPWrF3hLhrP08AB6IykyuLaxhAVUwuf84nqsaiw291pPEOXjD6Bm50h+lr9LEkhLbU3O3Ytt7aPdi0wACArMzmuTzInXGqBrnS0AUcsVQlXbc4ua4AgrIcOJd6+wtrI7hDlFzUi1Y/LskwUqEnJTBOqaUNLiCyTseXEsBV8tq5NCh7IkGYGuJ9CyAzJ24NXuc9B68GL32ZgtMwol2T+zxnRibesEUDpafX52WAZbH42nS/cWC5L+kDVKWTMKs19F0+7L5Pdxywjs5j2tBcby+C1hWDMAlw4xbdMDar2pjnWrnicwlQM9dXyRsiQka3RQPRTDKqed6dmFrWxtZbxoyQE4fW9QoHTcK9Ia2x2C3WsD5/azBtobyAuoVJMNBRg39k3s3nWJkeSBrGq3lm+W6WxVume2hnaHcG3RDrxQnw+R4iTQ8qT7VdNAJ3yn0SxvwTCnN+imLu0JSgtL1AxSN9ZIQTLtPltENauCl0E31ovWRqXzZGWYWvOlk1xLNb1ffsWr2NY22rhjFufW4okGlbYznjyImdMp07omCST3xoRwpql+m93XluRV49dV09liz+/2BbGoqMaIcTOsCJ4Adqdb1uQQqfGEpAB6QPzxrB8EV43eg/Wt45NWVAeAmfk7sWHvGE59zuvhSbGbtGCost7a4ehpH5D8X63jP5Xl4Gt5HafdnE4n+MxbDmaP6XCDryKuduKZznzWwNFnsAUuSvtYHhp1SdoZMt2YNXiAfZKZYCtCR+k8SQPiPdTL6qfw/3Y4ygfx6Z1F7qWaEXPN/CpTAyD9n5+ocTW9uq8299EbZA2mnRYzfVyVJmluYbMbcKsVO2Frin65d5nmzePB0nKpxabCTxSIrIOdGQXiFuPltrwqU2OhqpE+vbPIJbjKx5YsIoTP5e1lPN7fbJ+M0stfN1LIeQuIsVZQE5iMsXkFHq2oHaACzRSqZ43pdLWwvUHJ/GtaKE6bSNojxzKYgtVNBca8nR6pXUzwqU8iGLAWlALWBwIYN+ZN4xk9WHB9S44RgFrzyuX898rGEpdZ1XOLK7xRRhsKCHciUpP0nUoZ4NkbkZYlsmQtLquGHZbm7W9VrGdt+7SR+128Aah7dbg7dy0YmC626aP2Ao5lBN5tax8NPaAaFmCnyX0xv6gBsGHsJ92aMTuvzdWw9QTZ73Z1QyHWNLoaOd3SYgSO2u7ZeW53LgsGsFVKVUtg7e7xvGZcMEhYRgE5YuAB6VdMVkPGe7qZhMCJKBcFxXw7PUGpue4JYnVnHlp2XaZw4WquObWnh8FetycbdloMC0tqzYJGGk5XNGgFqLSMJ9Mm7gYcoOX1SySDr75bVCbXx4mEsLqhUI0BoAQFOjNPa3vV6D3y87ib0njWmE7TF1pL8ehEQjK1rdImz5zY4e6d3iDWNeeyVYv7o7Zilpk217OWd0zcDDszyrSF9qJ+hilFMFsdqB9bAOf2wxkI8NmnM3THxM2859c058POimLPm0PY9chglDNiUjBQaSUf236luzaWjCUgP3ZJH11670RCHI9AfvC6YKBbHymg2OkJYs64Ni4YBqjzqVkXFpbUmv30BGVgr7I4kBUngckHgIAwNPBrm/MMC7gbIE+MpxQcnf6AmqOioSTAhaOGS5UdllXGt3WMYgsW4eBQ31nyGe2+1hl9J+IW/GJhUyUbEEdS3DS+ZDESlrEXrbP6mYY8tu1KaTHSrHEAgKjFghEpaQztu0dYub6kUa6vst7NLmhlqxgp27xKK5rLDwrXMb1Y35Ij+4q4RfMoqYIdjmLD3jFweoJG6nMI4HqVWIItpV4FS8Atzqk/d7rCJw1Gph8fTgyfeQw93zLRTPNFGr0kJk8IMCEhmDl6t0tUiIH2uG/oFTTtTFUxVs/drYiR0x1yg+LoeY2hJMYckJolJxKS2q1M05wKwMypTmfXgek6ReBYMg2esDDuknfMwClPGkWa362TXpb+iURIAOM5JxJiVyPpVw43CNADeo78R7dNx81lVZxRaUFJXaLp0nMZ0nz1uQpbSE20ikVwIq72gyoU60SeGAT98jT+1nKhP1YzDXZGjLX+t0zYbrhuyaBZS14smnsOafMNE7/QLh41jlsmbOeAcBIomVn2rHMg6Mhc8boLVEocuw5cBMANsCY863jT50fvEv4kMwCjTzscxc1lVRBxm7OjSGRbeLD5KnONFNNCAZlREXD9YYXFmqp1e7Ilc0r41ZhBfayb9o0GkBi0PregGV5wItKlgJgIHWeEAwoMNvqibaW5ctCYWLCkFIq01rqgpqwQz+3ONZix64sb5f7SXJYAsFCcbPz0zHVj201XCMqSEwnJoF6VlYjcD1zNqyuQEO6MjEo9QRYqn95ZJJkqlZmGzv+j26fLs+axngLA5rax8p2oqZFjJpA0v90hLCnbagREEnNEe2rD3jFSiNTWXPdXT2aVfL7FpXsb9o5x8aaEgCdqK3kdqSKudOFzM7zNKZR7hwJmF5dsc5M10H7JjCbQG94TkRDKr3jVEGwBwE6JG/87kRB+vuWLvOfJ6sBnkFM8u/3clldluN7oAqLRV9jNgkcKlud258q/yf1SU1pQkK4djmJFTTlbslbXFxrnJNWOGfdghj3g1oeBPBvkVuR0hxIqP9O+Jfzq9GZxWXVCwC8LX1lRtlBSHKB0/QP3q8+VoLsrzbUGhaNYo5QtG3eMl/1r/vKG8ky4dyxnwFNjs84YwLLaKca8eE01N7IfFK6T9FK5F1LChJvLq+SYs1yhmIVzx3IDwBV9t9NjmDl6d0LmsHV7svl+WVK+RbrNeYQLohE2HJ7TtYU7eK0p8J6UGBzQrjLx6XuJFEA6noRjuZm5PMLMidL1ng7gCPtT/fhwYvjMY+jGYtdP2Q5Ln0Iqb25nuYy7znyxBgDgPMW6Bpvb4oBTt2iSE1FpwLxVNlU6RfJhB6RmmsfCUrpMG/rY9itxV4XUxN8xcbO87EtrWKtwZ+UG7o80jaTpun3yRiPbBWtVwlHseuMCJr66UOKFx6qvNP0Tu5M/BwBWzGaNDl3YTsTN+CCOpbjEzpIM7UPV8pJeWS+ZS8Lf4txaIOi4xFrLssAXryYU2eEo5pfW81icrhAzQy6i3Wd53XRNp+YCArh/31xWhXnZLTIIzaP9gyWwrG4K49VrLeLPM6PmxS8ggw7VfJZM2mLgbEletdFWPGYnxIjIf2RAHjOU3n71uerjUhfC0zuLkgb5cTVbTYtHMC+7RaYujLjngWJPnmwvNXzz9X21oq7c9VlXmaZYa6/1wQyq5rtLQgppLvk5pc3VGQnep9olyQJSt3IV8ghvc7NbmRZ4XbfscNQotqeDni2IgvvszChnRAJcIcEOOrLoGAm4WjabNc35xjm0s6KYnrubcXjHxM24KbdOumloAqQUyrWx9AWloE3rFbe4AB3vca1YnI5vYqJJq8tjyYxysKiIu2lDaW7UzrK6Ke5aaIKPrl2mPPfUP/tIay513nOiW3YuPv9jF68kiCimhdJPO5EQZue1MfPFQd1qrZbXT8btkze6iyi0PenZszT+bR2j3LXXbktv7IQ1eADTR+1152zD1LiSz7/66NGaKw1GlQqh3ZDTyHE4bBmA2xbtd4uKN2p02U6P4emdRfzO/NJ6UOFK2GZ63HU7cjB/fDP3v6xmKrevr5Ns2I3FoR8qlkljoLsKjpa9y3NfiKMpHEewujOPLfriWIqZaljd06wwi4Rw1pkRA186zXAiIczK2wknEjIKSOpgZ0axurHQ2PveNeR5K4HKzojhurHtuHfzdVhRX4YFJTIb3K0VymJe51YhpoJvtKcevPIZyeQPBKC71a5ryTVw7MU5rQOEJdPSeuIY9ErqnBIV4P0AyPNBAiQgFSxLJ25xlQO8R10aatkCC4vd+B+ynidVop5m4FsOTg585jH0Xx3FMpWntsnjwnaDoJK4VgAwmEtOG9YTZGaBnrHDUVxf1GRobelg6bBApSjVNSrCFmYsgvqe6gD8tGWGFCosBxAWVtSVswvPz7d+0dWApsbdy7U3iEeqZ3BQI4GXwaGgMVcocQnF0olbGC96phZjnBpDTO4zhmY4HMWDzVfJi9Pjk+vF7/ySeg6GfqK20hCsYEktFTF3NHYOeou4Ad7iaEpCMS0dr/rfRq5zbcwLS2uYYV3eXsYxJjSmedktuGr0HsONx+hD22dUTZu0ebPHKvcIpVm1w1Es2z5VaYvlgLh6rsZXsBCg4I6Jm2FnSAZ4vkp3R+ZxXbC9KbcOC0rr+LIFXCZbZzQJ6GKlCsl0Hmj8qxpK8N4Hg7gfvTIv41y7zPSx2Okx3Dl5w3EvG0MY1AMQVfssIKlc7HdNfpGrtlLKQGL0yI2CmWGFz7klTXC6Q6zRs8OuFs2JhLCopIbxMW3kfnlpDhowBUsCYrq08TvdIY7/MPaEEp4BsEXLmJ+uue8JYlPrOGboH9p2lbTOaK4FADhdLeVqR9zitIgAgJBw3WPCUViaH7Mdjro57QVYC7ugrI5dBakvEgqo9sWt5Zs1Da06i+TSo7uAMZ40HOl7zkrynKM953HvePPdMw2hzg6rmAViwNXvdXuyE2oP2BkxHidVvKZ9dnNZlSmUUPsaPSdhdOaEDnd8avxzi5r4fbLgMBOdYSZWkOMEn6252a28jo/WXgknIhUblGdfj0UxkhdkRiEsAXEkxaBBuoZ46cQtMqYOwPwC14KkK0CMgFdtnWjMi0q3G3SA11Frg8bz05YZLLAuq5nK8/yX/I28PsIWZpY+do1L7Ju+p3X56OMwfzwvu8WwFFhRS7pohqNsebwtr4rH5hWGaQ6UrEC33sj7wL1/ntudyymfV3XIekaPbb+SnyMhd23LBFkZXp3luzbPk32mxI3zrv823BgBQ8kBqGr2ap8tyat2FSFqTuSC6fQFOXYs4Y4XwOrGQq5bQElQ9LHQ35SW2ImEXFc37zk9DcEBEBfWJ/pJ4mXsgwc+88IBoMzKme6Ftry9TErzlD88IBKya+iH4tEdU5lICo//nRMJ4dmGIpfxE8Cvq6YbBaOc7hDnhdYZp4RUijqh0HyEf7btiwnMibAFrJjlFk6i+AnlJkCl7PkSBTBlwh6cdWbExUtYZ54sZo71AihsEdC0R/QduS0szGlgU2tCYTOAfV/JH1LiV7a3uKyaLwxr0ICcpzITEzy3OxezCtoBGxh/ydsG/omYlV7+Ov5uUi0zVTNH71a+rSJ5wBYxa/0B102sJygvZmFx1iYi2NcW7sCsMZ14pjOf3RzsjBgzzvwsaaZtWeBrcXk1E11ylYAtcHOpItKakGaHpcnazojh2qIdrFmj1JeEu4d3TON1W9Uk818vq5kKK27GKzzZXuoGlSsGVw8yB4ApI9ysR5QxKj2sYiVU/1wUSADp4X5AAHdO3iCDUSMh4MNUAHBrPZBbjnIxurZwB5zeoKz7QXuerF0ZMYijKVhUuh235VUZmYqcniDvA9I4kivCg9u+JP22FUNFcyKLGRcHpAratvzMzoziibqKBCHl1kkvG37Tm/ePdM8Oadh0Vz5t/+kMoJcRcXqCsCxINzIbhiDqREIQH6cY7ekxA3ocC2l/GRdafQRilHSXLHYRseT3nHZTrTmndg669TpWNpbg0e3TcX1xIwuv1hFTA0xMA9EviikhRuTmsiqIIykG48hj0lxGbi6vMjTPsIClFZs0gUmc0LIpjqVwbRKdEXciISyvm2xqwHUXDVI6qHVY3l7m7jdNS2/QanUO1u8Yj3PP7mLaUXr561izawLmFjW5690VStD20hrOHtshrbiKllJRTifiugjpQqDTJ/cej69PcxvKisI6Y8BIF6q7iD66YyrvH46Vg1yfawt3cD9zs1tZQOVMQaqNp2qli62svaIhX09pC0lDxbEU486iMT9YNUvhFEyf9DsJAETIMdrjNmgfREIYNKiX8bKqoYSTeDg9QVhnDEgrS5dkmJ2eID6IZvLztDZzs1uBgOAsVzTOBKE24N51ekE173raYTd+EDbw+/rCpP75+j4y9hngCiJdZjKPWfntXESRBBLCI9Ggp+omybs1LeYWUfPU8Li+pNH93wKW17rWDj1eQQiL9zmCjjHHpK7KpxE4qqjZJ/3x4cTwucAQacmuG9sOO1MGxeoaAzsthvV7x2LWmE5X46DFKRCBoYJddlgVfBEuE/B800T5XWYUN07ahtX1rgmTiM/i8mqDKBkmPpiWCzadKz9oXRNJjJcICLcypZZP3g5HEYDDF/fMvA7AFqg+cIWhgQEk47C4dJuhPQBgMinhaAKDPbugFcdiMgvUip3Fps+xhnOnO8RtGsWOhNSeP9leyhfedwvWu/PUUtM5kRDWteUAADreGOriSNNg1r06HCvrSlmbSdUpaY4U40A4Z61TX0Dzh7fYL1UEBDMtTkRWzkwPSCLudMk0ioB0mfESfLrsAdOdwk6PsW+7ESuQGWXrAOHx+SZPrAy1rWfV0IMVHSlcLcxpYJcgJ2JWsNWB3LMuSv+YLxUSHHojsgIu4vLCIAsIAgJ9PSlYOnkTCyh2OAonRephqPK07ss6PW8XLkz92GBc7LAsuLQwp0Fe7oMH8FRDOX7VWsmFpkhbSvN7pjMfSydtYrzT/qF1JHeeNXqshJqnV7Bm32EFU0YckBYbNb652a383fxCiU/YQmrfhXs506VuXvYuE0wWJivguP7hnsrkX5nUyMHeN+XWGWeOnmPtfEyeSQ5qFpB54hWjtKCkDrPHdhhWJClwmq6Tm3dqPvxprpseuTM921CE+UUyNspJMxk37/jWNuUZAfNP1FbiiyXtbL05HqRRVVxNoP5Va6URk6W7+5x9VreBN6EVw9M/JxcOes+bhYbPf1ZUCkFaGk2dsftW5XrcXFaFKy4/zIXV7IwY3v8wi/FU0znCzdhEfuVaKl6vImjt7vGSQdQsAvozcmJyraZM2AM7LYZFxTXudw44TTKlnOQzYCkhRphWBAAsiDsRmQb2+aaJck90h/BsnVsNGDbcgGGFf6cnKDMpKTo4L7sFcCxZQZwsQalx/P3kKlBMDvUlhVgXp9YZWhrRuIW8kYfk/s2IYVHpdt5Hxr2oaMmxY+nuMOk7x2J8UQVjEpC9ChUnoqozp8VQfeAKE++avz2dCR7z4AFOGct7qCuExeVSk0/pc+20GBcHNPqNBqTFtCfIcWZ8hjJiUqnWE8Sc4mam1XOzW7k+hm7J9ioNAGBFvUzSceOkbWpwluFatbozz1XsqfeuG9tu0mJFS3jeMdtVXuqJDHz4XMH/uHBwzz33wLIs42fIkCH8vRAC99xzD4YOHYr09HRMmTIFu3btMtro7+/H0qVLcc455yAcDuOaa67BW2+99anG4/SEWEs2KNgHJxLCu1HpGkGp1Mi3c92ebBmk5xH+6aCt6ijAzZMkE/Jkeynml9SbWlDFcDxVM4kxS4yG0x3iMvJ6u3olYnrO+F9jZg0mxJJ+hqxt0oMRlTmUgv427B0DPYMCayMhGdajsXTu28vkJvsbAC5MPSL9GtUFpxMRpyfopmjzpDbVGfprC1pZW+9EQriv6hp8v/IF1wqjg0crngzY3Ujr9/qiJtjpMWZm6fKk+VqDBwyhYM2uCfK94kbAguGjfEHKESyeVA07K4rqA1cA8PjCs+uA6R+cDIeslVHxFqsatQqYPUE2GRNQfQPKgkP98FoqbRf5mVMAq17BNkGbasnLhdol/Ii47e7ptBhrIalq8q9aK3mvMEMUlv7SXHhHxSJsahvLaRrtcNRY2zQ76qaLVQwT+QXr+4TG/mjNldjaMVoy3dpecLpCHCtx++SNhjbOu2+l8GJ8hM3tY6R7lkoJqgcMMpORri5/22RavcH0usl/WNqHSd2odBz8bnsRB3tTcLfua2/sec1H2LB+KVellXWleL5pIpbVTlF57DVrnwDXUbHTY8gd9hbj9eZy6Vqjpzska57BlKh1ZTcMJTz3OCly3IrB2rB3DOyMGK+ldz52ZpSDoe1MqZmmJAJUCdZQlISj+PCjTHcukFpoXTlD601zMqqEa9ZMHZ+rO/NAKRtJeJo/vhl2RgwPtUzHEzWVeO3tcwz6Jo6muIHH6THXeqbAibgWVT22JOk+VONZXFYNr8Y5PaDukgYlDPR4UorapuKIziozjjHX5WVmTqd5z5DV3JspzRLsQsPj87gUUSIA3VXU6QsaLm2Lc2ulxaJecyGiGAk11hn5HWg/dBGfxafqJsnYEc1VjPbe0kmbXBxq9Ju04rBdQXFJ+RZjvfU+dbDDUTgDAXf/aEk6ADcLmtMXxEMt0+X+JIY6S8ZYzS5o5Xtl/vhmzM1udcequf09VH1VQjYuru+i8P3c7ly+B1d35jEdoPTegLTA3z3lOY5FmFvUJM9QegwBy2GawJmTNEWbjovnduca53pJXrUscNjl7kmD30hSq+Z0Ar9C8smB//E6B/fccw9Wr16NTZtcaTsQCODcc88FADz44IP48Y9/jKeeegojR47Efffdh+rqauzbtw9ZWVkAgH/8x3/E2rVr8dRTT+Hss8/GN7/5TXz00UdoaWlBIPCX5bOlHLgXPfRvCJ4XYA0VQNoCGBpEIiALcxpk0LCqeMhajCS5kQFF5G03t/LSiVvwSNWM4wb/6X3R+wnBWJ73iCgmVLhU+Ylnj+1gE6TuIqS3Td/p+Y31PuePb8Y5oS48sm2G8d3C0hrDbUOfi0s81OfkVqFXuVWafKrk6H2WrDBU2Zhy2dtp8qLWC8PoONff1+fGef8jrgaMcrR730mGf8a5YnyY0eWMVTDWNkFo847Vm0Pau8Za9U99T+h7KhnkD38D47LewYqdxfiX/I0yUxTl4E+SKzuYEkdswD07TiSExWXVWF5b4TK64eQ4pXlcW7zD3WcRWTOB/ZYVHvVaFDoc9/xouL110svsKsTroI0BgMx13u76duvz0fHP+zCQWFnYeMbTDj3n/d47jmT4+m7BerwxcDZrdxmPWi2CP9sPfdalBL4kqW31udDZXdlYYtArwK3Eru+rRWXb8VRDOez0GG7IacTRWDrW7h6POyZuxtF4uhRQaAzdISDksHIl6TjpLGoVyCFcvDvdbhVeasNbJZravbmsCk/UVCbQQ6c7hDMvPIqP3x7MVe2JHi4p32JUDfYqSo5HT//s3oF0eeR4D2HBqElB89bPiHru2sIdGBzs5Ro29Pzc4iasbilIsAgyrXLcuIJktDphD7C7mbqrklXT7lXxEarmhxcnBv2yE10wdbgzfxMeapmOm3Lr0BNPwaqGEmSe343u98LGPnUiIV4nY4/QeJPUIrgptw7L6yZzO/+SvxEPVs1KuL8SNPOEJ29tAe/ctPvzeHeAXpVap/8nwv2fox0IOZzdyukJYtbEnQjacTzfODHpPtfH6K2SbDynJxjoM60S+v2o44r64ud0Wh8NAAN2wvNy3H2ndYXkX7SUID3zk7k+9XbH8E/59afdnE4nOCniUzAYxJAhQ/iHBAMhBB5++GF873vfw5w5c5CdnY3f/OY36OnpwX//938DAI4ePYrly5fj//v//j9Mnz4deXl5ePrpp9HR0WEIHH8pkHWAfG/5c2X+nTl6N26ZsJ0Ddcjv2M2VLcz/4Wp9yE1Jz3v8yLY/Ixh4szhoZldilHXQtW36Z/rfzzdNhNMj3T90kzpnylGWES9BJg2mE5Gp6h6pnmGOTUgzNmkE5xY18XidSAhLyrcojRPcwKnyLYZpnbRyr/ScBwC4s2JDQhzD8toK1+9e+S47kRBWKsaGCJ43OHq2sjzQ3G7IaTQZU7pI0t2YkwRCmUSrSFpvgpkTO1zzveYWQs+Tz3Gyi8K7XgazQoWi4GFWM6MJjCj9Jk1sy+uX4ONYBgDgwepZ8j3NFU0fy/zxzYgNBPhdiUNhujx5Los549owI6/THbvlus45kRDmFDazYEBjBsDVawFX+6bvafpf35PkyrCstSJpMKmOA71mg4FbJ4nGS73Pml/Nf5vmbIejMjDUI+B5++BxdCutn0h85v7mmW4wqWoXAOygw2fNm8qY0wdqY2IcCNcy6PQEXZcqgLXmTjQgz0mSok6r6kuMfWBnRqVmPj0Gpz+AFbXlLOw9tO0qLK+fLGmIvhdVsoMEECqWKFPOkzK4AMCi8u2wM2KylkFm1K0ToOZHGmU93SrBrZNfTqCbswtbcfRoBuywrKSu08NlNVMZP8Z7ljDWiOi73i7gaqN573DdF2B5TYVcH3We5hfKsZZf8apLT5QApGtdn2+aKC12eg2LzChWNxUYblyAZIoXlW5nv/9ZYzqVy6rG7EU0axGQuB6U4CA9lkCDqEaFnWVm0sod9hbHxbk4c1+bMuIAZ5mj/n5WPRMAsHx7BQudaaGY4R5DLm1P1FS6bStcUY0MPpP9rrLC8IWHpGl2plTWUYIPb0ZBQEtjq4qL6XiiZ2m/6GeM7sWbcutYyPsoFuY505gpMQHg3vvG+dVjU3pMq4exDMq6t64lV545soiqAnZeC7MddoOgE+IYdMFA279cSd2SCSpunfSytF5plruFpTUGHhj6bcMSZ8zvNI858C0HJwdOCoYOHDiAoUOHYvjw4fjqV7+K1157DQDw+uuv4/Dhw5gxwy01n5qaisrKStTWSrNnS0sLotGo8czQoUORnZ3NzySD/v5+HDt2zPgBIN2KVHDe9YVNCWb+dS25WFYzVWqIeoOun7mW1lA//DpTS1k+7LAkYnxhn0DL7tVS6387kRC64mkucdCYVac7xHmNAanJJRcn0tJRJg6nJ+gSCiAh2I3aXbt7PAftct5yD3Eld5VbJmzH6oZCw/zqpl2T/88Z18Z5o2k+djgKOMCmNplN4uEd01yCTQyNp2/3c9cUDUvGbOjA9SUE3GBiwHS7yUgsiuMyvMJdO7gEWl/zxbm1WL9jvGG+JyBT/eoG5T7RLQUmJ6KCD9WFSxlNCI+8LiqLis7gHi/oVV+7hSW1cPoDeL5BMus3l1Wx8JRs762s1YQA0kZ6MmjocxNxG6sbC7Fxx3g3OE8JbQRrGgoMxoH7dsDuFI9un85CyLX5rey6xWlPSSOvWVZIYNHxnJnV546zXzKH36n8AwtYVNFTd9UAXIvM/GKZDYv8rHXXGEC5l6gaIbq7F+9T0tYp4LoBEWnt4HkrsGJyrzxbr/ZjzGYmnNxmGDzVZ53ukAymtLV9ozL3bFUWE1oPpzvkZivS1pDWlwKGvWfa6Q0id8SbzBS5+8/CI9u/4O4FO5HR4UQLmVFpUewOIQDHdR1TAsi0kfuxtkm6R7C1qc9kMuyMmFTGWAKz8tvxRG0lt8NjjYSMOjJG0DUJAaotmTFJfamy+yzOlUkKnmwvNc6hji/OzKbRHpmPXz5HRebI2rFtl1tUivbevdPWJIybYi70O8Hpk4wnZb17sr1UuoEo5cO6Pdl8BpiOCfOcusXEkMA46jE/ZIEEpBWJgq+dniDaD10k6ZbmfkX1CgBgeMYHsAYNYFrunoR50Xm9ZcJ2fPRxWCrJVP2CZdunYn5xPa4vcdOm0tmhvW/UJICrMDOyO6nfGYF+c08It8aC0xPEU/XlHDSsJzPQ8eUVrOQdLy01H0cz4ERkPZ+V9aWyto1msdm0cyzTGl3BpLuvslBJMRCksApHYafEDUUdz9vW9pu6/4gmLp20CbPHdiTlEQDAEm4gMZ1TvqcUjpbVTMWy1gp2PSOhYEVtuYkLJdRa4VhCMpH545vlmE7zOgd+KtOTA//jGCouLsaKFSuwYcMGPPHEEzh8+DDKysrw4Ycf4vDhwwCA888/33jn/PPP5+8OHz6MlJQUnHnmmcd9Jhk88MADGDx4MP9cfPHFAAA7wyVCzzYXJpoClTaR07nR91phGWZAyHQqLGb+iDCk2TLIjXNUR6TbhvdS1rVbxPTqzAcxFEvyql2/xYwYZhbsZM0tIOMNiNjzVOhvYbkZddT49HEQMWUmMS4zHOnBq7PGdEpi4QBwLDzeNgnT8na7MRQKX1wdOuIWbSINNTMq5MfqJdDaXG6ZsB12WNNuaXfe9LxdsMNR1i4lCGC69UbDKY1TFxB4zTQTNfn1EqHXs1ksby9LMGXT32cFuzlHNwkCJDA935zHmtXeeAqPeVnNVKkZ1FLHcrvKAuON03ARJbVAK+rKpauHOr3L28uMi4suJ2ZOsszvCLyCRFKNm8Yw8OVhC9m3KhhG3xEO+JJ3XGFk7e7xWLNrAhbn1soAP2Km1SU5OWef0b9u1eruSnPHlRqH0/P/s/fvYVZVV7o4/K61966iahegxqgxUWMUgaKu1P1CcbGCPw4h8KM52ARTjYeDhzYPR4/Hbj/7lrY7X/KkO8foodumtWkJLU2H5hBtwiESRKj7/c5F0cRoEpOYqFC1q6jae6/1/THnGHPMtTamtZNuPuJ8Hh6gau+15nXMcXnHO6IKp0/wBNdcrkGvsxs3nmv2jgpaSH4nQS4I/hDx0Tj3jKEbdMzvAYDYwYjWUNKmMkmA/o7jKKOGDTi9BssXjihDYtzey+RZPHCyxOSVBOEr1IfAOsp1XpU/wsWVpGLs5qQwdPYG3FnQx7kpqqO+dY7c3BR7d2+9/i0AhgWKzrCbl7QgQtS/o0P5vCbENBXkD/QmokoB8R0c6lM4aMloFnSS0NisMWvDyUvEcO+iF3i/0ueealvMe3R/XyBZnfYIJcu7Zp/457PMmvqBGg/ZBl+/omIYbjyJLx5fa0UBAbV+lrIr5paY2YitKhjVJeeJdEyF5IS+G+CB9++6qh6sLBtSzxAGvVwjfpejaiGQvPamIir3BsZIPzY0n9+r8rB81Nzyfayu6Gc41wFR/f2hxYcwOzqpcPMTUStqL41QALjpxp9bRfbIGOKE+omoIgogj702rIh0gJLEb68YZcM+FF2he0Q7kJyUw5FZN66e5caTKkEeJsdMGkxf7b2DjSxvImocOHT+KGdKMLDxPk1GzPmNJ5UcCO7rqM/3KKCKEz7XsxAgUgyA5QAArspN/19w6480A6NhjCN5Tu/i3DLNJucllIyzcqYiPrP0ufGkFRm+lJvnOx/oz4ftvduv3DhYsWIFfuu3fguFhYVobGzEoUOHAADf+MY3+DOOYy+M7/uhnwXbL/vMww8/jHPnzvGfN954A4AROF4iFmIgoigBJaGR0LRoTX0AvgNHYCo3VnfgyuiEYePwgScH6/Fnlf+iqTBhKbMMHdBegqaiLuOxSZMGYXsk/uZEo3VwDw8UKuHMk6jHoVkQpHdEemy8RAx1BWfVz/X4nNnTtkKi37O7vY6/zxWBxZQfPzuHeeHJM3mwV7MhBDxYxHYC36wBCax761/AQ4sPmaHMmuaL5qn2xfAmowp+BOD3yo7g6GC+xSZBc04XLnuCaDwzUkYh0RRtQeNEJjjTJUzGkfTQ8jMDyrM3HsPrUx+BLwu2eWbut9Yex+zIJLzxGObHf2zWxVchdKlAU/vi0mcvahjI/SkVDnp3U1EXti06avWRkqu5zyJiII23IGWkE/HUBXjBRMruX3jMmi/1Dx9B+IObJ1i9MuRSPNWq4QakyOuKwJRHoJRFWDR+1hikseeZaI68cK2IRkLB07gFqP7omRurO1iBIOz10Zfm8bxTPx4oO8rjzMT/TjAH2XffBzDtsrJMUagbZryDzy9qVUqpNNombd754DuoQBLLhwCUgcYmYWD7uyuYtx4A5t/yY3xztAwHe0rNd4Wxxw4LHW05+yMFC3S0vOJ6GQlj7DBMRCdJqsEDe7urVIG2XFVUilmKBNc+9f2bo2UZGYb431pubajqNOumG3mY11X2wD+XZYp8kYwQHvi1Cwbhv6u896QQcdLsRBR/+em97Nk9OqiYY/jsXDAUrIf7C83eEx5WYvPxJg3kaVHxGXudxmPws4zSSe2uom6jLArooZkEWMatjHIcOFmiCncmNJxPfJdkYtWnXuM+/1N7DSu0y4tO2oxygCU393VWws1NoW34NjzXvdBENlyjuH71xEoF9Ro3jicl50ztACftYE3+EN74qXIAkqwnOUzRZ2YaI5mYdqxIB+2FYy/rSE4gp+AjV43bRr3v4P9z+0Fkars7laPt8ZblBoYj4Dibq1sYtttU16ai2CKJl/pPaxKMVrDszk5bSdX0Pbpv7yrq5oJ499YcM+vn24QiVJANAE6/cZ2ai7SjokHjgTsStvHJfZlh8skcx2fGJeo7Udh6EwEY2yXWvA8QNfiQyvSXt1/7DMXjcRQWFuLs2bPMWhSMAPzsZz/jaMJ1112H6elpvPPOOxf9TKaWnZ2NWbNmWX8ALXBEOJuUh/sWHcGG6k5sqmxDrquYFwj2QAVVAHWQ7q1/QVHn6cO5d6QcV0UTTGFHh+2Pjv2W+k6eraxYxWwcHRp3oS6li9g7v1PfYuNxPceUPQdM4bEMVI1BT2LHqzeHlI37Go7AjSeZ4lN+n5kayCMiMMPwgLmf+Cm/h71X0uMYT2Jvh1HIttUaHPGmmlbMdC8wTt6biLLCBQDE1/1oi2Jw+cu+5WqMGlMqYS62EWd79/d2VJu5mRH2ylnVTvW7d3fUGY+pFOyBublj3mmsqhjA3u4q9V4t7LcuepFD5DEnjb8eUMmVX2teYbw4AvvK79DC/JHjawAoRZ+8Z07Et7nTAx4Pugx3ddTjgmeH5+UFJdvm2mZWmNgAFEqzn1ZFuwiOR2xPoWel1NjvKWm1aDgn0zZ0ixVI7fF8tK/xoqHqvSPlFtRK0mGuyh8BXK3kOIKtBAgpDLLYj5urqoavXTCoQ/+2EkFKKhtgYi+z11KffSpQKM+mlzDsOLs66jMaIIj5tvcXykh8ZrjSMvxIIZVKQbBtqOrEHqHEubkpNuS8RIydCFakyDf5VN5kFKdfvd7aj5YnPuChhgOlPMSTrDzsHSk3WH3KG0m5YXYm/f29I+XwJqPsqaU5DeZg0R6Xc+XGk7jm6vNWouU3R8uwYuGIqrCeiFlz/vHsd+DMnrY9nwF4xP7uCvhRMyZ6D1WLfvCF3w4pVnfMUxAbd0aKeenXVfRaBj6zGumo1vqKHl7DluG56ruc95NZedvdUccJ3IZFTCZHIeS9Xlk6bHvnfai6JZ6DlfMVWxFFDzpGb+X3/natUTKPDBXgYo3uLmWUgwk7PnLVuKLKlvOsn72hsNeOnOj2n2u7uKaKG0vj0ZY7GP4Fx8dDi/6vZXRy843DgfZuKNdE//1Q+fOK4Uri6B0fXznxGQCKOIShn6LJ/3NUxlORFKr/s7utDk8P1ahzOzOJBxqeVzCgiShHheGTnA07liwoEmDJ6WeGKzkp+pzOKZO1PCi/ZU9HTca+r6vqMfJTO2A4ahmgJeV5k4QZwvgiCluJvviw/ea0X7txMDU1hdOnT+NjH/sYbr75Zlx33XX47ne/y7+fnp7GiRMnUFurwl5lZWWIxWLWZ958802Mjo7yZ95PI6tXHkb4Dh5vWY69ndXYPVzFSZRES2clB01EMTuiCrBIAS6pMQGwsCJOeKkAkpCTRWDkpUQ1E6i5cYXb5cs8YT8veJmyhxdGiSHhQG1Ddaf5zmSU8xMePXEHf488NH89sNjKA4AjqDRnJvHSD6+1OcbHwswtLHB8YHtLI39293CVqvysx0u0geSpdPNsTCqNSymGPsMG/PNZDEVw8xTukjzWXiIGx3dsBU7SL0J5Pv+s8l/UL0XyorxgyNNPvNBuPIntLY14/sx85ekhnLP+8+RgPc+ZHHPQeJM/JxgRJbN5iRh2tddzJMVPO6rgkcCme4mYHRGLGwhF8FJlbL5oTw/V8Lx5CUWJRxW8ARU54MjUpG1YW//W0mNHq8rZ2VDYq85RbxmPRZ4Zq2AcRZN0BIrhM2SU6n9zMTPoaELEV4ZfTgpTntonlPtB+8fNTWFnuyp0trW0WREFNC/nImhNtW2GClA3fkYwEiEvWkGVSXSBrCAL6FaI1SjqGQgUr7udMAuoM/jFJc+yLGFFQ8yZGxcwHt98jwy5DVWdmPKiJkeG3mnBoez1lPkVzJNP+4qUG0poTxiFlNiJWNYQLFO/a03+kEUFzJ55jZffWnucmczk3LMiJBS/n/18Fty8JEew7izoMxjrwNw+3rIcXiLGz814/oT3XX1I55H1KqNDQtSoz4cHChFsB06WwJ2ZxB9XHDJzQt58APu6Knm912nDgyInQbpi7oo06mDTKVvy1QEeXvJteBNR5dCSCck+5cb4yIkkLaIANzfFsD2irPUSJrJu3S/CW725tll9t1JBS+8q6sYv3s7D4b4iKyGb9q80zqTRsn+0FAf7S9S9lYzASSuILBwFV/zLvuUgSKI1L0HqVT0W64zo93yFSBokfFAbWJuL2/F4y3Im8pAOIfo3j2OmOttBMhN5nzzWvwzP9akIXNKPcL85d4TkWSCx16JPdcNjCEbV2JifmbTGJZmzDpwsCdD4+hY6ATD06kGHivp4IBKcod+XWvN89wP9+bC9d/uVz9CDDz6IEydO4Pvf/z66urqwbt06nD9/Hr/zO78Dx3Fw//3348tf/jK+9a1vYXR0FJs2bUJubi4+97nPAQBmz56NzZs343/+z/+JF154AQMDA7jrrrsYpvR+2+er2tjTs7xk1CiEMN5nujxIEK6qGLAMia+c+Az/3rowEjGeQSqo83jLcpNcG0hqliE7yb7z9FCNfdjpj7D0GYubAW5AQl2yUQQ9Nns7lKfx49e8a/dfe+KpFLylpNNlkpPCuE6U9iai8NOuSXLWApQYJQAhbLSwJ+XPem+GiAlHXHLCl9TBU4U8j48sOcCYXTbmBCSgqaYNn69vNQocYISvfu/ekXL8SfdneX0kPndDYS+W3fYyww2s5CXtPWPlNi+JlRVDiiteJMSGKPj0z2Veh+UZ01h9Nsr077JzDM5UGpYyAZfXSTKoBC5JOZ8PlT9vJc3t76zgCt6ASkjm0Hdgv/lp136eVgi9sZi5zMgTHsCY7+6og5unaArlHHhjMaxcqBPofTGOeDjK4sbSrIAdPKUgHbKgXBC/v2OgAcTrTr8n6B/z8E9GmeXKuhQnoszZ7uao3AYL5y+81ZYhKtZ1c20zPF3zw/Le6byHxrlnjKxIO3jk+Brug2STormR7CkAFGbbVzk7axcMYsqLqgJ6cXPm1ld1W0qEOyPFLETrCgawqbaVf7+xRsF0li9UyiNBPZyYZ9ZEJtHrtq6qh88Y5VU8e6oYP0/OtCBGkmmF8nOeHKwPOT14jsTP7irqxk+TszhyQP3h9ZBz7wE/uPAR+3f6zFqsTzD7/O5iBX1SdWFs+bO6SpBBiKrq1Fx4ITib7J+bm8J+KtCno1fS+ROcT/iqMFfj3DOqzkhcVevlvan3+Vd777DOOzcybnwHMScdmicJ2wsa/da4xDkmmOzB7lK4OSmLspfWlJi2MpEjSCXWzUmFI7pSZjgB+WiNTewzofRLp4Kbm4IfiGbTH6qJQuNeV9mjqrOTHpAw9xfnbcnovzW/2tjV9w/NiRXVzHAXyMrLXiLGCrx0hjGUNkAkIj/nxpOoKrL3c9BRQfebm5eENxazEAjUP5JRvoz00pxe8gnJzgf682F77/YrNw5++MMfYsOGDZg7dy7Wrl2LrKwsdHZ24qabbgIA/P7v/z7uv/9+3HvvvSgvL8ePfvQjHDlyhGscAMDXv/51rFmzBuvXr0ddXR1yc3Nx8ODBf3WNA9n+obtWYWEd4EhfIf6k+7NGCIzHFD6T2W3UgTp0uoAPxKryASWYz2fZiUZCOQBM4SRLKScqwgCGWHosG0tPwhuPGYy84ysPjRa42xa+CEAUYxIXM/89FQEcn5OS3HgS3gXlhaRGAuNHP7tC/YD6Q0JOUJRK5ZTGuLujTiUG56Yw54afhi6/HQMNtmC/EA15frgv8SRXhbXGISMk+vJ7sOGw/WXHxxePrVVDoJLx+pl7OmsA38HurloFEZqIMkwgE/zqofLn+TnUb0AZDsdevo0jO891LwQ8s+Z1hWcBz+HvHeorZn72raXNFlYVAO6tO8b/fma40vLEbK1X68sc9TT3+rKZmrTn2UvEGJfPhp1oG6o6Qx7YoCeQFArphaR3c9OQC5kU501EVT5CwuBx+SIK5hdoFiJvIqoiZuJ9TL1K/ZuZxKGBIstolKxOcvxlN7/O//cSMXz99n80ifuE6aexCkNhVf6I7TWfqTyhG6o64eaksHbBYAgq9OCi7yA3MsXjAQKGi/BWE60sKStEb/v0UA0cBwpr7IAl7tqKXngTURwZVFCOpup2Prsr5p2yImtfKD3BcCdi/QLEWOJJ7Ohcgv3dFQzbkQnM+0dLeX7WFQwwzNKNJ7GvsxK7OurhxpNwUg7+sU1hr48MFFhn3BGRDrnOBJEgMgJp3Eml3BuLYW1lrzFqNasVeTGlF91LxBim5iVUNXL/7Wzs7qrF1bGx0PgtyI1j/j7cV2QZenSmqIChHIubm8LTQzUC6w+LxpGUaQBYWTwSMma+eGyttfekUkeOHlqPzbXNWFkxZBwbAM8t3QNkkB59aR68ySjumHcavjC+JHyH2HuCjWTA3pFyc0YyObnGzT0iI5L8HElooM98JlkNKANiY7WiByVCDi+h2dvy7DkBlLHvR3wzbpEsTdEv7icl84po2KryAWvM8k5wHN/qGwCm6nXjSc6R2d9doQo7ZjLoROQyuOZNNW3YWNuh5LCIDHkTUaMPZAkqYM1mBOi8P/E+Ytai+1buN5oHvlfGVN4Gze356Rn23AEWzJB/Nh52ynEfqA6LZPuKJ7GhuhPrFthzfKm1DyMHv572Ky+Cdqk0LoL22J8henUE/vksVvaairqM14YxuXYRF6bBJA9xkEdcC4oNhb2Mp91S3WwX4BJFbbYtfBHb+5dm9JBZgnYshnsbXmAWFClA6HP3lLTiycF63DHvNLMScb9d9V64PtZX9GBfVyVWlg3hUF+x6lOWZxVkCgrETM1/NwvOFdOhgl78fU9xm9OcfqH0hKKx1E0WxJHfIWWIn6/nbeXCYdVfiEvWNRGSP644hEeOreEiUZKtAwAeWXIAj/SswrqCAavaLQA7KiLWyGKDCf5O95fZS6YiFmMJrRtcWONiz7sDXgMn7cCP+NhWf5QrDSul0RTdg+NjbXkfnj1VDMcF0ufMvG8tbQ7RPcqxcYREF2OipFTLIyYgY1KZ31zTgqeHauCnXfgXItZ6yf0nW1NRFyuX9H43V3Hcy8JXm2rM/uDcEV2wySr+xBSNMMmxglrQm44ASdc6nxmLqyViZo4nonBSLnwRQQi2TMXbqLjgPSWt2NGxBG5Oyi6UNGEbwLQHLGNMQuUu4oELzq+c5wfKjnKeAzNskVPCC7Au6XUlWSef56QcOLOncVdRN5JehJ0NUnlYWTbEyaxSHrkzk1aRpA1VnVZxtUxt2W0v4+jAAqvfmT7PezaQW7G2ohfPnirOOE/B8UrmMSftqLHrfq+Yd4rHhIhvFYvy38mCn+VjfVU3ywkr2hvYp0Fnx5r8IcOcI9dHy+BFxWe4Lgftg4Y5r+D40Dzj7ZcFy/T45F6k9SH5IddlbVUvV6lXpBCw2Jqairqwq6cWbnba3FMBeS+95BULvoe+799o5sE351Mm+8qia26OTRUdHEtQZniJGFaUDeP5M/OVcu4qAwGALTNILsr5EetMzFqZ7lIpqxvnnlGUzIC1HoCWXZ119t0/aYxIOU/y+W5c5TRQ/gI5AK25oH5meaqGgChqSXvhYs2biGJ1+UCosCm/S8sZLtJHa+jq/e0DiCkI7kPlz6Pr3Kdw/OyckC5B+5TPTPB80dq7Y5d0EbQ/6WrEjLzYL/+CaBfGk/izqqOX3JgupXb5m0+OD+9CFL6mqfPGY8yVvSZ/SHgqTLGQ9VXd5ueSeQPgZ9Ah4oQ4X2O+x1V1SIaq6O9MpLMtgXn/wmMhQQ0oobZjoCGEgefvjsdUGH48hiuiE5ZHgcP2cQX3oPDhoSEdQs5NwY16lvdGetO9sRh78gAlxDYU9rLiLkPpFi3rTJUjQWFeMgzceBJfWvp/FEONSGp+sOGwpUD7MUE16TvwoIQ1exN9xzArTEQV9ELzam+ubVYYTBFBeKRnFZqKukLhUyCD5zeTJ4UEufYeuvGkdVKChoH6IcBVt4VhQJCqw2cU44mvmT22tzTal6qsbus7rBj5VEE54AHzJqKGVnbMYMElu47lfQLwwKLnlac8z/SP11Dj9LlliErwu0Vf2DAgr7/2chE3PSWW7h6uwubidmt/0RoQbprnUXiEQwxNWWkep5w3Ok/k/W+qaWNvoJurWDj4PE6EaQ+lMkYOged6FsJLqPPWVKUSJre3NOILpSd4zqw5cezzajXfeMSDHj36TlNNmyUjvEQMj/Y1cp85UqDfe++iFywqSH5V1ECAKCnUma0cI7s76lQiPffZZ3lxRWyC+0U86+w1jqWNJ/qXGAYArAiHm5tS0CYBVWCGLEFDLOdC0mNSk95SjhQB5vs+8D+WfMfq9/XZ7/LcBqFCxBS0r6MypNRRwjHBJ52Ua51X/3wWn1F65uryASWD0w621R+1FFHq6/GRebDIAAQckhrlB8gCeYfP5Nt5aTOTxniiOjUiZ8Abi+EbrYtYVu3pCBQ81NEjjmL7DnpOfsram3Q+Q1h/Up713yRz7q07ZuClnn1n3VnQx8nMh/uK1PeEHPXGlcywInTaAJAyiu+rgGHgTUZNBDii7pPsnCSO9BVyf9hQ0+/YPVxl1jvgBHTjSevOks0/n6Wir/Ekj3NtWZ9xFjDlOfgOyAj9CjRvPKYiV77DTp01+UP23MMYPmQYVBS8ypEmOMCW+hO8F77aeweODc+32Loo0kRjpYhUKBLkOXhw8eFLn63ow8jBr6Vd9jPk5qSwYM4PLZYYEliu44e8Hm48aXubHZ+hPcqCVz8OXe7kxfMR8kACMKXkdXiXioFRY0VFXJrrCgassDP9nP62+MWJ8k4kHxHtINHIMd49kHQkG9VZABC6FKzP+vZFSx5f/pkWrITrX1vex5+lBGSeu8AcUOKfzBkgrPba8j5ev3WVPYYulqg5taLMBXdEfgBdfJkUM+sSkoo6jLAMzpXMs3DjguteKC/eeAyba1pMAiwZfaQU0X4ifOl4zErcpbauSl3iT7Terr6fm8L+0VKbrlDXDLBgTWKdHm25Q1FFJmIMt7LocfVYnYhn+jVh+kUc6CGDSs/PllqF211ZZmAOzLChFfD9o6WKKSsnxUnAXzn+GdsQg70vAJNEV/Wp10ziv1AUH21Rl/WudoVfl5WKGQcdUADpHZIWENDrP2Z7PJmiOC9pQRB4zxCmV/eJ4V0iD8SNJzMWeaPvEJWw8l6a37nxpNr3VsKpgydabudx2UafwS8TjSsp5KTwEaTCeLCNAunGk5y3QFCO+Tf8BPeUtHJ/KNGb4CCUjM5Ur7m2R3lfV+V7KkhBo8qNJzmPi35GeUZy7ihnTH0AzHJGYyZFe3dHHTZUdlnvomeRo8Fal0BODxXH+shV4/Amo8rTOmkbO8/1lsLNS2JL3Qm8PnUVAIXBpzFY2HEyzDWs06KalA4bsc9k3gy/l74bZDGbmcTv1Lfw/rCeNRFFTb7Cqe/vrrDGLJVvb8wmN5DnLRgRuH/hMewYaMATLbfzuPhzecqghOdYjhxAGZ1kiAGw9gidmQcWPW+NQ66TlOc+MWqlXLjxJKYmVe0Lmg8jEw1Mh6Nw0hfi+JrG0+QykEMBACMQ6LluPIkDfWUm2kRr5GhnBrT8cnyGV/GcTkQV4YWOJlDkZEvtCc7bYXgyjfN8FtMSu/GkifZoA+Cp1sW2Aa6NBnovRbsYyiWcJrQPttSegJuXVHf1Jc5W9O9VIfmJJ57AzTffjBkzZqCsrAwtLS0X/eybb76Jz33uc5g7dy5c18X999//bxjhf0y77I0DLxHDyVc+HrqU3Lwk9ncpPKGbq/iYg4KQfvd4y3LG0K+t1Bj2gBLDwi2QqOYlYlwcRXml9c9JiEwq4UAVWt28JHtz9nVVmkRZ7ckl6lHAYOXd3BRW69wIxLQXICdlLlPtIdjdURdOSIYRCu5MRYVGP99c3I6kH2GubutiFnSDQTiFG1dVfOn/2xqOaiFnlHyaA/6/KPISTM7aWvciK77srctL2hebr3MAXJiEtQDsw3wW4aZ/1lTTprxqdHlrWNCmmlZW2snrRoq6nE+rMqi+GCnhfH93hZrjggFsLm7Hgw2HdeVeWKFsUmxlC9YsoEbPZkVHK3+ca5Enog7iIjzQqwxLKlbE8+ODE84B4R33ESp2BYALLtGlBACzopM8fnUpGkXBG48xJeBTbYsNS5bAqpsJNXuTDPaOkwovPsMx45XRiKbaNmMUJpTyaoXedZOK/N6Rcqyu6Ofqy2quDLUxzd+2RUd5XPwcWuuxmIV3/uZomfldyjVFmrTRwTlGeu4sPnutRNHzvfGYBbFhJT8vCTeWxvrKHuztqeLPU54O7QUar5unHARSCeDPxZMh435dwQBq5r8K+MDJVz7ODFqA3vuOrkugk9HdeJIjHZkaGcDrqnrMnggYYeTd9xIxteayT/QRMf8MGUJYJq+u7Le+K/cvOwpEVWxAwVAMg5ZYH93SOpmY834oF8Mxz905VKvylCDIJoLwoYCSLPu9dsFgxjHxuPW7NtW2Ap6C46yr7OFoLp2HXW1qvVYsHDEFuvSe6BieYyn4wb7QfmwsOZX592IdvETMsPc54JyD4PMsWSTHLff6hSioGB39oWevKhu0jYHzWbwGbk6Kn+NHPX6PJaPFHdNU06bmBeacWPe2r6CLVP+EIsluPGlVLyaGPJbf2hlDMtmbVkb4vu4KXn+KotAz6G7kZ0EXthR9kmxqzqxpRj8ECRh4PsXZ9iZV3hc5SWgO9vbY1dpllJajVxnW6zexffOb38T999+PP/zDP8TAwAAWLVqEFStW4PXXX8/4+ampKXz0ox/FH/7hH6K4uDjjZy71dtkbB/BV+DKoxHuTUawoHzYXgCibDgSEoeNjSaFiFYm6nkni1c2bjFrsA1IJZi+RYLJhj9F4DCtKR5RwcIzHmpKgpOeFvP+PNivPmH8+iyEqAPBcl66enDZKJnkOONwK4I3XrmblSSrW9Pn9nRXYVn+UGVz2dVVCJlLJZiWPwZ6TZ4YrecwTnihvL4wnx3M48ZXp9TTFIMEh3HjSKCWOD28qgj+r/BdzMYnkXvboOArzbH4By+tJuGxAKZ8r5p3iffHMcCXjNp20wydkV2edUdrTDgtcwBgL8NUlQI2NCcEM5I3FEHPS8ODga63/j1JMJCzqIlhUaz/Rz6gYkzS4JpQhd6C7nJNemTmDvKN5AbiALLAzM6mSjslbxoWO1F+zZhnFf8mcszjUX4SNNR3W2lJ0AjDJsLz/LWPFRBacadeep0QMqyoHwkntOgT+1Zb/xP3YP1oK+AoKQ2wh1L45WsYRMyt3Qex/byKKg6cK0VTRwZEnNy9pDDL9ToLLUbKxnHN3ps2XT30DoHJ8dGRxVaWKBjKF5ETU1O5gJd23KgVvqm3ldZWNlMB93RVszHoJBUXKlFjqTUbN/JByLi9/oZRRZKqrey64IrZu/rtZlhECV8uaC1F7fgU9JBwfSKlzQ/NK3lWCyLEXH3bf+KwLI3ddwQCz98g5lwxSElZHURD/vPmOk1aQUUfXAgCgEoDFM8lhQ/9+9924otMdKWcY3YaazpDyvLxshKMugNh7AYYe2Xda4/3dFSjN/z7fB5KsQp6PXZ11Kpn/dIFKCNeRJSmDAOD5M/ND0Fgps70LdhVwcmjBVwXGyMtuKZLSUSRoOAFgT1e1Zfx6iRjWVfSGWPDmfuKn8JIRC2rnTBnHBMlXOr8He0uts+U7PhDxsaz4NLxJVW2bE7+Fcsx1PwSE+JnhSjUvuSkVoddni5xvO1qXqmhadzXvb5r3lBfh9aI8DjJsQjUDdOSA2NimvagyUn2HCT5oPqxn6XWh/jIaIaBLUB4LJ8+LCCzN9eqyAfxWTTd2tqooO9cHSjuWg472X1NRl72+l3jzoaDI7+eP/z7Zih599FFs3rwZ//W//lfMnz8fjz32GG644Qb8zd/8TcbPf/KTn8Tjjz+OpqYmzJ49+1cxzH/3dvkbBwFPPTfPweG+IltoBhQG2aKOB7i+Yf6Iq0RaehaxD5BiYSXu+gBcnzHFDPVwNKOGFszSkyM9JIAd7ga0IuyYwjw0TrpUKz71A/ZYSaVFhvtZWY4byJE7M4kLXgxUGRUA12e4u7jDqnZM7aHFh9gr45/LCjFwZPLcAlBl4LXHZXNxuxK6ehx7OmvsefDBRgoxTq0oG+aqmVaF5JwU4tEpM8YZKezoWAIAzO/93xqO8dodPpPPIWZWZFzg8/U658ENJyNSZAaAwfrmJTkC5SUUtIUUrRVlw7x+nu8oeIknYEUUBr6Ypybih/YEyBtEcKLxmEVdd6CnnJU89W7fLpSEwJ4bV9zwV12ZMAaETrIjSNH58zm8Z44NqWT4Pe0BPu+JqMrb0c/1z5lif964CPVrhQsO4FwxbfjF9TmkEDsgzqP2wocgKo5dEZgMobULBrG7s9a6VGlNeC31v3d317KhRhfl2gWD/HsnpdZ2f1eFBbmQzyIKQt5HUJEDMgAOaugJj8kBeyY5d8Sx+efJSygN9HUFAworn1RMbNb5T6jzq4rF+cYTLaqCL7jlR+rzwhhdWTrMzwCURxpXT5nIqt4rvo5OPtrXaCkiEgpFytDyharQ1PrKHjTVtVnPoWiGm5vCP7fbtJ5ksFtKisi52D9aqvIoApXZ6ZzYnna175cUvGRBkz5brRRYP+Lj0OkC1NzyfcuIB4CbPvmWUcaCCnZaJezu7azm/lFfj740TyXOA6HaN248yTkytNdk21jdgaEffIIjLbQXyEHEaySi3Rb1pDa4m+raLgoLomesLBvC6oUDcFIulhWeNns6O41lC08ZRTIAu2JZ9ItsUKVyLoToO5aDwI0n8X/aK9n4JEP29KvXw42lVcV4ulOzTMV5dcf57JTZWN0hoIFQ0eqcFI4NK+Nn70g5M6TJ+2lfV6UlVzmCo43ZKS/Ksuex/mUsPzdVtaFmwSvqWREfK+ePKrnUW8zPsQqLpQ1cyUuoaDrlBB3qV8xZhwcKeS9QZWd6fvC+Z0NSzD1HtMRZOz48L8zUNmlomJ/rK8WBkyXYVKfuM8p/o/MX1ALfSeVaUapLPefg3wIrOn/+vPVnamoq9Pzp6Wn09fVh+fLl1s+XL1+O9vb2f5cx/ke0y9442FipQ2nSWyyEFldg9ZXieU9JKxrnngnhqo+9fBvWV/Swt+ru4g5OpCXPyUMNh9TzCWMqvEZuruKFZouewum+fncgWY48iKxgkAdXCwX/fBY21zZbdH383bwker53k1L4fUd5sLRQ9dMu9wcARzyIg96bUBEDN55UYWuAk7l3tplEaYlr/8u+5dhSozDIflTAI8gwS8S4Yi319Q/KtddRe0mf6mjAod5iaxwSysSKhPC8He4rwqbaVqXQSa/wZBTPdS80yqZWltx4krnxd7QttTxolPxKl5sbT6rqtfEkV2f2EjE7QqTXhT34rOzZCZbehSizSrl5SVwdG4c3oap0y8gVsfQ0FXWFFAYZ8qWw9qaaVrUmeh6battMvxJGEWZB7ztsjFHiLs331trjgOvjycF6vP1OXH2XPVfA3s5q42Eat/c1edY2VHfCP6cof/d1VeKLFQfV17USR++iUL83FmOFa+X8UaufnFMwLvqu+7uunIpJ+dhU04rNxe3YUnvCQADiRqnc312hvLvVnWJfqk0svfOAMfJkP/Z3V/BcEe6cLmJpFJBsea5rIdycFJpq2vj3N1z/Np9bE60D1lf2KG8vPSeq+rW5ugVuXBcHoxwVuZcdHZEhL7vmjJeGFSuAmo6RFQv9M0/kJQAK0hBMej10ukDJQj03G6s72MPPxmiegXARlSl8pQwBYCrOfd0VeGt6Jo89aAT7rs/7a2N1Bw4NqO9fzFiWe5DIJADAESxbbjyJO+adVp/xHDSfvZXPg5eI4V/ayizK1I5Xbzbf1VGwH7z2UfbArl0wCC+pmcqoSrCIwEkufG/MwEkfb1kOOLAquj/Wv4z3hEmoVncQk1zw5NjOKs4fmSlgY7kp5SgS99bu9jpm9CLFlhr9+1BfMQ6eKoQf8XFsaL6RgQCOn52jiqvFDf+/ZWC4PvARo0w9PVTDjho2GslBEPMU/LWiXyukPsrmvqYHpP8KMAWtyR9Sckk3qmIOwM4hzEmh7tZXee6DFKhuPMnRIZn/QXuanAr8fd12ddQzjBE+cLCn1MAg6dnamdA49wzWVvRa9wccn5mYOJlaVD/PlO9I9/3W0maGGcm5p/9zBN43+44r2ovcR/mOXe31nBskmxtP4pMf+wU73w6eKrSiVJd6zoHnOx/oDwDccMMNmD17Nv/5yle+Enr+z3/+c6TTaVx77bXWz6+99lr85Cc/+XcZ439Eu+yNg72j5fxv8vRuqz/KguXAyRLrQn1ysB5H+gotQU8CaV93hVJ0PFNldlu9xiGPKSU5KIBljgB8cHIzhyrz1DOCuQrq84KthQSAVgicWdPY2dbAQnpdwUDo+5yUmZ023s+IXZnqvy9VyV73LTqCL5SegJMyW4KSeqHhS+urFYvTjde9bY0R0BhJIbCt5pnowaaaVnjjMXzpxGc5krG3q5pDwZTk68aT2FxjJ9QFmxtPGq+q6ItUcKxokJ4fKmtPfVMTIyIPgTEwrZ4PZn3hz+akDJTLc6zvszCfYXu5t7c2ws1NKey9VpQ2VnZyn3Z11GdUPElhpGTRXR317K10c1McqaDxZFqLjdVKGSJaUernjralfKn6aVdhr2ek7BodBH/JM8qhHO83R8vgC8/tIz2rzIuJoUXCplyzPodOF1j9pbwE/qj4HVUyd3NT2D1chSk/iqfaF6tok2OiKFKxJLx5U00b3JwUVsw7ZWPQxwMYYt9UM6c9FoR8SQPMj/iGhhXAeCqbkxjf+PFVVmL22gWDcPOSYTYtbcBSdecdbUttb2CG6BspHyEKRT1mgnfIufAuRFWV84RJfqdIXRAyeaSvkP+/p6MGV0UTdpfHjVFgMc34jlFutAH0/Jn5xsjXc/nHFYdM33Te0p7OGjRVtYf6I6MB1loIOSnPJ1E9B50nlNPlXDltFZqzImqBXAg3nsT+nnKebwkD5YjUhHHguDNNrhedn5zItFFidZ4W7yFKKo/bMpQTW6URIubPG4+x7D88UGgU5jylEBPF5sGeUn7minmnLppfF5wH7oOeA5If3oSh5rWMUZ3cLeFD8j3PdSsGMDcnhYHXbuDvbC1txubqFn4e5UXtaFnKWHnui68iZxIi1jIyN9QX+ryXUDj9Q6cLcF/DEbXntcyWVZzlWtGzVpfpfCbf4X1Nn6PIIqCM4AN9ZSoyWv8Cf4fGLWWFNNyZOW3cXoMnWm/nPJ7g/pVrsqXuBACTk2DGofuZdjiyQ3uG9r/Mcfje96418DktW/keu9QjB3A/0B8AeOONN3Du3Dn+8/DDD1/0PY5je4x93w/97HJql71x4E0o7N7aBYPsfSeecm5iFuQhkj+zrPyZJuy+vaXRSoYCVOIiJYSOpWdYl+b2/qWKsSCuvI/eZNSwn7i+LQgcXyUQJmIX9aABANIO9nVVGnYiwKa6FLh8bzpiCe3t/Us5SfSvBxazcudNRFkoLy9VeNx9uqLs6z+5yvZkBuYK0F6fOmEIJRSDy+7hKmypO4E/Wvwv1uepEYe/l4ipudNefzku2YJeFRZsAeYWKVyfHqpREYe4Df/yEjEWmGvyhyxFhNdrIsqeZ2Yr0obF5jo7OkLvpAuEva0BQe/mJbGnqzoMo9DNmTXN3usnB+s5GkEGQNBLBsDAbgKoi/OpGfx5KthGY5DvPjKgID2k2Ll5SWyqbuNxUL/pkiYllpN2dVjb6pfAwzI8jeA0VLQnyFgU6D9FMygSBcBEeMT3OJFfROQoGuSNxxgaINeAMMT0LFK6iFLXGk+A/Ywr/+r+Hugpx7qKXvU5ohbVciVYWEl6Y1VkwOQg3Fv/gv2eQPOS5jxTno6XiHHOTTog4lfOH1VGn06+lqxOwdZU1AXMMHSTbl5SRdxojaQHVCuw9y56gcdCGGqOQME+739Qfhh/3rMSxKQi+7C7s9byPs+ePWH1jRmohLy0lPtEDId7VfRhU00rwwnduKoIbhQfzUIjcPOZjDDpJPIuRJk9jPYa/Z4iO95YTMDLzPy2jGoldqa9pusqepHtpixZpj5gzri8AyTTzL6ewH6iKMqsaaPA55n3HT6Tb0UiQ+MUMoU85aQIkxG5saqToagbCns5R0k2a11EvhO1G697m/fvE623451krlFONdwMLmyKZSg5u6+r0mYNyklZ80Pv3lDdad0R2/uXKnkRYJbjfsJe/5QX0QaaKAZJctV32FGxcv4oKK+IGeXiKiGZIy8XovbcTkaxvHQ0xHQG6GKWAsLrTURxb/0Lal8JKNPOoVosmXNWJRBP2jLUS5h8NmJP29NRw31mhwCgdA/BaMYMjeOxyzpyMGvWLOtPdnZ26PlXX301IpFIKErws5/9LBRNuJzaZW8cAMpze+BkiYHEJGK2pe3ZFVmDAnpr3YuWd4p+d7Cn1PJskbK4vaURB7qVR+Kp9sVYXW68+t54jKEtIeVHK8HEow7fwVvTM+2LWCpPWqAw/CdiMOWEy3XjSfYceBNRuFlpI6D0u0mpBYznpbHkFHa2NsBLxIyiKL0JQqFhJgVxmR/oKTeJxBrqsberGveUtOKp9sX4cu+KsEfEN3PpxpOYHZk0l6/GeIdYJcQ4pIdJUXz6RpEWP/cSigPcuoS1sCCB6erv0r7gCqN6DzTMeQUT6Wz+rpdQsCv26CVi2FrajDsL+mymITIw5LKLy5O93oHP7OqotxRJ+OqCpjGQ8RBUbIJKrKw/IJk8gtSTTPcn6BrZgy72wP7RUpU7ov//1wOKsndTVVsorM1KO8B7gr3LBBcLJLfTJb52waCCtOjP7exYxJ8jA5reIZOrM0bkdDG0iymBK+adgjceU559x+coy1NtmsFjLAMlrmAKuadEGZ4HTpYo72bKhTcVwYp5p9iAD8K6uGmWGHqeLHhnsUoFxgPfYS+oG0/i6MACXh/qn5cwXPLEMZ8pKkfyYVdHPRs2PnkfycgT544jBxei2DHQYPfPAyzYiGhf7l0BbzwWqo5M50yu0blzuZa8CCqMqpOwcmpIgd89XMUyF4CtGOamsLmuWcM9fF4DCdlakz9kQZfgg9dW9pmjlBoPTzkcgDIspRwIenovxkZGzboDEnbSv6Qrprnzxm0iCitydDGo1kQU2+qP4vfKjvDnlhWrnDY3V92hRKG5p7MGMSeN2bMnlEGdl8SWmmbz3THb6KW+ybl6/SdXmT7HVd0GWSWYztGWWnUfUnK4XHt53uHDqnIOgO8c696i/AxtIFswzkCkjuBtnORMNSVmKgiQN6bOwcHeUs7LemDR82avZaWNo0nUflAvcDgB3huPGWea7rdFm+476mzNTGJ9TbdlZB0/O0c5QXIUbEvODa2jSoI2coANFr1GVfO/xwayl4jh8eblPKeXeuTAg/uB/vxrW1ZWFsrKyvDd737X+vl3v/td1NaGaesvl3bZGweW1UseaMdnqi7/nSzAd/DkYD2WzDmrviO9txqHbR1UhqKoEN1yTYn2RLP2GMjkIMdXGD66SPOMYu4lYlhSfIbfSY0iG248aSogC0gHAMP7X9HL8J9t9UctLzv319W5C0GccbUSZpkShrNdEw2RNQqsudV9fqp9Me4u7sBDiw/BS8QYyx1UPLbUnlAsEIAlsKlJzDwAPNXRYF3ohJ8NKUf6cmT8PZTywNAcgfcmONj/WPId+zmC+WlN/hD2d1fAP5eloB0AEPMt3ujjI/OUwh7Ii5BK6jvJuIGuOD7vE0Bc0I6939x4UtFxCliRl4jxBcmfdX3L87Oro54T0CS1J7Gw8P9lDYJ4Eusqeo1S5DsWrGv5whHep/IyojGur+q2LpolJaf597uHq9Q+0JEBgjIwOwYlnndVZ5ZCgRok+3vKTRXbSZuiliIHNK8bKrtM1GwiilXaOKe8BmIoCe8jBes51FdsxkuGtZ6DFWWa4SziAx5sVha9pk8O1rOhSobT+jKF6acCZQzZQ0DZz1R0kZVAh8/VF0pPqD7F0uBK0zSMc4pNSNawkOtEkUBplAbbtvqjqpq3fuecT/yMf6cUVeFp195ld0bKODboswIXTwnRMifLzUvalbNFo/0MANdcfZ7fFXQqUKEoyepGxpn8Dik/lDdEz+DaE9pAril4Bf75LEU5ORnlxP5VZYNW//yIH0p+5THTPtJ9IbYZMm4jjmcrzRNR3hP885yUhZ9np06AtUhCT3m/OMB/WXSCv7uk9DT/25GRWJ0j4MaT2FDZhb8eWGzgsQ6QE7HHtqujnufx6aEaZbRpQ+Cpjgbru3KOWY7EBQvVVITrHJAhs3Ools/cjo4l6rnti834hIHsn8+yyA4Q8S2nD1FSk5OK51bPqZudNvMuIilu3NRGcXNSDN2xjNaJKOA7eGDx85xDsbdL5WVRrQ0vEcOCG9/kugLqIT47f9y4iqCu1RFGjgoLA5n+TfBlAFYdJnLgrKtWhrDKF1C/48h2hmRnwBjrW2pPoGvoVobOAuosNRV1qYJql3jkIO07H+jP+2kPPPAA/u7v/g5///d/j9OnT+N//I//gddffx1bt24FADz88MNoamqyvjM4OIjBwUGMj4/jrbfewuDgIE6dOpXp8Zdkc3zf93/5x/7/r1Fp7Ruf/GMgErerMUphq5Og6N8kYPn3WjFjL2jaEYq/7UUlL4SF554wigxj1wEQLjXkPQf4GdQfQCkh3xwtw++VHcFf9i2Hfz6LS54j4ltMJPTMVfkjKtkq6mFLZYtVBVaOb2N1B/a018CdqTy5ezprrD6tmHeKL7rguOQldW/9C+yNXlfZw4mQIaw5zUXUZ+wnX3wALE7w8RhWV/Yj5qRVXQo955zwR5+jtRNzTIry/tFSa56Dcx6cDzZsdJEyot908xQbEyXXUfPPZ6lk1XiGMQJWNIH7MBHF1trjqtq19ADqi2dzTQueHqpBNCuN6V/MsKh2rbnS/b6npBVPDtbjC6Un8NPkLOwfLcWKeaeYmSLYgt+n8brxJPy0C18ndnoXVEEuN1fh9Dt+chPe/tEVljJu0WKSVy3D++RnEPUh2VdW5Y/g4KlCXq+L9ddaN3meELj0UrQJwPuJFKug4SUNZ2sPiD5vqm3FrnY790CuJ3GuB3NSLtbuLu7Az6Zn4tDpArPXqDlqbjbVtlrRGvrc+qpu5k33khEg6fD41lX2KNjAVMREB8S54vdEPYDyiwIc/HIeEPMU6052WlVXD6zDyvmjHI3i+ddnk/YTYf9pzYLz5yVieGDR83i0+Q7+XVCO0hxz8TGxdwB1nrbUNOOpjgbAd/BQwyH8Zd9y6x0ry4ZYjq0rGMA/t1Uxy5ycB3luim/6IQZO3WzPkZDLXkJ5fJ8crLdk/Yp5p/DRrDEu5OjGtWztqWZZfce807g26zxmRycUvDOw73hsun9NNW3IdaeVwyLD/VFzy/fRNnybjiTBOFNy7PtHykGSOWsXDGJ/b7lxLrm+ZaRyXwIyjZ5z36IjeLz10+acecCqygG1vwMymec76gEusHFhF/Z01GBzXbOKCgT3o2PLvruKurG7qxZuTgp/UH4YpyauxxuTV3IxMLm3LtbuX3gMj/Uve897QbZ7SlqZ3hQ+sLGmw8pXkMnUwTXk8XoiajURxeryAc4fI2ryoH4AwBqvXEdqwb0i59i6AzPcw9QXNzcF/1yWJb/oWamfp/HD+/8E586dw6xZsy46p//ejXS8321Zi+y89xfdmBpP4m8WHXhfY3riiSfwF3/xF3jzzTdRUFCAr3/962hoUA7JTZs24bXXXsPx48f585nyEW666Sa89tpr76uv/1HtsjcOPvH1P4M7MxtIOkpR8JFRwZSCfUNhLzyiY5uwvZQbCnttoUCHcTJqFN08WyjwYfVgQQZkY8+WvvRI2ct0YUihnkl48mc8qDFHfLgzUpbiZX2WhE3gwiFhwkYGwO/dWtqMHQMN1nuCChXBgdQkgOcCAB4oO4qfJWcx5tlSHiO+VaX4jnmnFX7YAVaVD4QoLoNjD65NcC2ofbHiIB7pWYXNxe2hqtaZLuhMF/bGanNJeOMx3NdwBOdSuUqpk2sEFW6mSr6slKZVwatMfaAWND5W5Y/guf5SrscQuvC1chny3ol+01ryWZB7NeUC025YuRBzsLW0GU+0LTPe7PEYmmrbFENKnrrAr4wmEHPS+FrzClZMg2sMKGXSnSn2TEKxWz09VMM/y2R00xg3VHUyzCJ4RkjhIAVOjiOkNAuv2Zr8ITx7qjh8TmCMxWCBLrlO1vdSLsNzgucDAP6s8l+4knjo/Fxk33HL8jhJ9p6SVvxseqbVb2n0BZWDu4q6Dd1uPBlSOjhC6iI0r9JgDhlTwuAKKl4kL1hJCiqs0tgURlMmg5gUsk01yogiJ45slnykPaNhPyzvguPWz+c9DoRyl4J7J7RGeo7uLu7AztYGHm/Zza+jZ/QWyzETc9LYPVzFMt/q/1gM9za8gCkvhqeHahSLl+9aeXOZZBt9l6k9c1O4u7iDo8RyvchLvrNjUchwDspb/p3YV/KsXFSmBBwj8B1lQEZ83Fv5ooq6ywhi4DluPIkV806hKO8NfLX3jnBfdII4YN/R1n7KcJduLW3GEy23h4ydizkhWG9wlezf01HDcCMJeaV53dlTz7UOgnvjYs6K+xYdUQxXCMtuer90wNTc8n10vHpzaN1D/9bndV3BgCqwKubC9x34FM0IzNOlbhz8t+bf+kDGwd82/J9LbkyXUrvsjYMbn/xjuLkzABjvpPTEsHI6GcXK0mH2GFn4dsfHioUjODxQmNFjFTQuMikf/DvNaMIGRMD4uLOgD3u7qtk72FTdzgoXoA74ivJh44lLZI4crK3oVeFwfYluqW7GzqFafOr6n+OVV65jXGpQweELJXCJy3HJRj8j5VZe4EGFDIAt9DPMWXBeqfnnsuBHfTSWnsTRgQWWIQMoBZH5wBGO6EhFBr5ieeBw9UW8dfctOsIePTg+mqrblTIl+30R40RevBe8WCgaQ/1mJTiD0sif1QmT9J61Fb040F/GLEjUfyftcJLexYxGa07GYlhb1WsudsfH1trj+Nu+BvgXImZuSKkS9R543BIKEogaXNRjKNcuw+VIn91Q1amqeAZrkYjzlskgoMt6U3VbxjwJeMDGWnWxZ5rrTIqCjCTBAc81KdiAyhUipY0Uvz3tNUBOGpiK8DPpTAbhQzTuVeUDONhj6iGwUheQSW5uCh/9yBje+sVMewyB/bmlphlPtS3ObGzocUkDy2pRD252Gn7aVcXxgr8XhgIcBUfa3tKIFeXDqo6MftedBX3Y211lnffg9y2niuNz9CxTC647yfLQeZfPlVHi9/AY8z4CNHzMWA5XXn8O7/xklhVh5jUNyKygsgwA3oUoNleZcfEzhLcfAHvyGUoToEcG1H7Ldafw1eaVGddUyu+1CwYNy5cwrIJOhW11L+Da2Dn80Yu/xfPUVNNmFxcMnNnQ2mgnyePNy0P3HDnB2DGQjBjGrUwG7cVklzQ8iEI67VgyPlNeUci4o6YdaJn2heUIAzg6F+qTpGIV6765uJ0jWhe7P+W9ezFnABvwCERugmsZMB5C6yPmmdbR9x38vwsGGQLlpByOIqR+kcIP7/viJadIk453z4n/jKz3aRxMjyfx5OJ/vuTGdCm1yz7nwJuIKZ7/8Rie6zPwEjeumUuomJHncKESLxGzhJ+bm8Lhfp03oLGTjD8nBSVXFEQhD6Q+rNKjsKmuFVwMRvYzoZhy9nZVw40nVTJxbgrnUjl2om5eEof7DAe4G0/a+RC6sWGQiMHNSeGp9sW4Y95pfO/HV/PYKGFPNilU1td0h4Wo6C9xZ/vnsyxFm/7e0b7E7qfuP1G90bxIge8lYiGjwZuIwpmtihcdHc63hCeNf29PFf8/+HvvQjSkGFkVJxN2MhgArK7ox/b+pQb37DvY3VFn7Y2gZ1MmtfN7Whssw8AbV4nKDzYcBnxgZ1sDJ2WqCzq8lpQbQu1An6HgXFU+wOMgmBnhiGXCMjXOB0moC4QuAzeeBNIOdrQtxcwrJtT/iTWHaBQpZ0WydgD4oyX/AqrRQPPiJWIhBhpaG3kJMltRBqaTvV3VlmJ2T0mrwQbnpuCfz2IDTJ6PdZU9cHNTYcOALtaZSYX5DjCE0PeD809r7sYFC5JutCe8hErGlowfe0fKsbG2A27Us+YBMF5OygkhesF761/Awb4S9V6NCd7Z3qA47MVeo3n46U+uCK3xxqpO1N36KvebziYz/IjqyRsqVQLn3q5qrCsYYBYoN55EU00bFi14WRmeEQ93FXWHzx69s0ZRXG5vacTGmg4c7i80Z3oyqtYyAIXIpCgB6uy5uSlOOpe/t5RC8X+uXk5JnwRP0nlMpJQG+y7Zs+SzAbUXqOow/fzcuVyLwtP6m4z3BYNcZVY6QohbH1CwqTX5Jv+HYUC6D1TxWP3O5FFsrO5gliDKD+BaOBeiVs6QbPs10xyPXydg0z1Gyuz21kb80Yu/Ze3xf2hWWHiWNZoiNXhGuDmKBU86tCxjQrDq0e+D83jfoiMhOlnG6QcV+FxVrHFzXbOiCaZzqp8pGb/kfePGk9hU06qSeOmOlUX19L85lzBucln4rtIJyV4ihlULB8385iXVXkxG8FT7YjV+wSIezK/ZUn8i43yyzKRxa/lj1WzyA+stk6oFucW2hS+aWjVCd3mo/Hnc8vG3cKCnnHPS/rXwyEuhpeF8oD8ftvdul71xAAC33awpqGRFWlJGIgbKs6z4dPjSogvFMUoSCf2VZUMsaPzzWcIbagwIAKZw0LhK1GSviI4iUFKjVCIpofK53lJdIMswCdDfK8uHeFxSmdta9yLg+vDfzTLKcTzJBdNo7AdOlvD7WHEQQovYjoLMFwDgJB3GGlPVUW4kX4W3Sybu7RhoMO/xYBtTPqwkvA1VnVhNlxLAF8u9DS+wUkiXOBdamzAF2gAoj5Ae85r8oTCsSyet8dwSm8V4zDIi5N6QCiMJXIqS3FXUbTHmOGlRlMdRl42C2qj/w/UVM0w8qYyGQPvmaBmI5ciNa/50X/2f8d7iMty66EXAdzA7OhG6XMk7vKJsOKwA60TKsXdz1f7zncwUo3lJ67L//x5bbV3I0uuXqfnnsvhceRNR5kbfUNhr7fOgB+3JwXrF/KOfS1ESVW0avGZJP2IpAPw8se7kZd5c2ww4fmh/X6zuhZdQ+QfOrGlLqZFz01SrKqE3zHkFe0fK4aeVmHVSgejegkGGbOzvrsCy215WEAdOpNd7vf4Fi8M+aNBIhhIvEcPekXKmzARUlWK4vkm8zUnBm1aRjG+OlnH//7mtiqvVeokY3knmovXsrXDjSZTd/Dp+MqU9bJ6Ituk53TtSzonJVKyK5ydHFIAS/afPq8lR8hTQSZV6/EHPKb2XDBWSe95EVCnNUZ+VcQDs8XbjSTWfdpkXk1yq59qbjFr9ov+vKh+ANxHFrFmT9vfFPqfzeeBkCbOL0ZotKT7D8vDpoRrsHq5SETtm2nFCBgwALJlz1hgvmmqXDVL9M2bD82FgULAdPcSkQ23l/FH7PvMRMnrozDtXTOPOgj5V3Vufcyfpms9NRbC17kUrWdhLxOxEcYCNeTcvqcgBdEKyjC7Qvni8ZbkNPQM4Yki1OWRfaV6pbhE1bzzGjF9S0aZGTFaSectLqErxTdXtPI9cZRy2Qq5Yi9S/Q/CrAJSWxnNnQZ/Z93q9Q5DSQOVvLxFjggF3ZtKmcSUn27g5Uzz/4j6lYnzyHD28+Nv4yonP4Hs/vhqI+DjUV2y98wulJ0KQu0uteT4+AJXpf3SvL/122RsHbm4SL3//OnMo9MHhg59risscG50XpnnTJeqDF5sbT+LQQBELbmIhAczh21TbigcWPa+SgfShXTLnLOBrg0DjJPd2VvP36EDu66rkfrq5pooqCzdflUHPFCLc0bYUbk4K/29tjxJkHpgi7uOf/Lkd+tZeRFIcNlR3Wh4Ub1KFbH3XNk78bA/EmgHAEsgsTMQBlJGA+xceM79zlNIEICOkY29ntVXBkoTp37zYyJR2tGY72xuskLRkvKH3X5M1xnR7bjypwslCsabPPtdbynuFKpuy0RBgTNpQ02kZg88MV1qG0Weq+nltN1Z3sBLOl3JuigX/o32NXLXValrIA8pwLJv/fWOcBJTwHR1L4MYV37ZVV8F3eAyH+wvNPAvDjZ71z22aOWVGii9jeTmyoZKXNLUxpLGh50iF/I0h4CVi+N0lR8PJjhFf4YTTpo/kBaS18RIxNg6JCrKpqEsZFa5Wkh2lXG6o6gwx53jj6oL1JqKsqOxsV+H+/7n0/wbm27HOFn0f0LSy4zGsq+qBG7cjF14iht2dtdhY3YGPz3gXALjwIHnj6NI+cLIEjXPP8HePDiywlGUau8rtALM+UeV0LxGDG0szVFIqo7KS+JH+whBmHj6squWAOuP0e4I2Ofo9fd+/EUdfmqeMa6l0ikJKQZphGTni+ZmMYkXZsNo7AWgKQzoTMTQVdWFtZW/I8KHGcFCSezqyx3Af37H3vm4PL/02P8uKWPkAHB/Lik7zd+X7Dp0ugJubwvnzOUyNTHJhY3UHe8eJuceNJ+EkHd4zzWdvDSnoNGf0fpIBrCRfiOL42TkK/lfWh3vrVEFNNy6Y6XxV0dY4DRzuP2BqrMg7BoChNCYHSqDuAnPv6/khyCadfT/mmfnPTqs7J4C5P9BTbskFqdCSIi3rdLi5KWVAa7m27LaXLQOU1vqZ4Up1/oWjj6vUJwJRfz0GuZ7euDasRL0Aeif1/cnBegtKtb+33Ja1F1EuLS9/PBkqUuhNKgfNXUXdKk+rytCASwSAFTnXz9rXWRl63+baZtTd+qrquysIBxwfB7rLsbqqn/u1rf4oNtW2Wmv0FQ1J8y7oqJao0AzHVzDJS1yR9nz3A/35sL13+82YIUEBSKFKghRZlYVFEic3B6xUSR5/bzzGni0Syn9Qfti6jHZ11OOxfuXJIcF5bCBfedc6qy1FmL63oaqTueIB4IInKnCSd0AITFZefNvL5iViePaUZhhygZir+vjmW7OtZ0nsrDcWY08iX645Bk5heV08B6vLB5SyNaaUtqDgzJR4DR+K6o3m2QF8TRMKR1VMlUIajqJrlcbH/QuPwblimqFAlCBN/ZbUnEGqxicH6xWLiZ7HzdUt2Fzcri6LcUXD2lTUZRljh8/kc+RIGVv2M785WqZrYdgKzANlR+GNK275DYW92FzbjD0dNRz6lwrlvq5KvqyCcAoaC106EXhcXVS2raXN8BIxbKjoMpeTTq7l+SSIgrwMxaW+ubYZV1w1Dj+qq25PRhVVaGCvWoqdhttZlyJ5IbVhSxe7G08yflde1rx/XHNRB7nfZf+JinhXRz0XLiPmDy+h9vH2lkb7uwDD9T5f32rGEk/ia8dXhAwcXqO0UbaosBocw03P+HH+jIOkH2GqWU9UHV+7YBDZbor3/xENv5FKBXkmpdIEKAXu8Jl8UM0VN55UbEU0Ps9WwuU5kkYMKXSUx0BtY62pEyK9oFKmEbUl9ZELnQl6xoyNHCKeg5nRC9wPmjdpRLjxJHZ11uFAT7ldn0CMh4xDSxGM2zS+lqNBO3gomZXkIjtedD7NsaH5/N1761/gIoCyrSgZwT0lrexljTlpI/NEDYXbK0et++Sbo2WWEmvtNddXZBGeqaezpPAMn4lnTxUzE5w3buqJ3LfoiP0coch5YzG42kEi4XIr54+CKDXlvIegQgIOZMnktGMII8TcsAwW62Ht64Djzc1Ow42lrUiAHzWOhmMv3wY3njTOHBGNiokzRLlADBHT78rO0cnWvq5uzHUsoOY5Uw0UOXbt+CPFnY3vQG4VAKsAGf8sEeMIHc3T+goFH3xmuBJwEMjlgL0edDZ1PQXO7RFtZ3sDWkbmhs4Ijfm57oXsUNve0hhiAKO7zMlKWw5GK4L5Pmk//72bB+cD/fmwvXf7jTAOyIumLgHjmYEjoDMBpY+gPhuqOtm7S4dTeeGFl0Bfil86/lk79Ki9brLSIe3JbYuOhqkutVeBvu+kHXVhSKyuVtIfKn/eFH8iyk3Rl7UVveYCcn2rONFFL3EHFxeWiXDhp+d6Fqr508WmpNdJKh1uXpK9bGzUBLz1X1z6LADgp8nZHEqndqCnHPAdVggebblDhYvblvLnZOjYGofjMzQGgDXn5DneOaRp4hx1gVusOAGPLL1DGpVeQkFenEASHUOHoJSop4dqVOi/9XbLa0XPZO9dwANrebjiSVM7AbZiNZ5WiffSywcoxUI2LxFTGHYYbyU9g7jL3XhSMVrk2MoXNVlwjpOA6d/6HbLQ04bKLr48ty18EcStDqi12FDVqdcuHE6Xf5fOfS00byHMsugnF2MSl6s3HsPutjp7P7tQybcZnuHOTLInVcHARP8ktEjsaXVu/dA67++u4LwiADzeOwv6LFgGVfDmPgT2BLUbPv4Lu58ZZBk1ggzJ6ItU7vZ01LAiZq2vaHs6RU0Ux8g5Ny+J9ZU9toc2EePnrKvqYccDU9US9WPA8GSlU/D5U6PnUx4F9ZU+Q9WC2dlS2BtKFA6+i6iDZfMSCo4SVJgB4Pkz8y1O+l0d9cwKRM9dV9nDyi1gimzSXUSF32h/EJsQvRtQ0YZMTc7X9n4jA73JKOd+uHmqWNb+0VLABUNMAR010NV86X3B+8CbiNr1EBIqMdkirAAQn3mBDdGJdDavjzcuigWOqz2ytf5F2ymgDSWKAt1V1I2N1R1mbfR8fvXESh4f0a7K+8yNJ1XEhx1Oaj9OTcYsw93NTRmjUn+Xo6KBKt40N8uKTmNL3QnjyR+PYWNN5rNB8y4hb4W3/tDaP0SrzXJbn38nbd8d7OiiJu60Owv64J/L4veUznvNXjsZMdPoBP98lu1QpHtN/8xx7STzzXXNF9UFPmy/Ge03wjiYf8NPOJRI8AzyAADgBC8A7CHY260URMZ7awV/dWU/Yq5dKRiuCa/LYiXsddFeFkrgBVTi19rKXuvABykjfVG8pHHuGf6dl4jhq713sFBlQac93l4ixgnJcHwg5RiDCICjPZmWZ23cJJDS+/9oyb8YT6OYL/lO/v6E8YjJsQNqbg4NFFneDb4gJqLYVNOKt1N5yqvctlSF0jN4C6lADglhukikAUaKkRS87oyUEbgi4ZfewXhzSjYPJMfC8a3Km2vyh0z1aRhPph/xLc8OvZMU8JXzRzW+HlxhM9iMsqovDVd/ViSxewmdZJ+wvUlcDExDJDI9m/pM+SeJVLa9fuMx+GnXgvRkuiSC0Q2p6NP/ZV0NjkjpZEWOdDkmYkVKmpVgHE/iox8Zw7KSU/ASMQz94BOhfeR4TghCtLm4HQ8uPqxqd1R1Wp4+Ny+JVZWmCBordaIWihwTtfsXHuNL3DKExbrI76zXScZu1INHbEXCeJLzqiqYmgjn7uEqFdES58RSknWf3/jRR0L7X/ZPGvXc55lJeFMRU5iLznxekmsDMGRvOqIqvAovJjeJ0x6PwfMdKwGXjG9vMqo82CJ/ZWN1B7bWGxIAL6GidrYXPEwRSh5gqTzubG9Qxvp4DOvKDcwJgMl/CMw3GYzEvCP3M52h4Ocv2vQ8NOo9CigjkNrm4naug0BthkvFDPXzx2JcoHFzbTNWV/Sj4lM/sM9ghrwz2ZYUnVGRHL0X9nVUankIOFdMW+eGFcSx8LjIOGPmMy1f0r4LJxChTozN4H/vHq4yEea8JDbUdFpG85OD9bhv0RF+99xbfsyRL29MwfEA2HBVocCSMbm/p9z20Gv4lXWviD5uqz/KDitZfK5hzivmDp1h7xGKkc2x8gABAABJREFU7h4/O0ex8Ok53Vr/okVlTvNJSdqAyWdakz+Ek69/LCRHLEeC3psUMZFjsL7nAMTA9M3RMvyXhhPc94FTN1vPorESpAywIV38bDnPY8Zh5I3FFK0tyYsPi6D9RrbfCCrTj980hR+9drWtDDgAUg6clAN/hsJPWtSCmmrwjnmnEY9MqQJcUZ89P5Jekr+TiBlKQIGf99/JgnPldEYPDbU/rjiEkcQnbJYhEhSaD5+KygDggkCAusCR1Aq/eD7zmItCVsFm0eT5RtlZXdGPa2JjVvE34gQvzv8Bhk7fZPWTxi9/dk9JK3a0LDWJt3ocwUQt6gd7Z6YiqkCT5Cen94t/P1T+PL7ae4eVVxCiz6TiaISppPeNxbC+phv7uoyBRXN6T0krdrQtxfoq5XmT322cewZHX5rHe0SyjNB4lt32Mo69fJvZJw7sNZ2IYl1Fr0qe0/uIi+vpBOWMBYgkfWjU5yTCkHd1LMZFxqiFjDmaF1r/AD2hLPYmL1p5ht6TJjVAp2mdE8m9LebWUnYyFSsTkmpTbSu+0bJI4eQDPN4Z54NqnFANDXF+6259FW2v3GLNM1xgZYmiNmalTq9RMC+Gal1wka4Mc8M/E/SxwfXgvJIInRNDlpCp3oQ3EQViPhd821DRpZwagQgWyxD9cyryRXNKygR5Vs+lc9S5IHpHSQMMey9LzvSLFgO8EDXVkeWcBM6GrJdyV1E3dnfUWbSRdPbo76Czx5oXL2Co5CoZ0FTZbpiNEDgHgFHCArJSOYF8zLpmHMtvPIP/01HJ7GBuXJESLFl4GscH5of2h6xvIvekO1Ot69aa44bymaLMGe4iuT+dpGsxysj7IDT3PlC14FVcnZXAoV6179y8JOpufRUtQ/PMnaUL51nzqJuBVMIytGfNmsT58zn8/3UFAwobr0XP2gpDlUz0v7x+UxF1LqeFrA/K6UQMzpQL56qp0BmgvcvPI9mvz01s9hSS57JDZ4HG1lhyiuW0NWeB+87xHPgRHyvKDIV4aB3pznLtGj1eMgJMuSZKMx2x6h7QWNy4eteqigFO/KZzA4DvolA/fUcVKkw5RuanwlGhYH2m4PvhQcmStG2Qq3vgPF6/588vOdpP0vF++4W7kJWX9cu/INr0+DT+6fZnLrkxXUrtso8ceBMx/OiHVxmPHeEoNebQn+EpXKlWINl7oIXb82fmKy9QxLeVGH2BrMofES8zkA55OP2YbxI0yVLX4VeC2/x5z8qQYQCQF8vBprrWixejCXgzvYRKZmNOfiFsr7giYb6nmxtXjDry/8/1LFS47smogWNpr8TQKzeELjpJM0eMIDtalqrLnrxJuSlsqT9hxgVzGTJNZiLGhsHacu1JTCvBRx5+upy+cvwzei1ErogILavn6jHNsDn5HU9DtgR7ye/Ut8BLxDihd/9oKVblj6jv6n4e6S800RSdayKxrgBw7OXbOFIjFYVFxWfU83JThnM8L4lFRS8xdIX6HPQKyrwSN08lCjNedNqx5tKdmQztCfkcLxGzcwLihlKU52f2dCgJnug26Xu0T79QGqDhEwUBpfEZ8gwD1t6kd/FzRBSHog5VRa/AzUtiV0c9/KhvRYJIgZXROwDq7MaTHI1hJVeP72MzzvMeJq8pPBhFKjclDFz9vnHjrX83mWv6LGkbdRKxl3LV2ml2MmkYcC5TTsoyoN140qKbJAVWzaf+su/AjaWxsbITcIC9HYLYIJBzIBXoQ/1F5uc6IkV9fqqjgZUQd0YKn7r+51Y0iPe5xnjLoop+1AN8k/vCEQuxxuyxFmeDZZEotra7s1adQUHBeWRAyb8j/YVKKdPnzxonzQtFQceMJ9idkcLu9jprawRzY5i1SL9zwY1vmrHnpDA+NgNnx65hRZzf6SgY0KoKk/Pgv5tlcqyg6ux4F9QaNtW18futqIJgDZKGgoz8urkpVdl5zMyxH/WMLKW9pXN53JwUrskeM7lTet7JIN7bU8VylKkuZVHP3BQ2FPaqsUaMhe5NRHHunbg1X5Nps67rq7qZKhmOz3N2RJMhuNlpFN/yhpLBJNsyOAr8bKIis/Pa6D3krKHmTCu1Jp1yrT0AwFBGew6OjppaQZwEPh7DffXftXMKfACubxkGvId1AUt4DpwpF25OyqaohennhsJeuFlpqz90TsnLz4naNBeafIAISmS0Z0Olost1s9Jqz2ellfEmE7h1PwgOSHfoqvwRe8/NTMKZNgxUTUVdKvE8wJ51KTYP75ep6MOcg39Nu+yNA/hKCG2tN1RrdLmtXTAYFjikUGnByPAKieHVh8kbj+HgqUKGnAQLhrEwz0uGPMF0gA/1F8Ebj1msH/Re7ivA1HhkVPDllJsycBj5Tq0Mb65pYQXfS8Tw7rtxFo5qLOpv3/XxQMPzdkjTB7bVvmD+T30Xng8OjUs2FNd4X4mvmtpTrYvZSwKIyzkQTnVzU3j2VLEKCc9U8+fMmrY8euurFeZybUWv5a3ZXNts4TVlvgO9g8K4UkYQL75c6+d6FrKBIhPS4TkG6+yYPcVr5jvG8HB9bC5uR9srt+C57oVmv2nF77b4z+z5FH3PmmGwyFLZX3bby/wuP0u/OO0YbHOGZHBSAkMGg+4PYYzjMy+EvgsA51MzrM831SjKzu2tjUIxAxveigPcKG9Mm6k9pgCYJECeTWsOfFtZ6xoyOGyae/63fv+EpznABZ77rqJuvSY2naiXiJm8GTpL2qAhbPE9Ja2WJ1pNFjhZ/6NZY6ZPYo/BUTKB6xy46mfeeIxhC2TY8pn07f6RostGlu9YyjqgoDNc9ZfOYQZ2tWASL/WTWHSkkUDtlR9ci1ATOSbeuKrRsbm2mY2oHQMNzInPnvu4WXPLaKV8roAhu6WmOfRaimwQTMybiGZkACIYn/qS+h4lwWdK6mSHjTjDJKNOvv4xozzq/g+9osgApIPlocWHACg8P8FUnSumrXP4XG+pUvg0sxLNnzXvvmPBXKVBIA0rUky31R8FV0TXz+Ohk4F0IYpDpwtCCcMsz3KMMct3mP439YuS6y3ygpoWVRhPzMOh0wXGeJTQDXGOZR+GXr2BjULaK8HP2Eau/qEj7jy9NoyfFxEVFSEURudwFT9jVfEQP5cdIXlJrk4MqDvGmT2dsWAhPfOptsUK15+ligQeELAnN5bm+drToQv6uSpPZsW8U3xvUXTMun+hoYkBpjFaI0IoWP2S51fQ8/IyaGfAwVOFZi19wPdVVIcgnbuHVcHC9yqmdqk0/wMkI/sfGge/tF3+xoHeAzvalrLXZFNNK9y8JHIj0xkxtIA5iIwdzeDNpWfv6qpTXtExEx6UTXol4CmB1VTUha11L/KFSowrdNnKapaWMufAeocUCMHogZunqRp1P2Wyl5uXtHjl4QCP9S+zlPR7F72gCjvpZK0ga1DIuy2SM1eXD+De+hdUwpzEe2uj6otLn7UuOkvxhskD2d7SqDzTMqcAwO+VHcH+0VJsLm7nsDV9l/HSWrkiA4yVIfKC6cYF0PjiMRvhvkVHFP1oXhIPNDzP6+nmJbGzrYE/78aTuK/hiD0OOl2+4rG+u7jDiihRIjlVSKZkWdm36QtRJP1ICDc95UVCSr6bl2R4gvVzcblKD6ScTy8Rw39brKgSE2Mz2KCi33ljMeOh02dBwjNkH3h/EpRHNL789KW4tfY4KyGWZxoBZUD/f8nC0xkT6inngPa8N24SJ6mvm2vDbCqAjmjQmXXA60JheEo+fXixosEkY40U/N3DVWywe4kYJ5sGZYEyKlUfn2i9XRun9gSFjOTgGgeUO/55Tnj+gkrWzqFaVtK9qQgrRVwZOTDX95S0ZsxdkaQCbl4SM9ykYT7SHz90usDITN/AnlgOBp5F/aW+P9UhCAZE2zlUa2Cbuu8bCg35gjcZ5WgGOXZIlvDcOBlkF0cvfK6r4uYZg2ZzTYuZH2Kv0uO6f+ExVYxMv+OZ4UqudSJlOMFmZF9o/HcVdbOTKdO5AhDaS95EVBnmuamMzgBAO0bSDpqKupjtSM69NxZm2AFg8kiEbA4qriT/MjkcAHN3Uo5JkB0JgKH6DBpJUEW75HsBcB4c9YPmNRMMM9ik0Q5HeOn1fRx81+bidoyRQ0Tvj20LX7SexUnkE1FQZNKNJzmfxpuOmMiAY87kzvYGpu4FTN6bVbBtIopcd9qWgz6smixuXjKUTM1OEb0n1pb1mfnKS2JLrcjP0oa+4yh0A+W98PMCusyl2N5/jQPHNlw/bBnb5W8cAAoP6IND4buHq+CNxwwfvT4YXEwGgr+bLkeCJGhc4x+Uq2JVdKkyQ4zG526qaTWRCFJ88pIMH9nVXs+KnCUwHaNskGeZBBKPhzxgCVNZV3qV6Hn+u1qIaIpDiTn0EjFVdC0AJwGMskzsNxTqPdBTHvI6WgaMuHQPniq0i53Re3VFyUeOr7E9QiJ86V2IcqEfOKoS6MbKTmuuKHFyyjeXOgDLy05/0+XpxhV8iugXH2pQ3r4dbUtZ4edLXLNtbO9fyvP2WP8y6/IwMCAlaB5vXp7RM0mNjBap9AX5xe9d9ILJQ9Btd1sdF9K7t179Xha5cuPmkrcMLvFHrpltbPrcH9qPftrli4sq+AYZszjZW3+3qajLZrhxfSv0L/emjBRQFW3+zHiMK2hL5Yn2d/PZWy1DiZS/7a2NobWxwveJmDHCEjGuOu3GNfuTb/by6op+OwKg21dOfAbeuGFAof3N86spD3d11FvsQ4BRLoh3nNdBJt1qdiMy4vm7BFWSycxjsZBCmGnfEY2tbG5OCm52OgTzCiZX72hbClzQCaOi+Bz3HQp+kO2KKJHsE0VadOIjsWzRe9gglvAGUpozsDPJPW79XORM8fwkYgxJImV7xbxTRqEMGoliHQ6eKgzJxKeHaoziqotpEszu0eY77HEAyHUN7MhS6jNArNx4Ers76rCro97a+/IzNJ885oTZE/Te9RU9FqRkQ2Gvolv11J2ypdbA/yj5lGFGvm1g2usXNjQB27C7f+GxzLJRt70j5YAjWLA8WIYSgJBRTR58CRP80onPWs/l/TppG10AMCN3mscWbG48yTSjcIBdnXWm/1pPOJfOwfNn5rMh6CViNjtUIqYMJAfs5CN5xHe7jrIfOFnCshaAWgsNE3TjinyEnAbcx9wUYk7auu/hqORiSZAhIaS0XhsKe9l5wZTmuu0cqsXW0mZlfAXWiZ77UPnz5r0Tl7aB8GGdg19Pu/xnSMOKJCadEpKZYq32ODZVtamwb1yE6gET0iPviBYAXzrxWUWDRslrvuIJJ+Vw93BViBnHG4tx0SM3L6nKmQvljZRXaiRw/vfxO0KCmb4z4WWhsfQkf18qGs4VAnokPIBBQS+Zc+4u7sCO1qUADAfznQV9NqMTzKUvx2f1T9DF0v/5XSLMKzmuV84fDeVIkLCXho3sC2OI9fBkhVc3rpIvpcLjC8zsX/YtD7FGyPWR9ITcKAFZ8JXTmrGgDTolgtAXmDUgryd97onW2y0Y2qxZk3BnGvrHJ9qWcc4BK9vjMauYm3yXXCf62T0lrYoJR1x63kSUL24n4vH4Dg6UsGFbc8v3+fkrFqpcG28iiqaiLnws611DG6gVly11KuLTMOcVLCo2bFsE9eB5HzeXMnxNS+v4/B1qBFVZlT+ioRQqAdJiaZJjn2l+tq3+KCvKqyv6sbezOsywpfcNV+gVNKP0+6DxRwalSvg1e+Cbo2XKA017Iaqele2GE7xJQXBzU9i66EXs6aq2zxM9IyLWMeLzOWDPuDSk9NiCic+ySUpV+bfl7aZn6iKPlrE5rmg8H29ejgeXHDYOClKU2bEi9r4PztMyP3MsWUHfk5HO6z56DgTLWl/Vbe11YpZz85LYWNlpyUEqouZNRq0ia7KRI8aScXRmc4zstGBRM3U+hDbSttSdwKqyQf7ck4P1FlTFavp5ZPAD4LWliJIbzwBF1eNykiLPIi/JuW/7eis4euLmphSMxfH52ZL+9fCA2uOcI+GAKy4HnUz0bpITgIG95s28AO9CNJRwDajzau0h7TFXEWtjKFEuzq7OOp6/h5d8mw327e1GYb5v0REFI5Wy9IKdsO3Gk2gsPYkLE1k8Nvq5NZ9acVfOKfkLTYXqRVlXkHsqSF/M501vdyvhPRnB5uJ2tdbCsbZzqNbkudD8BiI0gI6giTuL+rCrrZ6fVTj/dfO+CVOlnOi9mYUoYYzrJ1pvx+PtnwagyB18X+WmEHz5q73h9fyw/Wa13wi2IkTiFk0hXQYySQ+uzwJr2W0v4+hQvs2MIBhkKHrgpByVGDYZxcbKTtszL/igV5UPWCHMTbWt4WIkUFb7rs46wHewrrJHMSQhw+Winy9ZfxrnnlEJewKfuG3hi9jev9RmjpmOKHaIiI/G4lM4OpQPxthrJoo/rjhkefa9Ma186ihDkB0GgP0OUlononi44RC+8uJnsK66B9luiudIRjuCzDTB30ultufdmzDw2g0hBpVQwjjMhbas5FRGelT5nuD3ZJMMRfy9yajlkbKYdfQa7+qot/DZUvBb6xPcX4F+BLnFM82XHA83P7x3JLNTxnclYpjxkUlceHtGaEz0rCVzzqr5pEtSQkMEo0gmBpW7izuws60hxCoUVEYy/Z8ZTTL0Ofh/N57EFysO4pGeVSG2pNB8SGabDKxAwT6tyR/Cge5yENuRl4gxK4u1BsRcknJVkr1INpWNGH8AiER6B8S6QgmRAMLnJKnPM4RRkIECVM4de02TNrtKsLnxJPy0C3/Ktb/7HvN/Z0GfiqJK+Ur7lxStnBSairrwjdZFcGZNK/iPTrqUaxCUAVJuA1BKk2ZhayrqQsTxTC2HwJqFGLMy/LypqCskl8kgWFZ0mgtYcv/kXhHsV/7b2fBz0hYL0qKil9AyNA9uXOV+7ByqtdmapPycjGLVwkEc7C9h2RyUhXJdaU4piXhdVY+C9Gh5bkHmAP65fy4L/7muyySVB5iCAPB+2lZ/FNtbG1mesTIrmK68iSg2VHYZmFrwjpXtPWRYsFnMfIE58M9lwc/24M5IYV3BgJUgL5X5kLzRe3LtgkGr2GJor7kIzQmg7qIdrUttmKiUT7RPY55KQs5wz8D1sbpswCreaK2vYNkiZxBgzt0d807j+TPzsWTOWVydPa6Y9TRBCdW48MZjaFx4UlVfj6uicEk/gmeGK23WMnGWrPVPxABn7JJmK1p95L8gFn9/bEXJxDSeW/73l9yYLqV22UcOvIkYGwacmCOEFgD20pAQODqwgIUy45s9UyW1qbZNeXBmayq7nBQnbJFXltl94kmLgQAOOFTIDCmadUIlS6l+7e+uYEyv5a0LKH/UR1JepXDZ3q8iAMylPRE13pKclCrSQ8qK4+OBxSqU+MjxNZC8+hw2Bdgw+ELpCdtTnZuy4E8EhbgikgCgwqoWlVpAsZZeDe+CmnMq8uJNRLFkzlmcS+WYysDi+0tKTpu5GA8rnDNjF3hu2JM4llnBCf777uIOZkmhn98x7zQzpVC1WvlMOMCu9nrjNXN9e6/pMT3estx8RybGB3Hoes4fajikPeQO7ph32kSkxmMK4xzIV8iIQ9Z9lvMTDLtPJbLUmkwaL+K9i17g35OhpeBFZt6ZdYdeFfGNd16fvbH0DP4OwQh43oNJyYG1kF42GbWSXj3ZHulZBcAkt1L1XLnXJBRmQ2Ev4DmKyUO/956SVv48oKpeH+gu13AFh9//7Kli26MY9Px6ADyF/fYSCuO8dsEgNhT2csE0ih5wlFLLoPXV3Tx+KQt4XmhP0XjE3HkTUayr7LHnDjBRFkEIEPwDACWfeh3wHXzq+p+bRF8ij5EQH0+tnyzQt6REeSw558gBj2lXRz0rfAdOlrBhoJ7lcEQu5MHWc7OxuoOTe+l5Tw/VWF5W9U5jVLFXfMw8k88uwU1FTg7JdjcnheNn55jIDc2ZpsS9s6DPOmt+tmeUer2ekipXGgay0dpuqmpTdJYUtRCQt9Daa7w99zVP3x06uuFdiCqqS+tLup8xD/tHSzm6mpFSWO8nqsjMkTKISBUntjoGOkZ7Oaj0eqbf1CS8zaogreXPf2tQuVCSEY8+7wt6cTKwWTnnfyvYk5SrtCcpL0JGcJltyHf42VaS+JgqesmGAcFAZeIwnUMnvHZ8vjwHB08VYn2VLmbqIfP5DkTSqR3uK0JTUReODc3nsa8uH8AXlz3LxdbcvKRF1/r0UA1HNjyY8+XOSGFteR/881n2+wNb51JsH1ZI/vW0y944uL/ueS5AJGnr4AcOvGCjAOyQNzf9z2eGKy3l0puIKiy4FhpPtS02v5NKPUFfhmoVrt8F1lX0cmVT/3yWdRFYzTfKDQALMsFKilboqE/euCpswsKFxiKxqxNRrK3oBXwHj/Uv434T/hRQxhB/Xo9ne9vtIUE2wzEQCbgqX+LhrrXMgkM4fhJaTUVdNhTE1wVrdH+fGa5UBofv4PjZOZgdneR3PbjkMP/7+MB885w8+xLyEjE811vKihuxBRGlKo3Bmm9xsaiqxiay4iViCoeawZhgWjs9x1KplI0MJ7o84AkFjxIdhY5Dz6c8CziKVo8vGQe4MprI6I0PNkvRoos7z764rvzIGFO10uefaLnd5B/QXFwkt4KeZbFG6T2wr6sSG6s7cFdRtwphE5Vn3Fa2gzC20DhybVjVxeZ6XcGAUsYSMewjWkwZUaF59oA93dXYUNWpmDz03O5oUwY2fT4Cz5rn4DkNzrk3HlNsRVrm7OqoBxwfu9rrVbVknf8S9GpT0nxTTRv2dVZixcIRfqfEjhMbipRdlhKSm7IKcmVqVO2b5Asld3uJGAZeVvVMvvfjq9kru6muVSU1OmI/aQcCQ5xyU1aF37uLO1j+ErzLUsKFlxdQkCJn1jQ21bQaNjihGMactCEpkOuuDQoyVi1DmM6ca1eA3lzcbnnAOdoRT1rz2lh8CnmayevOgj41DkdByO5feMyisOUojlgTuVccz+DEt5Y2C2XSx+7hKmys7lCFLyUtq1AyJQSF51HAnraWNmuKTWBLZYvJpxJ7lvrLcKvJzExBJLNpHDTflBgraXNl8xIxdvCYgatcDZkLY+X8zEhxIjIxixF+X54BGrtMrmX5CQQUdeDRE3eEIqFcgG08xoQgcHymX3XjKrLgjasCbZzPJogOZG4KKflyndxY2vRR5tUlYvx/zk+ZadaH84wAhm421bQZIhK9/ruHq1CWb+Cez/UsxA+nr1JTLeikqT9ShzjcX4gHFhmGwmdPFXOdEi+hKtyriF9mOX+ptA8Tkn897bI3Dh5ru4MjB0F60N3tdUag5qbQWHwq9H0WbNr7S4Kyqa7NKAK+o8q5z0yGvwd1kWyubbaSo36eVBWB9/cYb7ozy1DfSewnKTPSM0KFrqTAJ+8RjcfNS6pKuBei6qAL77Xsu8Qly35Tktnurtrw7/XhIu+vl4jhq80rbY+ZHgvj/B1gc72hKKS8DDa0ZiaxvaWRf+8ldFKaTmh8clDhIX+v7Ai+dnyFURrykswwwkqBSIakd5MXyY0nWWFsqmkLJddaONBEDCvKhrl/66u6Qxzy5OEEFK43eAlbNLi6v1zR1wHgGu+ZZILJtCY0995UxFKKHm829HuyNkDQgLOMVd3uLOizPvvOL2aGEkLdvCQO9pdg1qxJfbHBVlryklhbqZL411aov3e2N9hJhvp5e0fKsbur1ngXJTxMKwmU7xLcs96UwvBKxiKZ62FFjsZiVmKoJAbgz9G+cNU6kedTXvL0vK11L+KrJ1ba75CsYXlJZTQJ/nmed6G4wRNFijQ8zVoX18fekXJsW/iiJk1QFzm9k1mHJqLwkpq1KpCUGMqVEevvJWIom/saf27/aCkeWPQ89nVXKKhbayNWzh9VbGrZaWwubsfHr3mX+zgzcoHx65SETWuY9l1rv5LhIteBq7cD4bnUR2VPtyJ42NVRb0F9aG/Rz9jApmqvmhc+mH8hq6kDwEQ6i2UFyQKKLgCwZDl959jLt2F8bAYKb/wx9nZWW4mej/UvM9/NM8qdgfRoh4OWxX7ERGZ3DIg8Kf2dvSPlODJYYGhmJ2yZQ+O7b9ERax7omU+03o7xdDbgOxypuKuoO2RIrJh3ig3RIKxRyuXgu9fkDxkOfNdU9+U50+v6zHClYSLSfdzfU45vdVZg/g0/gTcRxT0lrdhSd4LHSVBLN560Em/JISLPJo3NoAJg5oP6Qflj8kw6yrCWcwlog0nUQzlwsoR/n+1m8P7rZ61aOIh9wggnYoUbrn2HK5HL5HGSA95ElOGnbHAIh82a/CGsLh/A7uEqm8XKMZ8feO0GJXcmo1hVPoCdrSrXgCJzy0tHOb+FIgo01jSMI0reA4CSM01FXbjUKyR/aBz8etpln3Pwif/9CKJXaQ+TrNQpMesC5ys9NIAKN+7pNPRelpeLcP8XqWhLjQWSBxCNWvACDf5fYjvpZyvKhnG4ryijt5IPNVU1TcRMTkQQb0tYRqG8bK5tNlhwgZ8lBTrtuwo65fj41C0/xfdeuc7qgxQq3GeBf7YUzYDhJBMjg21raTNzlAPg/I0/KD+MkYlPmOTRwFxvrXtR09f6IY8RY2bFvBP+MvgcxmRL3C5hSyejrPhvqmnFrrZ6g60Wnis538H30j6U2Nemoi7s6lCwpPjMC0iMzQjjm2Me1pb2h3HuQGivrsofUQwsCcVuRYqVXC/ZR2dGGv6U8husWDiC58/MZ5w0AKu6K9JK0b2zoM8o1sHxyv2mFckNhb24OfstfLl3RSiP5WIJhIDId0jEVF6OhlDI8Yb+LfrJzxcKbfA9K+ePWgUHvckoNlW1GRYxEXkIjhGwZYaXiOHqT7yLt9+JZ14jsa4Srx/C/gZyejI9hys4X8SLG/y5XFNrLMJQy5SzItcy01mif69dMKiiFsTDH/HZGxzsD+XfBFvGPIBgPwN9DcpvgjPSGVs5f9RUoRXrR8x1wZyNTC2Y8xTcA1YfRd/mfuKneOmHpnYE3T/yvNA8Uy5BpjFbBhVBWkRFbdlWzh/FwZ5SuHmqovVYagYX9Mq0L7hvgVwnujfhg3PQqK9N1e1sCFj1EqBgmIf7iuCkVaVhqoSNqKcqMl9EJmfsU4a7Uv5/5fxRHOxWdUuyc5KYfCvXYvaTe0lWK850jpuKuhQ8VMgJebZJ5lFlcJlfRvN1xRUJU1sICO0v+nmmnKVM8/GF0hM4l85hQ2FDYS9eHr8GPcO3sC6ClGPkqd5HVn2XiSjLdX7+L7LhzVBFDKkitDPtwLlyGt7EhUs65+COw/d8oJyD51c8ecmN6VJql33kwMJRpsXFlGcEnQr525Zkw5xXABAFm29434WXz52ZxPKFIyGcq3VpwQ69r6/utsKL/B3hbWRvGIxn0o2rKMC2+qO2RzLoIfQMZlIaBuTBU59x4E1GVd91X3a2NzD8hxgwqD0zXIk9HTXsfX3tzY9oz4fxoJDXQQqypup2HsOGqk4LrkPjkhhlHoKGgQFgxgVAhUkP9pUAAL7cu0IpvJMCI0whXtfHjpaloUuE2oMNh3lOKUJD1XBDnqG0wwwQy8s0y4pQjrbUnoAbT1rsVCz8ybEWwHt/ccmz3BdKcJRJcfICS4zN0IMX8+b6cLPTfJk8VP48P5/Yn+Sl8lzPQv79rs463jOygBSvt+PDiXh8Jg4PKyX5qbbFZi+SYeA57PGjJEQ1LyKkL5qckz1d1fhy7wrzPL12BLfIRD8IAG9N5fGzKGHfOg96n1vKk/gdfZfmnX83bvZQyo/YnycWD2IVCiih6woGWJJaEAUAK8qGlWEwrjD0wTPiJF144zGsyh8xeG3JMS8SyKXBKtuy2142FZahDAVvMmpHNYQyQj+3vP96Xu6Yd9qSV4BiF/LPZSm2Kl8UAxT4c2Ih8sZjWF3RD29C7GnaDzk2Bl3ytVuRLyGr2CATjShEiQ2HknKpybFOeQZ6RNSmh04XMNTD9N9h+UY/I5Yq2Zj+0rf3kzcWiDzEk0ydSn1bkz8UNgz0u6Xyz2fIEfJd09zS+PnM69yX4BzIdT7YW2r2rO9YhoEFX9TVm81E6r0VT2JDVSffm2ureq2oiJubQraTMqQF+iwQS87zZ+YDHuDrvBqH9oOO6FNE3ZuI8rrQHpBrv3L+qCWzaT4Y3jSu1pYcNFOTtgHHRS6hjXKdKMz3RyIGeA7W5A/BSygmLujqzLQn9o6UKwhxIoZ/alF5hs6saf497UfKz3n7rVn8czdu5sZLKIjPusoeuPEkQ5mIyYv744q8rYQqOMk5Awkla3pGbzHRHSpAmdCQLg1npL31hdITSq73Fdnv+cgU3HgSqyv7GVrkZ3shSt9LsX0YOfj1tMveOAC0sumFLXFKFuXmmPDo8WGV4EuCg7P6A+3IQIFVMTiTpe9NqqRAOFDVWHNSLASkMUAChCpZKoXavMuNq8QwAHZCJXmUNG0dKY2r8kcyKmqOZkA50l9o4cqdWdMmSVpefCJJzBqnhFGR4j9hLuPdnSacKo0AUiz8c3YdBn7+tNmWsh++8Fb5b2fDP59l9Z93s+fwv5l2VIfx3bwkHu1rZLpFVgZEmJ8S9FRkyOeL8EifgnYsmXOWf06QBMDACQBwxU4al8SK/nnPSnu8fmD8MLSd3HwBoYDyQNLzSNB5CQPHCSovDo1fJ0h6E1FTm4MuW4HRpu/Bs6suz549of6dk4KTdoyCqTHaBHOg/gSjVmQIhJhKNNWn7ypKSXmp0z7xz2fh5OsfC8+J1JV9FeKn8TCHu4QeTRgjGVDnhL2DMzUUTxsCVO/EjSfx4KLvWGMjJXVfV6WhlqU11++gS9jNS4aYmwADJaQIGOUTqGRvkVQJI7/WV/RYRsaxl28zhkplDyfRWuso/x3x4U1FODlfwlGkN1Eal87saXS8erN6R1UPP48wzDQGNy+Ja2KqYjTVWaHzTg4XGgfBDXm8MPLwYpFYMjq8RAwHB0rMntZGmnchyvCZxtKT2N9VoQwNB6gqfMXK+yIZYOXSwOyPTI4LNyutxqHlC59T10RV7ph3GusKBlTxxYmocuhMRjPSytI8WzIcZl8vKz5tzqVW2g6cLMFXjn+G50tNtmM5wqSskMbS/u4KNrxWV/Qbg5IiHLn2vjk6qO49qm4OKGy6NxFlGlRvIooZroE1UV0CKzfLNdEEUqa9qQi86YiI5jmWPKXzSVj/gwMlCh4r87UA+NrIoIrmXiKGteXGsJOKO+fE6P21oarTGJg+ANfcn248aVU4BpSsIIeVc+W0MeITKppLe4Dyc0LrPcPcV/u6K7C/s8K6G0gOcJ/02sozKY15wCT+088kpAuAFZViximCOHn63/qOJ0cS77nfCA3xw5apXfZL701G4Wan2dsZDOdZSoqYDRIeG6s7rETfoHcGvhPy3rNCmIjhrwcWY32Fgj+sLBtiYUIwD/r8fQ1KqfLGYziXylUeyO6qcEGkhPHaSY8ACXZJ2SohN0RxBhgsInlhACgBQTCpgFXtzkgxnhoAbrj2HUv5BMBsTesqNOuDA2yqblN9pdC0CKd+seKgKXNPPOgeeUus14e8wQDw3xu/A2fWtCrWIjGtgA6vG+WUxyCVpLyk5aF1c1JsQB4+k89enk3VbRw5oLU/fnYOgmxWpIxuqO6EpKzky1/kEgSVPQuCpCNI12Wd577eVdQdSlR/6YfXYk3+EDbXNXN11pBBIZovomZy3SxvaSZniq8qBG9bpC78c+dy4SUU246v5xkAr7EsErS5VuWXUP0Kbg4sA1QlnVOHfFMAD0oxoH3iR3xjqE5EjQEo8OVunmIH4+iAVh7JeKHxSkKB53oWWgme9Jk7C/osFrKvnVCRDm9M0RMfOFnCzwl6YeE7psJryt7QQSeDZP3hfAJhyBETEO21fZ2V1ndo3gDFvuKfz0Ldra/aNIsQkTXdz4OnCjnKyHtvPGZYiQD4aZNDQHuHIjYAGMMs9xUZjM4V09Zzjw/NU4rbgkGuVk0J/MQWY3mGJ2zP8d3FHba89nTVdurXaCngqfosblyxtLh5hlyh53s3WQY8HBXdnBW9YI1RyoX5N/wktGaUaO3G1V6Te+auom4c7itS+WEazrG9tdEydrxxU5GXEnPZyNKeXpJJx8/O4XdJyBJV4JXGhfx/poRl/myOcph8LOscy8mgQSaNDum8YSeCln858Wm4uapYF71je2vje5IVsFz2HeVYc4AHFqlISFNRF+D4eKjhEO+Rne0NyJqRQmPRKSb7oDvGjSf5XjO1iYAD3eWqBkNAkSYyERof1cgARBRGPwMAVxIHlGHwXG8pP4/vXT2v68tURIX2dOica9ITZkPSBhMniwv4Eu1DhgN55nmOYE70EqowJN2RwXdy883fdJe5uSra6ObpujmMLFB/OWlHRUku8YRkH3jfTEWXJZb+V9wue+MAgIGpkEKX4QBtqmllz4sM7e3pVFVVV5QaesMttSesRDpJxXnfoiNYX9VtJeruHy1VSYVn8sOJpjrUStUg3bwk9nTWKLrU3BSzmUgFmAW0UOaIUUiOjT8/FcGDi75zcWpLwM6FkKFmeUlrz9AbP71SRR8CNGfkmVLPVXSe3BwfG6s7eBxffHEtXxSstNN4hEdxXWUPJyGSgkRKqJeI4SvNK01iLLPewIpqyP7dXdyBNflDnBBsJdXOTIYYeb7Ruog9N+zhv6AKPG0ubre8OhtrFIe09MTK9Vh228shr6z8N/XBjSctLPgFz/a0078P9JRz1V83nsTBnlLjeZS1B0Rf6F3rtddb7i3qj2xEk/jXA4s5oR9QIXpK9PPfzbLXEUoBoqJoB3tL7f5r6AHNu4U1F6wtK8qG+RncN4J5CfiHBR3S3mDrZxNRjljQfmZMLo2bIws+70vC/3uJGLMWAcCGmk5WCq0CX2Ld3XiSx0UFxGRypZeIiSrssH4u+60eoNZ8U7UiQeDLPZ5kxpX1Fcab78yaRsup2xTTijDKNtZqtpXcFIrnvAEAONCr4GB3FXXrYoiwkhadiMfPoOT9oBzhPRxgsOLzPTOJTbWt7M09cLKE1/WpNpXAH3PSljxV51jtBTr/ssI1GbMHesv4/YrtzUymNx6Dfy4LW0ubbcVWf+Te+hewu6POqr1C805jPPnyJxges65gAB/9yFh4fcR3pZIMz1EUwL75vHchynVuvESM4ST7R0UOhExWBzinB1BGqhs3DD4XVQodFXFbWTZkeZt5/makFB2nKIApx7Kl9oQyWAJwJeoPXB+HThdgMqGiQ4/1L7NkGoAQJMU/n8Vr5aRcuDNS+NT1P4cbT3IRtd3DVXBzU/jLvuW83vCA6QtRRTGeIRriXIig8MYfm3dG1R5Ney6aam2GH/Vl06em6na+26jdMe+0ceJ4jpIbk1FcGZ0I5ZRIKN++ngp4E1GW3RR5oLUn2lk6c/DsAmXWHAfkMFzlqPQSMfiufQYX3PimnaNI+432/KTZzxQxIMNU5sLwHLgm0vlEy+3IBIO+lNqHsKJfT7vsE5JlETRvKgI3O42tpc2IOWk8fmK58T7GDY7aUnIIsx8szPOvSLoEYP2fYQ6BJhUraQBsqT2BvzuxhCsdy88DABwfa8v7OAz6QNlRBZlJqCTiZ4YrQ0pb8Dnrq7pDIdCLJYjJf1MBlkxKZabnSuFHz3XSDvyYp+ZVJL8tKzmFT+S8w/3PlCTon8tij/J7JbRKzDY1mfwYUmoD/QSEEuDppD8ypCiML5JKAZXoKauRZpp/Gpebm1JJbl3V4YvnIk0ppb4FA7A8TnF778nk8osVH5LrOGvWJM6fz7GfSx64DEm8Xyg9gTenZzPGnJM7ad8HknX3jpTzWaN9GuzLxeYs016kiJccf6b5uOhcAlYyYaZ9czHjKfh8/1yW4l7P8E5KdPYmonBSroLxTUTxxcXPcdFBebbNQx1LPslCYDwHk1ErERGALZM8WEmKMpmU2JPknG2rP4rtzY0miTjwe54nkZhrFXuciGJjVadN5iAIIbiP+vucjA0hi33YUTVBlBBaQ5oTkZS7Jn+IITBWcq9MtBXnIdP5sIod6mdsKOxVOVgiwZ3GtOy2l3G0b4GVl/Ngw2E82mdY2L5QesLMbdqxknutMQULI4r/31XUbVfipUhayuE5ovHT9zOdB5lALAkWnLSj9mcGR1qmvf1A2VF8rXmFOUdCWc2UvE3tuo+ew3/7ZDO+eHytLbeAX3p+V+WPmAJi4n6W/eR360KegDrTXyg9ge1tt7OcYhk5EcWWmmZmnZNzF/w3zd8DDc/j0bblPHeW/KGio67/S+W7nCc5pov+PsPaADBy9z1k4XsZI5JIZHNxO57qWHhJJyQv+fbvIhrPfl/fTSWmcPwzf3PJjelSapd95GBt/iD/281W+MEn2pYp5VDg/L1EzBSdoc+TB3siasFEJAcxHyqJ1db42pBXUuDavYkoGueesSjq+Pn6EO8cqsV/qlb9l97fxtKT7EU90G28Xl87vkJ5dx3fMEc4PupufVV5l4LJy46vuN9F+J48V8Ybarz4BFvxEjFOaiMvL3kf/fNZgAfs61ERhFBCkwhjO7OmzbzONF6n42fncL4CFXAi7yG934/6IS8wz3XUs/CaQbaix1uWm+8KxYX6xV7QiSjj44ly081NWdWY1ZdVgjdhqp/qaMDail5sqmnFvfUvcFEtSyD7JuK0d6TcMEMVdRlq1F8YgUffvbu4A6sr+hVUIoAPpr/JQ9pU0wY3L4mVC4fhRHz2AtLYgxEDegYbBsT1TgqQgO9QXgYAbG9utLj0JUyJFdSJKNYuGFSGwXgMm6tblKewu9aaF28yymstIxXLbnuZMf7eZNRKpuYkv7GYwc8KakvKWbjoZQqxb+j4e46tQPtmju8q6r5oop4z2yQneuMxxVVPj/Rd5T3NTZn8Awf4856V/JmtdS+y15xw2HwWtWFAcD0A8JIKs+3mpFj5ubOgTyu1FBX0QZh4L6FhM9MReIkYNlZ12oqEPhPbWxvVvGovInPCBxQbNv7iBmJD8lEmZgPAskKT7MwQBu3JJMYy3m96/i0lSpwZL6FobfnMckK9OQPnU2ofEzyDzjhiQsZTThL/AFbkyc1JYVnxafb6Agq+InNYaK29Ce3djvrW+B7tazS1HS5EVd6YhhxJMosgRI0NA4ogCOWXqGFlv90ZKawsH+I5mhU1sBp+Lv1b3wdq3/u8h4g4wo/4WJU/gmUlp9BYepLlqYS5skf+QlQZP5JCNi/JNSos2J9Qam+49h2kPReP9Kyy11lH06V8zs4R+Vtafj3XtVDcU2IqhCwzjISw8q1emrjW2jMkFzdWdSrPv4DNOmmH94hUrsnr/1j/MsBzmJyEIoJ3FvQZuSnorGmPexNRq56LPEu03nLdNtXqopE+TJScCmwKeJObp9AC6yp7LOcOGcwE0ZXz5L+jZaSIyvrns7BzqBYr59k5OZda+zBy8Otpl71xsL9XKS0r5p0ybCPk8dceK2mpy6I5XtphgUJwE+n99xIxZjYIKmfNZ2/lw0dFhpjGT19+RwYKWFgRLpeVdM04cqi/yHquG1dY2jsL+jh8CgBOSl00B06WMPaVftcyOtdU9KWxkRDITSkFIeDJoDA2eaDk5e/Gk1yRV11kDtZXdWN3R51KGp6ZZPpPUx1Ve20CcB8aL60PJb9u1Mrf/p5ylYQo6kHIuZbeGK5sm3Y4F4Q+uyp/xNC6QUEkLG+dD42vjBovnO9gbVUvM1U1zj2jxp+V5vXhYkqeg+azt2Ll/FFsqOzCs6eKsau9HjsGGlSSlxbiZDS6eUmrQBAJ7N3DVYb94iNTiMQMTMFLxLCzvSFE38pzKas+O2AD6/CZfJVzIDGsgAWpAhDGsccUxp/yW+pufZXn+lBfsdmTggfdP59lG6EadrVy4bBV0+PpoRpOiGuqaTPzkpNib69kcDo6sIC/7+akFDuSVk7WVfZgfVU3aorPqktSV84lJfs/1xk4DwC7f3pdlFHr8AVtKRu5phaDNxHFM8OVljFL8xasLurmJXH0pXmYPXsC3mQUh8/kK2+sgIEwHEjriDtaFVyOk4VFo/FYzEmxNCs2JKeujo2zsUAKzMbqDqwjeN4FY+zs6aq2+ywpNR1hBE+7ljJB+0B+h1hjeA7F57yJqKKgnYwyuxLvHWEEqEq2CEFdANuBoAz4DDBRrmvi46qshPr9ZFQkXxuqZ3rnypJhNiSXFJ4BHOCRpQf4u8eGlSOEjH+CqbIBoGEe7DCihHBRz8J1fCy77WWUzn2N54i/4yuGHIpmkLLnXYgaw883MsJLKJhb0NlAe4zmaPdwlZ2rJOAmJCNJthF71zdHy3g9EqlsHD87R1XY1XfHlGegnWwgpR1TkI6iOFDQw5iTNvefmLMV807hB69fjZ/++Ap446p2jfm+vabbFr6omIf0um+o6lR9nKnIO7wLUSwqeInHLRvnGonIxR81HMSRfiND3bjJS9nTUcN3vBtPwrsQVYZ82mFDn+TO82fmWwr90ZfmwZ2RwmRazQEVd1P7LiB3tLHC0XFeRy0vBbyVxmRR+lKUU0fdg9AfNzuN/V0VcOMKKktr9eypYrg5KSyZc9ZyWPlkMOt1XlcwAD+m2IoO9mVOrr5U2ofGwa+nvW/joLm5GatWrcL1118Px3Hw7LPPWr/3fR9/+qd/iuuvvx45OTlYsmQJTp48aX1mamoK27Ztw9VXX414PI7Pfvaz+OEPf2h95p133sHnP/95zJ49G7Nnz8bnP/95vPvuu+97gIA6KIfP5MPNTakEVnFI93ZWs8BdtXAQgB2Cpf//3xOaZlAktLlxTUFGRVPGlYLvpGwlfP9oKXtD6BmcEKsvdSsxiS4+7SnbtvBFLmICKMG6t7Mabk4KK+adUgVpoiqsTh4hSryihEYAgOPjhmvfsQwkQHnC7i7usDzFshjUgS5bMfcmosh2UlaEYV9XJdx4khPL5OVBnkQ3nuSCO/QZaofP5AOew1jaPR01PDfBBGkvEWNKV/Yu+zCVbXVSH3wHVZ96Dd5kVP1O0LrtbqtTlUkBnTugqgDT+2gdnj1VjEODRfASMSX8yQujFYCnhwxsYkNhLw6dLmB4BI8x5inPpTYa6cKQlHSs6IwbnCgApJMBRSTQyEtFihYbNwCoyI5scl/z2MVauPEk/LRrFIi0mfu2V27BrFmTxrMncMyAgpr94bLnrL1Fnz18Jh+NJafgjcfw4OLDah9o43R3ex0+ln2On+ElYlwN1UoovIhAP3CyBPtHS9ExMkd5fgMRAoK33bfoCBuzgI5M6CiNH1FRFUmvSaw38hINwozUGNVfvi7AJRt/TyvD2xa+yAbaqvwR7OuoxEPlz1seVzeetFlLAs/iugEAe/QRUZSHB3tL8dcDi9Ew5xW4M1KqLkpuCns6a/B/OtQZdWekOIpKc7GhqhP+u1kmiVr8DgCK5/+A+yD3isyFkpSO1DYXt2NR8RljiHuOUjb1OIJY+Edbl4fkU3D8XMk3UwKtbm5uCvu6KvlsHTxVyEZj49wzVj+JgODeumO4KpbA8pJR/OkLvwUAKJv/fZ7rq2LK2KAz4eakFJ2q7gfRfgb77CVUMa1jL9+GgZc+aRnjgErcP9BdbhtpM5MWPJVghDTvkoyCIg3BFjTcJe6c7iySuwCwoULkxIzFcHRgATuuqO3vKTfylZpjII7SYbOk5DSeal+MfZ2VHOkjJZyNmOw03LwkkyrAUdCpbfVHeQ883vppa1x5kSl2Ij3RcjuQdtD2yi24u7iDjWE5j14ixrkaAJhCmfsOcU6JVpXWwldzsa3+KK7LPm8TeCQ0o53ncCVlb0zlkRBclOhSpfxi/YLldHj/EmW37BuPKQhNzvbYqAgaIABwsF89S561Y4P51iMc7aTcUNUJbzym7nNNjxzc05da833nA/35sL13e9/GQSKRQHFxMf7qr/4q4+//4i/+Ao8++ij+6q/+Cj09Pbjuuuvw6U9/GmNjJpHr/vvvx7e+9S380z/9E1pbWzE+Po7PfOYzSKcN087nPvc5DA4O4jvf+Q6+853vYHBwEJ///Oc/wBBN8yaj+MqJz7AQs5Qbx2e8rAxp0mf8a6b44N276AWbjUEoYZvrmpWiTo0ws7n2ReaNxwwPtv4ZKWrUBxKOMSdtqiOPx6zE5kO9xRb1msSXG1ytCRe+8dMr1XN0f8mYeHqoxnhsHNNPAOrnYrxubgox18ZP0kX1aMsdZg51NISYNQBwgnXG5hjqV9kHej5VxoWvoCwAjDc5wMxyX8MRuPEkPp7zrk2L6JhLkyjpnmpfbLGEcDVVUkwzMB2RgiLHsqdDJVluqmlVlHta0dtYZieiUh/uKWmFN268xLYibE8NXar31r8AQHkxm4q6VMK8SI5XGFpK6oWlZEmljvalxNZK2BvDHXT1U1rD8+dzONpFCguxEh3uK8KXTnyW+yMVhTsL+nC0bwEABbPgJFC9D/d2VQMeGK7Gxf4ukhFF88iXsrjMg5cZGZDb+5da3tMDJ0vYS01Ksx/1WD4wFaCIVPF6BAxcN55kL7hlUPjAO7/IY6+vzHX5WNY5uDOTiMBT46DvTdjKL9PuBuZU9sWdkbI88FmuLmhHimxcUQHfVdRtKRBb614EHB97u6rxXxcft+oNAIbWd+T16y1mI44aCTIAOu+A2Xc7h2rRMjKXZZtlnHq2skT7kqlzxXxfc/V5sw76LJAyLtdHsrVlVGo8ReEsDU9vXBnjT7TejgO9ZTgyUKAMPQB9p29mWcZyVhiIlEPijcWYUpaeac5kQMELeJNJ9obmB3Z0xkm5vEdIBvG4RZ+8RAz3Lzx2UaWOHFPB3+/truLvr6/p5vVYW2XofGXUXebl8PvHY7x+xwfns9c+Iy1stk0BLud2e2vjRY3DnUO17ESSyuvO9gamIfYSMeXkGFfy3ErgTphoNZ8l1xARuDNN1Xs3JwW4wPa227G7rU7J1ryAPHCA/X0KaraqUkNvJ6LMdMafI2Y30ivoHfp9PC9CPksdgfs/FoD2ZelkfjJ0xm2DcWNlJ0cP+B7XcpLq9PhTEbi5KiIbYjm7iGPqw3Z5t/dtHKxYsQJf+tKXsHbt2tDvfN/HY489hj/8wz/E2rVrUVBQgG984xuYmJjAP/7jPwIAzp07h507d+J//a//hcbGRpSWluKZZ57ByMgIjh5VdImnT5/Gd77zHfzd3/0dampqUFNTg6eeegrf/va38dJLL2Xs19TUFM6fP2/9AQA3N2kV1QoKavbWB3II6P+b65Tiw15s6HL3Oslpc22zSWJzdDGxtGMpSICJKhgvAqzCW248aXE8S2YTSmZjoSw9qkFBBRWGvbf+BeUFJS+j79gGDSmQ+ndbS5tB/MduPKloA+NJwINF40mNFGu6zPj7EOFTVynpO1qNQiQjB9b8aIF3VVRBIh5Y9Hzo8nqqbTFDPO5bfITnXXq2qD+khOVGwtzylsdNtPsWK4OCDBiaW6IZlMbkosKXrP6rOTUVXWktvUQshL2WSr6bl8RzXQvNWpKScJGk4SfalgEAjo/MgweHL99M4zMeYCBvpk3XaPGp01poZdSJeEbJ1pfWjtalDEGgs7By4TDcPEU1CCjoyrb6ozZTko5kZLvqkuWLZyKqPOa5KS58JmEVPAYBQ6MCSdwcBaOj4n2balpDrENwfOzvNQYp/94zCpybmzKQmNwUs0rJ+SH2MVJ8uGUwRkL/j3qsCDPG2FcUsd5EFF868VltqPjcH3utjLLNZ4v2SUzDNiaMseolYqqGSdzUVqC9+8xwpXrWVARbak8opUkrIhJrTe8/NKSe4/uOgnsl7LPGDg0HXKsCAGpu+b6lzHtQuG2K0qlfmD0o53tH21JrPADws5/P4vkg/P5R4f2kd1HkiQxvN66w79xvV8gd3Y/7Go6Y3KfcFFaVDSrFXcqVmUYZZ+ON+h3Ak28o7GWYD42THU6aFtowq4m8ENGIMW9dZQ+2LXxRGXo6MiONDqsJRfmx/mXWr2jfEE2tJf98s89I7u7rNGQWZBRtLm7H2gWD5mzKnByY+4kVd/n+hLn/MuW/ZTpDm2paWcbfeN3bqrheEPoIMJsQjYX65UQ8wNEOqTFDh01/DvUVh/pJY+NzM22oTNlg0nPN8skDK/5UedvKC6K9rWXZXUXd6ry44HX/fH2r9X76brZrnsEMewGmtYY5rwiUgHEGkIzY01nDjk8uxKjlIDErlc57DQAYZi2f8V+rm0PzdCm190tjSn8+bO/d/k1sRY7j4Fvf+hbWrFkDAPje976HW265Bf39/SgtNTi11atX44orrsA3vvENHDt2DLfffjvefvttXHnllfyZ4uJirFmzBo888gj+/u//Hg888EAIRnTFFVfg61//Ou6+++5QX/70T/8UjzzySOjnn3jsz+DOzFJhdMHGsK6yR2HyhLJi4W1121zcjp1DtVi7YBBvTF6JrpFbM3oNiaWE/g/AlFWPekDKtZU2YswQDEnBd1OTbDyAYD3Rn6+79VW0DM9FU3U7dnfVWgKy6lOvoet7n8QXSk9wATXr2YH3b6zusKj9LK9FgLFpXcEA9nVVcul3byKKDZVdijtaJFIG+08Ka+PCk8rbJlgZmIouRylpx16+zWbdyMA+tHbBIBdGYqU3yP5BDDoAM5sEPTJ82QdYQty4vU7UL+v5kvVDsMHweHULzifSNhMMtRXzTnHoPeOauT7vKTmnmRhC5GdIeXlrOg9HX5oXyg8AAGdGGk7Ew6r8Ea7HYfU7wNASnOdlxadxbCDfeDcpkVR/b0XZsKnSmoEhTM6ZG9fGPdUK0MngMtHZzU2hqagL76Ryke2aAkTyfLS9cgvWLhhUHP2/JFQeGq/eV1I+SFlCn72npJUVfulJxYw005lmer78mZeIYUnJaUQdT1M3Bthzgvs45qtCTUJ+ra3oxYHBhWpeZ4SNzOB6Sa/zusoewzol9qfVxwxrtiZ/CAf6y4CUg3VVPXweSSneWNVpeOjlszLs00zr4eSk4ARw1ZJVhcd/IWrlFNB5luximead/r2k5DSaz96Ke0pasaN9CZh5BzDv0n0m+e7Gk/jjikOcWE7zs6p8AAd7VHViYmqSZAZeImb2s+631TfB7sTebn12lFECSCrd4DPk81fMO8V5Qo1zz+DoS/PM58b153JSYdkl5ph/puU1yUE6nzQ3mc5XcK2s+0Hm8Ynka2r++SzO1QkyTgHAkjlncWxkvj32i9znVp8kw5GW+bIvdxd3YGd7AzbVtGJXV13oTrCeNRXB+rJezHCT2N1Za85UMsL5Oharnu4fyXkpYy76Dv0z1iugorY7e+uZMjX42eDPLCZF3QfSDbzJKFaXDeC5noUW2+Ed87rx1OL9lxyzD7EVVT373z8QW1HXmv99yY3p39qmp6fxs5/9DJ5n88zfeOON7/tZv9KE5J/8RBWMufbaa62fX3vttfy7n/zkJ8jKyrIMg0yfueaaa0LPv+aaa/gzwfbwww/j3Llz/OeNNxSPt5ubIfwfV1UmZZJSJjxzfOYF7ByqxRdKT+DAyRJVRCfgdaYD6MyeVsJaeyzvX3iMLw4WnKKQFwmJFaUjlheXPIteIsaeWrlKXyg9gYO9pczMAgAtQ/MA38Hujjo0Fp+yxtIxopIA/3pgsQkTBjwv1MhrDqjLnnGV0yryYjE2JWLY16kwzJQs7OamFDxE08Ox925mZk/90YEFHLplpZ+SmRMK87qhsBdTng1pktECbyJq1VYgWE1ojJ5RqMgzS/zn/JExxQS1rrwX3lgM/rtZIY//yvmjODpslHbpyWUDSLw/GEGSzUm6oUiWfz6LoWMAkJs3pSJUxe3Ga+Y5WFk8AjeuIjzeRFRBoXyV3OuNx7CqYiCUE0D/fms6D0cGCoy3SUKRRJNVe7l/adfKNeF1GDfniAo30ZpIKAKgK6cGwtuyn/JiW5M/hOs/8TYAKOw6KQAUCdN7fVdHPQ6eKsT+0VL2utIzWnTxLYIRravssRi6gnOUMRLgqaRR8rwStEUqbk8O1ltVyQGElB7JSOMlYlh228vWO914Es1nb1UFvOKmUB95y6koFUcYYmn457OwunyAkxgPdJer4lJpA9tZOX8U3phJdPSmIgpqEFBG2DDQ++y2236Mu4s74Iv9oaAWPhcu8xIaZz1DRU8PnCyxyACcpIt/bFORNIKB8Tv1/BAmPeQV1v8nw0Amtwadf954jA0DQDkNnLSSRZsq28znhKebf6bX5/jQPKwrGMCTg/W4r/67zApmORtcX+V5xTwe4/enPhrCux86XWAiuxqaKBVY+r+XMP22IlM+0Fh60vzMNxW34QNrq3rNfTRpjCJ5tlaVDsJLxDivwkvEbMNgLBZi0/ESqh4MRwd0boiXMBj7mlu+zw4SNzvN2HyL6U7Ptcw5o2euKh9QbFPJiBVppETue0paOZ+Ki3bmKCcAEUZQf46NqkRgirBINsEg579c93XlvQauR8aISIZWcC9fJQLL5HdhozIaIDuN/aOlqkK9JsPwEjG4MVO/o6mujZ+xuaZF7ZG+Yj57wbMoiQvkXpVFTJ8eqgGmXHsva5ZCmh9+jjYMWHfRcpkgukir3Bz4MIUoPeDwQBEu5fZhzgFw9uxZLFq0CDk5Objppptw88034+abb8YnP/lJ3HzzzR/omb8WtiLHsSfe9/3Qz4It+JlMn3+v52RnZ2PWrFnWH2pudhrrKnuwvGTUgtZsKOxlrCF/Vlz2ibEZjFe2MOYiLE54YOV9AAvBYFEYSYcKqAPsn89SHlQheCTO+fCZfGwtbbb6tL1VHWQqWOQlYli+cITfdXQo3/bYC8En+8PjzUlZz6f27Kli3LvoBfX9LNsrEXyGrMzLirAP5Xnz7c9LONSSEkNtSB4Mvuj0c/aOlCsBGsD4OyKXY3VFvzJgHPN+2b+V80f5d+s1vRugoFErygxTiTsziY9mjWF/b7nCgYr6EjSHB/tKbA+fb4qgMTRFKI1Bfm++wBI6dE1eNq38OLOmAdfnEPLEeDbgKHpU8mK68SQODSmP/tNDNcoo00bd7s5awFGKCSlPsuKtG08yU8cXlz1rrc2dBX1YV9mDmVdMqLXSc75zqJYvQSfi2TSeru354/XOM4oANQuH7RhvIs2PpEmkpNEDXeX4yVuz4caTSsnXCbhuXIXG761/gZ9Lz8l1p20lSX/eG4sxFGlteR/35QulJ8AUqLCNFG4RHwd6yjn3RzaKJgFQEDFRSG1V/ogFK1pX3aOcAdohcHRggXrXhGGBuWPeaetSd/OSgbwi824vGYHv+Dh4qpArQ1PfeQ/Gk6ritqtgD9QO9pZaeGLav3LcL3//Ojw9VAMnyB4UiJqE3inW3Y/67Ci5MjqBDdU2O9o9Ja1WjYPg8+TPiNxB9gEAww2lYfhuMhe/XduB++q/i12dddZn+d/0bBEJocjT483LUfEpk4jNcs53cKCrHOsrjCzZ3VaHw/2FCuuvZYV/zmav8sZjhk4VhhFOjvWcpl8FlMw59vJtDNMhetB1BQNw85I40FdmDOmUbZB6iRjWVfUwzCW4PoCCQHKkU6ybG0/Cha+K3unxstKv5VXHq0bhYPkmo8NUhVgk4T5QdlQVEE3EcLC/RCUjx9KWwUxw2h1tS61IE7Vd7fXsyKCzQKgArs7umLMs7x+O+uj5IGY/2aTslvtYftci2shLWsYC9yvtmNw1/Sx5t3NxR53M3VTUZd0TpC+srug3jjPdB2K5YwMuT0Gb+HN5STNHM5OhKF/o3yRPXF/LSM3mR9/PwBx2KbUP2YqATZs2wXVdfPvb30ZfXx/6+/vR39+PgYEB9PeHme/+Ne1Xahxcd911ABDy7v/sZz/jaMJ1112H6elpvPPOO+/5mZ/+9Keh57/11luhqMS/tu3vqlBsM3nGI7l3pJwPztoK5UXgqqUAsmakcOBkCba3NrISDRhvoJsjuO8BFhLknXuo/HnTASEk6UB/trqPnye9pQDY6HiibRm8RMwoseL3gDrcR19SntGN1R126HOmuQikl5ySEr2ESH4UApG+/0TL7Rb7TdDTSvNwsLvUfGZcXBSeY6IGY7ZgBVTCmnwmGz45JqlzQ2EvM1hQe6DsKHxSwCaimPaiyoDROPINhb1MK+klYpbSRJVIqR3uKbI486c8QcEoxu0lNNMMJUrL5GHHKPbSk07v4QrPCZMLEfJOCw5zNzelCvTIRoZPIBERAP644pDK+XBgQvfiomGF1vGtd0h+fTeukgYPnCzB2Lu5/POmmjbrWQB4TSlZLpTARmsavDR1ng43ccl7E1GT6yEMGKuGRTwJJIUHD5qtRCRzAlBJdfEk7ms4gjsL+qx+c5VeUR/ktQsfUc8QNTjk84IwGm8sZpT5McFvr98RTPb00wZSSFEuS+kIKCGHe4s4gvbLLmY3llYG2mSUk5qJ1lg6MiifgOaa10TABP3Au0jJAWDqHAhjQiZcbq17EfDBiotV20M/d03+EHYO1Soa2kSM8zh2tC7lpHb2UGeQSdwv7fGsK3rZ2uteIobn+tT5vrOgDzE3jb1d1ar6vGe8ucHIEtWGAYDlpaO8xm5eEj3fuwleQpEGpHydu6ZhSsTQRrIWMFh/N57kmhcky+H6FrnBXkEjK/euNGwtxTARw5QX5fPMjh2SU6JegxvX0XFhGEgnAQCu0syRLtdnauan2hcrWIk2HIjZbW1lr5lv3cfVFf2GpIGim8JRQ//+2vEVqjikzoPzLkTVvnLMuds9XGUZhNadFKAYlgx+BAninDZK8HVUDh43ERlYMucsj4PeQ/v7wYbD/H5AORBCuV2UOyIRCPSdiI89nTW47qPnOJeIDH9A7bm1CwaZCGOGq8bMdVF09EF58oVy7wD/0FqPOwv6sLO9AW48idJPvsGOSTNOc5ZXlA1zfyXpiWwqPxFKI/RMpW+a20u5fRg5AAYHB/G3f/u3WLFiBUpKSlBcXGz9+SDtV2oc3Hzzzbjuuuvw3e9+l382PT2NEydOoLZWJb6UlZUhFotZn3nzzTcxOjrKn6mpqcG5c+fQ3W2EWVdXF86dO8efeb9tfbWB6wAwUBjtUTzQV4b5t/2IK3p6iRh833iM1uQPWR7Kppq2MBex9oYeOl2AlfNH8ZUTn7F+H7wInutZyHSJUsgDMJVJtfAgjDY3LXApWdaNJzHDTaLs5tcZ/07Nm1BMHPTe3Z21aKpu5z6z0iggHizURUg9ZKCIxGO+YERRHzcviS+UnlBJUBnqG0hvDmCSpdR/lEDd01FjwzTiSXyteYXxaDhqbjYU9nI0Ze9IufbKa6GsvXUEzWgq6jIXtqMuZEdj2g/0KIPRm1R4TFIkAMV2w8qUZuvgmghi3bwxBeuhcUqvrzV/k1GsqhhgzmlAXVR0aVz30XPmuT74gnZzBBUlgB9MXc1J8nB8hhkFlVz2CkqFQly+0nsFAKvLBrC7o87sE7F2DzUcypzQJ/CsDEuTMJIAVz0p2JQ3QPOSqZESTVEFWj9iFpFJ/4Dy/KbF4txT0qpyY8bN+rnxJBuPIUYx6rtv1o69lfpckDHAy3TOUKHCV2fciXg8H248GYa9Oapo1vrKHjxQdtRWNuS8C0WRI1DJiCq+5IN5zf2YqE8gGahIB8zgLQW00TdmziUAVSAroeARXiJmRQykAbSjbSngGHpeohWWYySDDNAsSaI9PVTDCvn6qm4FOxm3HSZybdx4Eq7j2fMoICR7O6qVzKQoDVU2B5gAgD67v7MCbm4Kf1B+2ILcyPk5eKoQhwe0waqdQnB96xwtKdY8+IH9u23RUd4z3oWoTTfs+nxmARiYi85TCEY5vjlaxkYV/zw3hXsbXsDmqhamosw0X/tHSy3jl6CpvLc1NTMAy5EAAE+03g4voe5JwJA0AMBz/aU8L4CGiOm5ZqrkSVGs0lVrRXvUci68lc3/tgwjzaYjm1RgVeFDnXwsIG0AmApVwnoB4NhJvdaSVEDv76+1/T/8bgD40dQV/AzunzaCuI8JQZmq76ysSJo/31TThu90K5l54GSJqfvianKC8Rhijsf73km5LMslmYnv+uwA8SaizKglm5ubUs6CiSiyXDNvT7UvDpEXwAf8qGcMuplJPo9yzJdq8z9A1OByMw7y8/Px85///Ff6zPedkDw+Po5XXnkFAFBaWopHH30US5cuxVVXXYUbb7wRX/3qV/GVr3wFTz/9NObMmYMvf/nLOH78OF566SXMnDkTAPC7v/u7+Pa3v41du3bhqquuwoMPPohf/OIX6OvrQySivDMrVqzAj3/8Y/zt3/4tAOCee+7BTTfdhIMHD/6r+knJKjc++cdAJB5O3iQWHyph7wBwfU7IkZ+VigErFoEkKumBo7/vLu5gYUsevd9dchQ7BhqUAHftJEfZMiY3ZkoOk8I+2Fdt2BwcKIHKh1DJpn5AeAIm8Vo2+ezNxe12SXnRL0r0DCXATugkPFeESjMlV+oEYSflwJk9zUnU8nIKvtcyqHSxobULBuH5jlLwRWLyez2H/r26XK07ACuBnPs6GcW9tcfwRNsyKxE2OFecaJ5hreRnAfAeDPFWB+aeEq6t3+vkw7uLO7CztUExxaQdNNWqRLINhb3Ks+vBSoKDAyvx0EvEsKzklJ0nAMX6QnSiMhEw+P475p02ycX6XEnsLHGqWwm6jrlMOdFXJPhazxCeuWUlp9SFnnRDn9lQ3clVduW7mb4vEF2g+eD94QOI+NhW+4KJYAXOGf0sUxItJecHnw8AV37sPBo+/iqe6y0N739KeNV7Ze2CQRNd0EbAO6m48uIG1sE6axeiaq1pTigxV49xXcGApRx6UxHLwJTjtRRu17eiJkvmnLU50gWMjBNHaV5pnsZikImz1ILykc5P8LP8O5HcLltwHkjpbJx7BjHHQ45mLNvfW25XnRXJmfwsmjdSsClRVybDTkaxrfYFlccl9tCi4jNoGZhnn7eAY4Pmnd8XeC7n6JDTwYOdvCtkOyUk0zpZz/EcK7mcx+yZyJ+c4zsL+rC3p8o842LyLcNZ8qYjFvQ0FOkR7wnKMi8ZYcNzXWUP9ndW4KElh/DV5pVGNozHELtiCumUMfT9c1kqzy84h0EZJJOtA3fTioUjONxfqO6oLI/XxZIdwaihuG+C54Sjv/o+W1fZg/2DZVZCMiK+de+sq+jl+aC5obsWjo/V5QPwfAeHThdgXcEAro2dV/tOJjdPRYCka8GdAFVReVdHPfeH+rC2qlflCMXD+4AdizLRfeICXr/nzy+55F3S8RbufwCR95mQnE5MoX/do5fcmN5PI0ZOAOjt7cUf/dEf4ctf/jIKCwsRi9ln8IOM8X1HDnp7e1FaWspsRA888ABKS0vxJ3/yJwCA3//938f999+Pe++9F+Xl5fjRj36EI0eOsGEAAF//+texZs0arF+/HnV1dcjNzcXBgwfZMACAPXv2oLCwEMuXL8fy5ctRVFSEf/iHf3jfA/Qm1CRtqOpE8U0/tHi43bwk7q09ZvCUnmMZBgBYyScvEQuFDIanGzdFvrxEjCke6Xefqe5nCtCV5UMWXt1LxJhTHADT+UnlNghXWDl/NNTXsptfB6CUeTeexMG+EtTkv4KVC4exonSEscPs0dEKzFPti4033Zo/83sr+pFroD+c3C08xOTldQIwFwD2GgiPrTNbYcW7vvdJAFAeUQBwfS7vLsdKz4AD+O9mYX93BZ49VYzNtc22AkGJ4aJvwX8/119qvKEBwwBQCvUTrQrCcse805YCLKEr66p7TIEvPe5MXnryLq0sH0KwBekygxc8Jx9ORrGzrUEJcn05kud2PJ2tLgUZyclNWZSNtPeOn51jzQkAXJ/9rglN+3bfAeP14wRJahfxyBDkgGgdqd1X/11VFVf+PENCLwBcnT0OJ+Jbe4ZaqPAcgAcaFKxvfXU3Hl787dA+lFAeN09BbH6ezNPRAd86C0FPOY2F5iUIVwPAkbeI61tVra19EMgHujH7bb03wDhqgncwNnkiajyYyYiCKmilcuX8UXW2CYOu/9rXYSAiANgwoHGR8u2fz2J6Zvhg6ErZza/Dm4ji2GA+NlZ3mMiUY9bImTXNyqOEgKysGLIimYD6PXupoeRzKAoXbGkHKxcO28/JcJYBBZM4+tI8HD6TjwMnS5SMEnUjaG6CkEk2qHShypoFr3DEREZgxtIz9Isclvktw3NtmkmJjeeOOVZ0Syq1W2uOmztBR4wpIZifqT36bjzJCa5Ub4QqKpMnnIka9HxvqT3B82vJwwu6MrI2ggBYCqwas3rv6sp+cNFP+mwgJ82NJ9FU0wYn5agcg3GTR/P2dNyKOsi5Jzn31Zb/pB+kjZK8JBsGtM7/fenz7Nl2kq4au/7/lhoVYf3YR89hS90Jq5Ajec231h7H4Z4iEPSVag5I3L6XiCmni4hIBSM5Sm+A5djYWKVyavb3lHNCsjcWw6rKAevehO8Ylj2oyN+y215mimo44FwikjHbW0TOIfUjO81njUhMVpQPq/wMvZYED3VnJi3mPzeetGjT4fohqtRLPnIAwPff55//6E7/CtoVV1yBK6+8EldeeSU+/elPo7OzE7fffjuuueYa/jl95oO0fxOV6aXcyKqUVKZAwPOCzN6yoJebPEDLSk7h2EC+5fnMpDAQ9IGpIjV7T/B7MoIhPZFeIoatdS+qyIGmE8zk+Vwx7xQO9RTzYd628EU83v5pIO1ga92L2NGyFOtrurGvt8LyKlKjaIHsd6YmvXmZIhWhz2svWtsrt4R+ntEDFvEBD3CmXfhZOnkz4lteLC4mF5i/TN5diy5QKBwykZjoYAGFk5Zl7InOkDw9iPiWt88ak75MiKqRPFXOtMvGjqTuDM6HHNPm2mbsbGtgyIuT7SlISmCvWs8Yj+GLS581NIoJVfjKmaXevaX2BHYO1SJrRgrTGtIQpBEMhsaJyjT4HlICibaRvGoPNShPnzWOrkVwZ6SUR7K7ir2h6yt7OOFTzgMbyETVGOynjDJcZD/I8Vjf095vABzJgKfOFFH38ufoM+L5tM6h5Gq9TmvL+8yFK6IKRPHrp134U66KjtHaXMQze1dRN3Z31XL188Nn8q1nrqvsUUqf8OgHz8GGwl5O5iQ6RsswEnSwALCuotcoko6PTdVt2NVeDzcvaUeG9LxvrO7Anq5qu//Sk6n7I6MpVnJ4XkBmOuC5Cs5tRu91hmgHP+s95Jh8BgCL0lP+vKmoC7va67G6sl9R+ZKTZiKKGz75c7zx+tUq98UFnKQDP9szjGu+Y3nx5XslHe7qin4Fx5HRnoTKG5sZucBJyI1zz5j8m7iB0qn9K7zQunAkszWR5zsgYx5e/G1VCJSeFYhu3r/wGB7rXxaKAq3KH+HIKq+BGOcDZUcV3DPDHcHfoahqwlBmy8iB9L4H7+DgGgFKbhPsUUbK7q07pqLzF4wTJViLQkatMq3P5tpm7OxYFIrEUdK+LxxO/vksBcshRxjd2a6PRUUvoe2VWzLffXp8wfuH50v0n3IqrKir/hxTm8oIgZY/BJ8GoNb++GdCusZDDYfw1Y4VWJT/MloG5xnHp+Njc00LXv15HLuXfvOS87KTjle8/38ikvs+IwcTUxha978uuTG9n3bixIl/9WcXL178vp9/2RsHNz75x3BzlZdHhpwpzL6uYAD7OisZlgGYw5d91SSm3tbsEfIyDhQg44OmL6cNlV0W+4Z/Lgufq2/nfIbGuWdwpK+Q+cDpGXRpb1v4Irb3L8WdBX345miZddlTI+EdbMHLwLo8J6NqnDqps7HkFI4O5SvBKeABgFDIdWjzvfjuM4Xn75h3God7igyOOZhzEEzkEiHkZaWnLG92UCkmBZgE4oqFIwYCE7i0qAXnUAriTJcScZID4vIej2F52Yh1WdOzQgZlQiVc7mqvZ+VnVvQCdnXU815x84wCFYQFrCgdMUpZQOkhg0fypVuXRGDOQsaogHLRpS09tsHLh5QNWUPBUsD1WFbOHzW87qQMBvodhNFx3yQ0QDzfqmEB4LY5P0b11d/HM8OVBiojlA7aSyGDfCyGDTWdGE9nK8YewREv+0MKR6b1Dc4n7SkJyZMKF8HVvvPqfCz+5Cs40lfIuUSsvMnPkxIglNGgcrSqfMBiHJLGSzBqc1HHRyKWGTqXiGFb/VFsb2k0ciDlco0GL6GSWmdHJtmpIAsF7uuqxPqqbuzrrMTyshFcn31OwRrkuzMYo8Ex3Fv/AkdYLfimgIgACNc00J+9q6gb2U4KT7UvxpbaEwqiEZiT0LODziCRJyUNRzdH0Sg31bVxlI7eX/Wp19AxMgduPGnVFeD3iXMbNKbg+lbNE4K5yciCHEtGBxfVUsgwVmpkrMuaDRuqO3F1bNyqg0N3j2xbS5sVAQBgyX3aIxdzFsn55vNItVpcMEe/itAAcBV98lNti21FVtZTEDJtZdkQDg0UwYL/CJlIES1pcFryH5n3Iu2HbQtfxOMty8OOKHFn0T5iWGCmWjoCUvZec2UZTxerGyHeWXjjjzHy+vWmX75eGwEfk3WO6GfsHNQOIT/pqoijhhURBC318zR+eP+fXHKKNOl4Rf/84AcyDob/89cuuTF90Pb666/jhhtuyMgU+sYbb3ygOgeXvXHwiccfwQ3zEnjzrdnW7+kQOdor/1s13ZYSQnjNTArqxZQbVig928iA46Opul15BzLkLARx20Gvp7wgWDkQWHIgcNEFlRkh6PxzSoFcWT6kvJJCOfmD8sP40onPstB7oOyoVZ15fVV3Rq+vxL5agpuUNnkRStrLwDPkZ4KKKAtLqUyJ97CiJzGmFCLWcItDpwtsRdIJexlpLaQBuHL+qJpzYQRd7Dlr8ocMnvMigp3HTPMVMCro8x+5ahzjk9mYfCs3nM8hsNH0u001rciNTCn4U8DY4/cKJVwamLS+ue40vjFQozzdnmOUol/ijaW2qaYVu4erLIw7zRtfnJmiaNIjClvpLcv/PgZeu0H9X2LlheSyvNEZPMxBilXKwZHGA+2hu4q6FSUsVH+kAim96pkUDGte5F7Ve2Fl2VCouB15a4NFrFbMO4VD/UUhQ8HaO8kIe7FZMZRnSXz27uIOuPAx4WWFFA9ar+C68tpEfbhZ6dDcZnJCBBsb23q+g/NFeRWc/K5/RsxebMxoXHXQM879CeDpQ3IlgzERijLBjmIE20euGscv3s7L6ISQRt2qskElb4LrFlGYhrVlfdY73ktWACY/g+QLfGBznfJub6jsUixHgdy30BpeJF/Nm4jCSblGFhCUKOBEkN/JuMYXy40AQu9dX9WNfX3lat8mHVAkL9hX8qoH55eM/401HXjzwmxcEZvIWChTnmvpaHhg0fN4tPmO0B66mNMimO+ztbQZOwYaMjrurP2V7QFTLuqKX2b6V3JufbpiWKELRJ6a5QyYjKJs/vfRd/JT9vwF8nK8hB2dJvklox5IGwdgcM+T4RWMjlPx0Us956Bg3+99IONgdP1fXnJj+qAtEongzTffDNUH+8UvfoFrrrkG6XRm5MN7tV9LnYNLqvmOZRh4CUGv5wOfremDH/HZg+/GFY5wf39Z6FHeRJQLjHDT/95Y2cnKjRtXOEQWjLkpEzZ0YP8N4HBvEbxxUwBnQ6EpzkJYV/rswd5SLF84wjz/XiKmDnZQCZS5A5oWzZuIqiQuB6ygSGz8l178rKKl00Lla80r+HtuPGkpD6wYkZdBtAcWKay3O1N5J8hACBoGkoFBrg3lgHgJxdXtxpPY01PN/WXq0ACumr4b5D/3piLG2+oagRhKbByPYSqt5m15mWEpGkvNwJbaEyEPJgDrOV4ixgXbaI3pspNrSN+TireF7dWGyS/ezsPUpNkr1qWsMelsaMaT2NVRz6xUhJm2qEMB64KTkSc3nsQ/t1fZnt68pFZmFE66ce4Z5OZNmfkai4X6RQwiEQg4lE7s299dYfpGxqP4brDInpeIobHkFAZeu4FpOt3stDkbpGzS+tMcuj7ngfDzBTYeUDh4WbDJnWn23O6OOsNGFCgatHu46qKGAf3MG4+h6lOvcXXbj35kDMhS3nc6d7T3G+eewaMtd6hnBCrkHj6TbysLU6LGQzypmFFiadXPGSlsrXtRnd+Zpu6ClFVPD9XgqfbF2NNdjWDbP1oK/12T18NKw3REKWoaVy6VqJBCNaZ46glTTnTCysgEnwc3nsRDDYd4vrb3L2XD4IGyo/Amo3i87dNWP9zstMj38k3UBWBM9NZFL5pcDNojQjZRQTBeMymL9LMYCnYuC39W+S+Y+4mfKqa6hMr1+cXbeSoBN27kDD/T0bJBJ5AS/tvCbOt750BPORrnnrGj0Jq1imSupCYlCMyzp4oBH9hSd0L9zHewt7uK5Y1cI6IaBhRkk/oaVNjd3JSCxdC80N6PJ0FF96jej7zX+G8xPq5vQL93fayt6LUKxLnxpIK6Uks71ll0RESLonjULwAmQuYodqLjZ+fgp1Oz+J3Uf/oM5UvIRP/H+pdZcpUZ0MT+WDl/FEipu5NkEzHGRbSmvaejhr/jv5vFcmdrabOhPM9Lom10DgCV0O/Gk/hcXTtSHlHjmn7Lu8LNSaHv5KewrETlIsrcgPVV3bizoI/7T7lf3ngMad/lOfLGYyxX7l30AgCE2K68sZhFTkB9ONAb1oEuxfa+8w30n8upXawG2Pj4OGbMmPGBnnnZRw4kWxHDR7SnxI/4zJAD2J4JC8upE8O8C1H20mXyzAfhQPS7TdVt2D1cZUUIAHDBkSA2VLZMnhpiTHA8B79d2xFiacn0eWKEcXNSYcYMmXgZDLlGFAxpc73BYOaO5KB4zSm0jc7h54UgCgQfCEY4MjBbZArl0u8oH8HNVXjPooLXMPL69dzHxtKTODqwILQedYVnWSATFpefGWhy7ppq2hjznclTT2OA51hsML8MH2t5WvU6rMofQQQeDvSV2fUpRNSF5tLxTB6BxMZKT1vw+TR/QaWTvPRO0g1B06wxk5c+wGBhQcYu4nWjc1V366sqUVPMDXs/YRQZeFBrzXhXDb+4EFXjzbZpC2UlXPjA1nqdoyPnOUOUSobX36sRJMhKJBbRol923gCwNzbooWXaQOGRbCrqwq6Oemyo6uQcDeuZAciRxSZFeTWeMDTTDtZW9WrPn4HJ8Nk4fauKBGSYI7lO3+qsgB/1LM+yHHfwjGyqbFMFxwQuPgjRkXuUfqYWzTfYamJyC0QK4frYXN2iyB4od2IqguVFJy0a0j+r/Bf8Sfdn1V7rKrdkUMWnfoCuU7ew8Ua5RZQ3JKFl/I6AESQZiTJF1UKe52AEIUP0bF3BAPb1VGBjZSf+sa3WePIvRLGlqhlPtS4ORYsR9bGyZJiNzoY5r+D44Hx+PqDfodmsuML2TGNc0fzKHIwgexH1e0nJaWS5KRwZKrBlqk6Khu+gsfQkjr18W0YYIWDkyvwbfoIfnZuN8+dzLG+/NUcBaBWvgcD1w3fsPLMMe8t6v/49RU5X5Y+o+hj6O8tue1n1X8qSDJ51bzKKpiqFCAh+B76qCXFgaKHqJ91h72bBuWI6JEsy5dl4F7TukYFBSs55ptyfi+W/yIiXhBXRXqXnbS5ux1OdDfoMj13SkYP8f/r9DxQ5OPXbf3HJjen9tgceeAAA8Pjjj2PLli3IzTU1itLpNLq6uhCJRNDW1va+n335Rw4AzP3Um4pXmxQP38Hn6toVrGi28J4KzwQLTs8BfEd5TjSOL1hMxs1NwT+XxWHwvZ3V1u9+eOFKeImYihAI7+KyhaewrqrHHMpAeBuA5fkzgkeFYJ1Z05jybFpSyws/ZjyegBEyy4tO8rOW3faygt0sHA55sOEqw4B4j+EqwT5ROKlCpL5WhASTBD1zUckZ9txJTxIZBuSJs/qeq6sk00URT2J9eY/y8ExG4cc8DL18I7zJqGIycnwc1bSK3qRhcIED7p+bkwLSDnvxaGzkwaRLUT1E1YAgxXpDVacq5qSFNNWkaCw4DaQdVeHUV88OGgZfXPIsr7NSxk0hHFqH53oWKgiSSOpz4wqvjKhZd8dz4McEptd3eO7YUyaiVW5OyhQD0o+ZkTttXcBuTipjzopsfBkK+A0AHO4vtOoVSD56b1yFuD9b0wf/XBbaXrmFDevgvpYXEkVilhWdti4zd4aq6cAFi1IuuBoonQ1H8+xTC7g7vIRhIdne2hjigad2/8JjXHPCzU2xYeBN6OKIuo/EJa+SQMX5DJw3MgycGQSDUgYezT+t1T0lrYg5adQVvcyGAVXPJS9esL09rS4BL6n43xcVvGSUwVxVf+HNC7MVc5GvHBqNc8+gseQUG83eRDQjxI/eR/CVhxsOobz4Ff7MsttU8bH7Fx6zvuvOUJGVJcVnmOXIzU0BER8PlT///2Pvz8Okuq5zYfw9p6p6qm7QPM+M3fQ8D9AgRFAIJhDMhy6WgtGPT7pEvooVRflkOXZix459c6+vLduJriIZW5eIcEUIkYIxEUbM3U3PI90tBoHmeQC6eqqqc35/7L3WXvtUYVuK810+2ft5eIDuqnP2uPYa3vUuxfJFSr6GQNGzllb0GiU6N64oHGMRLK/sZtayVdXtWFQyiI3NjVhQqhiavFgESLhcCJLG8JV9nwWgPe0he0O09k/nPSIJCwge8nSfTUlLffRGw8C7mbz3G6afVGfxnCmgxeulI3sMkxNOl4cad2FldUfKmm49Ug14DjYfqeNkYC+mPL9PtjSy0sdRjuoOuFkJBQ8dU5EG4rRXVXKBhaVa7mnF3Y3GzdnNSaCh6DiPdXdPIfdlS2stkHTQOOOEiIz42N+Tj91dRXCzVMK87zlYWtFrKal7enQSvbxPdKubdorn5OiJa/HR+7mWt399SbNhQco1RfoAJWuIZc8qQOr4qMp/iWWt/A59j9dIyF8yDHYMFlkK+N5jM02HKUJMuQK6b2uLW+FmJ7CppQHeSAR7+gr4XRQV3t5ZgZuuf1fJML3+zkVK5pqaDz6vz90lLVb0gMbJ4xBMRw/Oe16RFpT3I//6t7jvDO/VdwLXntDFXaUj7smWRm1EiAXS0a+NveoeXFd3GJWaAfFCbb/JRdC6u7vR3d0N3/fR39/P/+/u7sbw8DBKSkrw1FNPfaJn/0YYB8dfvwKPN93KiWBwfGzpr2RIBh04We6eFWp9mLYfLeVDRPAaDgESXIeaD64QCoCFDV0IXkx5Cfb2FKhQp8ZbLs0fUAWwLPiIDSviMDT1S3tgOcSuPycL0QSViz0vzubv7+ktYGwzvY/H7zlA2EfNLacVnWvSASg8TQV6NPbznvoD/My9x2YiO2SHaCnM78Ui+GrVTrjROBoKj1uXHaBgKTIxcGtblRJwCYe99Ug67OEyirFjlAutcLHC7vqcX8HKtVAQQ1QTIS+ORaWDrHg/M1ChLo4sNRebWhoAx+f1pOjQruECLM0fwBfKDvAzv75/BY+fq66m8TI/3LiT/z2SzIIXi6hExuwEC7BbK46meM/mFb2YEt6n0+ydixgcrL6Ix0cz+HJhyImeY9rHRI17tS6+xh5wjVclpg7ek5qe1Cpi5Pp4fjgfOwaL4GsDxxvR2G899mcHSyxjSikyDtZUt2Jvr46sJRVFJF3GVIdh1o1vgYruqf1HiyiqP5PC6xi63/salCJLsKb1Jc2W0eDFIni0a6F6j6COJCVn85E67vMzAxWailjtv5Vzeqz95MUiWFfbxGvlhDwDwxlRcI+1xa18HnJD49jYW4+mvpk874whz0mo4oDZCTZi1xa3onVgmuXJbjoxDQxB1GNqfekmpiB2o6qS+p7uOVhUQgqjY0FXpFFOf/shH98+uBSdp26AF4tgVWE39nTNAWCqAVOfaK0OHp9uzQV8B+/Ep+D54Xy19yhf4FwEnt7jOztL+LNeLKIMM0dRwNJZ39ZeyZWoDx6fbgwQMjgYU60MTy4uSFWeYza0D46PZRW2vPXGjGK2oWEf3KwErsn6iPcBLp/gZx3qVXL0/sY9AIAlZf0G9uhrqCk9V58BbzyM73YuUkYLyR0yPATsCgCWlPer55fv48RXcgwBqso3r7c+57u7iozTIFfTFAtYDO0Xch419c5E3bRTKipQPAyrOXqeaX58A5mlNXNcX90dUtkSfWT5rFtT30wDH9QKtyzyJSt5A+beuPzSc/DGwnjy8Hz7PtSGRfvQLXhkwU+Vo07TXvtJ18x9AHJF7V9bKngO6Vn0f1LyrX2RVI7DKyNnxHiBJaX9VoVocgC4GtLkjYXZqWhBPfXzuPBYLKJkletjWVmPtRwsYzxz9nZ1FmNKZNxeH/F5GuvmFlNPZG1Ns1kvX0G6GqafVD/Xugh9b1NfDTpOffxk1t+2/3favn37sG/fPnz+85/Hrl27+P/79u3D888/j7//+7/HjBkzPtGzfyOMA28iZLzcjrnQAdhYx6mTiuNbWNJWeFgrB+tLmnFv6WGuiBkMK7t5qqLmQ427lNDVOEPyxLG3APZlsHOo0OKmBsACZV3dYayqbuf+3lXcZuAegAUrAYDLIiMG8xgQiNbYshNKGdWwKL5sKZqRnUDrSzdhamiMvU5zbnhTCbVzit0Erq9CkFACaUPZQWOAaEG/vu4Q9+Ub7UvxZxW71UWRY9ijACDiCCVYGx/eWBjrGg4bwcfeFpFboCMABJ96vOlWc3GKi4rmVGKWGa8ZiyDiJi1PPqC9Lo6olTAWVhVchbK/c6hQFachL7oe9z0NB9JWXaU+//fOxfz/J3oUkxFVTJ153dsAgIsio/x5WpumE9OwsHTQugw46VZyYOu1nzJljPcAKVjme+pSyXTVxfjGWxebi3IsrLzdeXGONPA7ZbLuiMnjSAnl6zNHBvOGsoPionM412ZLay0rhwAUXaYwirxYBC+evAZuVoIx6+QhVUnxZo2oUinR1T7e3cjGzdriVjzZNF+fdceOfGgF1UygXb2WxrpruABLtEd7anhMY8bN155qmavObGE3vIRrGQ87O0s4t8ONqorfPF9UFR1gD6gfUn24KvMs5z3IPq5rMBhi9tZDyK5YRNUv0LVP9vQWwM1IWpElL2ZyZGSlbPbO6rZtoMyONujvb+qrscYvmxvVis9oWO09+pwLZiWTOGipjFpyS+dzraputxQf/o6OMFLkd//xGVb0UkYLKSK8o6MM3kgED87dreZHJOsTnejTfdXW2KQnHQAXzctwE7x/3Ly4MtBFIryMBtL4+Hlp6sE8P5wPLxbhiLSbG1cRcG0IUD7WmmplaLpZRhG39rOInNA6w/G5onzLSVVh9+Dx6db719Qo4+bukhbG0vP35TpZYzK/kwo85yxQP0bDyL/+LbiRJDa31PE5DsK06N549/08tTaOwcxbzhYf+JuO27G2uBW/W9erlifksSEq73l6v39WFVILnhn4MMyFlISfneBcF/gOy226E3d1FFtjp3b6zUv5nDFBg2MiyvQ8cky60bhi6vL03pSQXz0GCTe9f+4etPRpZj9yaoxELGOT5CRFpjcdqVeGr5jnphPTOPFbGtzni7JeSO3jVkemP5+m9pOf/OTXDo/61OccUJ0DWUEQMEIgmCD70PxdzNfMPxfYySCXcNBS5/wBrcBuqNuvohYBQeqNRLCsqhs7Okv5kBIDAgArH4EaM+4QnpCwo2Nh3FmtefYFtpQ8Uiz4NHsC80oDQMjH+ppD7LXgS0WMJd1c0BxuaNiHxw/fan32y5W70Bu73qJcBMBYefoueVIlrnR9STOebG0EMyAlz8+AAYhLKA19KbXGGSdSvJnW5Sme40Y1DWF3se1tI+ww4d1FRVqLL/08rFGSYUMaJum8PXI+rHEH8ahJJ/0FLcb1cONOvsisXAQByWEKRcLiJl1VX0FX3kxbJVZiwUciuKfhAONX4auE7j0vzv6FmHbGx6bh76Y1SZmDhAuMqwT8ZQX9uCwygo3NjVhTc0R52+nsUZ7QREhVKM1LXRNAGRGPH75V1R+YaqBXyyu7VT6IrrZtPLcAV67V1U4lY8m9pYcVxEl+JyuJdeUthsaWPL16zA+U78V3D2tlg9aX6GXTMGqlnUuNwU7HvkI5K5KbnfeI43P+Q8P0k6m1SSZDQMKBk20qq6fIBL0vbp89hOnZ71g5HcEzYI0vZFNBeqNhrK87BBc+453T7W+Wx7R/ZD4QzZugJ07HEBXsT0pf09AvSzmSIou004fXXOseK6s6sL2t0sgEks+eg9W1bSkkD/TMJRV92NVdlAKrAezaFYC6F3a0laXSRY+KGgVt5XaED/ilMsWLGYa6IJuVZMRJkQtAWicBzcvi8n7s7i606seknFs5v3TPpSG04DmXla0DxgtVtP5h020p8pbfTbkloiJyyj4XOgP3N2FyeeRe5s9neLin8hBTHdMc3VN3kB1qnB/iOWrdO4pTkuVTcpDSULOmG5cbjacy24k8GJW3Aa5tQ/kX99XrWhEjEcC9sHMOZm7+0ifKOTh253+94Mb0SdvKlSvT/txxHGRlZWH69On43Oc+h1mzZv3Kz/yNiBww04XAPbOnSXqmQj6+27koVSkRECMKk0oDgD+Xa2MikXSMN1i3u0tacFex8lzvHCq0PICPdzca4ZqnQprsfRkLK0+C8DLBVYljbnYCEUd7OLKUt311TVuK58bNjeOWG96xsdIesLFlnkq40v38cuUuuLmm2jM9g6Inc254E4BiCyKstxRO3zy4jOlWJdxoeWW38UQQXlgrJoASeE82zecLQsF51MdXFXZb8yi9G25UYSvvKm7juZIej/MZBveX71OQGWp6TnYNF/ClTF5U6mNdwQn4ZzLgZiXgxNXxkZEBujjk3JMAX1LRh7X1TWYNcxTDDAADrwqlGjlBT6rJrTDwF3pGcIwyMsE4aGgllOaeLlPC4pJhQAqsnguCHWwoUxWouaqtAzzZNN/yNl8aiVmYdul1571E2GfBTqU+JKpjQ1U65XcBfO52DBaxkvTMQAVWFPSKsepKs5lJyzBYUqEq7PpnMuCNRNg7zLBAR313x2ARR/rWVLfq6BH9HlhW3c1Rh82iOvPjh5QjYHHZAMsEN+zhfx2cJ2Av0MnX6gePdi00UbJYBFW3vMznnyJ6Eu5CY/FiEXjxEO9PGSmUn4MD+CLnhaFE2vCg9SXDQHoM3QwF87vy8jOWV5r33Ig5G88P56ucjl9QUZXPRkgYPVR9PieBjc2NSpHyHDQUH1PQoNEwLrk4BkAzyMBW3peU9nOOBs2brFmzs7vYfj8x6mjZy3A5Ok9ib9JcebGISvSFLYso0kd7g9Z8XZ2KXFHOA+VEuVkJ/ix5kvlMkrIeVewzNK9LK3rZ++/FIvhJb50ll3cOFcLNU8Yy3T9SodwxWGQpm240zufLSRgVQEYe6HNkEGzVEe3bZw9hbXGr8jIL7cGcXcecPZobPY90znd3FKl9R4aBHjPdmSnsTiSfchXEivuno1reZMj0wfWtKO/a4lbAc5TBKvKmJJyOxs7niCOefopMNZBFLaddzWCk8/GIQY8/H0kqp4kY09raZlXDIVtXrPcdBUmLxrFL19Cx9BIdPbccJ3TWtQMk5cxDnFMpdwD77s1TUQ0qtvlceznc7ITSRUSU6EJuin3o4+Yc/J/u9a+3TZkyBXv37kVXVxezFnV3d2Pv3r1IJBJ45plnUFJS8rESkz/1xoGbEzcUgFr4eaNhA4PQnmIAqJlzkr/XMF39O+ghIzoxFmbQgk8/V11sSuCtrO5Q5dBJwIyF8ZPeOpwavdR00AnsUikINAuD8p44JiTu+lhWoITJlvYaeKNhPNXawIouCfU7a1vYmCGq1tNvXqq8QzpRUM2Rzi/QY/lWh4I5vDk+1cyjDsU3zjiBo69cDW8iBD/Dw5KKPoW/hlGE+fMhH1muufCeay/HsqpuVhpL8l+2vDtubhyIeHYCYdiHNxHC1o4qO6lYf0fOHxev0ooSXcgyN4S/B0Wh6Aslzbrs9Jqyt1dfQC19M+BnqUvNmTppqBP1ZbOsoF9faOIP0Vh2FXHeAkI+K6dSYQ8m03F/xH51s5XhtLhswORykBcIwPKqLl4DL6Zw4r5OSqXLj5g2vPEwoAsY0b7zk65KMg8ZZQk+WDl6rGkhvFgEp/U+pktT7RHV1y2d1XBzEii76VUTNQHU5X2eKA8pE25enAvNAcCb705V+R0BeBg9jzDGXA2UWHJ0q5t2iteT2MKI0heAWgstI6zkPF0d+5mBCkx42nDT8KGdQ4XsYCAjwYsZhXV3dyFDzwDg9+s7zXnLTrCBQ+9lBSkaR2HeG9yF9SXNClaioSCA3mMaQuVGkgqzPWZydzyqhE1Kko52eONh9s7KRrBHSZUqldEHK/bgnfemcD/VBKrfLyo/akN8HLO2XGFcNyfu8pkgLyvLthyTD0YFsqZExrG3pwDLK7vhblV7bUdnqVFq9FifH85PKdhFMonnm/5N8jzkK3jSeNgUydL7al7Ri9azHijfy3vTT7qWB73ohjc494XW1RsL438dnIeiG94wihrNyUTIKnZI62N53AnvDjCEjckGpLNmNKzGMKkMxOe6SzFw7hp+DgBVB0O322cPYdGsYSyceYwdGn7EgzceZsifJHawICX6XtrVWYynWuaqcVCVbd+Gr9BcWRF38kUJw4vWj3H+Y2GrThC9Vxo23z9goqD8t95XbjSu+fzN9zf11ag1CcJjfDU33mgYd9a2KIOc5Khuq6o6eB7lWV5W0K/uJb1+j3c3MnGCc5G6E+aVBPI3Eg5WFPSiccYJFXGR0VTHx672Yl43SfUMGF3DcmSJduMN71n3nBWpCaWe9yBEznIqaErbummnuG8XevtNTkimdtVVV+Fzn/scXnrpJfzzP/8ztm/fjpMnT+Kuu+7CtGnTMDQ0hM9//vN4+OGHf+VnfuqNA29CC5EJ4V3Q8A5qO7sVi1D7SzfyZ2R43Rs1AnNTX02KBw9JBzW3nDYRhjzl0Xh2sERhbHWiIF0Kh/pnwYtFFMaVwtC6sdFBwk4ncjpJkZjsg5lUlpX3sNIHgKMj/kcZmBoeTT8phKP2xWWUrYqe0UXlTYSwt6dAeYxnHGdjZH+f8JJnJ5DtTqrLi5LrchKiGJP22Ih8iJ1DhTxP/a9cw3NLsBXrYgBUHzOTcCZdTAmPcb9JqeD2fiYL92UF/dw/N1tzXmv6SFJevFiEPc0UAaDkMACmbL0W0m5Gko0ayQlN+2hB0TC8mEqkXFgyxJEBZ9JVlxfNDQlbKgql+wnPSbnAQq8JfmKhqNNFsOfF2UZR1sr/mqIO/OsRQ6MLKA+l4/h8mdN8u/DV/spMsqLrRlUCbUn+y4xL56hJ0XEzDgAtA9MtBg3AeG7dzCS8cxF0n77eTkrU4XNvLIyl+QMq6ZiiCaQo6FoiXkzV8KDEQp43CEPUc+BcTF5/cWZyElhffxDeRAhN/TMM5EDMCzU3O4EFhS/y2LivonDQ1RlnLEzxioJeLJk9yKxZbm6cWYwWzRoGcd3TsyY9LYcoGZEMeb2XJEvJT3rr1J7LTpjqvr6Zd2JX8WIRTJ06is1H6oznetTA3VgeyXoO+kx4kyGGhBDuf1lpL8sPhh1A1zuZCKm8IEdHK/W67z02UxlN9HxhuC0sH2Sl1otF8Lm5zaZuhvRK6pwKuDCRM8/B88P5WFzej+fay/He74yrPVPeZ1M7CkpbinisrOjkaIGMguD9TCVjNPZ/W0elUbopwjUWxqFeJeNunz0ENyuB7x66nRXdK64QiagAV6aVXlk3OwE/w8O03HctY8kyiB0R3RBrRf+nc+7ou0Ny/1OdBTcnoWqH0DnNTKJlQMOeRlSExI0kec89P6yYhvYem6kMCpIDWQlkumrfr6lqNXJV7ycyrGUCOFEcU9/J8KS1XlXVgTXVrSxDeew+DDscrb38TCCqBsdXuQFvZ9psPNnGMCF5Q8+TTSWCOynMR/AcuJEknISLzc11mEyGUjzl24+WskNqR1cpz/9zbeUKppxt7z1AFS50sxM41DubHYkUEd7eXomDx6cr0gv63rhACujPejFd8yjkW7LKohIWSeUvn76c7zlpPBId9EPz/s0yGmgdV1e1G2cpySVNONLUN5Mjlxd68z/hn09T27hxIx544AG4rogEui7uv/9+PPHEE3AcB//lv/wXDAwM/MrP/NQbB26msuhZmJFw04dx0axhpMO2poQds413PR28pWVguqUkAYpT3Y3GDa2neJYbjWNjb71FkeiNhU2idI65nL1YRHOjkyfQYQFL8B1ACQ/C3vqZngVTojbnhjcVDrWixwg3fQF958ASo0wLhT4ansCOwSJWWNzXs1gYX5/1gfX8L5QdUBhMUZjNCSp0EGFTXzGx3F3SYpT5jKQlxNXHlAeXvbdiHbyRCBpqBnnentO5DtKTioTLYyNB+exgiVKGp5pEW0sYaoWdPV0iQZK87/S8/T35oAJme3sK2IBxLppkw4eUWRLilndIe6Rlcmwix3hvyYDhfeiK8Yk13tJfaRKHcxKwoGlawVlVqViznmqeayfiaQPUT7roPWEqElP/XAFjsubKF2shmDiIx9t6f3ZCJbEnHVyb+RE2t9bii4271eepbkSuge8h6ajEQgD31e81EosgMVHzjtXV7fxeANjY3Ih1Vc3GWBUwwuDf+3vyjTcyphiF7qxt4TX/YfNtvE8BFaXYNVyA3Z1FfFZpf4Ycodzolu1OMgzBGw3jvoa9uKfuoOUBtSGLSO/B1f8neMWH7+Wp8+8B99e/gGUVPSbRddSGIqyt1pCT8bCVaOnFFC5/51AhR1f9s4rdykk6DH3b2lYFOODcDqlsBJs3FsbeblXEbVllNx6c97xiiJNc8ZRArqEoD857Hki41rOJfeeii2JwsxO4NGOEI1zpsNYLywYVlMcTyczkq8j0AM/BupomLCnttxQqCfug53FNGlFX5N3387Bk9iDv85VzeuyIpPawuzkJRVOck8BfVf8rM4TRPbSqqsNEj2gvCsgdsa/5YcV05etIoxdTjgrJDmXdO76K7rq5grKUKFDpPWNhRcfsOZYDATAMbP6ZDBveCQXDpGJmDdNP2tj6pAN4jkryB7CtrQqjXgYzNhk4qYPLM0b4OZdfeQZ3Vh9hSKgz7vJ9Bqj3O1Mm4V85YdZK5Gs5Wq6zki3PEGCiGuf03eP6nJgPQM1vXhzdwzeZvSha2E3yc+huvqfhgNkzwmEAKEjq/eX7AMdXEZZ4yLpLyaCW4wgahm40ruDIMh+Got/aIKCijW6eMbwWzRpmY5icJGuLW/Gdw7+r3hdgVNs2UKbm3XPgRBMc1eFItohwXcjtt5EDIJFIYHh4OOXnw8PDXB05KysrbaG087XfqIRk8oxSWXCALGw7URYAQx84NKoLoC0oH2J4BWAbCsGLipp/JsOmOk3TOJHuPM+1PPNASsJwMKnTSToMmaGCb/TMJbMHFbVnLIL19Qe5+iYAa7yc5CeSadfVH8b/OjiP+Zrp98sqelTI37cT2pyEgy/dtgPfPrgU8B2srWviatF1RcfR+tJNKXNXdtOr6D59Pc+LxIwj5KdNxKVCbBKyYq1d0rEuDUq043kciWDD3H04l8zi5FJpCATXgbxRsiAXKVX/97z9dgKamEu5dun+742GMa/4RatwWLp9pYw6pCQhpmsp7wvAE9QgdfjZd+BkJeGPhdImpMvv0zmS+GZKzg0aUZQsK78fPBdL8wewo6PMMvroO/eWHsaol8GMGl4sguVVXQojG01NzOO/9dxTAUKpLMv9wMZeukRw+lsmYcs5DL57VEZAwPO5vuEgs/a4OXYCcLr+Ub8AWEnva4tbOYJpyQmRjCnlBe/bNMmY9OzdXUWAD3yxcTe+f2ixUZ4+yISfncTMaW/ixBuXp4wRUGfvofrn8d3ORRzZtBJwAaXwifwV9py79vlaWdXBRQHvLmmxZBMVCZPPWFrex1Sa6cYm11IWVFxW0I/nOspSEojlnPtnM1Bf9iJaTt6cMtcrCnq5n7JIIY2ZabPF+EjuUsE7+S7uo5ZftN5Svp8vsZ8SjpdU9jHzGhwfi8sG2Lji55Ohk2av0x6RsmBBqX3fUZ+pX1ddfgZvvHKpfVcG5urukhZsPDJPGTu6CBgA5F//FoZevcpKrnXirilKSnei3k9UwNCLKXYrhnzKhGiRUCzHtr7+IDYemWfJIf79SAT3z9ujEpZpj4qzLp9zZ22LoYmGuZ/SnsWQD4R9Fb3Rc0OF17wRlayMhJNebgn5JfNnrH4L2XX77CHkhcextbU6dU+FPbiZSfhnM1gPCBKLIOyp+zNYYHMkAm98HK898BcXXPIu6Xi3bPoyQjkfrwpwcnQcL6391gU3pk/a/viP/xhbtmzBl7/8ZVRVVcFxHLS1teFb3/oWPve5z+H73/8+fvSjH+Gpp57C4cOHf6VnfuqNgy8cWoEdx2ptVgTAErheLIJ5JcNWQt75lP50bAhLZg8i7ru4KvOsUnwDTALyOeqLPgswqRCRkPtq1U58o30pvy/dpSAZK5gBA7YXAtCsEm0qDwIuUqvlkvdD0FLeX76PFQQSHMvLerBjsIgZO6wqtTIBMFjlMY2Czf9Ow7jTOOME9vfNtigs5fcBYawEmFzSKrLnWYMUpc9VnlVWPsX3VhT0IjsU1/CNXxxqZeGvDTRWGNIollSAxxuJYHFFP3Z3FCksdFUH54hwv2XlS6QxEgOQrHSK0l3FbWp/SiwwwEl/pHS5UUULy/s5YNwARulzs1KVOJ5nH2yw3j57CGEnaRisxDxuKDuIxw7fZnv80lWB1snA6vew9kja/aWVCyfpYH7FoNq3euz3lh5W+R7pmEACihPN87r6w/hfh+fBd31LBgT36KJZw9jdreBzi2YNY/fRAhW5ykrd06sKu7H1SHVadhKG2wlWHmK6ISxxsO93FHZaFZbTzc2Kgl5s7ynHosIhri5uKY7CCF5YMsQ1JqxzLfJIUgzNwNrxuMbDqqghRK2YNIZr8JnSCFtS3o/nh/Phn82wqsPznAkWJGpBpp20ay5lM+yzQI2U+4bpJ3Fl5jls66xko0cqpDQm/4NM+Jmegb5Q8mig0m5KH/T4pWJ4vnm11i3gqGDZEA9hdVmHcoYIBdxi15MOMr3vUljegrJ0LAwnK4n66S/h2uyPWDElZ8CSyj4TfYEygHd1FfHeVvlHgBvRd3OaisAATKXx0TBWVXUoum/ZXyGLvFgEq6rbsa2jMu0ayjEQKx2SjmFkk0aScL5YDibSAfS9J50o8vx542HkT38dQy9eZ9aLvPDie+Sgsxx9rjHuU/aJMEisfSHY6EwEMj3zHaBlhSZTILaioHECAIn3khe2cfC/PqFx8PlPj3GQTCbxX//rf8Xf/u3f4u23FQX6lVdeifvvvx8PP/wwQqEQXnnlFbiui+uuu+5Xeuan3ji44Ymvws3JSrn8LGU/7Kdc3EGaU2qSYpDYXAgjKYU+ewo+yoCf4WFlZaeJVgQuVlawHWgsuu7nZEgJkYCQsxRN4aUiOM99tfuYEtXqe4BybmVlJ7Z3l6tQt6Rwk4lMlJ8hohLyvb9qk+9eOacH23TI3RsPY3HJgKECFdEB/huBd5Mi7NoCPcUIkBd90FMmvfXid3cVt2FTUwNumvE2XnnrEv7s8uouVuTd3HjKJe/FVEVlEra0Vuzl1e8g75c1N/pCI4PAG4lgVY35Pyn19Fk3GkfJja+he/BmrKs7rJ6fxnMOx8ea6lZcFhnhdy6YcZwLIwUVbKLz435NhriidbC/ktLPUlSEd/a83rBYBAtLB1XBM18YyV2l9uWmsbpuZtJO2NR7r+aW0xx5kn2zlAGxTvzcQDSK+kTRJP6uzIUhD2Dg4uX9lc4w1dFGVuwTTooR4Ey68LOTyngQe1sarW5WIkXBpX1CxnhQvrg5Cawo6MXbE1PQcvJm5TUMe8Yz7PopSqp899L8AexoL2PI4zU3vI+33p1q1jlNhM7q01gYa6paGaaSLvoiKWDlnJC3mDDTKbKPklZ9YF7xi3Z+WDrDjt6t94IlY3S7s7ZF1dUIGFVBhTs4V3B8LK/sNtW09d6iqJgiTYAq4hhS835f7T6cGrtcMRJJw0bOYxrHSYryL8bqn83A/1XfqopGEiQ1EMUlStd0jgovpjjzSU6kRMHOQ518PmdPihND73+JY6+4+RUurgfgvGfTep+IxnijYdQUnkTr0C1Gmc9J3ZPW97XhIvuR9v4IRCC88TAWFQ9yAUwZHQzeI9acxBUVcPDnJIdT5jAw5ttnD5l9EpDtvM5h35LTD1c+j396owIrru7BdzsXqQT07jl8pqSMTteknGwoPYZDHdfjtfv/8oJTpNk4eOrP4X5M48AbHcdL6/76ghvTr6OdPXsWAP7d4/rU5xxYuQPBZKWYSdoBoJKKoxrLFww9xiJonHHCZkzRITpiKgCUl5kqWXoxhTm/s+YItrdWKqws1S/Q7/HPZHAYt67ghBIM+h3rqw4beskJs1TLK7sVpv1cxPAXE740M4nHDt/GmFiJS3ezE7jlmvcw49p3AM/B9o4K7qekcFtb3wQqXEVzwUbDeUxJ+R7pAaPEKQrVemNhbGurUnjrmOq/VSTM9VFXcIIVVTcaNywvUYNxdqNxS+DOK37RrCnAHiipKFChNS8WYdYk9nDrnxNF3+mTVwIA43d3DBYxHp7m2RtVOSLEmvTMQIUqWucYnD8p7vAc3F3SwgWTJMOLG41jW1sVvFiEvUbbOpTSJLGR1rz6LlZWdSg2DqhiQf7ZDJ4jKmC0pa3GvHPUJJ+6OSaHhudW50l4CdcyDLxYhOke3TeUEHZE/s2qwm6Tz6MrWFOSIVNP6jmi9drbXWApLzuHChk7zJej7xgFzleG+Ixr3+HchOyQ+tyXK3eZufPNPHmxiFqnYP5QnjHcKCfDjcbZs0p7mSvG+sq7uaqwG8ururBw5jHef0w2IJ0Neq7dLCUjvMlQSg4C5S44F00q2UFzk52woyP6fG9tr1LJ2yJnh3N6Jl1s77KT0AFVibqpRykzzpRJjrY1FB7HzJvfMnMQaN6ook1eUtmncl0SDt58+yIlv8hLm20S0S0lQyTZb2mt5bUgikepkAUNA/q9m5XgRFkynHM7sjkPws1McvLqof5Z1ridydQrjfeTiPYsK+sxsj4at/pyV3EbK5+rK9vVGDzj9fXORYzi6Dt4rr0cXkwRHJj8JGGoeMqQrip4Cbfc9A4ea17IHnVvLMyyi6mKYxEsLe3DhoZ99nrrff/I/J/yHAOKMekPatuxtaPKnGn995qiDh4z13oIGAYN00/CjcbZMKBiWbSX1xa3WnLfi0UsZitvQrH+SOPBzUlYleEXFg3xz4n5rX34Ztxw1QfWGpEhTQYozQfJ6vsaX2DDaHHZANpfuhFuZhJ3Vh2xcsLI+faFsgNGHozZBjqxPLlRkz+1oqBXFQPNTlhn1s1KqERuvQc21O+3zqAXi6Bk1it6n9mJxJyQHjX5UXt77Lob3rkIF24kWebFIirSIqKTnLxNZ84BnBFbvv1Nx+04ceIqXBVWyfNkGKypPaLmmZw3ek5ZRuv/L6noU2PLi6sCeZkXdlKyojL9+H8+rW3KlCm/FoPnUx85uO77X0f4UiNszpc8F/SkpXg7JQafFEoJLdDemJVVHXh2sAT+hxnwMz0bZyiUO4KckAfiwYo9+E7T7yJYwKyh6LiqJBz0oEhoSMRjNp100CHCGgJA+KMQvGvHTd6BDuVKiALBXEhpXzDjOC6KjCp8ufCmPtiwm8u4B/tlzWEaXGa6z9HPl1X0cGVb8njKvAkvZudUBD0gKTCC8TDur3uB8aqra9qMh+08Yf5gn+T75XqytxdGYZK4VMtjStCdce0xDns2vEcoherL6rJkr68onBUMc7u5Gi7SUYE7a46kVbwAFbXZfrTUjCniwc1OGMhRLGJCzNIrlgbeRh4s+A7uqT/ACfab22pZOaE5rJt2Ck19M5WH9kiddbbWlzTjycPzgYhvsc/ItQ3CZWhd2BNO9KWi0FxwLYMQHnkeqa8p2H/hobP+1s/0z2bgD2rb1dkQMob75gHITsINeyZqo+XHqpp2bCP6VWEo8f7SkD8aM0GhUvZqEJojYElLyhQMh6IsvP5SngW8/EGPvheLwMlOwBHVot2chFqTI7XMP//9Q4vNHg4Z6GQ6D3BwbVKiESJPRUa1vJjJNSEoqDcextLSPuwaLuCImqRLldAZzhWS8jPNvcCf15Ah3lva+8/FAXXU9r7GFwwBBEW7xP6SHmgrOhfwWq+c08OwmXSyPN28BaMIgI6AtjRYd9eyAsX85Ew68LO8FJw+j1H3l+EtFCEHUtYzJ3cCoyOZlqPD6me63AYAcH0UzXoV/SevM7CiNGsSjIA8UL4Xj3YttD9LTr+E6mdV4Ul0nrrhvPNG+2dB6ZApajcSwcPzd+L42JUqkTyqKM7DbhJTw2MY9yLmzhD5fXJM9H+SIYCOHGiD9YvzduP7BxefN/8LUA7KloHpBkoVkFHU+N3nIsy+xVF0HxYlrBuN46+q/xV/0fb7Zl/Tc/QcOjkJOOTUIlSE1nEu9JyDm378lU8UOTj9//vmBTemT9refvttPPTQQ3jhhRfwzjvvIKjWU1Lyx2mf+siBtNrp3wtnHmOqNwDKqq5o4+/wxS8oT6WQlt50Ptj6IvyXI1UqQe3iSRb4zB4jPFXOlEmlNOtL/DsHl1hJg8R40dQ3E1+ct1v1V1/MKwp6QUw6a+uamHlkVVUHH3z/bAZqbjkNbzSM2yoH+P3Jq5TSsbOn2AiSAEODmxvHNZlnmBFlb18+QxZY4GQn8N0Dt6sfCS/4fQ17+WfBMDFgvBNUfGdp/gDDM+4uUXUZdvSWsLfImTLJjBXE309zSh6UIJvL1s7KFI8oGQZuNI6Ik2RPvoScsIdKK2XrS1QdABLUdDH6GZ4l1JkeUgvWzUfqjFeVFHk9d14sAmjDhvaHjFa5udqzE1VMFJdcHFMYd6LjExcJv18nVG9vr8RXGndgc3utNSYAuPzSc/BiEaUY6p8pmIn63GXhEePlFYYBP0NsEXkZrq1tVsl+ujrylv5K43UT8qnl5M3GQ0s/197Yjb31ao8kjRd+aUUv76PwK1nICU2yMUf7iOeOIkk0nwEjZl2dSsDaNlAGq/ChZv+iP14swpc6RVKkh06yUxEdsB/xsL2jgp+5vvaQ8gDm6AiAC0D3eW9XgYrAuIpxa/vRUkTeD1uQPS8WMYZndoKVtaX5A3j88K1mXcdMhC4F/kjRpqSDXd1F8GIRtL50EzaUHVTrPxaABPqw6gQEDQPaZxvKDvJzvVgEzwxUsKL9/UOLeVxEG8l7lZh2xg0bGwCrwBhHAkeNoS1Zyai/bjTOSegMJ/KBnV3F8EYi6D52I5ZVdqesE+8PR+2plZWdZp0FvEieW1r71TVtcHMSWFnVofqRcFRkzleGlJsXZxinm5NQiZ0AF9xjeuhRkagMlZtC0QdAfWZbRyVDldh4C7DMWNFgIYNke7rPJKZSQv5zOrfNz/BNlPBcBItKBnm88izQ/FPNCDKwpdPk0uiouUcCfVU/UHuFWfjoznSAo69czWPiudON5OnySvW+yKks+Gcz8N2Dt6u+6fwSuTfo+e1HbzGe9sB8wQeeay8HoBnK9OfW1jfhv3cuNhG4c6q+xHMdZXi6rxpbOyuNcyAgh2kvUGPDQMhJNxpXeXzCMUHFy6iPKwp60frSTVhb28zsVMsqVbT7nvoD/Cz6NwCWm/Ac5VQQCde8lmNh/EXb7wMwe5nOAn3OcUStF2IEDGnj+EKvdeA7n+zPx2yPPfYYbr75ZmRlZaGiogKHDh36hZ8/cOAAKioqkJWVhVtuuQWPP/74Jx3hL23r1q1DV1cXvvrVr2Lbtm3Yvn279eeTtE+9cQBAhT2F0NrTU2BTveUkmJ5PemIcx0/vLU3TVte2wRuJKKUv18AWqHInoITT+pJmoZyA4T5uNA5MGi5rViQ94NXxS9Qzddh0e3slv4MLf0WVskHK7ucamtHSPwNuTgJ7BvKxStM8OiHPeAUALK/oVsKGCjlp6FPcD+GHXbda4+WiPXpsDnmZcuNYqLmbubIiQYHy4paAdnMSgOsrBWgixBECbySC9+K56kUJhy8Q+X7C9QYrAzMf94gOzWYm4cGuWUBt5ZwebOmvxNLKXk6iDXqJSTF5smk+3KyEEtQxwZceCLHz3zrqQ+tBxtGysh7j/fLEBanHzfNOl0leXPFcA/jgwyhW1bSzIPdGw5b3kYvy6QvrWx1L1MUvKFG9mKJg5L2llV43J2EZlNQvP2kKoBF07p76A1wYhy60VYXdeLqvmudxbV0THqzYw5f30speCxZBVHtfWfCvat/nGWhY0IO2s4tYV4ApJe9jY7NSvvKnvWH2ESlV2ji9ffYQP4OoIwHgqSMN1lq5URVl4dD6iAjpC1iRdfY1tSTPEyljmpqWom8/6a1j776bG8fyqi7u0xfn71ZGZrYxaOOXGcNS7ovg3zs6S1kRT9e88bDZQ67ihiclneaIzmZKsj/Ng1gr3ut6Hziuj3cmp6jxahpFVvTpFkkaWIcFLxT5A9vaK3FHYScbydYYdDSLvx8YK1dtJq92YAxUg4RkCq0P13/Q311UftR6rpxXPgNUNRkmefrZwRJ4ujjgMwMVgA92BPDr6P4YUYamG40rql4HSsaOh3m+qNAfrzVFDyo78VxHmak5EbiD3JwEy2zZFsw4buhQA7DSXZ2K0cmSw2EPd9a3YE/XHFuOAUYm5CRUxXh95qmqM0G8PhjNZoU06EmX542cEuyU89W9LFnkgh54MgS9kQjiN4/DD/m4s67FzAPlJmhj9/55RvbIZ3CkTyvEfM40iccdhZ2c0yWrRN9R2MlrJeuR0NhoLmWBRp5/Os+RZMq4uF8UsdaeenLAkWG3tFLRC8PxLfa7J5vmwxtRcKA7Cjt1dIugg/r5sqYDtKNrJIKtrdWmD0IOeOciXI8HCVfBHt2ArLtA2/8bsKJnnnkGDzzwAP78z/8c3d3dmDdvHpYsWYJXXnkl7edPnTqF3/u938O8efPQ3d2NL3/5y/jjP/5j/PM///OvYcSp7fDhw9i8eTP+6I/+CCtWrMDy5cutP5+kfephRd9oXYiffVSJN9+daiAxAgJEIURAUWh2Hr1FPUBDOggOsqhkELFEBlp6ZximAJ0rYEEdAmxGQcUzyGSUkrglLtQgVACAYVaQz/ol7AZWv0SF1GAC1rqaJsaw0zspqVpSlwJasMddE7LX4yW4ErU7CjtTqpcGE4qt39HlKudHJ1xRP9aXNOPJ5vnWZfTFebsthiUy8Cz4V7aij6QiR+rLgOM7DK8JNoaZEFPML6FMTBkHGUoBw0F+Jl0jaAmAFFhWMNmOQ8G6b0yDmCakX1ZwCr0vX2ezG8nkNopcpYFbnQ+KwXuLlEC5frQGmraSQ9ZhNSdW4vJYGMvKeyw60+CcTp/xJq7MPoemgRmpcAVKMhXwCIT8lERC9Q+HzwNfpGHPVA8PvjseAibMfj8fBfCXK3fhmweXmfM8FoaT4alozLkIFpQPWQwwshFzWToyBGaucRSb1bb2StX/iKdgCw5wT+1BViJo7TkxNqaijJta69OzPMm11zJvbU0zQ82Csuzr8/8FX3vhs3ZhpjSQGTeaSlkrn5dunRumn0xL5SvJIIKwKZbtQYIG/Z2aohNoHZiGdbVNeOpIQ8oaWzDHwLkhSBzBN/k7EyE2Gi2n0qRiA5p13dsov/hVbOmvNHCYiRDWVLQh7ocsRjLrbMGMMV2jvDUuGCe/r9eOYUICmiTHmEKV+gsSeN3shNmbwHllVtpzMR7GqgrFMCS/541EUFLwMk68fxnGYhlWP+DCIqJYX9KsIpNp7hy4PuYVBZLSAyQXVh8DFODyZ3Ic/LvJEO6v2cuRZzl+OpNOwoEzdZLhrMQCZBlLBEHT88mwXpLDkhlpJII1tUesZP7VNW0MiSRGrrW19vmUfQ/CH+mOcHMSWDmnB2PJDE5O532v9zMTkYR8IOFc8LCiG3/01U8EK3r5//7GrzymmpoalJeX43/+z//JP8vPz8eKFSvw7W9/O+XzDz/8MP71X/8VQ0ND/LMNGzagt7cXLS0tKZ//97aCggJs3rwZZWVlv/zDv2L71EcO/r5lgTIMYqqQlBQqVASLwtudwzer32mhCqjkXzc7gT19BWh96Sa4eSqBi5Uj7eFvnHHCNhhY0Pm29yLXeImUt9mxjYRonCEZ/pkMg2fPSaAs/zRXXwaAh+bv4mdKlgYvpkL4FTe/YhL19GXBhX8CF4GbnVBFW0TBk6Xlfcqbpw2DmltOm98n3LTFyJgNQzcWcGN2ojJXKhYh3wUzjpvETMoHyFFF0fyzxkBhBUi8h2AN1B9ZFIdZoAA0nZgGNxpXcKxoHI7n4LN1ClIWTFxdmj/A+PPl5d2WRwqADZGImaRdmVRLHqq6whMm4kHhWxGulWPxYhHOEQk2uvzJU8zP0cq5NxbGmXg2v2Np/oC1t7qHbjKVUcmDRknpenx+0lWeMv2MtcWtXA2VnkNKCSVsA7ZCa4wN7dXNE6F47emCoxIGaU7c7AR2dJcyDEPOC52ll964DM0ds5gLXjY3K8EGB41teXk3GwbUL6Vg0lkDV3d1M5NwcxIoufG11ImPO2yIu9G4rQDpcwwA3zy8zBhFUOfKj7vwEi7cvDj2H53FERhrXWMR7OgpAVVb9kZM0qf/UYaVjxJxkpy/AgCrq9vhZifwo0ML1LNIUZ4IWXCwTS0NtgEs4QIil4UiIZva9Ri1l5f3S9LB19uXwQ97WFbQb85NyOcEWDV4H3cUdsIP+wauB/WeBTOOK+9o/gAernyef1c37RQO9c5OCxfkPeSAk4J39ZoowarCbkPcQFFZrXC1v3Qj3JwEnjrSgKXlCu7D5zWw1ziapaN0TUdn6EUyCcN1005Z1br5vGcnlDF2LoIXX7sSp0cvBQBlGOh52tJWg22dlXxnSEN6cbmu7k7yfDxsQem80TDurG/R8EOxuCQvHJVIT3cOMV3RPqHKyz9vL+bzU3TDG+r942Ye2PutZTRX+3ZMMTZqXtwkJ288Ms9es6wE51B4o2FsKDvIBTL7X7kGsTNZppIwyfssIZsAvB+PmgdqGccF9Hwl070JVfHZG1GyOuGFTP9iEesu9WIRLC7vN/2WUB8tu1lGe45hl9MwRIr0ztNV1X2quC0Lyfl25ISjZHrNdnaWMGqA5DfJQoR8/O/mOuu8bG2tZhYw0gHemphi7T3/jNrPK+f0AL7DkVSO+Gs5v62jEjs7NUxYRJ/cTAW35YrnSeNAuaCb/wn/QBkY8s/ExETK4ycnJ9HZ2YnFixdbP1+8eDGam5vTdqmlpSXl87fffjs6OjoQj/9y5+LHbY8++ii+9KUv4fTp07+2Z37qjQOEfL7YZOVYK7ROFRk15GNNdSsL2+c6tSWWcFh4HuqfhVWVHerQaWxeLJFhBDIpSeLgEQMGhdQXzRo2Sh1sLz4pU74ukkKXVvfgzZZQ/s6h34VkdHASrlGMshK4InOEn+tneFZ/6KKRF6RDwkD/f2dnCRsx3lhYGUfpErpJCOZqwwlK0HNom4wg8v4JWcMKZ04C+4/P4HA3ACDp8MXGXvTJkIW1ZViNUFi9WEQpc5YiZBsoT/dVwxsL4zO1Xery8hxOeKTP7Rwq5HcRpGlFQS/8DzOYdSkFYwuY/UTNd9BydDrcHFUt1s1UFU6p6jErB+eM4UotmjeuPKDEBkR7N+Ggbs4JM4cU2s9OoP2lG+GMq8ttR0eZ2s9QURA3Rxm0ywr6VaiZLg7XT62EqffCpr4aU0tBrLc3Gsb+AcUW8/UF262LqmH6STU+3+Hqp4Bi+aBxLCvvwQ+bb1MMT7qyLDG7UO6K3B/kGfaz1ARbnkEyKvNU/QhSFJ5rK1cMYuLcWBjlLKWMLC3rY4Or9+XrWJn1YhHF3JSjvPCW15KeEY3zz5eV9aQYmTNveZOTEuE5ygsNtR8XzjxmnqfPLwCGyEjjnPY40eV6sQiuvPKMqlwMBSVkzzHUZf8HtYptZ2lFr4FvjIV5TN5omKukEiae117ABq0EW32W4CmmHsT15yJJbOmvVBWEx9XvyYNKBgc9Y2+vwmXv6CjD33Tczr9r6psJOL6CeZEcdXwjp0YizFRECgwpsVvbqjgfSO6dO2tbeL7hO/jZkVKlMDo+5zlZzhURXWKDU8vU0Dl1lzT1zQRVdydHEMmqpfkDWFLVh5IbX1MF1AS80c1Mqu9lJXDw+HQ+LwtmHIebncBFkTHbMw2YMep+/mNTPe8H3oM6yudmJ3B15hl4MYPzT3hq721tqwKxuPlhD7u1YdU7dCP3DwCWlPezU2NFQS8Q8tXfek6oQjAZF5RQzH2V8DSWL4p29vHuRpPnNaGqBxPsD77DSj61u4rbGG7jZiZNfY+Ac8DNTDIT0XNdZRZNKBwfzlQF53O0kr7nxdlp5DSMd1/3sa7gRKqRmquMkkP9s1Lw+DzmXG1ICcNJRVgNk92dmlEOABDxTF6cXh8kHGM8RnW+R7ap0l6dd8o4nMbCaozROLa1qigN509l67tFUC/Td1IcVD6wsHAYS/MHlPFe0WsboRdg+/dUSL7++usxdepU/pMuCvDee+8hmUziyiuvtH5+5ZVX4q233krbp7feeivt5xOJBN57771f08hNu+OOO7B//35MmzYNeXl5uOSSS6w/n6R96o2D24uO8kFnS1rgGyUsiBThZwYqzIVHCibx/uufbT9ayrR5blbC4mv+QtkBk7ilPR0U/qPLcrcuBiPDgVa/ABD8wvLy6z5QMjNFKdxonL3N9AwJ7yHBHz6dxcqom5MwsABfsSeRgkwJewAUplQo2tde8ZEyooo6TLhSX5BNJ6apZ2cm4ebF8dfVz8KNxvmChgsbLy7GTTh7+t3q6naFcR0LK89PLAI3I4n1dYfYk72pr4a9IxInbVVVdoyXifpJSgZBWKSnny/8mIGIebEI7i/fh+1tlcz0QUrh7bNV6JDmf2HJEH/HP5vBURJAYce9kQg29dUYqAVR8An4E/Undi7LgqotmjXMnjfi+F9R0Is761p43ZfmD+A/NbTwOyl6IwsS7RgswpPN843gF5h6AhoGaxwEKflk8uXJiSt5zCvn9OBQz2z85/lKwSc8LwCMegbLvaOjDG52AtsGygx9pd7rjzUttM4DK3xZiZScDy8WMZCYWATPtZdjZUUnG+4EHVxV2J0CDaC2s7MEaypbrVwCiHnwYhH8p4YWk2ivFaCVc3osfH3ESfJYqH8TyTDTOjoTrpm/7AReaDee72CfSJllD65WFBRETX3m3ffz4OYo5qDNR7SnP6mMPwDY3qrGThXR5ToC6ixuam7APfUH2FvPkRAPlmLEXkjtteaIkcB+e7EIdnaUWAZY1S0vq3+IHBEZWVo5p8fIQg07o7wN2mfs2CFICBlp2ulAuV28R4U3eHNbrfXZ2nKdj+KCc1no/ElD4ZEFPzVJ6GNhFTnN8qw9xB72nAS2tldhRUEvMt0Enh/OR/dRFYleW9vMUQiKSsk94+YksLdHrQ/R6QLAktJ+TjaVe8OXCp304I8bDLwbjWNqeAzeaBjbuyos44LeqcgozLoSpeUunQsBF9jeUQE3M2kIKVwfT7ao3BWuTXPOvrcYulR2lKMtSDoM5/TORdgAuPzKM6ZPmgZ5XU0TAAVJ3XSknp9NeQ50Jva8qCJMddNOwRsP4+EFO9XeCjA8SQcXV1+ORWwyivGwJX9p37e+dBPc3Dgr5IDZv0zmIaIpSyr6cE/tQb5DKefAGw3bxfk8YHNbLTvTZP4By1YXWFzer/LCoIw7LxbBxsNqz36rYwngOai65WWrz+sbDlr7wxs3+V2yLa/oNncPtFGYG8fengLs6FC5JbuGC3B76QAu+PYJogYA8Oqrr+LMmTP855FHHjnvKxzHNpJ830/52S/7fLqf/zrao48+iieeeAI//vGP8bd/+7f43ve+Z/35JO1Tn3NwwxNfBUJRVrTJg3c+3KRsXjxkEoo0jploKr1YBE7cgXPRpPE0pasYKis5aqV8WUE/bsp6Dz/supXpAGWFRonHJSwwXB/raw9hY8s8wHcUQ4yukMzvCr5bt/NiRMnLmHCwskZRsKbDnloFWwK/Z+xkTFF4/uOBBviZHhqKj6Hl5M2m5H2A/i2lL6L/C2Ycx5zcN6xiYf5HihrWKkyj+0+hb1Ju7mk4oBRfkVRHyW9MZSgK1gBg3KhsRA1qOmGvL/V7Yekg9vaopD2ZKwBoasLOSvZ2AUhhcOKq04G1x7uZwOUT1j7yYhEsKjtqQWUIkxucR/nvSy6O4b3Xp54fxyxw1l7CVQw7jo+FJUPMyZ2Ca5VUpxK7GsyJEHh2+f20/Qju4/Ew3JEQvJxkap4FkILNlueGFFA3J6EoVnWFa7jA6sp2lZynv090saQIBvMp5DO5X1n2enIUklwuGnroJ134cVUh+QtlB/DDptusvqU7G9bcivlKoemNh4Bx19pPsq0o6FXnWlJ6hnzcdNM7qLvsFLa01PJ3F8w4jr29+fZ8pikqRu3e0sN4vOlWyIrvcj7o3YChR7XmMKajiK6fsj9oXTaUHcRjTQtTcwTEnMizvKFhn6p8Hfg9fNjFygQls6R8ZGpnAakI4vppXrd3VqTKI0mtS1XpxZmWVYe9mM4DOVJvnR16hjQwqdhhuvHzz+SeTJeXRGMUhcRoP1lOKfFuzrfQ99/Kqg5s76w4757gu1LQnz4473l89/Biy4BH2EPFrNPoPn192nwXUrppDKHXsxC/KGlyyFyfc2yC8pTXPEClKytOS1kdlBkATH5L2DO1UgJFDPldQXw/6QMOMGv6G3jx2LXWWBbOPMbym3PgqNK2m16eybaioBcjyUyuws71h0Ji3ul5opK6dSbEXWMVvwtUX3ajcSTeT+C1L164RdCu//u/hJv9MXMOxsbx6n/++q80psnJSeTk5OCf/umf8Ad/8Af88y9+8Yvo6enBgQMHUr7T2NiIsrIyfP/73+ef/cu//AtWr16N0dFRRCJpEAcXWPvURw68Ub0ISQdLigYUhCFkoDhBz6T8uRtJWqG3ijkvsWEAH/i92h4A6uCtrOy0owDjYRaSMm+APJvfP6jwaFtaa9kTt6KgN1Vp0gLZibsK56yF6MamRswrEZhr8o4DFlNNOiXMGwsLLz0AB9iu+dYtwaQ9EXt7CgyWWEKTxsPKMNCf29JfCT9HeUCa+mZibXGrMgwE1EEWIpPzTd57LxbB3qOzLcNg0axhBa0QlWPJGxVMrnZz49jYW8+0p4z/9h2GfHkjBiNOnjMAJhw+rqAF2zsr+BkUDUgxvhxfwaF036l2BbVtrVX6PcpDs2jWMOAaOJQXi+CyjBF7rVxfeX4vn+CfE04ajm8ZBt54GD86tIBhMHIeF5Ud5fX+4MNoqoIlijpZ3uSwp/oS8vHm2BTug8xd8GIRLKlUffJGIlhfd8jMi3CMWN9Jx/YkIQjjdv8J5pJ7s/EuLphxHAAYJkNwLLke6h26E472HrfUKUiBTlDeNqCTnrXnzKcK5bRWY+aCR9gzMBzar6LmCSW+utE44+LluXNCHn/+9Pil1jqsL2lGkC6w6paXmbTAi6maHuSV36qhbDyvkaSlGN1R2MkREi8WwbODJVg5pwcb5u7jdYCnQvFbWmut7+4/PoP3mYwyBGFS9O7Hm25VMDkZaQOUfB0z5wcANjfXGY+obmtqjmBtfVOqwer4vJ8e7260ooz8kYTYZBR9iMbxePMCeLEIHpz3PAAtb3yAipVRu69+L2O+KWLBtRU0W9mSij642SoHhXMxdPsgHlVFrwRMRHUSDF+hvUAGGQArGd2NxhXbnMR9kxwTBgkcnw0Dmvv19QdT5oTpSfU8N84QkBjXR13RcTXGsNlrzF4jItDkuV85pwcJ3+VzAsfne0L2heXIaBgrqzp4PAtKVaT+u4duV3kxHI31saDwRXSfvp7HbEGkKPpD0SUAyWvHOVJLn9nYW6/WnSqQQxc9Gw2ryGHUFHj7QtkBzmVYWqFghn7ExhVZRivh7hMGHRAkCJH3+pLZg2YMdNdkJ3D89SuYRIDGskcUQKNEajcvzoaBpZOkgXs+O1hiDINYBHXFx7GsWuXESScDR/iEYUD3rxuNo+ymV+FG4/jhwUXqfeMmR0fec8QgdsG2f0fOwa/SMjIyUFFRgZ///OfWz3/+85+jvr4+7Xfq6upSPr97925UVlb+hxkGJ0+exFe+8hWsWbMG77zzDgDg3/7t33D06NFP9LwLfNV/DU2ffzdXYfC2tlbj/roXmJs4qETTRWEJKx2OJWG2rk7hYQm2442F8fLoJcYy12H/tJ5O/Q6Z3Ago7wp5qdkDqT/77GCJ8UZT9UVHYR4Zp64T8ChxU45JKmCAEqzEma4w/ma8S2YP8ndkwjFxn6+uMfhxwmfSZ1hp0vP3VLs6OE7C5T5tPVLNGEpSqBD2DG1fNK5gHAQlGg1z+Jq8P3SZVBWftIWpqE67Y7CIDYM7q4+wcuZ/lGF7f/QJsHIyqLQ8CdbxMEdOKOmLlWVdb4KrqhIUhJS33LiVYLfnxdlqblobeLzbj5ba8I3sBHJcO4pBe409RbSuPrgWBEVQ1peoJCm+hDwbwgWAFSFmB6HfC8PGGQ/hxdeuNJe6lBaObzCtuSpBl4xmNzvByfB0dqTRsKqm3UoYZ+XJV1GUpfkDymOnz8/IuSzu1/sTUZUP01tkrSFHMujMCXytG41jfcNBdYaSDvZ0z2EYAK0L5WvQ3lxeoS5RxdjhMrRIygz2fOs+cB0Aml+a9zGjRFEei5pDnVwfwC23DhjmFTcaVzASfekvLhtgAgJeS5Gc/sxABRs+1M/tR0sNTEfDG15+6Qpbodfto3i2wuFHFVXpghnH1TkXSbFuTgLrS5rhRuN4ZyLP/JxkpoRn0J5zbUdF44wT2NJaaygkxe9WVXWo/SQMS7mmAJiBqXHGCcBzTDE5TY1JNVi2tlSzQ0WOdySZZcnn1dXt7K2mRvu7e/BmlM06bc2TCx/9r1xj4CXQ94RWrvywUTz5TgDSJqPLRgb+ghnH1RnRXPNUVZganbegUk1GPQDs752t7pmJEJwJF9HwJH+upuiEeaeQ9feWmgjFttYqtA5O4zFwbQgdcXFzEiq6nm0cMAThW1d/WEVDx8NYV3eYo11k0O/XEKOcXOUA4bspRxVkXFd3mJ9pzY8eM0E5qW5H/vTXsTR/AD9sUVG5CS+C66/8EAlP0ZjLKvFEzSphkrKx00Hu6UBjeXvOQHilo4vepf7hYFtHpRUlBMy5pShGUHYhbDPoyXv8njota3ygZWgaLo3ErP45gXmjPr08egk/o/v09Sp61aCMwRQ4VlRBcZ08+y668JrzCf/86u3BBx/Ej370I/z4xz/G0NAQ/uRP/gSvvPIKNmzYAAB45JFHsHbtWv78hg0b8PLLL+PBBx/E0NAQfvzjH2Pjxo146KGH/r2DTdsOHDiAoqIitLa2Yvv27RgZUQ7Hvr4+/OVf/uUneuan3zgI+bj++vcBgBWSDxJRxmFTcik18roQZjfY1ha3YlNfDSdZEna9c/BmW4limkhYnjXLoyh4vh/tWmiH/lzTXy8WUUmVIuypvBqOsfQFC9G3F2yz2YF8BxvKDqpaBAnX4Ia113RBkYlA7Owu5u9IDwYlBJKQJ6/m4lKlrPhnM6xL0hsxmE7idgfA7wWUt8QbC2N99WGLXxoAvjJvh/Ls+I6lXMnLu71/mhnju5kGj6zn8QtlB7C+9hCGz13JgtXP0Le0ML68WID73bUjG2QEra1rAhXuIiNlWVU3Vte2GSWH2CfSeDulck77Y21xq4rKuD5fNEH6Vy8WsY0HMm5cIBjed3MSePJII6/hQ427eF4Xl/cbTyeF56XXUnsI/aSrCvlJfG7UFBsiJbzmltMpXmXqW154AvAdTg6Wytn2o6VYVdFhfy8rAbg+9vfkK25vvU8B4JKLY7wfe4dvUH1OOtZ8SgWH1wHG2P1Jbx3uKOzUWF7fJAwKY4gNu7Eww0/crATun7vHjMEyLH2LFvGxw7eZ5GsBNzHKExkUJs+D1kyuNf9f1x4hOkovFsHubpOjAEDBinRlWPIKAmBjm5OqhfJFCjQAk9CoP9vUOxMbmxt5nfYfn6HmljDRuk9PNqvkydb+6Vhb3MqGuBeLYFl5jzGaRKE5OdcyGZcS46mP29qqlIHrO4rAgbzltYGiQ3q/SLY2hkRENL43WOAxoOxRnRGOJIkcoyWzB3kPUpI6oCBSlFANmD0v75G1tc02m5Ze86ajM/g8k0OBE6bF/Ozty+cEZjdbMbbJ33+h7ACWVXZbcor7o8/putomlViaqdit9vQU8Llo7Z/Oyi8pwt5oGI8fWaC6S3UyhMJIEDGSlxRdX1bQb7OfQeQWJR0T9ZB91Os1OpJprQfVTnmqZS739c8qdot5VH9xXgTU+Ro6cS2yQ3Eg7nBl41ffvhhlU1+BmxvnSMuG+v08/954GMsK+rFyTo9xio2FOUrOcFTYa0tt4cxjeGjBLr4LgxFDGc1mCKukTvaMnPK1QXBHYaeRC5qQgKLOUj8gGKmbG0dd/knFNCiMGT/s4f7yfSn7vf2lG63/c/Qq0NjQSQOj+k1sd9xxBx599FH81V/9FUpLS3Hw4EH87Gc/w403qvl88803rZoHN998M372s59h//79KC0txTe+8Q384Ac/wGc/+9n/kP596Utfwje/+U38/Oc/R0aGyeu79dZbPzF16m9OzgGVuxf49IbpJ3Gof5bNjx2z8wSoWThk6R2InYdXeCSCLzbuVoXEKHchyMFNAkIr+GuLW/FUi/Lwnc9jQe+08KzBd0shJHD5AID3MoHLDF0XCZ90+GZAe+MjnkneGg8rj0bYrhLMWFkPKmztKo+rVS8CIlTsO8wbTnPOjTCnAO6fuwc/bLnNVmioXyKESphq4piW7yXvv8XBPhLBIwt+ilcmLsXmttoUrnI3W7HT+Dqqwl5jPU93Fbdh3Iswbt3C04r/p6ybfoY3ElERmyx7T1n7KRYyOQcC1+3FFGaXQ97kNQ/wd68vacY78TxcFhlJod50o4o//MkjjbZhBMDJSqpiebLfup1vX9L7VxV2YywZUUW7JK5e5OXIOebv68/5ZzNUYneaGhjp3smQHFK4aX5Hw9hQv5895muKOhByPEXnqT2ze/vy1ToT9jhNrgTtHSQdzvWwcnBGIlhc0a844wX2mcerccuIu5B85utLmvFkS2OqcUbvDuY96MjK6up2RJwk4/cR8ZVsoVonvyC3g/eWrmHB+4pw2GL/8JqHdZ5PdoIdI2nXgoyPqKlrkC5/qW7aKVySEbOKHzKcgvj79bm4t/Swyh8Q8i04T/L999QfwJNt89TYZK7IWBirKjusugIAUqu3yzyEhIN75h7g8xGcS/kMbmGD7w4+35KVJP/T3Bm0n6XMSuHhl/lSjo8l5f3YpQkuAKTuY/38Owo7ub4C/cw/m4HP1HRhR1cpqFAdgJSaAsGxL5k9CNfxzDqeZ22CsmNZQT883zHf03cjf5byNQBLHvLz6O7QHPxuroLHdA7fnLbGBgDLW5+ufpAlp8T9f766HcFG67GqsFtRjsp7IGLnOdZNO4UmXZzUGw9jUfEg9nTPATzgzvoWbG6tBXwHTlIRhFAEcnVNm3HMibOeMp7g+QVSZOmS2YOKylTvHUlUYckdPSeJd5N47U8u3DoH1//Pr32ynIM/+toFN6ZP2nJzc9Hf34+bb74ZeXl56O3txS233ILTp09j9uzZGB8f/9jP/NRHDjjngBJXtQV+d0mLYtahEGnW+S8A9SCHvUzS20YHcW1tgO825OP7BxerQzqupznpwDsXwcLyQfM5sQJ08RJ8RvXf/E2hVFmHAQAQ8dXvhDecLw9Nt+eNqsrHlmFAYVvhXFtQOsTvW5o/oH6n60N4MYXzd8Oe5cVaU9RhcKNhLYgSrhKyWhEiHCbNv/TSUW6Gm5PA/Q0vWOHxHx5eBGbUiClvm/RoeGNhdVHp/29pU+xFK2s6rD4GQ6ZwgG8f+Azencw1eyCq6PkaCo/Di0WYGo6SFPmd44p9attAGV8k7CWO2jUneK7p0sk1irw0DOD4DLPidRE5B5J+jjz8FvyNoBziYtjYW48dg0WWYQAAxKC1sbcea6pM7oNaE982DIQ3Kuj9lWOjudnaVqUYe/QcLCnvN1CUukNslFh0egAryT55mrONQXHRRTGlVOhxMTSDuM7JS69pUL3xMOoKT+CJnrlqv4yGsaW/Ek/3VWNlVQdWzulBZshUJuZLmNZC/A2ADdM93XPgxSKcoE3j3i28mIBg38nRnO1hwW6llcUnm+fznD7UuEs9jypljxvlz/Le+Q62DZRhS3+lHSUgRTgnYRnZy6u6UpSzDWUHWfnin+eaiMj6+oOWHCSnwLVXfGQ8wAFqR34HVVOeOmlgJ4H90tQ/g4tp8dhEJHFZVTfLYkosJqYnZhvT759zw5v87mWV3aowIlF6arpJ8nJv666w1pgihI0zDLyG5UC2ggc92Tz/FxoGC2ce46RPhxQ1OWYho+lcehMh3N+4R1M6OwZaqVtd0XHL0L27pIXnlZmkEsIQzklgV3cRf4dkrRwnPX9Lay22Hy1Vd4WOIPohHzs6S8FUznQedUI2wVSDEdBdwwX4aZOGwQbgN8E9t77+IOCpn+8YLFLQOnqPB5NDRjAafa5lrRZ+T46h6qb+kWHAZ3fE7quUjZJAAdC1fIg9i4wTKIigm6VyCdgA9FRko+aW0wBg2Pp0jtI/NRnDWc7H6po2vkdaTt4M+I4akwfs6S1gubClv5LHJ2USHN+ubKzPjDUecuwkzZ5aW9cEyZQn1059GFzHB0CKwyY4Jxds+w/OOfj/Qrvooovw5ptvpvy8u7sb11577Sd65qfeOHBzFEe3DKN7o2FsbG5kocNhu6i5hKl5Y2HGdiPsG7xvANv/D4eVh3JVdTsAoGjWqylUf+o/KmRsvCSpffZiEdxf94LuvzqsS8v7sKuryPbQ0AZPOnh+ON+iFLyjsNMWUNXtylsVN9hVSZlJjZNjcwzNJyhxSyijbk7CJHbr+bq/fJ+F46S+E646eMk6oyEW+KtqFB/733XPhzcRUgm75NHUHmU3GseZRDavISmRFk2jfuf27nL+P9VG4HAyKerRODzf5blYmq+qaracvJnnEYC5iOgdpNRr+rsgLE3OuX82w6bBi0XMvNGlSPtIQMTg+ojmjcOLh7CioBeLZg2zIqweBIbayGgD/XvJ7EFOmpMGJilkRH33zECF4kN30kvLWTPeMAYJgIyLbIxwMNzu5iTwLy1V/M4MN8GQEzJSnmyezxR+rMDRvggJRURDfD46eQnOxLN5DzUNT9cQM72PPKNcLpx5TOFw+xV84+m+aoa5eWNhPDtYgm3tldjVUWwZAgoOpvYtwT021O9n/P3Cmcdw/9w9xhs5Hjb0vFAwjLUNTfBiEcVORes9HuYz58UiCopFkT29V7/buUglcmolkPZX8GzK/2/tqAIAXHX1h2o9yaik946FTY0W8bPHmhcaBYAKEY6YeaA1onH7ZzPgZifw+jsXmX4Eaj0QnMIywMV2svaIb0OaZNTQi0XYcCCF1I3GDZSKlFVH5Q4dfeVqTiRmT/REyMr3ga+hZXHXeib92d+drwxIOuvaQFhb3MoKHc2tFSUAsPfYTL4PPj/3kIGRMCxMj/+coLLMTCqyBVJY9ZmvueU03Nw4WvpnqAJdWql9L55rot259hmRybk0pxt7622Pu04Ol0bIri5jTJCjQUZHUxwAWpkyRo8amJ+loWau2Qe85nS2xvW5D/lYXdPGDi6+JzKNIyFYkR06V+2qjLPw4iE0FB1Xz9QQKLpj2KjxYaIs4m4mpwcA/GXVDj0mB9OveVfBaB0YMgQNzyUq5V3DBUZLcpXzgyikNx+p42KabnYC1WXHbQeK/h4p9py74/iqqFxOAguKh3m+vHMR666A5+DO6iPgyvKiyeggfzweUnlV+r0k+2RbX3/Q2uuyzZn5muqHhCQD+J2So7igm+98sj+fova5z30ODz/8MN566y04jgPP89DU1ISHHnrIyoX4OO1Tbxx4oxHMueFN/e+wYsHQwpCSA9fVHYaTUHzBwbCcuUjBwgdQF4Az6fL/fS3Yth2pwtL8AfQPqroHMq+AkxnJ06kZLaTyBqjPyHLtgEp4WlbRoxS/8n4TgodQVsWhf2agQn9GJwK36jLrkaSlFAUZJyixj5OGYxFg0mVBzIXNRlW0ZXlVl3rXSERFSkYNxlkqBZTMJsfkTJ1k78Q/t1Rb3sqnWhuwqOwoez9/r7qHPU+EOec1Ju75c+ZCkpcORSUAsCJF378u60PuL3E7sycsTfGXFI9ydgIy4VcK8W0DZXCmTKrLUUCN6DK/p+4gG3kcbSAmGs9B7FwW3EiS2SlSPJiOunDX1jWp/5PHcNQUsLPGnmOzypCSt62jkg0N+qx/JgPeRAgvHr/G9GskgkQgMfKeWsOaQjhqP6TG+nDl83iuvVwZNhS90mvFLB3UJ1LqdETEi0WUxy07AVw6wQmMbjSu9mN2gtfwcw0marf32EzAc7C8qsuaKzfXJBm7OQmzD0gxpeJa0AXyzinPNSnLe3oKGEdNUQpiLltW2Q0/7Jl6DtpLunJODxYVD8LRjCNu1NSmkFCuu0tacDaRrfaR2EtrijrYWKF1NUmOahxvvXmxkUkWww1Yznijmp0sESgMGGB0kvP1k946eOcinKMT9MpKD751Jmi+tUH24FyFF5fVxAnfbVWpFrKP9r/F+S6e60bj8LMpQdyxnDtLS/pZppCCvbO9BG40jtWBPBdSuDc1N3CEgIqRbeqrUc8kQy07wXkM6fDnhPm25kDkBhEEhJuj2Jqo0b6Q8sPNSmDHYJE9BwShS2PLB6uhe+ciXHPD8R0mKiA5xHeTftaSij6O8HKitWB5I2MZCUftJ71/qFIwENgfsQif0aXlfdh6pBr/1lZi5SmR4SzvSRmtIVYiN5JEU/8M85lgdNbR8+2YZ/A7YhGsq1Uy8uvty7h/J964POVz3IQRS3tS7lXq36bmBt5rrf3TeU/S2GfeaIpkMUmB78A/m4FlBf3Y35OPhTOPGeNO193YUKbuhs1H6uBmJeAkXKyc04MHK/ZY/V1b18SGUdXMU+zIlOeV72My/rUeQmeS3n30+HW2fgIAHvDzHjvP6UJrvv/J/nya2l//9V/jhhtuwLXXXouRkREUFBSgsbER9fX1+MpXvvKJnvmpNw7cnDj6j1/H/6faANLTvKmvBp+p7ULr4DTb6yKfQ0qdhoV4sQiXTV9W0G/C2Xlx5XHPjWNDwz74Id8ws4iKn/R/ywOXYxQGeqdkpoklVPKWjBIA4rKSSqruiwxdW54dYkpyzNispFfNGkEhT/Js8Hs1Iw/RAzI0wfJIGeHE9QLE5UdJzjxPBNvSY9nTPQc7BouwuqYNO7uK2YNHffLPZlgeKva8ixAzACDpmAqfui2r7sb95fvwVPNc6xKQEQ+K6pDSLyutemMqckQUijxXOQaixEKZWHS0srO2uBXeuYiBlkjlR3sig2u1trbZeIvGwlxQZ9tAGRcKum/uC+Y5UXtvBRsl3K+qbufLQn7Hz0qyYsCQKr3f/TMZ/FnyVFKI++m+at5T3z7wGfaGqy/q/XnOViq9cyZ5Xc6/5F9fXtlt47PHjCeVi3/Rsx0f07LeNT/T5+O5tnIzPr33GKYQSLyTXN/0uY1NjZbcIIVhR0cZ1tY2m4J/NUcAx8f2o6XY0zVHfV6G62kd9Rnc2NzIuGNp5Mb9ELxYxE4YpAR9rYDMvPEtwPWxZPagKryYk1DrFoDRPX74Vgv/XjftFNxoPCXCKPvn5sVTohgks4I4eJpnVka00vZo10LAB8aSxrDa2VmSYmxQtdhfJA8pMZzkbUPxMSsKwJ7e4Fi0p31re5V1LtkgzjVyhXNJBMSPCt3BB/yky8nwPG4BqyEDbsnsQUuOS2gG/XxLWw28EVVckX7u5iSQ6SaM7NH9Wpo/YMkR8nbztjibgR1dpSYxXEPNSFY5UyZVTQBxtxkGPPXX88P5/LuG6Sc5aZ/G9XjTrer+zIvj8ZYF/Nmrs86aSvW59t7maMVwAeDAFNocDZtk33MRqzZM8P4Nnjmq+C6NJpkvSC0z28h1yuVbW9xq1j/QaL15zWQTBB1WboeYIx6vb5534o3LLWV9aVmfMm4dH891qSgZU1M7iqIZAM4kcvTPfF6rbW1V+M7BJfhC2QF+/qYj9UxI0D4wTZ19HTkh41jlJ9hjARTT07KCfoti2trXaYzgC7L9hsOKfN/HG2+8gSeffBLHjx/H1q1b8fTTT2N4eBj/8A//gFAo9MsfkqZ96o0DbzRiEtTSJIkC4LB0MO8gFPbU94hn/JzxuHIUwFHVZq2wsxbsT/QoxdPClsN41daXNENyG3ujYUUVKiIUxGLj5iT44pLt9tlDgA/2MgJGsPlnM7CssttgVs9FVAKYVoa8WMTyjgcpEqeGxtT3dJ5FukJL99Qf0PNM2HGYg+c7+M5t/1sLTPWee+oO8vOdKZNYX9KsFBc9l3B967IFlJK4rKKH+0ueUGfKpDFw9NoRC4VsSyr6bH7uWAQ7hwrx/UOLLbpW+t2S2YPqchOcz+vrDmFqeBTwASduoEiU5EheYy8WsTxfNK+kjLjROJ460mAnUwtlipoML7u5cYwkMo1Slp2wLqv7G16AG40rTniRTE9YVzluas9p+Ma21iorgZLfmZk050FcwAAM4xNUYras2UDz5Z9R7FXOuIvdXUXnZfsBtCI+Yu/FYEt4IX62lZAJ2IJeV+B+bfLilDHLOfedVBw1EPDO6/8vmjXMSqT8PM9LVBlFf962Al5M1VRgDHjDQfi+iaxY35d7T58vLxZhfvytbVUGyieiSzJh89jLVync+XCBMaZIQXJ9lnuLyo8aFpZoHKMJpSBtaaux1t2ak5EIfE95ib3JkNl/WtmVdLBqf6s5XlnVgQUlNlMPsaB5Y2EsLB1kDyWNZ3NzHffNzUmk5D4AwN91qxwAckg0Dcywz42AaFrOEBcpCsHDjTvtz+joA0NWhKzbfrSUI8dOyLPynWgfUN4VGdRKGTZ7zM1OYGlZH/dtXd1h9sj/sOtWSxHbOVQIuDD7fFTfT/q9D87drRwBSYfr1RAjnBeLqDoFmklLJrXzHg/8LZ0CNJ9NJ6bBOxdBQ9Fxo2TKHCdxVrcNlKlIi8zxElj4dMq4k3BFVWD9yHMRxdbj+ArPn0459R21ZymyrdeLvOfSoJwYU+/9atVO/vqmvho+b148ZOBJZEwnbRlEZ4NY39zsBBvUllNHyyWeK9Hc3DjW1BxhCCyNOcjQ5+Yaw5kcHjKirP4BqwaQm5Ow7in4ygBaXDpg9lm2iJQClgEla3/IZwDCoZEG+nxBtd9wWJHv+5gxYwZef/113HLLLVi1ahVWr16NGTNS9cWP0z71xoGbE8dNV79vHw4YJUElBjl88d1Z28JCLqkxj1R6nD3oFLYHrIPEhzTPViIYW+6DufupWNe6GoVT3tCwjz3B9OwFM45rT9F5hKwH7OoshpuTwEhSRRUs6sIkGNsIQHmbiRlCY2BJGbc8dlro/vDwIngxxcZCsKDg/G3srU+l2XSMUP1/Wj+rnq0VQVm5eGn+gPo/Ya21ESGVLuoXYYrVmibgOp4q8jMRMp/zjOEmkzF3dRelrL1aw1T3gRuNY2d7ibmwYgre8ZPeOq18i4uYEhwJmhWYQwCg5N9g5EauKRwfd9YcsXjyOYlY92N7ZwU2NOwDHFUUDj7YO/T6xEX8fvIcAsbzzl49OQcCF82XsOCil4qf/P9FF8XMpeH62NTSoBSDEaMkemNhBRnLjcPP8PhSBVTCJFHYSmWN943Y4/eWHub37+wssdbJG1OY//NFR7a2VRlYnVCm7y09rL6jixzR2q2vP6iM8+p2y0CG72B3ZxHP9bLKbh47zTn9TXkxSyr7OAKwsbkRDkXnAsmDgPD+atrOdM4LIgag9ZB7yY3YFJcSqiKVgr3HZmLzkTpen96XrzPnLc94P4PQF8f1lZMjI2kbd7oSa7r+bm+tVMW+xLlmjHl2Anu7C5T3nwx77d2ndQXU/pfPvuryM2ZcUUE/rCMpywr6GaIp9xDJXeVNNXP/3zsVWYQ3EeLzub7+ICsN/tkMO8cg4aQqalBwmLtLWkz0yvE5WdyJKzgmRThpD3uxCDb11bDRyWMRjeZG5bapnz1YsQdeLILvHl6M/3lgEVZVt6ukbYERX1XdjqfaGmzFNVsU8PID75BzSknksQhunz2ExZX9aDl5Myu2bk5CQMJEJIQiFbpJalb5jg0N+1iO/F/1rci//i1cf+37Zm/nxTGSzMTa2mYFpxFyRvbTwtET7Imi+np/u9E4pkxRzq2v71/B886wq5yEKiCox7tyTo9lsBMEixwX3+1cxK/c0lprxq6dPuo/pp4G7YMrLjsLLxZhamorHybQNpQdNA4dGbXWn19c3o/7Gl/gOVxR0GuKk2qKbTc3jqea5yr2tFiEIyaWs4mckWMCKiWdWpISOi81qvrbdmE113UxY8YMvP/++7/e5/5an3YBNm80gpdevkL/O5WRRHlpfL74iDEgGEIEtLJOFyoppK6fVjmU+Gonrpl7cuP4/LxDFnafPBlP9MxVtQxiEayvPwg3N469PQVGsaY+CoWGjBCCzLBXWXt4CPZEXj++3KiQWJaCjCwqHTRKDzWxM/a8OBth11OXwETICJYcI0wo5Oud00q6rA5KfM56DA/P+xlWzunBjh51WRKkKTh/MilQFoyjFvdCRjBrI84bV7jOHYNFwgiwQ9WMlxXedys/oqaDlTsAeHN8qr2nxsJqzs/DoU7ePC8WwfqGg1hd1c7rQ+vgRuOmoJyjoDGPHbrNRAPK97FiQ/klT/TMxeLyfsXnrT2hbjSOKeFxhjUtK+i3vF9uVF0WsnGBIf1uHoeEADlGCeMLwwM++ihqni0SIVdWd5h1yDaRsKBRSfzcgGYyCiY0R+NYVHZURUKabI+qNxq2ikFtbqlLjUZA7G8Nq5NJzueSWVa0jtbpJ711WFfbxHSB1BdWXJMO4PoqL2XEjg7Rc3z9nl1dRcb4hvI2c90BMgZ0Uv28whfZQGQFYkKcVwfY0V4GNzthonTjAWNeyKgt7TV8doPRqHV1h1lRv/7KD62cAVOBW3whYDw3TD+ZkkQv54qrylKCqjagOQmb+kS6FBn2Y4aFRp4p3nvRON56d6r1c0BVkqak6h2DRUxuAMfHmpojyrFR3m95QN1o3Mg6RyUIk8K8sWUe7w1fR3LmzHxNGWfibNBZgKfoJgmqCgALS4ZUoceYYjtbVditIDP6rNKYAXBxRwAM8+Ix6pwuTrglBVVHBv2Qj23tlZbM98bD2H60NAVSJkkJGHIyLogKBEyS1vL54Xxm4frfOqrjjYVtSBgZT66PNbVHeB9SwUz6/z31B/iOI2N165FqDL16FV5/5yLjgNAREs7dofnW68e1CMgJIpkDx8LwP8rQORnqM2fPZvN4jNIu9IBzESUvfR1BFf3Y2FtvzVk6x09K4nbIPhNb26rw1psXK5mma17UFZ5I+R7JyscO32ZyA4nqVsjg3R1FeOzQbSybt3dWmLnOEwyFjnIouNE456NR7RJGP0QloyHgxB2TfH/O5Ax6o+GUSPyF1hz/k/35NLX/9t/+G/7sz/4MAwMDv7ZnfuqNAzdHKVLLCvrhTAaGKzaITNxRX1S/vH32EP8sHaxH4YtheUKZylALFT/beCiYrlR6ubSw2N5dDjdqqs02FCvmlVVVHagqeEmMyQj/1TVt2N5eqZTh3Di8yZDxvFMkI8OwNflJ1748XChsf1epPW/acKDQ+LaBMqYolZ5AOX8AsKq2HesaDptf6FMok1Lfjk9Vl1hm0oTeA4fVzUmoJL9YhGn1ACOYHzt4GyeoKQNEfyDpcIRm5ZweLK/sxqJiU9rei0UY7iVZUlgJB+BSn7QnnQoerZzTA3hAyaxXsL210iiGDlB0wxvc9yd65uLukhasqzuMjc2N2NparS4h17f6QYWXFpWoS4+K9awvacb3m37HeIfz4qxE7nlxNp7rKsPS8j5e56da5vKFmvBCqYpbQHne1FdjJdrzewg2lHQVv72YbwWBA/edzgRdes8OltgeUB3iTwoRQ9/zz2YAnoNd7cVcZ8AbDbPyu6d7jumsD+D9TPYYuhlamZNKpEjoX1rRC/hKcZRYWifpqKq8/ZVYVt7DCjljcgGbw98Xz9aeZzaGxF6kPcNQGUBVMaakTk+cOcdnWlSCoIzEM1nhY1mgzxgAZlHyxsL40aEFykte1mOUBmJCGjfeWwkHk/kdEvrx8quXwc2NqyRUADu7ihWkQ6zVwpIhI6MmQzjUO5v3HNHSWk3D7VZVt7OjwM1OoGVwOlZWdVhKxnPt5QquSYbWiPFkerEIR40shVkYim40rgo6aVpINgyg9t3/blKG427N8Eb5GOwQgimIxXlS0tuuPcZDr15lR19Fo/WnKKU3piqpkwJcdcvLpjJwAJLGCdqeMtqa+maaeZ95zCZcCHhz6d1kPAKwYBLBaJqbrRJazQcccBV4/ftgMjxFsbzRMHzSpEQEjiNOANzMpDFspYNnRD0nLzRuR3IAZbBPCOa8qF2MLd1YGJIj8fRi3H7EV4xGUqZJZZfuCUGd+5zOD5RGMTu9ArAhQMEoG2ecYIN6Tc0RIweSdl7CutomjtSTIUiJ5zKxmNbCSTr2nqclIwizC8tpw+sVoBYGgB0dZbijsFPloxHkKmqiZ5axo6NqsgbT6po2SDKLC7r9KvkF6f58itpdd92FtrY2lJSUIDs7G5dccon155O0j20cHDx4EMuWLcM111wDx3Hw7LPPWr9ft24dHMex/tTW1lqfmZiYwP3334/LLrsM0WgUv//7v4/XXnvN+syHH36IP/zDP8TUqVMxdepU/OEf/iE++uijjz1AqnOwY7DIYKV9O3ogaQytkDWUd9qNxq2y9/LC2tJfqQ6eYB/Z1lZlnh2NK4UGsEOxOrlsZVWHEXAy0c1X1TTh+rg4PIrOUzfY44opr8cHk1GAGEVGw8CkqkkgE+Kkh8gJeRbfN+MehaDhEKQDxtrS54N9kMnV8B1cHB5VSoj28Lo5CaxrOGzKuzvAh4kceDH9Hl3wBWFbcZY5FIBSvmTVajePFCwSegIWEfLhu8qztmOwyFY2RePkPvFMQCVPqgtQ/ZySJbcfLcXqujb0v3INQ2PIUOp/5RorifEnvXVWQbvn2sstPOzqGkOfuPfYTB7rT3rr8GRLo6KwE/N8uaaYBZQQ39lVzFhbusTvLmlBpqvWgS7EYPNiEVamrMTpABzDzUxypOihxl2K811cuPROCl/fPntIQTN0OJ4iCVRpWFGP6oiWjoStrhMUkjkJhqilKJ2Xitoc8RBH7uDrcDld7I5SINzsBK7IPIcvNu7m9XGmTCqoCxREzUnqi08aGaIiueM5KQoCYC5ixpvnKQV7//EZnKhNioB/JgOLKo6auhG63+QBXV9/EL0vXwdK7r+ztsVEJWXSNm1tXZAs4QulShdXlJA6AByVsvDIug8AWNY8P5zPMuiZgQrLe5wdipsz4voq4qS9iym1MwDA9fFw5fPYdqRK/1//lZXA9o4KuFkJFUmiI6eTJwlmybk8UZXwmqLEZCdMNfFYBCU3vgY3qhLCnQlTi8WLRTiKs7auCWvrVESIInqLZg3jruI2PN68gN/HHlIp6yjCMRZO9RKLm5OilPTdFQW98MbDaD06zewXIqXQDhG6I9y8eEoF4T3dc6xiehRx8M5F2KB5sGIPy2k6W/QMVurPmkqplJ/FGHsBkfLPZPD8EtySlH2KwLFciAb2E6Ai05o+25o/18eW1lrEfXWHkoy4u6RFQa6C1KWAbXhLxjw9F3Q3r6zqsHMg9P3d1D8jFaKVJxRuX0dOaT3T0bDq+UxZcwCbWutZjuwaLjAQo4ifIreeam2AFw+x80Mat985uER0ELxneZy6ANuKgl5jDOl+0jzzWlBRTRqH/t0zAxWgOkYyv0k6fGh+OSFc0K8G2awu2PYbnnMAAN/73vfwxBNP4Mc//jEef/xxfO9737P+fJL2sSsk79q1C01NTSgvL8dnP/tZ/Mu//AtWrFjBv1+3bh3efvtt/OQnP+GfZWRkWNbLH/3RH2HHjh146qmncOmll+JP//RP8cEHH6Czs5Mzq5csWYLXXnsNTzzxBADg3nvvxU033YQdO3b8Sv2UFZJnzTiL469raNFIBKtq2jmR9PbZQ3aFwFh6poTF5f186QcFpfRaLSvoNww+gd+zZ1rkJCwr6MeOwSKsnNNjLgz9vZVzenBD5gf47sHbUzzn8hKA72BlVYcpbz8aNjSpEvrgmxBpigJ2niY/640opcmZMmmgBdKbJOdMeEM3NOxT2NhAVWPrPbLPlIh3LmIuYcfHqqoORbXqORpzbz+L51C8W/Z9UflRwwwBc4HJ5FZS7umC4WqvchwxYwSQh9zQL4Lfb62TXAe5jucizDwU7K/EqKf7Ls2bxeqi1+S+uS+oHAkAdxR2KrpZ/dkHyvfi0a6FeKB8r9lbYs6crCT88RDPTbA/wf0hq3xbYwiMPbinZN95D2gqU6m8WGF9sYeppcxvmu8DqoCRhA0F54zGS58j5XI0mcHywtono2Fr3byRCNbVH7YiEP7ZDPg5qhBacPwADL2hHEvguXKdgz+bOf1N3JT3PhtpgCoItqO7lFlh4DvWWgLA9GveRcUlr6h9Icd0nnUOzpncj8F9wlWrAZMDoyN6q2rbrQq9550Tig6JehArCnoV69n5iCXEebb6qc8m7xtNB7qquh3bOipTo6Bp5pr6dcnVZxhax8pj0knZ/ysKerG9syK1L+Mq8rOjo8zkQoh3UltR0ItnB0u44jvLG71XHmrche92LuL1oirfAFIq+9K6pMt5uaf+AEP9uCKzdCoQdSqEx9pXay7HvXJOD7YfLcW9pYfxePMCrK87ZMGtAEWaEfdDporwRAjXX/8+Xn/nImut1tcdwsamRqxvOJjeCA2sbYoBH5BBrNye7+5Jcx+S3E/5bJr38e/SyHIvHlIVkoVM4s8Hio6lyK7A3Q0HeGT+T/E3HbenyL90z5M/k2NcU9TBNRqe7qvGgxV78J3m27lqupSnqpJ7OV659xsXXDVhrpD83W/gE1VIfvCrF9yYLqT2sSMHS5YswTe/+U2sXLnyvJ/JzMzEVVddxX+kYXDmzBls3LgR/+N//A8sWrQIZWVlePrpp9Hf3489e1SobWhoCP/2b/+GH/3oR6irq0NdXR2efPJJ/PSnP8WLL754vteetx1//Qr2jrq5cVbCAUXfRjzijTNOWBcvYCzt3X1zrLA7cZkDsBSR5zrLsKyy23ijpPfBtQ0DbySiijvBYB7Zk6sjEI92LeQERwBmHPr/RHG5vbPChOZzErizvsUk/dHrczWLjIxgBPGPAs/sxSIMEQCAtfVNcKZMYvo171oeCnjgd/OcCUFIlWrpd+k82uyhEnhu+jz9flubYdZZ13DYzNWIijRsa6sChWjJK04VUFdWd2DvsZkmL4L6yThNH8zK4DlcBdMPG476J3o0dp+8qyIhkmBkwbHDD8yxgGr5ZzOUIqU9NA/Oe57rHNBFnpltPJL8PDnXgXfR5UCGAWAS6OjS+u6h2+GNhhXNZMAwIDpUmhteH6EwPlz5PM87YO9p8jbJPRpUAh+Z/1MAip6UcnOof5ZXM2YUmg0N+9TvhHFLf5bMHmTFNmVOHPPedIaB3McEb6HPLa/qwqaWBq79EWzplAT2Ao8aCknAwK/48j1nElHZm+cKhTpwmQMa2iUw1m5OAsdevgo3Zn0AbyLE0QKuQCyhNlIJGQ3j2OmrMOGJAo9p5mvRrGGraCKPW3h14QNra0ydiXsaDvAY3JyEwtJTQmzIt2Qv7SG5V5Sx6TBkhljKsqOTyvkRMAykV5nGKSMOdMYsz6mOhmw/WpqiTKkvGY+pXcAN+OCtqfbc+o4qfjcSUTkQem22B/YMGyl6fdxoHMuru6xzzDVOAHwUV1SWzwxU2Htb7xX2PFNXw6YeRYqCD/v8ksfdjSpSDKvugJBVNFd8L42G4SSUwba6uh3La7qsu8qLRfB4060phgH15R8P1ts5PZlJvPr6pfwumvuNR+YBjuH890bDCEVMlCF4/6XAagJw0SBsKCUiFeinF4vgj2993vo8N9+xWcdErpzj2WQa1vOJxlnMNe/VQHE9qXsE+/ntA5/hf3O/4yEeK8lTeR44wjSi4Gxb+hUkdlNLA+4o7MR39i+BMxqy3knPljliF2z7LawIoVAI77zzTsrP33///QuLynT//v244oorMHPmTNxzzz1Wpzs7OxGPx7F48WL+2TXXXIPCwkI0N6tLpqWlBVOnTkVNjfHA1dbWYurUqfyZYJuYmMDZs2etPwC4mJNUiNfUHGFOfS+mqqh6o2Hs7xORAa3kMBtAwlUUajGjEDlx9Zm1xa3mc45g1tEhvXvqDuLPKnanei1CPvb35DM0ghToVdXtkPh0WeI8qHxS38mbTgJn85E6plil7wHgwmOATujTQmvlnB545yJYX3OID44bVYbU2tpmM09jYcXdrMfnxSLMaEBhacLTktDzz2YoTLyALAAqaiMZekgpptA2s9q4Nu3k12/djqea51rJfZvbanldvFFDAXjw+HR4IxE8O1iCummnsKqqI5WSlTzHEVNBtfWlm0xVUcHo9IWyA3CzEyrJUs8tY9hpHPrff1axm3GxGxp0grG4pDhRUff50a6FKqnNM/CyibEIVlZ3mEs7ZCgKg5ccw+V8dQmtKuw2jBQwium6OpETQsaUfv5jh24zMBjAgtrQeN+cvMhMXeAC4/B0yMfdJS3cRy8WYcP0bzpuB6DgGIvLBiyF9N7SwymQJwBsmM267m240TgnbN9V3Iad7SWWIUHtq1U7jQdbGP20liur1JllvL+ueF1y42vwz2YoKJi+2DkR109VJixoHVTipJuT4P3uOIDnu7xfVxT0moR9oSxQ4jrPvZZPW/or0VB0XNWUCCj7biSJJ1sa4WYm2SPrRuOczCor43ojGv6ln/HsYImRJYF5AoDdXUVcNHH6Ne9aa8n90LUtaC64Qq/eK1RtXO4X+fe80mHr94vLlUODGLbI0BmLZVheTTLeaU3dnIRi85JjcHz4jo/lFd3WPN8+e4g98N5YmNmFqC0sHTRnlfamVAh1Xhft0amhMbi5cbQOTLPH52tefUF5Sr/zRiIK706RhXOG2AEA9vbmm7M3rp0FSQdfrdqZArkEFPSRPk+sWdRv6fBZMOM4iCbVDEjt5ar8l4x3XTgh5Pn2RX2V57SRQ3fVqup2ZXC0zLPXRvfFz/DNPQmYOgf6+VW3vIy1dU1YUtbP60ctqY3Uu0taLOfBpiP1Kef+wbm7LbnH6wE7SiBhV4C+dwHA9TGSzEpxxHG9oFyzv2m+6qadYkdS0DAj51PjjBMc0aF6C240bu65QPPGwrhvrqKpvm+ermEjINHcEnYeExzbcUGGWtAxurKqg+FjzsWT1vzId6yaY5+PC6791jjA+QBAExMTyMjISPu7X9Y+NqzI+rLjpMCKnnnmGeTm5uLGG2/EqVOn8NWvfhWJRAKdnZ3IzMzEP/7jP+Luu+/GxMSE9azFixfj5ptvxt///d/jW9/6Fp566ikcO3bM+szMmTNx991345FHHknpy9e+9jV8/etfT/n5DU98FXNmfYSjx67Tm8JUDXVzErjlmvfw0huXpYQWG2ecUIplILTHGNmcBPKvfwtHh69XXyBGE+GFlaF1QCn/m9rqOSkRMBcbQxlEoTEOIwvoC3ugfAdOwoEzdRKLZg1jLBlRCYPC8pcGhQWBIUHkOVhd04ZtA2W4u6QFP+mtUzAIonkkiICGjfBzdaiS+0xh+JEIK7cIe4BIgpOXFSdVBSAx6QS49e/RMOoKT6BFJyLPKxlWfNznW7s0MBVqLPwoRA6jJPMcJQxeU4Z2ZbVjbyyMmjknVXIkbMGdYgymabTeKX+nCXVb6xr2OAxs9Uf0n+lWqVKu/L6EX2jMek2hGkdO7gRGRzLNeMTzaQ2DkKfz/ZuNGgfcnzVVrXwpNUw/iUw3iT19BVadkeD4VxV2s/JrzYkcH0GQzhP6Twtb0sanm5lUyntmko3bnNAkNrU0WM+mc0LfJSM07fv08/ykCz/hYG1VC7o/uh79x65nyI+bk2BIhlznNTVHDORH59HQei2YcTwtOUJwrgENc+wsY5iOm6sMkltufhsvnbjKms+1xa0qT4bkjdhDzoSLW6uOYm9PgQ0ZigkI4XiYnSpB736Kt1+sAY2HDDaCR57v+/zzNGeF5pw/Q3tiLAxn3DVK0GjYmieWtZSknnTS7/GEqyp0SzicmKcghINgQSSrU+ZazFUKzI5gPgIu4sUiWFd3WDGQuQHIjz7H1lnVzyT4avA9PD+Bd3kTIawq70yBgLF8GQ3DmXThXKTG9NWqneiJ3aAcUmngSW5U5Rzs6ixWd0TchmLRPSTbolnDyHQT2DlUiKsvP4M3352atp4BjZ0U73TnKbj/llV2Y0d/Mdywl/J73o96jyChc+KoUKijipVRgjTlqsgK5WuqW7Glo8bMQzq4W0zDkLLOL0Oo1U07pfIpcszzaL+mu0Npj10SjuHRroXwRiK4p+EAnjw830CfaJ1GIlzgkpxFJG8XlA5h75Gb8NoX//KCg+AwrOg7nxBW9ND/92FFP/jBDwAAf/Inf4JvfOMbyM3N5d8lk0kcPHgQp0+fRnf3xzfwfu2RgzvuuANLly5FYWEhli1bhl27duHYsWPYuXPnL/ye7/twHONWlf8+32dke+SRR3DmzBn+8+qrr/Lvhl69iiEggBawtSoUfEP0Q/YOUGIWAOzvybdf4PhwKGFYP2dW3tsconXGXetgKo8KAN9hKr+n+6qxqqID6SBJW49o+jZXe3cApv9E0kHFza9Yl7KTcJiqdM+Ls3GobxZDOlgp81S/gxAUNzvBcAZ6709661ROgU5cW1bQbyAnIvlWXoAfaUjUyspOJSCFAHQzkxa1WzqPCntsR8Oc7OjFIlhQqhii/LMZXDFyyexBLK/sRkv/DLhRVX266cQ0eONhLKnoY7iQdy6CyzJG4J/NwIb6/WDaykDo2Y3GQfSyBIGQrWTWKyo65BmKN66GqY1ANTAHrYPT4I2F8UD5Xva6EQe/rHAdjC4wBe25CJZUKdaY5TVdAAxXeEqfoeAuSLjmkgDY08fj8I3SQtSN9P319QdtyFBWAovLBtDaq4zhRCLEfYYoeEZJ+aRMULujsNP2yJ3JAFztKaP+UHKb51g84U0npqk8kGT6cy1rNpzX2NLVtTkq45r5llBAgkhY1U41U4s3GsbKMjX327oqMJbMwIfxHGOoaEVxY3MjP3tVZYeqViq48bnfo2FLSXUzk3i6rxr9R28wCrS+oAm2JD3YZDzVFJ2wok0AsLffyKZLLxkxZ0vUCPEmVYGnfz1cafD7jlYgshJ46aUr+TxLWmUrN4EiWJ4D56JJxcRDEAm5XpS8TeQGmuN9TVEH71H5Hhn9XDRrmA0DQEUz7i1VVLsExVpYOmiN3xtVRRARMpA/QO//zKS95gRDy07AD/tYX9LMXnxLWcs2kDb5c0nxuaKgF5ddfo7lFkUviHGL3yn2Ke11Mgy8CbUuRGFt3RlCYbeaoxweJIeeaplrRRksb/J58jGeay+3YHcUdWMqbnE+qX0wqXIrnITDVXkpkZ0Mg4yT6g74+v4VyjCIRUwkovAk0+d6Y2Hs6ioyUa08nWStx/RPTRqOJzzoe16cjZ1DhfDGw3hTU9mmhYEF2rbOSuss8f4QNMI7hwrhnokYuJ94LxneFP1ztDeeKKXdaBw7u4pxV3Ebfz/IdvTMgErAp6Kj8MFJ5YBaYyfp2LJgzGYv5L9HImjqm4nFZQN2TaHAOrvRuGK60m1TXw2+e+B2jjw92dqoa5oYOUB9j7ynjSsty8gRc/D4dAOzu1Dbb3BCMiUc+76fkoj8+OOPY3R0FI8//vgnevZ/OJXp1VdfjRtvvBHHjyuF96qrrsLk5CQ+/PBD63PvvPMOrrzySv7M22+/nfKsd999lz8TbJmZmZgyZYr1hxqFL91oHA837sTDjTtZwO4/PoMPNiWepngaRhRtGSnj9LtnB0vw8LyfwYtF8J/mtlhVXjeUHQTxarcMTIcbjeMLZQew/Wgp7m9UuRX0LkBAEjTzCaAuIzc7gfvmvaDYigTe8Y9vfd6ODuQk8Pm5hwwrRo7heVeGkZmrpfmCC1cLCob0aCgDQypgLk9vJIKFxUM8B7u7FXxqe1sle6yspiMkj8z/qcF1i2J00oP41JEG3Fe3F4A2zBzFOLRruAC3zx7Czs4SleitlfIneubCGw3jkbk7lTcq6YCo3ra1VcEP+Xi86VbA1/MYoBKkqtPU1hR14MuVu1hx7H/lGsX4QZ4dMc9unsJPEzuNm6UUzEe7FvJaMouVLBoVyB14drAElIeyq11VkL0soliJNrfaDF/y8qK1caNxLk7k5tp8/25OQlVKhV1AzotFrCQ/GtfuriJQJdmJmFZ0tIcPUAYF0S0CsLz8jIuOKViQM3USzqRrIm8aP05wB+mFJC99umgBKabWPMQiFmsVPAduVgIPzxfOh6TDShJ5371zBkboRuOWgQ7NXMR0rJlJ7GwvYUgLK9EEA8hJYG1dE7YfLVVVe3Xffe2FJEWdjD/Zd/aWEpuUPjdeLKI4zgWMAQBa+6bzOjOOWOzH9z/ITYU6eQ6PyblkApSQLD/HRqXnQFLUkqK5oqDXGNCiP7yGwmMvWZLIQN02UMbYZjLAJOsNvY8SqWlfeDFNNZydYBrSYJRktS7+tbS8D4tLB8zc6v1P8E8ZOSXo5pPN81OgGUEla31Jsw1V0Qrcs4Ml+ODDKPeXaCm9WMQURkxTbdryShe+aMlJ6Tzg/ohIpTeqlLb9PflA0mGlV1K/cj5FVgJrao6kGMRq4YD75+1RxAyzhgEP+IdDc8HUno6pTO/FIlhZ1sXecz/s4++652tDTz2OIgaT08ZYwZfNjcbReeoGjo7cX/8C1tU2Gfw7GbNaQXWmTqp5z04oyA1Ri2vDZcqUMVNsTcP8rLkVa+pmJbC11a6VINeR5//SCezuKTR7lOZVy4s1RR1AwoXv+pqKWDzLd/B0XzV2dxSps5alojMpDjANywO0w0yw8jGDlIY4s2NNRo9ixulxVeZZNk7c7ATuKm5TcDLBprWne46hbqVIvh8wBoI00B5wfdXrnKOojD+H18rN+eUR8P+T7Te5zsGpU6dw6tQpzJ8/H729vfz/U6dO4cUXX8Tzzz9vwfM/TvsPNw7ef/99vPrqq7j66qsBABUVFYhEIvj5z3/On3nzzTcxMDCA+nqV/FJXV4czZ86gra2NP9Pa2oozZ87wZ37VRlSm1P7m4FL8987FLAwB2xtBlxTRDbpRpQjuGi6wL2B63qHfYyVEMqk83t1oQr45Kino77rnY1lBP5c/5wtRJDUlxcWxva0S60uaOblUXdbqd98/tFiz+ahd7p3TNRQSutrzuKay48qnRqBa1Yb1mCSGmJQIbySC+xr2mmeEfb6oLc7xNBzcPP+xiEqiEu+nn0sPopuTwGOHbzPzohXeu4rb8PxwvuGAFoLzvoa9+Pb+z6j3RXwsqehTSWF6zaQRR31m75Hk7D+ncN3fPLiMFSJAU5iSEhUyeQeNM5Q314941jzQZcaRGqmsC+X97pIWuLlxqyIuKY0bmxoZ+27Nqcwx0AWP2Os1Glb1OPR7yZMvee3l2qQkoVMlYT/Q58wke1moYm1GlgmdW8/QFKRED0mGNlfsDfmGTUV4MLd1VCrFWkcb7i5pYSUlaEhQ/59rL+e1oN/TObQgECJxW45fzrf6j89nBlDruLKmA5Q4Lj3L9P9NTQ38b8oVoufTO7a3V6YkePLfmvpwVZXCj9cVHTdRLkljqvtmYezHTR6OlxAQl7AHOD4nJnujYSZZIC8oR380fIow/vI8utE4J9QGI2rMVAZj0KlfIGWueQyB+U/5fpqbeuHMY8whH2wEP9k1XKCMC1HMjQrgwfFRVnCKZevGpkZDGCAYgjj3TBQFe2tyipkPUXm3YfpJPVaf10F6ylWUwR4rYObFi0Wwv382FpUdNWdA92VNdauZsxHzbpYDIV8ldBP0xgGWVxjv+KrqdnjjYTwzUMHRCt5H2nP8w8OL4OZqqt1MD37EM06bnIQV6T2b0AXE9H5gQyQaN3fOWBhTp46CiByo8BZDX8Qa/133fGzqq1GRt3Fj5AYbRVVYCc5Rjq2zZ7M5+iUZlGi+JBNR8J4xn0lTU0fkd/F6hFS0fHObYon6YuNuTW9qOzHuLT2s1ttT73yus8zsjYBxKqGetH63zx7iO1wmzNMYONKpDfRxL4Ivzttt5Upu6a8EJfDXTTul1sbV+0WjDvg+pwijLOin1/2lU1dyjqIbjcO5eJLRBEEd6oJrHzfXgP58itq+fftw8cUX8/+TySR6enpSnPAfp31s42BkZAQ9PT3o6ekBoCyXnp4evPLKKxgZGcFDDz2ElpYWnD59Gvv378eyZctw2WWX4Q/+4A8AAFOnTsX69evxp3/6p3jhhRfQ3d2Nu+66C0VFRVi0aBEAID8/H7/7u7+Le+65B0eOHMGRI0dwzz334DOf+QxmzZr18QaYE4ebmTRhUYCxx4StA5Ci5O1sLxGfNcLfSRhoEnlF+dCRvNLsPbIY1U966+B/kMmh1ztrW+Cdi3AYkJQ2WTXTzY3jjYmLLAYCEh4bGvbBzYtjZYX2EmmuZISVUFhYNKQ8EzkJeJMhZqGh9mDFHkvhYo/OOTux87GDt2FBiQ6dJh2jCAncp5rnRIriSeFUVvQCigYlzHJI3hG5Drovm44oY5ATXcUaSUYe+Cpx+z837lXzkAh4iQD2MkuFw43Gsbiyn4UxJZOuLW5Vnk/CEFPI1fEV/3uGUpxXVXbwZUEJbdLLy5eTuIA2ts6DF4uYS1xzjfMeSjq2d0dyWItx7O4uVJ5338Gu7iK+eLa0GcacDfX7LeWEPHZynVbXmUrNdxW3cUIynwmxjyfHFTzkK407uD/+2QxWoO5r2GutE3nuV1V0WLA96h81Z6ryvP6kt87aJ/6ZDDghowyw8u+APasALIYfO9lSRz7mHkyl6JRc5p5WVnRyZRIuf9eNxrl6Oe/lPGO8UF4CfMdKsKefUW2R22cPYeWcHrXHNM6YMN2tL91k1jwibi5fXe6tL91keYFXV6nkT0mRCm0obBso4zXb35PPSdhrqlvR1DeT99P2o6VKuQ6yBQHWPAb3cFBhZ2OOWIlEk4qlGRPstYT5/5qaI/BGw9h7bKYFP7PeJ6MPnGdh4HWA2g+9L19noICk0OrvOnEdXdIVgymBGwByQxOmj2NmfzSdmGYZYwuLhngvSllH7+Fq3knHzFFWAnu652Bd/WFrXrmIWNCYz04oj3pWAvfV7VW/12N5rtNg9KkyMsvOkQhXjJdK+KJZw4pBKkMV5lxS1cf7g2qbAKr2yso5PZYDLcXQzk5gbCKDz97OocKUyFAwEuYkjAz2JkLMtLOmqEMx4+gjYO0Xkj9ir1uGadiO7q6pbrUMTt63RBLgaJl7NoPPu4R98nz5Srb9sOtWVii90bCCdQKYGhpVPw/7bMTIRHU3GufxEUmGLKq6q6uIv5ti0Ah5R3f71tZq/LDrVri5cWUkiPPof5SBlpM3Y2WFdjgldERIQ5vVwxwDi4UaywPle1WEMTOp9k7C3DtfKDugk5sv7MjBbxvwwAMPYOPGjQCUYdDY2Ijy8nJcf/312L9//yd65sc2Djo6OlBWVoayMiWYHnzwQZSVleEv/uIvEAqF0N/fj+XLl2PmzJn4/Oc/j5kzZ6KlpQV5eXn8jO9973tYsWIFVq9ejYaGBuTk5GDHjh0W5dLmzZtRVFSExYsXY/HixSguLsY//MM/fOwBkleIvPXUqBrr1s5K9gxSU94R/W+NR19V3c64VUBjwh0fa4tbVXGemiMs8NkrqY2KVYXd8GIR/D+/Y2o0eLrC5J6eAlasVmn2FA6nQhd10l70NdWtKil32ik83nQrvFjEePjIK5mVwJ3VR1RRprOa4SMjiccO32YwraNhfOfgEuv/BDWhpNiVc3oAB3A8x8q/eLqvGrdc8571XR4TGUvSWyh181iEC8LA8bG1rYoLVNFcA8ooWpo/gHvmHmCFiYQ3eetovKtq2vXDlQfoiZ65JoKjL1LDLGEXk6GCXXuaFWvLn1Xsxs7OEnjjyntF4fmgZ3VXd5GCqrmKDpHWWVGpOopphQQyV/MUCp+OghDbFRyNSfbM/rMMmPPwzrs5CWxrF9ANWg8NI1lX24QneuZiVWUHIpmGklAqMG40rvD8OQmm7gQAbyKkPJNhD3dWa3YvDc9zfAffPPD7vBa/V93D8/pYk4JWkZLif6RgKO9N5jLumr1kMnwu95GOvqwo6IUzdRK+SOCHLm5FiujKys5UpVVDuL7YuBsLSpUCt/Fwo6UA0F5VCoOJJLjZKkH4ufZyVkpWFXYrZp9o3HYyCBaYlXN6VORERPnIQ3rFZWeBiIddXUXY1l5pKr36jolWEKRjLMxQLm5CmSYlY2urYQgir7flddT5DjLaSHz5lKArlSaJVZbyx5fGtetbXnr+scgd4j6RcUw1C0QLekml0bOlvYaN13Se93Q/JyYf9UMlB4jtiJ4f3GvORZOWIcH9HgtjS1uNVUtDynQ37PF8MzY9V7EruTkJYwD7UBWjtTJK+SXEWPVUy1wrkuTFDKXl4grlrKDo78beenijYZbhFMWaV6Rove8o7LQjEZ6QGbLIYzSOhBdCy8mbFVzobIaq8eOA5dXi0gE2mrcfLVVj+iiD5xaARTU9SVGAqL2mcu7lvP+XBQox4J2LWHt0S38lvPEwR0PIyL639LAxGDJtOlMeW9KxoE2WUamhvTQfTtxVDEC5cS6KyrIgaqL46u5QTin/bAYbkssqejAt610AwH/vXKzn20HZTa+yYcDOxNEw3EjScoxRTaUvztvNa+mNhrG2VheP1HJEGudkrEiDaDSZybk93kQIzkWTikK3QxmZC8qGlEPQs9eGDSS9NgSFdRxfRxvAsCSGTKaJCF5IjYJJH+vP/+lO/5rbP/3TP6GkRDm0d+zYgdOnT2N4eBgPPPAA/vzP//wTPfPfxVZ0ITdZBA2hqPK2CqEWZGABYDGfAEA0bxzn3spjXLxsUkGlAjTsQRZFlHgn+uYSIiFElzSzILimT/L5HAqULEfE1EEJc55j9Z084ZLZxBsN48rrP8S77+eZ6Im4aFMUTF/VB3h2sCRlbuQ8ALYyJPMP+PlifPCBryz4V3yrYwlWFPTibCJb1SAQY7qz+oi6MEbDWF7ZzUwbcu5XFXZja3sV98s/mwGfEqjl/BGLB2CYGFqqmbWBnyuZTeIu4CulnQoSAZr9pa1cRWg05/f5WCYsVh/h4T7v/NHeSONRtX4vGEimhscUTSw9y/Exr/hFHO7KV7CBwJ6QRovFfZ+GacXyxPsAsbvcUdipFCjBkgHAOlOyeJn1DLnn9O+oSNsvajS2otmvon/4ej5TFNZPgTII9g9rTL+omJtm2LLOnT5bSyr6lKdPr2HjjBPY3z/b7HU9Pwh7nPNwT/0BPNk0H0520kRj0vSRWFEk8xGNV8IRiHGGPrOioBfbe8uBcZdrhEhWI2t8woNM83FXcZuiggzKiVgkhV1H7pFf1uR+oPEtr+zmXBkqFgeopMml+QMW1JGfI8b57KAy2tOdOeucCWOE1z/Q76X5A1xrIOWdY2EsKhnEnq45QMhH/ozX8eJrV36ssac7u/x/kkWeA0Q8PNLwM3z7wGdMhCU7gZIbX8PFGWNmPdIUNZPPZrkpWYK0h5iMGmJNAtJEfnSxST6nae6RNUUdeHN8Kva1FsK5ZIKrTRPFcHDfUltZ2anWblTJVGfqJBbMOI69ffnnLbBo3Z+AYfSZDMHNUNENZ9I1Sd6xCBaVqQKX8txYc6TPsjPpwg/76dc+4GRYMOM49vbmW+NaVdVhCiLKqKU8wyMRrK5V7EtePKSYmQJ5BHxfkuyS5zN4N6dZTwmlmnHtO3jx2LXWmMpuehWdR29J2YvwoO4u3/Sb52pcGVxWMbRzEXiJMbx2/4XLVnTjf/1ruFkfk61ofBwvf+nPL7gxfdKWlZWFEydO4LrrrsO9996LnJwcPProozh16hRKSkqY2v/jtP/wnIP/080bVR4KEkT3zX0BcH2sqW5VfNY6vOiNhZUnhqz+8TBi59SGS1GCYIfd2fOtD9O2gTJmHOLPp0kyklSIQW7ioAIFD3bToUY3OwEkHQt/qjqlfr+3N99iK3rnvSmmP9Kb7TmMvyXB5ObGOUmTcYsyXM590Z6IrIRKzKroYc5rAHigfC/gAOvrDrEn81sdqojPs4MleKGj0BhAui+bW2u5L4QxB4A5N7zJ87y1pdpgwcd0wSnXXif1PD1eX3n1FexC/ez22SrBeuWcHmyYu0+NQ0cz6PL5l5Yq9uo9114ONzeOJaX9GpJg3keRGgrTc7L3WJi9lxz6FnzfFuPQuQizCwX3ghs1bBTeeBjPdZRh3IukFEU71DubEyTdaJyTfh+e9zMTOZBLr/cRQej8pBILC0qHeI9uqN9vYEuttay0yb0hlTFAQZS+OG83e7uld06Oa1rWOylwBADWGaLnH33laoO39k3iLZ0TYolKgSHRc0iJPGfnI1he3HPGc/7Fhp/DjcYxlhSRBuikeXkmCSOdmcTCkiHA8VX9gdy4VTdiQanab6xMTYRS4HmA4iCXHnUAyigFQAnM29sr4UaSag9qBctygAAKuhGz4QJr65pS3hdM+vVDJsrmjYXVngjsGf63Xk/K0+L51CxQcFVNC57j3Dg29dXgqVaVt7GjqzT1zMIoL1xQLOkYL3xMrJ9gE6J9hpCPhTOPWfBOeseO7lJIqByg9hp9f++xmewRfvG1K601CImk80WzhjlJNsiZDyAFUkP5HsxFn5nE33TcbuZFr911OR9ZhlqQFtSLGRY0QFWdXlXYbQwI7QEmgwEw7FfLq7qsqJE3EsHC8kFzFyRspbSh6Di8EVU3Z29PgUpwB/B48wI83rKA+/CVRhMVd3MSyhPuAFdknGPPPOUh7e0pMIZBPAQ310RxAWBrexUerNjDssoZV/JoSfEAr4Uf9tnDPa9kWDGeASn5CKtr2sxcaWcPoPY0RUIfrNiDu4rbrKjqvaWHVT/1+VhV3Q43J5FSKT2F+Qxqf1NuiBtJcj4JJQ6vqm7nHAiWXVk6EVsnYBN0ieWXbxL6pTG1ZPYgjr9+BQB1p9Ad3nn0FvX90bA6B2ywA5yErpsXi2DODW8qOZ6ZtPYGXMDN/OVG8W/b/9l25ZVXYnBwEMlkEv/2b//GEP3R0dFPXATtUx85uO4HX4cbzWQlIthUtr6T4k28ffYQFx9L54kEbAWePG3pePzZszMSgXaXz/gAAQAASURBVOM5+FxDs/KKBxQXq18BzytxN1seGh8p/TYYasfEzkTNAVKkV1e1p1aMHYlgWVU3dnSUYXF5vyqCFPQ8hD3cctM7eOmlK1VkobIT2zsrwHhpipQIzLyTcJQXG3b0JDiXPOaJEJaUDHAIlmkqfVhew6X5A9jRXsZzcFdxmyrIFPTCuBBMFalzBhjv3Pk8zsE5pnW9s74Fb09MQTQ8oYoCBWFVQMpY0/0OAHPoBz1pci+oHwDI8LC8rMe8U+8tZ8okVs7p4WI3bjRueWYXzDjOXPVWJEt4q/yki98pGLTyX0jJyrtyhI1mGVGx5kfm26Q5W9b6a0+vk3DhTJnEqsJuJDzXJJGLuh8N00+i6cQ0LJo1bPVNRu4AmLFJSkgRlaPnkrc02C/L26jn8N7Sw4ohS3Dr0+9kNBBh3zYaAvUhKNpxZ22LkgHEr899BEcizgcns2hbMzxg0mXvanB+5dg4uuEDc/JfxdCrV1lrkDbSQrUaPsiEn+kpxTPhsGe0YfpJHOqbpc5XMBKn53hVdbvFl58uQkafpUiNtS503tJEfeQ6LM0fUIo/eeapEe4AYLkYXF9vNIz1dYfU+dMKGkdmg/2JRbC2rgmbWhqwuLwfe16cbfZHIFJmwS91tABx95fWCQFsmSQjsXQXyPMXjJS6Wcrbv1kb8sGxykgOoCOixM6lP+8kHVV5Wexh/8MM3FYzgL3HZmJtcasiPZAe8IDnXvaN+lBXdBxXZ53Bqdil6H35OntvyuijfhbJhvN5171YRN1XnUUKaijqHHgjETw8f6ciIQnMk5904U+4KR502e4qbjO1TkR0gNqygn6GIP6iyJJ0BnD9HxqzB+vspD2/erzr6g6rORf1KcpmnUbvy9dZa5/23fo7rEsIOZJyz+v+KXjuOF659xsXnJedIwff/oSRg0c+PZGDr33ta3j00Udx9dVXY3R0FMeOHUNmZiZ+/OMf48knn0RLSyo1+i9rn/rIAV0SizRXNmGGZUiQBLX/YQZ7hUgxpUPjJFwDmwmYU4vL+y089arqdvaWsHKqf+dMmUylqRwxeFPCPDqeY4XIWdmTynXYN15qasR1rhOt3Ggcy0p7Ace3PNJkGDA2WAu+HZ2lgAemKU3BGyZcnH7zUlZitrdXGo7wqOEJ5/9H41yVmXMKAt5jmuP75r2gPGCZSWMYjIaZZeSehgMMs/DOqQQ4OEppXFPUwcnLKSF4ghlE48aLSFSSeoxMzZmTgP9BJjOGBD12XP0WyjO3+Ugd9h6bianhMfYiyTG5UQV/IvpY6bm/f+4eK4GQitCtrW+y3ruyusP0XzNpwHewY7AIq3US9Nr6JlauZQEgANjRZoxA4qpfWSnyKag+gp6brNwJXn8aN+GSyTDwxsIqqqT3Ls2nfzYDC8qGDJ2oUIIoqkIXrWQt8kM+7ipWCidHqwAuzuPFIkoJheI/l9h/maxOXjbpKXNz43hw7m4Luy8NeID2v6BzHDHr6I1EOMcnHRsN/b2m9oiV2JpiGOg9A8dXkTEdsbmztgVLKvqM15uMBJiq6dYZd+zLnPYTRYhkojUbSMRGpSOCQ69exdSL/F7A2ncAkD/tDfV17TGms7W1rQpeLIKmE9NUf/LiqYax7ue21io98cabLg0qLxbhBG9ZGZ5lT4CZRkYoaPx3FHayAayiouC1X1NzxERmHft7fMZdWBS/VgQyUKEdIR9P91XDmXCZbpWolXnOKSFUrh0ZfgBHq8/nOFhR0JtiGNw39wUrSrm9s4LPlNV81YfNLSK5n1h09POe6yozkeCxsKaJhiWj/UBFewBwLp7ERZFReGOaVQhQBRn12q+rO2xFEaSiS3+3HJ2OfzlSxQotRZOt6GMswgaHryPKy8u7DVWsOFtuNI7dPYUsv8gQDUWSWFXTjr85uNSaG/5n3DX3qYabWvJ+NIyn+wKFF0mGjJhIsvqBw7koXizCORNe3HhtlxX0KxRD1Ox1+MCy6m64OYoMQ8ojSzbpPSANg7XFrXCzEsYwoCjTSMTUGIGJwFGj+eE6RgFDhO9u7ZD6LVvRhd++9rWv4Uc/+hHuvfdeNDU1ITNTFTENhUL40pe+9Ime+ak3DijTfk/3HHixCH54eFEqzk43qp5JLaTZEdaXNCuYBiUG5doX/o1ZH1jf2360VAnXs4rJwZl0VPEdIdhkAuKa2iOqCNb7mSwkSTBTEmBQGfBiEY6GpPM4EE4UUNSlbk5CKf6ArSRBRUkI8kHhy4UlQwYaIDwJZl7NpePFtHKow7ReLGLNKwALakJzKOFJdxR24rGmhSrRcszQgcr3U2IecbN7o2HcWduCorzXsbm91vL8+B9lWOssvePqB0K5I48PoXuyk3Bz40rA06Wq12DXcIGdbKnbpiP1nOROF8T9c/cAUIoUcebzvDtQSfKO+b83ovjSn+6rtmAM29sqLfo/ObfE5/10X7WCWrVX2WwZo2Eg7CMSCA1v76gwl4eISmwoO4jxkUxLsSHlU0IxGL7ig+s+uFFl/B48Pp2p9Czoh7hYySiXMJhNTQ3WXEvub547vWeIYICMDqtpdhpZU+PRroXcf1JG1hR1MAkAJ2VyBEysFcBsX/S8lVXq8mVoFYBM176ErTObGzdeupwE1lS3YqV+9+a2WlyT+RHP9/31L3B0YtORenORQ+25DQ37+PluJMnrwUYhsZQATMELwMASYgoaKKE+BK8DVLIkoLDfQyevSVE+gzVDrBoMUQElpAJbpFxnesrJQkZ5IPpCfec19QMyTdMB7+wqZtm7uqYNXizC1JvOpGtqNPgqGvfMQIUxCgTVpLVOxOAyLigk6ffZgf/re+Cz81otA9LNUfUv3FzDZkVjcnMSzKoDaINWyFMqYAkow2B7Z4UlX1bXtOGxQ7dheVUXtrVV8fcfbtwJNydhw1CyTd0TLxZh6JpUvhcW6fX2zfgaCo9b8B46LwSZIrm27UiVdW7dsMd321Mtc/HNA79vPYOIHQi2CM+Bn+HxfcF7gFn/fMv5RXTIz3WV8c+CRpWbnVB7S/fjztoWJOMh1ddo3Ny9jviOcCJ5sQg7zJbmD/Ddbb1D3LVwfSOTo3Er18iNKmICLxbh87mutonzhXhOfRVt29FRBu9chMlHOIeQzjBFEnXUiPYKG2diDmmMm1trEYQ9sgwR82dFeMQ5l2fzQi8K8Jtc50C2VatW4U/+5E9w3XXX8c8+//nPY/ny5Z/oeZ9644Cx2CyoAx/wwUKJihZxWFi3J1saFdVkQJjRs4ZjpjAbM2uMhlnB9zM9PNnSaIXwJJ54S2utgjBleGbX6vClo0PQFTe/wl4efgZ5fgKMEXS5bulXWF1ZGO3SS0YM1En3g7z08hl7+wykii8/oWwRR7+bo5mc8uKGTcLxU3CypORvamngny8uOsq/3tJWwwqpM6G2JdGqknCXnlt69+YjdYohhi53rTRQkR7Cu1OTVaS5azHDzkRKiX82A48dus3AkUSdA5kkyl7QnASePDzfrK3j44eHF/G8B99pNR+oKjzJSpT/YQbvlWTCtYpMAeDcjuAzVUTBjEm+Oz6RyjhhVWEOqQvpscO3KYx82GPDQk0cUs6OG41jeU0XF5QjjLeaQwf317+g5pRyDmRVV5p7YcgQ9G5ppVa4BTsOz6tvG2Vyz1vKWLZ9sXvnTB4CtXcnc7GtrUoVVfPNs0i55P2fG1csTGL82zsrAMe3YFU5oQmsrW1mRTFI3crnNaZobBlLn3Asr/UPW24zkQs9X1UFL6lnZCesJFAAJq+AX+SjpuAkf57zXbSiDgD5t7xh5gWGRaWh+Bh+2KWiJFvbq+CEfUsh4qaVIM5f8B0j+0YieLRrIb/XzUmoXAvPwZ/e+jP23DkJx7CKUVRBUA2TQkuRAuOBNxGpCU/tn52dah2cKZO8Jm5u3LCwZamk62CiJzk2DOxIvecvqxRVL1F4Wh5lPc5YMpN/9mDj8wCUk2DlnJ6UfBn1RbX/llV1W0UYvVFVbJH6wHvKMXOwtbNSRXaFQQffwd8c+j14IxGlOCMQDaM9pOWyVPgk0xL9vOXkzXiyeb4VdaM5bZh+UuwD49nnMebFcU/9AT7LS/MH1L2j7zdvTFHpUh0BgtSU3PgavHOqVgjRcsu7cWl5n2KwArCgaNiKclFz3snEjGvfgTNlEkur1B1Odx/l47CmIyIoXsKFN6apSX2jNO/oKoUvapwA4MraPI++oGTVMDQnLuBQAJ+XZQX92NRXo2qV+A4qbn5FFVbT+4tlnzB04AB1BSdMlEyvxfajpYxSuLukJcXQ5iaSl2+fPaQINmitznMfcS4P1YTQ0d1lZT24oNtvIwf/Ie1TbxzAdyzKtBQFRbMYudE4tndU8GXkxF0kCaevk6modDw1N08xfhwamGU9j94rKcPgO3CIljNmEtikl4rgIjLkToKg89QN1vP9Mxks/FeWd9qhdqoXQMJNeL/ffXsqh/G59Dt1XWK0hSeLoD2UqOn7jqk8G4tYMBYFhTL4fgC4v3yfWA+jJO15cbbxVAnviB9R+E03z/BPu3lx3D93T0qdAppPhD2ljDm28rimupUv96XlfXCmTFpCddGsYbjROHObk0Lmh5Tn+cH56tKXCbUAOHlaemGcpPF6ujkJrK83nPdSaeewMtT7llV1q/XVFJzOxZO28TJivKj0DBk9eKB8r5WQDF9R9dLnKVn9wYo91sVPbFBBpcxPusbQi7spiZWUAN0w/aRKktVGFScF6ijC33XPt6AUdPGYcHUguqQNAlL0gDReak0u4I2GcVdxm12cib4jFCReI+3JJTiSG1XFoNxoHM91lMGZdHmf54YnLEWa1lN6HRcUD9s5G7EIHjt4m4Ih5MaxYMZxzrOhtnDmMcADF/fifSOiY25OApKKkua+ffCWlDkjaNiq8k6zTwDAc9D+0o3wxsJKSZPzrLnOh05eo96XF7eSaZv6Z/D8WBSqlC8RaE/3VZuogUjIpEZ1ZPb35ANxB9/Zv4Q/w2xIOtJBuGgvFrGSpi3q13MRjrh4Y2GuG0OKqjeuCr/Js7I0fwBeLGKxtgHAvJJhdmxYXloAf7lvpRkkySztHHKzVTI+sVcBwHcP3a5+p5NW93YXmCmnc1jVBW88jB1dpYZ1Rkc3V9e0qfETPa4PlMx8RfTBKJ5fKDvAzp2vNO5QRoTIN2EnVK5JSE5RHmXzbJniRtX+peJaADAt5z3eXyt14T43L45o3jjP50gyk2XMjvYyWExeAG7Kek+dlywNPc3QMsYFIk5S1RYJ2/SiOztLeF9RxXU5HjcaRzI3ieOvXwFvJMKyg+aB73wtE1ZXt3NOhBv24GYnlMEl9AJSzt1onBX+jb31fF5X17Qp5XnEyHq4gB8xkUkvFuEK9ZdFRuDFVC0TNxrnu5zutnTRTzcaR8vAdC6+ygatSH7f2NxonUlvPMznlCjJAWX4+yGjE6TbD15CGA+aGtjNSWDD3H3Y0VN6/r1zIbTfGgf/Ie1Tbxw4CQd1006x111esCRgvVHFXLC8shsbyg5iZ2cJPlvXhpzcCXgxgXENYF+XFfQrCEjcKD+ysWdIeyckNtqZMsnCRZa75+fHUpPvZLTAmTrJFYSfHSzBz1pL1fc0dIYVAlH0xM2NM+8yoIv9iH5LuJSMEnDCljZeHMdPFS6xCL5atRPwHFaK6Xk/7LrVCnnKtrG33oxNUl+OisJyrmKmYCjJaJgFH0MsMpNK2dDjpf49M1DBxl/YVZdR3A9hQ8M+NlC8WARPNc81SqvAAhMPNO8dHcFwM5LwRsNYVtFjRSuWVXbzd8lrSfkcXixieevJM7Ojo0y9U1RRtuApuXGjHBDLhQiJP9q10DIe1lS34snm+fx7SjT8zsElqRG0pMFGkxHH7Dq6oJ5JgHeQmR1n4XpoYBbjuvniCawvJXOygSGhPdLDTzzlmq53aUVvCoadL0g9n/9waC6CjT/vO5wYLZ0BvIdEFWJS/Ol9m1oa1D6PK88iVRxeXdMGR1cgP3h8uqVUk7JEbf/xGSkezr3HZsLNi6cUvHKmKmPQFANMjfwEoS2AgoZNv+ZdVTMlqjyya4o6lLI3rqI1VjX0nASWVvRa7/BGIsxMxFEx8W5fexxpDwLGI31XcZvFbgaAiykCikmFYG8UZZJzFDQ6ZBXap/uqTRSKmlZ6yLO+tqZZ4bj12AEASQf7e02yOjyVRyWN8aXlfXCjcVwUGcM2nTth1RgReWjeWBhLy/sAAHX5KhpTc8tphjEFmbd4XdNAOIiO2c1OsKOF1ndrexX3n/rQe+wGM9/6vN8/d4/in9ee5m8e+P200XBqbpZ61+LyfjaqaO75M6IuD8FE9/bl81j9sxl4qmUujynseiwnY+eyOJLwzEAFvtupo6Viv1DkUzIz+WcyMOeGN9F94kbAB84kslWCdKaSq3TmUvoq1pHr0IjIlvy93Fckv/JC4+yAS/d8fg/VRbho0uQP6M9tba1W7wz5xoiVtVh0e6pFySeqLm+9z/WBsM/3RdDBQeOSEFaSr5SD4UbjJjIbVQX25B0SzI+iu3VtcSsWl/dbkbNZN76FxhknzD3iqIjH44dvhZXcfwG238KK/mPap984mDKJlpM3Ax5MNWEdAnfz4vA/yMTySpUE+1x7ORcx2n60FPF4CGtqjuBnbaXGA0+byoFKQgVgQV2CXm0NqZDVLAFwVVk3GldhV/IeiERi/2yGwUqK5kaVV+COwk5m56kvf5EpM4mP2Y3GFabacyxsNHmZGdbg214MOCpxmwypFQWpitrqmjZLgLvROL7RrhK/3pnMSylcpD7kc7jSqmA5Zt7tRuOM+4XrY1WlvtBIgdGRiQfnPw8vpoueaWWIlBua16ByRpfzppYGPN50KwjGEzRMLG8MzQlFFfLiFuXqjo4ycwmNRDivwxsN21ERva7EUuXFIlhX2ySKFYl3joeRomTrtry6C/AdFTnSc/xnFbsN5GY0zMqnG41bylpQuZbeNS8WwWgyU+27pKu8pL7ykNN33WgcE2MRzklB3L4Q+ZKzvJgmesb7UzSi95Pnam1xq8KVxyJmrnW//aRKIvxiw89VQcLAxSUvQZnTIA1KbzQMR7C/pCiHdP60Z3Frq/KO/1NzDZypk1ioyQ3IG0drLxsnEsciaj5FAjY33QfyrpOSno71pGH6SZ5H+f0Tb1xuYCiegy39lVhTd4S9wdZcx5RnVcIKuEKwHvP6kmZrTZ0M40xgIy83zrInGNnZ2NzI55Q8/nS+dwwWWfKM3+8I5VrkBKjfmfUnTzjJk6TvWgw79EySfdJYZdk3avqlCA3U2O+sOWLki25LZg8if8brqjDiaBgtR6cDAFqGp6k6CBredF6nkFb+yYgi4oM7CjutKLHafxqW5SgsO8vPiRAQ8nmP/LDpNvM+T0UjEPIV7IUM7AB8zs1TUTKqN0DOj2AF7LU1zRakZ339QbS+dJPKt9Prs6yyG1vbqkDF3Zy3MyHzkb5QdgCPzP+p2usB+m3pIf9MbReOvnK1wuQ76r6FB4biySJ/9O5Vhd0WxIsrzOuoh8yzS+eJh+srb7t45vkiKivn9KjnToYUvFSMgdYWANeYkdF5AAyx8uIhlTdRpCrES+cFko6i8U1DhWuce/QDh99BuWxsZMgovzaoyYC/ffaQNWf3lh7GU0cabCZCBzj22pXY3zebz9ya2iNKZ3GAC75Csu98sj+/bb+wfeqNA8q0d/PiillHeE69kQicSyZwQ+YHab8bn1CKFlvqWvEkZZEFg+vbyncswrh5KWxlvgBFEUzirfr56irFdLRyTg9818fKmlRvj/SKAwCSjjaAdMVV31EesZGIqqCba499dVU794MZTqi2gVaO/657PpZXdWF5ZTe2t1daMAtAsR2lUKFqZW5HexmWlvXxzy3vp4ZYuTkJk8PhOYAn+Nd9Y1Rta1XJb5RAvLhUsf58b//vGuU+L45lZT1wEqkHvnHGCROFoLUh75wsApYmSYsTwTW9HkOcRGI6eUSDiqybk7CiIvAcq+KyWnLBbqRzNLxzEeWFkn0TfX+uTdEPOlMnsbSsD3cVtzEbh0x6pdyKn/QaxhLLG629/7KSKvXXCXkKvpOZVIW+9Fwws1JM1c9YWK6U5LXFrZaXl5RxGidVq2VFnQxGUZiOExwdlWjHEC7KJYlFcF/DXo5qfP/w71jzIy/5pRW9rIyT4u/mJEyV2xxTlZvmnyAB9Cz/owwL4nd/wwssB/Yfn2EZPveWHlYVV8UFT7k1ck+tqT1i+qwVNP9MhjlHBGOSCZfaqDnUP4ujlVJpIjYUUgzvLmnhOhQcwRPwJx5zRtJAeGqa+V1PtijlnqA5ju7TtVd8xN5XALgh832GaBGEjeAm8GDOv2+iHkqZ0Uq+qM3BBoAvopRRBQELGpPEBuXFVE4V7+1zut7HeNhEA3zA1VSVFHVzc1RtEzZS9P4ijDpHsnSC6NDxa9lDy2fS9bG2vkkZIoXdbDRZzTfzPu5FVDJytoINkTMIAB5q3GX2CMtH9e+agpMK3uc5hjdfw84AdaftGCyCm61kDed5ufZeXjjzmFFMRbTOSiqmaI2e+1VVHUzr+mDFHnbo7GgvA3wHS8r64WYl4F85ofqtn/l33fNxevwyEIMV7zcByVwye5AjWl48ZMafp6Fm+i5aVHYUa+sMc9u2gTIrgsZJ3MKA3NxSl5Lrw5Hz7IQVXeDIojCUqL85IW0Q6Zw/mXDP0aVsO8pGkS8vFsEl4RH180gSbk4CW/orOULoRuO6erGj/ri+ooyFMFZIefUclRMlfka5bHA0fWrYZ0IBjvjrfnA+oY4KPdEzl9diWUG/ekZuHP6ky2u4urrdirhf6BWSf9sUK9E777yT8vP333//t3UOgk1WSHZzshQfcWeZEf6ab9gdd4FLFbzG/zADfoaIOfm2Z9S6YM/DEcyfFfzswe+mfFY+V+Mit7ZWs1IKD3B8x4IlkRBpKFZFsSJuUrHEQBgcCZOUdG/pYTx++FZj3KSr+qwhSey9zwoozxJjHajDkPYZ2kPtxF2Fv3agBLXgMm+YfhKHemazwkAVigFwMiD9e+WcHmzrrmBIj3qhg6qik2g/eovNvDARYq9yMDnVeBV91BSeVPhszee+trgVT7U2AAkHy2u6ONrATEe+oqx8ZqAC3rkI5pUNo+nENFZGVE0H3xojAFPdluaPCuCJfUKGi1xnAKi65WW0DkyzLiA3yyRFn29PwfWtPQAo3PvlGedUkp+A/Fhc5kkX1139AV5/5yJMv+ZdnHjjcu53Vs4kRt+OMqY1yPkvG9Uj4L1Da+I5iv5QJkoKnnBvJIJF5UcBAHu65qTA3Xg9qJaH8FwyP72AbNRNO4Xm7ln4nao+9pjxOpBxcDYDftgznOrn9Pl2wGfFGw3z72V/eG84PhaUDGN/72zrrPhJF07IMxCUdOcm0B+eM/pbVIW1iidmeIotJlAbhOYof6au8HsuYpLLCdpF8z8eNt5HwSq1rKKHlTj/www4F0/C/yjDSvYPykhr/0Hh+ptOTOPPS0M1RW7AjozIsxLKjcNPOryHCPe/vaPCMuY52dVzrGrRUm7BUY4Iriw+mhrR4j5oObho1jB29xYaBVwY1ta+FHURpIykc33V5Wfw1rtTzWeTJilbwkjSzQOgPPN/1z3f4vLn+dPVwtWCqXsDIrrGip4DNphT9lugUnq6Ghg09uB3AeWMoXso2HdvJIL75r2Axw7dZuYr4Sref30vsMyn9Qty9p8ThlbgvuJ1D8gS2hM8hkBdBupfzS2n0frSTWY+fQeIeJwbISs389zo+h4AUmQx1xMI7D3rnAdkuLzXrLyjnIRVSV7K3Dk3vInrcj7Crs5iwPWxsHiIK1DLOVpR0Gs5CX/Rvpe/T7ybxGt/8hcXXE0A0vFu/tq3PlGdg1Nf+/IFN6ZP2lzXxVtvvYUrrrjC+vkbb7yBadOmYWxs7OM/89fVuQu1eaMR3HDVBwqXm20KIy0sHAY8wJtiBAYlgrJ3W3jaKm5+hS/NB8r38sGlzzD+VjdSmigczLR+gPHABr1NAOoKTygvkVZIHK3cBRVGem/LyZvRcvJmGwNNSdYiofelscusPqfFM5KX9lzEgoZIjzB7eXQxFRJ6XC064doeeM9hj6tViEwL9EN9s4yyQv3LTnBkR3qltrVVsaAmFhQ4Ollb72SKuriZSfbAyTmT8AU3J4HWgWlKQOvKkE8daQB8YGlVr6Km1GPncTkmnO3mxXGo3ySju7naA+34uKfWxqly4iStu45ySMUdQMo6A8AlGaOcrOlmJ1Ty7EQohUmFYEy0v5aU9QNh32JO2Xtspuk/XVLCq0fPevUlZRAcO3WV5ZUfH83gfYUMT89NgKVD92HPi7MZs82KELFzZCZtz7Gnqld7MaVQ7emegz19BXbejaMjGHSp+bpyKeGbfXFRU3LraBgtJ2+G7/qcgExedtlfXxvi5MGDC/YOm+ifw8XWFsw4zv1hbHVOIsUw4DVPuAzFsCID1AeRiyGfSW1RyaAyDEaFYQAow2Bc7d9d7cX885IbX4Obk8DQ0HUmX0jLBSRcqxKqqjysPKjLK7r5bBH9rjcaZppn56JJI+dkDlZM5HTFIlil628c0vh/LxbBqqoOC6PP8kd7tOcVv8jvI0+1ivw48LXsWFPRxnJ4e1cFzyftG8odccgIBazkfYJNbDpSz9GD8xoGOkrojYZV0b2EUbJJOSZ4qPmSY8lIwqv7GYr9iw2Dc4ZyVCppNYUn2Vss6+rQ3FLOzLa2KhtySkshcohozSUTl5udgKOjKTQWWssFM44bb7io7ZBi0DmG0Ut9wHSjIPcNqwYJnYENZQfh5iqKT1Z6J0IqKZgMAF1RmyMbaQwDN89EfBeXDZhfalm/puaIraRTxIciv6NhniMvHuI95sVUwrD/UYaBKNUcgTMe4r5eGTkLL6Yq2D9QvtckLuv7CoB1p27rqLTmWOZmGaeM7v4ZQ1AiyUzk/D8zUMHnZ0PDPn5u//D1yjDQjHP7j88QVNNKJv5ZxW5sb6/k6PrS/IG0+/6Owk7ed8sruxU88gJH4Pwm5xz84Ac/wA9+8AM4joMf/ehH/P8f/OAH+N73vocvfOELmD179i9/UJr2qTcO3Jw4XnnrEuwcKrRoRvcfn6GE56TLWFBq5B3hf0MpoCTUvnvo9hTP16bmBsuTRL/ncHB2gsuoS75zAJzkCPz/2fvzMLmu6lwYf8+pqp6qJdnGNp4nTa1Wz/OgmUamowg5+hQZIaMrR9eOY+OYzyHhckNCBhJCQmyICVFwdHEUCwVFV0EIRVjIGnueJ3W3NVjGNmDAg4aunqrqnO+Pvdfaa58qGeyQ308x7OfRI6m76pw9rr2Gd70LaH/xDoVJpd/NmmaKVeqPNx7WPMn2pSwTlYihgxKnDr2Qp9hSZCNlnS6QGYZSsjFvWHm8Ekbxobmjv3d3VJrkO50ETMoT/ZEKuRuNY3VFLzbXNPPFK/F/UlmT82QpndJYkbkLgqJ0d3cFjz0YEmU+d/3OLbUnbAMiR9HH3pX9U+xqMwVwuAKvENqNecO210cU9RpLZioO8stEi6j6Ks2z9C7dV6S42zP0nmRvMEXRZ04bBi7hGaU6EJQAmOEq4ywzZPpYO/tcIHdGJXvfk99vMMswSpRVtEf2P0cpmV5MJcdLhRAQIX/PURhvzX7BuS1k3JBi5Ph4pk1VIiW8PpKOvX6Or7yGel0bKwe4OCAALCsZwZbaE2a/OGbfUmG5dQW9Zt9LPPuMuGUIkGd5df4g192gi31NZQ8O9ygWkQfqjSJEsBVvPGwKIUbjCgqlmV/caNyCqEmvI8OvBBkA7dND/Tb7TX3haVXYDUKJctXnvYkwek/frt5PUSnR/xSPvaiWvberFHfnjTCUwZsOqT68nmkMklwDp2EDSBrijs+1Xqi/gC7k6AqKSM/Atpykg+Yzs5WB65hxr6/ugBuN81n45lA5Y8uV/BCySTg1LNhYkOHMd/BQ3VE7siscNQRJI0OysWwQ/sUMle9Dv6d+03sowTgat5R2ymGTxfGUHAF/TxJItA+pKAtHLMno0fNK73ejccVyJSCTALC2uovXpTFvmA0ZepY3HjYsUWK8btTQm5pfwDJE5XmxChV6JhF9a+8SNmCoudE4vtr0AetnXkwxj3nxEH6//CAIVns5Q+36ay9a/QWAg92FjL+fM/s16/eS5IOIR2StC3UOSRk3Crxz1TRHZXd2VsOZOa0U96SjEsGjcezrKGV2Kh5LjlDGAb5TgVTHIeXquTkJLs76PxafsOYrHSZeRbvVv7koI8B7ialVYxFDVR5VML+/6V4J+FB5WoBFVEDP9sYV5I3yYnJcVXPnys85eJd/3gPtySefxJNPPgnf97F161b+/5NPPomtW7difHwcW7dufVfPfs8bB954xKpPIC8eI9wCFKXROF8EsoJgsGaC9EitqephRX1tVRcnLZL30hsPY0zzYrvRuCW8fB32pES0bw6VW5cVJw5T/3IS+J2lSihuqm3Ggrk/sPsVM4mx6hfqQo64AkYAGPhFNK4SfqnarXi3H7YZIFYUj+Caq2NMV0mJw1ZY3TEJjjw/5SqZbP9IAbM4eJMqj2FNZc9lcY1WtCKqWWz0/5cVj1oeMOkJtRQg8ewD3UVWUbltrYuNN1r/zM/wVM4F7QGYC7ShWF243nQI+3uL7D3hmDXY2VmNLxxbZUePYF+AbnYC+zuL4UbjKM0/p36Wq7C/cHxMBwrJwVFRAG8ybO09YkBanT8INyeBJXPPwM2NI8dVkTAJNWk9e6eJuoj2reHi1NoUWtEMGm10kdBYKQmQYA4N80dNtEgrFOtrNOOPNiBWFIxaz2Nv3kRYVboWFzh9xs1R9R1WLRgCfKTU5zh+eg5ej+da+wVQZ1AxEGncsoxs6Wf7FzLU3Oqxbm+rY6pMGb0CNNe8jgw+3bZEVTcWfXVzEirXR3jHoVl/aD+vLu/j79B8PtNerz6jk7Qla5ZsbjSO1rN34ommlWqZyOtInyWvua7NoQw5I+O8mEq0ZCXEN8aSm6PqnjCzDiUkXztl5OZ4WMErhBOEalOkKHg+OMeHiztBJ9KK80LG97b+OqwqHeDnkHOFzoJ/MQP+xQzDYy90KGlw07tTIjRQUMytrcusOV1d3mfRAUuc+YGeQvhhD5luAiFdDZjgO/VzzhojTEcxyOAjqmjCzZNRTMY5KXtsMBIExjGGTn3RKWaecnNMrQYApvJ8VPPlZyWsu4JoPevnnE2pm2HtJzoHoiglPAcOGS0U7coxxp90JMC363TQfMvE6vXVHSmsfHfc+AYA4MfxWSkQXMA4W7yJMN44n2vlDAFAQ9lJNkTOvHgDvEuqfogTd1XdHICdXtb9TvKXotD67uJibZNhE2WDUai5hfxUGTNuUyrTXHvxELa312FjraHP3j1UynvrUJ8qqvnPTYv5OfcWdAOuz/uH/payQJKBWAnJ+gwuLh1VdWdiisJ77cI+VVW5ttk2hCk6RHtD78udrTXY2V5jF8W7Utu7iRq8R4yDc+fO4dy5c1i6dCn6+/v5/+fOncMLL7yA5557DtXV1T/7QWnae944IKvXi0VMMpPjs4IBgBUY/2IGsxTs1dy+Upi62QmL/UUeyr3dpSq5LFsJ6MN9+cpzk6uYMGSYfu3CPoZ2AGD6MTcrYQwVYcBsqG4zIXCtBG/tXQK4Pp4dqMILr76fDYDa2ef4smFPYUYSm2qbWZmyipqQEkcYWs03fmA038B/YkZIH+7Lx5tvRYGEy3AVDgOTghyMMlyKsJLFY46q8a4t7+ZckBRFSL9fJnoeGM1n5elonyrUVn3XS5Yitbm2yQg9HfqV82cpFNq4obkGACQcrCvoxbdbNb5TGJeHT81TF3dG0sAWLkWsCrPyvTJ6RFV2qZEH0bsUQf/3b7HZbAKeo3vy+wHfUfzpScfAvsbD2DlYgU8v/Q5Dl4gPnC/IQCNl1Hjt1c82FbUbT7eeK1LUALX+uTMmbQMnGseONkXVR5dYSCe5eTHFyASYSs672qvgJB1ckxHj+XdEwR6CAPDz0ygz+0cKAN8xyfTQ4fAxtc/uLei2lQGm9jM3gjQI3WgcfsjH4b78FAWbFXXhLZbNzU7gQG+hwXIDDEsgg8ZPuqoGxqTxLO/rKmXvOaBqFrjZCQW/EImpKdE0iJ/pPfI7yw7xz6marFLEIXIJYCsvGUkTIdWc7RZj1kTYor60xsxKIriP+7uLmaM+2Bgq55uoCBXzIqXZv5jBfZNc9VakUFcQ5ygegHUVXSnrQmxElLciZYMXiygDOYAP39dVqgwogp5Ar4nw+O8eKkUyoZm89Fyd6M+zHRNi7x7UdRCuDsdYK+GIX64qGmatsWsidQj7KL3jFUU0IZLyaY8Gma+8CdthwJHIqRCaz8y2ogcA2BC2oLCkLEfjWFk2aB5OhjvNeWWXFWmn/gT7RJEdANjVVoXrMxQsh2TlSz96H4DLV/rlvIbsBJIJFw3zR1l2ASofieV8VoL3OBlNAKzI3qbqFhsKR8n8+hmkDLtZCVPHRhiM3niYo5oPlog7RkQDAV13Rjva1EMc7GirteQPoIwxht7qs+PmJFRegecwzTb9LefGIgPRBfQUy506y81nZuOQ3iv7e4vYccMkKrIfYxH4vsN5G240joeXPA83quo7WDWUrsT2Sxw5oHbkyBFcffXVv9BnvueNgzX5A0bg6ubmKP5vK3wf8uHMnMbujkoFj0gYgUze39X5g0yFJhVnEmBPdDcohUWHSOmSomJTfLCppHtM4eO98TAbJQAJG/Is+djZWsNl4zl0LULThBUljyI9Q4a3pVBgxQRQRa60MfJHlft5zPcXtyrhPBUyHlBBp0ZhaIY4iUbPXlPRy14166IX4ek9nRVGySYlYNIorQAMDpxfoBS3FSXDgOOrJDJtANAznISo9eCB52pTUbsVnvfGw6oKdU7CGBUhXymxGi7jZiZN2BjKE7Vk7hnA9bGhUkFBqCASedFpj3gxXbRoTBl0lqKT1EmDEZ/rHNCekPv1vqIO7OkpZ4iAG9UVs0WS+Be6dJg7DfMLvfOR0mPwJsKqmJyMHmjGjmc6lOeaqDcBbYzRugmB6o0rilsJV6FL7LnRBawI08Uvx+OHPew5WaL2/GRYKXuinoDEO1MLKkRuNI7dnRV4eNHz8C9mqAtVf++bQ+WWMkhJlStLh8x5DRRD5LVAwKMLqNC/gAnQZxpKTzJGur7A5HUQTh0hnw0YPztpWK5mBDyMUcPd/8rkNQAE/C0Ajm0sG7Qij148xPvKjcbN2IniVdOjEgMY95+iaTLfR5xDNzuhoCPxUMpaWOtCMtSHSixN83vyYEo2L8DuD8MRBQ5bzhFFK4PV4CV8aUtxi4HZTYSV8igw7N5EWDErjRsmK8uInGHvCVoT6juzjWUmsbmuiT/HEKtsE1FYMe8U9+upnuXsNPGmQvyOp1uXmAgseZpjClYIH+h96VY9OYJyWSrOk4aeFwm7Yi9DVzKTTHvMsmc8jCPdC7GiZNgUtxTz4I2HcbCnUFdtBkMMN9a0wn8rA3tOlqD0jlfshabP0VzSneE7uDtvBPXFp7jezXOjC3gct9+iCqN5lyJM13tfkYKS3Z03YoxdAAcHFnJ0i+aAnlM7+xwaywdSIt/b+ut4Pre31jMjnjLMTV+pPd20lH/HidHi+nkrngM3R1cpd3ze2yoypj7zRNNKJVNnGN2C9wfAOUXrSnUkxfVT0Aw0fxtrWllpv+w5jDvsTEhxfAFA0sEnyg7zu2mfeRNhPLb4oMn10wYWAOuu2t9TnP69V0r7lXGAZDKJbdu24aMf/SgaGhqwYsUK68+7ae95tqJbvvRnCF8bsrxL1iHLTALTLsMxJNOC4wJJKpQmDq1/MUMlMIZVeBguLE/KqgVDyhOlBdmKeacQDU/h223lqYxDAu8rBSr9/PfLD+JvulfCmwhjWdGoYSTSyVnVd72E1v65piKiaws7qUBS/72JMNaU92JvVykrkMEkSlLGVpYPpjDOuJlJbCjswo7WWjDzxYxUBiMWwKJPFsONEGZb6o4j7odSPBvKCNJbNMC8Iz9DChPTu8rfT4fUGucaI8fiZ9dMHwyrkdz8aRgdSFnYXNWM7QPVZj84ZjxyDVbMO4VDffl2NEevA42JWFEeKT2GFyeuU4blTzOB66asdQTA8IMgG0VwPlYtGOJcm6CnNDgPaxf2qYRv+RzBvEKfOfhyHsYuZdnr5+m/6ZIjli3feP8ke5PF0KTHnc4rL8cS/D39/6HS48x+ctlnTISxrqKLvWesPIhaB8TCI+eKjAq5btToPf7FDAOhgoKQbG+tx+baJjzTXg83O8FsRdRqZ59Dc/+8lCKHxCYi37GluAVPtyy14Aty3d53zRh++upVhmDAc7CxthVnYtfhrpzXsbO9BvVFp9B69k5bxoR9pjOVe1aueWPeMPYPFgLTLq695byKGMKcSSvfhhQp0fe1C/uwu6vCZr+R+1j+W8gcZnTR8oTk8t15IzjQU2idR9r/TtKx604EWWDIOEnaZ9C/kAFn1jSKb38VvaN3pJ4T8qYGc4skXEXv/2DNB5Ugap7l5iRsphy512IRrCgZxuGBBWgoHlYRSn027ivqwPaOupQEXX4PNc3uw7JaFAFszBtmQz94jpglaCrEuUGAgrNkh1SUh+8lwFLQ5doF1zF4JuUZk3Il3blNd+6XzT2NG7MucK0Yli+S3S7gYQc0U5+uayP3Ysq7ArI0ZQ8JVqp0/eb8O89JK7MAxTzX+eLtvD8klNONxpnVLvi+dLKH1vly8iT4Hdqr1v3r+thU08L1ktwsQ9ryQP0x/NPxZfAjF/Hyg39+xTH7kI531x/+JULvkK0oOTmJF//ivcNW9PGPfxzPPPMMVq1ahRtvvBGOYyMPnnzyyXf8zPd85AAAe78pIdeNxrGxplVZ88QvPB5mOAYdKt8DK4pURt27pBN/XZjwf9Jh74sXi3BFThKgh/rzsW+40KqKTIdzS+0JCxNP1TgJg/o33QpX7GYncLRPYKy1ctX+4h3sHSSP5OPlBmZAOG7LUvYc7O0sM5e5UJJkc3PjONhdaBkPW6qa4F2KKCgJCaOQbwQjvSIWwZa64/jsim/ZRpmjhLWaVHC4/ev9tcwNL0P71F8ywnhtJJe1rwT5zjYD1aL3eeNhrCw8KeBTjiVwKSGQvbrRuEmejUUsw8ryXGclOHfCmTmN9bUdrNRIRdK7FMEtWW+xsuzFIpxER9CjDYVd/LskHI44sWEwpuAnXiyiqhiTYZCG1Ua2fV0K29pQbMLC7DknXHVMwQH2nCxhD6WfdE3/xL7Zc7IEY5eyzDxp49idEWfPM4XYKcrhTYRVFVDxLMKG+xczrETydNAE8hw2lJ7kKBu/H2D2E0tBkXsHADwHmW7C5H7oAkTyOazgS+92QsEpVlf08md5ffU7pGHgRuOKmCAaV3tDr6nvg4v0AUDzwLyUS9+LRRD3Q9hUqxiSHl10CBsKuxR/vX4fef2kEvDTH88ykA/fARxgR0st2odmI+4ruXdt5hhXv2aFgTyRRP0p5ormZH9nMVdUPz/8PngTYcNS45u9RHtldUWvpQzv7qjEJxd911pf9lrGInig9ji8WMQqXGVFc7VTgBwiz40uMEYQNb3Xfr26x5Zf+rxQYrUbNcX51hX08rMpObf/+7eYr1Jkgao3azlJnm1EPNtJoZ0Csl9uNK4Mf2Kf0c8lmJJ/MSMFZnm4X9FPHupVcBnq77MDVSl3DMFbZXSF55DILDyj6B8YzWeP/KoFhuXH0QxiXiyiGd4MfOjQC3nYq6u3W+/ShkewD2o9wKQIMrcLAGP3nWlXyZqpENc5uCe/36qjQntyTWUP9+dwX76pRxEVlYA9h2UcR7FoLWO6UKYPbKxqgzceZvkLGJkezHmTfZBnRjZyFN5b0M13pJLpqf7WJXPP4L6iDnS+eDsAlSdmWJrMHO7tLON+yQJ7G6rbeGyrFgyx7PdiEbT0zGdoKAAbQusZw8CNxtkw4PnzHWxvq4Pz40xsrmo2Y8tVkKKUfIsrsP0ysxVR+9d//Vfs2rUL3/zmN/GlL33JSkx+N4YB8EtgHLg5cT5Eh0/NSwnNUdEwVgIdnSBL4Vo9Q6QEbKzTOQeykmc0rmgdhceZfufFIjbXdm7c8C7nxvH1/lrULjzDwpIVQ9cHIp6iUAU4lyBFcQawuOgFy9vxRHeDSUCLxlG78Iy6KEhQU8KwDimT8PBiEcYt8qWaK/CUro9tzUtUbQj9LjKCmIkFSpFzo2psf965ylLo3Nw4xr0Mo2ilgbeQksPf0Z/bUnec30G0jgQzaCgetpII/QsZ/L1DL+QBroZ4+BpeQwwWLhAaM8fAGw+jvsQYkeqHMLAfwUgiL75dHZXmMhEXjTtDQWtYGId8TqK7O29EKXMdNbxHctxpsw70DFHc7Kme5SaSopPFH6g7BryRyf2Hq2BKK8sG4cRdHD41zzwraABG43h4scqFKF3wEuD4SpmdsCv6kiEV/r4yDh4tO6IuHuG5g+uz0eZNhRR0aso1zBgRoTmSTRPyBaWp8b5642F4UyHsbKvBHTe+gcOn5im2Gwc2tlq821ziqQbTzsEK7GqrUhR++pKE4zMTkUNc8BlJBdnSxvbm2iZVxVScOza6YxFORmbFifag9tiSA2JNWS9qC0/znNOzNtU2c07LRDID21vrsWzuafx971JWhPyLGSlGAY3djSic+LKSETxQd0y9I6I85K9P58IbV0nVBOlwo3EgIrDtnqOMPMeGct2T32+iO+NhJK5R/yaWGmKYIm8noHJBVpQNW/CwJ7ob1F6YDGNluSngtrGmFU+3LoEbjWNHe401h15MQ8Eudzs5JnmUZO7+kQJ4k2FU3vV9nlc3GjfK1niYZcKu9iou0uZNGhz5pxb9hzIoyvuMnHbM3jrRn4e5N/8EzmSIYTpuNI4NNW2WM4gNplyVh8PzLMZjRZApn0XIQcrfuLegW8kJMsKjccD1OQFYwlVlAvbahX1WboY3qRL9V+cPYl9PCc+TH9SSRJGwFfNOGTafnIQt81wf21oXmzHQmmnCjxXzTrEcYvlP9K8ZnoEG6f27p6PCyARNcOFNhHWVXh+5MybZ8JDsfcTIdPjUvJQ6InJ/uLmqOr2TcPFUi2BOIocOGWphz3yX1oueMUPA+YRz4JtD5YDvwH9L0JHCjpYfHczD9q5aXusdHcaRtbh0lBnqWEYEGKp2dlaz/kCQaDp/fpZhlFPRIrme+m+5zJRjNxUCPJVb479/Cs+012NTUbvFnuhG41d+zsGvGjIyMjBnzpyf/cF30N7zsCIqgva2oUtddOlQf74JCaaBYjBMQ4Q84fpYV9GF3T3lhjtcQIUsiErgd25U0TamhdL4wKqKfuzvLrY97zAeC6bRE+FQWbxMwqQopDhv/g9x6syNHP5cU2kKfQEmlEoh6bUV3djTpRKkGspOsqIZhAfAhQnZy0JAFL6/mAGfhH7Ay5su6XRTUTu2D1TDGw/jobqjeGXyGjacrJBywMNCLW2YWM6TXEMZnpYwkqmQYpkhOIK+7N+azkHn0Gwz1qRjoBDyuVCeuY8tasL2trq040w3Fyn9TlNkaWHeKzg5ojDJbwepCb6DfybedTmIAM817dkg5AZIgW5sqm1OGasM27s5CXyi7DCeaFmpsLKymFqa9WosG1Q5DAG4QBCSwhAOUfxKwq+4LxMqx+T3lv8HvnjiQ9YziL6Uz0CgIFS6Ob2/uBVxP4Sk7xoGHd2osBq/YyKMLTUnLGaX2tnnOE/Imw7ZicLaibChqt2Cy5EcAqA8r5GkvTaXgwkKSBIVgVxd0csyzSoeFXxGYC69mKJ0fXM6xxQ50314tOwInupZbhc19MSZErVQJKxpQ3UbM7XJObOU0xlGNktIESnCKXsXMDC3QGFC/2IGfmvxMWxrXWzLawf4zOJ9+NzRD7ORZ51TKhqnc46+OVSe8m4JB0vpV5ozt7ayC3u6yy8LjaEcFkpoDn6X8iOCv4Prg+F+SZWndXhwgQU/sr4joKkpdxmUJz8ET90JAQiOhJUBKu9L0qYG10u+kwzR1fmD2KuJO9LBYS8LpZS/A1LkZxA6lwI5A1LWKnh++LM+oBhyFJRtbWUXFxejvj1UfwRbW5alh6QKGBcXIhT/tj6r966cZzc7oeZJG76l+eesyBd/X0D05PNS9nNg7j61+D/wheOrjNNwcgKvPvbZKw6CQzre7E+/O1jR2c+/d2BFf/u3f4sXX3wRX/nKV1IgRe+2vecjB95k2NDHQYTe6G8tdG/JeiuFApOfob3q+0cK2FNBzc1OKFxtpmF6kJRvbk6CEyHZKw5zYLe31VnvWjb3NHuZOYoA20PkjYct4Sq99jJedqw73xJ8WxYdx5kfXmcl++4bLuSkSQ6jA+wB+ul0rtolro9oeIrp7iQ8QDEuqHesK+i1Qqvs6Z85bYWauXCa7gdDjaAqVT7Tsojfs7VlGaa8QMjeV94xhFSUgzwnG2taDXxFzwt5muQ8OUnDsc4F6jxH0aPSXMZdcJEbDd062FeAqzPGLX56Lm5zySiMAABfeZaeHahiTzP/Eew35DVKZzxQXwFwFMqNxjHyyg3McgWAoS+bitotVg25BtTkGQgyS7lRwykPKGWA+5UI9ENfsJLubntrfcozuR8ajvDEibvZE6p+AU5qt85WToITveH4FtuTNVc+GApGNR4AZTS52QmGUTTMH4WbncBv1rXjiyc+hDV6zrzJMB6qP2IZ6dZZ0J8hSKDs57amJXh2oMoyDIgJyZmpWFO8hIFpScPAGw/jxqwLJkKTEHuS9p3v4NrImMWGxYaBMIiDtTruzhthZ8HGmlaOdsjow4bqNpOTEo1z4qKlNGZ4JuIZcCPtGy5E85nZWDHvlNrzGmr05RMr+Wzfk9/P8sCLRThi4Y1F8GBJEzbUtKkkUcdnJRuuiAgJaAxDIshg09ELP2LYtXhuYxFLDtA+kYa7H/Hw9f5aNpI5UT87gevCF62IXWP5gGEtC3vsbSZGMKmASTgY/c6iMybZKc7Ht4aLTXGvskE0lg+YsRBMRN8xDMPSSf+y5oCEaD2++Dm1jzKTcKaVQXa4L185j97I5Hm5J7+fo5Ub61otWUZOgZVlg4DjIwRP9TUnwTKH1gmAdf9Q4Tc5bjcaZwIC8wswPGdvdyksel0xt0EYFiBkDOWu1Z4A5z3peWCoGrFyeQqyu2TuGTau0hkBD9Uf4fvOglZp2eWHPcDXieuOukO21J6AG41ja9NyNXfTIb4PGGaVsJU3LxZR9ZZidsFRaRh4sQj3fV1BL/a2lwGOj/XVHeg9dbtVgNC7pOvIiKJrhpzDseBbFuRY3zNfOL7K/Myz5/1KbL+ssKK1a9fyn+bmZuzYsQOzZ8/G6tWrrd+tXbv2XT3/PR85uOXv/hRuNBOIuylJqCQcG/OGsb+niJOSr80YA6Aw1pRARK1h/ign6K6v6sSu9iojTHXxsP/oKLGS4+h9q8v7zMWuPfPrqzqZliyY8ORfyEjB/ElvDOH8L3d4f7/8IL5w4tdSvb7kFUwTHQGEx8mzvTwcBfAdOAFYFXv19Lg44VEmx5F3PZiQdrl+iIQ6/lnCBYhBSXhCAJ34273Q7gdgkrYGUuE1MqLBY9e/9y5FsLGuFTs6atBYOogD3UWoLTyN9hfvYE9PSgQlXbIzbC8YAHx++b/hDzvuSbtuadcDAeWHvJd6/LwvpWcqMD/yWXB9ONMu17HwxsNoKBnGod6FcLKScEI6vB724GYmsa6gF6+MX63GHphbVm71+pbf+TI6R+6yqmzTfMoKxunGKMcp97f0ZG+saWX6VC8WweLiUZwYmq8iB8HIUDDC4PhYXd6HiJPEns4KrCgZVqxbMjlcRFUay9S6w/U5QsT7I+iBFB5QQMmKg30FcDI8ns/LOR+CnlE5P8vmnsbhgQWW157WEGGfoRlWhOkysoGVBDqfgT7TZ6R8CEYV7i9uxdnx63C0b4E1ntI7XkH3ybtS5p2TMEVU8+0a5y/paJ1FqkBRvqD3fTyMdZVdmPLC8HyXk28BBbkK9pXn0fU5MgHopPCucrXWlFuWZRJ6AaRGlqRsE8+sLzytEsHTeKrps+urOxTvvR7bitLh1GJkNCekMNMciORmwI5CybZs7mlmlgt63GUEun7OWTSfmZ3iMf+jyv34885VuDtvBNdlXMILY++37kQrMsUPNu9hQoY0v7vm6hhef/Uq6/tWxGo8zAmzNA+rygawv6fIcrTRHqPxyIgXRVbprHM3NFHAqgVDqi6Qb0eVrAR3nawdJB/htQn7WFPSh33DhdhU1M75aG40bpLBAwno9Hspuy8X/bXeF4uwHkDfoWcAsMYs7+aHSo8rFiKK8viwIwv6TJHe8fji5/ClnhXwxiev6ITkOf/rLxHKfIeRg6lJnPmr/96Rg/vvv//n/uzXv/71d/z8975x8OSfIXxdgK1IKqyApcxRo8+sK+g1BkAaWIr8/KeX7McXuu7GluIW/NOJZXBmqurGlw35kpCXDD6C6YXD4EK4He5fkKKA8jPHIurSJ6VMK2PyEiXFTVEPahxsAKIDKA/UrPA4V/iUbcGtr2HklRt4HBuq21Rlxah9kdP8kFCiPm2qbzbCXirjl4G4eDFF+/kbNZ3Y01/GypC1lgFl8O0UsXVVnVxZlyILe3rKmTliXVUn9pwsseaDmJBWLRjClBfGW9M56D53m2LLaV5hKaBWPy5FgIiPxpJBq2gXABtuMR4Iy2ujKCd3CuNjmdbFCRjIWNAAkIodGTRpw8oBWJE119I4COyN0CtZiL8vYSuPYxE8vPh5fLXpA+Zdl4EnyP2wrqAXuzor8XDdYXz1+AcsaFF90SlckxHDvp6Syxq/6wp6Lb5vyRIG38Fjiw/iyydW6skGx0nf7gIOMpZ5sQiWlYzwz+7OG1EwJ4IqeKoq7beGi9WYHaO80npddVUMb/5kJvPIAzAGVdgwzDSWDeJAT6HtRLic4azX7ZqrY3j9R7NSDTG9XmsX9qmCbDmqhsJXmz6g5ifsK4fJZebCWvu3UVrYUJTQh0sRNFYOpOx36reiRDY/k/0luETas0tFwujMaOWY2Yoc3zJ0pBFlGZjSYAwYVARje7z8EE7Gbja0vFmJFEOqfs5ZnOjPw5a644g4SXytbxHWFfTizekoDvUuNGNLB4MJng8gRX4xm42A3XG/L0WwrqaT19Yasz5r/sUM+BEPbrZizJM5d240bqCbAeP0kdJjKVWO6dkpfczwsL60yzJwLEeLb9+vQUY5gsWlgxP93Ipy0sHy8pOqBoxjn28vFuGzv766Q9VbkfA5LYMfqj+Cr/UtsuaHlfrLsBRZOoU0pJ1UB4j87Ip5pxSEWUPTaG+szh/E3o4yEFGHBXUTLHCW0RO4F4LRD/o3Occ21zYxvXRj3jDXFKHP3VvQzXc59/1KNw4+9S6Ngy/89zYO/qvbex5W9MFSBSkIwhUAmIucCh1ZIT31kd1DpSYGJQWP/hELk5wEc80/3bKUvTGSK5sKp1CSGl+8VAAqGrf65iREtMP1cfT0XPtn4v3s3dMK7qoFQ3Ao0XrMQFm6z92mGDOyE2wYyDHTPOwcrEgxDOhdCc+EQBvLB1RonWADgVyLx8sPWc9xZ8RVtVqoC+6xxQdN2FhEAIKeZGfmNPZ0VqBh4YjVl2DyqZuTYHaqYCOv856TJaDiT7xGvhGmRHlJ3/Emwnyh7estwaEX8tB97jZ442GlEOt+SygPF7PTuRgHuopSw8aSVz4ngY21rbwnHQ1FGR/L5HHRWn6i7LAxDHzgc8v22OPX7yA+8VULhuCTciIMEHovABNyB5h2c1V5P5gFw1HrkrhpmuecC1DlxpXSqZuq8AnA08XbpPESi/B+oMJo/3CsgZWfzTXNgK8qOe/rKrXmyBsPwz9vWKp2D5XiodLjDLnZP1IANxrnBH2ZhOvOiFsJmzwH4yoh2b+QAW8qhOOn5zCrGaA+f7RXKbmPlB5jhZdgNu6MOPZ0KmrF9bUCypVjEu3femMG04YumXvGfN8BnPEQK1vPjSrDf19XqXFcBKgUpXfWi0Xw5ltRbK5q5qRgbjpCo/a6msOvNq/gysSAog/m54q9SePm6JBQNu7OGzHwhRjBjYSBCwBhHwd6Ck0Ct0zQzzEwPPqzrqCXZc++4UILEuLFDINVY8UAw+q8iTAWF7zAY3NmToPYzKSCt766g4tXMjxJOIYeXvy8KgimmZTIGLgqNG6MG+LDl4bHWAQnBuajofQktjUvUYrleBi72qtw+NQ82yCXhbeItUrfvFw7gTy81MeogXFtqtfr64lE0Rlx5eAQ7EJEJgEH+FTFc4pEQ0NFDnUvtOqneJcibBg8uuiQJZeeampQ9WBEs5R1Uf3cjSTtglyOWYONNa14fMlz5hm6gnHKXoVm6dFQVp6PYIV40R4tO8J94jo4IjJt7WWdyE3yxppHvZ+2tizj55Hin+nGUw0DwcD0+GI1NpkHoNZNwaLWLuyDFw+l1AE6fGoeNlc3G3kfNUbx+poOO9rrCGID/TP/YgbP85qKXlWHBfY5ozmk1tyv9iStuReL4MBofoo8tAy38cvP/6/ae7+95yMHX+6qwZOjq20PkeTzdYwgWlk8xFUtEfHY0yc98t6lCB5bqjySLGgFV7vEO3vjYawoHuFQpuUhuhQxVJAB2A1gPGxubpwTOFO8gwGPDxDwKIukxnQwjhQPS8DLAxjPCl3YDSWagzsIl3EUX3LESWJbyxI4CUexUmSbMOXDi57HV49/gD1mNHfM+x3wTAEmMflt+64xuY7vMF0sJ6VOhFE4/xUMjtxmeXSCXsW1C/sso0DCySz8s+iffz4D/3PpUWzrr0tNGvZhlKZAYmtQCZOeuC11x/H1/toUL6XlmfKMoiI9SFIBbyg9yZ5Ca64EHOmxRd9T7EeBOfWTLvyEk5ogJ+AUjEkNQB0AtXeduItfq+5Tnje91524qxQWHc5OgTzRczWkg2B+RwfzOB+o8LYfYmDoDouJhaJsbk5C1d9or8HmmmZOaKdKvrT2gIFTEK+4hBGkiz7RvKUkWeq9v66yC7s7K+DoPBVn5rTZJxq+EEyMlC0YYZReX2rL5p5GZkhVZOYEwgyP6ZgZhkjnHrCiR2sX9mEimWHnMqXzggbkFO+14Gf0nD66+BB7muW5uhyXvZRbMpkfQMo5tL4XhIiJGiqX486XEAuZOCqfQSw0HP0MJN7zXOuEYG8qhC1VTYYxiJwhAsbljUXQWDGAA32FcLOU4vhy7Gok/BBOvnxj6lh021jTyvkr6Wos0LMthZXuEg3FQsjHlmqV+C6hXJeLgtHee7soFSDkZgDaRYn0/Fkt9+4vbsWlZJZtPMDAl2674U289Mp1wJRxeKWrjyDfg7ibAgWjuVpdaiJu1OQ5onoHwTMtz6KEABG5ARIO3/+yDgHNfbBmi7UnIj6PjyKLQags3XGrFgxhX2cp4Ki8k4N9BewcSdnX4r0SMivPMsOVRLK4jF5IFjtrvSc1xDJXQZHeuBDC39T9xxXnZScdb+4fvLvIwem/fu9EDkpLS9MmIjuOg6ysLMyZMwebN2/G8uXLf+5nvucjB3/b80EU3/6qFcaGpynQtAdrU1E73CxFeclebC3sGvOGLeUCDhiqwJeoLOKkhfSDJU1wcxKM9VS0lUb5eGjxEQvD/PDi51WSrBDEJDCfaFqJ2vwzVtKo9bcQ6FYRt4RjPK/jhvbTv5DBVHmy0fv8CxmmEml2AhJffqhHhcstj70WdguzX1UJftG4UlL0u31Nj7e1dwnvOC8WUVR5WQnbWwY7RPpM66IUryZRZq7OHzReuIiPX6vqU+NOOmgsHeT+D47chk8t2Q+iSCXvz0P1R1jZfXM6aq3ptRkx9lbDc6ykSp7fq6axrV8llFcXnTHd04mkfOl4qtbEuqpOMwZdQ4M8ljT+bS26MmXIV1VhRWPPlr4UJMUcPePegm640bjiStdJ7JYHiDi1HUWL2jB/lN9NY3NCHhqLh1LfrfcZ1/dwzDhkc3MScGZNY39PkbowZ6iIha8TOdMaBuL5yqBVZ+H46Tlc+RUA+kdvY8WbaBfdXONV29GmEky3D1SzJ5b6K41CWZ8AUMo8X6waekLrdV9RB9ysBNZXd8APmwiLepD6a8/JEh6379p1P666ZsyiX2RlFuAk1z2dFVgy9wwnL360viUlAfhIx0IVDZKMLeIcO7OmVbQHUFW/s5UhQeuz52QJ890Daq9YexKwaWJjChIHQNE0Oj7vOVpHNzeOv+9dap2LhtKTKhG8vN9EGLSiTkm0FDmTSqE3HlZwPnnWgZT/s+c54rNsYjk3FbKTYEURvm8NF/O7aQ7oLEnDIBgVJuWUSScyk6x48x2g95mMqDw3uoCVxb1dpegdvQODp29R943MU4KRfWwYjBtFk5KZVy0YUj8POHCkE8GNqnoO25qWKNki8PFuNI6jA3lYV6Aq1xMLWNjVCd3iHqF1ZiNGRCjo/TSGdaXd8GIRJXuE7P56fy12dVZiQ2GXFfEjdquXvn+dqqMhoJv0XVqnVQuGjGKboSk7NUGEnD9ARQ7XFfRaxAG+YNj6Wt8ibK5tMlE5qiitP3t/cSvDbAhqREoyPNWvfcOFKuF3ImxkG/UjaVOAujkJHp83HsaKsmFLVlHdjGeaF8G/mIF9XaWskxzsK7D2hTeuKWF95SSQrfXsnZZuQe1gT2H6Aphjhta9dvY563x542E0lgzymsS9cNrColdU89/ln/dQ+9CHPoQXX3wR0WgUy5cvx7Jly5Cbm4uzZ8+isrISP/rRj9DQ0IC9e/f+3M98zxsH3ngEvaduhxuNc0gfDrC/WzE8eFMh9kyTB3pTbTNfkvt7DGYbgF2kh94xETawmqwE4EFVZAQ4Efm50QUmZJqjyq8Tm4oXi+D1+Az4GeaiJ+UZABrLBnFt5piVeAaoQ76uoBfedIgPONVFcKNx+BHfhCN9BxQed2ZNM+0qhY+prDygBapOwmPhQh5PcTGRsrWpWrHVfOrIeqNcBOFKY0Zo0t+HT81jph7+LGCFYTfWaOYfKlan++PMmlYVnqNxLC4e5bXyxsOoKT1lwRoQ9vA33Sv5YiEM/df6FsGbCGNTdQsO9y+wvFW3Z7/OIWJptLByK9YJANqHZsO/mKH2FO0HqaRkJzjPIS0mdTwsai8o5Zgq0tL3afyAVoLg8FioMeMLREE9YSAG+36w27BV0c/8pIsD/bYHTiq0gFZYPF2x2rX3BV1kbk7CUCZGbQWG2J+s54uaI7RvrYvrksqHoLlzpl2rT+V3vgw3GmdYWhAGQkoWz2EsothR9O/JK0p9362rFf90eoaCjLRWwUk4KV7OxrL0kB4yCt96IxdTXsSae44CkUIR8nH89Bz2Ru9orWUDiY22q03EYkWxZiNKKIgfreH+7mIFS9OwRjJUvfGwMqbHFd+9NxnGzvYa7O6qMPAfrZA+VH/ErJOeJ+dqxbxEib6WZ3fCrO+m2mZlmE6EmYaZz4+GYlmRCVpfB9hY3abWJqj4uT58zVp0d94IQ4+caZehbQqPrRR32j+1s8+Zei90fvQZ295azyxOEipChqTk9Kf8Gm8ijJuvP2/6xQ4joWk4PlaUpOGF1w6GhsIRNlwBYFnxKKjeBil3ZPhR5GD3kCpmuH+kIMVbTmsfVAC5MCG0USFgNLuHStmIhOPDIyY1YuaJRbC/twgAsL6qUzkQHKpBA1aMqe3uL8Pm2iZ8c6hc3VkEC5tUjpWdgxXwM02x0bUL+wxjTty885tD5Vxzg/bH/JzXeJ7JKPTGw3Azk3YhTN12tVfhgfpjRt5IiNp4WCUK67oVy+aeZtm0rqAX48kMONMOrwXd0bQHHlt80BgEPtjwuq+ow9wN4l3BdnhggfW7E/2K2MSdEVfOCk8Yi9kJ1Bec5nngddfw4oeXPM/vAmCKOop+SCWYziEZrtQIbkRtTUUvDnQXwbuk4KtPtyx9W3jXldB+WdmKZHv99dfxe7/3ezhx4gT+9m//Fk888QSOHz+OT37yk4jFYjh48CA+85nP4M///M9/7me+540DNyfOivCM8KT5BQk7T1/A0yFWJp4dqEJ5nlLA11V2pTxzS3GLfekFPMWEcVydPwj4GkMsPGZ8GZMi6esiWh6wurxPPSMax/szLwIADnQVYV+38agxI4WjcdtJU8W1+9xt6tmTqjItvwO2R568NAS9eHagKsUzdGA0nz3rq8v67HkVFztTQGrjZ3W+8jxQdVuruJW4iNcV9OLZgSr2KtKJ9TMMs8uOtlqsKu83EBdBxbZeez1P9CvYCWE9W4fmWJhveA57yAGw982LRbC2vJv7L8e0tXdJKiRHz6XF+iRgKBS5YPpFiq64Pu4t6Ob1aCwbxMa6VhbU3oRWpEVZezcax/uuGeM5C9YVcDOTeG50gVIM9Fik0gUAb07n8HpGMvV8aLwtPyc3jt3tldbaOiHP8G9PhlPe7U2GsaujEstKRlih9MbMnpJz702GWamzWlIYLPTeaRePLT5ozbf8tzsjDsf12XPmh0Vi4VgEnUPKIykx3+zpzo3jhYvvNwaiOLtU4E4aZjJ5lfMMZsRt9jBt9JJcYaPWc1h5p3Ozv6eIjfPyO1/m/b6ydIi9vaSoSvgRXLDyKI3Aw5r9xdXRmN095cYoFYWnALMfGA7hGK8p7VUyhL1YxDg2hOfZ901Uk6rFs2Gv95R3KWLXbBHBSamcymKGq8r7WZbuaKtVyoz20vL59QHH9eFdimAskcG/+0DFEF6ZuNqMQxTk82IRtJ69k1l9KLdEJqXvaFWG2IrSYauPtNay7262OjuXJjMN5CxbGLG0Nr6josWTSoG+v7jVggEdGlxgPf/oQJ4dhfV0tPcySa30d8P8UV7DPSdLOAIlox3emHJ+TXmCBlv/fn9/oZY1DhvBVGODKpp7sQh2D5UqFhzqo46irygcMYb5tMsOtkO9C7XsgxXxY3mTk9DV2LUhF1G5OGsru9i5JufzhfEbeM32DRfi8fJDvE/9iKdylPQeARQz1dMtS818ihwGmXMCAEdPz+V/72qvws72GnxksaHkBWA5H758wkB7aQ94sQjn0Kn58dX+1gYlNTcnoaLZOmJKVMMWfNE1hug9+f1oPXsnNhR2mb1L770UwfDYTdb9xGQOADsKKB+IvvtI6TF7Lzm+7dSJRbC3swyLi0fRUH4SX+pZAfjAmtJeXNHtV5ED7Nq1Cxs2bEj5+Uc+8hHs2rULALBhwwa88MILP/cz3/PGgTcesSqQAsKbRgJeCzMSCPcWdKN7WCnguzsqlaKgGR8AMMbcm9BeEBEGVe9UgnnfcKFSECp6gaSjOL+lkCKPFuEnPcfCTO7orFFWu2Ac8mIRLLztR/x9ggXI73ljyiNJCZr8c6E8fnOo3FJs+TOXzAVEHp7G0kGuQBpUOmQok5ScvZ1l8MbD2NmpKgN/cskBfqYURpwgBhilTCjH1DjKQ8JQ31O7uirtd9OfQH4Fko7BXur/UyOct5uTUMptkKJVwLEsD6RQXhztvd3fU8QVc5WXXl/w2QnE/ZCiy4NKFN7RbipkWs8UStobb+aqD2gss7WWek/zxR6Nc7SC9lnryTnc96mYqSNA/VtT2aN+RkqVqI5KF5KblTBRIP1ONyuBFcUjuD7zkkicU9CMYKvNP8NjlsnQCpZkWFWq73oJfsg37EKirwCwomSY1/pwX35KaJ32hDS8ATBMwBuLYPDlm6yvSMXFv5hhRVZWlhpY1SOlx3icZPDSWrm5KtHRmwzD19WenaTD9Kj7RwrgOGDjz43GFSmAvrgP9ppz68QdxiTD0ZAZXdjNzU4AYd+si/S6QylaiwtVgm71wrOWZL87bwRujknKdbMVfntTUTvcaJzrQADAWu0MobGRB90X80rGuzSqV1f0Woq3m53A6qpeoZT77One1rKE12kiKbysej80lg3y5yX8bGNdq6qrUDIMLxbBof58tA8qGKWpKGv6wM/VOU3B6BsZhJQTJqNj0piR0c6LF7OtvUNtY20rwz4ArRhn6boWtDdjEdQuUFAS2m/8fF1NmGqXBGWY5RF2fFyn6ba5n5lJA5OhqF2ucox9r72IIZg8lswklpWMWNFR+v3+kQJ446ruA79Dy7LG8gGsLu+zKEHJiOY5JKeHjoIQZIxkuH8hA2sru3DXTa/zM5IQhfGmQpxwG7zDvni8kR17BB+kdfJiEQWrlYhZUTmdDPKg04f6vaq8Hzs7qtXchz3cnTeCxtJB6970xsMWtM6NxlmOUtL4ttbFKZERQBNE6Lk60FuIwtt+aJ4rvfO+qhjtjYdV9XCtG6zTBhRcoGzGy3x2+Tl6z9yYoWqnUE0mJ64O5lNNDdZelgYOy7NoHM1nZnNeoZsbx94eO2fkimu/Mg6QlZWFlpaWlJ+3tLQgSxeI8zwPmZmZP/cz3/PGgZtjw1j450Lx8GIRU5wnphgVWIH0wcraof58C34AXxkP8sCR0rihpg1eTIXm9nWX4JNLDmCPVuQkbIYOOwAFtSDmEK2YKZiSY/V5cOQ29tRR8h57HWM2fGFDYZdVlAywYRXyEnKjBs9OHim4vsKmEgY2jcc35SKjC0fTXMb9kDU/APBnVd8OrFMCjk4Os7C7UR2BEIortS3VJ6yIze+XH+RL6JHSY7i3oBurFgxhQ42OYOh53FTfbPoJwzK0kwSxXnfOB9Bj5mI2ev4eLz/EHixSYtjLHPCeM2uMb+bdjcYtYywYAqbGEaCYYW5h/DO9Q/fVzUlgT5eiXHWzE8bTFfZ4nPT+vR1l5qLTRc0Yz+3a3j75zkfLjuDo6bkq2kVNX8jeWIQN5g2FXaouAit/yvjkok76ovRdH62Dc1PGTXvcT7pKGWHvpY8bsy7ggbpjHK2geZORNwsaQtGVMaMoSE+sr/NmaMzMO56TsGgdpQHkTQgoWFKtrZudwP9YdILX04tF4Ou/aTxS2ZBGvx/xeR/xvpOFywJKrhuN47r3XWJoYPMZVbWbEukJRnWgR3mJtzUvsaKVxMVOyqCTdOA6voHyAUCmMgRCMZcTh5WyAi4U51/MwL5OBfG7t6Cbo6P7NDUlEo7Zi0KxA5CiZALgvAplmBphRnh8Vua1Y0V6VinqIGVbUMYFK/laTZ/Bh+qPWFHXxrxhyxgDwHkk3qUIdrTWskJO60l/u1ED/WsdVsaMM3Pa7kfCTa1jIRQYyR60vqqT58KNxvm8BeeRmnPVNEeN5OeOn54D/0KGdX/JZ9C+f2zxQZaBN2WeN+uaTJ3HIByLcpNkn/xMVUjtxR9ei6uuimF1Za+V5EuJ5W5UJcTymSV4YqCgJAA2yinCKI07Grdk1qLvyGccGM03UcPMJA50F+FAl4IV0/y7OQnL4fBgSRNXKt43XMjvoIR8HpLcD9oZOfjyTUY2xg3slyMvOfb+33OyRK13ZZeqP6CNJ3J6kCH28tQ1cHPj2N+l+ulH7Bor9DnKT5NyV8pT+CpK/6siaFd+e/TRR/HQQw/hsccew7PPPosdO3bgsccew+/8zu/gd3/3dwEAzz33HEpLf35D7z3PVnTb1/4IK0tfYs/xzyq2JS9uQHhNPAdBlgEABn4gON+tAiSCvYGfB2FIBBTe4GfuLeg2VThlCBKwlC7un+TzJg5yx3hW3GjcKnxD/ZNjfrTsiMXGJD8PADdedwE/OHdtKksO8ZCTkhucKz1H9+T3K0NJC9AVJcMGJiHG9viig7oISzjt87YUt+DppqWAqyr57hsuNGOX6xZgiGHYhuD8VwM1yqecb2KsYA+7MNbk/MGD4lvP9Gx2ItGnxxc/hyeaVuKB2uN4umUpVlf0WlGfB0uaOBeC9uq6gl78W3M1F70JUoNaYyMjQ/DBX65R/9YV9GJXRyXcHJvjXEJr0tXWoHl0ph0LDw8A/oWMFFYffldblcXsROPcVNSOZ5qVwtpYOYADXUXMOsVjC4k6HgGIF52zh0qP4/X4DOxqrUqBh6wq78d/dJRYbEdspEXUs7kYlGAhC85jcG3vLejGv7bUmkRnJ42sCHgg3ZwE8NNMeDmmwq9yFviGISVQPItYcUh5uOPGN/DimRtso2rc7FFZVCs4DuI5TyunAnINMPUpgkxazM+e6TFn/b7hQmu8skgTj4VunpAPZ9KFc7Up6rRi3ikc6l2Ihxc9z9S3VKxSnV1wPkuKfHVsWc4yUKylrDNgrZGQNbQWvMd04UNqd+eN4EBfITZXNeOtRA4ruEFGm7UL+ywnEvchUBRrY00rzsSuMyxpgf0nv0cF+9K1R0qP4ammBpaJl2sp5ydNgS/6nCXzLkXg+A4+XNNtM/fQ5wSbmjXeWASbalWNG+9SBMjw1DsjSat4WbAWCz8nDUvT2uou7OkqN3kiwYJ+6c5uuiRdvY821qiil0DAsBDz71/MwP+77LusoG+qbcb29rrUPRZY38vJ4rRyQfYLqXefnINgDQxqW4pb8HTL0pS7IV2NJKpdFGTqUnlTk3j1E398xTH7kI43//99d2xFLzz53mErAoAdO3bgK1/5CkOH5s+fj0cffRQf/ehHAQATExPMXvTztF8K48DNyVIHI8MDpl0jfNLRhwrOblbAghfQWASb65rY84awZ7x8gQsg2FIEnKhWmKJ8iaJK3A+t7JI3amX5IA4OLOS8iobSkzjUl59qSASMEi6ypg0LUqqYr15XhE2pUCppNANj9GKqWm3zmdnYUNiFnYMV/K61VapQ1NtVg+QKusEqw5cx6uR7ATB9p/W7n1FEir7fWD6AAz2FNv4XilLOUmrIGAj5QDz1gmaFRCgom2ub8Ex7fYqixx5F11b2SYkmCkD/QgZ+s75dwbCcVGNPzp/FniHGcrm9SEYSfy5HUG/GIlwhWfYtOBfpns0K4ngYG6raOVGa9yNRqrqK4lQan0EDWCp6C/NewcnRW+1+SGpVwp5LmmIy2GXhIM9BffEpzt+RFb4vVxSR5keeg7cz9rnvCRcN+aOqSmxAvsj1X7uwD3dkvY4nuhv4GQ+VHsdXW1aYBObAenrxEMrnvoTukTv5M2sqe9ibGfw8zdflFB+5BtwcH7fe8Tp+8JOrUtcnzR7h+dD7g2l1p0JYVTyITDeO3PAUtrfVgYgSpIIjC2XRz665OoY334oqhZwqVtN6S8XtfAacq6Yvu47cx8soSSyf9LpQleN04065J4KKlY9URU8o4ME5l0ZEOiVR3kfplEVpTDgJh6ufp/RdFPCTxTflMx8pPYanWj7AY7AMLZ0TcTnjFwA7KGROkKwiTU6RW9//Fr7/4vXWfSSLia2v6mQ5mO7uYDknjbg0RgUA6y4LUmTL85iu8F86J5o19stUwQZU5fDel2616ajFcxjOKA0weY6ClZWlc2gsguL872P+zB9bhegQ9k2SsujvPfn92NNewUn/W2pPYFvLEiAzCTesqkVzAUa9Zle8cfCJd2kcfOm9ZRz8ott7HlbETRdsoYOyrqBXXQ7EghPIyJfCZW1FNyf6AUrIPNO6yBw6mdQH+8Kl/3sxhbWEY97lxSLYWNWmwvm5cYvKDRBKuIBhqOJJPvfj0At5yjDQfTs0aHCNfEHTmKi/lIikoT/0c6pwq0K3MB538iBG49aOIbgLAH4f0dTtaKvln7m5plDU9zqLDC5c4zet8foOGspOWmuxomjEKkzFsChaj2gcTsKxKoACygtPaxssIEWC+L4iVXTmQHeRWUPHx8oyVdDmBp0UznOoP+NmJbC5vgkPljRZMDEO04q8kjfi0RTljulhA/hi8rLu6qrk8Tmzpq1ifNYlNB5muJNFaZiTwIbqthSFz9oPlITrO2pdqaBUiGitfMUrrt9D2FoOxWt8vAWN0p8lz/HD9Yc58kU5EV4sgnXlXTwWP2R+Ls8OsRcB4LGcfOEWe0xjEeVl1219jS5E5himqw01bYpEQCuDK0uHANdH89Bck1NCe11Sa8YUmxcljFOhLgCGkYh01Jiq1Cob56Q4KklaYrsZviDWf8/JEsswAIALiRwLRsSUhWIvdZ+8y/oM5Tqp82oURJIva8u7eZ1S9kfMMB8xVWxOAj/4yVVWvsMnyg6bTuqCff7FDPNe0Q4NKIYjJFwcGM3Ht4aLrXwFuGDaWSo4KKN53ngYb74VhReL4ECngnmsKe+FM+mmzIVz1bS1jsS4RmtK73yg/hh/z7+YwUncB/sLFLROQ2Gah+ZiS3ELvLEI1lcLWF7QMIhFDBxyMmxy2SAoh12VgxPc59T2nCzhwnS0761EVH1GrMJn4m+utRP2FCOdXActOx4tOwI4QH3RKSDss1wlZjPq21PNH2AZSWQacH2sKe+15Bv9XsKT1PvA0EwvphTudVWdCurm+oqkYyqEV358NeD4aKwYAOfW6DVycxSzEs0B1SCiuSflNXw+ZEFR11T02oxKrm8obZMO7ivqwDOti5jtSEWIyHjzzTs0GYMXMzDQdBFEAKgvOM3Jw7KPgDqfZADI/Bpu4h7mPSHOOZEmUHuo7qi5M3PjGHz5Js7fUzICnH8SdA7+e1ulikhqyBexo7lh5QzaP1IgHFy0DpePPl8R7Vc5B/8l7ZfCOPCmQka4jinhuqu9ioUFHQaZhCiF9reGizkpyLBSiMuQBJNOUnajcVXngA66ViTIk8M48Khi6CAoQtC7CuhLzoWlgKXzGhH+1pkKWZ+RSiq3gHdcjmlLcYu59Hz1XvK0SspCbyrEyWLyfaSIS4+TVJ59zWFPh3N/bxEniVE/D5+apy51ncB4uC/fjgiIfBAS3gS5kePc2rycsctb6o9jU02Lta7euGJpUoqfj/9dcYDH8r6IqnNASowXM1h5atsHqrG1ZZnh3RfPle+RTFOkoKfgi+m7sQgO9qjCSdfceIGfR2vJSWRTIYNPF5KO8yJiKneGlZDpUOqlTu/29B+9Dn7ShTcVwtqKbsvbtrezDFk50wY2phlcNtc3mfEF6jZs7V1i3ik8WYSflY0vRzIqdW0BADyW+XNV8t2W4hauFEpGJSkT9Cxio9nZWc31KADgYFdhANcLToBcV9BrJSY/07rIGHbCCbCpRid/+WreGssHVH0DkXxJjYwtGq8bjav5JgVQGFXy3wCQ42r4X5rqp7znXLP+bHjrsyMNybivZMOeLuWVl5A6Nmrl2uj1aswbVvsnJ4GVJUO4t6BbQSqIeYyQVDNtaJkTd/FA3TEFgZFeUvpbRrZ05GB9ZacZC8kEsZ821zehYf4o9naWMT3m2souFTUIVKL1YhGFD6fpEWJvLGkS83yiQc1JYHHBCxx1ofl7un2JSjzvqsT1114EIp7VL/VhcU6zEhZd5/6eIv552E1ifXUHnLjD+Hy5V7a1LrZlgucYVihxftKdHVLiN1VqGaCTUmVfCS7afHIu3OwEM3tx4S/ZaN6EvE2XuwDPxrUTLTY5s6ivu9srFXxRK8IL5/yAP3eguwjrazpSqnXLPUz0q9b9lp2Ad/Mkj92NxrG3u9TKt7Jr8oAZ8vxoQjkJtaxZW9FtR0l8YFnRqDEUgn0jB1wsguahudg5WGE7enISuPX9b6lkfY0OIHYyYkFkT79sBIfV/5Zz4Ebj2Nq0HPBUvQPrPpPRhjEzf2sX9rGhRrku8ntuNA4vHmKZ4sSdlLv0Sm6/rDkH11xzDV5//XUAwNVXX41rrrnmsn/eTfulMA4AdTBWFI/AzY0rj6JvvBMAuAgakBru9WJG6Xc8Bw+VHoebq8q2B0OEdIgvJLLhxSL41JL9aa14SUt4OW8EAKYnJOUl2C/r8w5MlVf5DM0yIwvRyPeuqephIbWtvw5IOipZKVclKEtBzF4w7S28r6iDhYl6mTZoQsYDQ32ylCNH0bZurGrThpfxWnnjYZUcJjyxlvFF1SZ9xwhR6PoU42FsKmpXnk3BPLStZYminPMVVCOYJAffweeOfZj7uLO9Bg8vCnBJ69NC9QzIw0qXFRsPMhGNlGXfvtSlFzMoqOkyOn8+yn2zPLoA87kDUFzU+t1f769V9KaBfUI0hfT/+4o6zHroNWZPd8gDPIcT6OE7iukmGsfkeAZf+DSGZ1pEFE0LXZkkuTp/UO0bFyahndZ6IoxPL/2ONX/ygt7VZjxibjSO0z+4Ht5EGE+3LuHCZbyEVGdCRIksQ4yUN4qwEExAeO12D5Ve1ktI67ahsAvbm+u1F1Q950BPoYEAaqgWzY+fVGxWtbPPCYUeqWskoo/W2o7Z53xTUbuKhOToIkvZtkJC3mPqL71zd7uCrGysbkstdkb/FvO2tqoL3nRIURpnJOGNh3HohbyUokhUSdWLqbmhd/phX8kTirIQI9mY8u7C8ZlZi9aDjDtJIU31ae7J78f2gWrckHmRZYgXi3BU0qcxCwXOjcaxpfaEJTu9mOLUbyg9yYYgzRsldfM51PAgIoj4yeszTbRoXHnMvViEz9C6qk5442E11wRlE3v7xNB87B4qhXPVNEN6LGeQdlTw/6OqJgN7hKOq8rBUYGmdSRlmWmoHiIanLMfFmsoeZURp5xbP16VIiteejSnfyDEyYKUxRrKDZNmzA1XMoifPYzBKevLMzfDiijULnmKvc3PjaCw3UQzpOMoOmTwhjvzTPGUlTLK/Vn4zNMUyRRClIftgSROQcHm/eeNhq0I5jZuLitK9BgCur+8X8VlRVJHGfF9RB2LTqrjZxtpWSw4RkYgbjVvj4PkUThkpa/+25t/UZ2bEcbgvn/fO6ope/jcVeKV+7+6oxKa6Znxq6X4zDzAJ9UxUoSO1zlXT1juv+PZLGjl48sknMWPGDADAl770JTz55JOX/fNu2ns+5+CWv/tThK8xRZkoaXZ9ZadSPFxfXfAe4Ey7HJamRt9bcOtrOHnqlrQYyrvzRlTyJGDlMGwpbmHaU4m/BMD4+hTcZiBUrYr7OPw3e+qlYqHLyvNlJlkwHB9OwmXvtpyDIHY0aOhQ8y9mwA8p7yix8QBgXDxHRxIOj9ObCll5GCvmnVKYa40p/f3yg/ib7pUWppzGRrhy6ks6PKdMGuYEZ8LQXopgVWW/KmB3mUQuQF346ys7VUhWF6GRUIADo/nWulgVXadDSmHSNI1IOipHI3DhB7Gq9xZ0czVWGosc4+U8NjJh73JKK+PpA7kGNMf3F7diW+tis94yRyG4p4I4V4CTga3xTYSxtrwbezorUsYncd8pOTxQrCu7Oip5fzbMH1XUnoS/fZs9f39xq8LKuva5Cs45ACv/I+jBJsy74zvKg+ym5q74FzPgZyYVo4zuy6ryfuzvLYKbnWAcvBtVe6+y6Cw6T96FyyV4yjl1o4oJxYIp6t9vrGnFWDITma6KiPhvZsK5ZgpeTOcW9ecrL/ZkiGETK8sGVeRJP2vZ3NOKVlWcL4okrS/rYuVIrlE6OeBfyIAf8eDmJFB910tof/EOMw6RJE75Agtv+xEGh2+z9mSKEyRNAin1o7FsEDPCk1wALJ2cpPmVOQrBsQAwVMRZASNReonFXku7Vo6P1eV92D9SgMLbfoj+kdtTDPsgHt+bCGNzdTOeaV2EdVWdiCUzLfkZ/G7KPqYcAjEWknUkMx6vV6QN/LuOCgtStX+wUOVG5djPqbzr+2gfmGNyAwSGPUi8QeOnn63OH8S328rZ6SM/93be5g2FXaqWBcmRqRAoIZn3pcz7o59RPtBUyJxBOuN63ebP+wFeeOFmuLmK3nNfZ2lKsULup/6uE3e4UGi6RjKQ8+fGw/jk4u/iie4GcwelI2qYUPlU62s6sHuoVMmqtsW8vlY+HlKdBLx/dI4OV23Wn6W7YHX+IPZ2lllGARMMSFk3GVZsSIJqWMoDAHByEvAnQ1hT3ouEF8J1GZdYJiXeTODV3/3sFYfPJx1vwcffXc7ByFf+a3IO3nrrLfzu7/4uvv1txcj44Q9/GE899RSuuuqqy35nz549+Md//Ed0d3fjjTfeQG9vL0pKSn6h/Xqn7T0fOWAhRMk+uioxeSnoM25OIsUwAMBFVkZeuQGrSgcMJ7gwAg50F8HNjWNLvcKL0oFjKANV/BQRA+mRty4J+lzgZ5J5hioCc0uKS41wgsLz6cycNpheqpuQnVDeBoKpxBSTDgkMqjILaCiQ4+NAn2CmiEWwq7WKIw6KphDsGV5b2gM3x1RbJcw1oBSWLxxfpWhWZT4DVN9HXrnBeO6ER4uiCoDxVgHAjyZnAa4Ph4uOAfu7ii3YRErIHMpI2T1UymFl2fb3FvH7VucPmnwP3WrzzsIbV5f/ujIVjn5g0bGUasOscLgKV/+vLbVs2EjvvQz1bik28CcvHlKhXjLuhMAP5g/Q+xgrTpeAnqdtLUssvDJBOdJe6iI3hopWUc0Cy7DJTlgeN66foZPpuX9in9HaqQRrBevwYhpOlZOwcM1WtEm0r/fXwo3Gsb6y06wxjUvCUGqbjKLji7OWdIyX1gX8sIdVlcqLfah3oT0VM1VFcYLduVEFw9hScwIA2DCgOe0cmq3WIQ3V49qFfabeif4OXcJ3541Y67FzsAIRJ4lLiSx4E2H4VB1a99HNTqgiaKL416EX8tgw82KRFBYwNqozktjVVsXVczn6NZ56TgAFG1lf1QlvIqzoaWX0U4xzZ1sNvEsRDI7eijVVPdZ6yHUiBwVXrab9rp0bB3oLLcOF+3ExQ3k8PQNNlDkKbAhI2Zp0sKVaRQ8eLGliCBpg6jrIZFmrn6JiM1F49p++1Rjdel4fXvS8yd8SNXSeaVGkFa7Oayq94xUji3xgc22TUgaDhkEswoU45Vi+NVxscls8hxlzAF2zxTey7sBoPtxIEkg6ytNOZ3AijPah2dhU18yQPO9SRO3LdDUWona18r3dpfgfi04YORAP8TvvLei2Il1yXXe01CpZkiWMMoBhqGS8bKhWNODrqxWc6tPLv6P6IZii1lZ0W+t2+gfXY1OduqvnZv/E9uo7sKNv+p74jbpOjl4F9z1FXrxYBDvaa/gOkDlBn12613oP7WtFHa4qWy+49TWF6/dMEUEyDKQcBWD2EzmTMpNqDaMaquUBf1H1LYYh7xsuVJGqmIi26/nkvazvZ9rDayp77FwjDalzHB9ryxX71P7OYjzTvMhEh6509/EVFjn46Ec/ir6+Pnz3u9/Fd7/7XfT19eFjH/vY234nFouhvr4ef/VXf/Wu33v27Fl85jOfwYYNG/CTn/wEAPDd734XJ0+efFfPe0fGwec//3lUVlZixowZuP7663HPPfekVFzzfR9/8id/gptuugnZ2dlYtmxZSuempqbw6KOP4tprr0U0GsWHP/xhvPrqq9Zn3nrrLXzsYx/DrFmzMGvWLHzsYx/D+fPn3/EAZRIxYCuYAFIuRMknDQDPtNXzv/d3F3MhK1lchQ4iKSzSq6omxUAXKGGSscmTtsJHCpGjkyydhKuUEmF4nOjP43C2TKLyxsPGO0awARI0xAaSZXjSE6L+gBuN44ljd6vvOD57TinRzM1RGE4ek4641BaetpIzaZ6/NawKl21tXm5BG9zsBA735eOh+iOMgV5f2ZlSfIiV35CPxrxhLugCgk0J/H/7i3fAzU7gd5YeMvPhqLWUkKi780bsS0lcXA3zR7nIjPIsGYVq33Ah4zu98TC8qRDaX7wDm2pa8EzrIlWh9FIE2/rrsLuj0ihgYl1XlQ7AzUmwYKfwuwV/0Er4061LjLIZSaJBFwDbXNNsJbSRYcVwnKiGzGlDhNcJUHk30biBWk3Y+4MSb7kllEcd0EYFgNbBudx3bzyMyoKz1gUka3SwsiWrhNJ8EMwgYNBS29tZZlW0ttYqbleC3dVeBa4Cqo0M+eztA9UcPuc+SWNCG29rK7qxv7PYrqQtlRsNP3m6eSkrAdvaFD6cinKZgdrrL9vu9ko+G9b4JsIq+ugobDX9fs/JEqt2RlDm8CsFsYAXixhcvI7msFMiR+Php1W0gWl0fbADQe4TwPx891BpihIbHCPlSLk5CeztUVHFJXPP2EpP1MA7KDJABbdIsbJqnQjl2Jk5rXLAcuOq6Jv+edAAJCgSGeYkm7c2LzdQHK1sk3wmQ4CfRbSxSQdcnE6ONSfBBv5XT3xA/MLH4uJRJc9z4/jUkv3KGRWNo/elW7G+spPzZZ5pXmTBjmQOSPfonbZzQ7/fp7uHIDf6vHuTYTyw6Bjc7AQ7sbzxMJDlmYKTIr/i2YEqlpfujDi+NVzMjol0cpKbD5wdv5b7tq6kW91RdceVc0Cw/bAcy05gTXWPSYClfUN3hgM+xxQFIuPw802rrNfTurnROFN3e5Mqf+y+og480bTS3peOD0cr59LZQE4Nyin0xg25w5rKHiX/NBMh7WWaUzcax593rrLeo84NLMjeyCs3mDnWzjkrijdhir9Jp4bcC/fk96vcNxf4w457+EwDpvYBFeIE1HyumHfKVB/PTvDZ2zdcyHejfCfNg4ocAXDBOQq/VX0Cv2o/XxsZGcF3v/td/NM//RNqa2tRW1uLp59+Gt/5znfetjrxxz72MfzxH/8xGhoaLvuZt2vHjh1DYWEh2tvbsWfPHoyNjQEABgYG8NnPfvZdPfMdGQfHjh3DI488gra2Nnzve99DIpHAypUrEYvF+DN//dd/jSeeeAJf+cpX0NnZiRtuuAEf/OAHcenSJf7MJz7xCfz7v/87/vVf/xVNTU0YGxvDr//6ryOZNJ6Bd2N9pW2+Y3kceODyUIsDQvRmrCToGSLvtlQ2AXWArUTdwIW9qagdMqGYEqHd3LjyXspQt8BjO7Omsa6qE87MaWyua0oRquTNd3MSzFbEkQHA5lsmYRXwchzoLrKLngCMLQ1WoA0mONLfrYNzWTGE6ys8v/48RzBybS8woJKF6cLa1V6VArniAki+g/3dxewV4bV0FKxJzvdXT3yAFYu1lV24Jmz2pTcdUkqWuN9lZOJgTyF2t1eqn2vPtncpAifhYsncM8wVv6X2BPfh2YEqgy0P+ZxgGIwIrSnrNaFhX3gCA/vm6/21lqITiqj3PN9ZAHiO2puiSI+MANAzdrTVWvuI14vmzUVaT+X2gWpsqTlhXTCsjJKiKJXepKM85I6vGFYctSZOQueB0F4LFAKi/UDjpznh9dBzIulj+XexiPKE0v/Jc03joUT3kF0VdU9PuUmepfWdoSqb0s++NVwMd0acPe/0DmlM8LxohRO+Gu/R03MtZXpzdTM21rRyFEAqC4tLRrlSKyDOSXaCo5rkLeR5S4P9VcaKbeg9WNKkPPGOz0UdAZ18KzDRsnAizR8piPTsyoUvAlB4/4eXPA8/UDiMFSGh6FC0cUvdcayYd4pzrY6fnmN7SsciWFfTaWRpTgJfOfpBI4s0q5Wcg9wZk/acxCI4MTSf++LFBG7e8XF0IE/lTSzsg5udQMP8UXhTIXxu+f9lT7Iz5bIsZoeAjCD6Wj6HPVVlloyDpMOGMLMu6egNGcrNZ2bzs75w4tfgRuPch11dlcb5QFCPGUrhk7DGhXNf5XNEyf9eLKI85B5MlFicZYpWf6e9jOeWWPosQ2tMEUWofC+H76dt/XVm32syCk4wFufnRH8ey5ndukaKC+OUoErivF7jhsHM2s+CaYyMxmAkWa6J3EP0XMDcd9tb6w10FsZw9MX+52dMh1hpps9ta1sMwCjRbmZSRfmzEnASjrVG0liQ99CGinaOBN76/rcAByjNewkA0FA0bMbu+vCyPeu7/DzK8dCGkBdTFMWPlysjOgij84VsvregG4dPzcPM8ISV28Z99hx2erIuQl3KFagDvTTPDNTiim7/icjBxYsXrT9TU1P/qa60trZi1qxZqK42NLk1NTWYNWtW2grGv6j2v/7X/8LnPvc5fO9730NGhsktXb58OVpbW9/mm5dv78g4+O53v4vNmzdj4cKFKC4uxte//nW8/PLL6O5WQt33fXzpS1/CH/7hH2Lt2rUoKCjAP//zP2N8fBzf+MY3AAAXLlzAtm3b8Ld/+7doaGhAaWkpnn32WQwODuLQIbXx3631lXaAObYwUT9TwiMdBSN5TQ72Ko8aMwtlK6yt9WypCApaTX5eLKJqIaTDoseUQslKuO+gsXzAXLZjqvqxF4vglow3LW8nK1gk2MbDTE8ox8OKDYVsZb4EAPgq8bYxb1h4pBXchZKdqJCVNGKKb38Vd+eN2JVUNd52a+8STo6TyY2ULMawLICjI3y5a6WpofykEZieqjVAayW9/1aFXgCO52BdRRcnlz3RtFI9YkrlBwDA6kpBvzoRtrjqgx5shHw4M6eVcqPHksImQjCenIR1mqTnjKglARNZkZc6z5NQYAEgqb3kfthXvNU81yYS4GYnWKniRHsBuZKKrReLMIROelbpnWSA0mdJAX1s0fesSpneRFjR4en9/3+OLQU88H6RnmHqQ5BO0qGKsL4wbsYMHSRVapZJkwDgv5Whnqc9+ZIVi89FwlXJpvric7MSKZjjdQV2VdYgFIJlAWGgA8btukoFiePCdL5hMts+UI2LiSxTrTkroSJ942E09SxQfdHK9sbKtpR3ysaXtw+eE28sgkfrn1fvjIfY6/21vkWcUC2fta+7JLVOQkbSyAfPYaMTUPPZfe42AMCF6Sxs7V0CRzMxESEBP4vW2Qee7yoAfLWPDvXZ+TpWFMkBdutkdxq7H/YZHkR79/HyQ3CjcdyT34+xS1lmL42H1X7TCadBI7mxbJCpRHe3V8IbD+NgbwHczCT+uOPD2FjTik11zfh/FivvKUFumLpzIqwoP3kADj5QMWRVJVcREt86XwQTSslzclS/D/YUKmUzy+ybhxc9z3Ows71GJRuPRbC/q5ihrMFo2KQXYScIAFbyJVzHD0Q5LNmioyBMqKGhdfcVdVh02gR93d5ex31Yu7APjXnDaCg9yfNNz93WX8djl5XEVYeEEaDzAubd+RoQoTAOeG4t2RFLZYljo5jJHnSUXVcpV2MC943OZWPZoPUYOgOPlB4zcyMMP+oHySBn1jR2ttYwFI6e7eYoA5LYC785VI77i1txoKcQ33/1WiDpoHf4Tlba2VDThiw9x8qDCjCQ0T3yxeONCDaKQpFRvbNNMbt9c6jcRJX0vlhX1Wnlj9A8+jqyImXRf5fmvMs/AHDrrbcyOmXWrFn4/Oc//5/qy2uvvYbrr78+5efXX389Xnvttf/Us9+uDQ4O4jd+4zdSfn7dddfhjTfeeFfP/E/lHFy4cAEAmCrp3LlzeO2117By5Ur+TGZmJpYuXcpWU3d3N+LxuPWZm266CQUFBfyZd2N9TU1NpViBgBYuUyqEXn7ny5aSRorhA3XH4I2HUZ5/zihLQvGmg9L+4h0pJdcBqETmuIs/qtyfCqOQvOQkuHQlXTcnwR5pAAwfqL7rJTRWDCjvVtzBX3Y1piTt0vPXLuyDm5MwzDJAqhIjwooEOXCjCqIDx1eeSu1NhWeo3kgwW0qL76D3zO14bnQBvtFcpzCz2sPP2Gcd9l5ZOqQwvp6jCtCFfKY/lYokCT/yVh8+NU8JR31pHewuVApxLIIDAwUm3A9hWEyG4Yd8BevhhdEGQGaSL/v9IwXYVNuswsda/q6p7GFPnjRoHqo7asOz0iQtSgPRSTopnhoAaZlhmFkDCj/rRuN4bMlBPFjSBG8ybKAw9I5AIjAXnRqLsBdp52CFBZ9zo5qpRY9pdUUvPE11y/2RBtx4GA/UHYOfdM24wr6iP5RJd0nH8lY6V03z+njjYcWKJaAPdOHTGt9X1IGP1LWmQMlkSH6vxndLeswVJcOqEnPSYMbhqAS/4JqkFMMTkCc3JwGPoEg0hiBNJCXSF4/ws+8r6oB/XnHik/edvgPAmvv9IwW81wGlWLg5CTMe7XX/6XSuek/JMD/LilQIhW5vbwkIMkdVrKn+yqpSzfCiz6tlzDiwxspzQvMuopDehDLkSCHqe/E2rgHg5sZNkj4pL3TGcuOoK33BMIk5Zk9xMmyQVWnMGNCW8QBgzpzX8ER3A+4r6mD4B3l3LeWbkmKFMnWgp9Dw01PUMifBkJEdnTXY3laHPSdL8EDdMQO5CfvqrshO4O+O3G1Fg4L7CYDNpqbHOeWZ+4IN8GzlOChc8LJ1/txoHP9wrAFuVGPKHR9vxXPY8ADAUSQJOVE1T0w3uNr9pDnLtA7eRBgbCrsQhETRGshnPztQZTzSYc/IMoo+5iSw52QJDozm41D3QktpDyqUVmQPsKKOXiyCfb0lOHXuBjhjygHSWDnAbHR2voBvRb34cblxy9HC8+DA7AkYJ5wbjeO50QV2PknCRf2cs+YsBZ5rOXuIInxGnEk47s4bMX3ITLKx5Y2H8fX+WjSUDKtIp6AqJ0Y1uQa0XkFYETkOV5UNpHUc1M4+By8WYda2+sLTcHMSWFdtcincnARKF7zEZ0zKLX6X68PRMGEnIZxdev8vmn02Zf6vqPafiBy88soruHDhAv/59Kc/nfYVf/InfwLHcd72T1eXyl9ynNRcM9/30/78F9Wuuuoq/OhHP0r5eW9vL26++eZ39cx3bRz4vo/HH38cixYtQkGB8rKTZfT+97/f+uz73/9+/t1rr72GjIwMXH311W/7mXdqfX3+85+3LMBbb1VsNx+raVYX1aUIOkfvBGAUohXzTgGOr0qM5yTQ/cIddqgd2pOhL0Cy9oPhbT8rCWfmNP706D0p2N+gV9ONKqOEmGekkkgQg9bBuYoaMezDj/gpQsGNxvGpiufgxSLmsBN1KHn4p0KKvUJQpnmxCJysJNOQkndNeq6lkgfHN4nTopgSQTucmdN4K5ED4sjnphlgDr2Qp2oN5MaVMpt0LIpLvjwIuiXGuba821xeucbIwpTLz7eEqYCmkJKj+qLW+8tNH4Q3Hmb85rbmJfyuayNjgOsr/LCAOmw9sdxSyoPeY8qLoPkL8rzzHpBQBdfniAX1eU+3upC/3PRBhYmOO5axx8aoY68xYCuk8m9a723Nps7AHVlvAJ6Dhxc9r2AX+iKWShfBEvidogItjZmTn6l/UsnLScCPGEWAsLANJcbj/OxAFZJwQfCjB+qOcR0GSq5PF5E73L9AfU54LN1onJlEpDIiPX+kvMpozZ6TJSYSMhnmCFnD/FGea28ibBnv21vr8eG6buNh1p5UAAy9IK+2jAT4SdeaU/YCOz4uxVX19sO6ejIAy8tKBg2gFJDqhWfVekyGzZ7PTmAiqZ87I259n35fu/BMShIeQwf0x1fMOwUqROVHPHiXIvC1IXj7nT+FF4sw7aFVmE0/t3lQsSLB1b8XRlwwXwIA7z2Zj0Sff/GH16J+zllsbzf1KSyFT0IkZVRU0zan7NuJMMP2yvPOKSpVQNUwEAbZ2lKVsEnGHOULeWPKyXTHjW8wNS3NHcv5ibDymPsO/PMZKuI4pmFlHnDy5RvtcYyH8Rs1nXwm3JwEywIJXZHYczJwGMop9glTZhOsRvdv52AFJ/BypCigcLvRuA0l1TAqmQPhjYdxd96I/gIgIbdWn8ciNlNZLGI7EwSc1rlanTsi9XCzTU7cinmn7LMwkaogy8ZGkDDa6Mzz3hN3hhv20HxmtjFKxFjgGgYuIiSg/bSpqB1wfGVsxCImyuTazqJDffkc2aPvBmm9aR/5FzPsnDjPwTUZMbjRuKoF5Dmq5gtFTcbDaB6ca91HzcMqwr3nZAnWV3WyUd3//Vv4XV4sgrUV3SkGiq/v1A9WDShY7sI+IKQMjOMavneltv9MnYOZM2dafzIzM9O+4+Mf/zhGRkbe9k9BQQFuuOEG/PjHP075/k9/+tMUvfgX2T760Y/iU5/6FF577TU4jgPP89Dc3IxPfvKT2LRp07t65rs2Dj7+8Y9jYGAAO3fuTPld0EL6eaym4GfeqfX16U9/2rIAX3nlFQDAv3Soy0V5xR2ud+CNhxUrifY0AMYbW3rHKzYuXAssS+mPGgUlXVl1+RkAXG2RoAGAVigyPIM/TZjiI25OAs6UqxRv3wgt8gx+oetu4zUfDwMaD8nKhGMK/dTPOauqQYZ8U/1W97F2trkopTdeMnrIMdQXiUrFYxEFzRBJfDRPjm8SwGRxuN1dFeYyDvt2RIWSZyfDrBxLqIMXiwC68NHqql5rHA5V8tXvWlfRhZUlQ8bTqMfDSjdd7BNhvDJ5tQqft9azsn9fUYdVqyE4D0QzmFIvAaYi6iOlxyyj6aHS4+x1vye/33jTshNWPQQngO/eWNPKUQuaE286ZEWx5H4kLn4aJ+2dv+9dCjc7ga82qeTJTdUtkCxYPJchD3B85U0320VFqYhaMWow1k4gr4DGvGzuaWUw+VC0m1DsMGsX9mFXdwXg+NhQ2Y6nW5ZaWGP2gE6ELYXWzUkwPIzXWkeNCOrBRo0P5plPx0zhxSKon6M8YtX5Z/lnlHOgzp6Itum5lRAxJB0F/RvTyeVjEVC1aYKXEHTOmzbJ/wAs7LLK2RARRh11IWcBJ95OKpaZJXPP8Phcze1/uFcYFyLpvX7OWWYYIqw9wynF/HLEjuaaEoM1XS9VspVFAd2oomEl+SGN53UFvSk0pZZCBFgOGAtWocd6Ymi+ZQTck9+v9obrY0NNW4rC58UiJrfGV557isjQM91oHN0n7+LIlJuVUPkRGpbDyamxCMMjD4zmKxmXmcRLP3offDozId/IbjlGD/hQTb9JRM0yRo+V/+Y7SoFzfDZqUiqp01pOh/Ct4WImB6B7iwqkrdSQmVULVLSWPeBaUf73tko7oiQMXNoLh4fyzP2haWvlerk5Ccv7zjJH53hJaCrnYAg4lDcWgX8hw7DnZSaZYED2ZcW8U+p+HtBRKmIazE7gs8u+lRIJ88aVPN45WAF4DjP4yL7Tv6Wx7U0pJjgVOQM7Q2j/kPNnQ2GXZZg807qIYYSLi0eV42kyjGWFo/Z7PYcdadKBQtE5NVc+6guUnHxudIF1X8T0/V278Azg+gpGS7pGjnHsMXmHvuNWLRiy6jdQf8ghsae73DJQ7y9uhaMjh5TrtedkCeCp/RnMT7vi2n8icvDztmuvvRZ5eXlv+ycrKwu1tbW4cOECOjo6+Lvt7e24cOEC6urq3uYN/7n2F3/xF7jttttw8803Y2xsDPn5+ViyZAnq6urwmc985l09810ZB48++ii+/e1v48iRI7jlllv45zfccAMApHj3f/KTn7DVdMMNN2B6ehpvvfXW237mnVpfmZmZKVYgoDC9pFBsrGrDA/XHjDdQb5LNVcLrB6D3pVsvO/YgBtryUNLPSHkaN8LRzUoACQd7O8s43Ly9rc4wYQCmuJQWYH5IhfrIoyI9+fSujTWtWFvRjZXlg8prRcmZnoODOrzefGa24igXlZnp7+bhOYy9dqNxPLb4IF9qXkwlrRXe9kM1Rg9oPWuiL40VCsogLxHCYzozpw10INd4bNkQ8B2sKhlgz46jlWZKRpZeP2puNA5Mqy27f6QA9xZ0s7eLlFB6156TJVZSK42ZEuXYi0zzJPGejlqbR+tVEbQlc8+YKqUA1zQA7MucsPjXZajk+6eaGozHLhZRSrnrw5sM432RmLn4dViZ9gBV0qa2c7CC94haG1jQHku518/aQbUU0oXk9di3t+iktEDCa3TGpPImhY2CsL66w0SpHLPX3aiJgpHhqxi2HD5ntCbemEqu291VwUXcdnZU44G6Y+r/ejwNRcNqbYtG08L7eBwUDQjQ0FK0ic+mY8bIxgOAE/1KGWo/OVuPy2fDxLl6Glz1VlzY9+QbJiF3hoJIcbEr3R9nysUzrYuU4TRrGk7Is4yaNRW9DN9o7Z+LbS1L2PMsoxtkiDWfmc3/B4C5OT8BfFPEiNb80UWHVE4P4bejcfVd3yjG66o74c6IY8GtrynK2JyEVSWecMs0ntX5gyk1Rvy3FLb68fJDeKZlERKeDVUj6Aut2Yp5xqHAz7kkkmNp/zsqr4IKjknCBkAnjWepM8u1Qi4DmXJz45gRnjSy3nOwtqyb+yc90s93FvDemnvzT+w9RPthSsmdq66KMS8/4g4cLY82FbULJ4eGiGrHgZW/oMfLdJM6d4ZhglKZntQkFfq8y6KLG6rbsLJkCK9NqXvuYF8BvMkw9o8UKIY4vR4El/twTbeJ1Ij8IVn9mOh63eyERfyQokgJaGXG2WzcV9SBPZ0VeKjuqBmjjha4OYolblNROxzPUflTAChvihOSdcVwbyyCw6fm4eH6w1wh3c2NY85NKnL1552rWG7zWgecM3tOllhnlhVjHf1eW97NhuSGqnbAc/Bw7WGeBxrjmjJ1Pna015jIecTju5ASz9dVdgE+cLRvAcvbxxYftGWvjhhz5EZEN+hO5f5q5x7BjFuH5hhnnQ/7XtSUyfIc35BxEd5EGJ+qeM6KlNC9y0xg42p/bWteAucnxmNOz19d1sf3+RXf/gsNg3fSFixYgA996EN44IEH0NbWhra2NjzwwAP49V//dcyfbyIweXl5+Pd//3f+/5tvvom+vj4MDytnxgsvvIC+vr6fmadw5ozSJSORCHbs2IFTp05h165dePbZZzE6Oop/+Zd/QSgUettnXK69I+PA9318/OMfx549e3D48GHceae9qe+8807ccMMN+N73vsc/m56exrFjx9hqKi8vRyQSsT7zox/9CENDQ/yZX6T1taPTJEZ9o7nO1B7QSjd8xQIjPXmld7xiJ1FqT7E3GWZ8Hx82wfzCSrd+zmeW7DNCEMCqSuUtPjo0XyV+aS+jG1VJd1TBmBWEYJBEKIjkkXxp/H3Y01umFGGiS4sZnKWbnYA3HcLKUpMIbBk2SQd/VvVtFhZfPr6SBQh8wA/5GBi6IwWqABjhJT1Ju7oU5p+8sm5unMdFVY1p/g6M5pvqzSGfhaJ/wWTbr9eVNkvvUJEgomgEVNKbrBLJnmLdpCJH/dzZYfJYfr/8oFLmph08Wv88e1YoR+PvexVt5dG+BdjeUm8ZVUFvofLcAEg4eKazjhU9Yprgy0kL8adbl5gLWAt12nMS5gDo+RBwGzcrgflzfmh56bkvAotPz07hUJ9Qe5kv/pBvGbJj53PsZwEGlw2oyy3icZK0NxHG6opeXBsZgxs1FbFJmSm+/VWzPhoCQl47NyeBp1uWsrfQu6SUg50d1Th+eo59pvSY1hX0GvgIwApc7exzHI2hWgKrq3rVHAT2Bjd5fvXamF85SnnQe5/w73J9JB0k7TU/0+O9xGuYdNU8T4WsRGiEfMVspGuxeBOKKlcqKRYsw9GMNNrrPP+WH/M6PXWiAfu6SlOSDWWS5+5uFTkbeeUG7O3R3nOxn8kjSUrl3vYyiwYVvoGCfPF4I+AA+/sLFZ2rXJOYSRY9NGgKfxGbmczPWVGsoSqeg2+3lVsGN8FoQpGkkck6H4nOnGWgxyJcXZloWskw2tNTznPy6aXf4X9/qLqfjQyqwB3cK36GBy8ewvnzUUMGMCOOX69VtRyeaa8HXDB9JABes6d6lqt36flZWToEmQMQzMWovOv76t9ZCYbgkMOD1mpnew0OvZCHg/0mr4WqBAPAJ5ccUIZcRPV733AhO2Hg6Arm42FOCGYlUjspCm/7oemba845edr9ixnwJsOYnj3BrG1f61tknGHCufPc6AI801FvVTSXc+TFInD0veDE1X7f2rsE3nhYGZmTYZz54XWmvgPtMYHDd7MS6s5xzXj8ixmqJpCGCBJrFZ3hv6j6FtdloWiqzGOg6BIgIj5xlw1sd4aSKbs7Kg2yQPfxqZ7lyuMPJb/dnISV+8d5Dfr/dC74c4KJjKI9BEGiuaufc9bM9XiYcwnHkplwsxP4/LFfV8+YDGPVgiF8Zum34UbjHNmGCyDiK9n4PlVkjiJzgDo/6WqN/Kq9fduxYwcKCwuxcuVKrFy5EkVFRfiXf/kX6zMvvPAC5+wCwLe//W2UlpZi1apVAICPfOQjKC0txdatW9/2XfPmzcOtt96KTZs24ZlnnkE4HMa6deuwfv16zJ07922/+7PaOzIOHnnkETz77LP4xje+gRkzZuC1117Da6+9homJCQAKCvSJT3wCf/mXf4l///d/x9DQEDZv3oycnBx89KMfBQDMmjULW7Zswe/93u/h+eefR29vL+677z4UFhYyx+vPa339XM13WCgzzn88bHnRvVjEMGXosDMfUvISB9gxZKi1Yf6o8UyKS+Vzxz7Mni5vPKzoLD0HSLjGy6+9g98aLuYibHQhNpSetEPxQmHwwz7uzhtB69k7sap40DC9TIWUYJkyS7uhrAM3ZV5Ql8WFDBPO18rmH3d8mMeing9WHtdU9rBQ31Kr+I79pKuMEMqXkHRwGqNP3k4vFuFxrawYTIHokNB9qP4IqNrkb9R2cv92D5Xi3oJuvC8zlpLsS38ThaIXEzjXMcOFbebPYeNp7cI+fOH4Ktxb0A3n6mk81dRgFBoYjxspz0GcfbAxdInyK4SHh8esYW007+bL4N+bsLa5CD9S32opn96lCE7/4HolxEVRM5kgaJRJ3+CEddtc3czeZfJuPVR3lPcyNYpgqRC56G40rjDJRAWZncC+zlK8Hs9lOle6NAGFeeWcHb1v3MwkK8NuNA6PktqJ1Uvil/U+ozHtHirleVpX0AtohSIv9zX+zM2Z5wFoph66iAVsjhViMX/0O5lDQsakNx7G9tZ6hmtZkAu9rx5f/Jx5hhiDn3Thj4eUDMlMWqF++I6KDFEhQx/YXNnCv39s8UH1b12Lg8ZHzGZegAVGNlImaD/cW9BtaG6nQumLUelGHl53hvKgU96EfMf6asXx72YmVc6Ehvatzh+0zqiE0mztXaKcDmGfIy0yr+Mjda14tOwIrw/BaJLxEMtkaShZirWOlO3uqLRYjNiQoEiQ6+MLXXcrmFxuHLdnvWE9lxwzJHN5LeNasctM8pmiujdudoIhSPfk93NBO57bcUWMMOemn+LQC3lcUE7mfFFrH5xj1iHIOhSUKSSnyAOvZckXT3yI4UONhcoxdG9BNxvuuzsVBIfGRM8mJ0H/yO3cb3rHlpoTah4SjroTsmyjRu75YKPPNuaZmiAEK+Lo43hYGZ6ObxikAJY9vnYMsbNFw7uIAa/5zGy+a9cV9MKZOa1+lnTwUO1RAMDBnkKO/n+6+f+xnudG44pAA3Zky80xyexwfJuMQO/h1fmDFiOUF4ugdWCugk2RU++SqonC6IAxIyu39i6x11e/u75A1ddRzkSz7vfk96N61jn783qdvjlUbura6Lnf11WKzx37MLyxCA6M5qOxfIDRDP7FDNx5y0/B1L2eY+kz3vjl770rof1ncg7+K9o111yDZ599lolxnn322ZTqyL7vY/Pmzfz/zZs3w/f9lD9/8id/8rbvOnbsGH77t38bP/zhD/HII4/grrvuwp133oktW7bg2WefxQ9+8IN3PY53ZBz8wz/8Ay5cuIBly5bhxhtv5D/f/OY3+TN/8Ad/gE984hN4+OGHUVFRgR/84Ac4ePAgZsyYwZ958skncc8992D9+vWor69HTk4O9u3bZ4U/fh7r6+ceJIVsBX8yYF+GhFvm/1NhKX34qwvOMusRfMX8QtU1c0NThlvdB1cdXae93hRetpQSAPAcWwhCCYcLiWwAhiFDJnzJy5w89/t7i9TnxsMm2Vh4Snd2VuOZ1kXYUN0GPydpsMf6MpBsL4xV1QJ5b2cZK97bWpYouNO0yxAawAgsGq8bjXNdBDmuIHe9NxnmsOXX+haxoGQPn/7+N4fKFRZcK95BVgyqaGt583MNrIsjPOL3VNNgZ1tNyncpQuRdiuAzy76tPPtTIXxm6bdTGHaY2nE8zJAlVsoc34ICADAXRSyCR0qP6WiKgTGwcur68PRank/kWHvEnRFnVhUp5YIYb9pbYUft/0dKj8GbDOOZtnrtifdZKfpa3yJ4lyKpUCzCVzsCxiLgOrzmuXHs7SwzfaBx6n3mxF04OqdmbXWXYejQCtnR03M52mAZdJp/f2XZoAXfkoZCQ/lJAIpmlvpIDCQE1+E9oyk5paG5prLHKkK1qmyA329FYhwfO1pqbecA9XU8jC/1rLDmjs6AE/IsXLObkzDeO2hDXCj52weqeS881bOcveOHXsjDpqJ2FN/+KlcrP/2D662q3DTnNFa5r3e21aTeio5IOpV85wEj/vabX0ew7R4qhXdJJWjS/gUUR3z9nLPwxiLct4dKj9tngLzRAYP7m0Pl+HLzB+3+XcYoZ3hETLC4kbLs+tbPKCJCRjAALl64tWk5M/sAan9trGlNcUKU558DoLzqjL3XxqXsY9ynmiqOwquLfXTqnILezghNAr6quE00sjJ6x/uL8mgmjBwDVNSTDHEAxsivP6JyWGAgSge6i+CNh3Exka0YyzS0xc1VkbVVC4YU9EtHNmXuiJuTwKrKfpPQLQ2eqRAbjdQHSbCRTgOTNTy8qRDDRO/J71fzoO9h8lgvm3vanEFBfa2er/7aP1JgMcJtqGpHRMs8L6YYhrY2Lef73w/p2jRJxzJkAXUPAcCjSw6xo8SLRXApmcXzweOPmTt0b1ep5UByo3FsrlOGBhFVuDPiODywwOgh+v5cX9PB5yR4D7WevRNwfKu4HAAk4eKLxxvhRuMovO2HJiJLcy+oiamtreziM0q6A0I+PraoCS/96H3qnUyWIL74X6lJ/yLa/w9yDq7UtnjxYnzmM5/BoUOHcP78eRw5cgT3338/zp07hwcffBC33XbbO3eo6+b4vv8emSa7Xbx4EbNmzcInmlZjz+gi45kLNII2bCpq5wJo7JkQv6efk1JEv7+/uJX54S2GhnFz4cv3biluMdAm0R4pPcYKTfAydKOqoAqxI9Dz7i3oVl6CmFEG4KcKsOC45ZgAW9nyJgybxZbqE8pzF/KxrrwLuzsrUuZiY20rvtFUZ+Hk5WXNP5Pv0N9dWT7Iyqicrw3VbZr1Q12OjXnDCoJESr7ow8OLnlch6DRzJtuDJU3Y2rwcayp7sG+4UDE5dVZY60wXpjWGcUHFGEzQi9qXgXcpkqKgU3E46QkPXiJyrTiRNhbBNTefVzCGmMIYBwvfBNcUnoNPLj2AJ7ob7Dn1kFpng7CvAbSYG1VeYn8ypPpNF45vlMcHS5rwtb5FKXPYmDeM/d3FbOTRObGUN5orvc/SnUl5pqwxXmY/XW5NZJ+9SxGdQOpgdUWvqQys52JtRbdiitEJjcTDz0q746cY57Lvm4ra4cHhqte0H+ACyExy4nDKGviCVUesPTyw6ybd52kNvHhIYeA1Pj1lP9FzxFwAOnJAhjb1IY3M8qZDnJRMF6qTcPEHy7+Dv+leab2LvuNfyFBeXoFND8qEFKIBD9hc34SXJt6nsNu094KyKnDGrPkUv0uZ25Bvebq9mCoqtbe7lPehF1Oe3cMDC6y9SWxDfIYnw5YnnOhdLRmhCyMGWZMQ8lPYdNLK6MDP5N0SbMFn29HS1IiS9T2ZFC7mzD+fwRFfQBt3vsvK8+Va2rMXNKTlPRlXBoLc0ynPvMzYpSyV543kyzU3XsD581H7O+Oq0vwzrYsUNHLaNRTcXvp1CcodTpAOnlu9/o+UHoPrePhy0wftZPk0Z8Ga/4CclHOWbp4fqj/C8xXcLySLrfHo80H9vL+4VeU7iWgEMRxJPSXxRgKvPvZZXLhwgXM5r4RGOl7h//xLhDKy3tF3k9OTGPyn/33FjekX0SYmJtDU1ITnnnsOTz/9NMbGxqwCwz9v+0/VOfjv0KKhKftSEImJgBH4z7TX82fI60e/l6wLQSG1rXUxe5weWnTEttZ1/EpCI+jAER6evBNPHW+whSh5k7WwONBbyF528obv7KxmLzMcH+srO03S2bjtubdacNV15U3vUgRrynsVO07SUQqahj4RRER6aOAAO9pqOVRN88rKH0wEht8h5vFgVyFWLRjiWg00dsKB0lxTZASAhf92o3EuukaewnVVnax80Bx4sYiiCIXiz/diBnIk11l66XiqchIqZ0JCp6ImskIGGXmoaH0As9esC0Jyc4uEZPoZXVRuNM4XG80Jr/1kmJPaKTwN3xgGxK4RfA99ntrDi583kSwdWdlS3KI83WEPbnbCYI9z46i86/uIzphUCY8TYTOHepxkGACwGHPoZ6sres1lK2hEATD0zxsPG0YW/f3rr73IY/EmwmjMG7a8ZBKGwmNGwBM+w0C5CArC/ctJMCuHG9Vc4ARz0mcx2DbWtFr7ZPtAtWK70hc+JY67UcUoxPvMUtwc9Rxd/E/ujQ21bQpeUdVpF4FyfDxSesxSou7J79cVxc36GocB8Kkl++HmxhkS5U2GOZpKyZQqSgMzz/T9aQMdI/iUM3MaXzi+yshSgoZNqJwRZ9a08rJ7il5S5lCsruhl/nU2DHy1PtsHqpVhAFiGAY9JK9zB6u08BxoDTmvKRojr45P13zV7TXvc93aWpRioR0/PtQ0DbRS5OQnDKETw0okw/qO9hPu3uEgU6HRTq5Azc1HUjiBvrGm1Pe76s7Juwaa6ZutRVgVg/aw1Zb3m9/pZK8sHbRgbzP1ANTNo/qTT67eWHuN3e5cU7CU3NJkSJeH3yciGjgrJ3DE5x+uqOzm35q7bfsJ9ej2ea7zxsQjj8B9bctDqP6AcCHReGkpV5NCdoeaBoockP638EZ1jCADOZIifQYxg8GCKewZqMwBgGbGpttl42YmeWkMm/753qaoNIyFWgWrmgGYrdGDdffzxpMNRM5rbTUXtfE+70TjLgNrZ53h+vJiKSJNhIHWIIGRzW/MSa2x7uoyj8emmpfhv036JIwcAMDk5icOHD+OP/uiPsHjxYlx99dX43d/9XYyNjeEf/uEf8PLLL7+r577nIwe3fOnP4M7IUF6yCTv0trJMea79ixmWghts6uJUtIs7W2ss6k7AHHg3W/Firy7tQ3YojnlZr+Evuxot7/MDtcctLnnyZLMCEHg2ewBc2wvFClf+ICc4pnhT38bbBKikNImf9MYi2FzXxMJTjo/GhqSDm+54HT/8/vtYudk5WGHmCcZLsbHO/I5+b3lkpnVY2fWxvqoTuzoqlTJ+IUNVEA14DeUzUp6pvV4rSoZxuH8Bf2/Z3NOWJzAYfQBgKSGX82rLvq+p7MHezjI8tvggvnxiJeCr0LBM3rLmQveNvsc/D3jSaW/Se8MZSSQ0BeaW4hZF+UkebOG9DHqa7ivqwPaWemyqa7Y82W83Fjg+6gtPo/XsnfCTrk15q/u/qryfIzjWOgb2mfRAyvkNNi8WwfrqjrRJbyke43TeVRGdSPcZ9izquZLz5CQdUzxKenxpHd7OCy1auj7J6GFKcrCYp3UFvYZyMDinpGjRe3WyftD7jQwPmHTZELHGrcfyUOlxlXApop7edAjQUC/Ccl8OLw6AIxTBfsu+L5t7GkdPz1XyVDNdkUIfjEzSuZDKv/yZNSeBz8nPUpST5fploqf2oilZyrJXRBY4QkIRo3RKPpD2vAajjKsWDGH/SAE+UXYYX+pZwQYjMfPQOj+66BCealuhIKHiffKMW+/WBpUcCxf/0vtcepXvK+pIKwf4ecyKZM/j5upmjIzdwDkQpHiTdzk4Fw+VHsfW3iVK/jTXs5c6WDiSvlM4/xWcfPnG1PMV2PcpEUIZPaY5nwyrXIhpV9UHov0X+DzdLQBM9CzgsU8nT7wJ9XxSsOHruZfnk/aN7L+OjFDuGn/GVUYUQ54vGeVddRRWVCTdnpNnpWH+KA69kJdyHwb1BK5/IfbOY4sP4svHVqaNyKmI6yW8/OCfX3FedtLxin7r3UUOBv7Pf//IwdKlS9HZ2YnZs2djyZIlWLp0KZYuXfoLqanwno8cuDlxFgBUGr2xXGGKCdISrDBZfddLqL7rJfUzUSXyjXgUjpfK/0+4bW8sAiQc7B8pwK72KnzuxGrrc2sruvF081J4lwwWly4nYj9oFNhqQGE0ydKlJETAeJv3dihl04tFsLGqzeKIZyWFvLJjEcycOcEehpRqiblxPNO8yOCv2fvo8/y50The++ks9eOoosy0nkFe2xmqONUnyg7bl78WOg+WNBnPje9gV3sVX6xkGFgFcLQHi/8fEOTkKTw8sMDylB89PdfC+VNlaq78HDQMtJdUXviNecOWF+nbzeVoLB/AUz3L2TNPytKDJU24v7hV4TvFhehGDUc+/fzRRYfgRuOonX3O7B8xNjIMALBBuUZjhiUzCT2fPGjbW+vh5saZk97yVov5hOOzYeAkXDT3zUNj3jCckAdv3FBQ0txKb5S1ngEDlPIZgkqZ9X5tUO/qrExZ101F7UjXggmelzMM6HOfXHoAgKIzpv7SH0qApP5ytCXpqGge7ZkJobjoiuX0HpIjlmdbstDofcW4bME25o1FFG85fZYudNGIGpL3esAYXFXeDzeSZI+pbKV3vMJK1eClm1mG0NlzM1RRJ2fWNOA7WFfZZXk1g2tF9J3pDANqhweU19+XDg6RNOzFIoDn8D6ln0nvZjpWNPLIBz3uwedLWRB8vlXEUZ/vfcOFjDnnYodUiM2HhbWWOVR+0jX0r554TyAp9zstZfAuRfBE88qUfvF+Sjp4qqlBKZAeLEXYzUpwsi15+ykn59PLvsNVdR0NywHA3uytzcs5t+bZgSr45zP4M+SB5jVJ2hEyNxoHPOVh73zxdt43XixiqlULb3z1wrPwYhFm/NneWo+VFYMpY6bqwFTAj4vCiRyH9dUdVtTn0brn7c3gm3PF0WktDx3PgXPNlKIAl7ViKGo9YYodArDy10jmEBEF3wfEwKWjWzQ/Ki9PQTbJmOK8Qto//H4YYgD6nefAzUwygsCdEeeIgBuNo6HspMWWCKi98b8rDuCBumO6T4ZcICT3qkimBtT53TdcqOBOuXHrHDz5gW/gy00f5JpDMmGc+3WFJyT/MkcOWlpacO2112L58uX4wAc+gBUrVvzCiq29540DqUQSl3aw2AiHB33FfNM6OBeto5pbXBymQy/k4beWHOPQnSyFzkJEJCZz0wJHFbxRQoZo6VbMO6WEkKZKPdBdxIKRwqMkiA70FFoXIFUxpPHtHKyAm5VQl3+2YcFgtpCQj/M/nGldlFzlN+BVpWcqajMgWMZeXoReLKLYHCSsQZebf+LE3ernAubixSL4Wt8i+FSBU0dtmHZRt2UlpmKkm5MwnlztDSKWImudsxNYU9pnQ4N0KB+Ob6oYUziXErdoP2jaOwlP2N+tICcPljRZHNRkZNFae5cUfOnr/bXY01lhir/RZTZuz/VTrR+ANx42lWVpn6bx7jXmDRsDQz9vVaVN1XqoL9+aY3l5ymJDai7AkRY3JwE/5GNx6Sjjp92chKrwScamVL4DHlz6mZxzR1dYvbdAccvfnTdif197j93sBHsYiXJy3Muw5ozWxcr50HuSCwBRCF7syS8eU1G7HW218MYiKvkzTfNiEexoq1V90XA3QzUolE79s2VzTwOOjwO9qo7IQ/VHjEcuwH4ERxQe1HtsVdkAG8UArOKItHarK3o5ST+4H+gz+3uL4MUV/aA3ZmqMuLlxrtXijSnmMDc3ztJ+a8syax3daJzpGIPnlObgqqtiAGxK25QoSpq6G3B0kuolVajLjcYRS2TwOslohywEJw2lEwPzVdE/Mp6FUSf7wUmZ4oyxDPMB/60MKwHbmwwjBC/F0VA7+xxHWeAYD/6aSkVbqmB3forC6E2EsaayB/fk92PVgiHNvGMbR2zgXVIKpZ+haW9d9a61C/vUvaL7uK+v2CSweo5iYopF8ML4DbxWzjVTqrhn4QgsRrRcAymk/AFvPIx/blrMY3XS5OI9VHrcdmzkxtXeoiTysQhWV/byvLWP3mXt03VVnezFZshZLMKsd/ULT6O24IxxWon9P5EUcsR3cCGZbe+zsLkz4JlEc2qmuJjZf3yHS+N8Mqzr96gfbh+ohhuN4yN1rSYpfCLMCdOyngbvTV2HCI7PRCDc17C5K4mmlQ1KB2xU7ukxBckaKwf4jjh8ap6hntX3qheL4HPHPoxt/XUmSgYACZcTjBvLB+wIBMz+I2pu2R4/uBHwHdx82xuWA8ii9PlVQvIV286fP4+vfe1ryMnJwRe+8AXcfPPNKCwsxMc//nHs3r0bP/3pT9/1s9/zxgFcxS1+X1EH03sRI4K5/BwO2RFDEBUtCnpzKYHnqaYGRTOnFZR1Zd2m/Hs0roRDUtGEyXCdFLpeLGKqkmohIJX/PV3l5iLUXiJvPMwsSbvbKu2QqxZedPk7cZe9Af6FjJQQvBeLqOqwFAqFuux+a+kxc8F6UHACTXMWVAxJad3WtthiIWHPDXlNfHGpC4+zm5OwhKYXM0wyx0/PUbhMGt8lcYl7DlfQvK+oAytKhpmGbt9woaH2k1tBeu5kyNZRDBfkuSQlMXhpfq1vkRLYV02ruhjkySTM6AzjAV1Vrg07DcdKp/CvLdO1NSjBTUJzJJNWLIL9PUW2YA/5ODCaj821Tfj00u+YfBRpZGQnDL47kjSKEsB77XD/AniTip3lxKBiNfCTat/UF56Gm6WS9TdUq4q0OQPZ1hgsrLSgwHNmTQOeo/JHchKGHYPWL5BvAYCNaPJMW3Obk8CyuaeZ+pCqzjLsIgBdYOWI6mrkxjHlm1wQy9NLnmsyNGhsZNiNRVTFbCgl6+jpuYALpjre2rYMbm5cefTEGfYvqlwVpgHVY9nfqWpfEG4ZnqM41enMhHzsHynAgyVNFrVs/Zyz1hlCUldg1RAE6dGn70lPKHk5Fxe9oIp9+fZ+kRFDeo+v8wzOn4/asmtc7GnXYKNpfq2o0wy13gd7C+DFImh/8Q7+LleMJ3iMbNrgc3MSiF49YaJ3OcaIIXgGGZzelKI8pfMHgNmK/GzPOAR0oTX2hIuzNTMyyTKrfuFp3mMM34yHGIZDc0GsY3vby7Cns0Ix6IyH7aiar5wmG6ra1c1L1JG6ubmqgvxv1rUzVMaZDFnygaIge7rKFUWnZoFycxUlLLPNQXuQs40xQmN0Zk7Dm1BV4H2qcUJKK4CvNq8w49K/2z1Uaoy/3Dj2jxQo5iOdsC7bnpMl7DSpzH+R/01GYvPAPLQOzTEVkhMOz+3+kQJsrm5mY/HZgSojKy5FsLq0z8wbzb+Wab6GlMHx2bvOTjQ9/+pv5fHvH73NivRtKOxieQXAOC50RInuKb5HRO2ZlCbrpVCUMuKB8gM5MTgrwU7A50YX8H1ActSPeDzvnE+nq74HjfG1C/twU+Z5i8UqGI3k/DiSUdox94OX32c9i84JzcOv2pXZotEoPvShD+Gv/uqv0N7ejtdffx1//dd/jZycHPz1X/81brnlFhQUFPzsB6Vp73njgKo9PjtQBarASJekG40r5V0rUuwJth5gPPPUPlF2mBWcLbUnFJNPVwU+VfGcKbzlKSH669U9/NwV805x2I6834Dt9W7MG4Y3piITcBQzCPEy31/cqoyGzgr17xnG08UCDODkOWeW4oze312M31pyjMfJQ4vG0fni7Tpq4rDSRBVbAbCAWFvZxUlsc25S1qh3KYL3ZYzpz5n55r/Js+Zrb6mG82wo7MJjiw9y1WEJFyBFzs1JYEtxi0ryJG8mKd/kpdXCeXtrPe7Med2qDOrOiHOZeoIEBNdXhle/OVTOnkupkMkmPZQ7O6uVh088nyAAblQVeCPaTP6uVrTIgMgNTymPem4cK0qGeU69WMTyPNE6sDcLUNz+YxFsH6jGF7ruxu6hUi7+JBsVYaN+c/XcnATvK/iqCjOtnRNS3szmEYU13j5QjasjynM8XjShlAIdXXFnxNVel4qiVpak95mS6ngNxEXrn8+wE0iB1L9jEVXwLmIoCnkdhQeakgktpVe/c3trvVHYHJ8pOFnh1gbHlkXHrSqybm4c2ztrGQ5Ec0a/IyfA0y1LjcISi8AP+ext5rHTnEHBLwBgc22TbbhqyNjW5uU42KsEu5udUBEAkRvB3lGtPMpEeIYZkFHuKi+nG1VVk0//4HodTRD7zIWJ6Gj4gx80lvR8bq4xRjs7REhJdxQNszRw0yVdehNh7O0sU5FOTQhgramYk4lYBuCCI1EEFakvOG3BAFcVqwTcmzLPp0QEJGwmpT6CqCNyoLuII4vB6rUzZ06AquOScbm2olvNrTaE2NByTM0aOtNuZtKwjokoBn8GwjjOSnA+yCfKDlvzTYX9vtr0AZYLEvrkxQLMaZSTouW8m53AswNVZm4E9IWVQoaGiSi7qB+xf6QgxTCg9vCi5wHXR+fJu9SzcuOKcliSV0yFGBZHsnNLcYvKeXMV1NFqYV/VLeE+OwZiOSmqzOckLEbAPSdLlBFKn9fOJTcnwfcUAJM7NxlQqImSWvZdMCVxYT6A6ax52iOCESwzqSKq/Fz115vxKMsWgjQS+5GsBUMwYEJAyPts1YIh7O6swPcn3of1teJeTZPzZEUFdbv19tdV4ruo9O0kHDZcruR2pdU5+P9ni0ajuOaaa3DNNdfg6quvRjgcxsjIyM/+Ypr2S5GQHL42lEozCiPcg17dFfNO4VB/vpWcJT0wqyr7sb+nyEQfwpoSTdCEAbCS8T5V8Ry+0HV3WipLq290aAnGo70k9XPOovnMbHixCMoXvojel25ljwxFKciTH3aTJpchajzk3lgETjQBfzxs/RwAU7kybahk6dBCKi2dHIU3k4YW040qZpv2wTkp77lcoikZVHCh5jPhYnVFL/Z1lVqJg0HvJVzYSZqBHAKrrzLxLQ3t7GOLD+KpnuV4tOwIVzXl74okz8bSQcNz7oAVKkBBF5yrp1Pmfl1Br8GYywiOoN8DTJI4jYOSgxvmj+JgT6FFyUhJaMG5pEbfkcm3/Fm5vpQMpxMTnaykojIF7MtwwgWumzJjEwmYaRN2ZSLgxQwgCfgZvone6UjUJ5colqUgXe9lE5npuzJpWJ4bz96L6fJKmKlFJ4c6SZ2YG6Az5HdqD6l/MYM9r/xZx2f4yJK5Z3Btxhj2nCyxkx/JoNX9W1PVo7jRRQIrOQyCPwt6AS+XOMx90u/YWNuKHe01cHMSWDHvlF03JeQDcYFVp7nRc0uf50Tkn2YC101ddq95MZXLJdfv3oJu/GhqFo72LOA9fm9BN3a211hGvtw3btQkvQYTfFMcN9R8gJJDg/AzedbT7gMx35woKqkqZQL1VAjXvP+iYcER9LGSKjddlNDqD8ltkTOTQq0ci1hn3RuL4KFFR7gKMZJOCm3y2zW5/+R4G0pPmn0h1nTFvFMGpkiRGh/W+Iju0pIDfCYAJ+FYGH8ARtZohdrNTFrEGOnkNgBkZscx8XoOr8uGwi5LmQ8mbl/uOTy3gHWvW7+X8kwnLctGyb0kC+S46Pvrqzqxq7PSIkAJnhdea5HsnO731lgDd7EXiyhSgrjD0SYqrEoOIGfmdFpyEmv/iruMGpGNeOOTV3RCcvGmd5eQ3L/9v39Csud56OrqwtGjR3HkyBE0NzcjFovh5ptvxvLly/nP7bff/o6f/d6PHOTEMf+WHytLW2McH1v0PT4EjeUDKaG3w6fmWRdE0DO3v6tYeT7pe9owAMAeNRbGngP/fAbeSkRVhVNBV8jKY65imJEVFglug6SD+4o6cKLXMBH0vnQrR0DIg76uqhNeLIL/aCtRhcvGAooQlMfBkWazMAu3D1TDG4sYzKFWHImObkON7REhCI8bjSsIj2RAADgisaW4hSM1NC4KL0sBqLx3+hFaGO/rKoUbjXMhNrqEvZj6fmPZICeZ3503gi3FLXyRudF4SmVg6WGm/q5aMMQC8stNHwQAPNWz3PLKrCvotSIjz40uYMiJhJEAgB8JRJq0F2ZXu4pcPbboe7xm3C8REdjdVmnWH2C8OhsBAi5FMA32aOt5vjtP5Woc7FGhej/kI0wwOcGsJZNGG/OGbVhb1EBteL5IOfTB9KkbCrsUTAKpFzIrDFChdedqoyjzZe74XJfhQJeBTlGkxfp/sOn9Qu9V+FyzF02Va0cV69IQNSfhwIm7HCmA46vwui4qpzystmfOzUhyNMCLRfBAzXHrrHIxt35jrAUVI/Lkubl27giNc01lD+fABA2AFJz9mKqa6sVDTPlI72gsGwRCPn48NROba5rhxSJ4vqPAjEdgbq3k1HFjsFFhQfia5lIbhfI93Dc9/9IwWDb3NHa21eD46TlwZyjIlTehIm5ciEsmCUcNjI+qW/P6+w7Dyag9tvig9R03R8FM0hoRIUX/StEGNsQru6zPr6vqxOqyPiv3oXrhWTP/CRfnz0cNlapn9pgjyRMuRaxCe/R/AAwxdBIK2kLzt6fL1DDhQoCE0NTRrq1Ny81YHXMm1i7sg38hg8+9HD/9e01VD393Q6Eat5N0LMNA5pQdPjUPROfrXYoYT7tYA/LOrypTRBmbalrw0KIjbPR8dFELy+rgOyii78VDNjFGoCL075crGtOpiYg5m4BFhLG2rDs1MpnG5enFIiYxWxeADJIfUDSTm6bylb8ndkEqlAnAjvT6DsOwGAYZtWGLbBhciqCheDjFyfFA3TGeOzYMxiLpnXT6rl6dP8j3yoaqduUMnDnNOoYcgxeLmGrpYszUVzcax442VWtmTf5A6mReQc3x/Xf1573QrrrqKtTW1uLv/u7v8L73vQ9PPPEETp06hZdffhn//M//jM2bN78rwwD4ZYocpPEkEE57V1clPln/XTzR3ZDe46w9TNI7yhSAwnPsjUVSKC0fKT2m2Cio+VCsCtqaJ5y+m62YKfZ1lqZa+MSYlBvHpqJ2PNO6KCUiANfHxuo2PtBBjxk1J9ODH3dZMNBYCVM9nsxQyYlpLn8rKpKuaAxRrQUVmUs6pC65y6WnRVLBpvGY8HcCtIiNecPY31PEHr90RZPo+TLy8/jS5wy1YMDTR+9fMvcMjp+ew9/fUNnOHk/ZZ3gOlpWMmM9exmOVtl86WkKeNGIscnPjXOBOfpcvKQ9AxGfcKb3Tv5ABP+ybZwa9uwHqT5qPYMSL5vbAaD6z00jPkyyQQxdxusJu/F3yygKpZ4v2kXiG5T0V6wqIPSW9sPq5XHRMN+kdpkuVxsFzJc+18BIGKRvpPVvqjuPr/bU2Dancn2J+01G8potqpRtLsEnPPwCmbpx54yVc/HEuQwk3V7aYYo76fWsX9nGdkuB75c/uK+pAbmhSsc6QhznhqghDdmq/aYwAOKr2QM1xPN2yNFVepPkuRejYyynWhAtV6f5ddVUMb756lY4SwOxx+Y5LESDip/UiW0XsRHSBvNYbCruwo7XWppsMzM1rUzNTqrwDaq/clf1TtF+4Eyf689gxQcYSPYeV40T6KCz3VZ8FWsOH6w+rei7ByIcs+hWIolrn6O0KqF2KYHWVKQoo5U7KZwMRLngOQmMhJGclIKOnaed93KbxBoyzzs1Mcg0OinQFC5wBthwPjkHKu01F7Qg5nqroHKCl9mIR9ogvmXsGR/sWpEbP5D0W8q3IQYq80xEgNnrE3ltX0ItdvRXAlGuihg6AhINHlxzC3/cuhf9mpkooFzIgncymwqDW72g9BM1wiswRd+bj5YfwRHcDthS34I14FHu6yq27mOV1oC+JNxN49Xev3CJoJff9xbuKHPQ9+4dX3JjeafvHf/xHLF++HPPmzfvZH36H7ZcichBsJIh2tVUhx50Gko5lGJA3lp8RVRi/9dUGy+fMmubkIen53dVeZXCxUAwBlocrN45fq+oDAGypP268m1AYzqDnhLxG66o74Y2HMT/rR6DCajLygKRj1RSgdwEwXn1oPDklc2nMqzcexvbWemxvqTfFzvR4KCdDKnjmYcZLvmrBENxsHWnwHfvC1hVayUPhxQSMBUrBfbjOYGrpQt3Rog0d8moEhN7+7mLjvc82yllKElbCsZ77pZ4VlrfEfysjhcbx+Ok5it1E948qNvPcavpaOD6ODualeOzkszhRc8LuF+0r9UsTjvfPZ9gXtFBU3KiivKXLWUabnFnT5vcwSgMrKAG8qTIqYc2xF1PUm8RaxJeFvgx9z7ErfOuL8qHS49hQ3WZdXv/WUs1jkmcAUMotMVw5Wlkib7KltNK8aC8m9TGdMk35LZL9irzDcFXxLNpDvutjfXUHG5beRJgpj71YBHs6KlKZmjwohWMsYgwDHUHhuZCGTci394TEPEPBE5bMPQNKvH2g7phZs8kw75dHSo8piI/Y1+V55wAHGLuUZYy6hAsPOndoOsR7a8oLp8w9NfnzZweq8NUTH7D2A/WbPuuNRTgJFoCFf15T3ott/XVGwcpJqOiGGDPvvYmwqhECYGXpkNpbYZ8x28QeQ9/jSreaHWZ9bYeSNcKDLr2vJMM3FbWbiIImpJC33p6TJfCmQkp2poFHUt7ShUQ2DnYXIti88TD2dFbgie4GnBiYz4nQB3oMDPJPl+0xfcxKYGNtq5WL5MUizDjlRuMp+UZEESojaQ+WNNmGcE7CMgzo73vy++HmGkw/oOCG5JV2kop6m+TUzgA1dVCWksLvJFRlYf+GSRPhiAWirbLPnoOnm5eqz+nob+HcV/H+m84DUHj/HW21lqwPnvP1lZ3cJ4vGlOA4+l58pr0e25pUIcVgjYSH6o8oh5OW8fL566o6dZ+F0RswDNxo3Ha40Zn3gMoFKvmaEqAnkgR1VH81lqm8B3dGHH/fuxQN80fhXKOisU7CMREWOef6u5ZjimSKnls3kuSicilGoCh098VjjbivqAPb+uuwp7OCz447Iw7fd7Crq5INHmmApqu1cSW1X+acg9/+7d/+LzEMgF8C40BehF5cRRB2dFUzxu6ZzjpzQVKyZtQk8rDHMjuB3UOlil5zzEBbzHfNpbKzo5rfe39xq7qoAU4O3N+jKv5+vb+W2Qg4POkaphFFwaceSqHXTx/+TYYm0KXrjYcVxdtEGIuLRxWzjDRusjUXdk4C82/5MSrv+r7Bj2pWgjWVPaw8ulHDdOOL8u6AguHcddPrWjE2ysW+LqUsKd501XdFf6dD7xqDSvOr/mGqgG7tXYL6OWexprLHMI3o+bw7b0TBXgClcI2HsaJ02DK4AKC++BQo/E3KgTcRNpctsURIIRz24Gd5fMGtqewBwore0Ll6moUuUeJRI7iVMhBJ0VdKaeVd3+f531DdpubKc1JqGXhTITYGeU58A02iRvMhEw7dzKTiiw8JakbBLmIWPz0EhOZX/m5dQS+IepP2rBeLoL7oFH/G0Yp0sH216QOsWJBy+7FFTWlrUwBKSdrWsgRwFSOHNxHGhqp2uNkJk/AslEpO3p9U3jylHPnmfVoxcXPjArJkErLdnAQO9+Wb8zxDsfsoZVEbWqQ8Ev2hxu5yzQaSlrKWgWC1emDRMbVeC4b4DMEHQ7qYnUv3ed9wIY72ahankK/w21qpdrOU8XlvQTdHHgky58Ui6H3pVrjROG59/1uWkfTsQJU6N9MuKJlxZnjSJBNrvDrBW3jPUBK5GE/9nLNwwyo5nRmXclVVcqtpeBAxzgBglq6dHdW4466fpHjQZRLxwf4C/t3O9hpOrPfGTKX3zOy42a+OMlr9kG95bWmfsAzLSagoSsAAX1USgElQ4qvvMOOco+eIckn2dtkRXVKmnSkXcHzck9+P6oKzBpYposmfPfYbPNbqu17CzsEK9XxxFqnCNf3bRAn1c/R6udE4GssH8LW+RagvOpU2yiR/RtGv/SMFfG8d7C9gI4QoTmm/kkHEZAWOUZTXa7Yc+E4KpIbe+eiiQ/Bi2njWa7G2sguOp4zwtQv78MAiZQSffPlG/PjVqy0WOu+SosWWRAT07F2axpWZwvSa0VyRwUIKuDcexqcqnmOmNTcaZyY/y9Gjx3xb5pssK+isWgm5cp9R5J3yJxyg+9xtgA/s7qrgOZfrcTGeZVWOpyiUF4vAz1aJ2Y3lA1hboWFSou4BGwbjYVbeU7RcQacq9w/Deh3lAKi86/uqX8JwKrnjFT7H9Ld/IcOM70pu/rv886v2tu29Dyt68s8Qvk4rOukSl8bDWFU2wAnG/PNAVVUZogXAF1U6j/qq8n7l1Q6GK9niD4QlA1AZ8toGv5vuIrjczwEVMt/dWYHGskFVIyFNIjAAK7xKYUj1odSKzPx9CVlyjKAJJv9dLtnMSmJzwd59uoApvK28TrZX8LPLvoU/PbaG18EKGwdCzDLcHlxLa37TVDzdVNSOZ1oW2VAR/bn6OWdxYmC+mqukg001LdjeUm+NnZ6v+imgLfr3q/MHsbe9zOKlTrvGtCY+2AhdmPcKRl654WfuAS8WwbqqTrsStlb4m/vnWfuYIylJF/6UywluTLN7ucq/tI/d1DECpsq33EsMYWleYUG7rD0SM7Ci4JjoMxsKu/CjyVk4enou7i9u1QaH6gf1eXX+IK6PXFJsQoBOxk1N6JQVU4PQLG8qpC5fAUnhhEx5vnVS/drKLg7bz5w5gYsXFQWshNNZ+09f9hur27CjqxpIuNZ8Ge53AL7D1d0BmGRRz8HqSgMRYZhP6xKsLu9jes10SfwNJcO4JiNm1TEA7P1IycTOtGMSy4XBV7zg+xh8+Sb+2bKSEVwTiWFPd7klS4DLwCTE/AVl64wbLuHDdwxZsMn6OWdxXcZYSsVenhMpF8T5vtxeZoUL4IRfmThbO/ucxV5kGTwaOilZjEg2yHnkSr5eKkmA7E+wIvbdeSOKRYneF7yXBOTmwZImbG1enrJ+1vN17Qcn7sKZOZ0CH5T9kHUaKMlYzhV9bkNlu4FCprk33dw4/Lcy4Gf41jlKWTeI+yQA/QzCIOmeLr/zZXQOzbbGm5GVwLR2qEh5wRj+hAtXO4JYdumzRvPC/QpUpJcyM3hHe7EIPrP02+gdu92+y2k/y/ssML9uVMmrvZ1lKXclP0fevUkHyPQ4J6q+6BRmR3+K7W111j4MnndLZ5GQOw9YVjaicqcoUjs5gVcfu3JhRaUffXewot5v/PeHFf1Xtvd85IAvdiDFMFi7sA/OtGvgKbp5MRNKI6vd4ofPFQIvx9D4UXhxf1dxSqVF+SwrKTlX0KjKhGCAubO98TBXFU2BzDi253jJ3DPK2zYVUhAh3zH8yYBKzgb4eVuKW3D89Bym0lTc8OrScKMqHP344ufgjYfxaNkRfm1t0WkAKgHbogUkXLd+356TJeyFsi4qR9HzranoVV5Szc9NlwpdMm40DmR6XFTNjcbxp0fv0R4Rh71dzJcOnT+hKTWtHAnHt7y+lOzmX8wAEo5VjZSb9orxvHvKo3iiL08pMZlJIOlge3udgTQ5Yv2g6TVFoiz1aW9nGeDCwAmokBfREmpP0OrKXq3sOmgoU+sWNAyC9HvkYaY1AGDNUfPQXJ5f8pbJ5uYkMPfmn2B9RSfvUy8WUVArkWsiC29RoiQApir1YqpGA3vftULx7EAVxpJZIMgZjcWJ23ksUknwxsMcsaEk7J2DFbg2cwzeRBjb2lVxpxVFI1akZ99wIZ5uVfVJGDogID/saSOaQddPgWaxgqIvzHUFvXi6RRUVouRBN2ogXXs6K4Cwj/uLW9kwAIzBQXAV5vKPxuEkXKW4aMNgXUEvagtPW4WdSN5I7DsllxP/vNyr2/rrsKmmBftHCjgRlZUcHeGBrxJTOZKi12h9dYe1J3Z2VivZcLWoMis8m/2jt1k/O9q3AN8aLsaKohHLwHGjccUnH41j7cI+bCjsUvAfwmSHVERxVdkAPyt2KUsZBp7x9DafmZ02L8QbD2NNea8tbyTBhGPWPJgT4eboJHW6GR11xr2xCM5Pm3WkM8uwUAHP8y8oKGZ1/lnLUwyAIZ3pDAMAWFY8auCWWSbSeKDPrq4u97I3bmPxZfE8ScvMdJ4TYTSWDCoYqO4f06cKOlAA1tl0o3FOspdzRQbXN4fK8amK5yyoIzVSZp2rp/m7AHDjdRcAqGibdJ4BMFG0aBw3X39ePYccKb6uIaT3cve52/g7BNGShgEl3lMOmxeLYF1xD4/FmTKqEM1LsJBm0OAh59dnl31LfSjkMwz3c0c/zAgBGWV2o3Gsq+k0MidATNGYN4x9w4WKOnwqpGCHLnhP/O+KA7weVFC1fsEZVuybB+Zhe3M9JOR0Q0V7ClTpofoj5qxrmeZog/j46TkmwV/I4Cu1/TLDiv4r23s/ciASkoNeSOKilp7hYJKSbCvmncKhnoXG20qeLoKtaO7p+4o6VHif3jMZBlEmpvX6BLzX/L1YBOurTYJz0HsUbP+74gA+d3x1SpKtfN7Nd7yOH7x0rfF2ifFzeFd6ZoIJfy7YE5ECFUg3Boq0hHysKe+1KEnvL27FttbFVn/vL25VuG6aW52wLXH3D5Y04Wt9i6y589/MxEeXNisFQnq0Asm8cl4+W7kPf9q5Om2y8M9qMiFben6DSXdy7jky46Su3+X2BKBpDjsqGDu8r7dEKb/k5fbFGgaTlwHbyyyeyzR39Axfrb2fdFXSOtGCEhuV66cY0ZyQJ7xhacc9HsaW2hPY1rwEjucopUR60MibJcZCUYH2H96OiVgGGw8Eu3p00SE81fwBE60QY95c24TtA9W4v7gV/+fEUlaCAGWs7WipZcMkCBdxo3E8Xn4IXzzxobQJxIDx3vH/AxEB67PCQ+lG42ws+2GNYxR72xuLYGX5oGKakt5Mva5EORxcS0AZxdtb68E0yHHXnhc53zoSQlEcqrmQTm6kUCkG+ivl4cOLnk9JnrUoOUWfP1F2GE8cv9v0kRQVeX4vFzEVBhvtra/316adF/oZPz9wD1DCtiXHgvTSAXglYGSAoXwM0FgD4ERUoknV0YAUyuM00ThyzhBrW0pUzRqgWVtJF0tN0urK55NM31Ddhm8OlVvf5fsmcEZo/tN6s8W8+RczzLkj77Sc16mQOoOTrokWXWbt5M+88TAeqjvKd4B/IUNVmr4MIUWwpTurQUIISaIRHHc6EgvgMlFfnSwcpM7m6BBFgwHjWAnsU0mOkDaqKShJ1y7sw+72SjtnIzA2wNCxbiluwdPNS9+eXv0KpzIt+8i7ixz0/OuvIgdv197zkQNqbjRu0UCuXdinCtZQkqb2SnKSEiUjau9BY94wDp+ah/U1HUaYR+NqBsVls6GwC7mhSfVd4S0ibLpSyE1InirtPlR6nKME8pBSmN+bCOO50QXYUmdoC6U3wLsUwV92NYLLuev3W96PaBw/+uks4/kQ4+f/k9ChxDApaKmSvFZKyTvFQg4wXh+ibXMAR3v493aWcTVeLxbBtuYl3C+iHY045tkryoatKAs1uhRk+0DNIHYOVqjq0a5K/uIKtwKvS/PmxSL4087Vus9OCq4yWBk73e/WVnZZnrzV5X2MG0+J8Oh5eXjx86z40nOWzT2tvdJmzVjxgsINr65UHvp9XaVqP5FhAHBRLx5LTFVo3lzbBDcaZ0pT6id9j2nuonGsreriy2nGVeP8Djdbe1EdWAnB9KxlpSPsMZeeW/qbL/ycBH4yPUNBCwSFJfdLROAoujOWzMThvnw2DNxoHAVF32eP11MnGuyIjFiL7QPVeLTsCLa1LYavcfP0+x1d1VysCg4YV0wRRgB4orvBwCrSXPpB4gBERCRCr9s9+f1qDiR0Qc+7M3PaOls8T7lxvE8XnLMUE0edcTIMqM29+SecWzOWyDRzmZnE6spek/8QjVuGgZuZZAOdimE9vOh5Xl9JA0yGAeOvHR+fWrLfeC913zdUt2Fr7xLLW91QehK1+WdSxvlgSRO+1LOC1+6TSw6YPaCbTAgHYMuynAQXvIOjKtfzWSWihZiiruQ8k5wEPrP02yBKU3o2kTAE18E/LyKevsNOIHo2RS0pwZUjhiT3ko4Fn1lWNMpGknP1tC0jgjJIn+f9IwW20SLlEXl2o3Gsqe7hnKSdHdX8WW88jBXzTuE369v5/zLxn84BRWopogOoir0UgeNIF5TxYRkG4+EUwwAAPlzTjY01rdhU24wttSfUfLo+VuerQnWFc19VwxCQUb5PoOBRJEOiM9SduqGwC/Ad6w7gvD1NDpAiy2OGStZ6XzDaSrl7BL0LOOr88xkIQtXYcHF84I1M6z3WWjkq2shQIL1+nOfC5CG+OQdaxHxruJjnn84x6Q0AsLJ4iP+9u1OdVYpWqI449v6MRTg/6OmWpSn06oC6U+7J71cUwFd4+1Xk4L+mvecjB7d97Y+AUFQpUxrvKTH+P6tJxTfokQVsb+1nK/fhs0fX8s/5GQFsYpAmEVBetC/16LL1ad5h9Un/noqDcT910hd56IPvX7VgyOCRhcdqVdmAhY2k57nZKmwa90OYFZ7AswNV8GIR3DXnNbx49v1YV9ml2D6COP9YRJWKj7vGaJDKlIygaFxu2qhDENMpPD6UT0Hrwt+RuSKOr/ItOovSe7V18Zn1tR1WwZq08y2KI1meUGatIO9PespEq0CVzpOQeQj8rnTFaoQXcf9IgfEESRy1ZhDhojw0dwHP7uWiCIC6eCNOEv/cW6uSktPgbAHjeSPPIP9cFvhzfJWH0VqfXrkJe0DSwYriERzuyVfUrKJ6M73TKtIkvcV67HfnjeCmzPP4en+twVpryf/4ooNMWUvPDFKCct7AWARb6o8b7zNF+/SZClJbSipTuXZEtUmfu6+oA//SW4NfWzjEZyyIh6cIjn8xQ0UTpGc+sNb0fblXvViEKVat30tsMhEmpNuTRBcr5M7lvPWlC15C//dv4fWRlaLJ6KCzGcSs31fUwXk5Kc+mMyZw9tIIlfjxYISB9gwpdesrOvFvLdU2Zjw4ZxNKSVtV0Y/9ncXGcy2KA1pz5NlKpYXhlmszpj3OWQlU3/USWk/OsdeC5FPA007RUG9CQaKoQB7NQ/A+2VDVjrhvagTI98o5TfFwS3kssfyU+5V0rPOarlFBzob5o/jeyAL4cZdZfbxLETi+Y8kFQOcddRVz9HP/SAEq7/o+Ol+8XdQEgpKtJD8vQ09t5avJfLeAp9z3HTgEZwzCgXPjdmRD5IdQFMWSWZqCVuoOwfsp2KxocchEAe8r6kDESWJbyxJLLh/sKTR7UBRF42idk7pmKXkhQkZK2vPgfpBje2zxQXy5+YP8Tjp3hFq40iMH5evfXeSge9evIgdv197zkYOHS4ynfXVpHwAYjL8uxrU6f/CyGfludgINJcOA73ChKMBY2eydjkXw2SNrLesbAHtNSXHwxiLY01lhe/0nwsYwGDdebsvbTdRtwmO/b7jQeCp81afG0kHs7Sq12R60YJWGAY8vJ6EqIoufydyC3Z0V2DdciO3tdYblx1d4f/a4uWBvVGPesPF6OL5iKCKstP7zUP0RM4dZCSOAhf1gzZuel/W1HYyb/7+tVVhZOpRisDFOVz/rudEFKQVmJIOOO0NFZ2hPbKhugzcRZjo6fra+NKHhMOSlozGtIyYPTVfJFJowyoMb1ThdTYEKx3houD+BuhQ0T7R+rOgB7BGi5/qhAHUmgE11zdZ+PPRCnvGkkndRf2fnYAWeaV2Eq64ZUx8WHlirhX3cV9RhGwbkPRxXSrWbk2CPNL2LcjrcaFx5rnMSONy/AI8tPWgUMoHBBVQxJv9ihrWPaewAcKC7iGkLyZtIa/LEibv1OMzXqLgXQXsIAuYkHcswcLMS2FTTohSuHLVem2ubACjoG1OZxiIqCqS9zSsrBq0zu72lHk7Iw/7uYi6etaGyHQ/UHePvk4LizJzmfcZrpqNJtD8b5o8CCUOJ6sVVtGNb++KUuWGFXeeCrKvqVOdlKqSw3lpZkQamVLiD9L7wHTYM/IsZRuEijneNzd/dUQlmufEcpq7c3l5nM35dNN5NJ+GosQvlliJJjXnDyjDQ7C1rKnp5z5Nsrp19jvMpdrVXpWD6DbuN+H/Ix4HRfPaeS+z+4+WHAACV+S+qfkR8lj0p1I5UJHMirKKkWga1v3iHmVtiFSN4Es2D9uBzAnF2QpEUBKJK9HxSiHe21xjaab33LJYj+T3KudPzubqyl73TlLtDRtmykpHLOkg2FHbBfzMTzWdmAwAOdhXC0QYdv9cFy4X7ijpYfhwYzWfHATHbtY/cxXNBHnGOWOuoHmAMtRXzTvF92zB/VEfjbcy+F4vwzxxpPEqD1FFz6Wd4HMVnSlJf59botVGDVwxXlB/I+RF6j9N7OWctKL/19+D6WLVgCNtb6vH1/lpLNn6vs0hF1Om7wstPdyblAZDM5rkKRBipP4qpy9wJdO/KonSfXHIAXz6+EpLhaXWFilLvaq1Klf1XaPtV1OAX397zkYNbnvwzuLMy4GYkbbzuZbCJXkxBMq6KjOP/ni5Rl1LQQ/E2RWVSnncZb7hs6wp62XO9rqAXuzoqYRW0EcWZUtg1wh4oMVe22tnn0Dwwz8a7AileDzlu5kQmxpCshMqzGFqg8MvS6JFFzMjLmnRQX3KKGT3IO0dJhg1Fw8zX7uYkLDwmQgpO4mYmUzxDCPlwqcJv0Bvjw45EuD4+uei7+GLzh0D1DwCliHyoqh/XZVxShd66Kux10bjNxvIBHOguSvGGW/MEEzGS73aj8VSvYizC8xmMKhCFLHkJJfZZNsrDeLtGc80esQjh2ZGy33heXVPgx5sMY0XhCI6enmuxFVnf0QWBvLEIFpeMovnMbAuywZ8di1hsLN54GMV5L1tMNjx/9MypEOA5cKZdxkZ7MZXse/C1BbhtxpuKHSriqz0zHTJ7YjyMxxZ9jytby2gDtYdKj+OrJz6QanwJb+n9xa0YuHgzJzfK9V5RMoyjp+fyz9J544LrwXsv6XKla2K/AdR7rXMu+sWYZABO3FWY6iwRLRIebpIDroD7yecFI2vE1BSUTSljSCOrSJGkSIWMkHnToRQ5AcBeKxnZi3jWuU7LTKX7kJM7hfGxTCtXgr63vroDu9qrsLqiVymel2PCCUYidfFIN0dVDX5/5KKKNGmWp239dUa2aflPY6Gfy/csm3sah/sXYHHRC6xAA6pOxd/3LlV9IEYsvbaP1x9kx1BKf8U7uB9Ic54pCjEVUhS2DqwoQsP8URMt1HPAXnM3jSd8IozGUsNwV33XS2gdmJuKmddryTlKEM4ZEaXiu0VGZOQ+FfebXCsaqzcZhhPx4IQ8Li4n+8FzlTRe9RXzTqHrtVstMgDZGuaPKgpdJ1DkLHgvJlw442Ejywh9IKCM8r6qLTiD+bk/NnpGPIQtFU18VoJ3KMIir0jMm6VvpGHaChYE5c+I/JuUiAIA+E4Kex2Ny81KGNYq67tXeOTgNz+HcOSdRQ4S8Ul0/9tnrrgxXUntPR85cKNxpvl6pq1eWd1CAPjnM7CluMX6/IHRfOwcrGDDgL29gGEQGjMeV+mlp3LsXAhNeJWILYS+Q31hL+REWOUY+A4aSwb5ObI4k/SUAFAXpVRyNasPKegWfpH6or2A1I/V+YNKSGYn4Ey67MHzYhEc6s+Hm5Fk4eWNRXDr+9+yPBubq5rVe2bE0TxoFCiGzLjK236od6Gd0BhJGkXVc+AIzDf10c1OsAIh28baVrjZCWysbkNt4WlmiVhT3osvHm/Ehop2wFdJ3HfnjeDjS7+HA91FeHagCntOlqTgSYkFhXC29PMH6o/xRb6pqB1uVDFbeFMhONP28fHGbcOAWEs4YV16ufS67Bs2FLN3ZL1uKT3U6GJ5pPQY48trZ5/j328o7MLDdYcZG+zMnOYibVLJ5D2kE3+tCznhsPLrhLyUQkybalrUGmkvMSs/ulYGRVqoOTMVptqbVOen/4XbDH7fF+srciecuIv/ueSomaNoHNv66/DKj69G85nZcHMSWF/axf2Vc/lUz3L+P8OQ6HyORfCT6Zl20h0pABoXvq6gFz+cugrd526zcMPUj8ODC6yf8foIz9yW4hbjDRdr7YRUHQcqFIiEgmGsrei2DAOKeDSWDQqohTayPFiebTKKvbiuzZBwzX4T8yLPPBc0Cvm4+frzcLMVBaQ1HjE+ywlBfdRe3m0tKlqzvrrDeDAD55QiBkiY6AGNd1V5P/4/9v48Oq7ruhOFf/fWLUwFkprnkeIEEPNAAAQnUQxlhGaoxzBUaMqM/BjpU+Tok9uf89zO6nTS3VnxykvasZee3WoraqsZM4zUfIxkmqZFURRHzDNAApw0zxMHoDBV1T3fH/vsfc65BSmx0l7NVnTW4iIJVN17xn328Nu/jZQve7Kpul9yruT7WtEBQIbBeIAttS1oLDvlMB8900YRqj1DJeQwiIhI+azA7JROxNXnXK/3947cLXPMBbuiUQIZY8YTD3aYJLl48PRc+AVpORv8czYMAHPm+HzahoEdSbHnKhyNy/tY2Q5HKYrElKcUydHGZmFKDIlwPMC+XgtKqr/vzaR6NN6Ej3A0jpXzTpkzoSDEFwAoAlIY2VfW3Cgd9dlad0Q86nTOLShe6BkGM103BiDlmWXAqvnDViTck/wpPy8NLxZi1fxhPD9cRBEEa7/eV9YuEWiAmI8OnJpn6IPHAirGx3JwlGoMRHO3nD2va2z4QYg/XP6CGbvFYsjt/vpjFMFTHloG5uKpliVGzsQzxohWmpWJo7E5epG0/FlVdVwiYva5ZaWfPgvZu/bPWS74BWnjSIreN/rOYTiuLR/9PDLChM72Emco+qL9+tvn3jgAYIobsbCyPAPeZVN4oo2S6MKpmAOzcJ7Bis4MSkJ+aMlLqCs94whcgCp7qvM5mAwDE74boQtye2uDeXbo6QvRI+NEAY0lpwVS8PxwEcLxwOH6B0x41Ibh2BfqipKTTriSlcmNmvbu+qsvSAVZbqygfrduF7zLptzCJ5YShoCS095473Jnbj5KJQT6wUXcBHY1og0aHZFYp0OW3FhgNZSeFliMGDRWnoKEUhMpbC1vxvYWEoAX03loe/k2mefnOqoAT0lBrr09pdjbVSaVqu35sr3YNoTJ/veTfYvB9H/HR66ni/WKSQqf2zSnmo1KXTAJkN6sKaPQWZUq7SrA9vi+17VKqnev14WgpHkKs2Jj4lFq7pwvxuWOgRpTmEoZZQgwik84FRMoC4/RfjdTe9r9kqRygLDi+WkoXfNA6PkSKYGhRUPizOkdjmkMbX4kGsSwPK3cejOnpG6AJE5G5mjnYCXUxRxs1pV0eb4AOMnDG0oIenJvSRfgEZ3rqvnDhl1Jz8EDiw/BL0jjmdZFdOaSxOUeJuOGEnc8cCEbmg1oTdGgEzl6sm8xFO9ZpqXlPZWfxp6hElHg7ytrNwwkmvqXlYbnh4vo8s9LGxhKASWGSxKkhhj4ca2sBqGJuIwFuHvBUNb8MbRlQ20n3nr/MgBEAdk45yzgKdq746b4HMPdRJ6wB9tqdqXocCrmyJXasrMAqAJylKFrT1c5HlmyX1iybLrlcMQ4cJ5sseBSiihwW87eTnvxYo5EYsIxggJ6E3SlcWVmnns564V0DwiERSeESnV53hsRZZir80rTkDuhlLXEZDgaF2eFTZsqv7f+z2MFIPAyu7imX5AWwgH+3paGY/ALic6W4YrezCmCwjUc03PlyZzJeWV6Z45wFqaIWrQwRRFdDZX85pJ98m672YnmPA4A4rFnDL1Nz2sXOINP8DGGFHK7ewGRGuzrK5F539JwTMtMmpOZM8dlriUqqMe4raVR+h4m4wY+m4rpCJtHCdfKyDoZw0Qgc+YnUmZPeWb8/8/B33AqTEerNz/V1kjnkIkWPA09jUKBC1N4f3KGPHdDVReWlp7EupoePNO6CPv7iqlqvN08i5JbZAvNeVaUYyTuUFjzu205zsYbN3Uxx+x1hnRGjOtLvX2RkPzraZ9742DJHWcx7/Z3hRFG4CeAYCL9vDQJWq5kyl5yy5PAeFbGGP64dwk6Xr5VFByp6gtAxRWuy71AhoSnyAsbM9jGcJwEM4dHn2hZBoSegeOIEkJ//bvlPzPsKtDKFuN9PYW11b3y7sOn5wg2VuoNKHOJv/XO5fDz0rivrF0UJ27fPnCv/Fsw8jNSqJv9qnhwwokAyqoGvbm+Bbu7KoQRZlNNm/FU2bSdCvBnpEh512MMJ2MitFoG5spFJnMQeli/sNewNOjxM8NCOB5gd3eF8YQFSuA6wmQCAHFii1k294ys5X1l7VmYe8SUVGLmeg+83n5+Gh2Dd5i8iLSHzQ0tZm/o9aEK0a7HhgZkEijtSqQyVi34mfnk2RPluE5zgNOm8vDnh37LQFXyQwMrGbMqI+uLg9um+laJvjzRQgbEhkUdWSxWJg9CUTXcSR8MaXAuEADwIIWQwmQcPziyGjvba8mDxpcQP58Nck5008YF5/8AcKIOMt8FaTy65AVnffwEQSe8mVMYzeQiTMYdik3b28X7/enBasHQ7uvRyodW/sOxwBQxKzQ5ESpOcIuf9i9y8mAcWEcI7O6qcCFlyTi8DBn7K8uHRDlUGV/kCcO9ftq/yPUG2l7L6KU/GhcDhffOqvnDxruo81DsPJjnh4sEThiOBcREpKuHA3CYmdjT7c2akmgTQLUaVMaXfrKHlvaZUVZZ0WwqIyw2K1gd/VSUiuUBV2bl9fxhz3KBt91T3Gd47T+BElbmQxswd9UMigd+TVU/drbXknMjP02MPQVpkYUPL3kxO4fio1ypqcAtWleDla1nOmrNd3VtCIE+gvbmirmnJbqi2Cusf8eyq/SWtwXnvn5hL2wWMNlLM+j+sRVSKI8cBp4S2llu9xT3iRG6rZVyaL659HlHEea1NZ2yFOSxgHLFtPf9+90rXfiLnm9mPbMdNddffQHra7qc/nAlZcpDo5+xw4iNZ9uz/vxwkcnJ0/P+0/5Fzl7kKMD6hb1OXp78W/edDR8ACPLSeLTxBfM+jpjaxmJeGtuaG+V8/v/u/IWRz/ozKlBSYdpeU/79Qw0H9URZCrsF+ZG5ANDWN0d+tut4BSYyccQQCqMfG8P2/K+t7nXy1pj0INpW1wxI7gRV955GE/asiIOv8H/Ud+AbVQecPWG/WxAQl3JTn/HPF+1T2+feODh69g54nsKBXs0UMmoutgvpfDD1mX1xSOKZBfFgQb2xvt0Js7NCtqeb+KD5MD3es8x4ATKeURgjuQvc7IiAHE5d9vwvOpumTXICACgPuzsrpU/L5p7B5cGYGCAsFFmIsuK4rXUxnh6sxtb6IxSmHQvMhQJzAayYexotJ+YYT1ZeGp5vqFi5YikrOE8PUjVUVrgkWU15BKuyLkrbg8SelU31rWbufYWdbbXyPDYozPyQsF9VedxSPMmLJf0CRGAePj1HfuQoZ4AoW8Ioo/u/blE3wvHAKfYVJuNYXTFoCuqMBaib/aokMdpGXNOCE7S3NAsH7w/bu7O2pgfrqun536zeTwVwxgK8+8Ess978zHEKkRNe3XiVJdoSes7Fv6O9jhQ2ZfZgSsUchg+A8hr4OQAI28/QO/ZosiIke89MsZ9I4aljS6Qffn4aWxcfdi4a+iDtFfbu+flpwr9qzv91i7pFcf/B4dXmskvGsaGkB7ULCE61+0SpeNWoLy49pb22nADppX1RviRaNGaiDbs7iE9+Ux0ZVFI0jC95S2l15sqaA2/WFJ5oXo6Dp+eKUas4WmFhsJl2kb/nzBOTH3DzgPXVXbIGYTKOfZ1aUUvFpI9OrgsrBz497/GeZXhoyUsIk3Hk+mmTa8IUiTacwlL+1JRPc5v2ha3Myxgj49Gl+8Qjy84OgbXZSr4COl6+1RmvFPsqSFPROK1c8c+iUVleWzaK2YNsV5UPJwKZuzBJlI3heECRtdAjakZPkcF45SQplRZURIxR5cHTkE3ez2JE+CbqZePcU8o3MD6rMvKDFUdlDAOv3yDyjIpUQiKJXytvMbKxMIXdPRVCpwpFlJZ+QRqrNeyM76xnT5Q790GYjGMs1N9L6/VjmAzvWZ0jwGPlgl3RiCa/28H285kcD/DOB7MM856OPub5hr6Yqbfton38WUB79xmulp92zlU04gLAwcqvLR4w8NjxAN+ueZ7mjuFYGQ8/OLLaiaRHc45smeYnUvhe1yoasgXR48KMLPulT3of/Lh3SbYcUmYvbyjpMfPOsCCQzO04MZuMcKY75nFzxKUgTU4Iy7DzZ6Rg57vw95w5TnsmMpabwcONB+R58nfoYVdnNb7fvRJKmbzF+8qo+OGWMlPx+lJuXvjZ/nzRPr197o2DJXecxcmXrzfC3IN4s6lCrSLspYUfJ8WD/r1q/jDgKeTHSFA49IVaEeU6Bn5hCju6CP+6ct4p/KSvgSoNW7UUNtaRcfFgxVHB49tKxtqaHuPVKEijqbqfBJWdyMUKxlhAVTItiMbh03PwVGujESbaS8Xf9yJY8okwjv3acPreobvhJ6gAFDyFpup+wqFHvEwACc91td3ak+G53hYNuwC0Uqvx/DsGaiQ0zZ42u4WjcRFGYVLziDOTBf+xlJpVlcfhF6Sxv2chKWac2DUZA3yiMfXz00Jj23DHK3JRh8k4CUxFc7Svj5RV8ZRoA/JnLdVA2hNcM/fdqVBbkMY1eSP4XtcqBzfsF6QNRawWRgx3sj1LuzsrhXr2rw83iRLgzA3jpvPTwkXuXKLihXbXl5V1j+sH+EoUJlkfAE+2LDVsKACaKojByzbGVlWcQMAJpNrT5ydSuL+B5nTrksPSlzAZF1y6XJQhHGXfvgwZ+8vz4ITRdT+f6ahFx6nbqV8TBqO/oaTHMVR4X8izdf9VPHQMfpYHK+edoss4HlLV5XZKJtzeXp8Fb4oqK/b/hVUpkULt7NfEa1l42TgQU1hdOSgQCpEJbCjYa5ZICYMYr9+zJ8rNmHVEz4kkwoVVsTFqQ5Meb1kBwNQtkDNmKx5aySya/ybNq94zdq0GTs70Eym8NXk5VpUbQ8b2qIocixoK/O68tKybn0hhRcWQuzaR9fML0mJg2R5NqSqvAKS1gZ9xI3VOtAIg2OeoOTfMvW8bat7MKckVAUjpDF7Nc3MrPANtOzIwX97XVN0vsCyuWAxQxGZF5RAZWHodOarISaT2+1RciaNnxdzTCJNxkT1RA7929mvS/x/3LqEK1xyxtmAyvE9kbi3l324SWdf3zcqKE/CmfKnSzpBTMZoyHh5Zsh/bmhulX4K3100gqxFWMtkzE5QD50QLeS9YkbEwGcdUGBA0V6/Ddw992TFu5ftjgetIiDzXzkVaNpegwkrfk41zzsK7jPYBw71WVw1k5VjVLnzZdXyx8yEVw1vjl8nnVlaekLvxyY4l8PPTWFpOjinnu3qels0940Siw6SujaSZhWznBsuHMEmEEPbZ+dExonS2c9VsZ2HwFkVi62a/ir87ugThKLEdcQ7lJd2+iBz8Wtrnn61IV0gGYJgixgJhCqEENmUYJCLUaDZTTFa4f8xlq+EmTB7Mp/xxLlR+xsBAktMzqkQVj8Yyi/lnJI4N9R2mkqd+trALRPpmV78V5Uj/zH6XEz6OCGQ/Pw11PoeEo37+irmnDWsLz5eCAzuJcooDVuXjsQBe2qfwsjfNHCfj5EVO+1QB9+gq9yL7hMbQConAjMRFQcr6rPUs9kSure0Rb7Y9TzKf53MIbhJlkuDLk5Vlzf//u4tbpNKow+wUubTWVvci7mVI+bPqRTh89BarijyDE9Ks73gZL6vfn8RII4nhETYrYc7Q74CvtPcRYtBkVY/lip2jlMD97tRMhMrHrfkf4kfHVmYp6jweeX/Gk3mz+39PcR8mQ6JBXDH3NA70Fsu82R7lf87+eKjyMB7vWeaynUTmObqefmGKKqb3FJnxT7efrP74CVMhXdYm7ZOnNu2yed1T3IddndVmLDa7izJKi3NGI++K/jy6xwBSfH92rBoqx4UN8HsAZEU2ZF61ApX/RhzjNxKmfVvrYvgFaXy98hAGRm/Ewd4iswf0fiAmsexK7TarlvNz3hv89zQyRD7LydrWGRA3l103RX93S1kbnmptNLLaZn/RY2Ymu2nrIegzMa1cOJdDuUf6fHPdjGgT+TcRECPXNOd086JWbG+rz5o3OasRORutemzPo91stiIApk6J/v50cw7oSuKtDc5enFbGTATYsqiZ9rwtGyxY6fqFvfA9JUU9ZQ9qxjqWr592hnkubLko74qyxNk1V7hFz7rFomV/z86PUedyBGaYdTYidy43rmNgM8w5d070rrKiZSvnnULFjNfxva5VMob7ytoxEcbxTOsiPQ7An/CBKycBUCHE029dkzU/6kIO/mDFfvzo6F2mz9FzpscT/ziG5Xf1Y3/3QnnH+tpOiuh5I5c0W9GidZ+Nraj9uS/Yij6tfe4jB06hHGZn4YQ07fVHmqahqXLAuizpH+z5iMJB2IMhz9YemM31LcZjqu2M31rSSUp06IkSyZSebqKQqXgJBRwbnGu8HT6FVO2w89riASNsI0KKDQNu4Vjg4j09k0wrSZMJKydDQwVU3EBXwmTcGAZjliEVMS8ddgXdWFByoS7xnI7GBWcpgldXb33s6CqCLUTeYdcpYC8ReRXNz2xPGWDgEvws9nCzF3fPUIk8S/kK4WhcoEQA4F2mL0QdJWFvoYRpC8nb842qA/hKY7NJgBs1igvnOUgfQw8/b6uSuhffXP689M9ZT09XfE4auAcr/JsaWgWOoBgHPmJBQz6BdUJgSODoDo2LufzZEOC1oE57uOyypKvYWcWh4FEC9+7OSuzpLsPjPcuyYSFWuJy9a48sIziWbbiGScq72NNJXuGDp+c6HuRvVu/XHlHlnElJwB6NO/vkR0fogmTDwJ5n/t7W8maJRDD84GBvkY7wQbxstteazoqHe4r7pH+MCZfCgJpuGD5wY/556e8uXSjMPMyTsxdt4ViA+8rasbXhSJZnnucEMMmd9rw811mJ31t+RMZs4+SjibdhkqhBZc0VnedMDhnGE2FcYCo/7FmOgz1FjkLHXs+6krNO3wXWYnm7OcrGe1pdzDH9zhjPZ9RpIoZMPin1fL44SglAclcAIolwDCeuA5FIiVd2W3+d1GUAqGIze01ZTkbhdeF4AO/yKcCHjO+J5uXZ/QWkFgcAk7TO86PP6fbWBjBRRZiMEyRR76/oWgGEhQ9HqU4P53lwZHpLWZskRu/rLhWmOQDidf7txe1OTRiW2w8sPgQv4+FimhQups68v+Eo/HEL4snyOi9t9rzdR2WU7F3HK0zUnedxIpCEeu+yqWwjMGnOMs+7k5djOYJWlw8637WpRmVf6H0r38/JSAVgO/+H76VwNI4vNfTJXfJgxVG3jwzXGnXP47aWRqdiN3+P52xDbaeB2wIOlGp/z0L89eEmMVLCZBzbWhopcl2YwqPL9hFa4MpJ+f5luePSJVueeLOmkArNWZd73Yo2sK6RKVDkrFRmDp49Uf6/h4ddqc/254v2qe1zbxxk8WaPZntW+P8XU3lGqFgYSz+Rwo7OOkehgqccWAx72HYM1GB11YD5eZIYdDYs6sDq6gEplc4KLb9nbU2PsxpRyJGjVOp2IZVPh36KWBmiIUAnzGnBNDwrpMtwKPu5klztGbhSlkdKh6tXVR4X5ZDxsRTGjUAM2AhRkQiJrcSHrrcSviLuejuRcDwQLzVAVaKFoQNGyEexpWyMyNwCzmXBbVXlcYGePdOyyOSVAIAi6sZwMobtHfUIR+OUIGc9452pWdjeVi9QADvh+6f9rtcMMYXfbmiXdfibg19y+2c1UWo9c7GunHcKgDG8OAF+VfVxA9/RxaNUhjZX5W1vmPUYiaOpys2v2dZfB5XxHYrNVfOHZf7On084/bIraooh7rlRDj9BSe0Co+AgxRjBln7Ys9woHKNxKiCn944Xerj6yhGEyTga55wVCMP3ulYJ5piVYQQh1tb2GMXBYorxMq5SYysXfoLO2hNty7Bi7ulpI3H0GXqeDUnhJNFd3RY2l41w6xJGTOGBxYfImNcMSPAUame/hm9UHXDkirNvrLySbW2LpXiSGAC6CBqfkb3dpabvDKWwFCX4Cmuq+p2EZGftFPBMe611BumvqZum4BeksXOwkiAxLMP47xnueWs7foc1JtrTK8uH5P8A8JXGZkc+/JsVvxSv/6b6VkeRueyypEmc5AjHRICnWpbI2NgpAF+JQms/n/vjReEwPNXWuRsLc6i6rMAz4cjSNUWDWFPZ7yiVlFxs5pKJHAAITKRy/qvg4lkPVhzNVtRiCqNpYhbaO1ws+8s2CKVooo6+PtddKYnu4xmKyj3VTpWovZlTsu5OsTIPFIWOyBq/II0nWpfBmzklCff8+W39dcgkQumnU/XeN7AdIfOwoqViqCsyJtYv7IWfl8bCW94RuJB8djyi0MYUrrxi1DmPnAS9qbQT/oxUdk6D1bWt5c20j3UklO/eFXNPOzSzsga8nr4iFi3djx/3LnEcZ4YUBHSeNPGFn9B5S4kUwlQMjx+7k+ZG35k7O2qMI85KehfdJKR38xm3nXY/OPob2NlW65w1zuUBSA7Y+ShPNC93IgUb6jpkngGOSoGMDc3GKLrGSNy5v75o/7ra5944CCcD3HztOWwo6XEug3CMsI3qvKHvazl7O5aWDxObi+UVASDCZs4NH4g3x/ZmelO+XJRFiXcMHZ2n4KU87OyoMYVoIiHGcCzAnqES+HlpohUECVhvIiafAWCwovo7nGDr52SA0MO2/jrcvWBIPmt7dwAS/CrjUwXLaZIpuW97usrFcAmTcXiTbtGhMBmHNx5Dwx2v4MCpeZLArex8htAkQXMfNta3Z4XB/UTK1JmIK7ngOfENANZU9pPgHSEPm7qQI5f/cx1VrtLN0AGeszHCp7OX0qa+5EvTjiJw/gU8DRXQXkqALoKdg5VYXXZcPCy7jldgU30r7l4whC1lbfgolZD9IuvsqSxFhD2cVE2Wfq+C6bOk/AQx6dhr4Oelsb+/GDs666T/fAEfODUPiCk8UvWS8Np7Mbq4el69mRKoS87i0eX7iCnE2l9ZGFPlZV28crHomhp2ZIG/I+PVF2bgZ4yy5hkvIMP5lpYPi9KxS/Nwb6xrhzdzCh98NAMAnMJSABzmK0682zNUIonGssYKsu/4onWiSrqKsJ+XlshY1CgHQFSwISWAslG7q6OGFJWUZWgqbYRZ8+DnpfG3h1dI1KepcgB+QRptx+/IquTMF/vW8mY3HyDi6PAL0uJ5Za8vJyEK808B5RRwUSaEHlVrLjlpokuFKawoHzZGvU5WVBdzjHGi92aYJGYwdoxEI6lhMi5GKxum66rJYDvQU2zmNDS5D34ihQ21ncT571H04+nBajCTTzgeIOYrqQkgsKq8tChRomT7ykQGClPYuviwzIVEKFhOZTxEKSd5vEINDEjEI5yMSQLtnqES7OkrNUaDjmRQjQEPjXPOouWEIUAAgJVlQ+g5dav07ce9SxxPOK+jvc93dVaL0cH9ZwjilrpmendGJ5PqRH9OTme5wHNm129BTImRhJhyq8brsdp5AbZjRXIGbOPAA+BTEqtD68rRWW0orK2laOyu4xUIJ2M4/vr1VHxrUYeBG+oIGlMP+/lpfPRxoTNPuzqrEY4HLvGEbmuKBh3Y4xNHl5O8ZrYuj4xz3o8sPwCdf6QpeZl2O2rAra7WVdCtcfq5GVMPKBI1F2+81iW8KZ8iPZpNUJLe2VCLKTqP4wEeWnxQ7vL1tZ0SIWdHZe3s14yBYUXzGR72zaXPO/fwzvZa+S4ncEvkTRdB4znxpoEFXortCyrTX0/73BsHyHhIhT6F5fLoYHHpdUk8ti6UI30LiBFBCz6httRVSc+8fbUcGFbkAeB3GttEeP/gyGopIuUXpOFdNkVFpBDxmgNgSjxutxd8JP9mPCjz7DuMGZbgXr+wV5RNLl7j56fF6wRAOJC9WOgm/kW8NuKF1c1PpCjnQAvrVZVUqEXlhWg5ezvCpGYEGTUXK0cp7AuZvY7CJGKFd+2CQ34iBaTo0ubv7x0uJuiXT/kAKocSRxvKTptw7XgAdSHHinrQ9+tKNIe7VlJthcQJ2RcY5Yq9xBtKeoglRnv61hYPIJyMYf/JBVhZNiRz9PRgNfZ2lWFbfx1R4rJSljARDr6sJZEtMAnJ1cWvYHNtq0Qy1Me5Wftkd0+F0z/G2ArTCsOq9F6BZuqwPeC257Pt+B1SOEz2n/YOctEuAKgtOWuUJ1YgeH9MuUajX5gyRnFGK8v6M0cG5luKFGS+w2QcDy8+gCPH5wm8gi9YrtTJLUzG4eVkpAjS5voW59IWg5hzgQqJGCCK3V+/sNfs+YnAJLlPkTH+tfIW+b3N7f5E83I8unyfs38k/K5rgPB89rx6s7N+4URA51nvh71dZWiccxZ+fhr/ZdV/d9cnnww6G7u+av6wUQTYGEzGDZc7zJz6iZQQLEjtAoY/+ApLy4fx0WRC3rls7hkcPj3H7C1RokOpBuy/k2fGPRoX9je730xXub9nIRUorG0FAmUK/dkJ5hrzzkrvruMVNOeBkuiHnQcjyiEbNAmCVd5fd8xg8gE0Fp9x5JeN+15VeZz+YZEl+DNSDrUjYKKc9nkJxwPUFb0MP56Bl6uFrVWA0okyFaZIwdcf+3rlIYRJA8kEYKIgYKPEeM05wjr7hg8FxhitE7Gy6oSwR/kJImHIqsxuwRMlUs1jyskITGo66MhDdYdM0qvlZHKihMx4NBkjdpw8MkJtr7gNKwyTcWEOs1s4FiDuZcSwWllBdwRVMDafW1M06M5zfjpLywuTcezuqnCdHDF9NmNmP/Gcrpx3ypnX/d0LZVw2OQIrzV8rbxFKV5HvOkIXdcYBIOPdzlEbD+BdNiXQWum3TadbkBbH3497dfRKkVNCPP26dbx8K+AbmQHAkXffO7pa9q8dgQCA7x5dA7+Aar147+Vi1fxhk9cQwmFQuqSb+ox/vmif2j73xoFfkMLleeNgWlAAuDKeRJgkTLk3a4qUg0jBLW7ChAHXc/ftZXtwpG8BHqo8jHAsIB5sZWjAWHlnIWVzU29Y1CG4ZVu52lDSYwQ+Q300zEAuoTzy6knIdizQxox+eBDKZbSro0a8HzZbyOHTc4ywyLNYhpQOwTK9a1JzlmvGmn3dpdjftVD64UBytMIn3NwKTjEf9tzvHS4WBVGdzwEXSHO8LXHXsAjHAmzSRa9UbkYSGlsG5iJMxqmQVX4a3qwpXJkzKlGHMBkn4WmzKUXwofbP7q838KQH6g7jmbZFQrHoF6bwXHuVGJEcNZI5sC4p+6LY3NDinLKdrbX0j7TBPfe8ejO26+RlvzAFlRti6+LDVLBON2J2MTdlbdHLEq6Xi1hD1ri/UQ+53TehxrS9psz/r5WecDKGjv47xJPHe0jWxoYU8fB5rHye+MKy6HttpQsgL+3ShaekH6zIbCqlXJ3Ym3kmQucrSZozhauUGNH8XF6b/T0LnXeGY4Ek9QMAMp4xFjT06EI6X+aGFQFeK7sSs831H2Vf4d8jQzSg3pRv4CC6HelbgDAZxx/s/z0yPJltZNQ8j+d3/8kFaOuf4xizACkf66p7nOJoYTIOb9YUFQWz+O39whTWVPXjyMB8DJy+SebjYN8CZy/w3Kyp0kwwIRBePwEAqL79damvwp9lZXb/sXLn+9vb6oHQQ8Mdr1CtDCuiEyaJEcaG+/F6sMOEzyzTYfKzWV7tPlFKzGyWUXPs+FzhhZczrpPo9/cXZ62VXfhQlEaVnTDt56fRNkAK25cWHseDFUcBX4m8s+XKhhJinNtST579x46uMs/ROTzf715JherGjbOHIiom+nbm7HWOsSZK9liAK3KSruJmU5Ny004NW7aGo3Gsq+0mGek4c1xZ8PjRO53o27rabrkjoHRhOw1lQsp3IHxGJhgZF44StE+cVWPGKPcL0sJSt7amR4yohxsOOHuDKbttT79NZSr7oyCNp5qX6N8bOSPGkwWzE1IQLtxms4gl4xLtzPNJxtrGJkBnSqpXW3Lt6cFq+m7KLQyI0NAUO4xcVpFFyUXUd5iTl6hlu+TxjMZRV3xWns1zLfuGx54fiaZa8/Fk32KoayfJsVVI1LvMouUnUri/LFKY7RJrX0QOfj3tc89WdMuP/wTwCyU8CUAE4zfrX8D3u1d+IuuACj14PkE+Zt/+Hq7NH0Hby7cZL771vXuK+7CrowZe2iMP4YQVeuXLKCfEw3Uv4Udtd5JHjj0rYwHW1fSQB972tgShSaLWLYuVwYItACDFLjDYR+e7mv3g5mvPSZXjcDKGVaVD2N9bbDwMlpfDz0+LZ1HCybmhwBmkv3q8cokFIR6ufwk/al4JKCrksrunQgqp+XnmXfNveg9DJ2/KGu8nMu3YYe1AAZO+YYVhLDVXJLbX3Zpb9jTzZbyq8riZA82gkfXe0bgUWmPqTS/tYXnNCRzsXwAvRcnWzIoFaG80K60Rz54orNwPxlLrfhZ252O0atxh3pCwNbPB5Ln7UMbIjC7MRhTXSbExsx/9PD33p24k75MujscwLZXx4cVcqJP/dh5+Z/UxPD1YjXtLuugSjDC8rJx3KouJy5lHXv/IHrWZYex/2+NSyoMaj0l/nedq9hFbmZLvM1NZ5N3R8zTtmsfNvmS2LnneZEwIDfxEypk/e6zrF/ai/9yNOPXqdbQuU75RzvLTuKe4z9CV5pnzz1Wm4QMNxWcEqsLnBzEFbyKWxUyGD3MR5oeyX9ZU9Rta3cha8BlZNvcMJWDb0QPNVgYg69ysqe4zzpOYcvJUbHlaN/tVzC98z2UDGomTHLHYYhw2Oc9Ea+194PSD58gDGovO4NjxuZ98xiYCmUdbBgHk0GEGJtlDmtHHXk+RCx/lIiy0akVMJ2un29u+xYoXUzJ2vjv48468BcC1S2Q++Gd6zwkEyDoTtvOjbvarcm892vgC5XFNxVA971XcVHCe7h1L8YcPMOOUA3uymPmi42UWMIrATHM2xwPcetsHeO3la+S73K+GO17Bsf55jsy2ZXeYjKPwTICx8nGH0tneMzJmzT7YVD4oFc/5fX9UvQ9/1bWaZHLKz9pTAIDcDI2T7ze+T617FSAH386uGjQuPE199xQeaDiM7gs3o2vodonyry4fxP6TC8zeHokDceUwVgkcKCc09QkAR64KG1j0/h8PEP8wQOrq9LRyjOWTOOnO5VABTQArSoed2j/OO8cCuutSPi51tqL63/yPn4mtqPUX//6SG9Ol1D73kQNACypOSGR2hpRvsL6RWQiTcYRTMeKGB2EEX33nSnw8WUCHhpUr6wLkQjBcGdPPI67rTbVtJkkuJ4MfHb1LLh/b+8H4aTsMa3P631fW7ni9wnHNWZxxPScYJ+U467LSCbI3X3sOr71ytflO6BHOnpPVbCpXLYBTYcz831e48YaPTR85WpDxXA9fboZwuxkPjy55gWhCQ4hX1BZCQ0M3kcdHX5yMn7Rp9ZyCcVz/oCCNdRW9JioyERPPIodi+Rl2rgG7DdZXd1HyeEg4fTEc4rQ+4USAcIpyK9irhLRHih1AtH6zpgiSkZ+Giimoj3OxsmRY9hGmfDKaphPc7PlnzGkBVXRlY2u0ilgonPAzwwNyMw6kSAr3safIjhx4tPf8vDSQ8oRfXl3Mwck3r6XP6H1kK7ZeLHQSV8PJGMIbJrCjlfj/pUAO7xnG8lqGgZw5vd/YS2avg72uXEiJ9x5X1QXoElW6P1ElCIB4T+1IGEDKlyhlKhItyZhiRdFGiYzKOROSk8Bez9yMNsDgzJ+cR/3zXccrcObtq7GmbMApnMXj3NVRg5XzTmF9VRe2lLVRNIyjZAW0Xi39c0lJsPKg/JyMJKPamPIwz9R0gEcROye5nudc74XGOWdxsLtI5lm+O9MyrhJEfsBygKMlv3U/AAD6KUlEQVSAAiOzPMYyx2MBWgbn4KkWSua8v+4YQetiChsqDQOcrMV4IJE+9p76iRQKZ0w4XtQtZW2kvGqsd8vZ2509cV9ZOxpKTzvjXFk+BD+PElHZqA6TcWxrW4zG0tMi28OxQGTKtvbFRiFlhTE/I0YMz9Wq+cMOfCsqf+HBGJH5aawuPS6/2tVZLZ+/e8EQDp+eg9Jb3pZ5FnnGY+FIkBX94pwZN3JAuQQtQ3fIfvnBkdUke3My6Dpxu9w79zccFcY1G/on+0HDwshwy/Yn7u1hA8Mzdy3L7mQcK8qG8cZ7l2NNdZ/kKrW9fBsAYuWzvdkNC89INfAwGcfqqgGMlY/L2B1nT25G8l/8RAoNxWfg52aEwMF2Lgwmb5I7j0ksGEIoc5n2Rb6HYwHAhRODUEgE/ESKoo95aaIlDolN7sm+xQQn1Pk6fl6aDIMRE5VkXWN5tVXkUJHTZk3ZgLzXloF+IgU7L0CiGvoOzdw8IWcYiNyVFttcOBagvvoU1lT0Y21lr0QMpVirnt/a2a/Reqd8IATumncya70vpfZF5ODX0/5VGAfhZMyErz04lzMwvUfXz8lAaaWAsXen37oGUB7WL+p0hDALu4eXvIgtdc3y8+eHi6SgkoTU9T3OzDIA6HsW445cuPz/fKKK27BIMw2MBUDaowtRQ4NYCVlaaSmmVuPLNjdmEo3XVPYL9lLqFdhQGd04t4CFf0Z5aCg9LZfmnqESQFmsOQmTNA3Axb5HLzsPBgsKI/zvrz9mwqPjgeRwcI5EOEKKguQ7YBolOp884fAUQl0hmA2gMBnHlfEkJdvqkLqtJPozNOQqJ4NjA3PNnPqG01yUMmvdvCsmcaDPXEzMOuWE22GFdhkepsew+0QpwpE41le5ZesFWsUK4FTM6e9zHHK3ErGlzxx6ty5rAOJtZohZNBk567tiBGhYgXxQ/608cCI6nwkaJPV5Uy09PxyJixEkj5jUlVLzrPB8ImWYOJRHeTsKkLwKWIZA5N8IzX7gBOd11T1g6AhduKZAYBQSEY4HeLJvscBb7DWSPtuQFC/yewXngg+1kjEZWs6FfFeZO3BqHnZ1VmNbfx2eaFlmxjRKz1lZSQqFwKCUR95sC/cuir2muZS+2vk0gORX8Hoe6VsAf0aK5JAyyh07SNgY+neLf05MM/qdjXPOyl5brel2bagkfRky39v664iSuSAt1W6bqvt1pWW3mCLn7gDA6EieA6dimk1JjB51z9a2lka0DM5x6IjZsOO/vZQvlL1ce0KSVvlZeW6ROO4XQ8C8lI9wPMANuRdMbgBX0rVkINKmQGM4FpDSaDlYGCbCSm3f0K0ov/VNiVDyGNkjbSt8ABEthBMGfskQpXV13SbKUGCqPd9T3Ed5KSEph0+1NhJ1KvdXFwTk7/HcsCEy3f2ytqbHObv2HjjYvwDhWCAF6tbXdjrfBYCt9US32zI4R0gq/ETKgfbZ62CvKye/t718m5Ncbb6gi+NxboSG+do1jOApR/4sLTvp4NMZcsb9EmM7UGIcrZo/DPhKyCXCVEyILfyEznsIgbqZLzuOFz8/7UT2JEJs/z/pMhoxXI6/L0X4rPoJfFcCwMONB9AyMBd7usso90Ofc5mDCXLMdbx8K539FEF+X+gtyZ7PS6l9kXPwa2mfe+MgHItj9m3vS0LuhkUdoqzIAZ+K4aHKw/IzboWztLeiIC1ebz+Rwq7OamIB4IMXevDSHn505C4pfsQHe7OmNHuw4qiu2mkJNp2A+njPMmxrbnQNjqjwTaTkMvUL0kZZ5kRD/awjfQsMO4EWDtW3vy40o6dO3WCU+h5KXhZjSVkK5Cd4U+EpvP/hTLQcnyNKdpjUVJE6aVeaFqSPLNlP3M6AXFS25wMeebHZu6/O51DRIt9gyddV6Uuead60Uv31ykMOMwX3f3N9C8LxAO9OzoRfkCYq2kQKjyzZL4JaOMk10wiHzR+sOOqsgVyqrEhO6MqVHLX5hGS0e0u6jILoKUkEt9fUzKuJJMAzkSgAGhLh6doJHG0yc2nP9daGI4Y/nplCxINMMIG1NT1GMdVe73AsEO+u3T/HS60v8abqfuzrdC9s512aOYiMcZM/klLkfV5VfVyS/XiefR3Ol6TicXf/+YkUtrUuBtNUcv9ZGbUNBPqHdYZiFNHYfaJUfn9PcZ9zQXhWDghA0UJW7kQJsM+HTu61+8DzYPdZ+qDP2P6ehWiq7ne8nfZn/YK0wSSzZ1ozxLBSu+t4BeApbFjUQVEuPV+PVL1kFLmUh6YazhnwHLy/s276DHI/trWZJGg/oaF6MOP+i84mkUMAGRV8btKhji6MWGsRmqiZn58mD7sVSVo1fxh7u8rcXA4Y2cwJ/EzFy+3ZE+Xk/Tw910T1eEyyrz1JaudcsEeqXqLPTASU82BFGzbqavOyvsC0+zCcMsm/3qwpIOMJG5SjHHswCbXa6+7nZmgtRuKy575RdUCiy+I1TqTQ9xrlhYhyynloXMvBMqC2d1DldYnmgd69+0SpIyc4H2JXZzXVFpiRwpNtSwHl4WI613xX31X2XPAzeXzqnKkGDwC7uyucOQCI4Uv2AUeZPc3CBBgqXq2kshFrJ+PbhoY4QKxcAm5rF9HY7Pw6kcse0HDHK26elIxJw6e4boE+J0f65xsGKsDJoeNIF0fN+AztP7nANSLiGYmCA8Dayl74BWn8Vddq1BW97EYxrSbw0PHAqcYspAcz3OioyCWbFY/ZhmKUG/OjYyuxrrZbKIVZXjsOlXzjYFCBchLCv2j/utrn3jhAEOLVd64E8xzv7KgBPAjHtDdBGNMTozc4XwvHA2QyvrnEZ6RM2XgP+N6Ru40nIKbokmDHatp4Vzhp8se9S8Sr5CeIIYQ9y+F4gI317a6CoeEV+CDXSWQ04U/9spgSZZcTBZkD2i9I4+ElL6LrlVug8kLDGMJeDEXCZGN9u4TS+Tm1C1/Omkd+psr4jkIBAPWVp4CYwnMdVU6UAB7ww57lxEZiKTo2hpax+jsGasjQuGzKYdjxEyk8p1kuOHGQ//3YUaokaXvTHqo8jL8/thh+fhq5vqblS9F3Hmu5y3i6Y8ZzLOs+FuDxNjIaNtdTuFWUNVZA84hNwq6XIJSKo3HikdewG1EWLAYK+1lmYXVfZhhKu2uuuki/y3jY3NAi3q0HK46SksEeSn1p+YkULqTzBcsavTx5T+4ZKjHzxWxZ+iJZX9sJlfEpQTYVE3gVPLocc/LSeH64CE21pHjeW9JFfS5IY2t5s6HbZdpSS0nf2VaLMBkX2JHUCdFK98a6dunr/XXHnHkKxwJUFr2K0rlv0v8nIuwbVvQpnIw5hZZWlg5lJQynVMyBTXiziA+eL1cVD81eUx7BLmaQx47xuLbRyAqIzfwEWMpibkbYfJ4fLiKldjyQgnX82XA0bjyIWrlU53OMYVY1oOENoHoLUzGJJrGCzcqNDa0IJ2PuRW95Q9cv7CXPs1UETMY0HnMSHPn5zOLEEQGA+kM/NF5Nu4JtOBnDvq5So+Dkp7MSvvn5JCOAnbpAoBcjKmOmV91Q0oPd3RUUgUqkcE9xn1PMUfDxOvdmLJPjzJEYBRFCATtp1Z53u3GuwPyb3qP/a1YswBAv8DPsqI5fkEblbW8gTMbRUH4av9PYhnuK+/C9Y6tln60oH0Zp0es0X6zo2SxPtryyqI+JiU+50T9NkWzvaylEFnrOO/xECkcG5zuQMIYJwqP78OElL+LhpS/K/vIud2mpOWpo9/WJVisCNhYg19d9Z5gMU/Fad8k3qg64SdSWh/3RpfvkHpVkcI3D391dQTJsYS/Jr1EjF/1ESiLAfiIlziMAaCg5A4AcSn5hCg0lZ0yEyIqA+jPIccTG2+oaDeW0jId7ivtcyvFUzJHDu3vL5XklM96WnzuOIw/w0hSRWlPZj53ttY4DwkywMhqcp+ClfAcFYd89u3soWrf7RKlUmue5Nc4jY3ACwPpFnXio8SVnn12K7QtY0a+nfe4Tkm/6/n+EPyNHkge/tfh5fK9rVdbnWaFtWnACKeU7NQkeXboPj+37EnDVZNYhlWRRnVTE/Pt2ATMHf6wTDKVokHKVlixLniEsF3KgAiUJzwAprXu6yqU/TvJgJClQPOQFaaIDjSnnMpYkVlgeKu4PJ/NFQqoyvtE4Hlm6Hz/sWY6GO16REL3t1Z12zrTnY92iblL+M960SXBexpP+On20Eoy9FH1mXV03dp8oxabSTnycSoiCxOPZsKgDKRVzPdejcYEX2PvBLnEvz9AJvbUlZ9H1yi1mfSNJk9GxZv074umOvm+6ZGyZT3u9Iv2OvpsNwVgQInUhF+tquyXxnRWor5W3SCVwAMi7chwT5/PgTfpCpzvt2KaB89ifu6e4D7u6qrPmJut8JOOoKz1D4ezIfEEByM8Aky7rRxZ8xSq8xo0TpqPzwYYpt5XzTiE/NoXdPRUG8hMdR2e1zLkdtme2LT9B1JyKaRMjfeQzx2d6Q0mPKGqPVL2EHxz9DePp4/7qNXpkyX48duyurPGFyTiQm4EfhJLQfG9JF3a01UtyfzgWEBlAZ6Wcn2j/NpV2Iu5l8FRbowN1kvnl8xr52+mLTuAVdjauq8DzDjjfua+sHdtaGmXvPLD4EJ44ulzk3b0lXSiITUm1eXut19d2kuc5kiDrjE335xtVByi3zIpgZUV5lGd+P2Jw4X4i5SaF8s84uZTfOxLHw8texOM9y0RGrK/qogRzjmIA8CZiUAWUR+JlPKciOHyFjbUdRCXpuePlRGq5N6bZ++truqgQX8ajc3vFZNZecfbWRIDNta3ivOI+wAM21bThH442EIX1NInIcj9FzlyUDIFlFRMh2KQF8p2UpkG1Za8mt9i6+DB+0tcAdTGHIJB2sqxl/InDKfSyZNG0e5XHMU1i8ifN2XTyzV4b+3PTycPofRx9T/Q5zu/0neOMVzuvdgzUaOeXj031rcKSBB0Vi+oSa4sHTH6j3rue8qDyqFYS0h68MHLf+pd2QvLi3/gPnykhufmFP73kxnQptc9/5MBSaleXHcf3ulY5tHM2/GfZ3DPYO1ychXH8wdHfIMNAhzPtxoLJLzDhR78whQcaDss7bKwmlFH4GecP0EFlujj2dAj3teZIBwAVKMy98X0ALs0qAKi4FZbWRsCDFUfFk8Gew5ryM6ZKqJU4KOPRfdjSoHH/eWnXSxrxhiCmpMrtbQUfOVCLreXN8DIe1hQNGrhBISkk7HXcfaIUXsrHhvqObIMqBO6sPm7C8lblWzu6wCHy59qrEI7Esb21Afn+lJOE6ScImuUUz9L9cSA6gMGR6z5L3/MIYtb1yi3Go6XX0a7dIOFs6//wyKBjZUgStO336bA2X5aJGRPSV744mqoGKPlMXxp/XLOXPmNjzLUC/sDiQwCATNrHd5b/HBdS+VlKPStg4gFOUjKd0nzuTiTAniPLa+xcmhoatKujhnDNXKmXm53sqPem5BZYeRySIBmEjvd0S1kbNtW1uoWb9H7nPq5f2IuC2FQkzA4HHsW/29+zED9vq5LcHT+RcuBxk6HZa569TyzK2HAkTso/QzGspN4w7VMkQp9pQFch1nvkB0dWY4OuCg1AyAf4zP2wZ7nzXIdyVMs2hqFxtEpYfwCpdMvwm3A8IA59QKKb2/rrRHn71rK90o/1C3uJXjfjS65VtKJ6OBqX+g/b+uvw348udX4nVeEnYwKD/Gn/ImfPfJRKGHmXjOPpwWq8PzUD9zccRV3pGekrQDkkEpnk91gebsDIWLtwmsyZtWfEEGB5FtdOE84Ny9cwL0U5FQAcJwm/60dH7pJ3b6zpwLMnyimZXivQWxuOQBVkpHAbGwYPVhwV42HnYCXWLeqWtRK6UR8QFiFAIm3frN4vkYRnT5RL9IANA3uN7Dm8ewElZm9vJyiSyLZ8Smre0V4nTibegwJVHQ/wUONLeo7MHUIwPc8xDGSudASFZRhDvMJJYn/Kcm5oA/3J5mUUNeJIKD/Dyp3xC1NEr5ufljXjWjezZo25jgaQIS59swwDkfMWjMxR1IPQ/b/+PUfb7ej+hpIec4fzZyNRCG4i1znSqaGETQtOmPyCjNET+I7wMh5Siu6fNeUD8AtTZBjoO1KgfIkUef41EuGW3I/lbqcPUO4ZUr4wACprrFHD6pJsX+Qc/Fra5984CBRuvJkUVk4C4w2/rrZbBP2OgRocHFggnilRZiwPpyRnjpjkRSf8aR38J5qXO3APABQCjwgrW5A+11FlwpG2kaDDu/y5wvgkha8VAE+h+vbX5b220q58hceP3WlC2/lp3HztOeT4GahASREm6GRdFjz8nm3NjfALU8THbSslUZpPZYTc9tYGrK3pkQvviWPLoWKKKnfOMEKUK1syFtqbNSVY5oY7XjH9iikKg+r/O4moY4EwLQjDUWGKKOFgFCYxaqJQHuhEvmm8iRzVEEPOirQIVEYndPkJDQVScLxapLyZCxaeEoPuiWPLBULEUAR+jr2GyZE8s9a6X3s7ynDkxDyak0Dhzw/9luwl4Xf3ACgPT7QskzF99/AaHOxbQFh17UVl6FQ4YbDnXix03gm4l0SYNDkBfn46i/HDhpypnFAgFY5REqUkZOPUpiW0f67x6gApoE8PVuPhZS86njpv5pS8d2d7LWbFxg3lp143B6KhPWtexsP/Ud8hie93Lxgy9I6AwHzCZBy/t+SImRSdKxMm47KWtlIUjlHFaT8IKUF4IkBj2Snr+wqrKo/jocaXZO+Ho3HB/U8nX2xYnRRgmjS4bfksJyAzljoZJ6YYbXj6ULJHueYK9+mvDzfRPB9rlH55sVDoNm1PPQAgUE7OAHt5bWOE1zYq/7jPz54oz5J3e4ZK8FRboxiOokR5htHKeP8h+wQgWWvLcABmnkbcdwuG36JYhqeLl03oJNqYwr7eEtxy3ccyTnUhx8lH4aRcjgi1DcwRo+6VsauIsWbSUBsDkCrJ3G+JaGr52Vhy2sgXmWCaiwvpApnLByuOalpazylsyS0/Rt9fU92HyUzgOCD8GSmCj8B1unDunMgTAKXz38CPe5cIvOuyy5JA2sOuzmqB5ch3tUdcIjCJFNTHuZKXJ2fdZjMbc435Jp3kHm32zziCixgl4XM0/eJIgZyhrY2HUXnbG0TjOmbdIZYxyetoOxSkP1auXLQf4Vggd83m+hZZf4YVuc+xvssMYBGyAL8gTfeEp5nW9L5fWzwAzrlSQUj5NEmCIaqLOQJtA2gMW8raaHxpX9b0saOrEI4Fwqom70yYnLpownk07+ZSax4+A6zof3Wn/zdov5Jx8N3vfhe1tbWYMWMGrrnmGtxzzz04edKlubr//vvheZ7zp76+3vnM5OQkHnnkEVx11VVIJBL4rd/6Lbz55pvOZ86dO4evfvWrmDVrFmbNmoWvfvWrOH/+/K8+Qg9454NZcggeWbIfYTKOLWVt5EFmXCUgnpeUimFzXStyIkwVouyxwshJoezJTpBHfE3RIP2Ok4Y1jejlwZgVkoV4KQHjaWy44xUXLwtIwhN/pufVm8m6L0xhVcUJdAzNpkfy5QNSsL3QI2wlX4qjcczMncCxPsJ8x70wC5fJ3nI7HLu3u1SUEkdoaMHIERGeoz1DJSYBUo+TlQVJtFXGSwjlOZ6XY/3zssPPkzE81PgSmioHHCH6k74GSoSu6hUliCnYWFFvuOOVrG2hLuTAT6QorM5FtIAsryBjV/mkCEzAN8wW/N2tSw5L/gGPbUN9BzaU9EhVbsktsKAdTH8rc6qx6Ny8mSafhb3PdQteNvUeLGxxFs2hVYGbBf+u4xWU65HxqFCV7yZmqowPFVOGXciK4tjwA24SebAa5Sko2bcEL9C/YwPMMjhXVw4SXte+uPXe4H7t6ymR6sjhWIDHe5ZNG4ZfMZcUqu8dvpuibikj5hzImn6Piim8NXEZ7eGMR8wj+uxxQcFwPMDm+hYHLsNrs7pqQJQodcEYeogpPNWyhJJpp3yhPiRPNBnk+3sWUj4S96+Qqhuzkszzv6Gm00RgfIXK294gJdGqDeLZhdNi5rvOftDt1KvXSXLwjrZ68VQ7HvZACcuSrAnTwVpOFumnXeVVJ+HfU9wn8lHmXedSRCNYzhnUhjZ7iz0f4vEWb7bOhWGlRuTMWIDnuiohrFZsD2ZMMb8wGcfKihNoqhjAlkaqsmwnTv/pnc/isaOrDDRDy+DX371Cnsd5Ziw3714w5BbqsvKquGiiKMT6fDNpQTgRRIw0+qvl7O2OYrl+YS/8BMHSJGnXAx5vX26+G9Je4DVfMfc01U0YC7Cnu4ycLRaMNJyM4ce9S5z1a7jjFbm/WK6EyTgGhm4hKMrMKSD0cP58AuvriEiCc8ZYRrFnndaU7h+Vp3MMZqRw4zXnSXn2SanfWNth5aqREvz8cJGzd21WP8CsdzgewJt0qYxVSHPB90TX8dlZMqWx5LTzPHiQfXBPcR+K5r0lP2djnNnC1hQNmr0HwHs/F9tbG9x7UTe/QNNPzzB73dAre4adqcBiHPMU5cxpOFWBbxU0jBAMeDOnsL9noYytesEr2NZfh9UVg24Ew+qvne80/6b3shi/pKlLXJVW6rP9+aJ9avuVjINDhw7h61//OlpbW/HCCy8gnU5j9erVSCaTzue+9KUv4Z133pE/v/jFL5zff+Mb38A//uM/4h/+4R9w9OhRjI6O4stf/jIyGeOR/spXvoLe3l788pe/xC9/+Uv09vbiq1/96q8+Qrsa5kSgQ/R0aQOupz0cD7ClvhlxL4PtzQ2YmggcNgUAJlfAU1KW3c9Lk2KuQ/S7OysBD0Yp0YKRS8tnhRL1Bb6ptFPoSUWBGjEeavZe1M1+FRsXdVA0pK8Ym2raJPy6qZpCtseOz5UkX/6el/Jw/PXr4U/6gNJVfi3aPU7ugw9SDHjMkeRg6ZfdPDjCihmUoICmmv6sBOa1tYb2Dr5CBoYVpbTodQnh+wVprKkmnvrHj92JvV1l5h3cv0JtkGiB6hemJGEUvkLL2dspwuCZy45pSNlTZlMAcoIzfIXC2CT8whS21h3BhpIeYt+wjCEJtyfjGMvkyEXKfdnZUYP/0VwnOSxLS09mh64t1iMo4KGGg87JZEPGjjB0vHwrXaKco2Hx9cu8aiiL/T6Zs/w0XfCaPvLuBUNm/fUznh6sJlwuy9GAIRee4wGTebMMR1bixBOpqKp3OBnD/fXHDIWubvtPLhDD0R6HjEXDMw6cmuecHef92kA6MFBklEJPQVn4cPl8AbFO8fp1vHyrgc4xQ0lIyfRN1f2oLnoF21mJZmNHEQ3kvu5SUYa8WVNSq6Ny3muArxDPN9AjAG59gMg4vlbeIhBCAAId23W8wqFADXwX5sCRE5krrsNgOTf2DhebfZXxNI2vgj9BxogNRWB54wehW7+Ea4xY795U3e4o1/YcU4Vx11jNWjdbbmgPs52PgphCTm5K8ofku/p9nOfAkEPxxHsAJ/ryux9e8qK840BvMZ4fLiKIE79PK8gtF+8Qo5u9tdw8vZ94jtYUDdI5hFZq9dquLNNJphHdio0JAPiH5gZ9piAFBakjVm0UrjI+I4Xrcy4gHI2T11hD/bw0yVm77olQaY4FOHB8QVaiMH+PcwKi69dy9nYpyJUVyfYBfJgrc/LsiXKS01pZlvtRz2f17a/T/HKSv17Xt96/zFSKL0zh46mEYc8qMBE/W678tH9RlhHK6+1d7jpR1Lkcl51Ir80Diw+JM+rYABEDcN9ri1+Wfu/qrMbJN68VQgqO6u5sr8X9DUfpntdzfNv1HyFTmDHrr2FEABy4Y93sV+EnUobKVedJPNm32ER9QZXI4Wv2KT0mhg1xPQ0xkrn2S8LAPHtevRkAyVV7/biFybhA6MLROIZevsGBC4bjgTAc+gWXNrToi4TkX0/7FyUkf/DBB7jmmmtw6NAhLFtG3sP7778f58+fx7PPPjvtdy5cuICrr74af/d3f4d7770XAPD222/j5ptvxi9+8QvcfffdGBoaQnFxMVpbW1FXR5661tZWNDQ0YHh4GPPnz/8n+2YnJAdXaQV/IiBhpKFCktxjVeZkhWZLwzFT0EiHG+EBDzW+RF6WkbgoZupiDpSnHEHkRATGYsBVk1I51v7MPcV9eGviMgmd2zAJ+xnra6wEN92yFLLEJx/icCSOh5a+hMc7l1GRF+VhY107kulc7Okucy9jfradOGy1T3sPP2NFxZBUW7V/zkquKDLTfDf6fLv6pp1UGP0/zxWvSfQdUU8lf0cS3uznWBcKPMAfiyHMC0VJtRsnecmlm/IomW+asTxYcVQ8xRTy9USxDEf1Re/DSRifdiwSNaDvfrvmebw1dblTxOyfauFIHE21/aJ01c5+DW0Dc+DlZagKsQdwETB7HFvLmzGayaXLKrJfbY+pM+++iSJEIUZOnyxYFo/TfpbK+FC62F10vT7tPKiLOaQwWUnfa4sHsPtEKVbNH8Y+zeW9rrpHIEV+Irvas7zHIiL4pERD2Vdpn/DN+a4jwp4PZ66mSZ4FyEhkFqXoXrarLfPz7f/b35V36TGoizn40qI+7O0p/cTk9n9Os6ukO3Ml0Ta4OR9WAusDjYdIQbKS1e25ufKKUXz0caGbdGwloa6Yexq3F3woifXfWraX4FEKbgI5R2U/IeE/a++Oc9J59rnnZ2wpa8NTx5aYmiXTnHvnezpZOhyhxNH11V1Si8PZw7rqtMCdNJ7fS1PCqKdznWRPWNVzo/sUAP7jop/h+Y8X4tiZO6bvl5V87chtK4/gocrD+NHhu6gfU5T43LTgBBXZs9dbz7v8TEe/dup8kaYFJzB04VqcH8/Dx29dBm/Kg8qhiCwTd0RlenSdHGYtPfZPkvuPLNmPx5rvwv11x/BUa2P280JgY0M7Ygixo63ekS9bytqciOHdC4awt6PMGNqaNjdLliXjBHHVkUB2WDjQ33M58C6fQuOcszjSu8CZM3UhB0rTdtuEFeEo3TG/2dDrEJJE381J77Ku00CZP6nZ+6d+bj+eueunl1zyLut4S1b+GYLgV0xITk/g6IE/u+TGdCm1f1HOwYULFwAAV1xxhfPzgwcP4pprrsG8efPwwAMP4P3335ffdXV1IZVKYfXq1fKzG264ASUlJWhuptBaS0sLZs2aJYYBANTX12PWrFnymWibnJzExYsXnT8A8H/WHxYM5oZqba0r460AQFUQtSXOByhLyfIIr/n4sTvl//wcb+aUw61s86FDeQgLyIu3v2chAGDr4sNyOJ89Ue4YBvy3c8HqhDPAHGr2RPL/19b00OcjeNMwGSfPgg/KPwi0guApPNO6iLCNoedUSQQgiYjyTjYhFV1u95W1G7y/FZ7kzx/sW+Aot/bvla8c3G903FLoTP8RSkYrgsHrdV9Zu/Hic72HZByKPVdjQZbny86roCUyCbN2f8SDGXpkGEQE+5ayNoTJuPDnq4s5QAhKDOcx8Tj18//r4ZXyjocXHxCvEMMtVFwnK1quDeVH+qcva46+hMk4/rLzbqmVYc+1A1Ww+gGQJ3KvlXwvScEANtR1yHpHv/tk32JhATIeYcjfq8pPEBwrGZdkaabMtFmWZH1GzVrbRuqm0k5EE8W9WJj1Tklu5zXjszNCz+CoGid9c2MjgLnJ/fy0YL75GWzMT2c0A2Y/3FfWLp427r/ylVOZm2XLfWXt8PPT1O8IvpfG5TnP4d+reOj8zk+kJLLp56edKvCUoEn/v6e4jzjLreJmAIyiMXOKzpiVyyD710ro5/yc6Vo4EncKSNkwMok2ZTzqEyspoUdzUEg1R9S5HEeu2v396ONCfL3ykMCRwmTciUYePD2XDIOJAFsajgkjXTShUpS+fLPP5G/bcLGSdKUv1tlRF3NEkX6qZYkj/3mPRGUAQPKZk7eZ8pVzOQC9N6JnimmrlYlOAXTv/Js7f4kmjrJGqoZHmXr+fftvSR0c9lJvKOlxCmk5d48eA0fRAODx/b9B/S5MCWGBEGN4Rq7ze5kQYWv9EexsrwWUhw0lPdjTVY6XX7kW588n6KtMixqRsUD2/wHg4cUH5KxvKOkR4+nLdd3ymcIZE3JHPnZ4FRB6hBhQLjyWI3jPtC3StWQg3wvHgywo4fPDRUJr7CdSBto5Es/aTzff+JGRTfkunn9t8QC8y6cQjsRxpGeBA839euUh/G5jC/wZKZETmxtaZE1/f8VBmXeb6tVudiI3z60UOuX71SaxGDNwVu7zw0tfRCsXCLxUm/qMf75on9o+s3GglMI3v/lNLFmyBCUlJfLzpqYmbN++HQcOHMB//s//GR0dHVi5ciUmJ4lB4d1330VOTg4uv/xy53nXXnst3n33XfnMNddck/XOa665Rj4Tbd/97nclP2HWrFm4+WYKq/235mUiNHcdr8gKiYsw5HBkYYqYUCIKq59IkYfdUg6m9RTAYH8BVqw9h72EcfKAUd7496urBhzlhj3kq+YPm0uR36sZCPwEwWrE65hwLyouSMWhVoCqJcJX+M7ynwOeZoawxrqzvda54PzCFJaWDwuDzLb2xVJZkeEmogiPxrFw/pvivVQaZy/Yfg94YAldGtzfreXNRiH2lXhCOXeAQ7t3Lxhy1mZb62LnAhRDxieKSr8g7XLga8y2UztC8/QLz35kDm0I2YaSHsGVM0yM+2IbiSJcl70oXh/ARAT8RAqP9yzD5oYWQAFrqvpljuyqrjz3fgFBf3iv+nlpWvMxC1Ot4M4zKBR9T3GfVPu2m+2RE2iG7vfOLqOwyLrEsiVqwx2viALBa7u/Z6Eo2X/R2SSX7E7tHeU8FEpcB9bU6LWIMB8xzaLT56SVDK3XfXtzgymeljQVn+HpZ+gCfeF44NRSsN9lPz/6f1YeBE6kFVRmNAqTcbw+fgW8y6ZkX22sawdiCjsGaighWUcew2RcHA/nUwWGhz4ZFxnAEEVey62LDws2GZ5yDGI/nrHWCc688D4jOtmIY2HCncNNpZ3YWNcuybgAhIWN14XzUwDKxZA5Ggsc5fjeki6CPGhq03AicM+oxeqWUobBReVpDdQy1u31eezIqqz1YiM0HImT8amr34bJOFZVHncU4ekYi5w1to4IyyebCMJuKmb26qa6VhrnGEExhDGG51t7fpfNJcagHxxZDXUxBxvr2h1jwE+knERRroa8uUEn/Fs0rxzx+X73ymy4VSLlQMF4jCLfraT2nYOVhn4bRNLBa8b5UTKG0Thw5aTsV77nnPtGs/fcV0ZQsyealwPQlYj186TWgqeI+c4yhjYu6pBzsGLuaee82wb6j47eJf/fOVgp+Sdxj3D895Z0UVXtGSmnX+I8CE39GgAGoqNlmacN6/XVXcIgF5UN9rNXlA+byJHuv1+YwlvvXzZtxWZ2Kq0pGkRTbT99lx0myTjemZpFlMQwcnDHQA2+tXyv6YeWDVEt7pMiV/6MlHxW5sG+Fjz6zMJb3qExj1CB1ku9eUp9pj9ftE9vn9k4+MM//EP09/djx44dzs/vvfderFmzBiUlJVi7di327t2LU6dOYc+ePZ/6PKUUPM/sVPvfn/QZu33nO9/BhQsX5M8bb7xBvwim3wTssZRmHcwd7XVoqtbVRT0Xe79s7hkHp8/fCZPEcsSeHRY0a4sHwElR/Fl6Lv0lHlgdRmdsOv2QB0744A2LOuBP+KIs88UtHi1PASld2XUyRhfVuEn6FE/FOCVzQnnoHb0FkgBmKbp2sTSeJw5H33jNeUnUY+WUFYQwGQfiIYbeuE6eJfSPFosPXxrcnmhdhm8t/SUNO0fT2+mQql+Qxp7eMoTjAZ4fLnJYMRzPq857WFl5An4iRV7f8cBU6+VpLdDF0ew1nAjootQCMxwPTNXsgjSQpsti52AlvFlTmHPDB/K8utmvAlYBIjs68XjPMipKF7iKL/d5x0ANVlbqsLyOhtyUcw5+QRqzZo3JO8LxAHu7yvBQpYk6kaGjPcAT9O9V84exunLQ/B4UnfL0PuEEO1b8uB/b+uuMcRvTeGVmVmFPrl3dOkl5GVxYSC4kZcYouGPtyfbSVmJwfhq7uyoAD9jTXSZeW1Hk9DOYcpfH40SxYPZd1BvEJAAADJNQfhrPtC0ySj5/1lJAAILgNM4560TmiOaUPN+stHO9CD+RooTPJCXcry0eIAXIdhIwG4k+K2uLB7Cnuwx1xWedz6yaPyyRIzbenmwhatCVFXTmf9q/SFjE7D6LEWPRMgp8xtp/j1S9lJUfsL21Ac901Io3XpTTtC/zuqJ8WPbAY82knK0pGsSaqn5nLnd0m6jrxXSe/NxLeVlg32Q6F/AVvIyHrfVH8M2lz0vi/AOLD1HBP8sZYv8dTgR4rrOSFCSPnD/hRECy21fYrxNvV5QNO/2rvv1119HiK3IkWDLCTvZlRTgqw8PJGMJxKnb4348uhV+QhnfZlJw/XgPu88HeIomueDOnsHOw0olQO0w9IDnrTfrY3trg9I2VRvYMrykaFGMvHCPF+Mljy7Ihodbcz57zLvwE0YCGkzH6bNrH7hOliHmhUG87VMBMtDBC94PKzSD2Rp7MhY2z39bSKHteCCFsB4XOi+EKwgDJsZ2DlXI3phTRIEvkxLfu4kQKKyqGsnIpdnXUwM9PyxzZURR7LCuqhgwLmUVBLXOk775nT5TD09WdOUcjTBIdrMz5RICDg/OlKJudLwHoys36Tl429wzWVPeJk2LPUAly/LR8lxL4FTkytcOtcc5ZGctfH27Cxrp2PHFsOaA8nB27GnYTB1Kk8TwsLTdORo5uSNPyanDwVpKddpX5S7mFn/HPF+1T22cyDh555BH87Gc/w0svvYSbbrrpUz97/fXX49Zbb8Xp06cBANdddx2mpqZw7tw553Pvv/8+rr32WvnMe++9l/WsDz74QD4Tbbm5uZg5c6bzBwCFsSdj2FTXKiF+9sKxd6dpwQk3BA5TXdTGEt5T3CesDxwR4O94GU9yAmzl47nOStfjypvSgr0AcCEV+me2BzFMxikseyVFYFiwransN0pe6BHUpCANpH3i/s83ybNCk2cJ6eeHi7Cuusf1IGhFjZN07d+F4wElkvHnOORr4T+bygeFaYQvwqzkMgVJAAcAZDyBAnCiKLONbCrtJCaK0EM4QknfNjSIoyub6loBDzjQVyR9zcJQ65Dyjla6PKLYU+63l/Id75RDZQrgzNtXy1q1vXwb/II0nmpeYtbSovR79kR5ljDiNQVMf1kRyegJv3ChALEghLqQg5VlQ2JsLJt7Ri5jUUA11nv/yQXYf3IBvClLqed3FqRx8s1rzfxbF+uWsjanT3XFZ4V6ksfNzB7idUqb2h3RcavzOQZC5NGaejOnyKi1Pd3acObaF1LISLOQ2Mm5a6t7UX7rmwKjoM5AIhbcV4YIcZ/sv6UQIUNw2MtnsTypQOFIzwJsqCdolXgYrcRUJ9naWlPAnDPZAxNUyTgciUsU6Ll2qrVhQ7kAGOeAMsmg/I4ZcVPzAoDjMeZ5WlfVYzya+uw3LTghkJEwGXdoR+2+Z2HwxwLUzXtF3n/49BwTsdBne89QianozHOQouqu6mIOnj1RjlVlJ2ReN+uoLI9h73AxEBI2+id9DVKXIBwP8ETrMvy4d4lU0uaq2VyPghXYn/Q1YEXlEHnhdQXvP132nOQeHT49x9nbXa/c4joL8tPY37PQyG4rMZSNKJUTOoopR2Q56dguaLb/5AJi5rmQYypF51KyKss2ew7Y0OY5Z2X1rfcvE0cTK4j8fvvs7u6ohJ+XFhYsTmKVu2MioCgds/SMBXj57LVQ53KIBlSTGfgBjfEnfQ3yDqeQlm7r6rpp/XMzyNxMe1KKwuW6NNfheOCwz9mONgC4+dpzWbCh1RWkKB8ZoIrN/gyqswFo+nHQmTp8eo7D2sNGXDhKOYN5J/KdaAAUYfzDsQAHe83ZWb+oU3SCzfrO82ZOSeRc6Qg9n7EHFh8iOKaWIch48HMzVCslYZ09AGEqJux9a8oHcPj0HOzpLiMnhTaknuuoojXU8GHH4eUDR/rnOw6TZ1pNdOnw6TlGl9DOM2dvWRWww2QcRyy4L0c3wmSckrd1DtVvN7SbApvTGBqXWvsicvDrab+ScaCUwh/+4R9i165dOHDgAG6//Z/Gon300Ud44403cP311wMAqqurEY/H8cILL8hn3nnnHQwODmLxYvLWNDQ04MKFC2hvNxCAtrY2XLhwQT7zz2764Dw9WE00Y4zH1bjCcCSOW/M/dGaCLf/Q8nSuKRrErs5qbW0b3CxfKMpTRmHVNIUSwlWeGCbr6wgD/dDig/IuwPIojwXCRMTFSNTFHMfjbAtTOxGMWXsA8rgKLCjjZXlYAEMLt/tEqaPgAVTIJe5nnLB6OKK5xbXHTJKnCtIObOD54SIsLTvpCCkuFGZfEF7KF8/ZukXdWRcyACDtYcdAjVyS0Hh+hvg8UvUSfU552NFS77Lg6NCwjfuWAj6WbNha3mzw0lYeCWAws1nKJoeZlSf0mrxP7m84akLJvuul/0bVAbOOOsKwvqZL1sVPpBzlLZOmKsUHeotlrx3sLZLLOPos/r93+VSWEsG/sy9oXg9h70rQRXxj3nkyQpJxw+wxSFECjqjYMBbu+4MVR2nvxpXzO2SIrpYjJDbUij33o5lcM8faW2eP6ZqcEfScuhX5vpVwqI2ijbWkyMvndbEtHp+wg3AyngdJJOS2qbRTeMX9GQStA4whnlUozzqTa4sHps2jAUAFlHIMjaHMS8SwWL+w1+C/ExEjJJIPESZpfJxz8GAF7Tm7PgM37v8jS/aLUS70xmMRo13/m/ND2oZnu2PmqGkyjq3lzYbqNfJOPz8t0Jv9/cXy3e1tLq11mKRaG18rb3HhGyGxaN1X1k57PxXDUy1L3PmLOG1sAon/cHgdfdaKPNmKju01DZNxA6HxgM2LWuEnCPctHmDbv2PlyBw8PdeBjWysaye+eQ9SXVzOSUFaIlBiYHPf8tJ4pPFFSL2ZiFHLUVuJeNgsQlr2JjPmnvATKZJDScoFmRlMmPdlPKwsHwJiumCX3ocPVR4GFByZasNh/YI0Nte34Lm2Kpl3ng+AculsA4rnmJw2JnJly6Q33rvczKn27rOBfH/dMRNBjFEhRC5giYznRnb13cIVyre1LoZXe8FZdz8/DZWTnVj+7IlybF5ERoFQkerEeEYDiJFRkKaokvJwf8NRx+kjDFGf4JnfO1wsxj4bl9w/hylIzznP0yONL7qGVaFb98LX711RNmx0Af48O0EiNZfsdfMTKcn5YDYmeDTH00GiLrn2Rc7Br6X9SmxFDz/8MP7+7/8ezz33nMMYNGvWLOTn52N0dBR/9md/ht/+7d/G9ddfj1dffRV//Md/jNdffx1DQ0OYMWMGAOAP/uAP8POf/xxPPfUUrrjiCnzrW9/CRx99hK6uLsRidGk1NTXh7bffxn/9r/8VAPDggw/i1ltvxe7du/9ZfeVM9lt+/CdAkCDuexhBsWLuaRHs/owUwqkYlbePh4Lptls4RXzMSPtZvwMwLeMNYFnu1nduvOY85s76AAcGipyfhxMBNlR3Unic2VCYLz5SHAqewurKQew/uQAq4xsoyJSGgnBVxZiCNx6DystgadlJHDtzh0lg8gE/nkHTghMoK3wD3z30ZcyZ+w5uSZzDwdMaLjIZozFHPAhh2hehxH1nLxN7q5yxTcWwYuFJiryMazyr5WWy+22/a8Xc0zh78Sq88d7lCMcCzJvzDs68fbXDShGOxoG8jHi+RGmbjBlGHy6Y5MN4wyxmEnm/bn6e67VlppPSotdx/PXrs9afITjX3fwx3v9wJr5btwttI3dgV1uN7C8/J+JVs58/HpCnJvIZ5/N6bMIOkqIKow5LyXiARxtfyPIOq/M5ULnhtAwtfoL6Bw9AipRydSEH3qwp/N91/y++deheiSZdcSAPHy7KOJdTdDz3FPdhV1+VFMv7k9o9+A9H1lGkodDsjXAsAAKFlQuH6SxOxuicaraaR6pewg9aVpm9b0WCbKYb5ITYXN1GESWLcYXZXqpvfx0dx2fDS5Ghxexk3NYWD+C5vgrcX92MpzoXA1M+bpvzHubNep+8wJG9/EfV+/BXXasRbcJ6NhonGE9ATCUNJWfQckor2Zb8mJYxZzJGbGKByj4jUdkyHqCx5DRx4dtzMxHAm/LxVNN/xdeeewhhYca8U5MTYMqniCbDuUbjWFoxjCOD852xeu/lQl07Kc+Vi9UDfnLXk/ja/q3wCyjZfGd3tSQdO0XzrPX2C9L4j4t+hn934LeBvAz1w8O0MpfX2a77YtcH8RPE9vbsifKsNQLImYG4worSYRwcnE9rMxkjeViQwZaaFvy0fxGdjZwwS2kMJwI80vAiHjuqk5sTKVx+KA/nlusIzlTMRO4UsLXhiMnDmqZdcXkSH747UyIesh4ZD17ap/vDkhPhhEnqRkCygd8ZPQfy+YwH5GaAidj0ZzRp2NTstbHn1F5rrhC9p4copB156SvkvZqLqctC4KpJWStmyZF38pmMnLs1RYM49s7tqLrmLYKARtaXXqiAQKHgTA7Gbk058pJzMgAIq1P1glfQdfo2ikjaZ0cbeTff+iHeePNKM8e6z9BMZogpVM97FV3HZwOeIua2MQNRLL3lbQy8foN5Lp/5EW0EWkVTw1TM6cd0zGZzbvgAp16+XvrPY5E+x5ToLvAVvrt8J77z0u8AvsLG2g6CAo7GgKsn3ZoeHmg8ug7KvSVdAmG21/qKy5P48M3LjDGha+748Qz1f8qHUkm88dB/uOSYfVjHW9b4J5+Jrejwsf90yY3pUmq/UuTgv/yX/4ILFy5gxYoVuP766+XP008/DQCIxWIYGBjAunXrMG/ePPze7/0e5s2bh5aWFjEMAOBv/uZvcM8992Djxo1obGxEQUEBdu/eLYYBAGzfvh2lpaVYvXo1Vq9ejbKyMvzd3/3dZxpk6Zw3HWs6TMZxVe4oHZAZKaq8mvLpgmDWj6hHTQFI+2iqGMh6fu3s1wwzQMJlqrD5p5nN4I1XriYoSSbiicxLS0VSSc7Unqb1C3ud5NMNtZ1S8RmjxhPTWHQGfh4l1zLO+vfvfAl+QdpQ2ClP8J4AeTS+e/DL8BMpVF3xBg70moqwjNe3MZSxN/PoUgeMV549dFy9NC/teq3THg72aNYhHZLmxN5wLCCqx7w0Hl26z5n7g6fnkmGgvfRn3r4aYTKO31zUa3CkHnlPopVP/dwMeWksxhE/L21yA/LThklJ84qvqein8POFHNezq/89MHRLVsIwz5OX8vHua1cCAL594F4KETPEIydjvL2W5148gflp+ozlqYlFKlHzxb2ns5xgO/GMzLV8Jj/tGAb8fO+yKcdTyvAJLgLo52QMTAjAokqCcvxfbb9Nz9dnYvRmb1qlQ8YzEseuzmrBEodjAf5TxxpjGIzE8XDDARNxysmQYcCQBA94aPFBhGMBjSP0oDI+efQjivTa4gFt0PhScZuhaBytCJNxdL1yC5qqBvCbdb2yL+wq57tPlMKPZyjBXCsNr569VjjCBT6g544NA/7Z3QuGyCDggmEe0FQxoBORPZw+dzWxhAVh1loJ3I7XI+2bPaPplYVnneFWvIdCjwyDpMu+s2VRM5Sn8LVjXyOmNEUeYvYkIu1h3ty3SXFhOFVhCkcG5juK4tbyZmSuNB5MXxeF5ITWr73w+7JfdrZRlIU93/yMtcUDpviY/uy/b/8tbG5okflxmJZ0FEneybkagAt91G1XR41E/ng9mhacENn+cMMBgp+wlzY3g/qqU3ig9gglLk8E+N0lLVhb3UuQH+vdq8sH8cOe5ZjPeTqTMZxbPkEQsbHA5EaFBt7Ezcln09/9+FyC+sERs7FAqtdy5NmOdMj49TkJk3FSFG0Yn+W8kOgn5xBEmNJoDhVUbmiiQzbDUTw0Hm8uvDkeIBFMClRvU2knVeNdOAxMxDA1ZxxhQcaJkESpROXc6kj8srlnUDv7NQycuwHnzycMVbCOmEhugFbYN1e1YaJoHH4BVWNfUzRomJXy0saAynjE7z/hZ8GbEHpYW9WLhmteIdhbkqJusr/SBFvdWNWJrqHbBTLraWpV3hvzZ7pwZz6jyNFOxYI0vlm9n3456WcjEnTeAe/VM29fLYQJrCuEY4E2DD3aY1bk4zsv/Y5AAHcOVlLfLkvL72W/MJOX1jF2tNVLJXuWj2Eyjo/PJQzsOaQ++3FCDHgjuqr11L+I1PKL9r9p+xfVObiUm13nYGHFhxgavilLqQmTcayp7hOuYPaWAq5HZdncMybXIIJRn47/mts3q/fj9Pi1+HCykIrKgNhdjp2YgzXlAw5WVzyh2qLfUNJjWB1ASa9tL99GPNPHVrqXgg4xb204gic7lohgXzV/mCILF3LwO41thFXURZUcrnDGYStkcYgDyPJWRiE2NjyDvUbM5297o6Le/sbyU5TQ6sFR/BxvGHu2+e+I98l+1o355/FMey2Q8bCurhvPdVZiRfmwJItOh58Ur6b+++4FQ9jbV0KXhQ6FP9L4In7YsxxhMo5Nda1SjIYT9laWDuFAfxE2L2rNSh7kdeRIBTIeHmg8hL89sgKKDcDpeNjHDK+1PQ/3lbVTIaBp5sFen0+rJWGvr0DMytrwVGsjoDxcd+tHeP/DmWhacAKTYSBePSg4rDSOp1GH0KP9Ze+nHY2w+xmOB7i/7phDF8hernAsMN5+q7/lt76J4pnvkkEwHf88v/sT9hFHFD5xbiwP5pqiQezursCq8hM4cGoe1Pkcp4aFKF66Urr9bPEq6iibPYat5c14omWZc+bt/gNw9v2m0k4ZL++RVfOHsW9wIV3mGiO8sbYDz7QuyvLAZkX+tIeaZZjDKx8dG+/JyGfDMcoZ2tbS6LzD4VW3Pan2zz2YsxTZO848eAory4dMJHO6ubLqQkhtAB5DZJ/ba6su5AgMxTZGHJmo5SUA8cJm7Scdxb06ZxQ7BmpEVq8pGhSYp+MVt6Je00VYnDnQUSCOaHNkVe6u7jKJhgJ0dna001lqqhrA3u5SeJO+oQqNNI6cA+TkiubAyOf02q1f2GscWFaTaKZdG+dT7sYwGZf5DJNxrK4awEgqD20v3yZzxNXmP6kehZfS0ZbovTxGDqO8yycwNRHgT2r34D91rCF5oiM0zvOsu9c+v+sX9uKV5JXoGpxNY7LuPr5beW6cPTYWYEX5MOU1xENSuGe4e3tTaWcWG5tALX06Y03V/djbXZoVveaI2a6OGjpDhxYjzM+YCHlkb5pigPQee79PV08iuj/DsQm8/uCl52VnHW/54s8WOTjUfOmN6VJqn3vj4JYf/wn8gjw5+Mjowxcp7ARMH/YD4Fw0XsrH7yxuE8XdeS7MBakuUgET+HRx7OsqNQmY0xRLi75fhCNffFoASWGT6CWpBaLAZ+x/M66an2VdhDKGaF8ikIfphJn93U+63Lc0HCPmioiyAQDlRa+h7+Qt9DNtsEQFW1S5c95rGSVRZfCTWnRcUQV3WoXCgkxJcZ/pLnJtPOzoqHMKlLGSuLZ4AM91VpoxRZQDbrYSLWO1lRQNFxMFhvcUQ+Q+SSGMGloR5Zm/wzC16Of4Uo5e1nxRbaxrJwPUc99tF35zxnmRCnM5xqpVXMze//Y8+Hnp6ZPNkb2XZU1a6x1FlX+3tqYHaRXDvIJ38YOjv5GlZDRVDUjiLz97TXVfliIU3Zv8fy605vTR2juNc85OW5SKzzB7MMPxAFvqmrPqr4SpGJDScLwZKYFLyu9tmBWf/0mGkLkeQcfQQ0QejJg9yIoOG6oATAJjvtknTZUDjnLzSc0xhsYCPNBwGE+0LCOvq1aGlfLgWWw7tlJrG97IofwO+2ytX9hLNLo+xFB1DCA9JllPbbA+1bzEQK8+Scm1CoSJfLOUtAcWHxLmozAZR2XxK+h77Sb6/zQyhNv6hb14bewKdByfLe/eWt6MJ/sWk1HYXZp9vq0idI4sS3uuUR9xREVhP/bZEseYHqeTpzONLOS5nk6hzZIn/D6+Q7QcY3kSLQZoj3s648Pu40OLDzoyJ3pfSN+1rOfiptykaCLDemJKnGWOE8Ta9/Z4AdB3Ygori4cJKWA5waZ1NHLzFLy0LzkMALLuwekcCPZdvKrqOF7sKIE3ayrr7s4ySvn709xHNG8jl7Zx0PDvPptx0PLnl9yYLqX2ryJeNP+m94iTWXN9r64YhJ9IZRX+8pPZxURsHN/S0pNQMUWeYE6csxKt5DtJzXmv6cD2dZWSoPAV4JNg8OxKouOusg4gSzHiMCEzJ7CQDccCNNzxCiU4ZzyEUzE8tPiggQsUpKlsfYKS7dTHuXi48YA894+q9wmEyQ6FO8IORLmpLupy6m/lObRwa6r7soQNhy63tTQSTOdiDpglhuds4PUbBFIAD25xMt9NFv0k5ctZP+3tCCdjCCcCgmIl44SP5e/lp4UC0hbIUT5znl91Icdc4ApYWkG0iKygAppKUEN0nh6sxoaaTkmYDMcDrCg5CYATv60CPDFT08AuFMfFhey2unxQoHG0r4yB688wVKbTzZVAwiJ1BCTx3saqApK/glwXLuAnUph5zWhWQisXcWLOdIG/6L3zeMsKhwGGf06UnUbBCMcC3L/4KNXiUB4ay05hRfmw1GjwEylnLQSSMx4YhitrfJzYt6O1Xgwpe9/6CUrg/2VbOR7rvtNJ8uT52dtVRrBD/U4/kZLiQ5sbXPnB883vXjb3DJ7rqBKmFQBSLExdzEE4SuwhjmIgH/QEpsDQvZ/2axpWTaTwSNVLIiMeWvoSwmTcMQy4zwCwqvI4EFIBKj83Q/Apz9Tc8BNEfEB5OUrGIhAaTSm8qvq49HdbS6ORGb4S+cnzv7e71GWWgitjtpY3m3VlJVUnfHpp34Fd2oYBfcnU2IBHytm6Rd0CC2MDM0zGsbOzhuqI5KUdAgimDuaxcu0AKOCp5iVZhe5kbXnfTQRoLD8l8mxF+XBWv4USFQBiCn2v3US5Zbpwl8zLuClAVTf7Vew6XoGOodnYUGsKzz3RSpzzXM3b3v8r551yIWuWsWI7DOzxcA0Zo/QyHowghGuLB3CwX7NnsewoSANBiBUVQ/ALU7jyilGRheFYQBScBbo4ZaT5iZTB5kd+zmsKUIKwn0gJ/a6XJqjg/pMLXBlt520ADubfMQxGzHn2EymB0agLOeJRtz/ftOCEKZqY9gWey/uU2de4JogdSbf31+ZaSnI+0FckUB++e2xaZ/qBkc1+QZqcJjEz5zY0jKlkpR6IxbTI98OBU/OgAoW1xQOSiyV9nKYwIP1Dn3tLBkJhevl0CTUv/Gx/vmif3v5VGAdDZ2/A7y05Igd3n64K+8bE5c7nVK7mcbYPA0caQIwRDD8SxoECg5d9ZMl+w5DCB0x7Rf38NNZW9dJBVMb7H44HeKD+sLzXwBSALfXN9DOLHeNPVzwLgJglGF94bGAuftxLl5mfk8HH6YS5yJJx+DkZqcrqXTGJx3uWyXv+qmv1tFEUMymeFGATSNCNE07oXhhoknHhaV5aetIU+PEgxb/ga6FuHU5hu4lAmtgbEo7Sc8NkHCvmnjbekgIjsDl/wS/ULBAZT7iibcV3/cJe7O0q05eUErpYh4mGBaLyqLIs6DJYt6jbsIZMxuRS2N1doRmwCDu663iF9BsZDwf7FxhmEUtxRcYjQ3M8kLl9sOKo1FSwG+eXOMo+P4f3qd1so5WVa/bqAg7u1WF40q1pwQlgMkZsTRbGeXQkj6hvGV9veQWdPcfVQMcCh0432tdNNTr/Qys2TzUvEQjEbQUf4WBvEeF+rSYKvlZi/Pw0Hqo/KOONGttspLNxEY4FDjPObywyPP22x5ONi/0nF0glX+lDMk7MJlahQlI29AeURzSDAJQ1f6yosfPAH/edPcHzyFVp+T2Sn8AKnA8yaHQS/o97l7hGYSpGBRY1jvvqnBH4hSmiUBQGHk/6FSbjpJBolqC1NVR924sUvtvfV2zORwg80HjIMN5cRYnL95Z0yXngcQGUm7Fl8TGRFU80L3eVVg05Uhd0ROmfiGm3nL0dm+tbxOC+Kj5qGF6qXpK96eenSUZNxpycHon6zDAsLkp7lx9oPES5DHZ0yuLA57N9bGCu7JnDp+fg95ceNBAUPhdWzgZ72QVqKAvmyW3M0Bo7B43PFM8TjyscJdm1v2chAG0EWs+UGg+WvOX+bGpohZfy8fCSF+n5dhQj9CgXh2Eqeox3LxiCn5vBwd4io3BaZA5s/EmEy4atjMTBVaFFVqRiWL+w1+kfFwZl2Js3ayoLymrfsd5YDNW3vy5zwncBN39GCt9c+jytxYgZJzPgRetBsPHP9M7Sd36/zg3gdXCqDCct9i3lUZFC2wmjP6OCUFj/wpE47q8/hnW13UaOhpQX2FQ14LDdfXvZHieiyFSv9rqGo3Gpd8BRy4cXH3DmhO9UQDumdP4FQIYLG3J+YQrepZ5zoNRn+/NF+9R2ia/6v7yJl6u/Tg7DN5c+D8BSuMYDbC1vdpOK9UFbU9PneLDvK2sXij7bU+knUvhhz3L5nHgpmIM4GUeub7wK9EMAGY9C6PxzhsnMSAmWF572zidS+E8da+AnUm7lwgif/c5BKgzEmF8AUjUU0OHsAiOgRZGMFs0B5RDsP7mAhBLgKKb2HLOCv0uHlI+duYM8nVqYbtD0rOK1tiEB+u9wLKCiR1roNi04QX3zFXJ9otGT5NWEKxDvrDmOR6pecpTwLIU1kRKDgT2h3swpKkhje86ZnSkEpB5AxhNuegBYWTIsz/Z04Tk/kcK6Rd102QE6+gHH8OB++AVpM9+cAM8eLA7fy5fc+famfHiTMfGSiSc+Y5Q9JwplPYvnhRVXO69kZcUJGdMv2mienuxbTNS61gX4s9Zq+Q5gIj52iNvuBxsR8jMd6QiTcYdBg5NZed13DNQ4Cq/Ma8bD+pouY5wDGMnkGQiCxek/nTJhPKekuOzrLpXxMdUsG978HabgtCkBJWpjP9OHMxeOEcxjsYwFXDVJlJkMM7Crc3P0MJESrn9+53rtUZ45c1zOOD8zTMbhxzN4erBaG35KcOhsLIq3Faa/tiOEK657MWLxmXvj+9lQLh94onk5GRwjxmjd0VIv5+H54SJZl+eHi6SAG8tGGw7x7ZrnyXtvwTT49zNnjos3+t4SWvtlc88QXEI/hxOCV80fxg8Or3b2IQBsqW3Jwn5nRSS1ofC3R1ZIoUb29voFaVxxeZKShhWcwmgAyYkn+xbja+Ut2FDTaebWlrWRd3M0NtoXjhZtKOlx+6jP+oaSHpchRxvFV+QkhV7WCz3h83fGrSNoO9rqoQKFx3uWkXzWRve3lu3Neh/3nyF2vI8++rjQRGsK0nh46YtZMle+78NEkX1TBG3X8QqZ93AswI72OufscoR/U2mn7Aem2t1U1wqVH6LjxGxR4CVHyVrX73evJDmvI/d2dIzHE44FhsYVwFOtjVKfZO2iHjN/0yTGr5x3Sv69Y6AG/+3IcuzopDPHjjl4bm0SLqoIn/ST3SdKsa6227nb9naVOffHjNgEOQ4sB4s4S/hHyn1PmIxjTNNE8z5Zt6ib6u9AO+4umGgB58Kxdvjlhq7sAV9KTX3GP7+mdu7cOXz1q1/FrFmzMGvWLHz1q1/F+fPnP/HzqVQK3/72t1FaWopEIoEbbrgBW7Zswdtvv/3r6+Q/o/2ryjnIugj4EFpJkzbu1IbW2Nhju0Wf6/wugitkzKjzbgt36fTPxo8y/jg0//emfKj8jKlkGUmo8/PTghO2k4wAC08JCB2qnYRkY8ZtbO8njdfGNUfnGICjAMh4IslmnKPBEJOmiuz5DpMaP95Wb54Teo5RI++KKa3ce5KUZXusbA/z+uouwrha/ctKcLPoFBEow8ZkJ2tqocPzFcWbT5cTYVPWOgWHbJpVCwt6X1k7trUuNgpHFHc7FqB8wevo77uNOL2t9dha3kwKj352NOmZv+/lhgZaBDeSA+ikR16DyNpH81mi68c0mPY8Z2Fg9bw+unSfw7ykLuZgQfEbOPnmtea9/9T79Joy5eV0LYr9XnjLO5g/4z3N963cPWb3fzzAt5f8An95mAx2Pt/yXH22VcaHSpMnMJwIiC52RiSXQ/dzS1kbnmqhKICD6Z+O9nQajLAkhlr5J7xudy8YykpwdJJ9/4mkU95vUQw5/07+zYoZKzUaS859A5CdM6AAhl0yTIUTcaN7hNdLfZwrELwsmWSdTXu+4AHelCdnYzoqT0f2jxvFy94j0+2nrIROa12nayvnnSKPP2PhuSo99yciE7Jkq+fK9nAiwKqyE9jfV+zQ2jIVdjgWYFWF+T3vzy1lbVQhXVMJO8nsLCsmAqyr6sFzHVVYWj7s5MncvWBIim/ZuP4ojv+TmrqYI5FlyQ8BMF1SrhAy8FqmPdkj0mcA0dyoMBnHutpuyfkKk0TpKvVYrHvWROkge4hx+zJGnTi8vqYLM4MJPNWyBHWlZyShW/aSppsG9F6y86s0fToXXHPu3WheyHT5JNPkLfAd5chTHdl5oO6wyX+Jfncaqm12xjxzpARv/n//9JLD57OOd2fNH3+mnIOXOv/i1zKmpqYmvPnmm/jxj38MgGj4b7vttk+k4b9w4QI2bNiABx54AOXl5Th37hy+8Y1vIJ1Oo7Oz839q336V9rmPHHATT7IVVmZv4drKXnNQPBa81peVR/hZrpRqfde+HB0v+DTQD/ZEORdqbkYKLzn9zUsDnF9QkMYD9YfNdwrSVP04N2MiDQUGTyuXsm8ZC5YgsIUpMy7Y4xevPSC4TxvzCEAw5BtKerKoHuXzn2B22nkU/CzGn3N4+/nhIqcAFkBFdiZD8vqwMF1b22O88TyHHinvG2o7deQB2FDTmTUGpiFkzDy/6/76YwiTcaF+s5UM9tqwcLWNDL8w5eB7pWAPj0FZdI16bJsbWuhZWiHgIm1iNGnFdMXc02IY2HszqhT7BWnckvgYKlDORQMQ/pk9fvQL17C6r6yd9hbXzJAEctfAY8MAQDYdL3vc9fjuKe4jGAnndOj9tXLeKTzS+CKNXdMuRj390VoN3swpMgwmjPLGsD7urx3+tz34u9pqHO+tvbf8PKvY2niAgaFbREHxC9J4aMlL5LXWUSwTFfCcegdPHFsuaxsm40Dax7K5ZyiCnaaqwX5e2hjbkZyacCJA3DNsONtaGhGOBfh6pQXtip7DlIkAqAs52NNdhjAZx4qqIXkut71dZSIPAIItHugvktwcT1M87myvNfuU83XYOxlRuLlPosAAYGiSN6lry+jx7h0utvJfIIWv2DCw4YZ+fhq/vbjdrJFVcZwNc++KSdMHPe8r553CmqJBp28yxzpiwQUCAYjsYk99VG41LDwDpjmWZ44HVFtBP5sjGqsr9Xv1snK0l58bJommk6mUD5ya55IqMKUr3y1cG4L38YiRHXyWAKoczm1/z0IHJ+7npY3zqYAqQUt9AA0NnAw1hE4zMfF4bDnk56XxXAdVAD7SR3cG54ywYeAnUri//hgVHIzg+Ke9H0D7V1lnyjYM+NnyjJG4YcYqpMiTChSikXM2DApnTMhcPrp0H8GkLIXbpnc2L6H98K0lvzTyXUcOZU5Z5iq6O7b118FPpNDWN0fG6Nyherx+PkWLG+ecpf/nZARebNP/AsC3lv7SzcGzDINNuso4y1l1MYeiMZyjoGX61vJmon3NoTon7Jjku4jz3cKROLyYEppkbjvba7GzrfZTmd0uiXYJwYqGhobwy1/+En/7t3+LhoYGNDQ04IknnsDPf/5znDx5ctrvzJo1Cy+88AI2btyI+fPno76+Ho899hi6urrw+uuv/1r6+c9p/yqMg3AyJiF+f0bKYAn1gSxPvCFhdlEC9YEov/VNrK4iPnWucBiOBdjaeJigEVEjgQ+XTiakEKbOEfgED9KuLoJWSOKjbsxbvWr+sGNYSFJikijgZJzWxRFOuAm2TiJmfqTA12ickvtG45LkJJWXoxhO60z5CcIwm+qiynAme/pPaHkVNbxF4CG6X+yp4c+wQuIXpgh/qsOcT7YupWQ1xrCHHpWmtzzWYTIuyuKu4xXg2gbsCbUVRjtRl9/vJ1J4b2om4Ck82byM3s0wMhas8VDWi+eI+9A456zx3ljzz/9+dOk+2XvhWECwCEvB5nyRytveoGn0yUA8eHouQTIKjEHlQI/0OoYTAXZ3Vgp0Z9ncM9leNW6eUQTCsQCvj1/h/DoKCeJx2/MN302Q5/ewMvfsiXInaQ8+7a/9g0V47NhdJtl5hoHq2I0v2DAZF3gHLPgUJ8vyBbu00j1DgIZ/zDCwKXUxBysqh7SnUDmeNuEHt9bs43TCzD1DwDyF1RoPLMmaOlHcL0yhtOh1IKZw+PQceB7NnY2bdrzUlsx5onm5VGXl9f1hz3JjUOs1sZlZ2CD6P5cdEoVGYGMw+SVrqglWycbhT/sXwc/XZ8PeS3ZyJENG8tJZ68ORDkmy1zVOmqr7ySjKo+9yBXEp0sXfL9TGolUtel2NVlgmAqqdwB51/g7XgLASP2WuFCnce4ZKnPmyWxQmw2PdMVCDtTU9dCN65rMtJ+Y43wMgWP+V5bQfWWHlRGH+PmPXuR/z572F+Xe8TV56SeSGM69+IlL9Vt8hTQtO0NiZmhIQWZxM51IUIJqQrGWcDSHjuWVl00+ksKOtHg80HJY1cRiodPvjmr3wEynUzX5V1oPvpHA8kLO1rb8Oz54oxyNL98t3G+eclXuKx7m2upf6HM8Yp1BC7wPPktPWPWVHmhgaS5E8JTDFDYs6AEVyY+RCvijKPzj6G04ekTQrCghQzoafSOGvDzfR51MxidDY67Sxvh1bGo45ThN/hptrsqe7TGoGwCPvvLqYg5vyzsuZWVNkEU1Yz//rQ01ZMF/uO0MxpUhaRjv8ODFcf+eJlmVCpWvvC36HUKV7gJqIOblnjXPOyn74PLeLFy86fyYnJ//pL31Ka2lpwaxZs1BXZ6i56+vrMWvWLDQ3N/+zn3PhwgV4nofLLrvsX9Sff0n7V2EcAPpQaOHyVMdiEkyJFGpnv4a/6GxyOeOtJLzel2/Be+Mzcfl1F8mLqz2j/61nMV2YhUSzF07FiLeZDxNTmxakMWf2uzgzdo2DL7WTnfiy3tfnHmRu+wYWoq70DGEm9TNZmZILCeZi4XC0FCjLeEBMoejmd+l1U+5l4eVnpN4A4i59XDgVI1pNvpDioVFM+J0Faam86k35JhydSJEHUwFh2pfqizt7q4F4CKU8XHfTx/Qg/ZlHl7wgrEsA8PRgNTytZHiaejGcCAxGVXtOPQ0v8FKevOfrlYfEUGpacMJRFNZU99E668J3dkEqxkmz50wu3SBE9cKXyRNTkKaoS2h56QrSOHpyLoWSC1OYe+P7Zg0Q8YYHoWHj0BjwMO1j52AlZl43QsV8rO8BFsQhRjSRXl5GFICZM8epSnRemuZAz+nBvgXGI6TnBfGQwsiJlNDM+gVpHBqe5xiR8u6cEJW3vQFvwrA+8Zr4BWkgJ3T2lH35Ns45a7zeGq7hF1BRJ05OjDZ7nfYMlNLeCULs7XfPh5f2nH14620fUAXwVEz2xoaSHvLOTcYIPuMpqII0Dg7NI8OxII1vL98jUcWQWURiCgtveQfqYo5Tb4QUNAC+jrrFQylcxUnq4ViAgVM3w9MeW6UAPycj5++2O94DdCIxAuXM3YqKIezq1RGnIJt6FQApsTkh/nLl01RILK5w3dUXMDx6nZv4qJXCw6fn0PwNFxNNbV5G8qwAqtzrF6ShYgqNZafovYkURSw+yjXv13NjK5tPtdH+QW5GCiY+P1xEhcf0GTpwah7Nk6dEPnqTPpSuI2BHWZ/rJ4WaZSs0HfT1z+aIYQdog2eKzozKUCExxBTUeTcZFTDyLpyMAXEypry8jEMsESbj+PmJUuRdNiHnkp/pJyhaqEIPtbNfk7V56SQV73qo8SXj5JiMwRv3HaOPZdTJ167D6beuof/r87Omol+YtngelOUJn3/Te/AL0jh98WoZpzh/9N440E80mfxOlgvIpSJtUshyKkYyQGk4TsanvsQUJYfrfvJ8VS58RZJm//zoWgA6Wdqi7a6d/Rr8/DTyYykjXwA81nKX7JNjZ+7Avh46u2xw7BkqATxg9g0f0jzFQ2OA6bFIblwqhpt3BJLv4xiLbFj6CsjNCFvS3uFi5J3Ok376BWmsLB/CvDnvSL+8d/Nkjcrnvw4ECgdOzSMnhGYL8mJUqE9ZuH8AeKa3miJ7Wo5+u4byGL0cPY6YqfIO7RyAoujn04PVZFg3HMPugTKEqZiJXOq+XXXzeXpeXsY1nKBJK3QBQz8/DX/SN3JUV3m2Px9GmJHYcVh6y9u44vIkGstPwU+k8M3q/TIfR3oWoLL4laz74JJsChRx/FX+6C108803S27ArFmz8N3vfvdf1JV3330X11xzTdbPr7nmGrz77rv/rGdMTEzg3/7bf4uvfOUr/0thXJ974yAcc6E+X688RMIyN4NV84fR8fKtxov8gU7asTwwXizEwOs34MKFAmFNCUfj8Hwl9JNqnPB6fm7GFIuyPAovv30V9nWXEkOB/tlTLUvokIMue4Y3SL9tWtGcDDpevhU7BysdA4OhDc54Na4xHAvw98cojEhczR6G3riOPjTlO95kNREz3hpddXdTXSv1QUMN+D1rygdIIOeZpE8ANKd5aUnqbiqnEPvh03PoIE7EhFnFjxOVoucpvPvalQaiE4R4rPtOYnXSXOVhMg7FrBRc8MYOc075FFodo+iQd9kUHq4nWkdOEPfz0tjTVe5APvZ0lhN/tYY+CKuObuJR159fWzwApHx0HZ/tQFK2LnEjQl4slD6dPHljFo2cwNrSvozRm0UQMZ6fi+8VYtqmQ95Ie3h+uAiKIQf5aVy8mE+KImCKdOnxrK/uIkVrypdwtjAiac95OErJp8h4LvXmeAA/nkHPqzc7xYOUpdA9vOglbKpqNx5k3baUteFI/3zDCmPlUAAwEQC7ojjPE/8/5VFl4dwM9d+KUqiAsL9sgHElbS4K5sczotgLn7ryqIJsEMoZ/asunbzqA2DFN+3j+OvXC3UwtwcrjmJTbZvBgk/ERIo+Un9An3+KVinLSxmOBXL+Xn/3CsJ2J1LwxmNOvYHDp+fQmRuJOz+3q3LvGSqBn5PBtw/cSwpdCLz7wSwc658nCgOPmZlfkNbnZCJGScb5aXyUoijVx+c0u5kCjvXNk2jBY0dXIZxhnbUJHYFlI33UROn8IHS86wyjkTblG4YrAF9dcYToSdOUlClQFv0c3hN+fhr31zbjnXsICsQwjMOn55DMDUKSXwAQevAum5K54vWV6IfeQ/6MFH6/6qhTS8RPpOB5Cv+f4iOCEad5MxBNNRUTTLmfl8ad808hHAuIKSrPzLnKJdYnMXp43+rz6cczwDjN5Z6ucppD5eGhhoN0DnUEJhwPCEY3Gsep0zc4eUCSlzCq56iGDL01RYNGLmh5Ek4EdG+kqPotFKBydF7RpG/kacoT+RAm4+gZuk0qOjv5UJ5Z39cuXo5wgiLNgq3X0Vs/N0PQTPsusz3RCjjz2rUAgEfr95sIQR7BPXlv/enin+GNTWmJEK6p7HfyRPh931y0Xyo4h2MBJuePy30ZjlL09czbV+M7jb+g1183IV3pO3mL7JPnh4tornIzUJMxQAF/UH1Ixr9+Ya/ID74/vnvoy/TMiZiJMI7GiVEsLy0yjPsbJuMUuQv0GnDkUjspP3zzMoLOxUI0VfebeQVwIZ3vaG6ZWWkTRU2kjLOC5WokcsRr2Td0Kz588zIp0Pq9rlVOFKTn+O1AynN0jkuxeUp9pj8A8MYbb+DChQvy5zvf+c607/izP/szeJ73qX84P8DzsjPWlVLT/jzaUqkUfvd3fxdhGOJHP/rRv2BW/uXtc28c+AVWiDY/jQ9ThSLY9nWZZNGmygHg6sks6AUzidgKC9Pxfbmu23jOmQmlMIVVVccBgKjJxjTG1FfYtKjN9CWRkgrCuzsrTRhYhz7hK+J7h3vRqrhFf6I9BFvK2oyBo4ynxJupsbWhy6phY9bX13bK3EhTwI6OOiOwLG8NK9nycxgBHY7EJSKzt7tUvJh2QnMUNmJ7OLi9PzlD+smQAQm767lev7BX4B2M9V9X2w0A+NHRu5xQsQNrUvxM/X6+rHwXdvPTfqJ85N/vPlEq9JJeypNnMkMKt0eqXpIIjl+YEnyxYIetnAQbfuPMCSeOayXd9qza8yVFfGD2yNpiE74vveVt+AVpkzipw+NfrzzkwC78whSgCCblJ1IuHFMbhfcU95n1TKSIDUsrgz86fJd4w+Ar8TaF8AwkKeLt4z7KHPsK95Z0OWsle54x2YUpNJSeduYCMYW/P7JYPruprpVC9YURikHb66gNIulD0sDxopAi7svKeaewct4pPH7sTuzQ+GNZZ/3ZH/YsN3UetOEFkMLLCaMCr9HzoeKh/HtTqcmLsek1w2Qcz3TUilFkoIv0vTVV/eIEYBnC3xM2KJ1IDU9ji6diTq4NPQ/0DAt+wQqTyvi4f/FRGbO6mCN7XBR56xzbHnleO3t+t7Uu1smy1DcHyqI8PLTkJXnWUy1LzO+stWG4qBgBhYTzlgKVowQljbZwRNOoRg0YED0sR9qcJHAFNFXQ2eL5PdBT7EZ2xi02LMs7ybkqDPHZUtbmwOgYniJMZXqMK8s0jC5QIqd5XbkfXM/hqbZG+Plp7BkqgZfxnLyBuuKzhkqa1ybfyBD6hxulspmY+HuPLt0ndSC4vf/hTKcAGs/VloZjAICf9DWYitRaaQ8nYwKb9OOUc8cR1erbX3feCQD/qWMNZs0ak6jN3uFifK28BXWzX5XzWbfwLL7fvRLbWxskpy4/Qcbf1ysPyRkLk3FR5EOdBwTApVfVf28o6ZE8gR81ryQn3piGu3HzQfc0yzfebzHlyHZ5D58BZjHUkCNeT3tP8vzv7S6VeyVMxvHB1AzUlpyVvgq1LSenF5rzgJim69b72WYV475QdFMzmNlRXn2vMp33JdsUPkPOAX115syZzp/c3NxpX/GHf/iHGBoa+tQ/JSUluO666/Dee+9lff+DDz7Atdde+6nDSKVS2LhxI1555RW88MIL/8uTvz/3bEVfefEreC1zI956/zIK+U7GDF9/JKtfXcwxCUIRtoMVc0/jQC8xbXDuAjeH3cJimJBLK6IQcwVcJ2QX6kse5BVmxhD72Q13vELeQeu5Tu6ApbQzEwUAYUwIR+KAZmdgzCl/b31tZ7ayoFv17a+jo/8OghvovmRV+7XGCwUgN5QoBACHPchPUHIdckKJtkQ55JkG1IY41c5+DW2Dd8jPti4+jJ/0NSAcjWPL4mP4uyNLoHJI2ZKx6xAvlEfP43XS87Nq/rCwNU1XSt5u0cqkJEw9Z72j+8HeE/bvwmQcqyqPY3/3QrmYo0aX3exn2kwpUSYlvzBlEpcBB+9vs6k4Px8PsKGmEzs7a7Cuugc/Gyh3vJRNC04IftpPpLChpAc7BytRdPO74g132DxCAD6wub4F29vrgdDDyooTeKljIVSclJRlc8/gYG8RVlQMEd2ixQrUcMcrODY4F06Vat5fNoNLqPeVb3lSJwJ3nZNUBXnPUInDbGQrBVlzzfPEjD8WgxPTA/MzNtR0yjnlgkp2xVQADltR1joEShTwcCzA2upeByccjgdYU9mPPb1l+mEgz2BMZe1PRxZo9hGZr/EA66u7ZN9cf/UFvPX6lY5SFGW/ATQkIWVgCyvnnSKYEIy8WV/bKYwtIgN8hZVlQzjQWyxyCsrDutpu7D5Rmn1G7PNj/zvKqmKzHNkMLnal4k95tt2YTcyWP41zzmIqDCRCYL8HMGfT1GLwnKrdayr7KV9rmjOvzuVAFWQcdrpohezo/RFtvM+8tIcv1fU5ycA8X96UT0qrZRB8UjVvu3/yf3v97btNf3fZ3DM42LeAqvjqd0SZl/wEGZAqJzTzMBLH+rpO7OqupvkMQjq7+p6Ybm/YbFGSr8IMSvadw2tgGXv2XqSkdxcVYDMJrS0ewOXBGLb11xGrV0cZ1td1Gjkbfa7ezwgpz0nq3+i+yL7ku1fnHvp5aayaP4wXOsqsZGSSfQ1lp9H28m1Z7Gl3LxjC3t5SpzK8w4gVhFhRchIHB+cDoUfywpLXzlpHGbX0XbCq/ISc66x1H5u4pCskryz/NoLY9Er9J7V0ZhIH+v7yf/qYhoaGUFxcjLa2NixaRA6PtrY21NfXY3h4GPPnz5/2e2wYnD59Gi+99BKuvvrq/2l9+qztcx85OHr2DszMnRA2BoamAKDwqMZUAoQHZF7p6OXCdH9e2tAQcnMKtFjVCgHgO8t/7vQnTBKMJEwSjEj+FBD0ZW8vRTOELUUnvy2be4ZC/pZHM3px2p45+30snDytyNrMB+whfPZEOXFdM4OI/m44GkfXK7eQp8uak+c6K7G5jipAShK0rtzoF6aACd+ZBz8vjY2LOkz/dDn6ab3nXP23IC2YTgBoG5hjBJv22ofjAWpLz+Kn/Yvw5fpuYSDiRNbVlYPwxmJStObuBUO0TmmC2DDHfVQBYdyrhKXHTIXOcJQSyZD2DXNKyhPPMDOgiAeWC29pw4n7v79noZOM6SdSjufGmRJr7nd11Ihh6RekpW4Hz+O2tsWS2OcWd/PkUuMLhoso7WyrBTKeQ70KACr0CDKQoIReAJSEDmDOjA8oKXs0jovpPGMYBgoIQine5SdSONBf5HigDg7QXB7sWyBKKScmCjxmmr3s5WZM8viMVNaF7+fp51iVe52kPA334PncWt6McDxA3exXnTkNk3FsqO+Q/ysNqaEoi2F72nW8Qhdx8kwkgessjAW4r6wdsy5PSp/sS91mWuL3cF8lElLbRhe9gvzxZ6SwvoaiLGHad/otz7ISiTeU9AAKRtGZCPDOB7OcWgLieYx40lcUnXL23oFT84xndRHNz67uamFs+UbVAfiJFNZU9uNAf5HrrU6YokzMqCNwKZ27Y5933qvsZRZjjSve5qUBj6rACvmBVZyQ5RcnwUf30nPtVU50EICr5Om2obpToo7XX30BYdrHXbWDgPKwsa7d2X9M1ersSZ3PxMX9ZLzjAY70LJBokgOZtBLEmxacoLOq8xo217fAmzVlmHxC2ls3X3uOIsaXTUExbE6Pm2ugcESO/2wq7aS14Gif3p92rlSYjFMuh+7Twd6irKjCgVPzHKUYoPuUFXZew11tNSa53crz8xMpTIUB4f1ZBnKkiPO+dB6bvW/tv1kurK3pcZ7LdN98N8teUObfz3VX4qmWJQhH40SdOoNq9ggigL39Oh/x/vpjlLw8I4Uj/fMRJuNonHMW9zfofAB9H7LxIVCjcbpzGK5oR7Ht4nd29G9vd6nZFxoOas//rbd8iIM9RVhX2SsF/zixmvfxH1Xvwzer92NFORfF47w0gvWJwT+R7TSZrrbRJdV+1XwD/vNraEVFRfjSl76EBx54AK2trWhtbcUDDzyAL3/5y45hsGDBAvzjP/4jACCdTmPDhg3o7OzE9u3bkclk8O677+Ldd9/F1NTUJ73q194+98YBAAy9cZ3rZVPGC8SYSoAO4jOtpPjYwpGbn0gJTlVYQxgGBIB5ke0mIUzbqx/x3oTJOB6qPExKccY9/GEyju+s+DlhbG3+acBlbeGQKB/klC/FgmTYTHVmC1VlqPOItcQdrw0b4D8q42Pjog5sb2kQhSlMxvHo8n3w89NUWZS9exZrzzNtxE+9tbzZhLYtAS3v1YWbAFJy7DCovX4cUu3ovwPhGLH08Pf5kt5/coFzKT8/TArLQ40vwS9IU/hbj/mRJfvpmdoQ8vPTRohbzE5+YcqBgkFpvuyUJ8Kela2o8UbeZdcLGY7GBZt7/+KjpPx8SjzPT1g5H6NxfO/I3fLvMBnH/XXHzDxGv2dHejSkaEvDMfiFKWxuoMqeXkxjlccDYUv6RtUBSjrUz9lS1obdJ0rJYCpMCWsUoHMc2EvOUAvNhCFe3tD0wabC3brY5HDY+5rfqyLK33R5OpzMGo6YXBmGq7DBxL97onk5/Pw02l6+LcvzyJSezlqNxJ0zHCaJ+hOgyOPW8mYHFrCtpRHnP04QPr7QUsCVKe5kvzOaf2HDgjiZ+6HKwxLl84Mwi042WrF152Clc2ZESeIzbcEmRSboPhw+PcfIFvszgGYTsqI2Vr/3DhfTPFt9sT/DxsIzrYu07NNznDJ5CQ800F64teBjIoNgSJ4NAVS6km9C54zYc6HPEMkZS1YyhKwwha31R7LmrePlWx25ubO9Vs7KOx/MAqZ8iviBGF82lXYK9j86Vumn3od1RS9Tn1jGMHxM5x3IfClPzs6ernJ6v96fOwZqJF+NHkaOAc65CccCeR/vN8mv0PPAOPbtLQ0S/bR/v725ge5AbfT+4Z0vYGsDzRUbHRyB58R2pK1qvZGKwj2v3kzr4yOLMhOgs3BtzkXs7S418rGQ5KWjzOv5vK+s3dxLo3FxegGQe4DXlO+whxYfpP42HKF9nGNRgXPOiH6n3XgvbK5rxQN1VFx0W38dGcojOu/GUzgyMB/b+ussKKXpA7d11T0G2srzk/GwbpGpjhyVeaJr8N1kKf1hMo433ruc7iTLscMVs/k9EyqOvz7cZFjMPLM29l53aJ1HCCq8trIXl3L7l+Qc/Dra9u3bUVpaitWrV2P16tUoKyvD3/3d3zmfOXnyJC5cuAAAePPNN/Gzn/0Mb775JioqKnD99dfLn1+F4eh/dvvcw4qkCNpUjDwIrFhYIUWAQv82lEJlfCgF1M59FZ1nbkM8P4XUZEBJdLpdecUoLs8bx3g6jjdeuwoPNRzE+1MziAPbDsulYpJ0JJ5DDg/a4XAOQU7GyCv9CSFB+f+UTiSOZ3D3giH88jgJBC8WmvB0pLjJNb/Ixfu/Oek8I1r8RGV8qIkYQY36quCNx4TxRWA0PA5mWtH9iD6DBZmXn8YN156ny5U/ozxKvuO+TrMmatKHl5+BSvnSTynaY3thEymo0CNldpoxAdorElOUHOkpgXQ4hWqmYvBiinD3oUe/H4nj/sajeKq9EQAJUHUuB97lUwJFc96T9iX5cEtZG55qb6TxWcmufiJloB/6Arv+6gsou/Jt7B0ogR/POJADe4xqKobYxRjUNZMOJCKc0phRnXQZpmJ0YeenkXsqH5PzxgFoeNowXRLOfOv5y1ofXUTHG/dxbYuHd5eH8ArSUEmNb9VJsVLRd5qifjz/jy5+gXDdNuxCr0vR7W/j5JvXYvYNH+Llt68iONNgicwlz5dAwXRIfmvDETzZpdcmCLOKeHH/uAhZlLfbmcNUzFQKVnBgiM53rD0mUIKYcpNZYfaq892UXqeUe8Y3lPTgmZ4aWbNwMqbJBHzaq7xHJwKCZFhwH/65KDlv5yG8YcJAGMcDeHlEAsDnCnF6Zjilk6IVaF01lAgh4E/4wNXZ9H5ROQYAeQVTmBjTkTaWb1O6kjevYbSoogV7uuqGC/j4XML9eWS9wpE4QRatcxtOxVA0+20MnblR9h/v3enat2uex1923g11Poe47vV4vHxyFCkFfKtuHyVopmJk9FtyN/ZmHjI3TTjPvK+sHds6GuicTPhQOZTb4uVlSCZpxhg/CLPkky2rvYCgSnwXzb/pPQy9fAPJpCl3vW+7/iO8+s6VZk9lXEXXbtGzzedzzg0f4MzbVzuwF/nMdNBUXve0D88Drn0+jveWKAPVtYr88bzCB7jIqN3CKboXGV6aNR+cm+MrXNGSg48rM5r1R59PfX6idze3/MF8jJeMZ/3cObvWHvJzMjSumKIk93go+1aFHtRkDLfd/j5ef/cKgQrChzmvyThKi17H8devl3cxBJPvSW+U6nOEYwH88Rhw5SQerDiKC+l8PD1YjXAyhvK5b2Dg9Rs+sbiozJ2+c/138hBeP+GcFYb9cqRjbUWfC1e0CjR6sRCzZo3hwoUCmRNM+agsfgU9J24HvJFLGlZ0V8n/9ZlgRS8O/t+X3Jgupfa5jxyEk9rrnJNBY8lpA6WJG5wvAKhxk9G/ct4pYvQIQnQcnw0vFmLqXJ5z2YRjAT5443J8+boBvPH6VfDz0/hx7xIKRWqvZDiuowopwiIzfCUcD7B5EUFy/MIU7m84ijAZx7ryXvEkCOzH8iT6BWlhSOIxMV7z+eEieLEQKu05RYAYd8x9fv83J83/2XvCY9LP9WIUki4ueBt+PIPfbmhHOB5gbUUf/Lw0gtfyDLwnJ+P0Ixyh+VVTvoE+JVLwfIW3Xr2K6Af1e5hlhAXa5iqdvMuUflM+eeWnfGH8AICn2hvF68wJ0OFIHL9fSR5zYWsYD5yaCptrW8Uw8AvSholE/722eAB+TobeZ7HI+DNSBJvQELBwPIBKkFKvYkoYYcKxAA13vCIsIQDwVGujMaYsukcA5CXVCWRfK2/BOx/MokJVOj/ExiJv0hAuNUUQqUwilP1jr7WfmzFc8PGM1KlgwyCcCAi2E884imw4EUDpBE+VoeJdst9TBJ9SOQrY8gHtEZ1keU9xHz1HQ6zE8xutwaBZtB7rvtPx+K8pGqR/K2Do5E0AiN0LIO8zX848PpXx8UIH4e85EvNk61JhIAqTcTEMxIvK1Ymt94YjcTknTTX9lOA+buSCn0OKop2f5EBuUr4kfQI6WsHYYhi5woaBnWi4vryb+pQwhcPC8YAK/KU9bKihqCWz6yD0xAHA84g0KYpXXjEqcBwnInDDBFbMPU05OCDvOa+nSmm2Ms20sqZsQPaS5yvpGwCEzIRiU6TqaBl7HrmxYcDva1pwQjOC0TlfOe+U4/UXr6v+vLAmAcbrr8+OyuizMiNlzm3KI6rItIehUzeagl/jgcP440RmknH8Zefd9B/9mE2lnYBHe5rl/ve6VtEv016WUpu5aSLrmdtaGuns5afhXT4lEcmvlHfQvguMoimsYzyXoVaIc4gdR03EZP8MnbkRSGnl1/Kch8k4Xj5znfGqRwwD8dxPxhCOxLF8PiXyy+c1ccDV+aP0XysyAWhZqOcyHNd3GIyC7weUg/P+b07Cmznl1iCJKScCIlERyzPOya9z5r+DNeVurR4ey5qKfjqHkzGcXzoBxEM8Un+A1jknIwQUtmGwoaRHov7JeVPyzHDM7F8/x5xTvreYoQkTMdIF0p4TxVIT1N/X372CZOSEduBpGRtOBFhVeRwDQ7c4tKQcWeJ7UiX0XvIBXElG9+NH7yTDYDQOPzeDyst0sjYbgtPUs7Hv3PRVBrrL45UCnKGHlSXD2N1TYYroTdBd1LTgBNR4DPcU9+HChQITeY3THdX32k3T5r1ccu0SKoL2eWqfe+PAvjCZskuw17C8GfoSDZNxCckBcNgj5JFWEuj3u1eKwvjHNXvBRWoEG6k8qtTrA5vqW/X7QRUXNeb7qTbyej6noSo2ntAvTMFLe6JccAJYmIxLcTI7fwBKFwbLS4vAXlk65EBj2Is93cFfMfe0QKX+/OBvEavJ8QrCo/dSRCR964RwUDNrkCRqzkgZTDtDOazmjcWM0WAlB8+/6T0KcQNYWkqVBBl3vrrsuHCvA8AjDS/KWglFJSAVINlwaaocoGTRUcKD7hioEYXynuI+WUsW7g7eXsMconhL8ZSmfMBX2LCoAzs6KYnNL0jLHuPv8pyvKB82awRjAHF10yePLhNli5Rr653JOHa01cva8fMb7niFfq+LnzUtOOHSpvLF5itio9DKMc/j1ysPUQGjeadMVE3LTA4/r5h72oJPAe+fvBpcKM8vSJMxbBdk88iQ4TV4sIIM31XlJwxUzDJ89wyVSD/ZALLxtnZFcwBQGVLYBfbn6VC9NaeyhLy/ObfFCsf7M1LiSXt+uAg7O2vMZczQFGVVi7VYiRiWsq11MVbMPS0YZ0qGNgsnCpN+Jr9/V2e18brzeuan8VwbXeg722oNXFEn0nM/JRdDy7WPPi4kOcGe0HHytoajcRzoK3ITjnVrXHha5g4A9nSXSf9F+eRaIjlEfenpeiDMkS4sZxG2nlXzhyXfZ09nueQzhMk4DpyaR3uD1yVQDqyQkiOVzIfsYe1J53dtqW+WdX1n6jL6uTIOAQeTHhojzK7PEI4FFFEZiePvjy2Wsck66jYd9podHvy5VZXH8d07/4djiHCBue2tDQ4mnvfC2poeciSwvOd5sCCJgKXw8V7wiELbTxATjdxPeebs2gxxfm4GG+o7CB6m5VE4EVDNmIyHY/3zpsWUJ9O5RkZzio02tu5eMERVfq199fxwkXmO8gTK40BWEpS3xHBePzeDl9++SpJnbfgow6lkHfVYftizXD7H8pprIqxf2Iudg5VEggDASxo2KyfPSu8pYUxiWc1zCgP7AtPPTkP6sbLiBO5eMESyPy9t9AaGoupoCMNveQy8XupcDm657mP4hSm6j/Q8n0trD77Oh5wuF4irmiM00d97S7pkbwqsNT+Ngy0lkovFsiMc0/SzhSnDZhdzc3Nsx8Ul3b4wDn4t7XNvHPj5aVx/9QURMFKhVrnemygMxaE4G4t4zWBhBsd0cZb8NP780G/R7wqpqjEfNPZkPj1YjXAsoERenXC7sb7deMKg6Qy1dc+Ni5Ctr+3E1sWH5f3erClSDDmJL/ScsPDzw0VouOMVHDw9lwpG8dgiSjVgFJ6Dp+cab48O3QIg5Vx7xgCIl2lnR41gXu0LdtncM65CxREEzcHPFTkBABkPw8dvltwFG0oTJuOEa7fG9dixu/Qa8gSRUbJs7hknL2FGMKHD2oQHlXEWUrKZkxOgce/rF/Y6ybAP1B92qvSKsqWjD2w4cXKgjd9mpTRMEi+7RCxgDB9WUBlL2lTTT15HqxCfnzAUnuw9DidjxOjDTemKrKEnuSYyFzEFpXGxst5jAdWBCD261HTdDtsoaVpwAgcGF0gfGstPAVdO6uQ+UynYxnR7Gc/g5DMeHj96J/wEJV87dRB0wUBJLs7JYEeLNoAs43h9Xaf0V13IEQ/fjoEa+cxz7VUmnwMQJU14+DlBcCLA+lqiC10575SjDHpTvqmkzs2j6recDBqOcGKmUdYOnp6L3V0VVGRPJ8iygfr8cJG5bLlegs0yFBqjnwYDoU7k99uKpW1c8pzYbU1VP1aVn8Cayn6hU14xV3uMWdaNB7JvROnS+H+pmst5IJycWJiCFwt1rQ+a439srZXvMwXr+uouxL3QJOXqcXytvMW8iykck3GKntq+A+XJjSQe7JoemdMNJT0IR4kfPkzSOX5G53uI4afXhokVRInPeGL0cdXwMEn5C19pbHaiSrZCPvfG92WPSONiX9qQP3BqHr5z4Hf0mmVDd+TOyDdOmT1DJWg5e7t4a9dXd6GpasCpcuu804PID2a5YZIFQHv6882+AiDP3tld7eRErK/qgjdzCh7nSPHndTXgcDzAgVPzHAPWL9AJwflp7O0uxR0FVLxs5sxxF1qaSBka4li2Anagr8jsRV34UXJ48ukd4VhAOWGWYZFXMGWMOytfB4AwFe3srJHfAxBmNPuceBMxYc7rOnOrc88vm3tG8nq4f54lEx5afNDB6B/oLcbzw0WUJ6HXfl0tkWKw8Tz7hg9demNA9AlVkMGrZ4necpcmILmvrJ3gQIDct+FkzI0eeJQHw844jqo9PVhtIjS2k2iGyadQF3McY9puLMfZ6eTnpSm3ZZrPXlLtC+Pg19I+98ZBOB7grTevENYZ5lNm5ZIP7Iq5p6Eu5mB11YAc/tx8k3Bqe5AcS155VOWUQ+w6KnFr3sdU7RfGm8ff3TFQA/gKW8ubKVkwpoQCbnsrec/XVfXI+9TFHGys7cCujho82byMnjkaF1YdUeDZc2QlSbMnWwVWJVLdlzXV2nseCZFvXUwJgo5QYK5k67Pinchii1Em8Uke7Ikn9U9XPEs843xpFqSFycZPmARoTk5lD3I4RkqxX2ByNeyICCfMAqSk7hzUVKsxi/qO8z6iURNfoam6Xwy5hyop8eyJY8vFs2obZqs0cw//3x4nK+b3Nxx1PKeOl5W9paMm5B0mCR7GXke7tQxSYugNueeFkYkLgLFhxm1HR53LplVARXjs0L7sYU+Jgs8QGs5Z2dNdRkZkZC/xPPIl81DlYfk5Ky7heICNDe102et1fnjJi2auMh5V5LTH6bsKb5iM4x9bahGOxrG2utdhO/qT2j1OX3gd7m84ai5wBXA1Y37nsycoufPAqXl4oE6vZ14a3qwpbKo2MCEA2FDT6SjL8EnhYIpdhrMBhqWGjV57bzt/W9ANOT+J7D25cVGHyKcNdfRvm3ueWUa4qi1AxuGBU/Owp6sc+/uLxXhZX9spz1pT2S8KR1SJ8BPENiT7IgLjfq6jShS+L9eZBMrtbfXwC9L4x5Za8fba7cm2pfLv7RwBS6R0Mmzkw2krWT+k5FKeo52DlXJ+NyzqgLqYgy31zWZPJ1JGkfUg8mFNRb+ZMwAHe4rMOwAxNMOxwCma9XDjAaponGQjU/ctCMlhYFHtyh5kekjr+X+64lnHq+4YhLrt6qzG3k4Nl4swdbECv76my/2ZXreV807hufYq+f9DjS+5fcrNYHODSX7nnDgVKIn4birtxIaKLvE2m/tN1zhJWkQHBWk81UrR7osX841jjZn6CtIUIbLw8hvr2g0rXAhTWEu5Set+fhoPNBzG480rHI/1xFiO3EFQntSXYYhNOEFJ2F8rbzF5aIVG7vJ8eVdMkhE8g5L5/XyiLN5S1kaUyrDuw7TvGANch+K+snZj+NmJ1crD5cGYmeORuBR5k2gT3wMZD95kTO7idbXd2FjXjm0tja7BrNePI8yiZ8BEtTiq5hCjeOZM245AoU+FewcBkAiwICwAzIqN4ZJvlxBb0eepfe6NAz8/jfmz3zHY4RFLwbU8cwdPz4U3cwr7Ty4gJTbjYXJce4gtQewnUk7FQIEPAdjaqJWNgjSeaF6OC+kCgkpohpS7FwwZOITy8MQxCpFuqO3E/fXHnGfaEBdv5hSeaVtEnndmM9IFSvjzG0p6HGW5qcrgOOkhpABedllSqPX2DhfT+xd1ONj1J5uXTQs5YkE598b3nbwGANmYbD1f4SjR5T205CW5/N6euhx2MTFRRqSv9Ffcyzg/8wsIU8webi9NXmEWbns7yxAm47i3pAuKi95orL/dJztqITkcofH+A1RI7VvL99KaauaQc6kCGdP+fpq7+8raHfiIPY9PNS8BQN5+x8ixlHNWsm2FQOYEgGJIjFYKn2xeZryEISk2DJGCAjbWtUtUiscvfeNwc8LsG/qeW9lYZXwxVqdbXx7LmqJB+IkUfnT0Lmd+WZlhylP2JvLnWEn4XtcqE2nRcBWEniioGxZ10DoGREf6rWV76SUxhf9w8B7zLugLeyxwkn83NfDZM+O35+TJvsWm32MBUiqW9ZktZW3aEFXO3IlCqmF0AMmWJ/sWu1EbntNzOVnzHo4F+HaNpqG1YAthMi6G7aNL92FnG7HlKF9JDo2f0LSpkQRou/kJKgqWUpS8CEVQH4AUjjXVfVlMR9/vXqk77Eniqf08bszWxQbO2uIBzfuvlUlLAXacB3qctufXJhWQxPDxAPc3HpU54b+5qOCu4xXwZk5J8TQAUuhJ5kcbiHuHi7FhUYcw9HAU557iPmdNSGmj9zy89EU83rPMeZY9fqb3BSDwTjbA/AKij+W1fHvq8iznkh3pkJ9FCvc5icWeEu8yy6BwPMADiw8RDaUHOUNcXHNLWZvMx98fM4UCw/EATIv7yFIy0HcM1GBnZ02WYQMAgU/ryiQQUUeHrJEFP4r7GcfgZcNuxdzT8Gek5D724xldh8as8xMty0x0MqmjdXqOnGialp9riwdkPOw8k/6HLivYH1XvEyNY5lnnlAFw+uwnqACYzLfeo9uaG83A2SGoFWsp2KejUlKtnaMwet23LD7mEB3k+mmRlw8vPuDsecBlYOL9ACDCCqUJIQpT2RWkLXg17+V1Gg4pRqSOmvAzt5Y3U/X4yUtbTbzU2Io+L+1zz1Z009/8RwRXx5yCLN+oOoDvd69EOBKHpwjDzB4UhJ5cpOx5ACDePS/t4yuNzeT10sLw4SUv4sPUDKJBjQgv/i63+8raTTVQMVKyi2BJfxhzbD23acEJ7OkoN9R+nsL99cdEMPmJFO4t6SIYk6VAcbNzJvj/UWEP0GW7uaFFPGvrF/YSrZ8FJ9je2uDMlXhHIgYVj40LlwGaUYETpwDjESnMnj8pWGQXQbLfMxaIRyja1hQNGv54DSvaWt6MJ5qXO+vzYMVRulh1UbVtzY3OvDvjmaY4kMN6Y3nenAJvo5ZCkEjhocrDpDRrGtvpKiaHI3Ep5Dat0WZ/znfn2Pm9Xs8rgiRBikA4Vc5n4H04c+Y4zr8z093HCpDiX9MVrYuMU35uFdbiImfqoi4gFXq4v+GouZitvbKprlXgSVlMGmkPD9QfxhPHlovyb1PnrlvUbfDIkdwikygJcY1sWNRBCnjhNPtqur08St58O/F5ujMvn0/7aCo5jr1dZdJX+Z2FJZ9O4XL23niAtVW9UtDNwUFbhaE21bViR7uhVGR5sKOtXp7HBeycvlsF3rL6MUasRfb+fWDxIcrzGY3j0WX7JCpr00dGoVG8x6eTjdHzHJWJsp52RFMruI8dXeV+TkdVtyw+JjCkhxpfwqzYGP6qa7U7Pqu/0bM67XkcI8y+0nlQdrE8hJ6R27pFz+zW8mbk+SnCz3/CXgOAutmvomVwjrN3nf5FmZ+iBbJsWTkSxzeXP4/vHbk7W37xnFp7yJnLaeT6dP2dbt6i391a3ixGuc2uY7O+hWMB/t2y3Qam+89453S/zyuYwtgHiayzyc/cUNIjTrdPGmc4GscDjYfwROsy3F93TAr9fVpznGRxYtb6WnmLE+1gmDBTElNExMgG7qPDZmhHDawzRBOYLX/vK6NIRJYct47PX658Gt9pW2/6rtfuj6r3yTlZc2sHfrj02UuO2Yd1vFVz/81nYivaf/pvLrkxXUrt0jYJ/yc0CSfOnJKD9f3uleLpEOvdcz0Tdll5TpQkLD5IIWYMaCKFH7WsFM+Ic7klDKSED/K2lkbn8LPH6r6ydhKeiw+TR9YqjgWdhMzJkXuHi+UyenTpPqyuHJQiRPx+Vqz8REowofLeAiqkIz+LeI7FO1SYwo6BGqiLObi3pAu7jldgVeVx3HLdx2QY6ARimSsrVLdq/rB5v+WtEAGZjOO5tir5+SNL9psLJeK9unvBkGMYSAK01VaUD8NhQrHGE41AhMk4nmhejr9c+TSFu7V36vFjd8p4ftq/yFlPfqafSEkI2+6nrTTxvgEoCd3x7nigsL+n8EfV+4zXnWEQDPOwkyJnGPiJ3ReAhDl7zRmf/2TzMvn9H1XvM88pTGF7ez0pJXqN/6G5weybjEe0eufoQl0x97Tw+vuFKRnf1sbDBofPiXxe9tyT59aKfHjUX6WVED+Rcmk+rWJVKRWTc3PhQoFEXRCSh+vJvsW6Twoee+70GMUw0B47BwaoqRE5x8NPpLDreIXkP9hJlOxxC8cCipRxsmxhyqFKtaNe0fMfjgXwPJBhAIjElbW0PMe8JveWdJlQv54D9vbu7qpwFGTGifNnN9e34OnBaqyt7qV3aBiQ5IHoNvTGdVgx9zQeXmKS++29xbC66ttfpx9ePSky4YHFhwBPGQKAwhR+cGS1ScjV+9iRh1oZtSFgtmyU/ICxAFvK2hwDxXZGyDnQBbHgK/ywZ7nzHvbaAsC21sX0zIZjODN2Df6qa7V5p95TrGhtbTxMPxsze0nOoy1vAkU1YyxqUPZOexmC/9CZoD46BcUmY3iibZkYBuLR1Z5nO2mai2IxGxo0rG5t8QB9NzDrHibtfCgN27HrQXh079nyS9adoZD6+99etsdx6EAhax5YLgBWciw3LQvWFpvo9dqaHqiLOU60zvZ+KzvHqiCNv+hscu4zABKpBIx85PXi/n1r2V5JzJ0Yy3HHYb03HNX1P6wx2RGDcCyg/udlSNbkp8X5JvNmzb39c+mzVsS/XnkII5k8Zy38BCUCy2d1pXfeK9zv1dUDTr/g0b60yUhYPofjgbPXbMNA5rbQyD0/kRLDYM4NH+gP0NzYBvRzXVX4ov3ra59740BoJpPxLE8v4IaEhf0laSWDKVfR9i6fwkONLwlVHXmLst/reN/0RRL1KgJG6HI04Sd9DcbLPRFQRWbdb/aA1c5+DV+vPAR4wA+OrMa+/oXyvGVzz8hruRqwn5OhwmOWoNje0kBJlJagsPtuX9zezCmZg5nBOF5/9wps76gXQSOUjizgFRUfk6ZAlINWe2TJfvl3mIzj/zn0G9Zl5Bore7vKSHnQXrAt9c2iGLKCevj0HHccnH8xHmBXF/VdkqJ1kuq3X9oonqPfrO/9RI8Q5wXY45HfWQoRQhh2ndDDprpWFMSmhAWC+/fj3iXwC9L4yyO/CT+RMlVWFcToyOLiHzd5JOEkcXDzHPHe2dVV7RoQYwH+8thvyue4Ui5g1li8dUmKTjx+7E7KORgLcKC3mPJprKqZOXlp/KSvQXJNGLMLQBSPrHm0lWfLyOB+PVhx1CSC63PDyrefSOHKK0a1IghAeaid/ZqsC5QHpavXcuML8pvLnpd5s89cQ4k5I4CGDmlSAdvjuaryOL0z9JDrZyfxRROCZYzTeLlZWeSET1EqLNw6PABBSB5+Vqx9Ze1pTWKwqEMucQCU/6IVjh0DRKUsnOZWgTlWesKJAFdeMYoDPcUudMZySPzo6F3wC1PoeuUWMSD8RAp3LxgiZUl/bsOiDkcGyjhB8KKV804hHA/wUMNBmhOL8YfGBDHYuDGenRsr3ttbGgRWxNBIwfAzVIYLWGl4RV3JWSwtI/Yzu7rzyooTlDxqQTN/0tcgzhqe2/lz3qbfRyiDOcocjhEz0JNHCQqj4iGePVFOxqRee468sgyUvBOtEPLd1FQ1IJXb7TmyE/wBSNG3FWXDEjUC6L5YW0NnXMVDbZzD6X+YjLswL21AspLtJ1L4OF0oa8oMShRR1/CfMcOGhI9yTQSNoWT6c7OCcXnnnqES/OaiXrlbs5KtQ0uW6mYzlanQc6qH26xt3iTR+n6zej/++nCTm5ibjBNV+EjcvR/4KHvG4LKbX5CmqHbaJ5jjuOXssw1Fi+xA5hPmPC2c/RYea75L7hmGKGaNUZMBkONR6SiwwrmpAskhFJ1hLHDyr+x5HM2QB51JKWxjl+du1XzaN2uKBuWZZ96+2rDr6UeLg6/g0yMl/8tbqD7bny/ap7bPvXGwo9N4Ju0DzJ4tEXaFKRzrnQd4iujWWPhYXkc+oD/uXSJJo35BWhSoqIfCrtg6XcXbMElJXnYCse2R4ETJqNDsePlW/LBnOWqLXyYhlJuRCyKZzhEFgKsBh0nCQocjcSy85R0SHIUpJDM5jgCWqpOWkPcTKaiPc2X8uzpqpG8sfAX3y8llMdcD5hem8IPm33DG+dixu4CcUNg7VBCKQPUsnvXN9S0oL3qNit8ACKdiZCRZc+vQDbIXTaIZpkDNZh0B8mZOAb5rBO0dLpakZ3mOxvXev/ioef5YIF7vLfXNJldAMyZxZMNPpLCjrZ4iJYzfjghp3lv/o7nO8bRGqeu+UXUAjza+QPM7EWBdZS88XWBOwsua6YYmRYnX3s9LY2k5CXnGsYsnWrdV84cFTiKXW0EadaXE3oGMiZIU5rsFsWyv24q5p4WXnp+/qvK4gylnb6mNOf9x7xIHorOxrl3GARBdJ79jdfkg5he+R0YIe6FnuJWft3cQTOr73SuxtqoX62u6hAEFAGbEJ8wajwUEFZhBcCfe/17K+/+39+7RUZ1XnujvnKpTepRAgMHGGDBvkND7gSReJpjgZmiCh2bsJk6IM1y7aWe54/Gkr29n7p0kd2Z1OpMsT2ecpN3J8rTHHQ+JLxfHzRDHDsa8JSEJvSXeYLANJrZBSKVXVZ1z/9jf3t/3VcnO2L085jq112IhqU6dc77X/va392//NmG5AyNS4NueQh5TS0eYm3EowENLD+CWyf2Yf8dVlBdcwPqKDmFlcaOahYpJC7gOBhtRAhMxclJ2dpWD4Y/Tpr1P75kkel6GoAkFqxE94M3fzU5Qn7rpSsmPecLoxm3iA4Qfo4R5P+ZJkmpeaATMMuTmJPBE1StwcxN4sOYIdjVVEetNTgJPH11FDzBZ0wZpbaXh8VOSep9pUAnNDvDsEe29dXOIbODRZa8J5aYz4spB0o95aDp3J46cmUsRhJg2zPafno9bvX6i8TXeR9aFes2TZ6dhc1Ergr6I6PhNi9s0xI93UFd5ttU85Irhls47/jlKDh3QydNQeg8gdjlms6ICYNBG7lBYor0s+9sKtDdZ3U+w6QHpczcvLrVleA1uWtwm7HU1cy5ILpnkNanDnztO33tNeTceX/6q6M37i1qICvQWiihtqWnQh2dVifznHUushN5fHyujQ2QA+4DC+XN+CmzSiCZwATceQx6nurnnqebMcFhqU3A72fHybP1yK6qbqufcvLgwBPkDHlbNP00OBlUI8T+//kd2pIVhPooyVg6c5lxSf+u9NBVuTgLLSk7JvsTzO+iLwOEkd6EzpfFfVdELNyeBlvMzLXYhNxqXg4ToSUBgtS+1lMMf8LCjodZqLx3yAmyqbsbloXy40Th2N9l5DMJEqA5+r7YWpUXob0rJsBV9IvKZzzmY+dP/C3DzpAIle0AWzL6Csklv4oWm6vSy93yIyE5g/h1XcfL87XBCASrmvoHWCzNQeuebaH9julyfWo3Tj3koLzyP9jem64q7w6pIUSigir8me4Hp7TWwoGbF3ztnvItL70zUtHw+KKwcCuB4PhX9iYfguIEUhfGHwrTZRJJYPPMyui/ejvENObheMUqFdXITVPBLJU7JO8RD2FR6nBgXUqoW+8NhTL7dqGQ6EgJcYOrt13Dl8kRg2NUbnKrCeH9RC37RXk1F2voiQF5izIqWkybG8O6bE4S9aeod13D13fFW/3Kla8Yucy6CjMP7EfjRJNxsMlS+e3C9TU0XVmOh6O+kMmbqOBhVjv1+D6srenBb1g3sOFZDHsL3sxCMj1OxNFUTgXGz5ncBiuCYyc4M/3j+eA1cL2lXak24mDS5H9evR5Hdm4PhgiELOxz4DoK4i43lbRbPd9aEYcRHwlZV07FEqh5zRVWuaKzmTqAKhuF3WcCUEalGbIo/FIaTldQbtmLMua+8WUL13N9WMviHvJs1xmosVs4/g/09C3XBQrOStcIrS2VaTv5TxvqW4mbx2M647RouvTMxbWwFox2zsbzWe3GFYT4EjNEGzvVAVlLuCzeAE/GpoJKqRGy1Ud2H8yn8eAhuXxj+eCrw5A+F8cjSfWKY83ckH0oVqLIOxmPkDi2c/g5Ovnmbbk+SrFmutitzlp0BxiHLjcYRXFe5WsYakqq5vBbVocYcI6lyq65n/cNjh4SdX2PCo8y1w8+880UHk/+P82i9MMN6Dld4nTPtXZw5f5tUHzer5hbPfBudF6dhxm3X8MbFyfKuPLZmRe0g6SII6B3WF3Th/dEoUY72e8iaPIQ5U97DyTdvS6/cPBLC1up6vDeaJ8xV5jrhStFcdd18Px5X/vtdi07h4Ol5KJhxBd2np8sa5T4vmPM2skNxtJ65k/rLrPA7HEbOxCEMvZdjzQVzHvC1ge8gSLq2fjf04LJ5Z/GVW4/g4aNbZRwlP0PdY/Kr2bi6jJxTUgdD9cv8O67i5Kk7aJ9T+wRUccwpt/TjnSsTrMr0XPWa2GQc2buCpIuJ9RFcXz5MeQLN1Vhb2kVV0o1qx2afplZNTsvXGAoj1BdGcqKOJJgVkrkyvNzbqDDNuWkFM66g+/w0YjVS68CJUHE4fyQEx/MRJFy5rxv2EVyLwJmYXnGd37lq3gU0nZwt+705Lgvmv40zb0/Rf0tQgcTQ9TCSUR8rSk/gUPsiC3rlZifS7BXW/W52guaAekf5XLVbV88evqkrJK+Z8xcIux8x58Afwd5z/+Wma9PNJJ/5yIFVaCiSpEWRlcSZt6eQIaNCtf5ISEKLbnZCaA5PnroDW6vq4YR8tPTMBgAy+g3Prmz46pS9sfo4WntnwY952FpVTwY4AIQCbK09CscJrIMBe3XuL2qBWT0zYC/WqEsHAzOcqjwtiLtSDdT1kvIsSY6Lu/CHw+g8QZvqjdoh4np2AgQDqmqvWdk15gGjrhRGkcrHMU9gKe9fiwom081KAiMurr47niorGglyj9YSI4TnJhEMkffKyR9FMBTCN6teJm+cEZV5980J2qOWcLH+jm7pX4ETRJJiWPgxD7t7iqUOQTAYJh5+1UcnB6faxlLClerPHPlwuEqzCfWKefpgoDxF+9oL8MuuSrnf3bWd2FrRgK3LjoB5vZkhaUtZEwDCmm4pbsbLbZp5ir2GzzfUSSXkwKPvP165F27Yx/XrdPAaLhjSY63exVGJy1bBNt/B+rndZKQa7DJme1i4ErjrJaWiLM+xYJCqfgZJSj4FgFcVh/fWkkYLChIYkQeutvxC4xLLs46Eaz2bmab8mCdjxn3Cc5jHAlCeUS+J26f00YUjLkV3hsPixf95xxLcV9kM10uq+Ul0hs836MJDF0+TUSSsThzV8rU3zs2Lp7HF+DFP2EoQOHRI+4CDwZrybjxa/Tp57JXxHgxpZ0TQF8HdC08K68mWSsp1uX5hgvQhJo/oqqy+Y7FAcf89UNmIx+t+S78bMEn+joy5cnD0nroDgIZaBcMhMprUXA1MuGPKweD+ohaqsGzAqTaWt+n3iSSJ7lX1yZdKjom+EKilgsmsnnxCe6+zE3igrh5+zMO20qN0qPGSUkXYisLEaE5f+tMEWrrnEJ6cdd2NCJyQj3sL26mqdsKVdv1F9Wtyn/beO7G9/CDeeGOKNrKVLgIIwsZz2wn5wEgI/oCH/9FQgfqzs7F6wSm44+IYuZ5NB4OYh/uqaI1z/pOblcRzDUuFypVhcmxQupEk7itT+n3A0wcD1T52UATDISpYFvPQfWo61ZVQ1eH9fg+Iu+g9dQdau2fTnBl1aV6PuuI4GrqaK3ls/D3zgCgG71AYzpBdGM7Ug0fOzMXfX14ljG+sCwHQPB0K4921wxT9UBGuR+tek6jrydPTdLQj6ZDjSMk7FyfRGo5TNWg3QhXAMerSOKpDlB/zgIEwri+naN8LjUvgZidELz1e+1u5ZzAYEj0aDBvtMiLJzHwE30HyFhV1j+v+x0jIqgzPUXsex81Frfhp23JsKW5G76WpwBBVrN9Q1YpHlu6jg4Ea0yAW1o6nsE8RqGxj3FWfs/xJyXE0dc0F4mq/V59xrsWZt6fY6ISREB6v/S2S42k8+WAgeQfKtuGDgaw/L4kHlxzR7VI1XgRapdiJzrw9Rdd2uZklEzn4ROQzfziQIiJA+qYDw1OYcPHS8XL5jBOn3ChVQgWgDxKGEuXwP8Mc/AGPijLlJvDo8r30XYeYPeA7BInhhN+ohqC40Th5pQ1c5JaaBoE8SeKnEi4sxMnS/Fl14TkAqmALdLjZ5JMOPB/b6g5ZhvyWJY2UxGaEgTnRjN/phUaDf9+his4EaUlZaMqYeOrI3fAHw1ZyLyV2Av/xwBfwxeVH9bioMD2HWxkDvLWkEZsWt+G51xW0YTSk31s9d2pWn9UHrNB3NatcA9PgG9CJiAAkEU4SAmMKFmWGUz0/Deqz79QCPNewlELngl8lJhJmifpx613kvQ4oMdg6UCZVjQJuS9jHDw6us5V/imytO6L6G8C7tqfkVz2lacwwJv4VgJ3IakKcmKpznJFAakLwYh6e66jBmrIehC9mw1HeWT6wsTHgRlU+h/F8hjFwu768nCBa7F2lhwTyrKAvovtejc3l3+XLOoAL4bLn+fJCA8HadnYRveYz9Susdw/CPvwBD7uaqugAbhxEUuFAXDyMqXoZb2/i01k2FHYq6EWAva2L4To+zXVlZK8qPUFQFwdw8kcJYqP0DYf9/3RFvTUeX1+hE8gfrDuMVHm+sRZ/e3w1Hq/cK+++te6IXoN+ikNEQQq4fop457lSa15cCA+sBPvBMHY01MpYM6XuSy3lpL/U+uEK5IDOiXKjcZtKOXDww8Ofpyinmh+Xh/MBl5KaLcpcw3PN82dNebf8zsxa/nAYtWWniChBUXwCVEzMHwxjnDusoZxugKuj4wXSxHObKYhZ/4iofIVAFTvbd2qBNQaPLt+LnV3lOlLD8BdFUuEPhrGruwzVc94QSJA/GCaqSkfnV/D4bVt6UKA5bjSuk30d0Hzndelq+I/pBd/VXCm/b607gu0rXoc/GJaE1NR5a65tk/feTJZfOf8M/JiHlvMz0/JW9I3U4do4MP+49S6VW0Nz7/ljtTSmCn4rdK8Mh407Ap16ubVYxpxhO3ACOcSZDiyeo08eXgu8l0VtUkP4ZMsa+z35dQfDVIRzMEyVjxU8yUySR5hqvDDEZ2Nlq5Xwv7OrHNtKj+r1pOBru1vL8HTrSmv+rqvu0M8eCpN+SoHAmpTTu7rL4EaJnMOPeVhf0QF/KIxEEJLcSaFzVu/7t8dXw81OEMRIObR2dFZZhAbm9Qyjfq6jBhurj9Pf8+KoKTinHU6qH79aWg+4GDu/4WaSTM7BJyJ/ELAiNzcbK+efIY8Mh0WNELz1s7G4zc8kTJ6iJO8tbCf4TYyw0iZMSZQYU1EmHWEb4M/Ne5mh/VTIwFiyblEP8Za7AVaX9mL/6fmSmCfGLCvqFPiBGOMMreB+4XdWXqhUJbu5qFWzPJh94gMba47jJVW0yKLRG6Mtqxecwt6WxdhYY9BOxjSUwZmg6GXDgRg66xb1UGGuMWhXAYwZsk2lHAUA4fBXfc2hasbLOwkXQdiX8Vpd3kOVo/meqXOEYSVqLJm2zo952Fh9nBLbVNid3+kby3+DHxxcJ9/xB8PYVndIvjfW5pbaLiQcgqxkf/DcReCgpvgMms7dSXSWChb1QeMCAF5WAiPv50giPeJUDMiJO3AmjqbDWQy6veBGBIsKL1meSr7GnPf31RwTY+k/rtqFf3/sC2mQneo5b6Dp3J3pbVfz7p5FvYiGRrCzsdqirFxWckoXbBsKY0XxSam6zf1iziEyDkGHwJTK4QDS1pAf87CussOCim0o7MQ/NVRSImiOvX4ZUnDPol683FqMjZWteKmp4gMNGDc3gXWLeigPhtfjEBk/JpTl4bLDeLp5pR3lAtLnAKDXe8yTcXUjSYJutRbApDDl9bx9xev4++MrxGu6pbgZzx+rhZtDff9yS4lARsQjn9q3Y0CdpH99iMErnymj5r7aY1rPsA5Wnm5n0ojVvyblp6VDjf4QXWUwnpn6f11FJxmnOZp2Fw5QU3wGjV1z6fcI0VLWzT2Po60LhY7XigLz/U39Z859s69SqEfT+of3DIV9N8fHSTr48vLDFi0296MJFbOcBTlav5mfm88zq0NznyPpWIUkAdIFQbafTpxg3PODdJhAlBTUyBkK0fvwM439KXUOmfsT7z3h6yEkbtHFOJfNO2vBa6zvmfcaA5YHIG3/sr4/RHPj6yuIujf1Ohk/piyOh4BRF07CQZCTBHwHdYVn0HhuFs3bTrWvGDrmg/rug/T7mO1UOtlJOmlzIe2+g2E42UkEIyFLx9UVn0Z953zcs+gYfnbXzpsOgiOwopmPfDxY0cWf3HRtupnkMx85YJGKvSqM/lDdQfEOfpCxycLsMczE8sTKPRJOZvgNQoF41tljxItUCiU5EJo3NjDMQ4bpgXdzEkQlqTya/DzxdPZ72NNeDIQDrK/o0MZrwsFDdQe1d8bwbAV9ESqCphgJeBMUryy0gcc/A9CRl8EwxoWG5WdLQgFeaqwQT4qTcHSStpnMpf7f27oYyPKRHx6SBGqBOkSY+iSQaE3QF6GDECfo9VPimJlYywcDZqBKVf7kjQvwUN1B6Rt/KCyhapkLPo2TO46gJubBgB6g/rsWUeOm7q1YP8waA+KNYRYV9f2LI7dYUaINlW36e8xWocT8WTdWvZ+RGO5G4xpKwMZkNI7GbjKMTVgUb3JjRSpGFQWgm53AhtJ28WIGucrbbPTphsJOi7LSGT+KuybreSsStmFDL7RUyYb47499gd4/K2kV15sciek+iHmomXOB5mKS5tbLLSXwnKRQz7JH1KzwiVAgBwPpp9wE6uaet95n27KDUmnc9F66UZUomXLA5MRc/v2lpgo5GHAfMVuYo4b+lRMFWFeucmSUh9kfDEuFaVM4siIbPehgsHjmZXmPn7YtJxjOINF/mlE/ABZ0C4COUmarvIYBD3E/JF5e0YfKS/10/So5GOQfyVY3ocbIwUBBaqxIFHt4VTVz828AJOH5vrpj4knmzzcuOQ64AV5orpYIIN/DzUnQwYCNdyWzct8bU4eLB3okhA0KDiXsRCZVam4CL7cWp5NGBET+IJ56BdmsPzsbzvhRSRIXvWYkqPIBYc1Cih4xuQHj9rk9bKyahbruqzmmI74McTFIHtjj/1y9ZnUyxz1wAns8hrUedHN1JXTLiHdSnAy5CV1czDHeVXmfuVK4PMNYS49X7pUx5z2rbu55eacNZe2y3uEEcMaPIrgegZuboKReIxHZ1E/CSAUAvmYYSkxKaD04GMah9kX2dwCdxGwy4Rn7kjnPauZcEMuIacAluqn68YeHP0+/p9SZEDH0HQKKHMJ3sLq4Vw4E9d3zxtQxqca+QO8MJyLv0Uw2wSLfVbogcO2+FHsiZjAy+g6C4RA2VLRZ92k8NwtwArxyUjtBMvKHI5/5w8EjZbTxmhST25YeJEYGDhcaipQVph/zEMkmphbZnJTy/N6B9bJhMKSCWRgA2lDgBNheftCmwDM4hgFF0ametauJ4Cem8trXVkj8/7kJOKOuhPv8mEfVQ+Mu3OyEDdEIB1Lci++zroQ2/T9ZegzXr0fFcBQxnunHiLrU3CjWl6pcjNyEGLAP1h4BEo5sLt9Y8RuaTQH9cyaO4pG6ffK72bf8M5KOwDBM1iHpb4NVIggH8s5srO5rL0jb0P2Yxom7ORQVACBYdzeXOPKFT9rw5vNGFnhB2oHG/JkjT87EUatqKXyCj6V6cMVIYaMgN5HGsc0sI8GNCOA7eGCpUUHWyDnwBzw8XHYYK4qIntGEggQ3IhpKkJPQBlTKJvjV0noxbDZUtWJrSSMeLjssxqGZyLent0hocE2vPvcnF7H7amm93P/pw58TT7S8X7ZBj6sMSpM2j716UyM3pP9ePlGITYvbMGkiHRLqO+cLdIPnZ354SG/u6hnbyw9qbngTrx3oOVh/dra11p85vBJuXlwMdevQZKw7c4OWcY1pY8+cKxxZMPvzN42l2hBRLFY/OUzwuy3FzRr2ZTgCti49Auaa7754O/x+T+pz8Hx6tnFZWkSS8e/mekpt1+3ZfUT1GPOw+3iZPXd9R5hu+pYNY0dnFbYtVQeZQOtUYr9xhT9fDO+4kXNi1AW5NDwJAMHAti49Ip8DiqaT54Xyyq+af9rqV/OA68c8u4YDzztVifzRiteBhKupXZUI/TJ/javDKkYnx3fwyIrXLCjLltoG3Dr5hubY79dwOvjAiuKT1F7lgHLHxbH35CLRVw5XmnUg8DlH5Qj9Q3ud6AKGyVE/K6Mx4ablu5hz0RzX7SteF49y0BeBM+rSfBqDeUYbk471t6AvQtEtQwczNFX2sTGijv6Ahx8cUBFRtUe60Tgd2NUYC5PTcFhyTAKPDjT7Ti3QkRUpWMgsSMaDAgf54SE6ZFY36bYY10bHDWsHiOpHc/8FQ+uicVoDihq58dwsfU3SfrYfo2J6lrMHxPhm9seDNUfoWV5S9MeWqkZxNMk86fcIXqoKc8pngEAzf1a/Mm0/+ldLqRbIofZFuo6RcTjZUqfgxnkKyhsAj694xepPcYgpBqU9vUXC2MYi0aObWTI5B5+I/EHAiubNG8C5tyePCaMQloQUT/iKEoIijBm6MyEDKawVW0sa8VxHDVWBVThuixEkMGBFKe+zesEp4uIe45mAUgwmowiHikdCYNpKfhYZmUCQ5Vvv6oy6Vtl2AGMzsHDoN+ZRwhkr7BFKWuW/rS/ogh+4BIFQMCDEHayr7tDeON+xIEuADmX+SXErdh6vFO9jGgTBYFbg568qOkkQMfZ8JB3MmP07vHV1wofCsZjuzawsvb6iQw5XzMzzgZVRlUKH51usNBYjjwHTSoVq8Ia4ubIZOxurgVCAH6z6Jf73xj8ZE24AAFk5cQxdzcWK8hM41LEQAKg6a0RDGsz3+32/ryg9oT3pKSHx26f04a0Lk+U7lbMvorl9HuVlOAHWVXSKwesPExMWEg4QAI8sI2Ydnh+WJ/NGBEE2Jf6NVQkYAIL3s+BMGiG4WVshbWDKgyjQNQMuYhpGCACEAvzd6ufw53u/Qt+JxnXF62GGYNGcDfoiurqtikTIQSak2JsYtmCOZ9j2rkqkTx1Y4FNfCMSMk96zkwjiLtaU9Oj1HaaoWBqswfTSmlATZilz6ZC8vXY/tS3hAkMhrK3qxKsdi7Gl8hh+2VVpQbIEGpLlW8w05hzwB8MEm1GRiOrF59DcPg/OqEOJ0oq9KhWyYkEjFFuT+UxeR2sWnkCWm8DulrIxdRtgVEwPBWDGmk1LmiU6y7DB4HqEDvAG9M/NSVgQTyfpIDByKdJ0ZiqkxNDjHFVL9Rb7MQ95Z8MYWEhsb1yMijHxafcx5ow/qKhJmR2Mjfmwj7qCs1LwTBj1ko7oWJ6jW5Y04vJIvqYwVcJwtTQ8ewq8h9/NrEYsz0wQ9p/3p81FrXihYYldiTdl7VpwqZR+9fs9IOJrggSGcjJL3HAY1QXn0HRqNjDi2gUw1XhuKW6G5yRxcuA23T+/B3IEQOBdiPhWArqlC4fDclhz8ketw5WlWyK+sG8hsOeCVZldvQezeyGg8YMLrCvuwsvHi/V7sm7hCFhI646xqofLvFbPuGdRLwpyL+PJI2sFhrSi9AQOdS8gHRvS85apxc1q2OIk4LGIeYi8F8LopCTWV7VjT4suzrZm4Qm82jrr5mYrumP7x4MVvfX0Tdemm0k+85EDf9DDmfO3iSeZvT2AUhgRraiZYxuBg+nZ1+kGqqy8dXgwq+MqRcfCFV8DVYUVoKRXUU7qXluKm+HEDWYhqCI9/Z6VbMnvuWbhCQonK2VL0QrlFfHJeN5efpCMMBA0x5kwahvKCn9o9c+AZmCR0PaAkaBnbGZ+zCNmmGgcm8qPY9X809jTW4Q9bRQB2bjkOHlvx8XJiFReRLPSKLcHDuC4AXY262TCsXC3zKojfexAckfMwktvXZ0gfcHff6xin9yPWGQcCvcyvCI3gT3NpfJuQtnppsB6+N05CmIwYfChRrzXjMs1DwbKkPZjHpBwsPNYNXlrchL4xmt/SjdSrC7+gLrHaAgTJsQwMkQGxqH2RXQ/V4WolV36eOVe8gqaFUP7iYfe9GqzLIhe1X07zphjg2G8dfEWaqaiu2w6MVuKKbm5CbxyogCht7Kp7xWLCgKaVz85fDceLjusmEa0V9cfDNOcSzpYW9FpjbFZvTnIpjm279QCfegxonHsFfRHQnDiKUnPqi+/Vv9FNUfoS08f/hx9N1vBQuLaGDDZdCyPOidf8udqPm2pbaDPBjwdSeHojKJ/3VLdqKNg2bQxc22FdWWd2NuuoEK5CTLGfN0O9oizjko1eFYVnyDml4Da89O25dojqjzUSLhIguZR07k7tY5Sxd2YlUygEjBgEYEjhr2bm0BTzxw6FE5WMJ4phPN3xo/i3sJ2SZpk6IH0nVorq0qodsayeWcBEOvVbpWPxG1mWV/QBX8ojJeOVch91pT1wM2jKrKaSCHA5qJWof1dVXSSrlNG6a7GKpkrzvhRgdkBilxhQOsHZg3yh8MaNmrUk5GoX0r+2GDJEMEIVfTQrOzLB6J7FvVK5WWev1trj9rsYKp4lpuVRH3XPF2sTzHqCZ0nIFG2HU012N++CGYBPAB4ua3YMv7uLWyHk9RRXYCgMnJYHU95Q9+sUsmtcVoDays6xTiNJbIoUXXOBa1nczV0B4AdFTdyNVi/M2yIv5ta36DpxGy5j0S5KzqlLWdiU/Bs/XI5GAQ3IlYkg9cMF0XkcTMP+Sybl1B0we9XkEufotGBuR9wJJDnBHv+Qe/txF3R9YDSnwM2JOnPVu3ThxffgeslCYKnPPAy1/hQY+SiSX869pxKCsZJJW43l+Bvj6+Wa2qKz+BQ10LSveoAIw6GPF352h/wJBKSejhPzByGmxfHntYSiXz5w2HMyL6Gm14ykYNPRD7zhwM3N64LCzEO22QnAAAnwIO1R6zwrISqmZs6N4GCGVesCoeioBW+3hK1afxl5av6UNDvUSEZhxgFnPGjhElVm+rqBafwxKo9VmIUv+errRQWF7yoWQZdLf6nW1fqw46iTwTI257mORlDwTNkyM2LixJfX9Fh5SBcj+cAAHY1VVECNHtuQZAAU3kCymt8YwwqNN6kTY50o/9TQ+ZuNuVg8LMeXHpYoiVmIS4WNxrHk0fWonL2RfiDYcUqxMkCmrnJTDSVIjbKwyXQjsD2FMtmFhi4boOBhI1p/l3ECbBpSbPV//qF6TCxcclxObS+/+YE+igngQdq6ym/gTf93ATWlHfjB4f+SMZL5sO4uDA1bahqtQyxZxpX2PN3jLwDEwbj5iSwZuEJqbydvGNY8h0kiW6cUflZPT8NIpaj6QcBmpPfb1kLQFUxD1GBHt7QpFonz4FBlZMSOEL9akHCTGpVhoekJv4bh1R3nGZDASBjtqr0BK3dwJHDhz8Yxo5jNWTQ5cUt2J4bjWN9VTv84bANb1Hy9BE6oLzcXGIZmpurm8XzngoZYIP9nkW98vyDp+dhR2eVNtC4EjVXCVe/v9C4REdB2KZQbV63qEfl1DgaipAyD2UNpkA4yGNLB91f9ZTiqaN3a8+0YbCxob6/gw6zh7opUrKx+riskU3VzfirFXsE2rent0giAFwtmQ9S/mAY66vaZfx2dpWLrj14eh5FmQDLCWJWYX6ufhnB+AxGIxlvw3iXe6j5sG5Rj4aS9Ov+8LLoYEdsNrb+ZBlJUt9LBALAc0coP8AfCUluAmAYvArWxDqI15DppOBrUtvpZqs8t+EwVpf2YsiP6KhJPAR/JITGc7MIYsnzPSeB/3jgCxrCEvMoaqqioRxNre+eZ0UX11V2aEM3dc8z+haO1qcyN0yveHaC+s9LWtdMCA/K+zR2zoPJLGZWc6cIA7VFSAsMJx7306bFbQAUq5bR7yxcvI3z43iep0WwRylp2jqcGHldfszD/Duu4t14HtaaTF1m+3MSFnzLzMkZqyAhf4fz1sRu4Tmn9qXGnrnUn24AhAOB0vK9rdwjV9stj9Tts95vQ2Gndnj49E7/9dBdY77XTSUBPsbh4NN+6Ztf/iBgRW5uthjMgPaAjBXaBmjBbahq1eXaORRnQG3MsGZqmHzT4jbyiPOGp8RJOvjfVuynasWDYWysaqXKjEb1Q3nWcBhbqhrxXjyKV9uKtGcmcLCmvJuiDCnv4ebaIWMu7sNh41Q2klQ2BvMdPuidUtkpAIzJ2JB6X4R9KoBjXFcw4wpxRcMwUsyQMYfGUxmHUsPb7Ol1gbUVnRIBMDeKxyr24clD9+h7M5wi9dCUAocAoJmu+H2MgkMmo9OHMbPwNfcXtWBHQ+2YRoUpZuE0ScQzx4QNAy6YdyNC0aoPuS8bjOwlZSaitMJfqoAQ1xMYE8rDh2bzbyxjQN/Mn/1+T1iWBC5jQNekzQDB4FQRMp4b6wu6sKe3aEyoAHtH3ajK8Qj0POf2wIE1xgwZgBtoGISaT9zf3H/OqIvACyyoxsaKVqvuxPqCLoz44Q+FqKWNjXrn7eUH8ZOjVNlZ9E/KXJU+cwINBfoAdjCr0JsaG7jUVua7T72G22YWB8N7WfBzyINdM+eCeHMBqlIr7FCGbrEYbxzIQZ490o3nZmm9rMZ22byzBOc02HWWFZ/GsTfuRDJh+7LY+Jb1oNbI+sp2XYjMmCPrC7qoKqw79vwca0wQCrCq+AR57JnxSUHnnFEHQSTAspJTONKxwJqLZvFAkyFPGIgMPWfCRsy/18y5gHHeMF5tLRI4yYLcK/jh4c+LPmZP8VhsVlZbFKOaGyVq4Z1NVR+6D1rMQb5eK5sWt2FieFCovp2rWUjmJ6w8AYv1bgxnD/chF19E3JGEeHPPBYgOmGFU/nAYmypadF+50OuZfU0GI5U5hgLjgTaax4LYWO9ojIcc9MaCNprz+0N0ozl3LPvBd7CqvBcHT8+TuWDOTUv/q3s7SQdB2Lf3wQ9q34CHZaWn7DUKyFw0dZmZ6/OXla/i+y1rcdMXQZv6MMLuR6vFkPBHsffKT2+6Nt1M8pmPHGws7MD8O66KkTNWcpM/GBYmCIAWsSgp5Rm7v6jFNl5iRjgx5dS/81i1rhxqhCuDUICfHb5LFMLunmLZzACIt5Q95b84WodXjxeneWEZt8zK1495WFPWAz/mITDwrDuPVYsX2lQibjQuMBdTcTkMC1AKUbCbrlI8CcPrwd65mIdHV+61PJCSDGo+L+FaMBYA6OmYaSXpmrUOxKPVb0ABAO3VNTxeFJkB4AOvtlD0YnNRq8DE1hd04ckj5KX2BxTUJXDG3hh9J82YY6YrNkyZ6YW99YBWzGbYXaIIxjPuyLqmOclLGsXLw5Ai/tmkyWSPoXUvFY6WhOuwTx5ss+9SIgLbl+7XSWg5ibSDAUeapJCP6UFTUzw3b4Q2SE5oLuzU10bjBKPhpFrl3d+0uE2KQvkxD99Y9bI24rMTAk2yvNhqk2I8t0DtYh6GkpE04wOAwH6strs0hzicz+3ZUtug35H7JCspaxbhwArv8/2dCaPWHHWztWfviapX4Mc87G4ux6vHiwVe4SfctOiZ+Z7by4k9a92iHor+5SQkQV3aMBjG11eoWhm8oQeOXgueflc3hwz49AiAEcmJJMWj6OYkpP6DOVd3dZdJ1BG3jOCRZeRpnJ93Vdrgxzwc6VhgR+/4UMWGVwAg7pCXXNWC4TwFjtjwczmvRigdcxOoPzsbSQW7M2VtWRc2Vh/HluJmfGvlS3IfTsReNf+0VSuC+3RjVav0PSd6pyYoc6Jx6YKL1voHIAdIZyLNhSMdC+z9RHnhZTzYY+5AxkzqgRiMd24uRS64Lkh99zzS/2rMXjlRgB8eWmvpv02VLdJfJtW2vEu/HY3yB4lPH46ts1hnr1tE+4gTd3VR0DwqrPW18gPY2ViNq/Fx9L3hMIJbR7ChrF2e9/jKV7T+MeA6qYcE7kPX03vTRG+Q1hUb5bkJ7G8rkL5ysxPY1VypDyyGLnMVeQgAbKpptp+n9q37ljTJnHy0QuupB+sOS9/dX9Si35H3IxVhNSPC3KbHKvZZfc9wIviwoj4cAWEUgyluXhz72wpobIwIl6wBM+Kg1n5g7IMMh1tT1iPXrVl4QutuBzjSRs7Eewvbsbqsh94habRP9a9JIMKR3dQ5ddOJ73+8fxn5UPnMHw5ebK7EybPTSBmqE7gfI+8GK78Hahrw9bq98h1zMbAS4MJW/DeT+cQ0Xk0Ig0WbBgBOoOj7KPmIDTuhLTSozPwBwoaam5u12auKrfwe+04tsDxw5vsDpKjTGDoMY1Ow4b6DR5fvNS6CLjqVijFVyvCpw2usw8cvuypxb2F7umc35ll98mef22exNDBOVop5mV5fQ4GRAra9ggJnUcwLLzQskefsPl6mvYPKA5OaeyEVmA3Dz6KOjRHLD3usmapOxjtXG57mBiJtV/c0C509W79cWIsQOPjGXS/rPjIMIXMO8eGDI1g7u8qpsnKuYq0yDHPzuYCGuLA8tOyAvmfMsxIr3WjcitawgTM4kGWFxm+P9GmIBiDQInNsdnWXaQ90APzgwDqdI2F6u1S7uQ/42YHvSGVcOAExmqRssJwvI++u3smNxvHgssNWXoGbkxAIEHtSBYvL8ySFjQRAGsNX6tz+m9c2jBmudsM+AoZycT6I4V19upUK/O05XgKAKmtLwTvVZ25uApdHJ2hjKxWWlrTft757nu5X7hNlmLvZBJHkOgp+zKPiYqn6CsDakm75+enWlfCHwrrYmQHLsXRDipeYoWepkEiA1vXmolZZb2kJ/LGxWXb8wTBebS3CS00VeL6hDv+haT393YCX7T89X3LAxKPrAJ6TtPrVzUngucal+Fo5rYdNi9vg5lG+QthNaicQQFTQSjetL+jSDoA8Y0ycQJwQ/oCHJ6pekfXkZifwYN1hwv0bXnbuCzc3gbzQiN1WNUap+xKSDnYdS4dLmXprw5JW6/MtSxq1welrJxCvB05GDdxAmKP8mIcdnVWk5/PiBB+NeWJ0mwfZdxN50j8IHHyt/ACtzSC9Hf5wmGBPanyfq18mtLoyZoCtq1XU5cGlh7WuTIGV7mqulJ+3lx+Udbuzq1z20B8eWivzi+fIQ0sPYEdjrRyUUosQpuphAHjywD3ifOBxl71oXFwlmENghAzPTF1n5sEDgDXned/etpTaIgc+01GkbIDNS5rgD3h4tbXIsld4H/1VTyn2tRnshoCGzynHIPfXmHDgjPzByGf+cODm2gYtb2KPVryOLJcW447OKjx1nAwnhiKwpBm4rIjyyNOXGjoEjENDqgQOMUCoxfpCI1HWMfbWVJ68abDiAkAJiY59SLA25dyEGM1p7xE4UjAHAB5fblfsNT3ETx1eAz/mYWtJo3UP3qjn33F1zHZK9eEBTxS0qbiZSo3//m48DwAEw81JhOvLO+S+XAlaaGO5f8bFLeOBN+LUTVTo4UylrZ5vVpt8tnGZXMuHN35X/t8qUObD8izzd8eaDw+XUX7Eg3WHrQMZ/7+lmDYOz0nKYc+q2BrY93uw9ogY1w+XHcaTLWvomcamwYmwqRSF5n2eaV+qjRvD4OSEZPN7T1S9Yv3OkuuOWgcpa/M3f07NcQns+8i9Uw5WbjQOxw2kMq7Zp9tKjwq15jNHV+KB2npsX/66PJdp+eJBaEwDk4UTxM3EcoCSSAF7zW8vPyhzideIvGf+qGX4Sr8mXGyubZKaJ1muAX0yInBSWfzQGqL4jeq+8gepHoc8c4w5yuLHPGyuIp3ChkWqkd17aWqaN9ecm2wgsQeco0qWI2TAw7ZlB8d8/obCTvn7+vIOpAr38/blr4seTM0R8WMeVZZ3gPHjhwQiwX3l5ibEgNtaYhi9sNcL6xA2hnd1l4kTQsY7UNV9QfOBaYFbL8yw+u/69aiMye7mcuuQw/Jg7RHaH1SC7/ea77EMYtHp6vlchZt13LNHlwNQSd0wKSiNOTXgpfe7cPprHes5ScspxHse5+RY+tukrFTY9HsLdVQgtV8tamkl5sGRHUf/0F5nrfvU72xdekS3R5xfdI1JDQ0AT6zaAzcax3MdNXrephBePFDTIOPxk0N3p+1DqWv0q6X12FzUSvTmUV2F29x7U/W6fF8dAkRvpexTfA07R6TeEt/Ld4QkxRxTKdQajcu+/Q/tddYepm+k1yTrMmv/YBljT2OdVDPnQhrVa+AGRBoxxq1uKskkJH8i8pEOB3/3d3+HkpISjB8/HuPHj0ddXR1eflkbWEEQ4Nvf/jamTZuGnJwcrFq1Ct3d3dY9RkZG8Oijj2Ly5MmIRqP4whe+gDfffNO65tq1a/jyl7+M/Px85Ofn48tf/jKuX7/+sRrI3kZWILwpP3X8c3ihcQlK73zTYqnYuvSIvRhMyJAPPLpCe9XNQkWpBp983fDMCz7SECfpaP7wMRYv88n7MU+XbFft4aRnYYYZCmN9RYf1TDFAFANBkKTw/N8eX60TUw0DxVEh0S01DXj26HKsnH9GDEdWrKffulUb6YZh6ebFJUmVYQ5unm7Dk4fXauXvkEdZWHVM9g3Vr9uXvy5J0swwIawTqi4BeyYZDuRG44Rfz4sDbkDY2KRDMBFA+M/dvDiebFkjCWvwHVxL5FISHWP3mbVBcb2njq+bm5CQN2Nwx7rup23LsXlJk+3FNP7f0Unev+813yPPdKNxzJz6PsKRpLWZIVCbloKAMGuNGN9qLJ4+8jn5zsbq4wAgLDv2BAysdwHshGQO81+Nj8fCBW8RdaaSVfNPyzwyk/xlPZiwHIew6fyzCZUzv8vtYA+bKTzW/nAYT9evQjwI4ZmGFdKO5xvqrHYHoQAIBch1yfO4oapVb9xqvn2p5JjuE8dey32JHPrZ04XGnm5dqROmo3E827DMgtFZRZfUsxwHlIOk1usvu7RnU7D+JqRHXbe9/KAchOGovzsBnuuoIaNYJXSaTFXcTzubqwAnQF8iR49tSgItG2PcFvwuS/r/l12Vcl8++Nxb2I61FZ1WRIAPzFIfQjlXXmopl3sJ/n/QPAjRfbsHbtf6yjjculG9ft1oHNffzbM49vm92Uh8rqNGDvsMm+A59EFVxx2GZzHNpnRMgCAUCMxS5nSeYWSOi8MxYJZcLIyN++BGBC+3Fcv6caMq+VzNC3F6RONU54ILRjL7UdgnQ9INSG/yHmY4j+4vapF3Mg9G0sdugF/1lJLBrt7huY4ai9oSTiARdPZAc189uPQw3hqeYO1LDIHl78rzBsPCTGWOJR/e/aEwnLiGbAbXI/R9rnWjjOEtS9RhO49yDN4YnmQlJX+/Za19oDbphJXs6KwymH+gyQyMdwUgSfnPHF0pUQU+1ArsbyRkGeoSFRrSsF59Y0eIAeSg4+hnmgdEvpebp7z6Huncexb1yvWSr5Pa16aTLGrYAQY0el0lHci5KCagoZTSd+pv/mAYDWbhSP7cof5Og1febJI5HHwi8pEOB9OnT8ff/M3foLm5Gc3NzVi9ejU2btwoB4D/9J/+E5588kn86Ec/QlNTE6ZOnYrPf/7z6O/vl3s89thjePHFF/GLX/wChw8fxsDAAP74j/8YyaTG83/xi19EW1sbfvOb3+A3v/kN2tra8OUvf/njtdCB5Aq4OQkqHmQcBtrfmC4nb384jLOxKVhTbhxojM/ccXHxqqeKbNDGAcT0NjANJaCVEwAEij5QmIKiegPgz7WiCQA3oPwCi25UKwOrIBpXP1bUZqKQcgmrvrX2KDYUdpKnXim/IEQUrDuO1YBpQwFYIc/gRiRNWaV6qK0hUBEBi7teXcMYy23L7OJTluGrPCxwVT4H078FkJC1yZNtjrew1hiFiUzlv7OpSp65q7uM2E+YwYeNB8XPz2HWTYvbJFkvMCqXjslkBWKB2NlcRZtfcTPgBvpQAh054n7mOXDxyiQkmFUm5hF8SLWzcuEFORS40bg1v6yEtGhcMPF8gAEUJMJIoJZ7gLy05hp5qO4gnjm6EqffupWSX5Uxsu94ofU8c9y3lx+Em5sgg3wwjM1Vzag/O9uKJMgm3e9JaH5dBW3OB0/PEyYe695uAEnChJHwPwZ3Pq+bnx29C3CgC2GpA+yW4mY817BUz7kcg24wquETTCX5XEeNfa2RuM9jzkbXAzUNeGjpATKsQ6rWiDIeJZql2v/I8tfGjBQ93bqS5oLqg/Vl2gP/XEcN9W08BARAXfFpDd3gxNfAkSJ1XAtC3nU0pI0m1nG5vjV3YMByg8DBruZK/PZYSZojxI0SNMofDhOLGD9PtSm4FrHzqaJx3Fd7DP5gWGpuPFBbLwd4ALL+JLcjkrQMQHJE0M+bFrfBHwxL8S2GTbDusuacYVQFBnXrWHOHaUpNY3LCtBtyjZM/irq55ykPSbEL8SGCC0ma67K+ax7pplBABxYjz0jgkgE5qEwdZubKmZGKHY212kBmaEjMI/IHUNV1NtKdhC5IZyYtu7kJvNRUQW1U/e1G4wiuR/Bs/XLcmtWvnSQxz3aI8VxR1LuHOhda3nI/ZlSL9w2WsWickvpVX4gEjrSJD5l7Ty6S8RHnQqB1mzPq0tzKNuZtyjgyQ5dEQTjfLGLX/NhU3YyXWggiJcX5eF1y3h07oXIS+Fr5AeuAsH3561beh+xJvBeYdVsUhExsCeVk4lwzSQY3+kZ0ZGWH2BecA8d9zPLKiQKJOrhRqlvB0DGOVAMQOKFjFIQ1HU3c9pta/ODj/cvIh8o/m61o0qRJ+P73v49//a//NaZNm4bHHnsMTzzxBACKEtx222343ve+hz/7sz9DX18fpkyZgn/8x3/E/fffDwB4++23MWPGDPz617/GPffcg97eXhQWFqKhoQE1NeRpbWhoQF1dHU6cOIGFCxf+T72XyVaEUBTOcAiFRRcpnG7gnMdiE7GKu/DiNfiI01hqDJYUAGKsmkWyCJMIbK/bj7dGJlA1wpQCOpsWt5Hxy99nRgeDZYbf6YHaevE4y3XDBgON4QECaNGvLe8iBpUxip5Z7R+LdcdgBuGCVRabgsnOYBRl21LcjOcb6qzNVzYIVcwodWMWT6oaH3g+4ADOYEg4zk0GEE46/h8NFcRsMxRG3eIzqO+eh601R8lzNhIS5enmJKSgksmKwp4aiykmpS+sIkcf1E8OxID9amn9B3ouWZiZiNsNAEg6iF4MY2jxkM2+YTzTZNsoLXgDnRenwR8MY0NlGy7GJqG99055xgc9W96ZvVzM/jJos1/x3M6dEsPgu7ni/efvpvUDrwk27g2DxGQBAQiyErgB1le362J6ilFm1fzTaHz7TnxlQYNg8/1+Smr+wcF1gA+srujBvq5FcLOSUoRQns9rg+fLEBnOqYZBWn+EAmypOIYdjbXYVK0LcW0o7MRLzeV4oKYBOzqr9FxOLSxljI2TnaQk70HbQPaHwhYbE/fh5qJW8mSqubm5qBXnY7eg5fxM+x3NNTcUxqoSzaqTysyWyrIijFtjzEuTpeqxin3CqS6HIYMS2NQvG6takRcaoX4xGJD4gJM6B2R9OwAccnrsO7WAPh/rvQZVf02gdVu88BJVjB4KY315B/a0lKaxuNyzqBcvtxXr5Euj77fXUSE5uf9oSArTpencQWJNEsYXPoD5TlrxL5M1Slhs+PMxGO8AMth2dFalFdsy+4plQ2GnxY5lsm2lFpP0B8NwElQ00WLyink2q5Mx5oC9B64v6JLidaZedvPiiI4bRqw/e8yIKR9cuBgXRzt2HKuxdWq/h5rSM2jsmitzKrXN7uVsJMYp2KUitvjWXS/hO/vvRXXRWTT1zpGxspiihsPYXrtfIormuPrxEFwvSftuQzUZwQGALMWsZxzEEA5QU3iW6FUDAOEAa0p6sPf4YtFVMi9SDnLSzgEPToIOSfABJ3AQRHw8sezXOvmXdXEosFmNPkjPpuzxG6uIOW0sFi6ruJ/vYNvSg2l7E0f14AbCFOUPDePNx/79Tcfswzbe3RO/8rHYil679t9uujbdTPKxz4TJZBK/+MUvEIvFUFdXh/Pnz+PKlStYu3atXJOVlYW77roLR4+Sp6ylpQXxeNy6Ztq0aSgqKpJr6uvrkZ+fLwcDAKitrUV+fr5cM5aMjIzgxo0b1j8A8BOkjJ38UaHNRDgAIj6FEBluxN7fkZCE+KgRSSDsw4moyp8DHpyIqpuQcKkoExsaYV+8A/PvuCreQgCS+PbTtuVieHJlRtNzvbaiU57vsCfNCzBl2nX9Tk6A5+tpUfsjIbnOzU4QM1GKZ49+gdRKgAsrBAxA8zzHQ3CyfCs6EvRFdOQBoIPBqG1sI+LrsGbCFUX9fFMt4AaYcds1BIGD4FpEv9d4xeKk7pM3blhDHEIBQVhCAZyIj9J5l3D3ki56V4PD3slOwh8KY09vEZz8UQTvU6XExnOzgFAgIXWEAjyx/Nea+jNqRBfMolA5VEbej4fgqwJZzB7CfQyQNzT10MDfd7NprAPfwTMNK8TLxdAL+U48BH84jN80lEpbnCwfTpYPeAFiczSuVahLc+l9g+sR7V1yAnRcuEM+332sHO0nZ1I/qkJiU27R0Tt/NKRZkkZC4p1cvOBNPXdU2xYUvEn9EwrgJBwMD0awsaoVTsQXHLqbSxutBU0aNVRLAAvGg4gvczxIknHsjotjTxfNz9LCN+DmJjDjtmt4/cRCDMUicjAAQJWl64mW1slNYv/p+aLJnuuoQfWcN2jzH6W1HyRtVqbieW9aY5YaCeSo1I6WJUAAvNhVJn11dXgc3NyEHAyyJg1RO0KBzBnuk0AZU07IRxBoKuGChW9SvlJOApNnv0/fYf2ScPFCWyWtJTdA4DvY2VWOptOz0uapG41j8czLdG0owP7ORWC6ynAfGUH+aIggLUW03uuKT9O8jhCNJB8ULGia8nj7Ax7+8/4/0s/MTdC4Rcj4/vqKV7FuUQ/BZSI+/qm7BM+31AjE5fYpfYCiqxQjmxlc2IBieGQoQNvv7tD5Lk5gtVf6lOGKOQl0X7wdQdLFgrmXsaezGPB8BIrrncf3lRMFFqvY1DvfUzrcx98foEPP5qJWMl4jSWFukvythCsJ6/VnZyN8IRvOlWzS51lJSwfeX9RCFZHfzhbjrGDeWzQGDKPjiEA0TvMDNEbs6EHYR/HCSxQRAukIhAJMmhiTfpCEYDVfhOPeHCfQ2nJzEwhyknCM594+pQ8I+/h1z2INEzQOBgCQeys977/U7cDutlIaI3XoWrPwBO2b8RCm/JdcgYaaUS4AAl9i/XHr5BvY0VhL72TkNbnj4mg6d6dECKrnvEH6fzREui7pYkKvmitqn9lU1YLvvH4vkJWkQzOvv5EQ9p5chM1FrZhwOBtudgItfTP1gTg3Aajq9lxjYVd3GZxc2jfnzL9CsEq1HgPfASI+nKwkjrXOF7gbwj4RI+TFNSJgKAwnh6Ihq8t6ZGylnXlxcW654+JwRsmB9v2WtbT+Ei7cnATKCy7QFzwfs25/DwDw6NLXMGX6dbmX6d0PrmvD+CWVHM7610+4MpfWlGp2Mng+nmlZBgCoLjJsAY8guZuqWnDgxAJ18Plgx9JNIcHHiBpkYEW/Vz7y4aCzsxN5eXnIysrC9u3b8eKLL6KwsBBXrlwBANx2223W9bfddpt8duXKFUQiEUycOPFDr7n11lvTnnvrrbfKNWPJd7/7XclRyM/Px4wZlEiGOFVVFQzhgEee9bAvIXemCls5/wxBCAyvuqv4152QL0ZpoBamMxgG4i4cVswJF5srmwEHhMsfCktVVgtvC0iI0zz1A5BKp37MQzBEi9r1knjv/Tz57urSXl0ETb2bFPAC8FjFPjHU7y1sF6/dGqWwMOziUPsi630EV+olBXPODDEc3WC8PgA7chLzqFoubzRuQJR4Q2Eg4WBjZSsuvTMRjhPAmUj3Cm5E4LxPuQjcfwP92drbkaAx+sby38BxArT33ileRb7mSyXH4HBE5106FARZvmyUuiAcsHZxD77fslaqJruRJL5ZRexA26oP60RvKKNx1JWKsTuPVVvhY3/AQ5BNdQDWF3RJcTKZX0ocN8CGijbyQhc3E/TCMJIfXbIPSDioraCx+zfVexEMh6j/AyA6eVAOTgIZGQpjTVEvghy12SvIQcB5J4NhrK9uBwJFZzeONrPfvTdOxuqByka8wDS3WUmB9fR0kGfa3LjnjHsPD1Q2AglNK7q7pxjBiIsgrCEprpe0+uhf1jbRz3HyrPKh+ecdS4CREBCnOf4nJVT0zR8NSX+391J135JJb+HzhT2Ycds16Vv37Ww1PwgC4YQI5sQb/fqCLqoOrIw9fyisqVmVdF+8XeYwDZQBZ1HVUZF0sK60C1zJm+dkfed8SYCHE2D0d7kanjPqAiMhgZ/9aXGzNoQMWsPek9Oxp4W42t97P4/6YyREEIGwLzrHzUrKszcUd0ifPVx2WMay++Lt5L3myrpJYohJTFFVoBUungtF1bfPl36GMiA4imIah/csIh0DLtbF4xxJyvefOv457GkpJbhM2MefVxwQ54Q/GMZbFyZjdcFJVC84L+/sjotLtXceU/hkNL97cYLOdwkcWb8mxS+PMetPJ+TjzNtTaP6p/BzWuWNBTa68cQsZaSEfgUdzZ2dXucbC90UkIdXNTcAN+5RMqg4ciVnDCKYOp0MvAFwcmgT4DhITExJNWJx/WdpROfsiFk5/RyB9vI/wuPqDYWAkhM6emcCoq/VQVhLvX4vKmJukGW5Y1SOJu3LgYkx8MBTSRvANPf8u/y4fm8qPIxgK0/eNiBbr/MH3aV7/Rf0WvScqOlkuluZ6SVz4qo9dx4lelCPqpiPl4bLDWFvcDTcngStv3CJtCAbtgx8fGv2Yh8buuXCzknigshFuhPaj91cP0z3Vu/yqp5TGbETvkRimvvKHKHn/+gr6TsE4ZTckHHJoDOvDmj8Uht/vIVBr4dyZqdQvSp84boAHq44iGAjbEb5Rg1qXK0jnJBDEaK8tG3dJdBDvOf5QmAp5cg6Fqj4e9JHDjNdVa89samckiXPnyJ76cetd+N2lifBjnlAQcz/PL3xLz1c+GGaplxoJ0bv2ezbLm7KB/KGwRCT94bDoglsj/XBCPrbVHkLQf5OzFmVyDj4R+ciHg4ULF6KtrQ0NDQ348z//c3zlK19BT4+uwOc4dsZtEARpf0uV1GvGuv733eev/uqv0NfXJ/8uXboEAFhepIxm9uAbcKEtNQ3a0+FDCl0BurKiPxTWhrwRHt1S3CwwgvJZlwiHHo1rQ7Lfk8q2APCtu14CAKwp74Y/GNaFTkzMOHSYkLGrJvabjRiuTGwaPL7aGO5Z1EvFvpTR96ueUikAI8a1MhhNMd+Vn5UbGrU9UlwVF9pA5/fdVK1xjAiAPa0lEjl5qalCPN8PlxEFnTN+FJhMlH1WdcoUebJljU7gAnSEJ+Zh2Nft33b36zo8mnDpcDIYxqZqwvO/2lKMNQtPEPWcwh7/dfM6uNE43o3nKWo/o/kJY5MzchQ4vLui+CSQdLCnt4iqEZvVkwfDMm6zst8DEq5OJlfy844lQg3IBaX+9vhqSJVPBxiKReBwrgTjoh0gGh7RcDPV3240ji01DdhU1SJQgb0nFym6W2PdOIFU2rXC7OPiBLMyDkB+zMMrJwro3R2I0csYY/EWxgx2DtWHu1pU0q3C6gpUg+eSimrs6i7TeR3GZ5uqWrD7eBn2nlyES++QM2FzTRP8acOyCXLkxGRd2d1cTnMkRVXI3PTtvuD2iyQdwYm/3FKCVHGjcTxQVw/mq7+7qkuigix/Ukd4+h0NtVhfQNEuqe7K612tefN3yYlQ4g+FJUFxT28RRQCKzwgURqI8Cr7G8+Cpw2vI0PAdqX8i0a8w5ciUz7qknxMz5jYItsIwN8Yi8+GA53VwTVdN31RFCdlWdMcB4AbYf3o+mnrnSP4QAKoBMRSm6ImiHiYICukvSf5U95eEdeXN39NbhKQaNNHThi7k73LfWfAUw6Fx35Immjv9WvcEET8tSX/1glMybwLfIWYXI1mUr6s3kjpXzj9Dc96AmLScn4nes9NonFVBLSFVYM+9yiuTolzKKbBm4Qn9/gZOf2tJI9xsqqwuvP8KE+/mxbG2rEvaxWt09YJTev8x6sgsKzklyfIcPSq9801sKW7WbG+qL++rbtJjzUXdshN4dPleS18+fXSVVEaXeW+0YeqUPvpsnDHeCo72/LFaqwaFP+DpQ4SR6C0FEw1aUwAIKacAMygBFOGTavacQ8DVqBnOaEKjRkJ4tn65fr/cBBE7sP5R81bmgdLXTx66R9rIsDw3J4F9HQVwFGTJH1bQzXDKXsw6VUWfVi84ZSU672kuBQKHco4GPJy6QIiI6jlvCFHH2tIuW9cYxUeZmIIjfABFGJB0hPWLdcwzR1dK7k1G/rDkIx8OIpEI5s2bh6qqKnz3u99FaWkpfvjDH2LqVJqgqd79q1evSjRh6tSpGB0dxbVr1z70mnfeeSftub/73e/SohKmZGVlCYsS/wOAKVkDaYVHyMgiekRnxIAB9EXkVP/uKNFsIiBDR0QtlOeZWjEUoOXkLKEqk01NJWQ931AHv9/Dd/bfC38ojL2tiwEYpeUNrKIwIKiNqnL2RfG6mlRtJoMGQ4v2n54PQCU0saE6Lo57C9sFgiLfV8aTuQkDoHfl5yhIjCgP9pYahibTOAKA6wT6IGOy0RgGDEBc+2Z5eTcal4Jx6ys65P1WlJ6w4DxyvREe5xoB/mAYP2taIWPnRuNkIDvKSFUb5attRYrxBdY7vNRYQZ5SVqBOgC/U6WI4/nBYIjWrS3pxX90xHDkz18LGmhVma4rOKlyqj6eO3E3h+GgcxTPfxtrKTt3HKTrXHyTsNG+0Vh9BHcSyE3ipscIygNk4/sWROuxqqbTmSupBcFXpCfgxYuWQdzc2WLnWI+iPeUBirn64ykhU33lo6QG5RnjVfUcS1yVhWf3PDE/rqjt0u6KUXMdez1/1lKZhZt8ZUdGPoTDuL2qxC/3wnFeHw9R5x89hw2/ZvLNkFKm+9gfDBAFwAjJAjMiY+Y6AYpdSzEH7Ti2wkphris/AVew366o6dO6KgpDxvR4uO4yCGVc00whHFs0Dsu9getY1aw00ds3FyvlnpO85efyZpuVS2FASH/NU/ZNQQLlM6p5IOmi9MENIDLhd68o74Y+ECLYyGMbqMjpgr15wSsOAFJwpyNFzY1dLpXW4XzX/tNXvgrlXz1lf2U7wvRbbQQLQgYwPTf5Q2IKpzL/jKunVAQ8vtZahfNYlaZd5SGUxCwnK+CvZUNWqa4w4em4yxM4f8MSxwH3oj4TguAHqu+eRTshJWO9u5gtwMrRZ7dYf1mQJcAM5VLHOBLT3181OYOH0d0TvcHFHABYBw7P1dOiychCMAntSjM3XuSLsJILnS20YAFYFXb5H64lZeP5YrVXHYd2iHlwe1jjt1WU9cm+mgzXZggCIQwaAhkyNhnDld/n6vYfCGobDh6QAmoKbGbtUe3jMQjdobTnjiUqYc3GSCVccAG40jrXlXXDz4qgrOqP72hw7VQeD57I/pMYr0AfnVfNPC/W2QD05t2nIlWdz3sCECTG9d4L+FmRTtKam8Cz8fg8bVFE+sx/caFwOYPtOLbAKkDLBxp7eIqvWSGPXXHDC9Ktdi3UkSUVDuf9/1rRC3kWeF/axcMFbFn2rPO9mhxVliqB9IvLPzkMPggAjIyOYPXs2pk6dit/+9rfy2ejoKA4cOIClS4klpbKyEp7nWddcvnwZXV1dck1dXR36+vpw7JjmZm5sbERfX59c81HkxTbyJAY3IjZns0MbipM/KhuQkz8qCVH7FexGEi9ZWQgFHGSDSE3uNQ0qNxqXkLSwWeQmpMqxeAhyEwSViOmCO03dc8QgNEvECzMFe0lYCQyGEVyLYE1pjxjqv+opRZDl2xGQrCTc7ATWlndZXkP22gCkgBijCFDBrC1LGgEnEJjHs43L4EapWNCu7jI8sKRBe35M7Clgsc5IstiQ/pubk9BMS4GDQ+2L8P8eJsy3m51IK8gS9EU0k0luAmuKeq1kum2lR6V6qD8cRnAjgo2VrShY8BbcnIR+B+Wlf1l5t/zBMFYVnSQDyWASYY/V/tPz8f5o1MKWm9EdP+YJfhbKG8PwlfYTM8mLxja2kfDnD4XxVyv3kFfogzw1/L1xcTgjOjR+z6JebKw+Tth9xUzFEDKuTMz9ffD0PLjRuC5wpKI2Mu7qfycW1vkrauNnY16uU+/JdJMAMOKrvgj7+DerfoN16hBmHliY4YmNNz4sZLl0z601R9OiSH7Mk+q5W2uOYkdTDRlMvNZyEkJdaR6mauZcsA5jYngGrjIGAhlHN5Kk9W/wiJsHp6Avotec8vT6MU/fd4AgETu7iP/+5XaVWxTyCV6l1rEbJSau7pPTJTl1fUUHYa2V3LOoFwgFVt/yeyyKXpHD6I0bObqglVHoT+BRgMakG3Sp/mhIogMMa3jlRIFmrMlNYF9bIZYVnyY4gpe05ribrSONJi0tAOxrL5D3kOtZhw54BKkyOd2NROlUMQ/JJ09P0/lGCZdqEHCkTSYX0uaOnkT6Ry5mSAdYOixaB5IAVrVaJB0smH1Ft5chaaoegD8UxsaKVji+Y+lUfyhMrDYqF0n6IzeBXS2VWFPRLRFB3gv4uyffvE0OPBxhMfvZHwrjkeWvCVWx9Bkn3vs0zpsWtylDWyf7AoAbSYqTKlVfC9lDjhEVU/vMnuMlOHJmrnxnXw8lwXN7JZJoQq8USQMAbCo9LuvNpEbeUNFGf2PCgqykPDMIdIVi1tkcSQhuUxHoAaKIrSs+beVocX9xzl19zzw9Jfhgn60PZcz0Je02yEj2dRRop5AZPc9Kwo8mcV/tMf2dSBLXr0ctJj2MuPKspnN3AuHAIr+QKOuAhxcal0gxUUDDfAFdGduPeTIXHSOJGiMuECh4YHZCj/1QGDWLzgl0kqMn/mgIJ0/eocbZmAdjrMmbTjKwok9EPtLh4Jvf/CYOHTqECxcuoLOzE//u3/077N+/Hw888AAcx8Fjjz2Gv/7rv8aLL76Irq4uPPjgg8jNzcUXv/hFAEB+fj62bduGf/tv/y1ee+01tLa24ktf+hKKi4uxZs0aAEBBQQH+6I/+CA899BAaGhrQ0NCAhx56CH/8x3/8P81UZDVQYeic8aMSAfBjnn0Sz07o5ErlgdrIp3l1Xap3GEnbuwtAqn2mVla0vJeGV0e8ngx1CAUSRpbvqvd5YtmvRSFYyXyB3kCJlYK8mZbnUyk33uy4sqfn+HLNA0s0BzLLvlNUct0fDuOZ9qVEhRY4eOP8FKtdP+9YguBGRLNtqP6RdvjQmHxj415VckKu+WbVy1Ll1s0mylkzf4BDm1xNNQgHEoZfX9BFFKTQYfdn2pdiV1OVbMrO+FG81FSBk2/q6BMZdo7QIjI97Myc9y1vnj8Y1gwkMY9gPWHD0vAdgVW50bguyMTh3EArYUoQVn2hDhy7j5cBvkN1DtR4WpAxLvzk6PkjMJUhSrp8qbFCJ3saYycFn9R7chtMw3FLjT32WdFRyWkAlPHW72lPbECG8ubyFus9AWgIQVYSTx66R1Pz5dht4gP1V0vr4YwnDv0sN4HNVUQvikBVRR4yDT8HfjyEn3cswYM1R9KoAid5OmmTseyN52bpKEag71N/drZlZAEQyBrDUYRuVkW2nPxROL5DVIAmVCWXGIUcZrphD/aoS2st4YqmdUZdbfCxsReNY8/xEjIW1Nx4uaUESDi6QOKgNnaePvI5YiZRRiHDufwBYjFj3WQe/sUjzs9lPeMEOqeII4NGpeQjJ8iQCl/M1u1VsBFOot20uA1OwsGahSdwf1EL7lO1SMz2ye/qcLS5pskaO9Zz8jfFnAWQt5a/b3rrhX2Fo5nsUU+pEq5fnP4zITrO+FFsqGrFoc6FWq9mJ6wDIjtOKiddBEAwUq5xIAe+nATl4oQMWIo6sD11eA0Zse9nCTwkuB6BE3ext2Wx1hXRuLR5bRk5DDh6wf1sSQBMCsUItqnayvPFzVFtiCQJupejKZJFDygcOgDUFZ6xoUuA6C3T2WXCFDnqzjTbbk4Cjy59TbfFLNSXk8BDyw4IpFZy4cI+wZZinkE1rJ/F78zwtq+VH5B5taG6VVswauz/ob0Ojedm4cGaIzIHZD0a4yuiIhBc68Ef8GxWLZXELA4gn6A3D9Ud1H3FkYUAUqiQdQJHSTg3zIT3MDuWH/M0vIv3ChUl+VVPqUDrdhyrkf7gytgm1MuZoGGhbh59h6GRDy09QPTSOQkr+dtRzIZIONSfqv///ySB73+sfxn5cPlIVKbbtm3Da6+9hsuXLyM/Px8lJSV44okn8PnPfx4ARRG+853v4O///u9x7do11NTU4Mc//jGKijSWdnh4GH/5l3+J//7f/zuGhoZw99134yc/+YlOIAbw/vvv4y/+4i/wT//0TwCAL3zhC/jRj36ECRMm/E83zKQydXNpYxM6OZU4JbhOQKjcxGtgKojhMEFDlEHIFGimyIIOgGXFp3GkfYHFkMIbuDuOWB6QIIODE6gerDmC5zpqsHrBKeztKASSDpycBBw3wNQpfbjyu3zLu/ZBdKR8jT/gwUlSEinTdgLArb/OwtV/MYKF09+xDWWDntWiwuMN1g2wrOg0jvTOw7hbYoj1Z2P1glNkhKbQIprvWTDjCrpPzLA8lnxgC3wHQdyVdqS2KUi6lDio/udrpk1/H29fmGx5iJ3cBJbMvYDG9nkC6Xpo6QGqAqzoBVMpaIX2kpPaspKYM+1dnO2ZhiBHJ6b7MQ/OqCN9yH9bvOgSUeP2e5g88zrevZyPvMkxDA5k6esMakMAmDn1fVy8Mkl/PqTeKS9l0wjofUJeEvctaqXQfo6m3/P7PSDLl6RbeDQ/mbKV5znTiTK0A56vDZDibszLvUqJ0sac9uMhOP1hYMKoTsZVwvMnSLoIRl2sK+8UD7lFvafGcvHMy+jquBNBbpLyCrJU4qzh6ZaEyBSa2M3VzdjZVgkn5FPuhfpcqF/NOWusbbkH0wKGA4WLhjwHScei90QWJdpK/46GhBK4Zs4FyQsx22bSovqjxC4kOsKc5yrB2wn5dFAIHJTPewPtb0yXOgV8rTUOxnow75cq/oCHBQvexpm3p6RR85rfDXyHcgeSjtzX1A2pEtyIwLvuonjlabRemGG9w72F7dh1vNIaj/z8QVy7Mt52hnBfJVzrQO3HPNwx61289cYtZLSpJGyZq+b7j4Qwa6eDiw9oqlC/3wOyfek7eY4yuITSOY+KoU2ODGBXd1m63lMHorUVndh7chHN66Qj92QDd/GCN9F9fhpK57yJzovT6LsJF05/WPpPoCkJMjiZSc7UXQCt65o5F1DfPS+NDjjwHZrrKeNHP6TsSyMhik4q58/9RS3EBmRAZdaU9OC15iIEKrnalFXzT2Nf9yIwvaXMQZMa2wn0HBwO631LraMp065TUj30/poqTOdszlc3j9iD8iYNYuBKHumluM0qZkrozWzEJyb1PDHm4lhRJ38khKy3IojPGYI/GsLWqnoNTxpDmHZ24dy3cfqtdEIUP+ECoy7qis5Q5MHsL1O/cl+m0ojyIcFLom7ueQ3hMvQ1y9fKD+DHrXcJy57oGkDPE5M6+d0syd+Texq6mP8m60PNI2Y5s+a0qT+VnRIEMVx6+P++6Wg/2cZbnXM/ws5HpDINRrFv6Jc3XZtuJvlIkYNnnnkGFy5cwMjICK5evYq9e/fKwQCgROJvf/vbuHz5MoaHh3HgwAHrYAAA2dnZeOqpp/Dee+9hcHAQu3fvtg4GANVO+PnPfy6UpD//+c8/0sHAFH+QWCLoBbVnwI3GbVy3Cr0JFARAbt6IfOaGfV1d10vfpN3chEBIjrQvoGep64WdQ4WOhcFFsVS4OQnB+u1tXSwGTzASwpbiZjkYUHvCEsa02ml4d+iHQLzt/3JZk3ye/BJRo/EGaXn3kRLRUHJfzTHAd1CYdxnwHQz05UiCsx/zJBQsniVH50iYBwMAwpACKBaYpOGmUCxNvBkyywwz0tALOnj7wmQ7whIAwahLHpFx2gP5TPtSUrAesQXxuLMn6dn65XJPhnecOX070SWqcPfXyglPbyXrKk8i18xwx8WJTSTuyMFA5sq4uBUtuXDaMEzUZmpWPuU+2lJJ4elkXNEccnuV59kdR7kamxa3Ab6DR6r3k+Gk6BV5npueMDcnIUxBbiSJV48X4yeH704bl/GTYggiPgIj+ZujDVf/hdqEYvTuL7eU0DPj9mbE/dl98Xai71PjvLGknZ4VkJdTIirQHj2BXXSXEXuWOqBsqmiBPxLCy63FOpxuJMgz5aQf88iDajDssMEGAJsrm7VnE7Rm7itroY2R13bCEa9tVf4bthc64UqSqhjsCUcfDGIeEHexrfSoRHOWzD9PcyXsw/WSaO0h48D1kgInBICv1+zVfaD6UNaYkWwp4xKnCq6nztwuECeTfcaMkgZDxH4jUbyYpw1bAzLEPzvjRzF6exytF2YQXWKcWE+2ljQSll71NUeH+vpyAd+xqlvzAZH75qul9VJR+q0Lk8EYbckl4rlq5GK5WUlcfED9nQ/aoQBrFvfSOzFEIqbpSDHqYtMSGr/9bQVSjb335HR5Ny5mCB8CMwxGFM2rWkvLFp8Gkg66T8wARkJ4Z5BY4/wBj9pkell9mmuiP+MuvlJeL4w0SLgonv8m/JgnES0xKJUw6xh7q9ct6qE5Fncloij5amaxxwHPqrwN0Dzfd2oBGZEjIVvHgCCSG8vahAKbZWu1juCKZ1tByczIjJudEBY9M2ppzafBMJ45utL6TBLMI0kMDmSRlzsrKVFiieQN6mTk5PRh66CCVMII/g5Xi85K0sFArcXn6pfpvJ6YZ1c1V3swkg5OnroDfozgScLApMYSgYOq/Dekv8x+kbYZ0R8AuH1KHznqBvSh33OT0j4o+lJTnjpMKAqhywXVR2Idtmlxm12gj4k9GCKVlUTl4nPWmpa5EknKXr+1uh7/Z+0etPfeSVGtIX0wCG5EsK3qMDEwDdnMUjedZIqgfSLyzy6CdrMKnyqn/+jbCE/QiooLuSwrPo0jnfOxrqLTZiVxAty3pAkvNCwR7KYU0voAz0iq50I86OzFGwlp7Lni9OaTvQkXGOv+wY0IMWgYRXwKFr4peFQ3GtfeTuXN7j453fLE+vEQ3D5SImbSnPm8VA8/APJudcyHO+ICt2jPRKAqKppF3Px+Ym3YUNWKPb1F5JVqK0yLJKS2sXL2RbScn4lV809LUjVL+axLaOmeI5vGmtIe7O0qsDxGG6pasbu53O5/teGuKuvF/rYCK0IEAMUz30Z7z50yvgJ5SfH4yHxRh5jUSJC0iT1hhgdpU2ULftVTqmETJu7UhRhoqR4v7oeCGVd0XY7UeRXzMHFaH65dHUcbUQCBNrCXWwzE31fwju/J1w+F4UR8FM1+S1N+quukOF9KXwV9EWLc4IJH5qY9HEZ1wTkpUsRj40bjVrE8a+yM9gCAl5VAfCRsvy97MZl9xHcgBac4CggQ7zzPlzHWF3vnEHcpwhB39IHOLPrEdT1MCl8ed6OQFxxgWdFpi7mGoyxW0TKjjx6v3IsnW9ZgzcITBMtS89Xy4o1RTI5lxm3XcOmdiToiwn1vep8N77Qp8++4ipNnphkY+sDqe6swWEpkcNPiNuxsIuarewvbNUZfwSPTdNn1CEEfjPcCiNlnf1uB9E2qLuDrZW6zPlVFyGQ+GXpX4CxGHlJasUvDMy6FxBxgQ3kbdjeXU0E1rmXAkWbW50p/c+HKzUuahJQi9T3MKOziRZfQfWq6XiccTTaK9PEzLI89R0MCqJ/TdYdEEpwAZgG3scRXTgKzgNeY16UUr+PxgAvdd8ZhlN9pW+lR/OzoXfL7vYXteLG+Gk7+KEXHWxbDHRdH6Z1vovXELDhxl/ra3BfVPFxf0CW1Z6ziZKqv5t9xFSfP3S5tt8g3OH/B1HGpBfl4vMw+N1jMUq9dV9mBl1uLwUne5hpzc3Qkzeoz9YyZU9/HhXO36ghmgvQWR054r0GECrEhFIg+EXYl1sHGOPtDVAQRgKwlmUspSIcx5xjrQWaeMtfT4DAuPvwfbjovu0QOIv8KYcf7/V8wJBHEsW/0/7np2nQzyc1eGPufLWIYDBj0i7kJHOlYAAQOBhIRjZH0fGyrO4QXjlVr70ZOQocj3UAYetiTarFVxDwqwBQlZhokHGE82Fhl4CNTmFBECSlFCEBC0M74USDuiIEMJ0jDzZvJgt0nZmgMLuNivST8XEo+c7MTlmeD73FjhKBXnLjtD4XJu+UAfrbyiA5SYq+jWIGCbO094c1qd2sZAGBfa6G0cW0FsaBYSVzqUNRyfib8oTD2dZAy9YfCcm1L72xSgCrsOj48ZI0nHErmqis+bfeHSpbtG83WmFDO9ej30H5qpmZ0ySHDxknoSqc6IkE5Auzdl/fjDYxDvXyvbKKdc3MSwlLC2E/Oafn68t/SuCicrunJ8wfDYhR191A0zQ0FliEJAPB89PXlSpTApNIU+IvvoHzWJbhZSdyzqBcOe5qM6IewY6mEeC7M5YR8TMkawJqFJ6RQE7eF30PgEFD5II4+LPB6Yq9ay/mZOo+CDTmTCUStGzLWYBu0AOIjYYvNi/uaKRulvoYPbXApjC+SDlbNPy2HG54DIoEjGy6cwIKAmdE+N5JMMxIQCig/QdEf8jgIZGBYMzrNmfMO3CgVeDJx9gDR9dbNPU8Jk3wwiHlYX6YTwLnNpjAWn6leCf8eWJTEnCyLpENMVUNhy7N/8gzBCRAO6CCVmxCP9j2LeuF4CrrzVjaYppHn7c7mKtE1u5qqZNy5CKAkpyqPJheAQuBIXgcASZJn2ddWKM9fOf8MihcR7eqt2f3KiHfgJFzxuvJhsab4jNYxho7jhFx/KEwMaMM6CRWBQ6xAijYUAbC7pUzyEQQ3bzoOlK4L3s+iw1E0jvwwFcOzkqEdutacb9aBP4BEOuXgwoeaLONAaxRxc3MTKo9Jja+Kkol3mI39JEUPuUidPxxG3dzzei6Z1xrCeiHoi+hIlLHXibGdTesuHKGiX/6Ah81LdIT6Z0fuovupCOqvekrFkbTv1ALAC7B45mW0v0EHpYCrTRv5DgwT3H28TBu4ipuf+8qPeTh5/nYsnveWrEEzwrapiljnKmdf1DkwxjoqvfNN4yCXkudkppUZOvjl48UWuQjnavA+8MqJAitXiiBL9NnFK5P0/uwDCAfEnKcir7zX3FfRLIdR1iduro4y86HTjAofPD0PB0/Pw+qyHilcCiAN6SAH06SmfpUohUvtoMOZ2jNSktVvNgn84GP9y8iHy2f+cLCpsA0A8EBdvaW4eSObnfuevjjuEjaSk4IACVNyuO65jhqtKAwmBD5lc7GhzovTqHfVJiUF15TRYoV4k46EMXerCodccMaEznC4ed2iHjGC+NkiJtONcQZwcxOYln1d2g5ormo3GsdbVycQPMcN0mgkxdDLTWico8qXEIOAlbpSOG5eHNtKj+LhssN4tbmYrlWJrRI2537MSViJa8xSkbrJm+whZni7vn2+hLbFQ5SdEOgGX39vYTsl6bHxosZgV1OVVKt2cxKazjIAbe4Mq1HGLYforQrRaq5IEreCHnAb/3QZGWxPHf+cJECbVHo0xkYiJdu7SccaZzZU+XeBSRkbIj+zRbFdvXKiAEHSPhByGF3aDGi2KAA5IWKV4kRAk85Pnm0wd6UdONX7bC1ptKn8jHGWe/hUpOvRutc0FMpkSQGsAxAAPFH1CvwYQVy2L3vdmitWImzSkQiWbMoGL7kbjWNLNb2jmWMinlCDkUT6mznfFazKbHvquobvwAn5OHeKIGjH2ubbESjVL/VnZ1tRFDcaJ1Yfvpevx5X/3396vmCZAUiyKNfUIHYxPT8Pnp4nRsTimZdlPB6sOWJBSKRtzSUELQsc+HcMw81RyevMHGOs21QiBX8wjIdqD9L1yrsvkI9oXJiSzDUCQJMSqOcfPD0Pnb1UpOmlYxXKuQEEinlJ+jvpSKKl7jNqz85j1XII3HmsWthi1hd0wc1OoPfUHfAHw9hWd0i1Sa/D6oJz1F41pyZMiIkzxpk0IsYoY+qZJIHpcDkSBYD+zhSoHG0y540iRDDXssWIkxKJ2VzUCjcax/MNdbb+5fY7tKYbz82Cm52wolk0ZilrVsGz3BzNvc9GsPSB4YXfvux1JDiylKdr/Mi7R4mQ4fEVr8jfGEroZlOFa5m/gRovw+HG4wrfoT4fCuPR2n1ynUDSIknJ/QKM6CY0lawU+jJ1OYDW3lnE+5+TwONLXyVoVo6uo8IHdbNdW5Y06vv0e1S3I6bhe7w3+DFPR96YZQmGk0et94LoZXsuA8R4ZkRh/7LyVbo3655UKCp0ov2+44Vw8kexvfygfP5Yxb6067fWHtWRTxgHUbU+UuGeGfnDks/84WBnczUASOEnU/x+u0AKb/xW+F0xJ7Ai3bS4DVxsyKTXTAvx8iYb1d7WTdXk4dm29KDcgw3EZ46s1F5VpvkLtCeFr/MHPDLiAuPdjAqvJq7ejcYl0vFw2WGJgLAyeKFBt128V8pT6g+FbbrBVDiEycqS2maluH529C48feRzeHTlXv1+yuvl91PCtCiwwNFFtlK9b+pvFnRIvdv9RS2aI3ycoehMha4+543CFGmXRYfopHmQ+bpN1c0WM5HZn6ZnXTOn0D1+2VUptHRmn5uVQZ9Yucfqc5aNNcet+eTHPIoKRLUXSfpJXXdf7TG40bjtRTa8vjzeVp+qvwdJF3uOl8jftpcfVBcYUQRjPsiYm5h4Fel6tmGZsGBYz+LDt8rZ8GMesbrE0o0m893457dGyVv+bP1yDPoRq//l+gEPW2ob9Lrm5zqBZgGKeZTEaRRHvK9G0REa64rXrPkc8qJqY8RiRGIJQJEmxVolh2sWA9NtFSE0vi9dGo1b6w0Abpt6Xd7bLCTlD4UFgy5rm/tmMIzO09Nl3XHuzQO19Xhk+WuUKxHzsHXpEctr7ccI184/c19YOsAw9keCsFy/ffnroq+EUtq32+fHPDxzdKX1zma/aO+6Y7X1wbrD2KwYkvhvPIceqK3X884NrD68xYvpduQmrKTZb1XvBqCNSpbr16P6GdwPqQUlczXbkalHLFH6/+Gyw1rH5un/Uw+CXEvEXLsvNC6x9AUn1UqfslNhOJx2CJPXuBGx/+5gzOv42aZhy4WyRP+q9vgxDw8uPSzfefLgPXJPcx/m6DgAPLqCiqdtrT1qtdvShQ7w1CHC42+rO2S/21DYyjczdbMY8mNQ3Lq5CcQDeg8uVmbqZSmyNxiWg80vuyrxjZUv6/E19nLz+VvrjqQ9K5X96d7Cdjx1/HNpbeX33lRNz/x+y1oLGve18gN6P1Djy1StPB4/OXy3jk4evMfaPwBdP0LWla91kcDcUhxPN6UE/sf7l5EPlc/84WAsEQVrbqYx28BLFV5ku7rL5Bqhk0w1kgdsg4b/MS3ZP7TXWSd/9iKZhWNYEdPmA3yp5Ji1YfK7+jHP8kBzKJ9/50Tnp498TpKw+HubljSLsehG41LmHdAeK5NbWQxRJY8sf03aO9aGwsrox613pbUVLsFRTAXmjB/FoxWv2xGPaJw2RoNe0ZQdqhidG43jq6X1OqIQGOOcbXt/U2EdrNhN8WOebFim7Gqqwq6mKqtf5EDVn7LJqrbypvSrnlIgAL6+4lXdvnH6MPj9lrXaADWe+9KxCguC5EYpSVSuc4yDrRqPnV3lZPh26mgLYEQrlOHLRjK9s0HDaCTr/uTw3bYRp8abPZf8N9MgkO/nJrC7uVxTA6trrTnbTwa5OR6yplL6g0Pcz9VTjY1Hl+9NO+Dz/25eHHkhRSpgGpa5CTxfXycGlylM68rePz4YyZpNoSJ28+J2McA8YwNWOQhyP6T/bGLHzbGSuWQ7d0VYH7xzZYLuM3MT91MSNo1nMoTAOuDGqGDjTw7fjZ8duQsP1I7N7pLaz18qOSZ9DSewDsTP1S+Ta3/atlzeY9exKtG/rIO3ljTK97aXH5Rrt9YdSZvD5nv4MQ/PddRIMTRL/znUp37Mw4N1h9M876wbLYy6Msi+07QB25e9Dn8wPOahzeprw9sv76V+54OWzBeDptiPeXj6yBiGofp85fwz8vdn2pemGWmWB5mjbYY+kP0tBQ5nzgVn/KjlgJL7Rg3oUn9KTl2/3UaTBvunbcuxbelBPHt0ueU44GrQ/J0vlRyjtehRoj3vET/vWAIEDh5Z/pr1zPz8Qasd/9BeZ+mbtWVdlqNF+ihPR7ZTqXV9FQF+9uhyWU9+zMOWmgbpO67g7uYmKCKnxvrJljXSB1wTIpWWN9X43lLcbDn8ANoTOOkagD5QqBymX/WUWo5K7n8+JLGONCPpps58uOywfNeKSBk61u/3rIrp5jtz9Pxmlgys6JORz2xCcl9fHyZMmIBp3/srlJVeRdfFaSmJXQ4+qPKfP+jJZ2YS47pFPRb0AoB1mufvURl2+tx8hiQCDaR68jzAH/tgEvRH4IwbFQ+VucHds7AXr5wsQO3c8zjauUBw3/4gKYENFa14Z2Q8mlO8X/5IGG6Wvg+3yx9U9KfjRq12+0OUnGV+R/4OWO+kqRRVX/D/xt+5X1bOO4P9KtfgvupjeKFpiXzGGNDUPkztd74fAsBJOBQOZ/yq8VyzP4NQoMfXGGsZCwM/H1yPACGNq+d5k0otaR7I1i/qwu7j5XBz49hS1IwdXVX63q7xbmrczQJ3Y72z2WZKwKWqoMGUkfRrhsLYUN6GPSeKdJ8b/5vjlTrPV5f0YF/7YjjZSTguYXtrF59F6bg38bP2ZWM+C1AJnKq9QX8EgUcsLm5WIn0+GP3E/fFBYWt/0FNUfsY89O3NNa0d5s98wE75nH6g9fb5BSfxSvtiIOngvppj2Nldbo0p842nzn2WB4qP4R+PLZU+fbCkHv/12AqrTYHvwnFV3g7rBpVL48QdBBE9H79Wvh8nYrfjt21FWFl8AofPzpX7bF7ciheaq611mZ5grftU2gp7XhFVriPjY67t1PGy7m14pseaQ+bvmpJU6wFJHAfGHi/jfT9f1oXXTlFdm+Vzz+Lw2blYNOMd9JycLvPISTj4mzW/wBMH74MkpKfoGrm38T40KEibh/AdnUxtGtRKJywpPIcLfZNw9eJE0dUyf3wHW2oasONYrdzb7OfUvjV1jPl+Vh+OqITpSNKm7R1r/zDbw21NXWvGM3kP8Ac9PL78N/jb43ePed1Y7ydzwXdw64xrePf9PNEFX1lyBP+tcTnc3DhWzT+F/acX6P41503MAzwfi+Zcxomz0+hdYh7lvWSlj93EqTfQdz1Xr0lADt/We6Ukdm9e3Iqd3eX2PHQCfHnJUfxjS51i2rPXCb/fv6xuwUs9JVrvBvaesrGwAyH42NVTJmPNOWryvEQIm0taCHJl7lfms1JsCNYNEpH6gH3QTDhG3LXXEc/BmIeywgto650l60aII9QeK3MtqdvHDjY3GkfiWgJvP/FdXL9+Hfn5+bhZhBOSl+NfIIyPFt1III7D+HUmIflD5DN7ODh37hzmzp37+y/MSEYykpGMZCQjGcnIB8qlS5cwffr0T/s1RIaHhzF79mxcuXLlY31/6tSpOH/+PLKzs3//xX+A8pk9HFy/fh0TJ07ExYsXb6rT7h+C3LhxAzNmzMClS5cyp/L/xZLp+09PMn3/6Umm7z89yfT9pyf/K/o+CAL09/dj2rRpcN2bC2M0PDyM0dGxizj+PolEIpmDwYfIzc1R9c8QnsT5+fkZhfUpyfjx4zN9/ylJpu8/Pcn0/acnmb7/9CTT95+efNJ9f7M6WLOzszMG/ickN9cxMCMZyUhGMpKRjGQkIxnJyKcmmcNBRjKSkYxkJCMZyUhGMpIRAJ/hw0FWVha+9a1vISsr69N+lT84yfT9pyeZvv/0JNP3n55k+v7Tk0zff3qS6fuMfFLymU1IzkhGMpKRjGQkIxnJSEYy8tHkMxs5yEhGMpKRjGQkIxnJSEYy8tEkczjISEYykpGMZCQjGclIRjICIHM4yEhGMpKRjGQkIxnJSEYyoiRzOMhIRjKSkYxkJCMZyUhGMgIgczjISEYykpGMZCQjGclIRjKiJHM4yEhGMpKRjGQkIxnJSEYyAiBzOMhIRjKSkYxkJCMZyUhGMqIkczjISEYykpGMZCQjGclIRjICAPj/AB3Rru/JZGdMAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "featureW = np.load('./mats/1_BasicDemo/featureW.npy')\n", + "\n", + "# Plot the weights\n", + "plt.matshow(featureW.T)\n", + "plt.title('Input > Feature Weights')\n", + "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "826213d7-7721-440c-b1a8-47fb613339eb", + "metadata": {}, + "source": [ + "In this case, we have more inhibitory connections than we do excitatory for the input to feature layer. Let's load the feature to output layer spikes and visualise them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7609eae5-f584-4c98-9eb6-3c3cf1e2aa04", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the input to feature excitatory and inhibitory network weights\n", + "fo_exc = np.load('./mats/0_basicdemo/fo_exc.npy')\n", + "fo_inh = np.load('./mats/0_basicdemo/fo_inh.npy')\n", + "\n", + "# Create a figure and a set of subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", + "\n", + "# Plot the excitatory weights\n", + "exc_plot = axes[0].matshow(fo_exc)\n", + "axes[0].set_title('Feature > Output Excitatory Weights')\n", + "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Plot the inhibitory weights\n", + "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", + "axes[1].set_title('Feature > Output Inhibitory Weights')\n", + "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6", + "metadata": {}, + "source": [ + "### Propagate network spikes\n", + "\n", + "Now we'll propagate the input spikes across the layers to get the output. All we have to do is multiply the input spikes by the Input > Feature weights for both excitatory and inhibitory matrices and add them, then take the feature spikes and multiply them by the Feature > Output weights and do the smae thing. We'll also clamp spikes in the range of [0, 0.9] to prevent negative spikes and spike explosions.\n", + "\n", + "Let's do that and visualize the spikes as they're going through, we'll start with the Input to Feature layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6c84239-c176-48c3-8954-25da5f989d61", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate feature spikes (positive and negative weights)\n", + "feature_spikes = np.matmul(if_exc,patch_1d) + np.matmul(if_inh,patch_1d)\n", + "feature_spikes = np.clip(feature_spikes, 0, 0.9)\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(feature_spikes)), feature_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Feature Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", + "metadata": {}, + "source": [ + "Now let's propagate the feature layer spikes through to the output layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate output spikes (positive and negative weights)\n", + "output_spikes = np.matmul(fo_exc,feature_spikes) + np.matmul(fo_inh,feature_spikes)\n", + "output_spikes = np.clip(output_spikes, 0, 0.9)\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(output_spikes)), output_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f", + "metadata": {}, + "source": [ + "Success! We have propagated our input spikes across the layers to reach this output. Clearly, one of the output spikes has the highest amplitude. Our network weights were trained on 500 locations from a Fall and Spring traversal of Nordland. For this example, we passed the first location from the Summer traversal through the network to achieve this output - which clearly looks to have spikes Neuron ID '0' the highest!\n", + "\n", + "Let's prove that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23", + "metadata": {}, + "outputs": [], + "source": [ + "# Output the argmax from the output spikes\n", + "prediction = np.argmax(output_spikes)\n", + "print(f\"Neuron ID with the highest output is {prediction}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", + "metadata": {}, + "source": [ + "### Conclusions\n", + "\n", + "We have gone through a very basic demo of how VPRTempo takes input images, patch normalizes them, and propagates the spikes throughout the weights to achieve the desired matching output. Although this demonstration was performed using NumPy, the torch implementation is virtually the same except we use tensors with or without quantization. \n", + "\n", + "The purpose of splitting up excitatory and inhibitory weights is to allow for extra hometostatic normalization of inhibitory connections, which has proven to be critical in regulating overall system activity.\n", + "\n", + "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e0473a0e93ec4d1a6f701eb0cc38758a92a51b0e Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 30 Nov 2023 10:34:58 +1000 Subject: [PATCH 63/69] Modified tutorials --- tutorials/1111_BasicDemo.ipynb | 488 ---------------------- tutorials/1_BasicDemo.ipynb | 380 ++++++++++++++--- tutorials/2_Introduction.ipynb | 734 --------------------------------- tutorials/3_Quantization.ipynb | 493 ---------------------- 4 files changed, 314 insertions(+), 1781 deletions(-) delete mode 100644 tutorials/1111_BasicDemo.ipynb delete mode 100644 tutorials/2_Introduction.ipynb delete mode 100644 tutorials/3_Quantization.ipynb diff --git a/tutorials/1111_BasicDemo.ipynb b/tutorials/1111_BasicDemo.ipynb deleted file mode 100644 index 3150d68..0000000 --- a/tutorials/1111_BasicDemo.ipynb +++ /dev/null @@ -1,488 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37", - "metadata": {}, - "source": [ - "## VPRTempo & VPRTempoQuant - Basic Demo\n", - "\n", - "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", - "\n", - "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", - " \n", - "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", - "\n", - "### Introduction\n", - "\n", - "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using our base system and the Quantized Aware Training (QAT) version. This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance. Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with pre-trained set of weights.\n", - "\n", - "Before starting, make sure the following packages are installed and imported:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c879cd02-82db-441d-9476-fff1925bf494", - "metadata": {}, - "outputs": [], - "source": [ - "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n", - "try:\n", - " import cv2\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - "except:\n", - " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", - " import cv2\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "bb45df38-e333-46b2-9161-80e6ac367532", - "metadata": {}, - "source": [ - "### Image processing\n", - "\n", - "As in the previous tutorial, we will load in a 360x640 image and show the patch-normalized version. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input image\n", - "raw_img = cv2.imread('./mats/1_basicdemoquant/summer.png')\n", - "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n", - "\n", - "# Load the patch normalized image\n", - "patch_img = np.load('./mats/1_basicdemoquant/summer_patchnorm.npy', allow_pickle=True)\n", - "patch_img = patch_img.astype(np.int32)\n", - "# Create a figure to hold the subplots\n", - "plt.figure(figsize=(10, 4))\n", - "\n", - "# Plot the first image\n", - "plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot\n", - "plt.imshow(rgb_img)\n", - "plt.title('Nordland Summer')\n", - "\n", - "# Plot the second image\n", - "plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot\n", - "plt.matshow(patch_img, fignum=False)\n", - "plt.title('Nordland Summer Patch Normalized')\n", - "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", - "\n", - "# Show the plot\n", - "plt.show()\n", - "max_int = np.max(patch_img)\n", - "print(f\"The maximum integer pixel value is {max_int}\")" - ] - }, - { - "cell_type": "markdown", - "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42", - "metadata": {}, - "source": [ - "The patch normalized image here are floating point values in the range [0, 1]. For the base VPRTempo system, this is fine because the entire system works using floating points. However, in our quantized model we will be using integers. To demonstrate the conversion from floating point to integer, we'll manually quantize our input spikes by dividing using the `scale_factor` determined from the QAT.\n", - "\n", - "Let's load in some model scale factors, we'll use some of these later for the weight calculations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", - "metadata": {}, - "outputs": [], - "source": [ - "# Load network scale factor\n", - "scale_factors = np.load('./mats/1_basicdemoquant/if_scales.npy',allow_pickle=True)" - ] - }, - { - "cell_type": "markdown", - "id": "82691809-7b06-4f0b-aca0-e19d293355b3", - "metadata": {}, - "source": [ - "Like in the previous tutorial, we will convert this to a 1D-array to pass through the layers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2bd6ae95-2a79-4b45-8a60-503079339739", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert 2D image to a 1D-array\n", - "patch_1d = np.reshape(patch_img, (784,))" - ] - }, - { - "cell_type": "markdown", - "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae", - "metadata": {}, - "source": [ - "### Load the pre-trained network weights\n", - "\n", - "Our network consists of the same architecture as in the previous tutorial. The excitatory and inhibitory weights have been converted to the integer representations from the QAT and will be applied directly to the quantized input spikes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d98749a-8f28-477b-871c-93626e96786c", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input to feature excitatory and inhibitory network weights\n", - "if_exc = np.load('./mats/1_basicdemoquant/if_exc.npy')\n", - "if_inh = np.load('./mats/1_basicdemoquant/if_inh.npy')\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", - "\n", - "# Plot the excitatory weights\n", - "exc_plot = axes[0].matshow(if_exc.T)\n", - "axes[0].set_title('Input > Feature Excitatory Weights')\n", - "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Plot the inhibitory weights\n", - "inh_plot = axes[1].matshow(if_inh.T, cmap='viridis_r')\n", - "axes[1].set_title('Input > Feature Inhibitory Weights')\n", - "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Display the plots\n", - "plt.show()\n", - "\n", - "# Print dtype\n", - "print(f\"Excitatory weights integer type is {if_exc.dtype}\")\n", - "print(f\"Inhibitory weights integer type is {if_inh.dtype}\")" - ] - }, - { - "cell_type": "markdown", - "id": "6de0fada-374c-4289-8042-5347b6b2ba21", - "metadata": {}, - "source": [ - "In addition to this, we will set the zero points for these weights. From the QAT, the zero point for the excitatory weights was 0 and 127 for the inhibitory weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0405142-f8bd-4d83-a78e-d078f911d649", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the zero point for the inhibitory weights\n", - "zeropoint_inh = 127" - ] - }, - { - "cell_type": "markdown", - "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6", - "metadata": {}, - "source": [ - "### Propagate network spikes\n", - "\n", - "Now we'll propagate the input spikes across the feature to get the output, like in the previous tutorial. Let's start with the excitatory weights though first, since we will need to use different scaling of the output based on the zero point." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6c84239-c176-48c3-8954-25da5f989d61", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate feature spikes for the positive weight calculation\n", - "exc_feature_spikes = (np.matmul(if_exc,patch_1d))\n", - "\n", - "# Now create the line plot\n", - "plt.plot(np.arange(len(exc_feature_spikes)), exc_feature_spikes)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Excitatory Feature Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", - "metadata": {}, - "source": [ - "We can see here that the spike values calculated for the feature layer are huge. That is because they need to be properly re-scaled after calculation. To do this, we need to take a couple of the scaling factors we imported earlier to transform the output to a reasonable range." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1bd2b7f6-c698-4a7d-9099-da9cc59ed007", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the required scale factors to transform the feature spikes\n", - "perslice_scale_exc = scale_factors[0]\n", - "perchannel_scale_exc = scale_factors[2]\n", - "\n", - "# Transform the feature layer spikes based on the scale factors\n", - "scaled_exc_feature_spikes = (exc_feature_spikes//(perslice_scale_exc*perchannel_scale_exc))//perchannel_scale_exc\n", - "scaled_exc_feature_spikes = scaled_exc_feature_spikes.astype(np.int32)\n", - "# Plot out the scaled feature layer spikes\n", - "# Now create the line plot\n", - "plt.plot(np.arange(len(scaled_exc_feature_spikes)), scaled_exc_feature_spikes)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Excitatory Feature Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4d9d5a9e-05d3-475f-beec-1c6d29923cd5", - "metadata": {}, - "source": [ - "Now let's do the same thing for our inhibitory weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1059733-2b2b-4c49-8ba2-2a52531f483c", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate feature spikes for the negative weight calculation\n", - "inh_feature_spikes = (np.matmul(if_inh, patch_1d))\n", - "\n", - "# Get the required scale factors to transform the feature spikes\n", - "perslice_scale_inh = scale_factors[1]\n", - "perchannel_scale_inh = scale_factors[3]\n", - "\n", - "# Transform the feature layer spikes based on the scale factors\n", - "scaled_inh_feature_spikes = ((inh_feature_spikes - zeropoint_inh) // (perslice_scale_inh * perchannel_scale_inh)) // perchannel_scale_inh + zeropoint_inh\n", - "scaled_inh_feature_spikes = scaled_inh_feature_spikes.astype(np.int32)\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axs = plt.subplots(1, 2, figsize=(10, 5)) # 'figsize' can be adjusted as needed\n", - "\n", - "# First subplot\n", - "axs[0].plot(np.arange(len(inh_feature_spikes)), inh_feature_spikes)\n", - "axs[0].set_title('Inhibitory Feature Layer Spikes')\n", - "axs[0].set_xlabel('Neuron ID')\n", - "axs[0].set_ylabel('Spike Amplitude')\n", - "\n", - "# Second subplot\n", - "axs[1].plot(np.arange(len(scaled_inh_feature_spikes)), scaled_inh_feature_spikes)\n", - "axs[1].set_title('Scaled Inhibitory Feature Layer Spikes')\n", - "axs[1].set_xlabel('Neuron ID')\n", - "axs[1].set_ylabel('Spike Amplitude')\n", - "\n", - "# Adjust the layout\n", - "plt.tight_layout()\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8e8e56a1-591c-4617-b3f0-2b41082b9a31", - "metadata": {}, - "source": [ - "One thing you may notice is that although we used negative weights, we output positive spikes from in this operation. That is because of the `zeropoint_inh` of 127, which we add to the final spike calculation.\n", - "\n", - "Now that we separately calculated our positive and negative feature layer spikes, we need to add them together to get the final feature spikes. However, we'll note that because the scales and zero points for the two operations are different they will require to undergo additional transformation to match the scales. In the VPRTempoQuant model, we derive this addition scale and zero point from the `nn.quantized.FloatFunctional.add` function which learns these values during QAT. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "274df441-3015-4bab-a3bb-1498a2a7837a", - "metadata": {}, - "outputs": [], - "source": [ - "# Combined scale factors\n", - "combined_scale = 58\n", - "combined_zeropoint = 61\n", - "\n", - "# Remove zeropoint from inhibitory spikes\n", - "scaled_inh_feature_spikes_zero = scaled_inh_feature_spikes - zeropoint_inh\n", - "\n", - "# Combine the excitiatory and inhibitory feature spikes\n", - "exc_rescaled = (scaled_exc_feature_spikes/perchannel_scale_exc) * combined_scale\n", - "inh_rescaled = (scaled_inh_feature_spikes_zero/perchannel_scale_exc) * combined_scale\n", - "print(perchannel_scale_inh.dtype)\n", - "combined = (exc_rescaled.astype(np.int32) + inh_rescaled.astype(np.int32)) + combined_zeropoint\n", - "combined = np.clip(combined,0,max_int)\n", - "\n", - "# Plot the combined spikes\n", - "plt.plot(np.arange(len(combined)), combined)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Combined Feature Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e557a0ee-5202-45ac-af53-c49ccc65b4aa", - "metadata": {}, - "source": [ - "Now we will apply the same process for the output layer to get the output spikes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the input to feature excitatory and inhibitory network weights\n", - "fo_exc = np.load('./mats/1_basicdemoquant/fo_exc.npy')\n", - "fo_inh = np.load('./mats/1_basicdemoquant/fo_inh.npy')\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", - "\n", - "# Plot the excitatory weights\n", - "exc_plot = axes[0].matshow(fo_exc)\n", - "axes[0].set_title('Feature > Output Excitatory Weights')\n", - "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Plot the inhibitory weights\n", - "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", - "axes[1].set_title('Feature > Output Inhibitory Weights')\n", - "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", - "\n", - "# Display the plots\n", - "plt.show()\n", - "\n", - "# Print dtype\n", - "print(f\"Excitatory weights integer type is {if_exc.dtype}\")\n", - "print(f\"Inhibitory weights integer type is {if_inh.dtype}\")" - ] - }, - { - "cell_type": "markdown", - "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f", - "metadata": {}, - "source": [ - "We'll get our excitatory and inhibitory spikes for the output and scale them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the output layer scales\n", - "fo_scales = np.load('./mats/1_basicdemoquant/fo_scales.npy',allow_pickle=True)\n", - "\n", - "# Calculate the excitatory and inhibitory spikes and scale them\n", - "exc_output_spikes = np.round(np.matmul(fo_exc,combined))\n", - "scaled_exc_output_spikes = exc_output_spikes // (fo_scales[0]) \n", - "\n", - "inh_output_spikes = (np.matmul(fo_inh,combined.astype(np.int32)))\n", - "scaled_inh_output_spikes = (inh_output_spikes - zeropoint_inh) // fo_scales[1]\n", - "\n", - "# Combine the excitiatory and inhibitory feature spikes\n", - "exc_rescaled = (scaled_exc_output_spikes/fo_scales[2]) * combined_scale\n", - "inh_rescaled = ((scaled_inh_output_spikes - zeropoint_inh)/fo_scales[3]) * combined_scale\n", - "\n", - "output_spikes = (exc_rescaled.astype(np.int32) + inh_rescaled.astype(np.int32)) + combined_zeropoint\n", - "output_spikes = np.clip(output_spikes,0,max_int)\n", - "\n", - "# Plot the combined spikes\n", - "plt.plot(np.arange(len(output_spikes)), output_spikes)\n", - "\n", - "# Add title and labels if you wish\n", - "plt.title('Combined Output Layer Spikes')\n", - "plt.xlabel('Neuron ID')\n", - "plt.ylabel('Spike Amplitude')\n", - "# Show the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fcd7e8f1-d9fc-48cc-82f6-6ca17c478c37", - "metadata": {}, - "source": [ - "And now, as in the previous tutorial, we can clearly see that Neuron ID has the highest output spike amplitude corresponding to our first learned location.\n", - "\n", - "Let's quickly prove it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2cf56e4-1bb8-47fd-869f-91e96c35f480", - "metadata": {}, - "outputs": [], - "source": [ - "# Output the argmax from the output spikes\n", - "prediction = np.argmax(output_spikes)\n", - "print(f\"Neuron ID with the highest output is {prediction}\")" - ] - }, - { - "cell_type": "markdown", - "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", - "metadata": {}, - "source": [ - "### Conclusions\n", - "\n", - "We have gone through a very basic demo of how VPRTempoQuant works and the operations involved for quantizing floating points spikes and weights into the integer domain. Although this is isn't exactly how PyTorch performs these tasks (a lot of them are done in the FP space, especially with regards to rescaling for addition) - it should give you a good idea as to how we can perform these kinds of operations in whole integers. This is particularly useful for implementation on hardware such as neuromorphic processors.\n", - "\n", - "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b780c62e-53da-46c4-b882-f2dac2ca75ea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/1_BasicDemo.ipynb b/tutorials/1_BasicDemo.ipynb index b245d5e..d18bbf0 100644 --- a/tutorials/1_BasicDemo.ipynb +++ b/tutorials/1_BasicDemo.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c879cd02-82db-441d-9476-fff1925bf494", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ " import numpy as np\n", " import matplotlib.pyplot as plt\n", "except:\n", - " ! pip install numpy, opencv-python, matplotlib # pip install if modules not present\n", + " !pip3 install numpy, opencv-python, matplotlib # pip install if modules not present\n", " import cv2\n", " import numpy as np\n", " import matplotlib.pyplot as plt" @@ -44,7 +44,24 @@ "id": "66b11853-6e17-4884-ac92-d35d814add42", "metadata": {}, "source": [ - "Next, we will need to get the pretrained weights for the model. To get them and the other materials for the , we will use [Git Large File Storage](https://git-lfs.com/) to download " + "Next, we will need to get the pretrained weights for the model. To get them and the other materials for the , we will use [Git Large File Storage](https://git-lfs.com/) to download the required materials. Please ensure you have it downloaded on your local machine prior to running these tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be03b081", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "# Run Git LFS pull command\n", + "try:\n", + " result = subprocess.run(\"git lfs pull\", shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n", + " print(\"Git LFS pull successful.\")\n", + "except subprocess.CalledProcessError as e:\n", + " print(f\"Git LFS pull failed with error:\\n{e.stderr}\")" ] }, { @@ -59,21 +76,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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rBesaCR+yMbZ5pkRISM6/yAgoVSSSNUMCiBJKaf0FgEQZzMBQB9nTpb1v2mqtKFV+yr+CytVp18Zo/G5z3gg5J+SUwczSd2r9Tsn4q85T4Cuy5YRebU7sXeN/Cdn5hMxErdXnV+5lFH1GrRWoLHut9dbZFDOjlKJra/uF5DNmmQNfe/b9xczCVQn+zshnoqyhJHNAREKrYX1PlyWtb/ZOgJ0fQ/eIjUXmsiIBKFx9rJWFR9ba5EgpKxwdHuP44BDcDzi6cQMnR9dxvLyB4+NDnAyH6GYdbr3lAmoPrFdr9CdrpMQYasH169ewGpbgWkFU0a+FrpDg/IcoyZhTAkPXgBiJGLUM4GEAF2A23wWlGWodkHPGrOvAqDg8OMDv/9934Ny5c6fSaWwPCUB5xCMegZzzhrXk0qVLG1YVQMpAv+xlL/O/Dw4O8BEf8RHYP7OP/f19EAkztbUfgQEXru1zARUkGwGB0d4PSLEvhHmmDSI0wODvD7efRqTOwJiRFKxsCsbN+9kpuHXLvjbiSWSAJjDUMD+M0jZGHWOOCKrahpUNSakJ8TZWhL4IU0vJmJbNS1WingoW62MFKWgcCR97D+ncjqyB1N7duuVgKQoPACrkCFHIcEUT/t6SP2LabM0NoIDl7wQ40xCcYwAkCsSwYKO/oQNrAMWuif3iWsGcmhDBJiAYX9/mwWhN5r/6OkVxmqgxXUpK32F/EHGY0rIxN5HmjGG2sVn/9ApOOh7rQfX1kfclBa3yXGP69qjNvSlzRkQgBSj6SRAk43UQuk4CuyqjFBPU5OCEKymgGgMzm3+QvQWjtR7toToFLjDEh5QJXZeRAkAhYp2LcE8CmKyfjFoJpdjGzS6EwUDlimEYHEAkSs7XuDL6oUcpFUMpqKWghLUagQalr/Zd+z2l7PzAQFaihJRVCTH6V8EvY0iA7r9SBBjFveeAzfrrY2/zWKeAAg18CIiRhY1rUWsZgQ+AUSo7/Qloa4DJFJjKjOoKgAJXZgF/cStz66eDFFdec1C4Nml3qpS2Pp+yr/VfsTWqQF96DFyx03VgZvR9j361RL9c4bYL51GHHlfuuxvXrt6Hk/UxKDPO753HMKxx93vfj53dDjuzOc6e38PhyRGuXb+KoRZ0iVBB4Kr7JyVwquAKdN0Ms9kMXdehDAPW/Qo5Ad1sDiLGalVB8zkwsKx3lTlbr3v0vfDxlHljLm7WHhKAMp/P8Umf9En4lV/5FXzBF3yBf/4rv/Ir+PzP//yN63d2dk49d8SRLRcVPvJ5tHgYe7QNZ0Iq/hw1E3gTTTBSJXOVjRg1KoTNFNSrKVhpTzMitL4mAHW0Yf3aDe0gABTvkyEJRvH54I37XXfnJsRq1PDC9XJdsMRUbtpy0BwdAFIAhDb/lACk9j63hMT5FFBQE4GYXJsmYiTVpJBEeBMxwFn7KY8xuMdgiMqJAFTI52xk0SKAsmgqJgtMICUFIb4W1tOABH11VNNS0T+25rBOHLWxtnUh1XSNQqcApj2/teI2mKnAtEdsWMGUkZMyCxenWadI548TUBUIcq1OlQ2stulMlEVz4wZUomBLlLx/TdjZGFUzVy18Y5t5Cxp820432Zt2Udt5/gkbkHQVw6fZtW5it/iBZR8zAZwALrpXOIFJBNbIEpvkTX6NYUzYfohrH15PQM6E3CVQEhACQtgbKmir0KgAk+qAqdSie7EAYOSUUJmRkZDSDACQckb2gQtvST1jGCpST6hJAK9ZKwARjgYcxMpaRmCl1CIgAQSodSznjJQzOp3olATkmUCXJahqcSFwJnChtq4BzFfUtl0YoIIGsAJ/p6xgp1aZP25AsdYyoknR/gGohUwsUvL+Zp1OPmamCpQqfUED1gYu3ZoWybDK/jFCTTq3YlVJPodTK7+NZ8Q5iEAwZaTALWLUaLnnghuHN9DNOuRujvW6x2KxQL9e4baL57E773DpnntwfHKAo/URQIR5N8dqVbA4OUG/6AEskWgPJ/2AvqwwlJUoBzWhKuDtuoTKRQAaATMiJAaoMhIIs6T8T4eVuw61VqQu4cz+WQx9xdHxIJZU6kGk4PpBtofMxfOyl70ML37xi/H0pz8dz3zmM/EjP/IjuPPOO/G1X/u1D/4hVdBuFMaNv6vZixIowBRmdiFYVRg5QzOCvinTNIE81pL8jTV8FpgfuBFgUmK1Z9nPaEI+DVmOxhg+awxvDIROE1QAq9zmkVYYzd7CSE2AGrclNyemlMNrmjholqTJfI3kLsmaGW9SoMNVQFKqCW5ESQATgQ2k1AY8XR6rJrEBAJO+ywGjdIQYG/TRmJ8JT50DRDLg8K8JO3ERBKHIzYkDCNO0OTQmdLpwnc5ZExLxs9MsbG4pweaaT59JoABGxZUh46lCowRQCmb3Utrvbk0R8CHjJRg8Y6gdKDDb5qprawY0AEhEam0JkhtGS0oL4JHlTVBY+zWiEU4NTLcWQOUEGIuM0r+zAFUHq6yuEKgrqDKIxZRtOodYEBKYK4jZAU4D50AmFZ6JFPgocEuN9ogMJDRXiAhLqHWAUMpgSrR8V0QIm6mdUF1JyCkj54QuZxCZawOohcHIbvWoyj9TNuAsn9VagFJRakGpyV1CrO8zmjZ6K0WsGoUIqRQkIgxqaWAeK13Gx8zKFq21cf1hoEABLihC+QbMjZdUamuXKCm/Mt6k73Ns2tyZVUnPLKqUEory8VQTqu9wmK5zqngQUNSIsVZGzuoGC30pKm/Gc9Jc3+KBSg7ImatamyA0pqBvuTrCyfIAOzTH+sAsfsDx8gD90OFG7XHXXe/BelggzxLm8x0AjMODG+jXK1RirJYDlssbbeKqvG899BjKACISFzZl5G6OnOdCk6xWsFpkbggCblKHC7fcIp6JQZTNw/UJslqSCiex3g0PzAOtPWQA5R//43+MK1eu4Nu//dtx99134+M+7uPwP/7H/8DjHve4D+g5G0IbQqnMDdU18BI0bd0AtYognPrlp+00s/LYYtPexfpy09i41JGLoW0we05A5PfTCXFl1RFYmfbrNBll+73WMSDyXTAa03jubEywsbgQDs9W7cSAQbQLxOcbOCLtN9t/OnbRntQ8aiBOwQpNBQqa+T2+i0g1OzVwtXEYgDP3RtaPeTJpBkTM1BwRVtT4GRzAjJBUkJyMEP+S/DkPBqDcrI0sVxMAc3/3xOsNoKSaXDMzoCWm+ga+SRc3ETmTrdYPA5wB/PuahDUnmIUu3RRkRyAsZn7ye0dNL9tYLntWBbiFL7QO6Bj1DUoTwRGnN5jBy0eTEqiIaVs04ISsoNnogRRN11rdrWkv7RIhUXu+AQghC12XyuBUAbXeVc7NelHMYtFiL+SmAKIZDr7ccmmgmyrMZZTMOsgE5Nw0eWLkTuI+1utB428yaimgkkCF0JcBKakFjsndQmZ7KLU6byEaIiZ00F64jgD//dGuCW2jQ6M/cwW26xLY3PuJQLUx0GiXHCmvDLfwyUe17XMynpFk3hRceq8nvA/x2RDh3vZbRSk6B6SKsD6XJ/eLUhb4i8umilIG9Ku1gMlSsFwtcM+lu3Hf1XtQuMfemT2cP3cLCMDu7i6G9Qne++67cHx0gDqs0M0yElUcH97Aer2WWJZEKEVoZL3qwbUAXVKALusJJlQuKOotOLN/DmfPXsC6H3B8cIjVegFQRdcl9KUHc8VsnsFifMLi+Bjrfq10wc4DEObzwbSHNEj2677u6/B1X/d1f+H7zUQ78vmaLQqNOCtYrCgsTCRVYUzCaIuACSWgNLGMTBH+yDc7QcPWhL7YtUq/j5sftO1ijJ4PBiifvogi2MPmMu0CBjpUG5u0CKQ8EAvAzYLpbtZYmYRBgrHAIXDR3ZV8j7UhOt++HyuVCrekDN/dA0ziM7aNzOzTByIUm2t9LhE8WDaOkf0l1Pqkv2x2p/oFlMjXbwR6R0jNfhiwMnDyQBty83uzRLkb0SwNpkLDmLjcK9/F8YTnhOujkCsc40jks1oquLY4CNkXUCAm85uYRXBTBH/sM5bM/BXG53qlvtu02+Z2G6ca1rgXiJDcXTAxkft6h3GGrTWaeyc5UoEuL/CVpwCi0OIZ7HvB3hIPUwMvIAXl5r4Y8w40t6+uXdXfa+UG3jwOrAmpWuBxIma9MCtYpBkJ9J/Gb7RYCmzwErHkpCQuIMAMjYyUxZyfUgMzTBmzEHtmbhvje7VWpKGAs1roqlhezLJX1N1SaSrM7x9cE6pa5JJYUiG8jYvwharKCrnVmkTRVOUgWrJH29RpxeYlgaiMeCQpWEmUwOri8svNTQQgi+aEyupWb0SmPFrmhxL5DuENAW1ubBjV6fzKPA/LNU5WJyilYBhWuHzfvbjn7rtwfHQNnBjHRzPURY+uyzjJhGvX7sN9l+5Fv15hd07oM7DuF1ivlxhKASGDQOg6ALVDoYLVMABDaYHxMtkgzmAAXbeLlDpQyjh/4RwWiyX6kyMkDV6uANJsB8wZ16/dQCkFZSju6kOVudzd2QczY7Vc4cG2hxSg/GWbb9oJIx6BCZUz7CTS7kmT65tW3OjxNFfOtA/WJvIBghxZCL1WxD1pqJlUM4mt6ibckF1BNbRA3fYVwSxHox6MNG3TTAa9vjFEm4f7k6fOCLDZtfE1435MAtL9l6nlyT6rXN03bIDCJHBb21MgBbdAzkSTeTV10zVg64YjnQZgfCxNCxzLhTGN6AAE4OqnVVQ1f66Kpg3A24DQeEadLn28Y9qKcR7u77b5OYX312Bqb10eCwyxPqlQ1XiVlJVMiIAkvulUq2cuWF+NcNgmzp4b3zd6d/PNxf2nUxnmAShx/f1VAZyzLREj6WNFKJyCIMEwXz5Tg/fy/+p7wlVZCXAYPS8F4NF4epuL5raynWJuAgY4hTdav5LSGjvN+d4w5YlZAUULqBb+IY9ISf6J+ya4lxR82AjNfUupBaOay86syEV5FaWEzCQxJpqtI3EpVQN2CwYMLkWipYKTut8V3JDyQgc4E/7lGY2svE2BQrzG4mVMGWOwxo0FgAEF0nFlA3mNtwD7l2NXr/6dgExZXD2aPcNUobjO95qmXCC6zUWR1OsKo6CB4sLVA79r1f6qG2QYCoahqHsOuHZ0A+vlMUoZMPQrlGGJvf05zp+7HcfHh7h85T5cHXp0OeP4+Agnh4cYhh4pActFQak9ah1QUZCyzEstAxIx1mWtrhmIqy+Zlb2tBRjYmc8w6zKGocesDshJJmDgAhoqctehoxmILdmDUCpjPt9FrRXDStZnGCQ5IwZmP1B7WAMUIIAUTMFGa1HrnmpsHjdCwjQrSJHx/Uhqbgi59UK4wbQfdt24W40xSxDeZp8jMCITdOE6UR6NCbLfM32GX29aUIUjZdkvpi+ertG42RgtdsA+bz5eiz8JXMIUmAgSHNmM5/ZUQem3VI8zQmqWm/bAcWNA4wHGfQcbwGmuC9M0m5lL3B4SzKaWKmo01ubShtksaCbsAhQMwGys+TaXhvWYAXM5TVsQVIDF9m1m8Ixo32VsoAsjUZy+R+L1UQvkAneJGxGz+tWrCU8EGkiWgTO2JmDS31NpVffVKJYLFAS2msPtfaldY89nUpcAmnCE9T21FZJZN3dPc/uMrYqsGo18Tw5WxRUmsR9tb5cwRmYBO+bmE/bTQFV0pfka6jjEKpI8y5C1/7nrTrE+MlKGg5Sc1R0HtXSlBnBbOQCl/WAlFJAn72UuDfho2q+BlFoZZSgYyuBTX4sJNEahglTTSNhzZWQFJwXiqphagsy6V8Huttnc39JXWyGJu2hp1SYHGJKFYlNrWT8+Y2zjrQEYsa8HqSKX1DNbU0WqhJoMEDVAZXtNlGGLdTFeyAL0SdwnR8eHOD46RN6Z4dy5c2BmHB2egIhw/vwFzHY6VBQcLw4xlAE78zlWiyPcded7sLMzB1HB9YOr2NvfwZm9fbz/ve/BycEB+n4JAmFxdIJh3UPcdoRhWKHv10ARDn/u7DkgJSz6FVALhmGNWgcACkJTQhn6EY/PSdyaO/MOAzOuXbuC5eLY1blaK6iIa76W4tYTKytRNeCeS8W6ivvPgpgfTHt4AxS3DqimE9EvxgzXZGMzLVf1p2tONzftR+oSwJ91qgBlwNU1f4lyiXB9CmCo9S0CmIDa0bZkCmMgC0ZkuPZvzzEGC8Sfyn51szHZhhQNiibvt9iGxitDrA7Mf0r+BgN0Zvo09xiFd7cJGM/jA7k8Nl1qbVgb4IVPE4RtrU8Hq0EY2te1gQYzwVZuQXU2I2wz43UXGpOdjup0uNe01KqpBcxQTfPmczEdO1Fy0/lGC926mSvN119B+XSaTAQYPSaINmiaD1XNYpuAVPuZNXi0uTckgm8iV127FQ3e5t3WRX7ZEFFEHisTnEc+LiQNfodZlUKrrMLDBucrOoItbTVNSuv8axCuYWVC1bwqGu1jd+ewjUEC49t8tLFM58PviQO3lxoIVhrOWkPF1iFlQsot+NTcva2mjQb5evRImB9S4E0SNCt7Hv7cnAgJLNaslBXcJeSaUXN1C12uWTKdDETXZlUwNx1nETsWcOuBuToPkk6sezsoXzInEodnSwi1+gEaBK+0E+sDyburPz/GvY34sO9BASeaoIXEkmZbagGT1FCJu95lRFJ+XyWVGaoMca24995LODg8wP7+Hub7uyhrsSas18fo6xoDHWKv7GNvfx9pj3F8+Tr29m/Dzg7QzeS6a9cv4/jkCLeU87jnzvfi2rXLGIY1Bu6xM9/FLHfgXNH3a7FWlF7AIzMoJ5wsFqjDgGHoXVxyFS8DdYQyMIZi88qgJNaQfhhwfHyMfqgYhoJaekgsJNQSVLFaHamrWVLmd3Z2ccu58zg+PsbJeoEKQi2yfB9IYMHDGqBUjgGnoZYGGkOvtQVeMomAj1qfXRsszhKd7ABgfC0QGOokS5TJfKJB2nDzMcY2Bihml24xEzH00+Ix7AbfEJYh5KBlM2XVsgxaEqwxZVHrjJHUAMQiILE29a+7AMT0p10TUpcJo+uiC270nSOLzfmKfTBtOoKTqTAfF24adRvELQu5TZf1va2LzbUtcgS4BjLFG3K6ID2t/1brhbyPIhAYm0B4o7lQ59E1Iy18Smym4XEEpfE9AMhcGwKdDK0apZQ6DswGWv0etxqmSK8EouqmaxMG9Sbji64sDp9tAAwZrAqDcXaE3SsZM+xWN8HS0/E2sGGvlq1jbg/JyGCGCBqdSPL/s2Z8tEBtACqM0X5nCxq2sgQ0oTIE8A0X4CKw2bNsTKDyZN1rzuhU0qSUJBsOze1jaeS1WiArwJxcpptLqUsSNJYTAV2WInBhX85yUquOgNqUCal2qCW5u8d+igXMMnjYgYuY9pvN1/aSuXxqzQ7KSimStTRxfRtQJm7WRlM0fR3CLRvzVQNImXxn9D9al9p4Ccd/Boij9oTIG4Irl4DDg2OcnBxjf28Pt916GyoX9KuVKsI9To6u4N5L17B3/izOnj0PSp24aJYHuHblEq5dvYT1aoXVeoWcCPe+/15w32M+m2G1OsHefB878zk4ycymnHF8dIihWPZVRWagDhU8NMWm7yXtd2d3B7mbYbFYuPwstQBcQR1huTzG8mQB5A7dfC4gjGW2zCrI3FL1z5w5i4/6qI8GkPDnb3+HgJacxQKYgDTcv5Ia28MaoHhzIrM4BNFOacreTOttsh6mqVUI8iM1y5p5P1oJonLlpkAeV4AUAi4jQTFCjExRL0AUjOyC0oBLc12dPm6gGTxp9Ca7p05N1uFni3OwHkQAcErzsetoRxtUN6b605vp5JRu02RtHOzY5h/3M7zUgefYahN64IzndJxuFhL7aVpic7vE8Vt8jnWhgSECUALzmt4b8cVYY7ZxjkEfW6Ck/gtQoI2tNuvfeEw3QXSx2Thj1V/WCTA05pql/DMNt8VVjFvVwRERoFolUcj00doc46Js9s4JqHPmj9F3LtDM8hHm3AvKTdaFKrlbKoHUZaD1NYiapm1gBSFWLUioqkXRhKcIIdi25kSgorUvnFzb2ldO3lcppiaf58RacdVAGLQwl7ki0GqIVEkNriinzr+AGXmngEExq6fcqkmbxaBWeP2JpLWCnBodOEjfWF1XJmjTrFlUEutcaX0SqhLwYtaSWisqtSJotWggJUn8RktsgGTmWEaSulTc/VJJU3zR+HCgCRu/0Yatwaaq0L7fsKwH2gHgFjZTOFgWcqOIZnT/wuY48rSkfIyBfhhQuWB3d47ZrMNsllFqwXq1xL1334Nrl9+PxeoYBQOOj45wvH+IWx/xSMxyh/suXcd73/VuLE9OsDvfQVkNWNcKHhjrpdQtmXUZF86eQe5mODo6QTdP4B4ADUhUAc3kykmAH8hqxAgddF2H2VwKvXVdAjqhw8QdoDRUSgGXAtSKMgwCYFlswKakSCJKxqybYf/8ORwcr3D50mUcXD8W40FKOHt2B/tnzuDGjRsba3Sz9rAHKFHLGllNdPdtaO32N3RzMmC1AopaFJzRqXUhAhOn/6QbmoFo7bB3jNC8mrzl9mDqDmhbXhHZctMyYtnxm2nWckvrqM3FaDPaeOzhUQO1sZ8yx96HEUBpv9td4883571ZaE7xZwAauKcBeroGwoTGlpLGLBrYdHDIDGAsEEdr4eqrxhmxMMJmldGnUXN7OSNkRu7yqDpoc5GYgJdnk06IMWrTvGX1q7/LZRuLVrJRxMgCOjVQdJSxM21hjDcDmXFdiEhrAk0Yd7xG14UVaIBog67Cw13jBCBR/hNhYDRtc3racyLtjjJ+wnOYGUktB4YqkgIWMEuBMsQQZI0JskKADNf/mptYhA6ry6BaJVkVvETkcTdVrwGVMNfdSJN3CwYLeBPFn7TqqA1GHEBl0HcbHbJaU4PGHtfI56foPJJZ4QTksLp/orvJ9hXiTmfRsKHWPdI+SnG4AUQWF6JAhwWIFK0QKkMXi0algoqKAUVLslf0tVeFr4Z0aZ33qsGyRGLF0fkgaLxbanvexn6aVaWCkTrba61ybKRTUygBtDRx3eAcwZ9lVwUtYWShzDI3UVGaronxLQDIXUYtZ7E+PgQAnBwd4eq1+3D3XXfhrvfdCSpr7O7tYCg9Fv01XLj1FnSYo+syrtx3GYc3DpEpg7uEnfkOlosllqseORHW64K+H3D58n3oug6L5RJcGUO/xrBeChDVbL1aelUUTCFi5NyBKImbaCjIucX4JepAXQYXYN0XTU9O8hyW9cmzDkBFZQLlhJ3ZLuY7u9jZO4vaVxxeOwSvhDYpA0R5pCQ9mPawByjAafxaNRJVi6bV+8ZMMfp7NbWVbaEmyD1aF4KJt2g5ZCv5HOMF2vssqOomzB1AdA2JYJDgpdjn0WZxjXwMDowBjUUgWrpstc1nkeUGysYCyjcckWqrwixHwbJudQGQGH6AxaSZaTTO58Y8VBEaxqQqmjsujt3N/woi5DO1NNSWiWGZUyPg5EDGVGYtPEaAlVc3zdzBgwJeIkubNLBg89hihKA9t2k0M78cZ6BjtiBF5lB7o4I4+Zw4DbF9fxPApes6+nsy/00It7kDxta1DQDvwX7QcuJmHldxeRpwNtChz2lpx5vPP02b5baYavUYuzZGwCkA63h/dANadpOcGSIds9sKN3BJjnMN3NrvhHguksWH+LOVd6SwhgYGGiho856YwSwRrbOZ7l2jCRAIUlgNKYO4IlOSImo1gUktEZra6cLW+5fEwgapDVKZAAUvYk3RfusxHzZeD98OZyGJoIf+qxLIrwRmZdilArNYlix+ROp2FJRaMNh5P6XFp5glja06moLCRAlDrSogxXIUQZmDKpJKs1OlK8YLIiWtAEueRWSAJVEOBdwMlAKp6/S9likVd3/bM9NA22nW4BSwgBmlDiACbrvlkUAiXL56Batlj5PjBTIl7O6fw97OLq5cvg9l1aPuFlx9/xVcv34F/brH2bNn8WEf9mGYzWa4cuUqTk4WoERYD4xSCWUAjo6WsmdKUfBlAFcyZ4ik2q/xDWcfLAX/SM9UcgCrco8LXFYQEbpuhr4fADDm8znm87lUmQWBUsbe/Bz2z5zDRzz+o3B07Qh7l66jdGus1seYzTt0sxlW/Rp9HfBg298IgOLsOAhORks7rQHCkAswIHhhPEhR0H5RLYkDE1b3BZMKNEkpjYkEpZTmB0RwP9jrm8K/ibonglhAAY2+jyOO/n+Q2UaU3ZAw34TkpmYE6wllUt7QwJm9z7XaYJloIIiUkdkGaANjsHA0ZGfiscmGrlrlMcSnwJiRIWv5V4kAqn4NqJkT7Xk29jAIe5lqryGwFQhxEgJIrBZFWySjgWDl0nfyaK6EGjgGXcb6BgZaTsGhFJ4XX+ugKdzTBG6gF0jQ82nanc3TFB/auk/96O3nJlg0wBEZ7hRMWHGyEXC0783SIF8IA2XNaCAVuPq50bEfruhDMDDXGKqvtwVYxqGH6qQRrCSttgrmUBG3kU0DsQQrN9OsXMl70+qfQEzeBNTEXuuB0KxHEVgZUGKW1FwhgSDMmNX6gWYhs7fqvgTbvkpatAzKi+LSt/3koMd5ibq9yQCLDI9V8JPNicW/RHrQcv9gc78wKgmCkfiRNl4DKV59lo0vNDog5xs6xVSFbeg1DchToJtNkBtpzqxg0XJibvZ2BAmBgsXTDjpMbm1SK5yxnKgMApOCeWZFkj5laryzKQAM7hm33nqbZj8N6Iuk/h4e3MD5C+dwcngAALhw8SJ2jhdYHi+xXqwxTx129+a4cMsF7O3v4fr166AEzGYdTk4KFoslShnQdRnnz57BYnGC1dCrtVatW4GuZRkkv0nK+quSxdXsX7DgJVJYQJWRuzloJ6Hv1+g6+byWip2dOfb29zDfmaFyQk67WJ4U3HrrI3HbLY/A6mSN8xcv4OTwGspRAtcBy/USs50OQ13jwba/EQBl2pxJqqCNGpFsTGUiZIZ3Uf4BuNYKLxHNrqFabEnbO+RBtwA8rWrcmcaADQSMOmnARQl8GqAao88N2UYG53225yvncc3I/eIMO43S3ut6gvOLIChCc0ZP1uGxhqOTI/OhIsPqmMQ0Pvt7avaPrWklFdG9D9iZD+G9zliDFUe1o8zJGUabH2PashF9PKzjoRYgutEisxJ0M/7M6kqYdolNS5oFL5rfvQ3N3HynvHcyR6wuAavxzaoZ3+TWkebXTmVtf0ufCagGYMdWFQcnCPQyeb4HzKLNdyxlHkdrfbAu2zv9uT43pa3v+IXyo5QxODHBrWC96vk6RISa2/k5FAPOQ/8MGpsQaiMdz8doLQzwg4LlbrP5abPa9zIUDSQ2ENtM780diFAgL3n6qh00seFmhMZFBD5mgrRWcYSAGCmp8PGuqhXVEIsrAi02RpSx5t6RLR1OQ9efpTBqqX5uUDtll0bzCNMzRqd2S8yPKTsNdIzpMK6FDbd9vwmkzart/C3QaUpyMrPH7WhqrCsFMP3E1obVIlS8DkxRpYp07fwARhIx33VJ9588o3DFtWv34cy5fTANWKyPJAOnn6MvAzoecNstF7C7t6MAGLh06R4cnRyBGFivj1HKArMZ0M06EBh9f4yhX2A+n4ES4+joSMAEASl50Rbl7XBeTimDOWEoGmNVJV6FNdEj54ScuwDkq1SdzbJ0u/t76OYJZ85dwM7eORwdDdg7dxbHB0e4evkSbly/jFqX2N2bSfZQ6XFyuEC/6vFg298AgBKIboN9jom1ofBNvbnowpGvBlRw8KjcsbhrSQKhWALwYrxEM5Ers1RQIZhHhYH2YBpXwVxV85TOUoVbRFq8wGT0CSCqYQPqT5gWibbTmNwCZFelHM2X5kONz4kCcjx3Zj1wYIVWPn1UX0B+OXVN4iOdKenYWZlm9Js3iwqH74A2UBPYdm1jXJuTZ29NCrBsnmztbiL1bU48eyqPBXTV9+vzhNm1vsnHLQ2WAAU8oY9sv0vxo5FLwxg8AGhlyGYRgWudcOEhLP80l04zWyvg4eR9NWhCOr9RaMjsWjaFhjM6uLDJNNJTBs+micsTTGVwyNT+52PVhIHxWijNiBUjrpG6CHR+igpcqCXUrZQ3ASjW7DTgeM103qYtPlfceaffb9f5z4mSIcNrc9wkpa6nGyO4pZoT4CeUw1xC0HAbQrbnhbh1WQs9z8djplonkyAVfZnEXQifqlK23t2baMHbiu/91OUQT+VA2vdvU7Sc/0Q+GPkwhZ9hDSQWpmxYVlLKUuhMY/+yrb0GhdoJ2VbbRVhHW4vi1p/qtCd8XOiaK3l8j5Wu51rUMpWRspSukBOyE5AZOWWcHB3jXe96N971rnegLwvs7s+wd3YHF85dwNXL11HqGo+84zZ83JM/BrnLWK3WuHr9GnZ278BsNsNqtcAf/v7/w9HhZTzxIx+HJ/2dv4NSKo4PruM9774TH/3Rfwc7uzO89a1/gnPnzuHixYsgItx5551461v/GMyMj/7oJ+EjP/IjYda83/+938c9d18CqJ1kDRaLW63BelgHKfrGBbVm9MOAK9euIXeEgoSziXDm4i1IqeJ9730H7rv0fgzrJXb3MmYd4Xi9QBkI636tIREPrj2sAUrT7I15Ai4ETmEk7bNx/EL8foOZONMk52vMrLFH3AohKVCJ8S7m8z2d2VlpavY0aLPOAGjmSTVDMlmRLikFb1HjFpHfNAjasOS431VPQ/NxsVmDTNhWZVTTINZN7cVASTTry6gmwjLOexCaNj6rmTBaA2CyNs1KMBJUNDbHGraQv/U+//+0RcBkwX4R7DxAc+Gi4CneY9VXEWlJrhuZoCMISjQat7uwoABZ41MEA9h3whANjBgdTOfdrIKnAi6jHY4fBfDAVQ9s3Mzkaa4EICGPgI0BsVE/MKUkA6U0vs63SIh1YvOTN+HtVja/3idBXpba2E7LgvHxGkjQ/kg2zTSYvfUXHEDwZL8Dm3UeptdwsC75Cb9mgQkWPFs3B5maumxz1SxgQKHagKzF0MEEDUAzqfU0DIPGtalrCtQq0I6AL5Bzp3u2KTWJyK1TDENBBU5CpGXoLQaFOYDq5FURCHDXio1HUqVPsYYYWA97w1yTzY2XBXwk0sMSM8yCkD2zST63wnOUITQEEquWWn9sDSV1uqVJx30GKI9W95aDztzoz+ppMRJqAa5du4Z777kH1y5fwW2PvABiwqPuuAN7O/u4965LQJcw398BzYrU86Ee1w+u48POfgTOnTuPRz3qDrzvvXfivXfeiS7v4fzZWzCUHpkZT3ziE3HbbbeBUsXHfMzHYr1e4fz588g549KlSxiGgosXL+KpH/dU7O3vAwDe+c534sqVK5KWb+thxSq5SAXa9eAAmUhEAyVG5QHrvoBqwsHRAY6WS+zcOEbpBxxfvo7S90AdsFwNWC0HrFYrpG6OPMugB+/h+ZsBUGr4OwoBBAY6bW5d8N8bcBjdQ9GlY1cAvoGKMasKJIIdVB8FkWuifkKrcE9O1asVioWklZ+2oDbm4v58t9Yk9YUSA0WrRXLzrxc94MyYqfjq6wg82Um0iaeCpA00Ar1TwZvPAkZm8QhIfCaDSdoFaXtxfO1ICJ2KLSDMTn6xAFcDCgK0gOYui8GKpzdhiNMzYR64KUAxq0h84s00aJtTitYp7V9Im44gxri/sLsEtQ1Yt8c9okCdinhcs7QCXcrgR4AlQapBhloR8kBhWDcT8B4f4MGl7eaxVcBnS8dOI3AqAjLMv+4TB6bhOvvbYJqkEpsFynZbA10mXO4fpoZXm1C18YVg4Ck4mVpBTwOB92eJayDjJvTpVoXWH5ffFgcWgIUI0aJzpzVNEqEMAwqxZmJUj4uRG/XvwLsYkpuj0TENVCVW66aOXa1TfvZUIXBidFkzkIbBwQRz8f0ISMB59rrxE0AtCxGYgmE3BdsgUAJy6pCzgJNu1iFnASleLVvpSsBQS0tnaG0YLi0dmlnOQDJwpem0xHDg5IrFJDDd1sp4nvBcAYHr1RKL4xOcHB+h1oLbb78dlXsMixWuXb6Bk501KmWsVkscHN7Aul9h1s1AVEE0IHcFXcc4OTnE/u4ZdHkXM9oB1QweegxDxXo9oJSCBMZyudDAdkbRFPVZN8fHfuxTsLO7i1oqFosV/ugP/xjrfmgySNc+63lTksJu9EUN6Ccgd0mDXnucHJ2glgVyOgQxo18sUatkGA1aT4WZQQU+Nw+2PawBys1a9DO2NhUh4+8bAygjIWL3jHzqJlzCvczNFRSZlaXKSauBoXXIICkJ5QzKhK2eLGmo3N6r1pqapDKlaLVqRSBGSgx3VziDbhpJjcKc5Vo7C0LuKx4jYgTpc4oxA6bRnIUguDgnmGzsuBQ+I/p+BUdxvFOJwgjWIGMUVlyL9Wlkvdtc45u3TdoYfXsTsKGS0sc2fZM9dQp4T39WckB2ap/VVCBrnYByunCNa5DUxM3MrtbbGrazjuBBtyYsjfYdBJHVxSAwwuFpTXagzf0EjIRxNuOA0GMyWjfwMSkyNwV21ddcPxtZqMyCI8+P1h17+QPSgqe60sbea+M8pW+Tz1JQmuzvCNbjs61vdfK3/2rAMCgLk62pJQBqe6YCFiLJxmlhW6bYWKC/gDEvsDYBUsWzyuSfYCELtDWLHjv/SJRQU3WBllxhKl5Uc8pza0g1BmNyPEFz6VIGPGYlNT6cU0Y369R6ktx6slmwkMEYnKZrkQP+zF1lPKoMkMwW1uMuFOlTJonRMPU00JNZ4/0ZNWRclYrrV6/iypVLODw8xO13PAJn9uZ455//ObgM4Dpg3Z9g70yH9d1LXLvWY7XqMZvtuAWrVOHLfX+CC7ecxXyeUdGquTIz3vrWP8HR8QH2dnfx5+94Bz7qoz5K9kwVl9RHPPax+IiP+HCtgpvwZ3/6p7h27ZoX4GNmrIc1mFliWahtcFsnl4Eslq46FIndHHr0yzVWam2qQ/FjH8hPMCZ1eT2IfRjawxqgCIE2rRCYIrTmN9WLguZ4/wLJTKoGCloLwY88Pl21ROGpb2inbUrnLAc/JSuH3ACDIXMrRx85DpGc6goAldqx47la/AMD0JSxRJONZIyFYHbcqHGVMF9xo420Q7CDlMlsbWh/cf5s3k1z8aaYJNnnOtwILk/TwEemXWa08BuzXTWtVt3aD7gh2ntsQ2oHw/dRg0W83kzuGFMUT36GYY8sVsbcgLEbIl4jvn0CTgGCNV7NYb6nAta0JB4DTVurjeBlNEErYFPtftT5WyWjXEY0ynBBABRx8GzBim7zdNDiLs1A95H+AHNBKAjAmFaAFoDtaxFoPK7hyCIS5ygAjPhcmvAYv972f5hrc1VkIncTkc6TuV+oLYYfXZG5CUkD4nEOALR06Um/kz7Xr1OrBJGcPg02K1By0JosfspLnjOQxs81cDIG2NBTavXPnL0YHwhgBaopJ8xoJv3te3AZNjaDWTeIaOMEdy+kqP8jJLe2WAVmi6sqpTRXmVlG2oNUMJaQjizTX2HbV9Oqi6ZMG6AntVIrf/K6LKyAXuclKX0WlgBZHvQEagZq7bFeL7BeLVDQ41GPfjQWx0fYu7CPPdpDrT3uvftuXLl8CUcHV3Hu7D7AQKrAetVjdbzEjfuu4uK5WzBLGefPncGZs/vST0vQoIrLl+/Fe+58B3Lawc7ODj7qI58kc1iBvb19POIRjwSq0OW9ly7hbW9/uwT8ViBliTEB2E8gJhKayF0H5ophGHTuEtAXLPsTECV0XScHGQ69FNcrVYKEU8bO7i5yTlgulxLXpbF5f2sAyrTFYbdJiEKnCeJT7+cpMNCNGxhjq+JpQrClq0WBQOH+UWwrC1OoLBvW/MQm7FKwwgBotUv02HFoCnTixjRTiMUAQ4I0p/1g1XwA/V8Ulae3TZAQBdh4DqfCIloD3Bqit1v6dAmnNp9mBjdmj9DTxjSDJasyiGrTgEfDstiUFuwZ+3laC3gSQLAoBCEcOjn+hG5Gh+MxToXm1C02BhDwcuJ+Lww4jl698Rx7bzOb68CizNG5TF1yU3fUDuWU0nZTVAZsCgTCAEx64q1+l0YTasCavX/MBVkLOAnJjOcyu7BNI61Yt+Voz2XKDrZ8niGVWAlS76Jh/tN5gJ/SzKHgXM4b87nRdD0TUavAm8Z8ISlIIEXn5hax+yuzuEIRAL5OFxFZ4pbcG9dn2p9o3YnvJwrghf1St/gmdeqMLAObQMn0HAOv1qpZDbjAzm+mhBGwcC089N0py95H0Jg7O/ixAZlotRXHdQUxo1SI61t+ARWZY5C4vEsZPEQJYCRkSVUP5SVKGYQnQawAOWnAa9YO+trIGsjZPEKztRaNYykteFd/7u3t48Itt6JeZ9xz1z14953vwNHRDdxxx6MA7nH3XXfi2uUr6PsFzu3vqTVjQKkVy9US165dBzPQ5Rn29xMuXryoxwEYEFJaSxlyHKPOTdgHNVjizOVlcxHpyHiI8wA9FJKVz1m8ZykFzEUqy9r8QbKUuBSPAer7Hr2mP6c0wwfaHtYAhbm2wllG2FPt3a81XznD6lrHAC12VFybUAzCduQvT1MUKGl+Ufgx4Itp6Y/+TG4MUARscx9VtW642bOYMGJYfAUl0rzohFQhp21qFC1x9VQysAbXypaTjVWTn2mhM7MBEJwxBgYxBSejv/XejISqxZRc2PAY2LgZF5DCTCoMTCjbBpky3U2h0NbG1jau2fieZjECWj0VW6fpff5877tc21JkRWO1UvvM7V1yNkg4JbXWloIMjCxuCFq70YNVezRBJIwjNQsHa18D7Y56fcrcxfmw7ymR19wgXWNT94lE+kiQY23ppTbYanFNMk8EOTbPWpyntgJtzCOXGBEKqoNssygZiHXIQgxU3RsYg1oBFK1CcYxXMdsP+b4KzPe0OdJ1sABPswTF+ZuCS1BQVHJC1u1pwjdnAWDJFBH9J/3SrCmrJZKilSzsUwWq1We9zSuHObf9ZO+LfWYHIukUpSDEV5BancPzhJ9Zlt4mzRWtf2IZUN4fBWKUSBSJMAMOqCnUy5k81+6oqM632inEUoDM/iUiAR2QdzEziDWWhuGCFBArtBixWmqTW16D1VvOcoNbeGpf0A89lDW3cRQLshV3jNWDGfoBQ89YnKxw6b2XcPf77sI9974HBQOWB9cBHrA4PkStvaTi9gNW6zXmOztSjbcAh4cnGHpGN+swywm33Xor3n/33ViveplJlsqvteja6RJZuvhyucT777oLFy9eRGbGbRcv4glP+Ej8wR/+H+SUfLzVqsWGCtC1Vk/FZpYMJ8uyEuu7KYDSV7AVRmSslhKLwsz6DOWjD6AYx/YwByiT6HzVNGOAnFwXtzOcyKPjN2qyUVu0yrDhdk81dmMy2TPH2sYo1bB1cSzowud2+JkEoLUS2hKTUiFBU/IyCfrXbVuBSmIW9MrlzlzgZ6OIoG1fOYjwzamR/dSEVvLHULOKwISAomqNcxOljDXwNgYW2o+wo+PnJHquvMUjtuCngU5Ax82EC9fBNXFZS4vBEEjlgIvis/zBCB4pZaIqiAPTqaXIJFvsCzWmZ32zA9BskMksVg48m8ZiLkOj3arRzTEWyDO5OMxhnYAdnVYHz0RuZohTDmorhzBXQEJlO6eDHeyL71kEkL+PbY81kOK/hbWJ7jDvg+0zBK0taG9wIEHImZBD7IG7LpVuLV000rABEdY5dEBH1j9jskJfYG57XsHl2KXR+rVh7Yq0p59J7YjsawYi9Jo10uUsB+1F8KVjoSx7urLDKwdasq1E2cjxM59OwrrvnT7dzdRldCkH8MwjC0yrHqv7eFRBeeTvUcFS9RyW8bhrKRj6HsNEqx/PUwCNBlD9e5asyECP3mc0kGRrM7WG+rqnhJaXzh57QySxKpbeWktF8RRr0ngJiDAt1emvWMoDEYjFOleGAcdHN9APK+yePYOUZmAkUKnu3ihcsF4PWC+WOFoc4+joGLVf48KZc1iePY+DG3s4OTnE1XuuYDYbpGpwR+DSjfZ9ThmzeYdrV6/jZHGMW+YXUGvBrbfegkv33dfAItteZXSarSR8JguYqBXvfNe7cPuj78ATn/BEJGJ83Md9LO5879tx/dr1wC8BicHM8gzAFRSjmbGMY3BpYBEKuJEtPlJAe/PeqVylzf1zs/awBijGvG3HiHZAjSmF68JdG89xDcO1cpyqiW5oTjCBQY4Kp/ekyf1Trd01HIw3LwJTEQuNMKwcnt++a5qSfD29JmjJU+2ayJE2CMhJw1TMFI1mnSBCiBVA03SdaVZ3URnEE+Fr5fp1xhR/iPBUaesoj5z4m/pN9sYRaEOYSxlH2hiez8lonJvBvqw1Z6yKpR/WZpU1ayv/bWl4cj+NTKkR9MRaOA0NWwEyAScmpMaCMlhTgJCF0EQXYNV2/c0gAhInH68vAni0bpGxx6q0o3mrYT8kQkYD6mxHAhAjw4qLBaEZ5mJktQmfWW8SdZvxJ2xA24CJKQsI4A5aA0jmx2g72cF1eibNMAyjQmkGugBIvRCext9o+rLOUyv+FvZ2FPgTpYSrCdJWoyURiS9/NsOMMpjJTeAxbk3WCcrD214mkAeGGnO3PlsxNTALSBgUpKcEqgXz3GHWdQLAa8WgY7eKqRablnJWs7/1BT5GgNBqkxgAp1aYrIhro07AyRSgSL+AzMGqIy/xRY8lp4hlHEjKAad4R0F0UZdCZbEgt9g3OJi2IE/7V2qBVZI2hWKKOznB3XRpYNRBDss7PjjA5Sv34pEffhF7Z88DqQM0SJsB9P0ggbZEICQsFwv0yyUWJ4fY2esw29tBLgvszHZwfHyI9XqN+d4uiAaACQkddtIehtwjJcJieYQrV+/B/pkZai04c3YfZ8+e8Tovtra5a3SYKCNRB6aK1AGl9viDP/hdPPIRF3Hu/Dmcme/h4z/+E/G//tf/D8MwSEE33QtWO6Zy1eJ5FjyvSqTJLK7gJDwRrLVlUkKG0PdQJJ4npQ6VGUPtT6GL+29/AwCKCS/jB+ypUPfbnHk3YDD++uam8o1H1SqulNTM/jTZ6De917XkqWSFC4ipC8MKtrmVwxk+JupyG5+bvxVkNJO3fkdNc/DvkdwqAugprFYm2x26be6ZrY6BIHEGhUC01i3XXm18MYUhgBHX5YgcCDZzfQRbWstBN9Lp9VkCg3fBbnEQxvAVoFTxaXOVDWr+ZDH9C2NzKwLQXGIQcWLP9OEAvqHjmlCYP2tuveMgUNVy1twe0nezILAPiVBIU0zTGNRJ8TgR+q7B21i1bHvsW3SNyCLo2CqH4+6ldkdWwSZxASEInUn7p4GTZBastm5EGTSZAxiNasC4NaM4c40mnWR3L7FYIOLam6uDqwbvBQXA6lgkq8IKe29M94cL7mjt8TWdCGR7t+1rs0pYqX1J8W0nPnu8ig9d07oZIKtSSuQuBoLW6KhWx0Tcb0Mp/s8JIifULGfjEGhkuvcT2EmEcPaqsYGX8jgexsYl/TVw19xhNh+npfXriEe0pZMIgUkhdR5QRUHW0lzUNn6QpJizPpcrkErBDPp54CcGPsSyY/tXrR3mhtV+OPCvFbJdpDpxrQLQh77H8cEBDq7dh/ve/x7U2TXcUh+D3XO3YMZ7UpxuIHCpQJXnLQ6PMaMOJ+sFjpeHmrkjNUbW/RJDv5Y1Vngz9Gtcu34FVIFh6HFycoL1eo33vve96LrkFs3VaoV777kHlBJOTk4wDGI+F8BWce36dedxBwcHSCnjxo1D/NEf/TEe9/jHai2YhAsXLuDatevIOQkf4FgHKAhJwPe1gVYDz8ZPMgizrnPegjKgm82QU0Y/FGCQtXhwUlXawxqgAEBOQbMDAJ0wFyA8ZnCAMg/T1uSDIOykPRA4uZmFBSxbqdXgGz/LqkyOPsubwZ0bLiDfyHYGSvOdNi1OPoj3GgBxH7gJKWO2SRFySi4Mkh4M6PI8Hs+eDNDFWW3ap5xsz7B050Qhoj5kb0T+ZGCuCRVAXCgBnDFrQNppGRj2Sw4ftgBN05glqI01foN0M7IHs1FpNVO4kGoQ1d07sXKlMW0fR8zWAoU1aYBiU3OgNoMKTGw+WjVSBQpaBdinjyC+fR7ToFkVqU4+88J/FVKAdVxfw8CK8KCwNwBkSmohZMjRTgYgzTdt49IgS9NglVHZKabZa1FQqAW4uc/MvA0yi0J7XqRrazzdv0E5cNoysGDPYEj8QQe3FgmzTcidAatxXIatC6jFJI0yr+rYPWRAZ2TFpAZWJPaEm8BlQq2D8xEHmBABmXMWd0qp6tu38UuWhQVOEuAF/QB1dRFJXEagUwcpWvVUAkxtfidAOayPZHfVVqSsdcQLtJVJWQY/04wDACZqLKHql7aeEB5TUeXQulDbSPQbpWnNbCpcQWUQEB32RNy38Xwg+9tTjLVzbttLBJAqWZqqu1gscPf77sTVe9+Fgyt3YU33YMAat3WPxW66BSgZKHOvJD3UgtnODMt+CSRg/8weDm5cx7BagUvRs1Ur1sMa68MFctfheDngj/7w/2I225OTzWvFarXCO9/5Ttz53nfqYX1AvwqxkgDW65WDydVqid///d/1+beaJGDgz9/xdrzznX8OhsSFFJ0Pix3yQ1QpWuRV6bTYM42lYjS5AAAt5gmaDSWksVbLnoGrOokPvb/2sAYoKUtQmsgvZbYMn1xrEcBY+WKVT4KcbcItPu0m77uZaaoxf9OeRbPIQRNr3zOyF1wT071o76GOROMTPib5WyPUAUCtNpRScwBxY6Qmx6PGR0lSBFNufntDwhbAZ7qJxWrYs8ameZljQjwcDSNmZnMBzqLrNJkBOxPHp5NMdzFmp2OcVJkVfrnpZhN3j36mBx6O6ooxYQDkDA3WeB0S2VBLc60w06gmAkyTqKYlNqACIIz9dODRdH77fbPdzORZPfZJTK6mfRqwZLQCWVFoMxuMhX9m74nzNnonh7/dcqCH7CkhpXCPHfpICmS8jobWtGAaC3YALZ00CWB2i8UE5PtzYUJiDD5OtTbGGZ6CFQMHgGfYmBae7HAki6Ey8K6eDvKD4yZWExXKBIwAGuU8mW/vta8NM7v53+gZJJa7MpQGtkaghoBCKLWglN6BiL2DGaMaE559VBk1A1UVthTGQRSCf/W06o1KwQY+RrEq0PNdkltMoHNbINkdfcwuoUi3CSl1QbAq3XC01FS1uIoFd5TsQOzWWKuk3en8l1pGuNrL7wcgKTEixYPDPa7MXIAGyGydc0XKwhFTTTg5PsL1ey7h6j13o8sDbtxzBUeHxyhDj9se+UTM8z46BrjM5SDBKtmXd773TiQasDeXLLIzZ89gvtPhypXLqH1F4uSxjkTA6mSFk7IQRbfLAAsgLauKMlSUUsViYnF7REgkINz4YN+vFCgYaByQOgGivbv5sitGtoPcMq0f2enV8nsR3TPUp2EUVxwGZnBhSZYo4jIrQ1EXuBkOonL7wO1hDVBySm5B8doFivbG5ohAfGzBhvYdCzpn0tloixPTg92cOmWA2igyVu+LfufXKAPWA6ycYdcmUBmsBwLL+9SYAGFyY23GTO7CKQ3T6knK2nnzsWey40KjFj7RvvV9Zo51jVWvjaK2zXP2Psn5HKSAyepkjOfJzYMU5sfQ+ui5OijYBmrawuisDpAGhqjDRAOGzSTt4BWAHE0va+g5J8qDmUMdDftZWXywXJTRCYiNoKxZPsabfAQIjJ5MKIdJcVLdmCcNWK0MSqaNAmwuGpAErp5S3GzDPWPjBG+suU2h3evPIB71M7YudY1+CBqTQuO+kIJhZNe0wqtAjYp0RsYBzBzm1ADFpmsPI0Hu7haazK9+bwBlNCmBT8i+HFtMSIsbihAj0R683gqPeEl8qwtL/Wk8vzKjMsaxZBq0a9qsgR9Qs/pY9kQZ+lEmhfGlUrSP3MBhZQaVAqqqANys/ktoFuTapjfMh11DBeRBxw0AFP3nsShhTjxiiwqSZnTUVG1De79lb2qwPBs/snlS5SaZc5RUOZHA1z4cLkk1+XwKKKxuEfU+G0BROip6bb9eYblYoPAJdvbm2NnZRcIcq/US68UNLBY3cOZsRlmtcbJY4Or+Hs6cvw08r1gzIw0FRwdHGPoeh4fXcXD9KhJV0NkzGIaC3TO7WFw7wnI4wWw2h2ejFnEpn/RLcK3Is07SqJ1uEyhXEAbQMEhQu9Jn6tTSo6DFxp4oAbmlegPyuwf8R+HkhyoKDfvuM8sx9BqLR6u2OOz8vFAZBTN3iTDrhIb7Xvga/W0JknWAogz5VO2QTBMYCwh20MJgicJybckt6UHLESRZb2qeMouBsQAef6mxG+yna6Ys8SqJpOS9ZxyoJmrAY8OXe5rMCP3dEEyhM9VSB4v65Z0JKyOCBVMqk+Ms2nBtTMpESgM7DD8kzF81Pixt2hnhR7WBEtP6GBgDEZfsXowuprjZmEu0HNVWDZUnK3GatcK1NmX6Ouker2ACYwQ0Ro9ozzRtj7UKRMAsmLzVNd+WGaNrGCppxvHbGgsNqhsHmhJpa54AqIORIk1MLE43awKGyIUjK+ORAYzjL+zZGsQhf2MM0PUxusdMc+L2uVsKVJMOqJVgoINbtWEiCf5s0+XKia+jKRBuCRLgwUHoItwLZrdkbriNqtSTAJFaPTQQskpl1FoKKprbKKYRTwu4RbeoWxlhMRJaXCzEN9k9lvpp+8E02hgTJc/UuYyKxGSfELcTnm3MBkDserPO4CbPsJ9uBdGYE7dSaB9Szn7fKOW4MohUMAbpk7itR6VGN5YE0fbCuAryADmkb5R0BIB5ABflVnaEQw1gqpTmrtV1q7WiDgXXr17G0eE19P0V7O7v4Nwtt4B4jsXRAgc37ka/OsbBuoB2MogLrl+6G+duvwP753p0swGrgxu4fOkydmdz8SaXHkdHh1gd3cDi5BCHJzewXi9AqWI2m2kG1uD8wOYrh/mzz6zvXMrI3S7rpOtoKdZW70SpUo4AEHDS9wsJrNV1YrVsgpssE1pSQNiSEgUCVQ5HQtiCqWtUFVRP/+6UB1W5rwaw9EDtYQ1Quk5KHEdtq/m/2w8urEWOAkDRZ5AtQlSD7M+4cV1r0etOYfibYIIc7SeSKn5Ja3+YqdvM9xlhQ3OVJHv9nU95h5ur/TlNq4jPBwE5Selnr80zETIUhBBcK1EwUAIzMqHvcsX6iLBJWvZBs3REkWLBlOFAwzDAaIkYza11MWhaTfCyPrddvAFKw1o2INHA0oiJMqOhLg7vbALFgJYzT259Pw2cjcGzMV7J4shBsxXQtinoAbgLkrIBPFs/ZdkcxumzPt4Pozl23iLA1sCol4ZwQMpoKRSuu7a5N4Cr747HQhQuyjgN7FvHLDVxvDYe52FrNZlDtuyIkHHiB9KNJ9wGsbEW1pqFawJ4g9tC+qgRONzij6BgPqEdEkrGpN1aEyxCGnRoIKro5LtWD6DWAcMg4MCF8MRiZJldjSZNoDVAHWltwzWl8S9UK1hjgkxRcfcht0wnf0/VOBZ9nsVImaspvm9qSQabXi9N3DGEOrR3WMFu6aMCXyS0IJW2PqZISN+0wmwliNXR9rS4b1MCitWSqoy+DuG0Yg3MrQV1KOhXPYZhiYNr9+H6fe/Dyckl3HrbLTi6dhk3bhxid74D8DFABbOdObrdfVy/scTxcIR3vfUP8OgPewLO3/ZooOzh1gt76NIOrl2/hqGssVwe4r6DK8ipomYGkmQl7ezsitsFwGw2E2vdegVAMtCoSB+HOqAySzh3rWLICHQhGYYl8HyxqJRa/CDFnBP6vsdqtQYYyF1uVqg05bi+S9S4IgAjKQ0K8Ccwdxq7BKRsqcWMPCPMOsveFBpKKWG+MweoP/VNp7WHN0DJnZuufJPksWACAD+qfCQkgJbGqJchmpjbF8yMHMyqLpwjDzQNidnUv7ZZ9SM7xIomzMxMXgzTQhk1myC1PuuYwjubrz4cQkjN39vAB+A1LAO/Ni11qqFYsCNYgxRNZCqQcUwQ0HTrX/VU3Qj6rB4Jagga5DRaJ47/DwJDGE6r0WEgwzT8aCFw36gyWWPmBuDEWnOKrx025MCQ/X1tjC4MTrkvzut0s0e3zwgthP75s3CKK4bbL2bFa9eIW4O8H8LcK+DJVuaO2Xgey0WS0WJB2C3moFlR/E0SHAlo4aX2rAbcFEAq8JL+5smoS+vXpE1tHSOLjAWYRqE4uZbHN9/8dxXYgjPZ3+2wRMfTrHfwvcu6zykAlJTVOppM4Avdlirug1Kk2iiDUQa5v9QqYIFNU5bgwuJ1dm5u/TI6NUDln02AwmhuYyYPM5JaHIkIrLw0B+uIW6JM09Z9VcN82ltOy94xkMksbpYy2C6X7DRXIBxHk/qDNAV+tJrmRtR0cq4QF7M+kUNmUwBrpSjgBFDqIPZN5SFmgVwcH+HqfZdwfO0uDP0JFoeXcXx8gJ2cgcq4evkenDmzL6m8s4zZ7hnkfAbER7LT1gnLgxOkch/m3XlcvnwN655x9cZVrFbHKHWFVX+C3b05dnd2sFquAJphb28fJ8cnmM/nmM/nGPp+VCzNaaJWiTMZgXbWOBJLABA6NkuJySsD/BaoKnGIMxQ9jNEssMzc5sb2V6A/oQuScnlE6LqMOki5A1GIRSHpuoSuy8g5YRgagE3UqfXt5krDtD2sAUrK4nuTWIz2OZv0NKZiKTUMlCK55iMsAQSBHpkhPGBLYleS+3mbSg9lVHLNyE8+kVQ5N1AEBM0xWHvsXAdHyWiBj/Z/2ZEET91LTaAYkDAFUyaKTYFtrqwEL0vu2izJUeomk8QsbJEprQ8t6JW9uBx089gBmEKTQXMOAjZavEZtojEDGGlysCkPTC+hbSCZm+bimZZn5/CcuAkNFLADlAmAseh19rf4fac1Ms3e5qJ9AVujm8V3+P0bUzMGQHa7Way4VtSU0I6OMlfPCF0ocAiAzmpbMbeaKAZ0TgM13Ey44o6wr0TrHdWJdAuZgm5fD6EXczlYrBcpU5XWTNOZmsswAZ6aa893rMXsW458vvVntFLJxdNJH7l6WvAkScVQnm5n0xYLpOKrCMzczYRxu5YOPW22l+BGLcZX9KiLyhVUlFdVq8kCgAdYjYsY3yZdbRYKtp8B5I6sUDpW67fVrAGUZoIiZVlHZGcJOcAMB8VNrSMIoCgAGANPZmVpgarGQaKYlb2SQH7ODRJQtS5IVFZapkgLSG5CsO1t+2mueVc0QvzaoGfmZBBWxye4es/dOLr0ThAP6FJFRkUtA/plD1oPWPMRut058myGNVfMAeTZLsA99vduwep4QFkcYm+PcHj1Gg4WC1y5dgXr9Qq1FsxyxrBcY8EVOXc4c+Yc+sUapTLmO3OklFHKUteSNaifBb0ldj4E0srmTODE4EKwcxCkYrhlTGmCdhWgbCnXLp9sFQhOd+5CNHpSwk8pK0Bh0S1yRjebYeBBki60srlkmyWvVG3AGyDYydGr1RoPtj3MAQrJZABykBMTQKY1A1DhNS7wI4TNtepZMHJdbFYAytJrdXuIQKyudJ4qoFr9CWlcLaVZKq6OmZyz1sZoyEAG+2PIx2O/GLeksUAB5HRRkBYRtVgFI97Q4QoQaih/bQzSAvyEUSmvQJMiUILVWB0tDGbmazP9i9BMIQqc2ovRMhzku5iq2gbrQ668oZ2ZILMjAiODihr9qWbu8L1PR2BcUzN5XKdmcQpDOg1r2cc0fg8ANeiNqWBkBQgCxT4fWUu4MXkTpkSEVCtAuYFHUigxmQdPD9Xf28sNeuhH3NKO2xii4LPbFPzRTYAnAIysTgxwdWBuoJK0thnDYqHgoNIBJuB1TaYH6tkYmzVNrAQ+bmfM8DiLsUWzvcsBxiR2Zeq+s9PPc06YJ6kE2g+DaqsiIPteKq2WUiTTAVZbRxg2hbg2ZiN/GW2ywN2QfRTHTClpKXfe+N7mYkTnVifFmloWYwZSfL7NkdHYdL7BoRaKBl6W+NNiJriiFIYfrqn70Ha68L7wHbVYwAamE1JlL5kutN/GaJjaeUVtLjUR3e1Zsi49KjNmCeBa0C/WyIUwn83Q98fqHhqQ5xndfAfEFSlnsYbVHuvhBKvhGF1H6NcrDEPFzmwPs9kae2d2MYBR6624cXCgbp5eAoAr49y5syDKODo6ASCnypdhjb4fUOogAKUU3+NOH8pnrczEMAwA5xbaoK6dWnsQOq9A3SxLCYwC5qGdQxT2jWU12rvMA8CJoDUkgCzyopRejnZQdz3r4YHgCq5WqA5adkI9AnUijx+gPawBijF2SlrOzkYu0h6AaaptRjwfP2xsaS1a2ja3BbpV20kE9+GG3QXz1bfX6ALX6UInSYMNZtYWkLQpUDHqX3uXCwxlZJbGBcgUVGXgWWubSPytbXJynJHUB8BEQLXzhgT8ECDn+mjsDEFjWIjE+hIFv09EUrO2Mi6LeeHUQg6ogZNNZh/XDyL43HrRLBsUjh+YMuD4nJjJ45+bVgCMnrnxHB5/Nj3tdwQzuc1tvI8xXT9dX0wD/yKwbT128Kx9HgmIEfOWe5iS+Km5adWuMckVDiCtV0mtdd68sqxxL7GiIcyFW39MGKCt+Rha2lwA0/o1sl84CFT51IemlVhNE7ffyzC04mYTi2dcC5sn69tpVilAtDvLBLQxlDoGq6c9Wz8AJUZWV3PuLABxjVIGcAWGyiiD1inRA+AqN1N6KaUBlEA3ca3NjR3BVDtEkTa+Y1Zgk0Kcl/bfa7BM+UpKG2Nugv/0vy3+xK0mlsmjZ/K0yq1aJK2IVc/BCYeU6JQn+xSoxA7W5JwjBpNYCKoClWbtarTu/awMLlpagKu48xygFPR9j/VqjToXC8CF287hcL2Da9fvxTAcIM33MJ/NsF6LeyalhLpM6CtjvjvH0dExThZLnDmzhzJIMby+X6NQj26HMFsTkCu6HcLypGDd92IhmgH7Z87i6OhE6SZhGNbaf6Nb5UuWLAFjWwQDdHWyLnJBK5mRcjupvu0BoUdmKUYngF/ck1wZqYYMR9uLpIkUVXiXWEvEYihuHaXn2qP2BYkI3UzpkTVejCFWIOZozHzA9lcOUL71W78V3/Zt3zb67Pbbb8c999wDQCbq277t2/AjP/IjuHbtGp7xjGfgDW94A57ylKd8wO+SAjxVKkqSs00AUfOUv/zo97DxY5aUSIlg7gTg56awIlcIqi88ZtaVg6ZV230j5iaWYN+YsaPGxP2j+2G6+sFIa4Bm3thYvC8BBPnBhNxONK3AeEOPpsKsRcWLXHGVeJmGgGnkqjALEKuAtDMYqthvFdM1gGBjG8V0TOanjbO271FGfaYwRtbaKRwBQJw6n5qx62jsrpDfK5cxyHTrAQfwEwR+mTDySR8cgEYBrvEe/nMkTKlpuE6oDZxMzSyNSUX6aaCXA4a2dxWurbiu0yz5PMrktDmJlp+xhm4m9ADYyDLHRkuuragQsvgmrWWToKvX3DcFE61drR9m8m/AqL3blYubZe/AChJiFHhqgrZyW4/TGhGhyxk5zzCfS7XMWhllKFivRROWvjO4r3qibpHKxKVggAnu2jJYpHPyfAMcaCbyLqsPXzeWHY4X6eY0hcbpSQGB1bSJabhlCKfWcjtqIVound/oPvP7bf4UoIgVoHptlWaNipaOcYC7rWErO680pcTvRrBsoEuy2QCMAIqsITc+GAJqkS2eTcdVKhbHx1ierLG/k5FnPdb1AP1wCNQV1ssB69URVv0KXZ5hNtvBahhw5uxF7Oydw+V778SqLzh7IWN3fx/Hxyfo10scvf8Ax4sTnJwscLJc4FG33y6gapVQ+orZbEeyODsgzzMSMgoI3AvP8LOClIdUs3hasDmyZ/LJ/BdXbapZLkI2ZiMJQzqEaLXmKucOWSRzBCXidgNQGBVFLXkMSzPvWenGbuQqVu3B+mb8KIGTuHmGjSizm7cPigXlKU95Cn71V3/V/4452K9//evxPd/zPfjxH/9xPOlJT8JrXvMaPOc5z8Gf/dmf4dy5cx/Qexy9yx+jz4G4OauvTdzEKYcNbQeSuUAYB5QCEcwELUO5bxPwJoDkhR6jgcCsI5Pn8emRAEb+46neZ4xgxIaq+t7VHBckrSiiOj0xQMmOr6cQCBznz8CGXCubupCI/uSVZe1+ciA3lZ6J7H86W0waOiAuCU72t/ZXLSxmyjQDQq5Jk3dbto2teVWNy5jPaH3cfN4EnDzDXghlCirILeVQ14vdNKoauwtwWx3eeKf9PnB0ZSG8X8BOCtfGxTaLmzxP/bcKBskAYLBibLgoKjcDCE+BT3w2hb41qhQrGqNyljgRXc0InkT7PcXSwAEE6j+ZQjl1fFyvqCkNPkKNV2FgVLNkrCVaR0lM3DqSeP3NAm3jGIrux7hANyvVbs9IE/VPDmbLGEoFD4y+F3Bi8Q2lVgkkNFcnW/Esq6sjJdfdkmrPtfgazdASS4FZjRhMoh0bfzG3tQEbryob+tx4VmvuinFFoP0bna0DWdcKKwoX9hBsD1UtJlfdMrRhLbH1dV4R69+Q5u2EU5PZrpKS9kXp3WKtqs0nS8q1g6USMvNU+xe5rHFSQ8F6ucThjStYr66DhgWOr92DfnEfulwxFEbfLzGUQdwlO2fQzefY3T+D3b2zWCwHLE/WWAwLXL8xoO8PAGSUgXH9xjUsl0uxmtWKy/cVVMpYDwNAcijn4eENrFYrlNojJTlHZ71eY92v3bsvO1Q4hWWhNoUu+Rq48hD5YSj8KQpnBmWz1pO74+1FmgClfEVccokzQKT7RI9sAJx/SsE+2ZNWm8nkYakanFwLQElBdRLwQg/ehPJBAShd1+GOO+7Y+JyZ8X3f93141atehS/8wi8EAPzET/wEbr/9drzxjW/E13zN1/yF3zlllMKEb4LUaHwPISE6xgyBmhZni2jm7Gg6lgeNLmvWGm5aNwDPHomaJ6kmE99tZrn4mXZ4/N44fnufgxTJELA0DoIGLSmxK2WD6ibT9Wey/U81Xa8VYXErFjArFUSVxygeaVaAFrQtVqtkLiLLGvD3sQR8kTCnkQlTtSU5d2SQDAD93bQLt1iwZiuo9oHwfEtBiRYBDjEZZn2K5uWYXh7D+8h56JgO7F0RRnJY++p0EeAnwcvWmxtNZtc0QAWVCjQbPhkHHEppcFZLXlsDEwARpAqDsX1CMDqxACth7qH8/2hVA2iI66d9EZqWO+zIdZlXMadP7zXFYAQnb+KWMaAyBU2nxkhMrgkvDDEwUOC5OSadeH9G13We4l9rKzEf4y/MveE1RTiN6AfKHxITqvIeU4pGAMz7IOttQbnMPNq3EaBO3V7RVW1/25aw/g0hiBWAltO3g90skF686EWBaanRMmagt2IopdXhUOVAzgpTjdwUIgDsZ4IRyJQsAK3CY7Lph9OmBhTbGEbKne79nBIGYlRoDAxXDKVH5QHDsBQrcOlxcnSI2p/g+Mr7cXxwGaU/BPGJHDhYhC8M/RqgiuXqCPO9OWYdYd2fYLFYYWdvjvUx4eToSIKfqwVEr7AuAk7KUHH92hoDEwgZ8/kOlkt4ts6qPwZQMPRr9OUEFRWzNAObf9z1oCB7nERE+TalxC71NHkrCKrxRW4dq7Ku5M/nkAGowbm6f73qMwe6qs0laokcYoBpLrZiVi1oMK9W+uXACx5M+6AAlLe//e14zGMeg52dHTzjGc/Aa1/7WjzxiU/Eu971Ltxzzz34rM/6LL92Z2cHz372s/Fbv/VbNwUoq9UKq9XK/z44OAAgedzZ0gRPbS1g09qUoakRza8XS4Tpfq1VMoY4flZ41Simoml8gGW48On8dkMzvT8Xz8biTv7mCYMa9HuHIGaujwzLCFoZWRQO/j61HjgTJQKVVubYdNZEEv3fqgUGE2MQYPLn4ILXWjtORw6fMvDhRhYCOmRUIhSqyJy94BKA5pJh0/CbFDJGC4znUwSw/a2ZG+Zi2Jh7Az3sJze79g0DvGYlmMatRKFpWoibTdockBYOVA3Hi2upamUuRJsnm3dDiPb7acIKIHXVhT3Ard/RLTUuTMhuLQNoYorXe7lV6LTfp2uitqmRxcAsKRF8j/qlNBfH0jTyxrhNyE/dltPn+d8WbOnzz8FK0d4Z7+XwGdCEgQgJhOMQpCooWyqXzZIBRIaWNx+73jB5j62v0Z7MfRGmH+bn/kBKnfxtrSi4GvQk4kFjSopbTtR9UtgzjIoC8hb8rhZBp60YX5fQ5Sw8oRIy4BlkxIq7NPbKTqwWTb49z8h6uvYRDEcg3pcVMjrkWQZTQeEeq8Uhjo8OQNzj+PAA/XqJ3Y4ALlgtFlgcXQLqEl0qWC3XKEOPWdch5YzKPfr1gIGBvlzGwBkp7+D4aInKwHzGWKo1Zd33WK0GlxNlgFSwrQyiDrkDUHpgJjE0wwCsVwWVB3H3FbGWyNyTFlETt00cu/y0IzB075EVHG98uuvm472ga2hr65meE5lmlY25kByToHYTs7SZfCgsZe1bxg6j6zKImnXQLD+l7wEMMGXrwba/coDyjGc8Az/5kz+JJz3pSbj33nvxmte8Bp/6qZ+KP/7jP/Y4lNtvv310z+233473vOc9N33m6173uo24FqARLgEbVofxVad/1xiWMQH71SZxE6jI5DdN8v7QoJiLw9/tVZt9MIbNeggUheqXU9AQzAJW66P1jzYYFhDiTWqBlYXHRLuqEwYXZ45ZUkItgwI0Zoj2swJeMlzGHIW/MRR4HRPZKMGCY9daCrULe3sfg3KoLIoM0nNKJKCsabuEtCH47AXRHCpybpxl0sypOgoer3VzN5wGHlV4cbnpZmSNPajmckNQllA2tF4/gRhNCxX8otp1BBxarCkep2CNSOYSzBvjibVm/DOLjQHrmtp6iFSV6bGgvRbTEE37/izAAU8s3TU9ZC/S3ObcBgvJlPZ1gNPv49+O7AieNTJyjwmlIYcDNWPFVXebELkWbDQnlpJ2JELTOtVCosUSE3UjQGJZMNYHL2WgQjoYo9p8KuCOAige/mf7FzS2kE7BloE8ixkpKlArQ7NguMWSBBDmvFLnbGxfi42DmxXNukIEVLFygBqQmwajt8PHDUxSA0iVW2C6vj+ljL7vMaxWKKUH1wHH16/g2tUroGGF0q9Qhx49Khg9FstjHB9fRaKCrO6fwgV1YOTMaiWT7KvKjOvXLqPUBKAD1wywFHkbhrXM0VC1fAOjFEZCws5sjlm3rwXqEs6fuQW1Eo6OjlEHy0TKyNy5UG9uuerWEZvPcZP9nZVeckqYdZ3ENmlJ+2EYnL+4ksZmkUNw+5NYU2qSkDCN5WlW84I6aMG4JAXyhjKAB1n52XwOooQyDBJwC0KtGYXtkMCq4R430dRPaX/lAOV5z3ue//7Upz4Vz3zmM/GRH/mR+Imf+Al8yqd8CoAxGgaitnB6e+UrX4mXvexl/vfBwQE+4iM+Qjb2RAJMNaXT2pjxAS3XUgV1beZ4vSrcxxsMNT63CS4TYgiWGrQFp6ZtMOCo1ypLajkcYTTMG2QJ3LwwkjFQZ8D2nVBj01CYHbhMtZSRxqLP4FDUytMe7X4dk/S3ugXiZnPv1qVTEDUDKhgnAaOxqfyOZu6aKlLVlEiuMm9V/KF2jT3bAEfV2IgRYEFjwq65Vnah0YTEpN+jjCP2KsAGxFxY6dgrICefxmEZM4iChcw+Zf83hExuTfFD9pKaWhFcP9Xmq9F4XIug4DeAyG0sFlRS0YDraK30mg33CBkongZwKkCqQEVxsBTn0wr5+t6DWGxcMqrlwcbq/nRINLoDW5PzNjhq2igCzUbrJjO5ZCRqWVDMlvYrgea+B1SrLEYrVDXbOznEJoIc1knUYmUiMIrZNpbpo4AhrkfEAdO977ywlJbVo2vY3LPYpF+iECco/c3M4NTKvxflk5QAFNXY1bxhh/25BQ6S+ZikU+N1dZ6tEFXL0YsAK0prlqEE2ccKPBaLY/TDgL0z+8i5A5MIZvPkW+n3UhjL4xMc3LiKM/szDKsFVidHGNaHIAbOnNnD8Y1DnBzfwGp9DPBCgvtJ6tT0VaxLXWZQyqDcg9dSnG61WqPWDnt7u+gLY7VcgRgacwFwTajUgHjXZcxnc4AJQ6k4s7uHR158FICEYX03jg8PPSRDarskcC0oCOdsKa/29Ql7yXjvRjC4rikRYegHn/O2z5TmtNhiTkkBqCh9rrT5/hnTmLn0HHAmAe8WKO3WXo1t8foqMlA82PZBTzM+c+YMnvrUp+Ltb387XvjCFwIA7rnnHjz60Y/2ay5durRhVYltZ2cHOzs7m1+wmZk3vsBUoI1ByTgWRJoeFW+bVwPOrJlwMa16uunsZ8ugGX8fXTxTV5AxjuoaLCDJXoR4dLhpRPcHjmxMBjyA5iIwbS2mFI7Hd/O/ExFQpRiYMb54TSv1PW7Sn9KoPMov0wiDC8BARJyjjb7BlDcGq9bKBJAGoLXTUAHKImysXkKVSlEuWIk05oeaBm1ykcNCuxsiuC049C1aDqIlwXT8oumCgKR3xxFJGWl5c85Zy1U3S5CkuWv2lM2LapWUtJokQvwREYhyO1dH+5lTbmDSGjf6NSEv2m0A5L4WBrRSO/Y+Mr6brZluo6yWu1orQEmEuApiRODDENPwCDRUXWN1iRQAxMjcAvBZuSnBblagAXUVpAYODMQbsK6kwMSKLbIVhbP4iepMP6v2mHIeAfvsoEL2WfajLBpAGQF5s9AQeUaMnStUhkFdLtgUPqylDtLp8x2VFIT1NotLtLoA8JTklBI4MdC1Z1UWgFJLRV+HdiKw0aKum+wd4ZGFqwTwFgGUnuqq7j+1a4XA3IrSL3Fycgzmgr29XRFoSeaFAOzOdnDtxn04OTnB+f4Cbr34KORujlolWDQxMLAcpjisF7h++V4cXL+G3Ufdghn3WB1fA3JBzoSDoxMM6wWWqxso/QKEHlVquIOZUPoBQ18wJPbsRTCQcsa5c+fRdfvo1wz0a6wG5TGVpEpwNWt10jpdnVjaIPEg851d9H3Fer1GrZKoQUQoA/tYuS0k3JJ8EwXegYWChTIMvr6lsJ/jpFQi/atipa+smZpkrkpqAe4KkhNDDkw1zsiMlLMrT5UF/FDVuBqr7B7CJow+Xbn5APKMP+gAZbVa4a1vfSue9axn4QlPeALuuOMO/Mqv/Aqe9rSnAZDI5d/4jd/Ad37nd37Az5azMcop32xqirJpx+BtLKQbqIkgxTegPperbayxUJpqQbEki/1UORie1wSzCKT4flvYscZjo4oIejwnEeGqcGR2QibS7JE49g2hxe0z65sJTGNyaLUXQC1lO86oPzM+D2NQJn/HWIYGHjg851QrGzMkupwkMlf2j6c0ggBLRd/AdA4q4QFixHK9acW+jUaAVIQdqwl0BE5DnwGy7G/5K1kEfcvSArP3NYId0UrFipb0/dGSZBYTmRsx6SYPOLTzmeS6quPpcg7XkA1LxpkS4tEI1RmjxMJUzahwWrZUQvbOOXiOcztdNwNRMasPWrtlvDQMK7tvoEhoxKwZBM6NCXJcIJJaDVYt2l+jXctdq0Y6DQ7X0FV7jN+nYtcZs/3LGl+BsPbZ6o+wEiMLD5kWhDMgbiTi39ncSZUwX2NALIqjOTKu5Ir2RBiE+i7OA/XZMQW4zRGJsJW/ZL6QgW6GWitmbKnSLVNHMhMJkrso8TEZ1LLdVOhWvVbuV4scF7FaDD1qv8SV+96Hw4Or2NsXhXR3fx+FAUoZx6nD4cENHB8dYb0+xnx3jt39c8hV+kYAVssTyRqpPYjXSFjj8r3vx2p1HSeHV8Aka9j3PaisUfs1wHaSsAyZiwS2FgZ4aOf1DJXBQwXqgHkHDKsl9vZ2cHysBT+ZNE1XgOnuzi7m87kClIr5bA/LvoALcPnyVSwXCwzDgJ35DMPQo1BBSkpDSnTmYgEaz6i+9tA5DJY1wOnODgHs+96VazvJGWgWHuMZUCuy7zmYm1zXSenNLLWtC2qtV+Uq56wBsuaikp2VkhRxm81mzUX9INpfOUD5xm/8Rjz/+c/HYx/7WFy6dAmvec1rcHBwgJe85CUgIrz0pS/Fa1/7Wnz0R380PvqjPxqvfe1rsb+/jy/90i/9C74xCi3nUkFwiLlJtOegKW40Hj8KxgC0Frh9Gjb76Goeu5toQlhjyAQxhrKx/XFw4vS59vM0FH1/7qyROyW0UlodiaaRN+EShWXUEP0evc80SLvfwZONVbU4A2D2fZ2OxTQAmxkT9A4sCcwaeEutTwyoJUfPJOBmvfBzePTkWX+VuXfCvLTnNT++Sya/L2ndicbAazj51a0vCprioZX29pTDmQv6TmMa9i9lO4G0adfyfIvMF6GYVVhSIsy6zhlSXAuZO6k6OetmI5eRLVQ7P6YJs1qlBHqnwmoog5pv2ccZEZ8LPdsXAQCNLEyBKdqNRJ0DQXMHVU2RZJJaIcSSytveZ/frdTzuj3yoTFepkXR/U+IRQHGtjghutiEEa5J+FcCVWybV3TNSUMxCgZZhYi6hCIjs+shLfP5Deq/NywjsscRuiMKzuf/dimrBuhEwAoAGw8ZKuVP+IkkkCYk6dDmBKsk/WCaWFBUbihzG54cciIoMQtVK3FncNASkqscIpIJh6HF8dA2569D3a9y4dh+u3HsXjg+u4AqtcP7CBZy/9Vb0NWFv/xx2dvZBtWB9coLVcoHzt9yKxWKBedrB/t4+Uk4owxKrxTG4rHDmTMLypODqvZewWFxH6ZcYaoEkLFQM67WdVoVSEpgLqlqIRJFIvp8ryxhL6XHj4BAniyVm3QxdSuAiFYO7bg6aZ6z6FQZmdDnj3LlzqAVY1KXHe+zuzXF0uMBQ1pjNO3TzOYaeUE9W4KGIS4xTA/+o2peq/BAO2GXdGvg3i+1sPsfe7j6GYcByuYQdFyCPJA3OHgMPUQTMhZqQKDuf90rVKYejBvRzmEIkQLJUwCooC50qv9eDM4kYH8Bhxn/1AOV973sfXvSiF+Hy5ct45CMfiU/5lE/B//7f/xuPe9zjAAAvf/nLsVgs8HVf93VeqO2Xf/mXP+AaKK1FrcL+1uwNtonn0WaXazdUasA2KVlaJwCNejaQElNf7SdrUBvF7wH9u8l+6GfNLC6f8ClMZto2TPM3mw0X6Bw/PBWs+HVBqIx+v8l7jalPez0FKESktVP8xs15n4CRMu2nFycqQTNkPRo8iQC2VDeXz6QndQKwU6IVlHjGTlgDK1wVmbT1nxSoJEpS1llNpgxG1Qqhbqo2fywUoGY9aZUa3AU0Kj9oMmKOLc4IRDAmrQSsJ2DDMBM194JaPHLSwntqQgdCgCyRnpWhAcM2rdyqjbZ4jQbqK7O+X0zo/TBImXQHP2ON3Uz1AEkauAKvlqJs+0IWIro4YFYpVhDBLeB2CtRoFNA3qcgbKdHpBrq1LdLU1igEUztwCPdYMQohBJD20cGlkW8Am3Kgnvjtk61fqDU0cq0o8x7NUYg98fVgHr/NhVRQxio8ddvu29jvk30dwVGZ8MhECagEEKPLCTW6mZNUlgYInBiJxdpGLE7ygY0HibvI6m0kSl4aoNYBlXuUskQ/FJQy4PjgOq7edwlldYy8U3FQByyWx0Ca4+z5Bbr5LnIldKngzLkLQO1x5fJ17HZ7yLddBJhR1ivcuHoJ167fAwwrrBYLHB8fgMuArpshcUZFwWq1gBV2q6WXc5GUF1RNcZb9IsBA9quc/LxcrLFe9tiZzwFeYbVegiiBchLXV5E1XK8l8zQRYb1eYr06RjffwbCeY7E8RDfrAJLqs5WLZjrFoG9VfEK9E5NL0Xpmx4C4JUz357rvJSi161DWUtWYOPnxLWBGR53zD8nOMcupZMgCosy6QqTuTafZ4DKUa6uOv7rb3BQyroxh6FWmPrC8c3rj+1PBP0TbwcEBLly4gP/z1rfj7ATYjAXb9PP7G2pjTrLBACCmoIbNHRlA2Ogxo2Yk7ExQWLBhYObQc65ZuNpmr24CSqZWnMiIjOFOwYj11xiVgYyRaypov6LR56ZlntK3CIZMuzPN2TTTeO/NrETRdNnG0hh7fJ8ILPlu7LZowibnkMKLOI+CFrjC05MTJD7DgH1KXppMCgzlNAYJolKLZjWobVj/T1oymCirJjaOARAzt9TMYD2jRFI57SA5SWknksqhs5zRdVm1f3uJCWxqtMloJntlOm4p6No8c1E6JW5WGF2jGPhsWRtE5DU+BHMljWVREJQFsA0BwKxXg7gA6qTkeQnAx9/bLBl2dosX3FJg4wHANN4PZlHbtGYa/QUXhwImK4qWiJBCrEUE9hbIKs/QeLXgQjPLyQh8wEWGvt/o076VzzyQuhQHJjbu6X4WnsJWusev8SB+tfJITEHTov0aBSVxLzo4CvyjVAlqNyAv+0eq43Zdh1nuJJ7GhDgzhl7OGxqGAUXLBQBS1qCUqoqdjJNUcVz3a9x99/uwWByi9EsQBizXC8kOW1W8+11/jmF1gjQf0A89QIxCjLPnzmF//wyWJ0sAhMc98e9g5+wtuHrjCLeeuRUZhNL3OLpyFQcH15BSwXq9lHiUfgXUgsVyAUoVsznhZHECHhhlWKMMa0hVY3GxG50QJZ/zwQvrCc/uMqHLkiLe95KZg9Rhve7FfZUIedZhvrOD+XwHi8UJVqsBhSvOnj0rZxJp7ZtSgFW/UGuGyI+qSgFY6t1YPJjxVd2hshZFkgBMnoiVLyHNMlLqkChjtVzBLC45ZQcRUNcLIWHdrx2MiDsmw8okRNoR3jcuEWB0ZM8dQnmClsQgBVt3duaYzzr83lv+DDdu3MD58+dxf+3hfRaPayObnzc93oTK/QM30+CqbibToOHPcruAXt9cF1598tTnBgahTXx2oaZE6OuDGXP4Y1OoA6YGbNw30qbQLAqjfhmx2X0GaKK1BRidh3Ka1Saao2P/pqbk2Dee9M0Kl/nUBNelFAVKChQ0kyMngNvn8V2uLSstUE7oLFtA3SSz1MAPI6QtQ+OXQp/9WXsd0JwOodKsxXKY+05OcoWefFsKoRaN8SgVQx2QdXrFYCday6zLbh51EUj2P10zTwdUN4KBB7I589XUAnAIgxHatmygnDvXkETZqZC6JTpviSTyH4CdYADYUezQNFwVzEnSMUspPkNSFTV5rEZ0faRSJDgULN4WzxBQMNIoIxoDJqBF9lSCBg5DM+Og8ThVxjRwRepPj22S0vpVAdEY5CdKYp0jGYdbdjS1260QSYGAApmmSQYXZAAomyDLeJsIPFQ9P8UUJlNqdJ6GwqrtV3eT2fgjoN9IBYdU1PU8RgNfdl/KjZa0oFcGASxJBbUWz5LjqvyiFgXCxcEQiLBarTCsBqwOl1ieHOHc2Q5pYFy/fhXgit3dOahLWKwPUYclVsMCpaxR1ic4PJCU1m6+g/ffu4Pzq9uxHhhl/4zScsF6dQweBlDqwEOHndkOzpw9j9XJIa5dv47cMYYq1o2MrIBAgRw3EMDKUypbvRf5rKi1guzQPBXwzIw6yFhtn6CKG6kMA9bDoDwKKEM/qtvU67k4TcWBFLhjs+ITONWgRBIsGDlQJqAnlzOkll0dClIGZh2hm3dyPVec2TmLxWKB9XplOwkguCKRZzNXxsSiC9GMlO4EEAmQmc/nbjViZgxaVqHRl+w1C/qvxBiGgr4f8GDbwxqgVC9nPP48ajMAYHrhSNNHQKX2PGY4y7XAtHg9AtGEZ0UA0hhUavcSbWS9tCYaPJEE306K2N+0tc0UhHsYS2ynpWI74zjlOkPqQuyqUYbvhVdN5lL+2ECBEchFQBXv834ELUGY8yRwsKqQyJYyKTUHEiVhAB6QKGlymYPwitaXJIca2kaUgMcEyupLJRH2bjY1BqLDEAGp5Z0nVi+2eCUNqrNS3KVqGKHGl2S1HFRmoABdyToXQmcWF+IMz9wTk5WWeRfhSGptGEOpMZ0ChKqxHRJkrpYYAFbWX5i1anSMUeaS6nh6T0Ka1OIQQKtAiIGuk9ihkhgY9NA7dZd1s04wjKUM19BnTdVlD6iEa3Mp54k2OW0MRgFRhtpoBDCoP78WFUAhe8zvVFBk8T2gaYxGQdIDC2PK8KnWRFup8PtISVB6n1pP7Lq4Px3Q1OqB+uR8o4bvRYDEQPFoht+w0lQWb44RvtIfQ2mXBxQwKlp5eQmSFuFOicQlV6UUPSngLsMgDoPIm1LGIy4+Eud3z+Dg2jUMq2OUkyVWhyucu3UHdd5hVdZYrhZYrZboq1pBhl6soYnQr3vcPdyJk8UCs509zFLCI269FcuTI+yd28GjHn07rl2+AT48wmq9wmJ1jL2dGVLKGIYeWA8oA0CdFEhjBd8SuNsUC6ndIcskIFRrhhBpHaxWm0gq6MaKtkK3pVSxvkJ5DSUM/doVUlMFyS1f5soRBcvjFOOaGR9QUFU1mL2RWEWmLB0fBqSuA5iw6nvNEj3x4GZz4UKr3DZLCTC4m1HBOrS2yyCWVCZCPwwoZQBREjchmgtd+IYo/Jwk7qRWxnrdj+riPFB7eAMUboGvQBO8TGNtvh3uZ9rXhGGoACUK0KaGQLSRGXnMeMwNAjXTujx0LW/MRKPFwd7f7nngFu8nEgO0xcBAez+1qkzdQVNmFc14G9cAesbP6UR1cyHRWuzveLO17095MhAytKbxDt6/JL5tAEhad2KoVXznlccujChQ9H8WfiACMql7BV7i2ebG4QELiGEFHjQBX+YzLsqw7XTXGBtimm0LWiXVklmeTdAAvql7MeQnu2afXIiSpsqP10QtPTYGc5/UlhFjmT9qAgBg6YttjgE5er75mTUlVi0gVnW2cHUw0QCnAvYu9knGKynUUrvCTMTuqjDXB8bklzEp7ocxTRv7J5gLTICd1QVKOatQVaGAsBf0X6lVVNFYNyWoPpSSnsMT+UbQg6nRlT24YRPJ9LDTXf1aL17Ird+qsVvp8ZaaC0Q3djuIr+01NVeNrbdJ6drqk9hxFfYcZjBLVouk5lcMyc5hsVihBiZbTJ5ZDFksmejAddBgSdkLs50O83lC10l8ytW7j9Gv18gArtx7LxgFy/Ux+mGBUtdaEkB6X6vFPhWsVye4duVuzOZzlMUNzOnDUAbGxYuPxs48ozsCzmAXfNjj8L4DJN7F2TNncHDjOtZ9D0ACXiV1XeiQIf2slKRQm7qnQAnV95VDeQy1oOMMSh1Ac1AuKIPkGBPBaSI5rYjLqB+0uiozWtZmVTqUdHcPNmZ2RfBmTRTJRmQSL5LcJUUAck6Y1QzmcNBiqarEFSTKbgGVuZ66doSgSynKm4SG+r4XHkoAUtKcw+LnYyGRKJWJR/vhtAM8b9Ye1gAltqkLo8lU9o3fWgAiUeNh0YxB5CXj5XvZqPFdU2ErmvmoR7CDsfyVxsAmmhabr5wAC96L7/L+hXdZ83x0BSmR2cZ5GRWCokYt9r1XiJUXTGbJHzbuQxSEfHrsi/8+YZSj7+N3/kwGqp0DAY8BkHmyOg9J/e/GxFpRrVpb8SAHXyQHFKacwVyQWEpxm2Cu3Ey0BfEId7FKSF0J9qqtEUQZ6DBTix0+WDUt0zTighZoam6iWDYcsAPTADsJlMx0Q+OYm0jzlh5NWohPaoawz8XIHerAQUBSVreQCS0O8yW8x8bbATy0IE5IdoO5Ri16v1mexruOLZgC6vZS/zqYtDCbusLqMAoY5VDMjih5ubpYbdcAUc1hE+pJq2B9l1F0knIBUk6kZTgwC2Aitf6QWolIQZxkFammXC06paX/EomPn+ydCtQ8MykAlEotILoFRAo5VR7v42Q5MixuHLtYEgEwejbQ9mbbo9TOu4IIQHEbNHABm2u2uCPpWz/0bqmJe9CtN1VjjYqUie+6Tl137DQkKeliVTk6vo5+sUapPfbP7eDoKCHPEuqiYN0fo18fo1TT9hU8q7BniNWSawH3K5Q6YIEB990r9U0Wi+sgZPTrisc88rHoB6msulwusLe3i4MbFVJuvbQgbFYF1mKDqpztJUoFJFOHjQ+0fZ+0vDvVim42k7ylfpB5cT4IMEjLACQp98AMroNbT7iSuGSYQZQFzJVhtI5xBxFFKz60bsk4uD8C4WEokAQDeNzMct1jKINUjJ2Zdswqv4xHCGAyF1aB/BPZavJCYoQKR95HrrDJobI8igU00P1g28MaoCSKIAKIu9TNt/YTU+0K7meX79viA2gb3DTkqAIBG8VmLIBuCnyi2c9+RJOrNWVx43fE728CiqQruiEsm4hiEaTm974ZYVQVpFOBMh5/qN8RxhUFJdUWBDx61tT6ET6LzYS2AxbTyCBm1ZQ0nVjrVCFVJCTUYtlXEPCSGKVqFREO1hYitwZY0bMBQGKemOuhjN82pMUjNHBAagYHNwEu4y1+3oi73hR4as6JX0sq2KfWOe2aAAs/Pj2pkLI6HBHYtLstmNWsLaOMEJBrWK2wHuk4cTp9kJR9B9BqZJQiFkO93mIoYuDnCDyp8JtqTl5HBw1I19oOrmv02+ifsloRCo/OmLHvC5+mnQnYjWMi75dAstbHVv+BIO4LmCsvdUiJMQyDKCzFAhMtbbnxGGAsKEbl3pk9SFE60vpcSgAR+rXLDWZULiM+NZ1nUuXK+Bl0v5WwtDVYYuS5dTQPru6S9EQ2VgO0rlSQAXtxZ6zXaywWC+zu7YISsF6t/EDR5fIYi8Uxhv4EZV3Qr9bgxRo7u7uo5TzW/RGOTq5hKGtUXgvYo+SgTOqRMIgzEgMZjMwVZTngqK4gVgEGU8Lu3lkc7+9jsVyAcwXXAf2wAJIIakmFbXFZEisjB/3plkdVV38FievYF0+ASpc6gBKGCvSlYNVbADiZx1VpXMDMwIMrLfpWXeMQZB3qQY1ot5Gtr7N9M4qRgvHYqjiDMAwWEyYnbq9WEhcT2DpqrCUW6Mr2SVVLk/Fuw9UM4/PsoAmqyHmmtNNl2w/T0IL7aw9vgGJ1CMLom+ah2kA4jI2ADQKwyUq+6XWTm2l58k5bpFEqtwvgzYlvWr/24TQzwk3umbbpZ1OwwkA7G0S1djH1JpN4ME1++pzTnm1AwubN5lkAdg3uElLgN85ichAzBXP63I0xA+4ycbCCJtTF95skIIHkB1jN7FYBVBXlaO6GApDGlNVVwywxFEmsMRZXYBtu2r9m9WiBjymFlHQHpyT+bX2/n+USDngrVb3H1LJYjOmbABqGIVhrdOxaEMpAhcw+TIagsDBzK5PuWnhK0lcFO1nvETO2WCaMkQDBjWi0YCcgK8gj0rVCkkJV1n9uaY8NdNTxeug8jiwgZAChARTPRNHTgNszAKRAn+RwApXG5wm1ZpW4xiBb/kV6Ha99BiGpJY8TAwqApbpoE+bSX4CzrrkKflDLHgovHoM1wwO2iKqNEqlVymj2lEwfAy5uwQPAeTxGA8c+E4SNmjZxvzaLjM4FNR5pQaNRWJZa0fdrLFcnWJcefV2BS8H7734fzuzvAbXi6pX7sFosUNdr3HLLrei6GYpWHuXEOD45UuvFoEqBWTZaKn/OwktmSeMKi6Q3D4PMpWSzFJT1Ee5cvEOteWJlXSwG1GGtxRgrBg1UFWuYBYl3KBr0ayKF64BELcDeC/TljEQz9Ms1hvUgigoArgWpS8hZn8UDCidxj7oCZ89mFAcHUovF9oaSNhSGqDFKMo0A9gB+oRG4lbUyg0oDDbY3uQK56wSocEGXO1WkTIFxe6C4uCBnb/Ws1qYR+bKfMzau9syj/kevgwXhyyV/S1w80SRJ2awI4gNlOxIaU+ZjWtkmUCEVbOZjM+IA4EwM+ilrga520iw12HxK836SMaHT22lWkgc2iTUo1VDuJNamAqdZaG4KenhsTQG33Hf/qJi5WdlfanM4siDVOurLtG9xjDV8H5mqCCPT6JShqKsACp4SzIrV+hutJ/oyDMxItQW5mZyMGr3PFcHfY5aTcYBk+xnnymjMtPSWCggY4ycV9rPctdRmcjGD3CVkJncRGL0XFcaZxuONoNKKvRndJ40zISj4Tgkc1WpjGtSsWAI5BRDRNObARjK1duiae00HBYfc3oCu6xy4tSlrYDYFRucWFJ9Ljbepzc1nKiuTxZO0FGXpo0/7SFTbNadZfKAuDIa4fRPX0Z4QGS4CkWw+mCVWgpKcA8UMNveNTkDkIb4XSdx+OWV4hgaHcbBkz1SjqWmfJ6CllsaLXMYpLRugGykQ4e+2fkanWlMKcLBs7g5miUM4Pj7AUHpcvnIvcidVVDsQDq9dxX13n+DChbM4f/Ychp09lBVjb3cPi8UJrl+9jmE4wdHhVazWh2AMmnI9uBCv3ErAi1quQe5gMEmcECND3ISEioq+X2J1fILZbI6umwlwLOzAvQ5V4xIt1kL/88QIrd2SpXy9WG+h4D57kL2fb6TZTVDL6VArupk8EzVhGKrG0FBbENYkD44C3XiIZc01l7nIDzmElTKhEqEWhiVZAu1kbSF0UR4MaIISdnfmUunVqvkqOKdY8jruA2CUXh0t9dXvbdlrU2soAZ6BmDOp1RfgOlZY7689rAGKoN2ihYUAg2jE1JjxCNVZkJfUgaCwGEAQRDBo0s6sYVPN0RgHwvWnNreakMarCeN3QXJqE4ESNdmNxwaBzqoxA3mDQMaPJVh1TYu5np53Y4JlfJ8y5QZ//R3yi0VMAFwsYKv55Y3pxXsT0cg9x2ibVC4w86rxVFPXZDeygQdmC3jXtVNGGgWvAokofIgIBQBXAxcNjDo40voVrV/mZxXLC6ekmVkZxlCixaAyg+x0W9bMi2CVMxpIGWAetEKpvseHZ4K+xXaQAiayWiI6RgfUZIXbdH7VKiGALjTTyCyVN6yHXehCi9rcVEt3rc0lYgGFURmwFXDrGYv/vYZ3CS2MrZjIUnujanBxKaXFa/C4fzDa8OUm4/0yH5WcPxu9sLpI9GqdW4s3kjvNzWP8IqUsMSkG0gJtRUDf+tHSSKOlpDFuAyEGSOW9ZVTTx+Igwp6T3o6AmmVekb6+Vj3rxsdoVkUCF7PotX03qoQLA4JaY6O2vWu0ycyeSt73PY6OjnDvve/B+fNnwXWJk6M1qJ5B7Tp0qeLo4DpKv8CZ/bO47REXcXy0QmLGcDTg+o0rWK8OsFwcSjpxreiH6oLWlrkjybDLJDFlAxEyFbFWMAFcwGh7gIvEzdTSY10KJNZWgudLrWJlLJGvNUCeUufeeyJJjeU6QGKVxAJZdG4S9+hmHYgYq9VaLaUSm9EPBbBaJdyymST5QriJW6RHbkmTV3pkQki8sN8bSRiviBaztg+AVmI+pSx1m0qB82ejXwcpcEXWLEsOlsFI+jziDJBYBdk2HJrlUXhe0VIChE7T8i1+ahyTcv/tYQ5Q1IytRYpss4nJf6wpjWM+yBe9MYRoVmuaZxP00d/d0upA0CKzHO47pQXFHB6qMnYn2DsbkVK48QEaN/+/PAvC0E3bgsYmKCdzTZBbbMsUnIzGMgEnFDaXPwtx7C26fMP0Fz6bAgd7V4sDmIAU/Z6ZgVCLJSJ/hHvMgjNdl5QSakpe9juWKTd3CE0Aio0pZUllzgoATAMRE7we/AYJsJM1EVPqtAoxkbjiTBg3C04sK4/JuEg1loq+Lz7jBlQIQA31XHJOIEgtFRjoseHos6uCIJ9etTq7dcanfSxspfhay1SK3wHwM3dMy7dj2tt4lMkxeVaRVc+1InJegr8iWC3CAECoVleC2Q4h1rVS158G/MldE7oTclKBHy0TPquwMuU2J0w2F2ZlYdcqbV3jHOkfbnGUNPhWEMy2uZ/fo6BLxjVagmatsmdqXxvYCq5dmyESeq3VDiANmrmBW4b3yd1VzLCyCyJU7PuCYSi4dO9duHb1MpZHl8CrfSyXRzg4PMLx3h5mO3McXDvEmd0Z5rMdXL58SdOH15injBs3rqJfH+Dk+BrW/QKlrFBKj1IHzSxSVpkMJGoGHFeUXs7wIajVgNHURLI51cP7wKKSKTip1eKy1TXrR5moFZSq008iRtdljRWRYyjmOzsopddAbhHstnssxZ9J4jo8kyZbTp6uobN4QjstmwXccLyy8bCRlYubSzHyKMvEqSZbWA4S3dnZxTAUrNdrrPo15MBiU1jUta4JBwVaeoCN/FVWKh8DNIYtAay1U6zPdqaPyF/ZM12XkXPy2CmtPIUH2x7WAMUrK7IWJzJthAELbOTa/OemRZppd7qJ4+8mIKdCxYgnpaTBmmPBDZhWA7egtAdH4d4+I0ruMho31uKy4wPVpsIdRrCja+Q+rmaubX2PJuIPpEXNWF5ALkB5cp0ai8aCrzbTcMEk6BZwK5O5B1IUcGibVf9wIDPS2IPgcWgRAIoD2FpH73fNHmLKbqXko7VH4kZAFtleARR4PgdzW/tEKNTBXEijar2jObTgX/a4HnsXVKCDmiZvArzUCuQoKOVxZn6vVYqRDT0jdR0yZRcuDk5EKkkBqtA/OY22jg714qDpWfl7SaWWexMH95gJWcCtIaM4FBszkcYdhJOFw1pajFll0do5qTZX5aRWownTNIXWq1tOyW0Vuu/JuyXmZqWXhAYmUkqu+ADBwgCldqqabafxV17HReIMfGaDZcppr8h3JWRpiEUnwsypMGpB7+BWVmHMA5qW25QcKB80aivNsjJhNb6nCvs6N5AjAKrqvNdasFqtsFwd4fqVe3D98r3A6gaOLq8wlBX6AhyROEmGMuD2R92BnHdwdHyA1fIYy+UCO7MOhzduYL06QSkD1qsVpPS9pCVTBnKndt5aNW12AOWs0leyUWxfMATMlWFQa4GdDiyWgpQqShXXS+XksWuu0HrWTdUAXakPUmoSEJAsfoIxDGsoIpLCiwz0hb32Uq1sebegZNbG4m7hVlLe+qZ2e2ZUWMah8ohAP6fVyrE+EdiVJD2sXUGB3LtcrkA5SdXfmDCh/K6bzcTCtFyiWSgNcMD5ZZft6AsGKtAhSewYpNYRkczB4LZvXRsOp2gnyAFOD7I9rAHKMBRPl3KtTSWj1Q1ghlUyRvPut3gCEDlKd8Gh2nNM9xMNW75uWqyCITePTYQ+nyJcVXKaSVaYSgn3RZAxDjp1C80IECii3gAwcr3koNdYUsT7c3/tgcALh2tMmxv1nnjUd4q/B0vKab0QrXQ8ptO8lszswM7cHeU0oBdBTejDtF6OPScB4GDRYCKPowAEbBRdB4+j4GaFEA0CqHUAjMaoaTpuHdK/20m3avmxgNb4fJhVQa031QzF5P3WjigjLa7dRNOuHIbWrHY+jwGwShVNKcblVhcOLkH9rFSttstA4rYGBhqYyM8Y8mdbPJLFqNguiMcaGPO0+QHGwcIjEzH5PFRVSsDQKsTqgrVuCzLyuc56cSkS+NiyeAR4cti/DoKVbzjUD/MTa5HUkZCHcWoXAA5KOViwnEybBTcCkpaRtek6AifNDKp+r4EWuUbSZu2uqBqM+qjfjq2eAnjX6yVKv8b733cneDhCGnrUk2P0i0OshhWQGP2aAZIKwTs7c1y/fhXd4gA78zkOjhbouhkWawazpNoOg9TWYZDGziR0eho3U1FzB4srBQwms6Y17pEVQKSsPKOSH1rHYCSWcACLawHMSprV/UkQ94OBW3X5Wep+hvKzir5vLkqpoCugdj6fYbVaiewJcyfxkO14Bbf0GeWaZZWBnCCZcmFvmlXcrDq23qS0KLy/SuSzryFhKGNayZzBxY7CGCt60/0PLUxZqxRz6yQ6GQBQtCpua3amV6dWJAKZO5sl5qSdgFxUufhbAlBKKZ7nDcCFiTRuAGECTuxas2QYQt6UmE0zrpW9ZK/c2wqAmT8zapmKYNr7HBY3hmqWHA7uKNGmxlqS5aQHxagJXf01UQ4alL3Hrh4HME011fDy0XNjHww0jECJz6iNUTVLav0bgZQJKDIUb89HAAE0ucfe7YI93CtTVDeuG73L+mf32LviXKimwKaZcR3RDgh6WJaONOXgxsAonTuSkhdz8+dkZ1jyWtGkJPBWTbVkNUoaM/GzdXIGUTfqGwHgKsyqVKkNU4pYFYYiQD7GWoyBmWUH6NyxMBnA/OeNUYogVsZXQqq+oEO4eyYnDYwjZGVecYu1dZU94wpDo5jglpKqwFUrzjLLIXZymZrqM40tNWCtv9FSeiXw0cz59m4gdRlcdP4zJLMrp8BfmqY+PSreAxMDrbaDDuMebrwH3LRK2dZtXAyWUucwUNGscgJkZK3MUGcxI1BQWaoefFeFfq1IpbgcFBQhaeqsClsNdJRzAAh2crTv7AoMqyUWB9fB/Qmu3PUeLE9uYH9/B5QYPSeA5hJzsJswm88AMFb9AuvlCnXRA6jo+x5nzpzB7vws5jkj7+7i+OiG0J8K8a7rkLO8u1Z1z7HMEaqkeKeUvEAl14Kq1k5mtZQZcGfJ7LGfVqZe3CICkqoWKovGP2boeUKMnDtXRkUODLp/9TkMzGYzzOcdhn4NsKT2MpEE8RICXXDgo1qFV7MSZT01PiQZP4Fki42UVt1slN2NIp8Z4NB7i7r+lFz9TCwWy3FS4JJyBvcDSniH8WI7kqIqyDeFJ/IOk1dFD3MFZXDSYnHOM8y1VsAFGPOe+28Pb4AyDFK+GHAG145uczGPkS89CDbTiqmW8aQZKrUqkkoU5IKlHV7m/uSYFaQ0NXqmCRNqBNdcQazPb6Z90xJrrf7+qNnooMdCl1UAjICABrxYkggTvIy/Hi0eC5yN0PX0WQHUxGsiovf07rAR24SMn2cALwKNBgBD0GZsEZzEPkz6ZH2dApWoyVB4hoM+o4nETdskYSQ5ZTBV2LHjOXeYdZ2n1ZaGLeX9RMiKS5K7GQlOBiYgVKPuuoTZrPMYGE+jt4wVy1SLTM/cnEXLj5tAUy3aJsBcMexCPjKaljRvDNoAdMAL8ndlaIUztUKY5YNdwHRdp+MQRqVSWKwc0yBsoNUEQRMEDhx1HFKgK65p2ztukYK6FlkLjYE9C8QBwzA4gLR4BWbRsEe0Qu2nCTz7KGqQY83z/l2nTnMmRDisD0tabdWzTfx54Z1ecLImmEwya40c+6FZKmil7s31y2BwSb5logLgFikvODcoX1HLBANl3QN9j8MrV4BVj73UYXmyBOWMvbO3ggAsFwtcOL+PflhjsTzG0C+wXp8AkMP7QMCSC/IZYNbt4GR1AkoFwOCWjFLUFQXWVOCCnflM0mVrCZkkjGGoSB25KwiamSfWF6sxZOnHNqUSUEucFUgWpa/swKQddJddUSl2Sq/uxZwTZl3GbDbDzs4ci+MjiNVNXToanzIiqqAIqTlPQZbyr6ol6lOzcnBbrfYYEwmstZ6yKUYAa7kFsTQVoAr4LEonFOgGrBZoIk8XNouZ/RyK7JekMogZUlRHawPJ6e5qqdIKh1yaPCG0kgim2MSg3gdqD2uAMhQ5TVOnFAB8osg1FgKlOiYUu982qDFjZxyKFBEACpPkkVOWrAKvC2GpjSH7R0FIZAbyGNPMxwzMznqAAhQiuOm8WSzU6uJ3jQW0f7phpYACq8Y85Xcgd0JwcpaNErjGKjgBTwAdNmJdguXCtUm4oG/upjAR1Jh9BBxTQDE+fhwNsJxiAWpja5aA+HM0P6Y1hus35lE7QxALQNKTXXNu8SkWi2JrLQCUdVNqTI0CjZyzA1ui1g87SbmbdUhdaqnPygAIBMoNsMn0NtfA2K8MMeMyt6BNZq0nRmhpi2P6ANrcWdaQ3KoHoLGm2Rqj5+Yrd6MQyRizzY32ZxiKn+XEYB+3PKgxMRcggZZMiJO9TPeOaHSa/mqMU83+LWbMTlWtwXJUx1YWMiGsfv5T9lN1gC0dq2jKyOY9m+DEQN5UAYqWlUazrU6RgQ2NUPD1lUkxiyprH83WYeAzA1XAxawLfQ1Zp3H3JOeeLVbArcQAZhlAWWHYyUDO+LDHPg7zLuG++y7hZHGMWy88EqvVEswZlQdcuXIJ/XAMUNGS8oMD55PFArNuF6vVGicnRyilB9IgCkFV4VagsQ1NKxeLk/S0rKFZcQRWUDIMA8pQ0XUZO3NRGotp/Eo7cgp3Rt8Pzi/sZPnqVipq7vxUwahq3UFz0YEVIA1IlLDmlQcn2+GaXIGE3OjO9klw7dnz5IntfKXEWQFikB1W/4QINZviKp9JTSNSa4kscgegkrwfJJbhzDRyjxZ2CTmRS2MXY9a06hJoMqd2fpnIXHnGNDNUOm9uJFFgQx7cA7aHNUARbcNKhzeNm1FHZxgQYn2LthCnCVoATQOlgrb1E3gAWtS1EkfKwQJgeJf8t/AC2Imn8Vowg1PT6ls58NhXJSNnZONnu6up3eUjd+uLFxsy64EJCBYByLLpZdM2vyRhrK3rL+oaNutUfB8LelfAF/vlz1SQEYNUdSBNgIX32nes46zAKHW4zVNk/K3AVRwD1DycaiggMG1k2oEcNiexHxkJ7aBChmgo1icpgqZpgUhIJL7blHM7uZeaHt5cNYQuS5YNB+tTo6hmjbLgSmbWjATJnhlKEfdOjPVQjU/GrdkFLHUTWE295rocUY7T27i66LhqbQOB1t8U+8wSn5KhgbB6R9IzOZIVuaMk4El90iP3jFt8LFVO+HEd1YQR+qZiWWgNhMdj35uZeXywYQUUbI3dlyN6AtwEb5aTBuBDcLVKlA2K4s3njsCmfhdpPSn4NUBmwCvseAcQzJL+KSfSEXLu9J8KkHhEhAEc/WHgL5lPAWOXpoHlRFWUmdKj3HILHvmIizg+OsAtlXELPwKJEhYnJ9jb2cHJ4ghSb2RAPyzBXFQoJgd2BweHynslsNXKAhhobMpK1iy0lnHT9wPqoAGpqsnXCgyDuOS6WVLqVToZClhTXeUlAmrArAUac8uW8kKKMv5S2jEa5GXbpRaL8ELZ731dN5oQDu58qporx3i0rQPUJQkBJ9Cf5uFnpYkMSY+OimrDtsLfBrWyU8q2sMggKW2f4JZd1jFvWP2ojVloompMlioHBLW8A3mesZNnSF0GSLKVqqYmqxDR+DktGBrd3txA9INtD2uAwhbIZFbkiZAy7YMqqz9QNUNlyF4N0szdtpFrbUxH4I08f9DAQX06g1Fyi3+Jx6+HXGJvZIUUiEAezJZAPHiEvfh/xwCiaelR3JP/i5YDaUHrs/BSR69G4DYHOhuBaI1hR2YeTXZJi2nFk0pH9RTCZmU0xupART+LzMC1amaUAFDShJFbv2J/o7XHvw/3tPmXGiZSNh6eMUO+Ju06ZgUyZNqcZrAUKNizsYgwSSRahgSU6btS8hNDBdjAGQVDYkPAwFCaZmPWFre8JFIw1irDypHnYp62YFiLN2mnidbACMz0zJ4+6icVs/jxbdxAs+A1YdqouYb5tYPHjDK8wBXUgskC1mxc4hojn7tSK4bCGIZ+AkgwAk/2DKsnUStr6qfMRyr2fcvqK5XBVDylM9KlAT4TBCNrAoXS/6qlmjB3F5p9bzwDarW1irMB88lpsY2/+Gfkdnq/NtshQFlqV5Bm79VSA00JRyotCEW5QFKaASjPFXgY4DduBdljU4WKSLtR1A3Q4lBkfzC6nTlmuzs4e+Eczu7vYWDG+ZTREeHo6ACUCCfHh7hxeEOSFwYpGEdp7qfO59x5ETmpLSIWi1oIFYPvJdY5siMdhLY6DLWgL2qlMMA4wK1g5ho1egdacUlx+clZN21OnLLgFXvJ4joU6CuLshRx4Q3OUVBZ122UuaL2bg24VwYz4Y1Ge3B6J1YQrl8kshRmamtnrSaJv6kCJkCkQI811V6oopRmSZJA+qKyoPFdCz2ILnXW7wxIpUyYUVZXNOlR8azgXTrup4hRFfphSCxONZ4EVaT+lhRqA+DzHIURQSbCDpWrrBYVi/Uwwep1Bpp2YcTvKVb+TDMHBJBDUFu+Wh7KgKpZFs4QQp/sBFi1W/h30dzLHvDb0t9a48nvJuCbq0GagagMoIeDG52DaKI+zbRtsTanCXhogTKGZkxZ0zKB/jy9ZsPkPZmT05pXC6UQ9BzfNQGibeMH83v4XpiXMuvcAaVImrOmGregMHIQA5a0uWKnG2taKhcxecK1S4IdRw4rl+/p5+zspcXiyKe1VAlcLcFqolaVFgybNNhUEDgXs5wUD361Gisxa6cJd52uam7ICmarKxJdda0PmcJ5K4B/nvSEVAOWUtNEBGPWonGu8VPLLmIVwFaHQqbagEZFKXqEuwuEBp7iGhtdWL+tdLykg47L2zNDLVJCo8SaFWbAagSQMeIhdnAcdI6cseo8juiNyM/JaaTZAIcgh9pS1mGCEfCsiFqAYto80OWu0Sq14otuXYwgSQdrANvBRxhPo0OxODO4HblAbX3NUkOAguUSwL7EKOzt76FLhFnuxE6QgGW/Qs4Z8905Dg8H7O7t4uTkGOAMYIacMkqpyAk4e+Ysjg6X4NpLAGywKJq7DRO+yMxiYMsZteo5OmVAlzsB7euiWTRz5HkD8rXqERZddtArGWXwSuGkIMBdHFXc+imcw4NqsNyU0LbWYk0BhtLrHodb7AoDpDm/dp1YZlOjb5UlpOBTalUREmssRyIMTBiGHsySdcZhba2QHlGSmkzmxofMUzK3GSTGBtQwDiXCLM3gFdQDjY1OfIdl9WkMHltguJx7xWpJIj3yQhI+ktJpBbGmqVegHVmBB90e1gAlFnICmmZ32gww4EWczD3BCMxU70lsMQKArxwBzXenR8uzaE0pdfp8Rk1BKDh4CO+PfZQeT36G/jIJKiVhFs01IwwjGBwm9xVPsY5ZSE0HHgvwafEtQBila3vWdwNWBlwCCIlzTkTNdhS0Ve//xkgRxrUJaMZ9C0w3AKAobE8zmQNy6m5OSU4TtmcSae0ctPL1CooYAAqQsvi6CQwk8YOLtuMmFF++ylWj1O0LcbW49cnGCG5BjCZoQS7ITbsT07C5h5L7gM0aUkqzgnichQVFxnl0ENqAqstRZlAygJRGfVU90METsjBssRiQH9Pu9JGitQFyJkigI2PESUu6S7ChZkqEta21iJApUmnGxhXBr8VNCE0mjFMfbdyNdoyRA3DxIwGTCEHFjZ70CkC112gttEdbfwQ2aJqoD5a9h5TUEqcWJoB8f1ldG8lYasQkAKj6A1tl7MkeIaEnLxYZxiHBs1Ynqnqwtcco6nx7hFA1oKpgE+TuOFCrxXH12hUcHR6gqvummxG6ecLu/g6uXb0BIsLZs+dxeHiI3d19HB0dIXcZ587fiqPDuyUGBBXQ0vMij+1U7QagmRlDX0GkLknx7aJLQCIGZwEvdtxCyuaillU2+jJQDpgwFsueYz9ZLiQAhVLgcVAoa8dqwPeCxZawWjlEaROQkJQQxnV3mluvckLf94jn6UjNSAOO4n5uRxuYspZhIQBJi9mBIVlfpj8HugAlpA6NVwHwI2BURrApgcFialbrvhQMfQ+Q0HaFuLgKKriY1VbAp8froMlTT0it3iGIS/tvkwUlNOYWUGeJO5YWGE9sFBbb6gWY1kFolQuNIYAaUbIiF+aEPAIgYoa2yBNblw1hTBuftK8MtYZrLAicqR2W7pq2aT1oQMX5FunuuJ/m0GgUFyPf2LktItBkJMbcq1ZerZqOG0GKRdjXUkbjsLlwM7cJDIQNZd+F/k0Frb0X088m4OTU8Ro4CADAD5mkZlVz87/23AQCKyCFCYmikfZoYJR1vtIkriNGJjhd2ZzZ/Me1IxHKcLOyaYCDTh97CqwIIB4xdQ5rYkOEAnJAn42mJWVIqu18NnMXh4G1lFPLWtM1qTVmt8Er50LBUrQitCVhoLK6ugbAA4bJ3VjNdZRAVDCA1bSu1pZJBpDvsUgP5uYi9vVoUKYVB7S4i+ZSsgfoHOls+foqCjGQFd2fAlgZXSLkLo/W1wSlnQNjjLpCrCZdmiOTWckk7sFK/BuAkg6RC97/P3n/GmtblpSHgl/EGHOt/TiPfFVlVtaDosoF2C7MtbEbC7UvtsFAdWPLF2Qs+Y+N/APJkiUakCVkWTKSDTKWbP/yP2QQtE2r1deoacttCvu66l4XGEgooIqinllZ+Tp58jz2e6815xgR/SMixhhrn51VWd1Y6vSdqZPn7LXXmmvOMccY8UXEF18058rLikVNWdUpJMg8tXUhRRqoRUSAqE+4Nhu1e//sIKatBe4cHVk2uH/vDrbbM1QpKGXGydERLs5OsN1c4OT0CLdu38LNwxvGYWAC54xSCu7ff2DddN045uzASxnEqTkeI7HZqnsA8VYd7BorqylDhC0aoxYdqRCspuzz3vgh4g6D009trjVkq4BHM4jcGfE5YcAzt7c1PZLcRdtMgC3D1KOLRVz9npsMBXmKLnHjRVrJrzoA5GZzEoeDMswtsegEu3icDAC1AUu1yN94RA8c1e6QB9eGEJo62j1237/Q1GidGuFLXXLvxSM+laTCSMo+nzjGiAnkBOGixmmccgYRI+cJ282MN3u8pQGKaIF4LhM+MLHkrEu6h80UrZFYywcPh6qCIU2HIlIcnWAIQ61DxCNiAQGIot4k8psdyQY5S4f3wzbo2AIJoJBobl157aEbEB5IfA31d880yvJ29CQijEHx9dyrbIaQ5XjN9u8rDfOG99na8deGKhgKg+/vzWkcv6vAQduCb79rQEubV4cYeTeYcR3BP4mIAAss7TScP/5SeAi3bRg+cuG1RDTKCbGJYiy6sBoRg8f8siqaErQaHwTB64A9wzrobjwyztL1TAAXMUIff0uJjOCwDAB0TH3Yu4mtI7NozMBdtCq++8YYJoomgxi8N0ZmxipZqLw9+iD3RgpTwngyVFxx1gXgWsg5LpDCMIQRd9PsaTW0eUSQlHbHV8WatGkvu42UUBuABhJ8Pg9TjOzGoR7tjLnRnin3sU/wCEPx7wFDILY5N6+9r/VGOvXy5KUUKBRTnrC3t8J6b42Uknv1Dlw9whVaFLUKEhlfaZoy0jS5fLilAqpUzMuCZZ57hVZLNdg5I91159WX8PnPfwaVt7i4vMSzz74T3/BH/gRUfB+rQOhfkIO9ujtS/Sf3ylualAEh89ClCi7Oj/Hw7st4+aUXsFpX7K3XeP3111HmBdvNOebtBeZyjlpXOD6ZsdmeY723h4ODPWw2M5ayIOeMOpuBSilBi5UTx/pIyQTFVLSJghmASgAqlKN6RSK4FAgcPbJNUDGQVxaLGpqmRwdeLQWBSKfJDmAREScHu4yApyGtJcVk1T4qCBVgG9pIVVqUgIndPom1tIBFXmosTyZAk1+D2HpDpDJj94f9m91GIEqI7Z6bizzskcF9s+iF9WYK0MdNx8n2Cm0dzw2gCAlU122fZZ0MSImiFgG8k7fZE4Y6d4jamrTeSXC1aS2KaUrY3z/A4eEhmBmvv34fb/Z4SwOUshQPQbmPpB4+JgvBC3fvt7sJ/eGODzX8y5i8vkMjynNbtYqzmYP8xGBUVQx77k60pRn5RiCKq7dJ0SSz3VCQBmAZ9+KGSoZIgDOypeegU9rt8QE3mLHpS1jtcWMaPML2gl9PkCBDmEzVG9SpRl0IAnTbpp8aWNnN13YwQtS9lHGjVXVPXNEMUTPs6IBmZ0EGWJEABCMIdPBx5f4idQKK6IWBEeYQcDKQ1sGXNw50OXWba9zuSkf+Q/NMxSDsEBGK50hELe1DbOqnDbDsGPQ+H23j5kZwBSLSNzVvSNWMSK2CSkPaiHa/P6XswN3mTcoJ2Ym5Jji1ArE9g1KrA43QgLDmnN2jhJdP92fa9YB8zolCiMxI+OtjJCRUJ8fy3zZr3DCPnItWSebPrYGH1ksFHg2zqEZ8J7GnptiauzEzdMWmJqsKmUdOkOw8h77pp64a7c97WxKkVuQpY29vD+v1BErmEEix/Hy8X2RCKQXLYqTglGy+iJTB4HHYW/SO1AIhda/V+Ea1FGgtuPPFF/AbH/0ozvUEVSu+4YN/FBWKmweP451PvxfZPw9VCJORFIc1unO0YKrraziIVZ1xfPQaXvriZ/Dw9ZdxevwQe3sJN2/exPbyGAmMZXuO8/NTaC04PTlGrRXLvKDUBavVPm7dvAGihAf37kNhVT7brY+Lj3P2rt4N0Po6tGiDrfGkXmrsRjEcDSIGT9mpcKXNQ/YqH0BRdYZUAovJ0Pfbji7SANPU9qa2h/hatq7kgqaP5ftXrF0RBTkRN1K1KrG+Yx9F20MVnUBqKSdxvOUcMk83RbTOzk8uV/MoR7CtcfZ0cHFFad/vwvnqjip3UIrFfOVCKBCX6iCktLZ9p1ZoAnQosw+wY44l+d5of4cS72q1wt7eGtMqI2Ubj/X6zcOOtzRAiRJLAM1b5SQtbM4yMK5p2EB3PPYABzbRk4jzEcLjDnBjyJ1AkBRkV49sEHakxgF4D53dyEEsuHZJcd54q2jbPEaS47Ant8U78jnMmVZHsAYdzAgNSHm4lNh0O69mWGgtGoOmidFBjfZJCAdlLZ1ozbXMcAz3TtgdBr+/NqF98Am2IKU625wGRrkqolh1J10yvIcVGDso72y+AUbQPeD4fPSEiaNXDVHboCOI1ghnpG4c+xyKdKFV/vRyvp1U12BoiciiZSAIS7svA3DS5pZllToQbdeeUuNhNL4BC6r3HYlNu3v9qZWeWlMxj6hkK0kNEbjmpYGASgP4MdCfOVt6gszANl5H3JsG1B//DRjYZ1ijudIqkgho98bE7f0GkNXGxoMHZKzGHkWyQd0ZY5tPrpTpS5eDxAs0fhUzuVdq70uZzTPOtk+0HH/MVxr5GECIppE7BdM0Nbn8MFYR9QiODydCpgROhLIUJw5XByiuMeH9TGjgugVXSURQi3njKhUPXn8Nr999BfPmHEobVC144Qufx8Ojc/zZP/s/otKChMl6WvnzEdJ2Xy3F3dZUOCLmsdsgC7YXJ7j32ot4cPcFHD98DWXeYLtRnJw8sIja4p2FxdJcdZmxeERqe3GJeV5cg8R6LwFqEQ5FW+dSBUWtNUQ04GQQRNlWla9Hq2qy5x5KxcoGYETVOqqrydATAMoEqtb/SFQ8Ec+ulOwAAdVT5gF6go8GNI4exXCoRXHsEjyqzP19Ptfj90Se3oCVAxOZ6JkzQCAc6RS0SHlEtGUg6DKRRWF8LzaiL7e1Fo5MZiu3nufFo4gwRBrAenQCfC+3NZgtXVzNka5JwASkvEUm9kolhYpVLQVXJpwI4zFbBaoMtmNvNWGaEpayYHu8AVH634/UvbZNAG1jU7VNLY4x+qAWl949RxjB8L4ILnceBrC3uA7vwrlNDmTccIoglBiZk4GZxu8I7883rGa0e5TBKhPiOq6i49Bv8Dv1ew4+Q8uPDp+o8RXhTWLXYIpIL/bxceCQvB5CpupevW3S6oYiUi1o1SwxMaHa8pjxjPwLLArSNA8ccAU5iyPdENdp4+M2D5HLD1Azpt8IaOmZFnkYxqKlsHS49qvzA5Eqi9d9VgxtyckjcyPI9JvbnSPaybFX3xnPrX9SoSXIaXwl/eP3ir5RtohKIEPpn1VSmNwD7dwnJyvxjUoRf1r9uaH0+YpozieNfNj6Z1AfS7smatcVcxOEFoAkpjbHLFjlqbiMJnDYxgToTkNbL0NVlkfKStklRIuOAEXbvTNlROJoTEv2/YCcRFqd79NP4VzN1lF5Zy22qIr9mxMQkVYj9/Zrb2XSvLtH2WOzaqwqppwqNbrt2jOI9Rrro0UdHdCcnR7j05/7BM43J+CVgreWwjm59wCc9rC5PMXD09fx+P7bMfHkgKe0saUA1aI9KgRpJa5kb8Dm4hyf/9Tv4NWXv4iL01cxz+eotaAU8kgaQDCBtLxKmJel92iK+1wWnJ+fI+fJnnGQcYd9wlozVIT8un02ot+x7uHG0B0lhRc5GEAh78ETr7Q5CbQScfGbj/EttQJRUeTnB7nekc/rDprtOYdzIqIOWgNIEBQFKGw9eEis0lEjAux7e4B+KEI6olS1hps+X00qvjaNGGKYKJrY+SJKOK519vVWl2IR7oY7tf0QFcYxE4N0C7FRS9lIy7EKraePoLAic8KUM1arFbbbjav++lf4PaUoDScCQbBsN9gum0YmT5ybw/1mjrc0QImQ86O/6Bt5TER/qmiF7QB2XfvUWKnmzff84s671YlDvmi4EVhhE589AOm/Iyr917EI4oxhVEVhnZEtlzlGBsJDHl+LDfqq0FnwMsYrvj4MyM3bHS1553r4p5V8UyPfsNovbCExOwekRxIaEByPeF2kbRoDiPc3eD+MUE/dsfhhWGj4DLXXmHrSZTdqMYARH/vEqW84IISskuiQtmoj39M3DVQO0ZF2c9TP1jUGAtzugs3rfm5lxc752H3v+Ix7lKJSaI70cWEO0EbNY+9z3HqxVA8jBwk6OhLHmMazkareh6Z985C+gRvP3QqwkSPVRL7SyLGxHHZy0l+PzrWR3EnzsFcOtDLpiHhF1Ioaeh2uw06k2K0MU1hZufkFHh3xiicDzmjXHxABgJOqh3lP3SGQwZExo+xdSdW6LVsvnV5C3f/2ii9xXk9weQTt+elQgRdRlO3mEtvlEioLnvuN/4pPf/J3IJhRdMFcixNrge3ZKT7zB5/A0fkx3veeb8ATt9+Gqa4GccNdojlapZkbKyJQrTh+cA+X5w9wfnwP5yf3sd2cosac8CoyEyJTQCpILX1GLJbm8qqUWgXb+RKlzlYFIgXRMTj6XcHnpRSxyIB2MralqMi0iFSRp3WLnphwJpASoS5WDGFvj/0jwD5b6hI29lCFCLUISOxPEnL6OlTtAAAp8mQUgii5j4g6QKhmDswe2SQDCWy9DU7dzv7ULo+H9UO+fuHEaXEgzCCaDERWQVXTelESkJtxEYCciB7rU1SdraMNKJltCl6jrxXYfm5TQNt6J9/HtNrvV1OGECDEUPbqJR2j+uoRViDnCaVWLFsDxnsHBwYKl24Tv9LxlgYoY8i8bfph+IYQ+1UjTRERQJsPPmk7eDDJFEH0KWlAJ0L/sZlR9yAjqhI/JPZGYxxgJjZq9HNScC7gxp0QpXYUCLl/YOeo2r1Sm2x9c28emO6ClM6xueZoKrnxrcGDADrrFm0hheEg90DiW8bOve26I49M/ew2ft07Yi8XJaBzcYI5Pty85VkDeIRxRcOg4zCZ5+6ghxWug2f/a9Ex8c9Z2HQke+7cBo/XMN5cf5GipJHsfP3hwF+jBkxHcNL+zf157/CC1LUb0IElc3h5/lzENmzb4CKlwWihWG/nLvGMvLS6rRMJD9qqTuJZ76axbFbUGoa3S8WHOJbpVjGA1KJyjF4KTO7eEah72iKtEmHks7RoSdxTGqsLWpxwOEIL5spyIUsSxtoc94VEBGELX7f0o8AF+uwzzbBcWVPsYMOAzriOgxsFN9JR+cQNhEUEQgU90+uCWrHhq6eBqFS8/uqr+OQf/B4Ob+7h9OwE89kpzpcLLLpAlq6aWzczXvzC8/jSSy8CQrh34y6efeI9ePz2E9iNtDqw95QfqaeHQdCy4PToIc6OX0NKxrvYzKX1WSH0/caapYZhNm2e8DtinMoyA8jY2zvAdpu9wsbKjW2O2CKxyG78HIbVDab2VEaQtHN2Qx7CbBIdqUO5ueEFu0d3EEXsGac0+a/cVaG+l4UzQDSsRbLUB9jAgCdgwEQuDmiTQpy7p1pBYm1SYq02Z85ntkU/Qx8lnIRx//G9swEO33+BBqSiQox8/oPVTJdhR8RZ7GptPYYv1QQOvWpQ3OmM9hyq1uixLAs2MB6bDiA8QHmv8jGahSbXnvGqzmWeQcSQ+r8bgBKOgHvkIxAJvHKdgd7xsBwDKzy8556re25jaiWe6E40Y7geap48IaqFCLYQjBDZZdJtjtGVa1QoUvOeCAOCH69/HINR12O4zzHlFX9fB9Z2OBHov1d46Ne5IuP74m+NPSTuwf8WBLHQekqE4cXQC6JFSQZjDQAJyfcqB2f+/hDmAsIbGq+j31t40P06vTsw0HLV44DGdQOAwL2vcU4BO5GDXZByBTEaovLnZmd8xGtCh5wjD2j0qtTDyCNfBm44fU9pYHjnCgJN+o7cJOVdnr86IZligxuMbeOPaE8rtE2LsKPZMn5m7PE0AljjVoxE413tg7iTMMTVK00QBj+qaAYPLTbj9sx2roN276F5DD5CVyIhO8ZODdlyC4bG+XfXVPvtIw4PudJ8V36lMPR+Tew2SUUc6vd9oOyAHoLU6iJYxp2opUDLgr3VhIvX7+FTv/cC3vv+90JWhDpXWy/VgBanhLpUbOdT5NWE5z/9B7h160m898+9D1NOTaStrbeI2hEhsWs7JYZuNtC64PLsFGcXx1jmrZO0HaQFPy3GILr4wsiutgcY58OqSBTRS0e9GgxORmYGcs6AVhTyfRf92fkUbGR2FVMzIrK+NKUIVKop0qoCxMg5WRuS4O+olxGDPA1nTlGsp7AHrZNzgIiYG06ihwO8lMga8NXSgEjM00gNVm+AyMmitl2Q0FzkIOlrIwH3Kr2Yh0b6NTJqdwri+nwNDeT5UEunlrLV1gq0pWPQHcIW/cCwl3oFSGITgKvVmoJKVSx1gUx2XglaAw3UAQfqDGlRSU4ZBJPsT4lcWv/NHf+dABTq4KSB7itEyTA2AzjoGw05QjEftY9feIVXznH13PB8oJKj1V6x04yQeJoE3T52+07D3qltYcZm3PhqvjFHamcEOG90XGcgH/l9/JtpvKgdMEL9RbTgBVMzpHHBo8EIw5RzahyTnQjNMBBjSL5v7IO9HZyOqzyS4Am012V3PLr36ycageEwfjvG0N+624MmFv5w7cM42RdIW/wtuoBHx/8RoDkCQ8AlomUnwhSk11Dw5cG4dIAWG+t4gfGXk1+1l+w2cObgpcm8D3N9NNQj4A8Oi11LBxvjPTUgL7vrqAPoIN9p01NpirgDULLL8HU+jHd4nRFtiKdAdoFxIe217hUP4AW7a+nqutp5/kAwbdsUiHC5VFOsTu74gEIjhq3rbrWUUNyCwqJ6KtU8S7K29CqCBA/Dp4xFK1546WVorfjA138AZ5cn+Nzvfxrny3knT8M4AiklLB7lSGKK5H/mT/1JHN5c43B/H0wZgLY1nHKkSoCcE5gzEifI5QqXmzPcff1lbOcTbDaXSN6DZd7OUMw2fKQw0gI5JyPBGqouUFi6JDmA2l5eYp63TUspHoFpUtlcniayclag6W0QjJPBNJDY/f+1wtMd3seeyKp5yH5PDLB4+tMmBaoL2AFeHICIMGJI3wSMiGijk9/JU8rOESG1tJR4S4TQ+iAmE3qMkC5Rkyew71Kbt4ND5DMaPe4T6wzo0ZXuMLc1P6iP9xXd9+QoGqEgdtPgLAQD3f8dUfsoKhGfsDb+BlZKKf79fh0+15ujAGCRroXVba02MvibPd7iAEUH77aTCO2X2DEi8f4dkAEaZU6wszOjV+K0UzUDB+zIBPpkig2a/OHGJhSRg9Eo7UQuKLgEdq6W/47vJdq5ri8HOOK4usl+ufePvwkgMVg1xMKP940TL94SGjTj93duxaPaH+OhrZTZNyAd0D26N7z7/bGQhvPouKADDHaiaoDTGMvYLFooXvw6BnwT4c8+VtSVGSm8Eepg1B+VBAl1NPR+2d1rxy5RGc5hSh081wBe/g4iAy/E3FNsTF2+vE1xN1yeNmvGHk7OuwLixmPHWPMwHsOauhqZi6qz7tnFcgnA108SP4uIrw9/Dr6eu9z9GNkJw06DqGGPnF4FRvbwBo4MdkES++/VDUdf3+hARxWavPmc9vQn2jMhjDM5Nmfyf4s/4zBcxp0P4J16j69VQsoWj6/G8nRSM7AsBUSPQS9m/OIv/VvQTcIf+WNfhwfHD4HTU4tOwCIVSzESsTqh8smnnsb/+bu+C1/44gu49+Ae/uKf/27srffBlbrhtfBu8/RD92aWGTkBlxcXuNyeQyHYPziAKLDUGVTQ9sFxDbX55gCkjRt5WmFnLXenIlSRrYu3ravgS6RkJaocDgPgUUZ2QX72P6URwiNVnKhzMpqqbhUjmatFQSxNEc+e2txj8lJv115hjvywGshyACaJIUuBqPbnWGoDAuMe3PyHNsf7/ImtSZpMAJDJHNtajW7QIjBtXwG6blKbiT4zuTk2Fjm2Pjzd4ZQ2l5l71NLePxQcxD7PgIm9xdNTQKXzzIZjTPcgUsteUv3VVPHwV37L7vHRj34Uf/kv/2U8++yzICL84i/+4s7vVRX/8B/+Qzz77LPY39/Hn//zfx6f/OQnd96z3W7xd//u38VTTz2Fw8ND/JW/8lfw0ksvfbWXMhxXohzUDU+gYbmyWY3bc9sEBS1cZhtmZ5JHiWkYTMsxVkTBmB2hWxCApc+iq3oOwG4kQIfXYLcQyg5tw2sdcQdv+Wo04erP9pr/cZBE7JtRsuZtnAg5M1IiD/ma0FBOCTklU1/0UWvnIpuw7Q9ZKjsxkBKQmJAS0Jo9Nwvg4UwGKPkf/yz7PrMD5Hwwxu+N59BRfm0/g9SMAKPpnSiZAFFrNqe9G3AroxXpr2n/M6Yz+vtqM/rjhT7yvlobkTvKRK/9ewRBip3Px/mssd6CUguWWqySQiqWWrCUgrks9m8pKMOfRRaUWlHE0gb1yj2Lq5ZKEEhVh7FEMzIyaIO0W/YquloVVRSlevO/6q/FvyWuv/rPZkiLa3ospTbjWsXWmlRfj+6ZdyG6cExq0A6uPcII7KzxYT2BLPVQ4SRVBEAaxka1q7r6ve6kv8Q7JquajUiuE5ST/UmmY+GuPygl5JwxrSas1hn7h2vcvHWIxx67icdu38Rjj93GY4/dws1bN3BwuMbB4T4ODw6wnlZ4xzNPA1LxB7/3O/jkH/wuNvUCAp9jxcqPY0yr1PbMn/vN/4qPfORX8MUXPo3jy7vIBxWr/YRpPyHvJaS9BJrsmtnBQRXB8ekRLi7OraTZ+Ui1FtRqkREDGb03i811i3y10D/MOIEqmK2aidj0mgLM2fVXSAXqoqiLAxWC7x9GulW1iptSFizVqltqFdRZUGZBLbbxG8FV4CbWp4d4eXv197G3WxhTgQEm+pof+R6cLNplwCbmk7Y9VYbvKsUqmUBoxpvU02ceGdWq9ke6iFotzuuoaCqt0ARLOw16QV5Z4/gALcKogILBKWO9t4/Vao2UJ6xXe2Ckdq5oLGnVZVZ5k6eEPDHyKmFiNnVpkO/j1iCQE0Asns4M5wyDA9X3wN21qG18VHf8hq94fNURlPPzc3zTN30TfuAHfgDf933f98jvf+qnfgr/7J/9M/zMz/wMvu7rvg7/6B/9I/ylv/SX8OlPfxo3b94EAPzQD/0QfumXfgm/8Au/gCeffBI/8iM/gu/5nu/Bc88957XUX90RuhGPHIpd7xnde9fYVOwEu/tcC8IEgcteG9/VfICdaIaXmsXkbZ6oX4z0c7/hoUNgZrwkuobLEGBk8MzH+x4977HCo312OM/Od43lmO1eO6ExAiAxOXfcT4qUBvrrHvEYx5hU2ol2ogFhIFMvxxufYxumAaC1m8aut7LzzBykXuXhRGqgGTTtYYLrQvzjs2jnj1sfQp0jZn0kZQBtkZfxNfOc2bjZGtsddt83/M0iu9wOD9+M5Zst7Nruzau1fGO+dkMZxik2xXHM7T0dxBPgnugI/a+mXSJyQD1a0rxIj8C0seigvAEmwLz9VtLr3mesC1x/XL238ajjc6IOn2mIBlU8+vlHnAAHwwRqhHj158Eu8Bfvz4mQc0ZKqe11Vj69tLUjUk3hExZOT5xwdHqEWmbo6TleOT8zwFmopXh6ZMv+l1cJD+49wOt3XgVI8aUvfA4f+9hH8Of+x2/DkzffhmVZQCAsy4yDg1tYrfZR54p5s8Hx0RE+/+lP4pVXvoTt9gJVF1jfHQGUhxYMbeiswooIosWrVmoP5xNbtWPso1HLrGqpkWqOjaI7kpZ2svcFqVIcvCaO8m9xAqYa8JsmxE4T0XXbB2KN97lXtVeMcYvIdTBs4CIj5eROobYy/ZiqRS0dtCzF6SA9nUtEADvvpj2gFqbxMejRaqsCowZ2iciIs2SgSs1b9g2BocrxsO2U4UDCRBdv3LgBKHB6embqvdUcAbZQVqtmbC1MtHhlFEOFMC8F8LEJQCnOGwpOoW1TfR30CNNVexV7nbgJfPNxka8aoHzoQx/Chz70oWt/p6r4F//iX+Dv//2/j+/93u8FAPzsz/4snn76afzrf/2v8YM/+IM4Pj7GT//0T+Pnfu7n8B3f8R0AgJ//+Z/Hu9/9bvzKr/wKvuu7vuurvST77sEQXQdWfPu4NurQPu/7VeOgkPgGfP3251MMAPoGG4uWBtKpT8JmuHjX6AAj0NndbK/75h1g4kg2tVBFR/c2MezfIeve9UQ68LhmsB4ZHwBjIc+O0SXq+3xD81QMzV/lh8T1OUq3sXM+RHgzgJPb+uK7ztA8YlivGc8WbQoAhMgV+zWHB9z+oBnKnXNqH5fR6IfxaXMr1CJ3xuf6+XNdLlbEeAyxab6RgRURCzk52BjHPzQJ3ojnMrxwPUBpIMQiGqMWCRRXIks9FGyfGcP9u78j9IhknK8ZElArDaUr14GWGlB7XlESjF1Qc/V4o9dUjdfVUxG7YLEDFK8s+goOA3v7+EYGH8cC2jhRxISKbA6RWlnwdt7i81/6PG7evIm3P/12yGzAYW+1B6aEk9NjzMuC1x6+hlfuvQJw6E1YVCYiTmlyIrT3iCIlLJsZZSmY1glnRyf4/Y//Fp66dYhV2sPl5blJkN+8gXc8+148/fZ34fjuQ3zuM3+A+3dfw9H913D08FUQL0haQVJRZ4UoN6AgIacOQk5k0VD3l6t3Tlao9VPyqG0nQPeUHmhsZ2F7aa3iwLda/xswUKtHVbx/E2XwKnrjCFirq2lr75wtPfLGbJVwWkIZ2eZPVPtUcVl4wKMK2VOc1hAvEZoOkgEta3VQYZGV1okcLlugsNJzWI8mdYBu6e6I8FOfqYalXICRG9/GRCgTDC77nK+Axf90x+ArW6uNy8tLBP+NEyNlQipWhk0afZ08OutRMvbSbfezWkSRUsJqnQEk0FYhXWHIRRcrokWKyG4ftrhfs39e1cTXrczrjz9UDsrzzz+PO3fu4Du/8zvba+v1Gt/2bd+Gj33sY/jBH/xBPPfcc1iWZec9zz77LD74wQ/iYx/72LUAZbvdYrvdtp9PTk4ADA8bediodj21OHY4IOOOSWjEtdELoXiDdvATG/TOJqTjCTtgUVVUrQMwwM7370RjdoxZJ0PFmbn9azgHoU0K/6ER3zB+51Dp0StGIpKi2NXZiHvy6AR6kHRnt/frHyNEbZnFxEcHHqNHP95rk7kfvhe6e5+j9z5cYPOcG9q45hgNobQSWue3AMPz7htmL/dsN4qGTt4AXMYdB+dk9xk9+t7YGK41oG6gG0FWtXUEtnvtkvwq2vr+xO9lnPPtH1fGZwRg2B3bnbFQIKS41Q2tgcUBaKh2/gksIhIL69Eo1+59tjntG9h4Xbuk2niOPcLw5Y6vxM+KTRnurSo6sOz3YdU4MU927oNGEBfp3uFaFVCO9SWtDB8JKFTcK7XoyPHxET71+7+LJ598HMt8gqP797BaTXjXu96JJx5/B7aXp/gvH/kvmPYzpnVGMdMB42aY4Z7nBSLSIjLM1FIMia3fzXo14bGDQ9x7+WUcPzxCkS1SIjzx9NuwnjJuHR7i9OQeLs4e4sUXPgssl0goQE6AZPOWqzcnFNMQUS8fjXmfIoIkgZsDjFJLZ4ZzQmycFCKXuM8WVZgX8b5Efk7X5ZhWCXWhVgkTkY+UbN+rVRtJV5UAofFxoJX8I7ppG+AR0RZJqaVA/B6kKuZlARNhteqVPfY9nV/mLh9AhCklwKPBjHBSbZ7XKm2+jCn6mDessNR06tpBRGR6MwGkPUoXdRvNjLWl0zs0b+YtIIr9/X0c3lijnmyQJ7ZojMY6rT5fYPsKBVne9VyErFqqFqRKZm+YrUs5CJSsySP5xtrXfl8fHcgPy/vLL8+d4w8VoNy5cwcA8PTTT++8/vTTT+OFF15o71mtVnj88ccfeU98/urxkz/5k/jxH//xR14P/sSOQW8bZ39f8C7GiTEe15sKn9Q7wCcMQdSgA0GLG0HL7rn7v3YiMcPD2t3EY+PuRl0cVXfVwoiixAQAjOh1JTUTkRKi4TMOj0fvc7T9Q3nnI+PRcjvmSYRR1tpLi/t3xbWbRw90omy8Z+RYIAy2A5rek+PR67hqUHH1PeNqoGiN3sdchhXeN4ooc+3G1Z5ZfJ+Pv14BHFeilWpxo7aBxcYyzkt13lLDxI9E9Hx8rt6XEoBuFOjKvFP0yMbVeXBt9OQNxrTxrmq8bzfSYtUH/bUOilyDwUX3YgzViXlXYFHTXWiCdnFdw7VdjbbEoNu/nfyLANMdKMf62D2GNRbP3h2Uq85DSA7sDtnufBzHw6pCtLWYP9ucYFkW3Lx1ExNlN4qAkoA5Y3N+iS9+8Xm8eudlfOHTn0J519tw/8U/QF02mPZWOHrwdrzvj/wJlAIs5RK/+xu/j+3lZdOpYWHkaYW0tqiNcUMUJiTIti/C+J3racITt28ilYrXX3oFm4tLCLZWwZOA7TtO8cqXnsfJ/WOgbkB1xunpQ5RqcvxQgqhphxTvjgwO3Q4bpkyE5CJDqr36TEEI4RADFxZ6SDlhlaYGrJRkUD41kLLOE2plzPOM9TRhKwXLPJveDysyGXcC0JZitDTEsNOS8epAsN+1Z+d7ErHzsMTBjMsjkPfMSmx8KntzA1rkEQGmBFEDuylni2LFLHUnQoPYa6PjzS+jH9cADHwC29D1TwQfzdqvJBC7plCkzVqqy4TWhNh61Kki1wnHxw+x3WxRokxZ1XstqftdBkqi6weRVXSpwtJOoi0lWGvFslQoGGlaAQ6kpIk92tbbUjoRPRxA/RtlJK47/ptU8VwXWn4zXs0bvefHfuzH8MM//MPt55OTE7z73e9GR23XeE10/UBcV71wvZfbT7TrvRvSNaK1b4ox+PTInmYGi2xzEpVWnXI1BbD7/ePmSlcMf4SRw4AOhg4dke9suKORxG4KSaSDudiAr6rzBl9FtbbJCP/OJufvF6Gt18dgOAcjQ0TYKRW84vUDADcgeD1Aie/G1XG7Zt5Z9GT3Gtq4YAANGi3Ld983mn8rFd/9OhVnxA9GTmnwsgggGSJRfi7HLzv33gjQLSw6Am4FIe0IEBJol9cUGghx4aoeyr0i4PcGgDy+r5FSpb1os61Fs6hVU4jLmts1Bcisb7immqx6pPDU4cU1kbzxmnZ/lp3nFhu/jezw7GLN+P9UQxgQ3WgEaB20GZgpCiN270P0CsPPOSoSTgJMwl8Lju7fwdHREd79nnfhxq3Hscr74JSQSKGy4M6rX8LHf/NXcfTgPh6+/gJu0BlWSaDF+Awn917Fg3v3UCuDMmPenGHZXPSyVDajoTljygnbeesGz1NX1SIs00RYrRgZhNOjEzM8tUCoQgQ4PT7Cpz7xcSReY3t2gZwZ2+0pStlAFC7G1cF2by/SU6MpcSOkyyBXnzgjGuuJwlJSRUFC4Eqo7GkcsbRdFYUUBicTTlwlgnICSQLVAlbBlEzkLWfCNCkmBw3K1rRRaxB4B74dB+ge9k0VV2ollOI9wMiAdWpKNTC2ak0QinSWpYlCoTXmWHGuFicDEDll1GXphRfeMiPmF3RIWw7zyfY8oKt2u3DlsMdI8XMROgjwiJElgSzVJbViu92CXeWYoCC1qAcAcyw9jTlNVhTB3J3KiESpKGRRJ19bBM0KEfo6vw68j069anDfvqoAyh8uQHnmmWcAWJTkHe94R3v97t27LaryzDPPYJ5nPHz4cCeKcvfuXXzrt37rteddr9dYr9dv+L1XwckO12E4rv485p5bZCEMV3sPPOxPaEN73f4edu86kKJOlor3jWjyWjAX391k33rEpp3EjQl6dMJeC8C2SzZu93jFOw0v1r+xfX783Dhuu2qBNjjqO7116QzQ1uvjx4OIIG8Q8o/nYU2/6htO5GY0rjGA4+uiHZC2zfUKWIp0QZM83z3ZMA+iXWHXtDVPwT2RMaIUwkU6RK3sE/3/11x7lBRHd+CYCDubQNVu5NFTcTuDRbQTZvcX+6axc49oAmntfNSVTB+5zohGoQOF9p/0+TzyUMZno3V4HvG8Gmy+fl6Mf/dbtNLJxvPCsE6ufn54FjH22t8AgqVC/IGidXse5rqI9x3ZuR//n0sKLMuMy8059vdWOD8+wtH9uyjLOR5/5hncunkLjz/xFATAiy+8gE/93m9huznGxfl93DxMUN1iu5kh2wIQY9ksuLzc4PR8g7OLGRenl1g2WyvVDaOsFagFOZptqq171uiFQ1BWFKm4mDdYSQLRykxYNb2W7eUWdy5eAsP0dRJbP51tqbaAKIVNRwqtFrjBB0CJnQgMLLM4ODURU/ISbeshJI1cDzUwwk64lFqsAqlYqe7Km1fWamXDeUqQqkikyOuE1bRCVOZACxJbJKMm8kodaZEJrQvqkjDwzkGgoYTYiK6FbE5Z1RAQud7M1u27RQoViKQ0ef+gWitIGdvNFjlnMNsuMRLMw+lsisM+laxB36isSkBVSOxNPm+jmacBmAqAwUoesTOnJlJGnRcn/gwVlEJDxqqpRBUoBsYSCOuVcWjg11hKQUQYQYAW+/yUMpCNwxK7WWtPQeMY2/0ZqdiquqwfFK5vT/MGxx8qQPnar/1aPPPMM/jwhz+MP/kn/yQAYJ5nfOQjH8E/+Sf/BADwzd/8zZimCR/+8Ifx/d///QCAV199FZ/4xCfwUz/1U1/V9+1u/l/50GH0HiExvgFouM6Bvw6A2OvuaQznD8+jy8i3N3RA1Nxe+xmuRTIabbuueMPupm+GNsR2rsSN6Lr7GQx1I11QM1BXozrjuLR0jFrEYUT2Uf0BAGT7bDey8f3j9X2lqBqueMFf4b0aZFug52kHYKJ6pVpnHDcAUZLSDbCbTe5z5ZH0CVwmnwh0JTp27eGRjevA8ngvYTh3x8iNjrbdwV4NMDi+8xpwPjZw61/Uf+7r49EUx/V3dGX9DQhhRz9luEd1Q3p1WTl2fuS6x+vqc9I32tB3iOd15b4JUeYJN1iPRqVEQ7W3px/Hpdg4UMM9xHWY/82oKCAFTo7u4eWXX8Lb3vYUDvbWmBLh4euv4XJ7js9uLvC+978fq5Tw27/5HO7deRlMFSktYBXMm8vm85dagFpBWrBKk/UQqjOYFIm8eFci1WdjF1EMYjMExRVORdWDPiuXBLDImyijFAGJV2YQUJkgdcYyF5RlwZTXHv0d94FekcHJjZoIahXnhFi37HjWBobE1VsZ5G0XQszOPOsO7hOTe/IMQQW0mraIKrgqUkqYVgyt7poNkvhNDVzVjLMGkAqZCBth6/5AAJnWyTQRarVrsc7eEeHw0nq4AjO5Po1HYqw0uLQoZvTnYUqD0Y49RgCxaBGA3qYiPAmbrQAAUW9KCG5r2/a0gX/VNFRs/0059eo8WEk4AKxXE1LcQzgDvhfyxMhshNc8dQCFanta606mCsrAlCZ7LsUkBUCMRRWo7vj4OBARJo7mhYIKag6g2Zg3X2f8VQOUs7MzfO5zn2s/P//88/j4xz+OJ554Au95z3vwQz/0Q/iJn/gJfOADH8AHPvAB/MRP/AQODg7wN/7G3wAA3L59G3/7b/9t/MiP/AiefPJJPPHEE/jRH/1RfOM3fmOr6vn/9RgN+yMb3pvFM6M7FhurXDU+uvOBmNAUky3O4fNPuL21he3GlEO7XhFo8ElUvXdK3NtwTmBI3fjmGTE08nMMVxnqnA37Dl7BOHZvdES+fxyfJsT0CBcDlgajgRSGrrq5O9a7aazdsY2SXH7kM1cN/RjtUd+s1D2WEWy1f9druDZqFQCIFAoAr269ig3b+YjIK288ooJIpY2coCsG/w1SIOM5cWVMYnEnn1sBYBoWpv7O/kX+fF0j5qoy7rWAIPLhV681poqrLjdlZLsQRHpoBC7NW3TDgMCG/eTDNXiaSOMe+vn73jtMfuw+z6sPx/R5enXc7h97xlYVrO195lnbfAsCZRjwRqaHundIUKk4OX0AVcHp8T3cOFjh9PgB5ssz1Lrg7OwU83KJo+OHuDy+D4jg3ut3oXVGkeIROa+kIAIrMC+LVZJoAifBepUxrTKWsvWqFK9QcQPLiZE0IcJmRTo3JxNjlTKmydIXtS6m/6LOGxNPr5GRL+e5YpktSjhNGeqpl1K8MesA8iyRQs3oEjPSxE3KoSwFy1LgfGTvn2XPqNRqRp8ZiROmPLUu9FaQaCL50cSTCK7X5HwbMR5XlUhamoHMKQEpQ9XAQzh/RJFizz7/LHLCicA5QyagVNNeIXf/RYaKRzLglRJQq4MOITAyWhKmgWBqACWacSp1wBLgBDGdI3pI5ACHW6WLrTsjJIsoQKatI2oRZiayGm0ASS01s+NbiA/esAZi/Rmgs7lUg7KgahwTB46mcVNAOVkPHgDzYteSst1HTlaaXJ17ZBL5dnPi0dx2Hap4g23v2uOrBii/+Zu/ib/wF/5C+zm4IX/zb/5N/MzP/Az+3t/7e7i8vMTf+Tt/Bw8fPsS3fMu34Jd/+ZebBgoA/PN//s+Rc8b3f//34/LyEt/+7d+On/mZn/n/SgMFGDb0K69dPXbSOdcczcMDzJ4wdc6KWdeOWcJCNwQQxnvMho/emIe3A1kO7xwrf3avv7bz64CwdxoANbxgHsEj3yr2nWPn2fhYByePfv/OdbwBgBmJoCMX4mpKSKl/94DXvkwEzHtf0BhZuKI+O46VvdBeb8ZUHZSp7vyxaE+g+R5ybWV9YUmHp/0oqvryR3REjXGiK6dQ3S0l7lGyN0bQFFGVAMGE3TEaXf/x3/EjmwEUfz281riGR76vX2x7TzD17fnxzn0ZeTK0Sjw9uUMqjoqj3k01Ill27/atI+DpN6E9wjIAaztbpIh2V+fVc9POYFwdZwudiyuG2rwREIl3hY5ydVvzwWU5OzvDC1/6LBIT7t29g6eeepulM0hweXGGZZlxfvIAy7zB9vR+87pVrNNxKI8qyDlQCWDblueiWDaXJiRH2v6YMrWROwuZVDuzkVgDIKbEWE2M/b01VlO2Sg0oSi0GDshpC2ogBkSodcZ2niECZGIsVQ3EVzNSZmOkR+DQ5y67qBpztv471SI3tRptx4xgPMOYL3b+aiUsrcRV54qcPNrilT1x1FoBWoBo6ulVPqVUJDCyR0BKMdKwzUZ1IO9pMe9sbHhOfLwUzE5fl84N8wQgpmzCk6K96aNWDJzGDKiVHS+LzZmIKEfUKqILMX9UvTLHe+0A8PSwOECOvcunLXXOY4ZHD53gSmyK3cQEFoVqKAMHqh727eH/sY7my4jEm7Ciwu6tqECEMDGjCKBzwTwvTcpfA8AlwiTswN05j77c+97bRSLf7EH61bz7/0+Ok5MT3L59G//XX/x/4uDwsN3wmKu/6qG+IWnvy2zQ7fWrv9JdGNBPNvID7GB0IwRbh4/I3vOV6pto7jYegbqJuhcyXn8Q5KI/TvRoaRHwa1rTW537lXv9Mvd/bTRqWDztNR+AIM7tLI4GAnpYPV7vVT7a+8zEyQbSG9AZ+SOXYeePJ53bIvdP1ypXysSDh+PPNEAT9c2n8ZPG+7zyb1P55Z3nEPoEiZPVEV65TrOTni4YxqEBuKuCa3aj/TqHefQID8v/f230yDewgMiKUThN26bySOWbxoaOdg3jWPR7u7od9g2K2nXEfHpjQDZe8478/ajLEu9r/xvXQ4+kEo3zycZR2989RUZ0RelUvBx5B+ACLlOFF7/0WXzidz+Os+MjbJcNpmnC4eEhRBXnp2c4eXiEeb408FO3XpIsHq6P3jVxSQKGhernpWApC5ZSsF1mbIsb+2qpB2jf5zqYNi4I3Kk+2J9w48baemF5+wTx7rLV052iigTzekWsa62q9fVZTZPxI8Q/G07Qle/lDB8zF+sS41aUopiX2ZhCDojHKjgTJnMgOGgmrVeEg4M1FAUkirKUBoqmKcP8WEvJlOrRnVqRUzaAQgC0QEEo1SIK9scrm5yzQ7A0YaRzAI94KGx9+LLPeQLnqOJbTIenKsoSaQtqhFkbG8VqNaHU0psxDpvlzlqBNTusXtIeK5JcJapK8f5JBnhErbKIW5sHuG6LdR4WlFYRxGxK4CoV6pG6ZpeaHbEoyda5T9rWkF1f8X2gNUAsBbpUTyfyjhAltf0SjUcnAns+Wv1n+3PvpSMcHx/j1q1bX3b9v6V78Yyb81Xj+Yinj+4hAwOq9ffKTlhi9zseTQM4Kr1inMMjHw1aB094ZC9uQOrKBnndsRv5GTbm4dIaZNo5x1C100J99sHxkkZjobgydoOx3jFQ4w2RvRIcg5EQF7+/eoz8km5IvAEh7Y696niyfurm6VwxwhEajfu2MDE/QoTdIaBdva6wHHT9cxlBRVRXBXCINIHdG9q4xyYcAIuIQG4QR4+/pY6GqB97JEeHjS44EwFcw3DE5odH5kewjt7YL3kkkrfjgBGiz9QuGCDEfzvzp82p/v9GHtQ436PXsEPc9eoZcd0MpQ6Y+ve0p9JfIzTjEPyIeCbt/DFX0MEJx/MAkGrk4aWBJCYgU8LpyTE+9/ufwr3X7mCeL3Hz5k2QApenZ5jn2frowLQ7QNXlvqN7TlfQLcEnULjRtPtYZkuRlGrKpwayzPDHM4/GmqbYalUkUMVqPWG1n5EmMyLGAaiNeNnCaD5vumHq42DRHkGihJwyFgFEjchq09XGLgvc6/e9VAAVRpEKp2WYtkaNe/OmlDKkfjnIkwqiyYCFVEAZIgQgWQdhIZRi+1BK1q8oZ0bOkVrxvdQ5UGQP22TllQFik23IAFNywAz/LGEp1cuSGaTmzKwmtuclQdw346xYXKNo7CqubY1DqaVOUurrukdF+g4q0GFNaF8bUCiPe/+wB/i+Qg6mlCzaBRnG2ccje/WTiarB9zS0FhSiBrPU6+41HEy3TdbMs3oFDzWQHvNP1Stbfe9XsjRXKWjgpHu6j673Nzre0gCFE3n4FVYCOjwo2RkFW8gtJXGNwWEvH3tTBwHQ0SuIl/3J+0YDitC+7m4AjgU4vOy4HupGp33VI+Dqy1wTbCMQaKPAUAvxXX2/8SZ2Ih6IVOHuYnoUGHXeQzsUrY22RU52rNrOvcT3N4N89WRe0ictL7r7lhCAG8HJo9EfhfFfonrKricxt7nRvWFqBmIH2LrjEyAg7r3/y67d7Yn3MqEWHbLni51P+hRsaYOIHEXKoI2lv9/mDrWIi03xiDzZG3sjxhh3tI03tpg2XupGreV5bK6wj4Erk/s49I2yg8UOfNpXMrUmcwHSooNqghP8/LsjAiJDe/oxwqUAICaB3p53FVSxtEFLpGqk5jyn3gztqA7rES0Y+XKMcqVWURAaEtQMc3JjR0RA5j5PCNatGIpVzpgvLvDgwQOcnZ3h5o0DaFkgSlitVqi1Yt5usF7vYbN1OXTtIXt1LRdx4TNVgvW2MQOSE+Fwbx8X2KJoQXJyZCUxyfk2f41jUhcPx6uRI6dpjWkyAmtEEJo4HeA6JkDkiyN1BqDNyeDcKaLLsjr5VkFpDUCMG+HvjyifIgBJbXuf+uetp5I9N6kOhnLu1+ARpnlejOjpe/o0mRrqMi8m8a8KIku9qAZfz6NCtVjZMIBIQ7JLhoiKRRa4txkQL50OZdl43iADP9ay2CMQ2u8Hvp7JbZB4SXYn3ptom3jLlNG5i3EWJynDo2pjWpOJkNKq7Q3CyThILttvE1+8wtjGnhMByfd2sIM+AaeVXXclUPJeZMX4SnZ9k80T3zdBfb+CV2Gp78e44tDtOISqlvpCiOcJWp8iCSOFN328pQFKW0iA783hrRFojIgMRp81ZJWH8kTqnr+diobdEqNNuvL9Ydxi43aDFHOnv9O7mEamPEK78SW7DMxmyP37mSJasWuIg9swppbcGu9+f4ABwK6BBqIqxWt+BaNj2myUT1gTFNj5/nY9cR2DlP4Ibq56qm2jHO63GWGRR+Zwv96+AYxRE2CouBj4NkHk7N/m3xGb0NXD75fcIgVueCOuDBF6BQgTmHXoI2IDE58f9V/IeQZGzkSLOF0FqN2Ie+QCBI+r+hu8KRnFPXrY2g+JsVYFqbaKiTDyFuHWYdxkmCpDLhkuaiW2EZr3OkS9PELRQbZdE2koHFMDWuJt3UMUMO6nhb5FzNv156bM4FpR2du/N0NxFdiiG4hwAgje4Tb4CWlnviWhZihBxt1I2StRQnkZbuhg10YKgBVpnfD4E4/j8uIUtS44P7u078lG0lyWGQzBXp5Qt1sjHHoenl0mvpYgNXpJbtVmZHLOyKmAsbS0E5bSFjYD2FtPODjYR9lusZlnzKWCJjLeiVgzwxgchm0DtTlMQAphQgAMMT4K0IXBmCHFGlMKvGeMKiBzA+5MxscAkaV3VE1x1gXJBIxE5kyaoJ3vxcZQbqJuti+50Jk6OR4mO7+/tnJflAXkRFzmiOZ1ZB/7oa1javsR+ThktooXIrTUUK1oG4zCrp81ynEjXdLXhI0UOZCN35nE/OIl6SkRVKv/SahVnIszOnfRENSUZ6W9ThbRT4RpnV1ETnF4cIDtdot5nvuEB5BgUb3EXYUWqqjFHLlaC0IXpqfm4JwlAJTAObnIHZoKrt1wf7+l3slVkj06SWrsYumoTZVN0yb2m8HxuzYj8WWOtzxAacCDqaN3BZSuEm4dPMgV7BGfH0q64mihdNAjPyvUUSoAavvhI9GP+EkGw2cLZrwP+3S81hqA+XeN27DvAfG1trDHVET7z8+luvN5+L3HhjympewyqG2A0Ro+wulXvWhPYLhRijHtAlxvNBktpRaYxt4fRl6vGOg3Oh7hVVwTRRnzvDFe8fO119WA2JgqeRScjMBrV8Z/fC+1wO8bRcVisxq5JCMfSRvK1OH94fnanGGXO++vAP0T/TmTRxxYvJqMYDviICAV/ZHk6lhyx7eJMoilRW0a72YEdNQjh9Se9HDf7HN8+O74nK3dDtiJCJIEVACWULc0L+8N51YQMWDzisnGaZqmznNC16SQGsDKcvkRReEgAauClaAkqAK8du8uLs6Oce/1uxBZsJ03mLebFqHYggBY512VBZAC1WqpIRCKGglVoMYPGHQsIvVCU4J6BcVSFhRQA1fJx3K1ytjfW5n4W2aseQIKnMxfUeruPFO1kl/BEJGzkYDFSYY1VSsoG3BZRA1YkIOPWsFMyN60jxoJ3/4uEgqiURlFgHjFUbYxrSKt95I96wVMFtmIykDOjEyhFm6eOCUgaeyZ9cr69CpKRouQjWud4d3bAe+MbCmo4mAocTAGYQDTZeGJBZM14vFolEXcUpa2FiMimRgIGYkeMTMQGo4GDYq7EVWx2R72pBOGqxRfI4J52VgJOnWOSuxTlqoOwrVFZAQVIGlADldsIidCpuREV7NnJAoltFJxAnk6VNo6jXWVnAhcEfsv2l1U9TmDthTb3Ppqjrc0QImjh7cBn7HDb8M77OHk8diJSFwx5Vc92fH3Oz12YlK0c7CDli7qZRV244axS9jbhU24cg/9PqAdDNiG1kv8RgulOgIq7NwZEe1GKTq6QBjlCMU/AgSG6+h4hlp1Q+Oy8K4hv27cR0PNV4z+zthHGYDf2Ah8roKQq9/ha8cuiXpTsuuuabyv2LrbAMZ3DS8FOLkKQMZjJzp03Xuuee3qnDRgG68put4KdsBJfF/jbcTPaqjcUqDagSiP7Ce/Keam5LtT+RVzDkBCauAE6ABrZ9X5XBGPDfRIo3+1kzKvQL8B5PXfRG8WVXL1S0Vjf+/MXbSyzojihTov51DK7KDQwA6jpj4PE7NVpLB14B2jmZQEr7/6Ev7jf/p3eHD3dchS8Porr2C7NRVX8XnKzK0sVGUBaXVJd0ZOsMhINs+YaMI8S6ucaKZK4SXA1lumSnjg5uHmzFitE4gVkRZStbTAssyopOCc0SITYQxVkcjOafbE+SxDNExqxVKKecawSIM5JOx9Z9gqZsgIqdZx2VDsEhEWIpjkvBPAvdEt+WK0GhQBSDFlG5cpW8nx4ikCU2P39V0VSAzOFsWtxfgSPLaRGEplB1koJGYvhbbZuCwVs3N7FICygXzW5AEDBbRa/MU5TKFnqWL3G/MI/vZaiq1WHxP7bhenI2npVfIKIhM17AJw4qlGgKAemABZM76Yg9GPLkCfRW2BWtTSoksQ2y1GaoK1sZYoHkBbWkYi1zbXGYAk56OQgbBMVkIsYIsGRcNCEYg6ATcqtuA2J55HGwdqtuGrgyf/nQAUwAYmzPEY4gXQ9Tt2jPauR2uDp02K/urv4jvis1af76AIBCgP75C2acaZIjPSJ3VX07MHGOWbtPNwx3P4mRHRjJ2yYr+31mOCB5GdK967llG5EIF2gJYii/EMECBXxmS3BLh9h/ZPt1O/gZfbPgeY9zgaeVep1Tc4x5sCGjEm4UYRoCmub0ghDJonQZtpUaLBkA1nvbLIIirm59bGRNpFhYjv7umf/lg6EXYs4bxuzOL6w4j5025XFnL9EYqOc4/nMWPmUYb43XXYyY1OAxZAq0Zi3xzjmlRdmU9HdrTN49rmxZXzu/fXQO2waMc52/lXCkmByYO3AIwPiDXu0U2qkw+Cf9I0Nvy7kRRpuLSoHrPUkpWkbrdbTFPG+YP7+C8f+Y/4wu9/EigFZSnYbjfWOdd5VyKKUmGeqAqKLEikSDmBWQDnkmix57jKBFLGUowDIbDogTXlI+Rpwv7ePrDMKK5aqgnYX69BydRrTc8iYVnMS2bOyMn6pBhIEjNEyf/A1pvU2Cd7yXt7chp8E7iHDAMbKWFvtTa5fCndyF55ZmGUbN4YsUnRq7FiHhMBKSsODiZXxCVwoeYc1VEJusLOI4KlCjxOgoQgPNueJ4KWTgkQbQJkljoRNdKxwBRRZbFogBBcywOtEtLk8A3ImxCbAbBS4rrI1wi5sdd2jdHbK7h0IEXKFnk2pViFKpuRF2p7knG1fP8SB5Y7z4hMGZYN9cWoVwEg4nozAMi4V4rYLy1imCiByEBb8FuIqyWuGNYMUC09EyKUTIwqvracNrME+ZXIo4G+j7E7UQ0l7q77RxS7v8zx3wVA2TFQMpSWqgMO+GvYHasdToR7gHUADgAaYAhPFugwpEJ9iXSVvDBwofTYzh/7tUccGhEpPE/VplNhD9fQrhmtqzfcvUwi7Lw3rlmqdGrLeD8R7vVrbeOgAw/HLWyPQFwFbLIzHgBag7ywNdfHE3YGP27l2ohIjWdF3XjH5+TKRnjVANsYxGIPIwZI0MzHQWnPaHipn83HgHeiMJEzBwZ1YP9kEKBbKm/3a0A0RGYaiH3jdNjukI3PwV8TIPqdNCAW79Moxe7VEhj/bjnjR8cfQFNZNYMVY+enJv9fO1+EgS2EHdUo2saHdtdDu6dhMK70ieCd5+yvAe6V2jmlLQ5tgNcnVeMCEWgQbeOmQ9T+76HvqL4CFEcnxzg6OcY8z3j1lZfx9rc9hS999lN46fnPoWzOoNE0TxerjBACs3VVV7H+W6UsqEsFVkFCtpSOiEmjxxW0HjFqlSIga54XOuOrnK1X0GykWgahLgWLMOri5cpEbkQFmTM8Wdfnn0YaO8ijdm6p0ta78SQsJZCYUar4vbAbdkEiQs7s6qBW+xiZg1h5ba5zTAsFqO/JIAPWzEZ8Xa0ypqRWbSNLe9AML/kGsCwF87KYMRRqxlD7ZLR9LyLllggzsOJN8hJ6tRJ7rkirj7uagWdSF2RLbf4ITAmX4GmQcGjFtUpEAaTuMLYijc4dFLTCop11Fg0X0Wak8WYiSBjLp/msvvUEeBKpzakO2Qh1hyAla10gpAY+Ipo6fH+LuDiQqkJw5RjjEPnnjNTfG0RKkHqbYxpGQ9q+zYmH9fnoXv1mjrc0QAkj0AcdaKp17im6kk87+ubc4yHm/OgjxnDcrAmepgHvlCQr0MCJ+AKXRyIO1ECKnSc2Yve+g38hjmA8vIjYXP1aRiuq0QwhzhveAg0X9mXHDjvnA9AjT3HD4dUq7WhiXD15RHNGgBQbwZWvePQihjFuaafm1XcPf2dij7/H+PJ4XeONjOcIFc5H0zK885G+mRLFdw6Ao339YETb3z0E28YnUoBEg5ffI2s788XD1oYXtT/XESwOQOQRzsoj5+QdXklbLzspvF5t06CjwpvoXd0l1QS8RsDevjeuyf7dPDSKaixCdDfuR/f44thJpzWLh7YZB++KKRyDodxxlK0fibw+jq08nKm9P76+1gVf+MJn8Aef/jSOj49wfn6Gs7NTrFYJD199BWcnx5i3F4gOxkawDP5Fgdf0opKtYUu5xZwTcE5Iwg0nz3OxTIrvVUyuzOlRPpsHihUzkBNErbHdZrPFKq/aedQ96ogetXRO6p6sPWqPDqmRWCPSVp3cuZTFCPwBKM0D8idh11TKAohgyisHGwYKEjOEwvCiXXsDjgBCaRn+DKdpQp4mEFUDT0xo+RQKnRRbPVX6MxZv0JgYvh/bd0RUIIB1dDnmcOYwXJeEhL0Bm5SSXU8icPb9SBXQssMn4+BkEUBslTIqbNwU8ShK3C/bxOXk+lThNV7ZF/t5FWAxoTofAyXn5fiHcs7Gk/ILoeQVWuEgC7oOkyOceJQ7US648i4zymIAtWoH/iJe9UQGTmINizvk1Bx0t4Nsz9fpuAAxavI9hrXNwzdKh193vKUBSkqEqbGWYX948AadmVS1g4ho7AbQIA1uR/O+/JAxDRO/GzbJq0aCvR8GS5R3DecmasaGYCG6iMyM4dCm1DlEVgKlkiqUd/kjWjsJUQbF1ogcjfL05KGN9r2DwiGP8L6NQ38hFBsHH6AbRgfPcACXmC0hGbyJATeNIYVdm0Zo+jJhBN8k2m4boAOaq8TVdj/ki5ws9JkSDxEP38jDuPumGw+93/sIOjrQaTlh8koRjvCyAUvy0mnXmGo5crTvGe5naKgHABW7cyl+F2nJBps6DgJwRUvEN+XxvPYe36yxy7YPgNQ7Z48cK9mN6jUcGYMtbUMMWPdIThqP/nuc+33khzFOPb/fPpO0jcXweNr8DlAyrjPy6EqtBSenx3jssdsAgDLP+MynP4Nf+y//CS+/+BKkFiyygEix2V5gvtxg3m6hUpB41aoU4nqrFJAypEgTTFO1JnerKYETIKUajwJowF/nYtOuWpjfdENsXoT3ryKQUq2RnNpnl2Lec8rJ+S/aBbTUQu3smhSqfQ0LagMnCoCqIqaYBcoElAzs1KqoTgqdkjXpK8uCRAxKNq9LWfr4x3oxJNBmTDwY8/odUHhlFIlYE8Jq/WdMqkWhsI7LAawMSPTxiNCN3UdvHCkKJLY9IdmX+nAb0jFNFHEDq5Bk1UlFZqx05Wk+S/Fal2N2wz+mc4GlkUAJUaFRFotARdm9lKgiI4CqZUAFsBR/dzbABJJwbthVYU0YLaIVIDTBvThSYgMVsrt/E5HxAEnbdzITUhBcHLxyMnBaluoABc1u+NZl83wpvq+SR2kcDFF3uHOLyjnI0XF961gQ9KaPtzRA2U8Zezk7ZKRGauvecajZWehUvMTPyraAOnhdo7cchikIWMFPUF/l5EI+kc+Mz1H7h38W6GRFR9KW2bH3jMbNDgcjANxatA26QwwCPOxpxrz5mejS6u4dgro2CXzr2DEO/hsHBHGNcdgwUABzN2Tt0054U8D7Q9i7qpW1hXAZCCzU759ioxQPp5M/vl79AweUxHBJ6P7MaQCF7pK3aw2AEBvljsdA8f3WvZPJfMle3ivDWHgQzoHodThJYSHohs3INotEqeuhxPviDfEcBvAXz/E65kkDC8OoR05991oGA40OXtAAxy7Y6+ftvw+jcjWtp9JBCTXCne94UaGzA1YGfkc7s/0rNvdIc13lMo3CepaetfUUOjHsZZTj9zVvGAZwxihiS93uzAGrpGEQ5u2MT3zid/HYE4/hiaeewsXxMX7v47+JF7/wWZydnnq1ivfGESNBQtRAiHTPnsZrgRgQZmDKE1StFDjnhDIv1qCPTXBOxAiHOQOqCQoTISuluLRAryKiRNDFqjcYVqpdYedIMGKOMiHzZLLtAeg4CJ42RtWjAk052LkyTS8EwCplzFDMpULE9tVMjFVmqJjAGZNFjKsWV8RtO0wMBpo+CdAiFRyNAKUa2ZdsLca+XLV6Mzqr0AGbhx7qseaoJZhsvaBG9IkYpGwAD14m7PsN8dD4b3AoAXfMfG6LVnCC9ZjxCBhytlLk4II4QTyikAagCFf3G1uDFj2zggH2taRQNjCpaumjmPYCcZ6U9/KRsrNum23zJRsOOBAVWQolbftqRE/MoVUn5njdlp+2bhfvsxSgEuDB+4iKuupid3a+WEz2szWq9AiaKhbVRkDue9L47zePUt7SAGVvb4X9vakttrHksfEhAFg+0htIVUEt1fOr8HChOHN59OaCCR4b9RByhk96EIRjs9X2OUjLMoV9bwBmMDWISR1QlTyUZo6Hh0bJFxARrhYMtxBsu+h4HT0Ck9CNY7yG3Y9oXOgAWOwSdDyt56J1GNd+krFDpY2lAK3M0c7bSF5eUZBSB5KEXfLUTtoCY8TBz9exxM77gcBs3fD6avabcCTvPUuMm+AAFOM4WYRNAJALTME9gogYJYoSVpeaZkIy7GqbfdsM0aIJcUXs5Nuqcq1nMUZH4qkpet8gtFELwNvn8ggwAO+gOoC5ne/QSAe4CWteT1w7+8e4fZztBvwtQxWRwsFqJ9EGFyXmNzngIO2eV392O090Zy3H2mZ2wl9kAbinzAbWbouWBJ66uimKCE5OjvDw/l381nO/ioObB5DLLV58/nlcnj5sfIyqas3pyAyuiHXi1R2Q4ht1JiQH69YLZ4JWRmZFXSqWRbAsFtmwTd8AUOjEMJNxICjG1DVu2AY9JUYtFk8jj5DG6sl5AiDIrA68XcME3h3Zn/9ufxf2CiMzjpmAvfUae6sJZbN1dM1IEOytMtaZMQqWLW60c0qtj9riaRO0WeHr1+fthGwAYAFWk5NTa0X03grHZUWTNSkEehVflOgSIakBOdOwYRAn/3xUJdmjl1oBKUgpNYBC/nziGkudzZnKE1brycZbDWT2NFgAfetNUz1KYI5JzKnwryziWrXCLxfB4RIR44Vo9b0XPX3ja7BKaftyc7adskTDjkCAlXR7lI3bOnPZDZDrvLiGcUR/1OgEfb+1dgtKjV1s9+aVShEdFY15GvfOyJmQMwz4kEAXJwY7CI6tJOzDVfvzlY63NEC5dWsf671VI0Yx56YySl5aCYRjxrDbJQcqES4NdcPhUCC6vqpPGqnVN+HwfKlLSDjICI5GSHm3f7v30j1Fn8QDu7uH2t2kxsZN3Uu0rTBKTM0KhomC9lxvU0+Nfw9hydGyUxOOIRAnm7iAlwRqfGUbGxFpuemWS+5D0g5TCfVbjWoRKIIBb16N4srHdkCGfbYb1TDwO7cwghJ4aJTCm0nteaBFsWwsckrI2d5roqF+cuG2MdTmgQDKPUVCFNdt3mnOCezCXgr7fYrxHvBjgDSNjUhNKyIJtShcWPkuyAaMKUnb4gbvJkCEXU1P6Sh2BPeS2/MRYA8XhUfY1LEREu2UUvPwLDoWGCM6HHSMFv1quQOKZ+QRphrLxtNju1fQnimhd8dtUcy4f70K2eO2aOdviQiLf4NJeig2ZcEyz3jtlVexPTtCrQV1KaYroq4yG3uDEmrIqMMl0sV0UYgIKSfreeO2jEhRly2MUiHWIVYIqgm1BF/ClGGh8HRMajhSqhmfZhqrWERkIlBx0qfrdpgGVMy5at18CSgQaFXnJ6CtB1W1Jo6NAGWGmFcZqymDUJFUMZEVAgQvhkmgjVtjcy4BrjlFXiprWitBMhWvGLRqH4tkc2bs7yWsyKtM3EGSqgY0ElBLbc4Qp+QcBkvlRG8aYrJxYWDK9kyCjMq+f0oN3Rb3Gpysm9PUSoWzmoOaVwk5EYI0KyoeOemRvlqlgVeoASPm0CQxHg9x4EFyA27NJ5fFmuyRuHMLfx/YAVU4IrtHOHZjX7GIgolHdyS4N6OqtJ+/enSqS1vEvsEDCb5nEdhTo+yRkeIl1OwApa8zB8/u2BQfGysOY9/jIkrV1+ZXVEUfjrc2QLl9iL39fZTNAkN2ydGkdO+TwuBoQ4AiglIJXKO/RkJvMa9oUQzf0oJLEIi2e5qRxoDnDO26VACR2sKpNVqVo4fRAA9rOgBSmDRwN+zoob8ohXU0Gx1L433kLgM5yuYdT7JP/Hb4vdnHHC1HntW+sXmdzMlst1cm+I3v8DwasHIjbF5OJyNGOXZbBO1j2sYyLqvb6V0AQ1EFIEYGJDYQFs+EYaHikCm3RUZoeW6/vpwyppyRk228afDg1YGA5d3NezaBxAC92OkDQ9kWcUrRa6PBxRY1SBRCavH8yTlRyVRnA6AM4yGV+lgOi9vm6G4kbUfwj9nD5dqeffTOSD6P1OdvGLiYX7FJ9fF2Ly08OOobZQO2w7Sy1ErySJe0P8mJm3HOUEWOAEeEoyOitLs9+8wdInmqbfq2817d0G2sbLMNTw5DLxQpFffu3sWnP/UJvPiFz2NzcoJycY5FK0CEgopoQ8+eYqhRpATAMio2Jxhe8UFGXlX3kKkWe5YAStFWXSHDvFYFltlKNa1yxNajqXCipaQBC0bGuudELYKU7CEjKkjC21U4wFGCkV/G1gIEUrYGezmZjHyqWK9XHqGym7XAjYH4HCX63DYcezYeTSAyIBCy8cLqfCvyPdRSTQYWEvb29lHmrRlmpdYsD6rN6xdRUDYSba2zjQcZN6UZx6YvY2FrKyMnVFkAMYCbvQeTaLFUTuPfWKVLTtkq1piQ2aqiFhGUUtr+qgoHO4pSnBPCwMTc5647XiDTqQknrlbrXC1akZAHrltExgOojKq4kQVQAwjqXBYyzZJo7tgiRn6eMEKq1kqgVvXWANyyBMzZbKUKingE0OeWsjvkUDDZc83ZMgUayrOgnes0yoT13SnVDZeKVWWNCzR8rkez1G94vKUBCqtinRmrg1UPxWVHpzFjEJuJGMm0CkAVTBUg9Q0dRlqNqIkrK9rvQ6obLTRopYrJuw6zG0OAkvtpbtTEvWSpFaVWRGjNrF4QusxbDxQsvomIAiE/qBqVCXaNppdgEwY7qNauWakbEqLBiIVxwLDRB0dFS+dBRL8WBYDalCcFPW9NpUdwGjgZvACoXQd7eDJy6n2Di5BoJ0+NG7eCdibyro6MRchSpAjcy7MNxnkmbgxT5qZ2maJhHBOyb3ApdVgRnrK4FyBhUBpRT9v1ke+Qdl5q2hnwa7FzBicKcBEWI45FxYxY2NUetZrnFkagz3I/GdnFhgHaCVsNAEdTA4K26QbgtsoH8YHWCEON4RAd0pwNbFIDES0yEU/Kwa8OnzPv0ScDhtfjWVFEZkZAS2gTNVKNAwDSBuHR1k1HJXZPjduFTg6u0jli7E3/To6P8OKLX8SLL3wRn/jt53Dy8AHKdouinZ9h683nlA+vVrGuAszGj5AgY3paD9Wl0wXIaqRHQ0it5FT9tgIARGfXpSpqshTLlD387ntYDIM5VYJaEHpkUC2o1YAhZW9SRwmiljosEoRNW3si0dAP3oxPTDMlM2p1x00q2I0St/1OAHEirD+3qmYaRdS62PqmwiBQsojKUrxkmNi0YIgw5YRVTpgyg3RCcVW2quH4EEpRFFFACBMAKaVFy4z95CuNLE1kYMDAoPjcKA6Uw6gL7JwSjkctmIjByEgKdyRiDcRacoeldOexVvtDDhKIBWABqpfWwzl1icGc7RlJgcKjSol36AKxDCPNGoiIEGkSj8pQbzyaEgEkpjILi4xktvRjRMuiyLOUITfeQJQR+UsxEB1FU2HnBIDUYorHnOwZqyCipWP6XdTXgigWt00B5Hf2hRa9a/7Gmzre0gBlf3+Fg/1VC/31/Dw1lG8dORVLKVjqAsqAKCPXbINVjfxjDbF86qtVyoBDZY9bGbKVeGXfZGPTtv4ZxG5k1B462SmgicDVO+mqV3Nof8iqFu4T38xEvIeBAxTzNG1yhEiZQpv0RKdj2sKwuv1qhjt2WAcIpPa3whe61hZ5sbnEYHURLP+viGIpFdt5sU1SYKRYESRi55J4aFCD2GoG0vqJUNt84E3FilT3RF3Lok1c43gE6bZt0txlmllNjyB5SBNE3oLcIjuJCJkMrFiEw/qrTNn7x7BFFGzzHaqR4CA1E0RSY88X7VEJKLVnz5QaYG2oz0Gi2ePUjHCk8FIAU4EZIR9ncV2MIFqOYdrgUzXyG3Rnke+CJ/L51/WAYp6JmHUSB5niOcoWZYu/EQAsqr24gRefZY5fqEUyKP6N2KCojUuDxYTGJWmYOjxI6jOuL+HwLvsmG5nb/swU8Q0xfi2hM+AYUyQV3L97F7/6v34Exw/u4+jeXWy3lyhli6V6p18WJM5OcheU6vIBVSDwaFBzlwmcPdUwXKNdl6cLKfoHwfeo6oTQ4r3DzOTW4uvVjXukGUKxWqo1GzTQY0TbxLk7ObWCEgOJsEhtqWUCgXIQU4f9EYqUJhh4tTldSkGebD4nVqxWGZwACKEsBaVUpCkjGgmCCAsATtTC+sTeo4YUYDOCDEVmwt7eGtMEpKQodYGJ2omls3xIEyuWBViKp9OWgmnKiN5qBlTUIpkEA0PMzSCHQ1GqrSzO1ngxOvlWnwdgazdAlMDRiRnBMenlx6UIlqjKcoAiEqmUPpbRyjcAoLUUKGAXWIs5bw5r5EHts5GejvSxinV2tn0vIsbU9FlSIihV/87YA2L+V58T3aFWYo9Q+W4jAkVt6ciE0DyPPdcAGapaWwyfH+b6Rxrp3AABAABJREFUqzdH5LbWgsoQS4A8CtVO6X+anSK86eMtDVBWUzYiWhiGxpwLrz5K5SqWkrCZuZWppWry0sICzYBWtn4b6igYjOQle9a/wSZRShkpJ6QEpClqw7V56eHd1bqglGKeuBSoM/Yt35iQPaxpct8eRlZtQEX9T5SvZt9kqBkVe8oMAg9Gs4cjxcfBwr8xS9StgaUuFKqG7sEGikQB1ITWXhsAyLzUubjXpw4CmDG5/DR52CDZHQJSW35euRO5qhbUqphLso3JXw+PUShAmeXVE3lLcCk4Oz9HTgnPf+F5fOD978d04xBf+MLzeM+73o2EhGlaIafUohomVGSLNOeMlE1roD1PhqfodhvIhfhSKCdyJSuDdGOf3AOJv8c0jLdi89B1cBUivcjtGRh/waMbCADkBDXuVVpmpNgBin+XA4HuhpkRb+Mo3q3UG+cFmdPsivE/olqMgJZKHDlA0Qsk0hw9P+6Ap31vX3ZXKyTGiEm83vgkw3i3E/qG3W5LdfypcVVG8Na+I5Z+VCNcOVQVZZ5x/7W7OL73ACdHDzFvN5jnLeayAHDyql+Xhart/LU6eXAAyare8K8BujAedj3VSZFQMwjkxEsdUi3B8zHgaWBWagDQXe8z5wxUI/db48OMzAR42a546q9HsqyPTaRGq4P56PILmANUlmIAhS1KG03tWARsnoJFSvwPiXFMHKE6jyOk5KPs1YwhsSBP1qDvYD9jPRFAxVPgnoIoFbUoCpKDCzGZfSIkikKFMMD+HFUMjPm4xzNTMoAybxcUWHUJlFGKtC7M8VC1lTqL+7L+rFR9/7VISHQoTolRSvXomYMSWNRCa/CL0NebAKTZevMQ3HmlBpppXC8BJGPzJgBeNp045kX8bPMo5kWL9kCcC2MRPhNZBFLKaNXQY0RaxAfMxmFK2aJuVXf4lxjmlCLsXExthQSPU2IJd2eViFrUzv6oQ5w3n+N5SwOUdlCgWN+01APMUkEAMhPyKmM9ebMxj1KUsmApxcFA5Az7IFMyFJ2YPVKQeldRaP8zhMltfpkRF1mheXJqqSU46S8jtffDH6SooBTBZrvFvFRorcaQz+wek/EWmrCbuqOiMCHDSBeI8RhsAbhRbOpCXTfDSHLZ70KcG0F+7YI6R38IBsDI6+ST1yIn02SMfLB93iIj1u2VuHu3FYqCgu2yRd3ahhgkwO0y43K7Ba0yNtstNpstTk5OkVOCCjDPWyxLwWuv3cXJ6QlWe2t87tOfwf2H9/Ded70bH/3IR/B/+tCHMF9c4vDGTTzz7DuwXq9xfnGOw/09rFf72CwzDoiQkJuRpBQgwiwssz1XK2828Mi4YkgQfCN2wGh8AKLRKPOwyXBfyMEejeoPIq98sPfWiB4NhsnmR+xoBrbCLtuj7JY4Kg5ZLf+PSiCvuCD4PkRe8ULuBWo/RTeUw7KKCqcrSeOKrlCqCpNgGFHBNcAkxnB8fecefRZerTRr/77ms8xdzArsBkcIJPG9zn9SE/SqClyenUNFMc8zShVsvTMsEdzwAKVpVBCALk2QPEzvV92cnzq0iGB//opqxGkXTVMXdGMy48j+HgM62UW6jKvWqkcDnGk1PoWwkzCpOwSJgWKlz4nZSz1dV8VvQaLElzkcfZNdqC54BkFKLhDnRnTKpvNigM+UVEuNslgrhy01OjFrM0pNrdmjdOxev81Be5/tvRWsFo0RFtSlYi7e/I8ZK5ePaMRTB0RGfO1VXcviKanUU6hFBVV8zQqZ6q03A6qloNSC1Tp5BZUtiBYhpT7sUOMGTVN2kuGM6ABs32+RHXOAF+Rk5F1w6O9EKldMR8Q1a3qxxBi97Q4HkYmx9fmurQu29aNk77Fj3Db1YveouGvVeaA2d1thxUC1j5QdO5WhYXLq0Xr2iHdE/IMIH0CuSkQqI0pKINK2TuJ6WvRXo4rtzR1veYAS4Vxc8Sh7bKn/3kqpbL3bJmFhPk0AspH6GOyeqr2XtKNBKyU24plTDVFJMS8LLjYL6tkWt27exHovG6mI1PYWKRAnkBGRTfiUXE/FQl+UGAmKPCmmVbZ8ePWumT7xyQ1rhIhaRgGIBproKSZxwSMDT0stUBibXQrccwKQ7E5KVZ+gAqkFQgwhRokvcp0QYSMcb3UByoKyKdjUGQ+PHuLs5BSnRyfYXF64wBHhxo0b2M4zzi/PcXFxgWVePGKRsMoTTs/PcHp2hr2DfZydn+P4+ARnJ6cWAaraQrNlLk0bRUrBRx98FL916xDbi0v86q/+Gm4/dhsPHz7Arcdu42ve9z68ducOnnn6aTz2xOP4/Gc/i69973vxwfe9D4/dvg2ihAQHXg3Tko8lt9njYQxP33HDHMmjZaM2R0TSroKLiDCEiQ+jr8FRcKJ1qgrxd+3UlDnAaWmk5v8CzR1TjUTBTgqiYQYyHoEOn07Jwax3qe3AoPNbjF/FMd3c6Jkx7OvOv8LQwO7PA6iI9Jv9blzA4cWhTeZI1+wYiwGcRMQyeo5wsuZ3UAUlNSJii8yYx8YqkJrx+BO3rLRVFfNszknysg9relbdoHOr8ujkbm3nNf4GAE3euM6eUVXnS3AFT9nSMfCwug6Vfs0L5gYiRMQ4SXHp/n4mE+My7plFN0SKRwStiqzhXxnD+Gig2+ZRgYp5yZYCsUho7KJR/lIWU05NDETvoHgqKhb+FxjPQ8QqVdJk5ciWXjbCqlSPBsee4xUudqE+z8hIwUYE7XoqVrqfwSjtGYh/nzmgBv6XUppOTov4kUV7VIw/ou65myNghjWxdU6esj0jWaT5FaIWrWRy/hAp5jJDpGC9t4cprbHdzhCtnjrsUU57XskBR6TibeXtEN6vAHqzK/FDRAqB4AhyShAolmLzLk/ZOYjU9qwAWY1nI0ZyFUXjE0V0BBjK/xHXaxFwm0quCNsus89d479GPyNfom6EzHbaczVMktp+hAakdh2UL3e8tQGKimcvxgGA/5vNqA5hZyt1q9abgAHARHGUCZrMK0gg75MBKDsLHoSkblDUvQCqWHTBl+69gs986tM4uXuEg82Eb/of/gc8+/53g/edJIiKxGq5XXWgAXaNjYKKgtOzE0x5wuH6EIzJymDTCpj8bvzh2yITRBWH+obeo+RqctFkzHRk7jyHYtyblBk1WVfKZfGUEgjVwgmoVbFFwelmwSc+8fv44+95P/Yfu4FNnfH6yREePriP0+MTnJ2dYd5scXR8hNOTMzx8+BCbiw3KdsY8zw6uGOvVhATCdp4xL0vz0KDBA0m+OZvRqVIhzpK3qJKjc61tA4ISlk3F+ckliID/+l+fw82bhyC23O1v/ObHMc8z1us1mDMuL87xrmffgd9797vwf/jmb8af/KY/Aaxzi3bBZ07zjf071ev5x5AlJ7SFbaHvoaaGqC/WgANR9TPohaifrzXWIwcuMN2KCDXD39vP6Wfw5y3N1XZOyRBuNYKv9S2p7Vr6PAEpWIf78fcQ2ef8pm1tYOC8UMdF4RHHZbUUDrmYYQP23YA0lKR2HU3JF13Uq3mOYdDjoRC17yEvg+xDv7vpMbGvWfuoaMLJ2Sk+/6Uv4GRzhkpWVTXljJR8ow3eBvXUhfq8A5GRToevUctVQGECZETGuSi1YCLy0u8KqHr5paPVYTCt4sT4HQBAOSNUnZelQFDdUPpcczXdnBM4ufYKs61lqW4Mdcc1C7Aa3Ip4/tZSI/UIjlizSdOIqlhlM1JwAmrcPIfwmQSRlB3wKqQUu29YOb+VwgoW9+4Th7BXwjJbOqkUSxklJvDEXpmjSOz8QH/mo0MmtRNH1StuzNBaZFIdrBmYMgDG7qESGYcm0uLiEYpQcCYAKU+e5jFyclksui11hk72GXFNG1FCyrlFwftaC74JkCcra1YnbkdkgtrcTYgS6Ob+GsK0a2J2gObps2JRPpHON4lWKcZpNAfP/ITslWamxNvWSLJ1Z3ND3Z56ao+C16V96Wnw2uDVgFf2JgrBPxqcEGljAXcweTeS8GWPtzRAURaALd9mpXmKquwTtsKC0bapLGVGrQUHqwNMk5PDEqFCMJeKs7NzHB0dg8G4fXjoUQ7xBZywniasOJvGBQiSCHdO7uN/+cgv4+T1B3j28Encvv00Xn35c7jx1ARZCEenx3h8/wYev/0YSpmReAIhYymCKSUoFmja4vVXPouL0wt84Gv/KG4+9jSIpyuP0A1GC7PZpOcgKilA5G2wueFUr3E3Y8fTZKAEFXMiXJBig2LRJFUsS8HJ+SkObt7A8XKBX/+N38KH/98fxrf/qT8L7E949f5rePmFL+Hi+Awyz9BqueJSClScn+OcH4LX3kvFlqzNvVRB9bI9W0SMjdQu6CVAKM8uS0HKE5ZiIlK2TNxz8M2N0EvwlmXBsixYryeIVMzbEwQlLKmlxj5z8gV87jNfwCd+91P4vv/pf8Kf+jN/AgeH+1bqazgCQfQ0/k5336MXRRfQghEcDakgSvjsl+SeLNrPLSHS8ynNo4sKKdKokrL3RoWH5WzDT+nVNvFcGzO+Rcz83+HlXInGOL7rlzpGOcIAhTEMcDReOwxMNUKce6zG1QrA4KjHvydIghIPE81nR6QB+zi3Gd83Z/93pDabqxvvdeNFRFAiFCl4+OA+VvsTbuwfInECqmC9PyHljMdu3wRrQd2cQSq53oU6AVwxMSN0OwI8R1Cnj4GnrdgMMeL+PI1jZbme9iwVS6kOtL2zsRrHq9ZiYXK4ci68WoLIyYqCAovUxhOYEmGaLN2rXFFQm6iYq+QhwGb1VIuCwKIeGSAQBMtcULSAxFLISgwpCqkMYiu1zQS03mBiVSuhgmvSCeyRvwyxcIU9+iTGwVJzGqgGJ4FRnJtWi3r1pfO6MmNaZyTypDJZtMfuxdRdldyDB7x6yvYcE2yLyIDv0a5v0v4WQqKM/fU+1mszzMtS7HNke4wURZ4mAIRSC5bFVFYtyMceZbJIhCRAqyU8nUvqIKpip6s3UbtPW0raoh0KcxSSA/poA8DNG4v53dM4ALA4oDVQ0tPK7E5C9ggHQOBkQIrgaq++nzBzq8CRGlEdixhb6bTzcNSj/w147ToDj6SkLWTjr3NL9SkpvtrjLQ1QPvfSHRzeOMPR+SXOzs8xV0EtgsvLDS5OLiCyRVlm5KQ4OTnG0fERnnryKTz9zDPgnJBXByil4vj4GHdefBn3Xr+PVV7hsdtP4Oat26hZQTxhNa3wxGMHOFitMXHCaj1hOkx46d6X8OlPfg71YoN6a4u6Nc/k8JlbePn4Lu4d3cczjz2Jm4f72GwuMNGEJ594CvN2xtPPPAnRBeeXx7h9uMbjh2s8fPgSclYc3rgN4kO4JiRiQhAZITbkm0JV0aezbQCkWLRiUzd46fW7yGnCetrDa/cfYrNscXRyjFfu3cMLX3gB8/mMG3s3cbB/iPsP7uPVV1/B2595Oza6xec//QUc3XkdH73/n5BzQq3WtVXVdF0A2GYriqKWm0YQSYeojkAwu2dSI7yo3lMDrg7iE5wbW9PumzkhanwVikQJmbtmQYX6tRXr51EqlsWkm4mye9+uQ1AVKWe8/Mpd/N/+7/8zPv/i8/ju7/5O3L59w0CPuoKnwgSVoptpImQ1EpqqkQSj5BuRTpFeYRBe3niMpFF/YxOUEw29DTSOS2h4gMnKPzUqrrRtVG2T82voJXwhEBjhV9q5nJgn5CDJWiLAvLcxUoSu7RBS6XFjWqOKCi011Hv2xC32f9eoY7TfjFgHrbpBtQdAh9+NTRfbfdrsaCTnuLFlWXB+eYlPffpT+Pf/4d/j4PYhvv4bvgHveue7cfHwCDf2DrC3v4d3PvNOyGbBA1EDCG5kFB3khOcfwLNX+DnJ2jlLxJZaszGAqWoqYcqmkFqKVb6pG8mUE1JO0Grk0MjPsxO1pd2qz5HElnr1OSGeolFMFj2HVQQlJ9n2sY0wlYNsBUi5pR8sGjiSqJOnmyItIEAhIBEm1xaCRyQ4ofEwQAmMDFJC5owSz7jWTs6m3O5J1FR1CVFFw0jZr5Gt+m61su8ihfctsmqaiHpw8pSnRkqV2tiZffTvkdpSGlFSrcleX4qtJeLkUR2v0IpUmyyYZysMiCaFIFPsnfIK87xxaX2DZ9WrhjrXI6JN2R0W9b5p/swTMHIe7YkZuBAvQ7boh6dvfN/0D/j73WESaREPFbSIpcI8Fksru9Mi6vwlBjxC1B2diL71v1tOIjIQ6HtBVAl2npoBV2ayc3OXRbDQrG9XdRfgfLnjLQ1Q/l+//BuY9vaxLbNNqmWBbGer2ABwdvYAJycPwVwwbxfM84LP04u4cesmKDEOD29hc7nBvJ0xn5/i/PwMiRPuHtzHwY3b4L1D7B/cMIOIrbOiXXJaZzw8u4+HrxyhXFzgiE/wxS+9jtuP38arx6d4eHGKedlCF0GiiikxtueXuHnjBm4eHuDGEzdw6/ZNzMslllIxpQwtBd/+5/4CDvZugVbhWXqYDYSL+QK1zLh5cBME4FILmBLmsuDk4hQXm0scn5/j/tEDPP+lL+L3P/FprPIeoIwXXnwVpVRcXl7i4uQcdSleJZOxyhlVFUkFr3/xS0gQaFE8hoxyfgHJGSlPxjJUxcS5hfKWxSqUilQnF8I2cY+mhMicHT3018qltU/5ztgHQC6hTRYlMt0Fz2nDK7C8iRkDQK3uJXpkyY2JtiSMNlHTe/fu4X/5T/8rjh6e4o9/4x/H7RuH+KZv/KAVaSSAVxkuRQkFoVTzQKoVmrruA+2sPcCjCi16PxjWUPJ0lUooICwtJWKkaQu5Rm+QKoQiCloIWhmowOJemIamj7qseDNqZNUcDcR2WojGuPomSC437zgIocmtXhbZ4xrmI0ts0h6G6WTxuF33dIcBUdUerdEOsFRN4CyiIRGMauTttunR4In1TdL6y1S8cOdFbJctwIzN5SU+9anfx51XXsHnPvMZvPbqHQgBn/zt38XBwSHmiws8+/QzeM+z78Q+Ex48uIdSjdekZBt1fH+U4+56vZEiMeIkwbzMqtLIyymzPxvjs5TFlGmjQ2yADo00CtxQQHopKoJ0aJGnWquN4ZBbqqqYawFNDpSTkdgJgBbB4pUTTLYPiotmkU8EKQop3vclK3J2fSAoUkITVXNBD1v7DixqVaRqzzJPKx8vRamuJeIsbPJUtBFczZkyj10cGBBWexFZAyAuCcCEHA3uFEAiT/+aWqxVp3CLFhLZ/qK+P0QlpZGNvdy6RpzOhTFFQGSdiy2Va2syUiRLRKICHHhaJVIhIT1RltjbojrPokvE1IiksQ+Jp96iR1JibmuyOQy6y+yKdRoTopWya0SUIuJm61oErhgba5K6U1t871Q0ErkBCl/p3tsIsPGp7vBYdZiDrl5n0dZzrBXyyUXDeq+tUZKtJXNKyTTB3uTxlgYo9147gvIJSj3HemKcHx1BLy9x+9ZtpNWEo4evYTtfWjualEBVsMxbPLw4M+/riUscHx9j2c6ALJBasUBRli3Ozh6ApwPcuHUbRQRle4xA6tu5YFm2WJYNZHMBArAlxYPj+3j9wSnoS6+iltLC5lIXqBRorYCXDue1SWNPq4ScJxw9fAhWxWc/+RK+/hu+HrefehLrw0M88ba3QdYrzKXg13/j1/Hql17EN37wG3Hr1i187oUXsN7fx517r+PB/SMcH51ge7HF+fk5lu0MrgDUw7YLIU9eEVQX1O0WZVkAVWw4IVHGwXoFzhYCnghQtVbwcylIWUyyGbbhLXNpioZ7e4SlEBZv8FWrIK+ScyKs4gfkuUeyTbeiIpELPWkAlKgYgoleFRdaC++gmn6KlcBK24yYCVOaWim2VULNqNqNjrWgN1JkIaAcX+BX/7f/io9//Hexnyf8yA//X/B1X/9eLLR4hJ1alIQodePlVQqsVr2CcGzE41hMrTxZZEFZFogI1nsTMrJVlhGDQchIbhDE6VKe0iECJWPXJy/ZtEhTryARsTAyeWpzvVqDKaOoYPGUQq3mzyI7aCsW9p0m046IiE3inhNXCJQ7idNIcQVN2yVAVaso6m579BsJr081JMPHVSsWhYK2NIkCxncZG1tGhIfQ2z7Y27BIwXO/9ev4f/zi/4yjo4dGIFwUR0dH0GW2ZwbblE/PTnBcjzClgvn4COf37uLJxx/Dg6P7bQO1+7a5R/CUhJJfv5MfNZgVfttqfK2UyYegwmL+ZmTKbJFG1YF9xD0aYERMbnwPVfV+Or3CQtX66BQM5Fxv2FZKAc+5VaNVKZDinAtRM75qMLX6GjOeiHNHXEtltZqwmjJUXZ01WySmLgmlVudzdHmGKK1mnpy82wUmhV21lckiMp5sFo88qlaknLC/WoNIsL+XfV1mLIt1SVYkqFiKK9I74lFL9m7Etl9U/x4bsWjkB/SoQPTbMfDpzwgG4HM2SYLEBIh6dNhBvsT54NFNMi0RqVikYg/Awf4BTuYLXycKVK/QgkUKxKNNoTMVEbdpyojUaUQzI7XZms/66+ScFQzOXGhvkQMihz2uVu7XjCDwB0enAwr4OARAIuocE2ZuURp1B9AE9Ay8chDiBhTVieRyJZIS+wXaGouKItndEL7s8ZYGKGfHr+H44gTbzRlWiZBqxQrAfPEQvGJU5yaoKDhlQ9rFJkyF4sGdc6+WkaZTImIlYwJClYc4P7kDkEJrwVIEi4uVAWgbYUw0KRYuvnHzEJytOaFFRi2kWUqFVsH+tPLc5tZCYl4tQQr89u9+Fr/z+19wSWfCjZs3sX/rFuZa8frDB7g8PcWv/+rvIKWEonZfIgDphMwZ2Q35xGtMZMqUNS1YgZD2MmhKeLBd8PD4wkAMPP8pinKecePgADcO9zBN3uxMGCW4EGrVQQrF3mTlspwYiYD1OoPTGksVnJ1fIEGRJgsBJ00IRV7OQBWGaIaRChdslwUmGZ5b2NQ6iFaU2Rjs2VEMex+dXt1gHkNmQkoC1QqVgmlKmOCVI74AoXDvyEBWWQQnRwtOi+B/++h/wXu+9p14uDnC6w9exzvf+Q5cXFzg4uICe9NNMBSH+3t4+9seA5OpZCqA+/cf4PDwEHt7a7z+4B5UFeebGQ9PTnDn1Vfx8OFDHB8d46m3vQ1/5P3vx8XFJW7evIGn3/423NjfB9cFB3t7UAjOL8+8OiXh4uISt2/fxsH+Hsj7eWxm68jNTChzwdnZKfb3V5imjJJmMAsuLs+x2W5tvjtr//zsEnMpOL+4wPnlBvNSkKcV1qs11qsJ62nCrVuHuHnjBh47OIDC10CxHiIREVYvVQ7yqXWA1SE84x5jtCWAti7wCoaQlYgKAxEijghbztY/pkoITXnpY6nYzjPW+/seKVD8pw//Cj7yH/497r7yqlXhIaOWAktHmdQ82DgEFiVSQCp0AR68/hoe3nsNpSxorA4lqJjaqKiH+6tXfsC8VGEDoIlcHwlDCgpRrSFdJEw8LUdA0KgJu836RAzEMFvloA4N5HrKzsBikCejSoRgXXZXKQMKk0xYKogS1nt7bY5Ktevi6BmjALEJwtVSAO2tCFLOSMmiU6GyrWLOSC2ll5cuxTlHBkArLH0hKuCawaHNItXBm2CparpJzN7vRlHr1kMIcDC2GDhMaxOeI2rAgWMWca+gUhinpopCyfZP8bROYkvp5sxYyCuLPFrByZykUhk5T4h0afBMcjJCapXFAYbXtFACK7BsFzBZFFmrQmnga6lrgsQzRJd1iL43kd6N1/p68RSQRyFLtX5EKSVwTo1wG0kYcwA9ezIAkJ4yghdB7CZ6W7SuvT/ebWdX7cTiiOIPQXBXw4XxBlvk1KJnIWBnkbRwUrRHfAhNw+bNHG9pgHL64GUwKdayQBeTmS5sqVNZzGu05k4KLt4uvlbzyjNjXhbUWiAe3g2SJKS6jHrBMis2mxnzrE5aq22iW1iOPJxq11QFODk/x3q9MtCitrBTzgZKlgXEGZkE8zxjWRas8go5T+bFcoIoYXN5iYvzDS7PC96BfWzqjHK5YKKVqcEWYJVS0/aoi4C5Yi9N4LxyJdmCNSnyVMG8QLhi0YSLy0tsN1vURcyIZ5usG52R54SbB3vQWmySMSETe0ml3SNRlPEBtRYjbLHpEdZFsLdam45LqdBqUs/ICWnKOyFk8vrbuRQY0zxjmhJUGbyasF0WqAr29tZQKRaZyRk5lE3ZBbWgyGyNvogY05SsIVcjgBmResorZAAHhzdQasX5xQWWUsGZ8Ju/9eu4+fQhjk8f4qVXXsR7nn0HHh4/xNHxMRLvITPh7U8+gT/6x96LvfUKd+9Yyuzua3dx89ZjeNvb34bPf+FzEBAuNhXbpeL+aw+xLAUXZxfQRPj1J5/AdrMFp4T3v/99eOe7nsXLL30R69UEkS22mwuspgmJGSdHx3j6bU/jPe99J/YODnFw8xY+87mXUasiT2vcv38fr925gyefeLJpYuyt1zg7O8c8z7h18xA3b93GUgq+9NJLmLcLLi+2WBQ4P78AOShYr1ZYTRm3bh3ive99D/6Pf/bP4Km3PYHVekJiGAg63AdIMW+3mC9nPPXY46i14uHxER6//ZjpA3m6QqEQTm6AKi63Gzw8ucCD0y3Ozuc297al4HJzie12i8QGhgnU1oTUGYkJF2enOH54hJs3b6CKYilb/Pqv/RqW4yPcmBImTpgXxemyuJopoFzBycA5YEaW4WS/4h6pCIgtamIzKA0VMLGds8/52MwHDzY2apheEVSgYdhKpOKaDUARSxsEEblWbUbc0mWeKtHaImWcEri49xpKsGrKqIngGkh2jYknYErIKRtwdFLjKpn3T2BQlP0OvAPAwDp7is3EyGboEikBq1IRvyew86WkIsjhjS8jugPA4GkjEquQocTNyUmJsF2Ka5gYF2W1WpmTYqNqoE0MdJRaLCJCgML1qShDncwMj4GVUlBLtSKHAF4JLhPg0RF4VVWtELG9pIMUi3AaQAE4udaJczcUapGlSGGrejWZlySD/Pq82gle2YIexbB0l0dBfYqRV5Y2WhkiltY/F99HpCAl5zb1tOH43iHX4tNX2/4tIh7NQ0st9ffaXGciVAayupPnACP6+RjxPCInHiF37g2R9fFRDd5NkHzRuT5v8nhLAxRsz3FwsMa2LChOOlv8KRQA0QBPNBYVQaRAOCGJLZTF1VEN3NnD38uBDBXn2wVnZ5cQmdDJT66O52x5k6kWFyQydL7dLq6X4YI2dYGKhTu3c8HM4ux+waIL1mTiRBFq3y4VpQjOzy5x5/XXkfas5jgmv3pe10K4ZJtvLdDMjsNdmkgqKpbG0i9iJOJabdOooCamBK1YREx0CDAiH0sTlkspOXnSok7kJDeZC9S1KJalIqcJqVrqRb07qZW0FcwXSysRZGZMKeNgWgGw8Gf2FNNqtcJ2s0VeJSx1xrIp1r+DK1ZTwq3bt1FKwen5uS0yMS895wmP3TxEIpixV3XirAHR7XbG7duH2JYZCRsQr6EgLPMZ/uOH/4PJ1tUFx1/8PKoaeNpcCqaccXd/jc/+3m8gZ8K8ufAKOtu8eZUh5CXbxQFY8ehWsYLy+2eXuFwWgDOO7x3hS8+/iFdeeRG1LphYMRFhf70CUYXUii995vP47V8HNGVQXuH8cgUVRi2E8/NTgMi4KipIzFhPk6cVPZyv3o1XK/bWax93A3GlFFwqcAoAKnhFCZ//xGfwW899HO//wPvw9d/wATz79Nvw8d/5bTzz7NtxfO91nBwfY5kXfN0f+6OY5xmf/cxn8Kf/1Lfg4OAmDm7cwt7eHu4/uI8Ka3D08iuv4pU7r+Pe68c4PZ0xLwXLUqw0V4LMaykHAsAiqMsWWguAGVoWbC/PoOUSDEJdDNiUzQUO1hmrbMW30yphvTAuLubGWTHdBxsH9igeqvHUesjfNt3q5bmm0+IpSN8X2B0AVEBIISzINHk/FDMirdrbOSutJNareNS1IFRhuhxq323Ro9TeX4pVIeZMsMY/FglKwXGwUyIxdyEv50qxwpoIQlwfwwxnZtdp8UZ3JmRmgmXsQMsMlhlWqSbQqOKVKi1VYQaIQchpMjIwyMCkhjCXAjul+SEsB9easTLuWhUZCVI80sEmOZ9SGFnfwWKOBJAKonCQ7rlaF2aPQkCtaaPpzth74URq2wsDBPjY2o5vTQw96rEsCpCtk+JaTmnK3mrAwM2Ngz1AEi4v557uaAEJE7dkDv0PIGrpo8KROVmEihkKmxARaQnwa01BA3B4xU6LxUVKMgBmj0byADxqm5sGTiIIAj8v2vnVBTlhjp/Pq91SfzJALDAhyAF8g5ybyew95SpYPF1UW5zSgJnbrjd7vLUBChWoJsxLweVmaxsOLITnKw/Rz0Zd0wGulVG2S5uYpaqrbgrWGdBsHvfFrDg732K7rSaKNGg1ROhL/YEYuAkRJtvgpmDsqydO2XQFSp1Rl+rdTxWAYDPPWKqV81kuMEHco5wfHmF9sEaeGAKL+jAbQ5woWZi2mHe1zAWAdenco4SFPMiXGIsULFUgWwFVNi4J1Grf04SVb3CXywaTGqNfDOphPa28rLBvIgbGrPQwZ5hxqBYlymCsp33s7e2BEmPx9Ncqs4f0E7KHWpe1K/yKgqla2FUWaN3ixvoQ07TG9mCCMmNbChYhnG0vkDiDphUuLzfYbJ1vgUucnV9CZcbh/h6efOImHnv8NrQUHB2f4O2PPYGcGPsgHB48ieppn812i2XZgmqBLFsUVDBl7FPCes/Kl5fLU+him5Y4S49g1RpYFvdabLNdpQlgwVIL3H0HyMShqjK2FzNe+uKXoGVr3jcJCgGbpSBlgNmbyG2BooQiF1gdPIkbN25gb7WPl5cNLjeXyJmxN00ugT83T+XycsblxYVFDybCxYV5OTkRpmmFJmFeq/GlFChLxsUXjnDnpRfw3K/9GvYP1nhw/3WsEjBJQeKEabXG7/zW7+Po5ATzfInf/Z3PY//wNqb92+CUcHG58QgA4/JyC5JsIW51IwKrWIl8v/F7rDy3bs9wdnwXiQuozijzDEhFzoLVegL2FblatReja0woKdZ79hVw5eWojlqvCFMScE3YXNj6Wor4GvHoSQWqVpeGpxbdUAEou7YM23cVqVhT9tJqAomX1nPyiIK4iJ/pe1gBVN+kg5Fi+XlvXeHrSbQAWBtQEquqMkKuV/NphXoYfbWySIOwe9U+nnYu96JVUVok0dIs5FUiIHWF6r6bibgcvJOsKwxIKQQpM1aTRXpSNvAnEmTp0L7wdADIhcLEwYTp7eSU3dgyajUSr6VqehRDtXrE09O1mkyF18nyAEyADaaFAjXVVU6mOWOaltS/lxmEhHleuqFXI9paqbNxVxSKqpbmr0UsckK5CciRy1eAjbxP2bRFlmqRQvaKKtUKSkYSNgOf/Nna3llqBZWliQyqyxL4NGnpEHUEEJyOAL4RDVGPjEDUW6qIOWQTt3u0xIufe0An9sx7p2wD2doiKIkN2IKMBN46gnuRhLjIXpDeDau7MrqXLBt6IavYccCqyUUP3jw++eoBykc/+lH803/6T/Hcc8/h1Vdfxb/9t/8Wf/Wv/tX2+7/1t/4WfvZnf3bnM9/yLd+CX/u1X2s/b7db/OiP/ij+zb/5N7i8vMS3f/u341/+y3+Jd73rXV/VtSxlBs3mPcOrDIhg3jT5ovXoiMhQzeGh3KJWT15KRVaYImJmVDWAsoi11iZii0SQgil5e3HLg9oGYouxeTSee6vVwt7k/WpEe+h3ma3TZzS7q7XY5BVTjo3+MeLodnu5wbIkpMk1MhiA1lb1YRECU9GMPHnBYnl5T3HYIidkN5RSxBdkQeWECrv+ZTEymThBM2fju9g1W27XC2KhqEgTULBgYsJ6LwPivA+eMdcZZSFAQr7Z8/JcnedTWlxTxTbUWgpyztg/WOPw5j729jK2IrjYbnFxdomLjWC7KVivDkFEODu7RHHPUqRic7kFM+F8s+Dk/BKPnVyCoDg9u8SNsxn76xVWe8nLUjeYixn2vYlMpbOG/LmF30HJyrelYJnFnzn8OZvORYTUOScwi5P6BEU2AAh7BzexVCCDQMUAq9QZiy5ODiYQVQAW1o5mdaSMuQg2i4A297HUihu3bkJyQeUtpimDM3B5edlKwaO6apkXjxqYsBcAJE5IyXvPBJFOxUXyFFoWkBZczlsc31uQSDCTglVM/XgRXJxucXR6CoFis3kJe/sPsdq/hSqWfppWN8BpBeJsUumwiGQUIYgDFcA3aFHM20vU8yNsz48wpQoUk4E371pRxcjGpap5xQgyqVeppIRpb4XoiVWKcXUSEaRUlK2pOVclT2nY9xthz/LmAhhhUr28koGoCAFFWhd+PdX3l+jVQ1A1OXMjSnclV6KeCglDbvuFICVLn9YKRFvvVplFgjyRqVG7ujV8n7LIhGl7iKp10dBeBWICXmKcNIGVNIuakCMUecpYrzKmbAAmWmaoWmrJDLx1uyU2MnD2kt9aql+rVTRak20NewnAwJmRkP2KKOTkyct/vSIOGQSr0LHD+RUe0bDnkBpJ1zCQR3fcaBdP0atHwJkr8pQtChY2QR0ctNS8euTbUhLxfOGpO8uy9fYYpqti9yIiWE3snZLJJAmSlzSTiWQG/VtEei8c5+FJLRBhT5UYjAhAGZSBRNHTCD4/A8j6uZia0rbC9r6UjZ+nCuceeWhncJ7bSQhoWi1iKb6GGyKVZD/Yf4JAUOhNedEiNmH/elTHq9k0KoUcZI1Kh2/i+KoByvn5Ob7pm74JP/ADP4Dv+77vu/Y93/3d341/9a/+Vft5tVrt/P6HfuiH8Eu/9Ev4hV/4BTz55JP4kR/5EXzP93wPnnvuuaHXxVc+tkuBAKgLOYPZ+gm4HmfbEKJ8y+SRzXNUnzxSFDorKhkCjYEMUiinhAzyjqYKZXUA1EPUiYemgYimXLX93kBLhCmdpOkPnDnQLkOqet+E6huvbQIFFsJNok5MitnUJ10Q7pRMjn6pputBZHll9bRjyI3nlFGo2MZaFVIKhDOqEmYVENsEF7YNs5a5CVmxi/pItWqHnE0PgpNplVi6iCCLp89gIm5LNVGkqgIkCxerVCcUmpfNamWXooq0Ah6cHgPnwLbMWOaCzbzg4tLHark0UaHMSNUUN5tiKVm0bDNXvP7g1FNnhHo2Y7so5GLGsmyxmbdWacSEzcJYk/urYYxUASoWdfOqoBQVOw7iKsjDxoqJGGDFXBcL6cO8DJXaPA9iS3OQGEeHFJhWoSBqxkDVylSXAixFMC8CEka+OMciW2znGYKK7bbg9PQU2+3s0Qi7/epdYkM40toYWJ68UHUjxaCJgKTwoAfIU2K1WtqDJ/NMz5cZOU+gueB0rsbfAjDPCzaXZ1itjpHyBOaEG3uP49ZjbwchoUZtd9uk/fpgc52QkHVB2ZxAtieAVhccU0g1gy9gAxlVMM/FgLOXiIbnR2zAYntpgK+KRUOngz2wss9dr5pRWJXQEOYGsRtl51r4Jl88chKqtS3fT8Fv0lbFQsTIE/u1230b+VXbAFi/QQMKiKZ7lDx64kCuWwpzBJIikYEqI40SWnO6aB6K+Hvc3wCTnKCeHPAKqtWUsLfOYNe/ib4eJuwG34PMCWPvBm6lo2iiXtEYsRFqtaLMAkU0HdTW2sGa9lXA+RwGlioSJ6yy7RdBMgaM91CLlbsya5hJm0AKRM1rFUOJxk+JdEg0AITv9z2Fkth78ojxT2yvt7lu0UcvW/YoE7mKo1SLsCRP3czzFvN2BqAebahNX6RpAoVui5dCr1bZnM7aAUAAbHs69jkrZTa7UjX4Gx6dopAiAAjiBGhXinUukznpMQvQov5WkSdACtou+qLU0JThBgQtukVAqPa6bk5Wjktt4A7kfcmc7B39lqx3UL+GAD1v9viqAcqHPvQhfOhDH/qy71mv13jmmWeu/d3x8TF++qd/Gj/3cz+H7/iO7wAA/PzP/zze/e5341d+5VfwXd/1XW/6WkoVbzQV4mEeahRBdD6NHTu8FnhYV9SqapbZyvOm7KFJl0auKpi33mZd7PV2DDXjlp9DyyNbp0/7fZSsoYor2lITpGJiLGW28Kkj7rJUhLQ+e+fUmGbVw2vGXA+kqgYOAoRpZ/wHKzwlb7VOwfy2LqOcPP20VPdWtIkNkQJcqxsTY8DPxT0B53ssS21e15zgngsBbBsKYJ1Tay1OsjXjyV75YSqnXrqoEfY2IamcErJWXJ5ceMlrReYEpoy6AJkZQgqigpSMt7J4Y4iQdCa28G2V6huch5axYF4ElbbYbC8xLzMiFLydgUsyr3BqUucFFTKU8Nmzzi5SVUUaECTfcUqt1nk2yh3ZiMnRNyOzl9lCMAWjnQ0UmmdffE4Bi1TMtQIpocqC07MjYGPVJDlbaqdsjSQuVIfIjvMgPLZbFw9fSxhcSxEW2PxcHEgkf1ZlKf6zifSp95ISIsxbK2M2WxJjXZFWE5gI77gQPDXtoeACd/a96dyOJ1VR5gV1XrCqhC0VlGUDxgaoi4E6r9TI2arElMwQECdst5YOlYhcioJrRa0Vm62V5tZaoZmwv9prqQeLjCjgUZbu8GmrOCHY2izhJIilgqILM8xxNaUdFTMgvi2IiMsLWC7eNHpi7oQn72RPaDM+Ui2Nk5Ir+DKueL9oDg5H1QQbkCcCiJP1AIpIpBeQE4XQozu/fq6cGatVQmKxHkW+x4TUu2ptKbIpZ6xWxr+ry4Io+YWGOqoAXJ3o6vw4jQSRDVhE6hSejqiuJcKux8OpdX9Xv9gqJpIWAMj2cOeNaHB2TFAtzt8NHztvxj1456mkKYOTRU5rKR45IKQp9k9AXcdFam2CjMHFiSZ4B4eHxjFMBCkKZ4sC6lU5Hg0WrUauJcJqsnFkJhQ4cVg7qGwVPi19Y+nPGhpKTffEnUOx+ZnTNIgVdnBLHgkynFTbvqUUdaf2HhszQyOtZ1Cy7tvEQFL7DNhL94mHzwdK8aaVyZqsUhJQpQYCDbTAHfMRgH/l478JB+U//+f/jLe//e147LHH8G3f9m34x//4H+Ptb387AOC5557Dsiz4zu/8zvb+Z599Fh/84AfxsY997KsCKItUZGFUMhKiTTiXXPdNLXYdEmuoRhbDtBTOQtaJNMh0bB7K4l06l2IkwypOIfAF79pKDfTUKs4md42TpP4grC+FheMAoQotg9cltogsnE+wKwxiWkXIh5NHYqpYN1KlIOFViBjq9byJbTThGRIa67rW4iqDyaSkk/ERyhLgiv1cdrqswHpK4CSoKthuCxYlpDRZ1EXsfoUAcWXI5ADFWrxXj9woMot7EQDYUl0mntQNVlSihPGfcrLzOBucOIMQgCQMXsFmO/fQJQDRZMNRPMIDco4aOfPempwVtkoK0gkgQIp5SAuM/7FlAyem0xJjGmHMUBKNZo5OMxEFwbgoUj3iQoScwwu0TSgD1jJBFYoFKVtTRhEnmtUw5g5+mTxl5PnoUi1fXowHpIva/IIJoIV2UziR5B5heMDkHk91AAkAyrZJCikyKxJMZbVotaidEoqqrSl1zoCYYWNy4pxUVALu0DlO7r+C/b1DzOWwpSyWsoAALLPxS6hW0CwoSaEJIN5C1KXHYSTSom44CSCyai87l3nytSpYKuAlr1KNh6UqmNYZpOLk09CXsaSC8WIMxKONdDgc5j1zUk9hOACNiCyAUrGz0drYOpmUewS3ecr+QKIKJCKpAKNUU0S2/d7nWCLIsoBShmkHBf3f1n+JKEFEd93JsFRBbfPGSJuKSqazYU5EdGinlksxLTeL6HIGch3SZC6Pr8xIye5NakQcFMnXAUCYVqZDVNXBEQHsIoD2Xb55wqqLlJxPokA0y6xafe/1hoVtAOGcqR7hjpke/2aypnoqXfXUNMaMb8Nk9x7NFOM5GW+IMXkKsEVgANfA8ecdlY2TVV5WsfRwRC7g0QtVF5Os4pzIKLtOHhnp8w0ImGBAgf1ZdZG7qDQzEKQ+jhYgUsD5Lu09cL7lWFXl99raUXjUsWkXgbtQrXrzzRgbu1BLPQ2xHnM2gaaB4iKRFEjfoz1mi+FOx6Nd07/c8YcOUD70oQ/hr/21v4av+ZqvwfPPP49/8A/+Af7iX/yLeO6557Ber3Hnzh2sVis8/vjjO597+umncefOnWvPud1usd1u288nJycA4JLmXmbGES5Tm9QwBGfkKVg6AVYpoNT1CFKyhoFmXF1MJ6/MQ5urRR58khiy1lZ+a1kaIxJZSNQ26+hHURUQBylp8kXKgBZpYk1Eirm6gffywijFY/hkhC0eywePKTALDUa5okVXrDbfbFBUCtiEV6hLtavJTLsSsUAt3UCuBike+cgrTGsjkW5rgc6Wyy5lboheXQ48csCQ6NVhOiMiisK2+BhWaWCcHgtPh+HnIFmpYGFpm4RH8AEsBj6zYrudEU23xDcB4/+EV+a8GxFLxZA160pMXn0EFFiExdtDmgiTM/XzNIFJ2jlIgwDoYV8CmMRDqtQavVV/jhYVrc2zM8O19FAoudgZgCJWTrueEhInm6vSN0ll8uov+07ANzV1Q+x9kIrzplQt7Rbvi3BtiFpFr4+cV5hyRpHSWgFYAkvAU0L2+SpL9WolB+SRtq6C6hoaytTKeIkIF2tgbz1hpop9FZeZAw5WjPV6hWVOuH/v0jbEgwnL5QUSWV8scYEFKuaFc7HKAR86CKx3VimEZV485Gz3t1qvEX2aciIcHmQvU1VIZdRK0MRg33RDMKo56V4uCTcMEQXllN0x8PA6xcbc9SHGdFGkZsP0kEdzg9hoGj+9asN0QtzIpSCuumdqJVlYpPqcMoeFYddSxSOf4aJqT2hEOipKQRMH58HvDUbGLYuXElvMHiJwcG/gPACXCY6xFRfUCpBxF5iz0/o8FeFznHJq4X1bPepA2xaJiDcKZOOhRfR4qcWVTC3tQ0HEdFAdqsamTxJN+hwIUjh7/dCIGrrAICfGxFbxA+dGBTeFI4JMDYfbfp5cpdkfeESmk8srhGhaGr7Y+CmucSMG+CafY00hFgZoIrJpFAfbIzFEoQBLdwVfjzQ1viPF+XwuxTUrebn2MD/bvmBf3F+PCHisB3hEySNLRuSPSI5F7aw6ihopVoRNZE8zQIKUsxPQY64zcmavNntzxx86QPnrf/2vt39/8IMfxJ/+038aX/M1X4N/9+/+Hb73e7/3DT8XfJHrjp/8yZ/Ej//4jz/yep0JSWGKnhTeppNSxcpq20GGTqtUW1TkkQsiCAo2tUKWAhBhf38PtVxayZQ/oIiE9PQJLCwGR7/DtQdhiL1sUQTgouCVqWVSshr/pRSvBHCvBGjy42GZLWUz5CAHdyK67UbfCVtkhqgNxfdcIeIaNXC2GqEzK8q8gLSrZFayUOw8z1jKYvdZBLVo89pKNP5rxhu+noyoFkbPgKJHIcgnKgMY5JaV0QCHqiJZva7tEg4kbDz9vFoaYAg5clUCcW0cJvFS0uoE1IwMncyLt4VsKTUCXDciyikdMLGn9dQWK0fY3b1g9vBngMDQ0AB7qq1WJGvMYhwekQYSrMTaNpsiBSkl1EpY5YScgSZUX00VtlRLOWaKnksMcR2ERLCokgCyoEVKomNqTCVq4VW7zpQsxA+YQuRcCrbzgpQyVm5Y4kpEzbAkB5dVFpTay3UhBE02NpwYZdni8vIU+cYNrCZBLRXrVUb2MvK0P6FsNpjnBeu9FVYTcLm5cFK2GS2uVi4dRr6IAGSRpkVqI3SbrLwxXZ56/AkAQCkHZqLKhXM2LFVLCmhlVJmtgsVyR3YfHsGz8TESai2m12FA1gGaP+dIb8EcVat4CZ6Xj12r6vP1pr5POdPB1o6tTMBLYK1Nnb1/2lshpQml2LMPImMtBXlKEI3uxb6O2n7h14ruISsqVsmAcKZqcvJSvUliBTShOv9CwilKDER1iljkjJhQlwXzbPyL9d7Kje9iJbxVm3BaSgHqvZ2e9GgStf8bKd+k9z06tbhCqqo5B1CkyeZDnhjVOSHwtWtcNm29iSIFFGOzwzWCd6rGEJH0IWQ1MrkQ7YBIwIshRCHsrU4KEH3HLGLgKaZhv00p7QClAGXQIPxGqk67XdHgvYwRIou2sc95i5INACeiKsw+EY0rSdRFOMNm9bGPSxpsgkfSOFt6Kd4WTTNjR+DErttFWGZP/dZqq8LnN2D3pqoIlvB6PTnBu5Nsv9Lx37zM+B3veAe+5mu+Bp/97GcBAM888wzmecbDhw93oih3797Ft37rt157jh/7sR/DD//wD7efT05O8O53vxsEYOKMrYt4iQBTysi6tm7BzdoTUk5t8qgIhBTTijDXBZezAAIsruNxfnFiqHwGJlr5pllBmbDKExReslsKIs8ZRzC+AVsQ7KqTtVheV8zmIeeMUha4ejNMAdGk8BMzpogIEAOLo9fQFXDjTL4wmay7qYoA7vWHtktwWYjUoh0QkBCqMMyBKrCW8samVxEsYmH/zVLMS0pRARU1/t0ztVBxkM08bYUIe1h8lkjQsuH2RheG8q3bdiOwmCdHXqVRpDbSHMjBpRuSJuOuHspVAMUiQEqCVTbdmFIWX3gVkAwIufKlpamscsPHXWH8BH9IJH1JVy9NjUOFUL2zqwEkj8j5xicKSAEoWYTKFCcdZia7HhMyihx3RVU436K23HNR065pfTQA91o97IwQlwIQZDufg5FCjF4j8PdbM7jac/ghfAfTjlmtMhK7RwQ4OLFoUVXBtMomLrcUC4vnbCBncXVWJUzujc4uPS/CWDYbFAKmVcKtGyscHV0CYKxXCZvLKC8FbFvq4WC71gVECXm1Ak0TUhUvxwU0m34PoWJTCsqyYJ0zGAxGMY4ER1dixs0bpjd0WQjbZWnuspXFekq3MlTZdWfUejTFs0eQKG1NtDlBJsiWXEOiQn0zFg8s+Lr1tG2AQPLzWGdk25KlLJ6qKK5jIp4KNu9VPRIW5aTwtK94msm6m7ddCUw2t/bXEzLFGjLisShAiaBi5bYCT2lX20OK76MGVl1j1NPGIoLLy2pK0sw+pxSCCqJiFTG+7ANQJgcQouLdQmHl99Jl8y116s34POqSU0ZBgbL06FHxCh71PUp7OjMiAHEEFwqwaErTslGF1og1UIsSRNkZEZpuTzTzOzu/sKi+VnBy0Op7OLf8kEXYxMeYyaKh29lI6FNOtm/7cwwnhh18hJ6PwrMEQp220ACp7kTJKUBYgrV3aVGWaGWhw5hwV7VVIMqwAbgasDrRN8CmvdVSnh691uIVW+pAMvdoGQHEFrUXdzA4rVrU8c0c/80Byv379/Hiiy/iHe94BwDgm7/5mzFNEz784Q/j+7//+wEAr776Kj7xiU/gp37qp649x3q9xnq9fvT1/TXW+3vgjaAsFft7h1hNa7zvvV+L27duYZYFtQr29w6axkgpBVOeMM8L8ipjXmZUrTg/Pcfe/j6Ojo6hBNy7exfbsw2efvodODw8hJAaoGLF8fF9bOqMo9NjfO6Lz6Oxo9UNxxBiD6OhEl1IEW6hmfxIIQgwWdzWPC8RRG4zJ/eWtAIlwoYEaLEOv3kCsUleL6Vgu/XFlCw8G0RUpdhOrUJCq20yFhY2Ixdkp5TJCWze62bKoGxKsVDycmtBZqvaqaVCkYAUlUiWVIJaDjh5mDex5WCj/CyatfmAIcEUYXPOUEirurAqnAEQUA8f28IjgEysLrmnSATMKLDMqUlhT2SN+ZCsdLjzbrq4s0U9zGOxKgAHJw0JuXGJEhn/XCJGEo+QeLiVKtqmYGQ4dm8cXu5aAVQQUgNv0SkYIKzZFN6NkmIEEiZr8AZK3k/JlI8P9o0fVKVXZhBbw8UQciIIQIqcDSQZF4axToz9aQ2eGLUWU9EkYL3KTpStDpAYnIGnnngce1O2KjgLB6AUxfn5BaZphen/Q97fhdzWpWfB4HWPMeZcaz3P3vutequsP1PpLx+dRLuTg0ZEsEWTVuOJiD8Q+DxRyEHAJBCSIKgn5UkCHhghQo4k8YegJ4o2NGJEiITgh+ZDTNIxYHfUzk+lUlXv/n2eNeccY9x9cF33mHPtt1J5y6ahX3olu/a7n2etuebP+Lnu677u655pV35dH0h1e1dnWmB5rAIIK1A3ZDNM80zg3Mg2pZwwnyeYNawLgWrJCedUYCWNRn8GpmR7A7BckVvFlAGgwlNjGXvqKDM3irkYPvExpgJ/+8vAusqxVAxT611snAKL7jKbogV6AjcJ1667W4wfmU0f7sVcE25FnGYBvOL3sWnGeObe+LguGN4iHbQQSJGKAOB9sE7wLPZfdgbK95ciPYE2KQoW+fvW1Kk6ZfVc4ZiL9ad3RykEZAGiATZFnE8Tti1+xvnXGi0LGMwnmGf0yuq2aOo35pFTktJ7FxvEdTJbgksrMubV2JAF0g7pDFaWAIB8SowgokXQoPsEQExs0TEi3RTN6xxQQUAwGbFObbUdWDHaLeSUcD6dcF0YeMKVajMKyZNMLOFiqLCLXrelMqiACSSZAlAbz3+vqFFaMWVKCcLeXqtVdIxuraObghdTcJWAnAp6r4hAi+OT85XLLgOWHSjvYGaw051BSskZ01wGQ06JBFNl7Cm1A76bTsfUKkhb14br+gd5fc0A5fXr1/gv/+W/jH//6q/+Kv7jf/yPePfdd/Huu+/ic5/7HP7CX/gL+PSnP43/+l//K/76X//r+PjHP44/9+f+HADgnXfewXd913fhB3/wB/Gxj30M7777Ln7oh34I3/qt3zqqej7oK2WgesXJz/jm//kb8Pt+3/8R+Tzh677+s7jc34uZcJzPl+E2685qgGgBnxLr2R/fPMIArMuKZx99F1/87S/i+uYRn/7MZ/D06RNWkswF67pgWV7Ds+H5mxf4F/+3/yt+8Rd+AcvDVeiVCxGdLA0J0YSKaYmSD/XzzoZ4pYjOFOMRVtVwLsJTNsCYLmBKmggZLoRa2GujlILZC1IGXVinRvEdaCIFVRV1XzmQuuM0GVOG5sjFMc8JZSpSnc+q+EkwdFxA5iAYmsjhk0050N+qyomoY9CqBsyJQ65uscHQ2CzozqDJo+yW4sMmVfxtugsqtxz/1uabMhdWmCOnSRuQ1PJJJLr0RL01peq4QHkzLQDYFfQ3paL9LfoV4x7kpFI/QBoSXnvvTTb9WaZSaqvuHb0xh50z7zu7ykZqjhF/7zL1Q5Q/yysnJbQ6DWBVpsKNwqXpUZox61iO3e/g+viIZak4ny9DV9Bbx6vHK5ZlQ8oT7i8z7i4XXOY5MLU0UB1P7k6YC9Nky7KiNi6s03TGPJ10D2QBD6YAciKrRYO1hvP5QhEhOP5ONiMi0WkuyCXhuj5ysSuOomue5xmwaWz8Oee9kZkxen+4vkFPBbkBW+NmWcDqG7akY5Yx5agikY6rd7ofSzRcnRF9EqCmCRdNGyOdkowbbJTD5sSSeqZ6Wd7uI38bm0BU5IQXCss0c07YKu9ZllN0B8XL3Kg41JPE5pxvjt4MVWDRQRF3SuD4qRvHYaaLbHJQG9AFUIwjKHQQTF2wfL8bvaLGIA92wRpTKgrOamsjZR7BTehZsnQfdaOhWEpF6Tbufq1CTQol8u9MUWSJw3NW5Vs62jYAQGffnEThO3rey4O1BkcX9Ji/TIVkzTE+iyLRahja8flE+hwHjQhbDLTW8eTJE6AXrNuV1eKqRgJYKt0TEC2Xvat7tcrmWzAqjc07qQvaAUJkQEIr0seGL+FtDzuFI+AAjiDk6EnytnRirHXw4Q7cB+ORtT77AEoeqS6hhSFIFtLwRDC1WTA6sVbq+wEyuo0aRisJH/T1NQOU//Af/gO+/du/ffw7Ui9/6S/9Jfz4j/84fuEXfgH/4B/8Azx//hyf/vSn8e3f/u34J//kn+Dp06fjMz/6oz+KUgq+8zu/cxi1/eRP/uTX5IECQFF/xrOP3+GdTz3By/plnNMdfvUL/w8gn9E32sl/6jOfxvn+jMf1irpuqL1j2za8fs2eJNM0ITXHVCaUnLG8vGJ+dkJ+dsF1foPr8gB3oGzsfnl5ekH1inO5x//lT/1xvPvJj+J/+1//VywvXiKbkwFIeSyeAFDbcGcZQOR8OqFtdCHUijVK7+DUgdTaxsbGY1FEiASkRGvzLK8IdvPNePL0hG3byKAUAiIz2tSz6VWHMpqjbTkAgR/Sd9FQKnwMCKAcOZPZiEgDI3pMolodQ0Dre9TFlISNXH4o3S2HetwFUEJvsqfk+tCGHChbA4AwaAJC1T6qMPQKIyODBLQYcS4iZ8JARykz56IaUQSAUR7uHiCB382GYVrImhZ1LQatVuZyYWK/xB6VjDBdCpqWf7eDZsRHrjlwyrEiZBisKbUWZgMGLYwJI7ra6WAgWRl09v3TCwwXmCVFXmxQeF3eoJwKzvOJf9QbKBiGjEwRMWTwFU/eEt68eUBOE8odN7HamNqDO9brhhnAPJ9gmTn+iL7grOwx62Ocs/oq41TucfnIHbw1bOtCsABuUMkTmcFK8NVV3tp6x7Y29uTqjmXZ0HvF+XxCKgVfft1QLCOVE+4uFeu6EJR2LqT0ulBVhABhUO7xfEaUiBhD+0Ie2f4AISmZfES0hagkJADn6cy6otYaamU7gN4duTDtwcqezCZ38r5QqAoDxa/c1FhxZ56QpwmWMCzaAUd1w7Kt7PnrAii++7iMiNfCiNBQ14j0pUGSjspt39RSySNYYarBATVBDeZgsD29o5yyxhKU1lA6gup2QPYI85QH0AhrBYcqzDq/A5lB31QMWwPWtUpHoXLYboNVAkIXAZgnlCQ2YNsEmp1VY747zZolXl+NlBHw8PAAS5nVSftmNNoPRIl+pP9bc3g3bJ1sadK66vtI0Xq5B2QA1E4gQGCwdePrRtAQ70kptJXp8PMdcLm6yIcGhxFuJoPd9Byw6yx3RoX3kO0fVkD7bs55FEM0aul5Pb0dPsfBZokyhm3rMJvwQV/mb4eCH4LXy5cv8c477+BP/y//Z/ZvmdhoLiUKY6fphJzPI/Kdpgl5Koxqah29N2qtCFe8HLlO8mOA0HdKFAShdpRpwvl0xvnyFJbTqL5JZvjv/89fxevnX+Qmrm6hMChqx9jIo+ImqgHGojUmIQZI8d4UmaQBGHzksZXbdlb/9EHPxiLJqJR9Jfjd8aRTIHakIVZ1AD3K1caQ2KsU4t83/zI//MT2DTN+6GISXBMHwRTt2XxOIFGQBgBJVSj6Y5zo8eJaS88QHMp+40xMW6bCJv5OwcZOn+7XlgSOOPmTjsFd3g9HDeoyKqcolLWxIflh8djPNc4R4/7H/XSPn0WUMz4F93o4ykGErI2Sr4g2e5A66IkGaH0AH16zjUEG1GXD+rjCehqN5kopsJSwbRu2hyamw4AqYIZRPIBkBZdzwXyaqWtItLf/0vNHtNbw5Mk95ssMdODx4RGu8dtbRzeyH9PE0u5N/ZHMd9Dl7qh1k+hSpn9gRUlKbVzHCGZiMRXNbhmoDlzXFc3ZPK4HGEoJllnRU9Kkbt0V3hu2bcOqqqBdMWWqTvHBWphxoa2tom7c/FNON/MmJ3bp5jw1rSHlBrDvAIWdwFtnmpoW8Gr2V/jzrTV4NzJkTt1QKXln6QzwnrHVhuXKtNDpdNL9bCNtVZSqTe7jnrdKIbZ3wHslEBrPgaW+JSc5Fu+VHnVrWLeVqbhpwrZu0oX0sQ6MIMdYZl8rmduST6iVbSCamJeUoo8OYNZQMlNRGc70cgimFWFcr6tSUARubq7qIx8NRPn8CGbNyFBFIDLlCacTGwFGJ+jeWW+WEivqhqMvEn1/OjDNZ+Q8U0DfCtbtEbUtMNtQSqEuZhMFkkLT1AGXtmhUHAKWDafzhKmw6sWdKc66cX0rJd8gEQZ2skc4LMwhfjcjm0GGNSMrDdbMATkJt2CCxcAwle5MxTmDjaj0S/Laab3BW8gMXFkABsPsc6Y5HmubxvdITxrEHMmvzIEv/NqX8OLFCzx79gxf7fWh7sUz3yeU2WDo6LaiGR1X174g+SMiQn1cY+PFSBVw3CktQAzLKCSYATj790hgaDDgmvDiZZSAkarzvgsMn7wzc6tMPHZESqGaztBE8jx0Ku6Opv3a7AABZAc+oyDKGblOhoxNDIQZzBrMAxF3DJHuwLTQ36Jweywcfdd/wEn1+s4/BMU46H0viLwlGQh9cnwgDTpVMZgYadeCfPiu2PqH6EuLx9jU4xr2DTo2+Lifdjx3P3xGoGi8HDhCKfcdTDV9H8Wd0UBst232wE2yITdNOEfbPUV4Noevu3WG3KMRRWHjeg98Tpy/B7DhqweFEn+Ne93HM5NghZqaw3WHtinGQ6sd9epYHira2pFAqn0u2lQAZJthN4JRlfIHYJQfuteGao5rXfHi1Ws8PG54+vQe00TNy9rWPbUV47GTBUnG7t45Sp+dtLhFJVpKyJnlo9frFa9evcA8F3zknSc3viQBhnulwWGSxiLDMU/anI2lMUklQZ0xpAy1aFFm2XDKM6Z5EhCT3f1g1eL+8wtbpRC6ZPVqQWwee7lrEdPoIBgQ0rmZATFmeo+5S9uDqcy4Lgv9erYoQU5KM6n0vXHzIdnaD+waz6XWKnfRfX1KKQOdRnZwx5QnBmgdyMjoKbRKgIOMr5kaXzamuUpR92FF29mKtApvx/Q8L5dofj5lTPMM5YNh6EjmsJLl46J1OBEQWTZYt/HcYGQE+YxVFCBw2A/zwlJCconoXdG8S+Ok1TMAwt7vR2uJJWCs90kVeR3u6uzu1B5N0wRvG16/fiOvnAw3bt4BmPfJa4NJiqaHUarJtHcEa7HXaD3SXhXglzc0a21iVRQ/2rXP2J6aU9n/EAtDa4CBYyjSocnlj+WIirJ+s8hgH9vG79iZHp5qbxDjFsx7gBIbAb8lia1rw7oY1rXhg74+1ADFrQHJZMIUPwv+IOriYwMyRMM+gyoqTBub3z6SSDmQyuqIuLk7N/TUHV6B5c1KQyLng6mdplt35xnTeVJ1QBsDttqmzW2vcA/qHlpYuB9xQdo3ozT+c5AJsXOGYE8i1zjz+J9YNkZpGgKU3S6V+iK9ea9MYASOEdm871Na/LSL72Dm8D8m9mGAEQ/HyDTYhXE4sLQQzmjgBiHdLOsYfh/xq5iEN28a/zw8ZPPBKvjhmG47oNsxgu1gR/fcYeO9fJ/yKgBU66AblgaOu0lNOatoohUG9PtYPD3dgpTjS16ow8OBBh1xHvtFm6s03hOKsaPttjTUa4dXg3lBToVMQI8RacOFudcunyExgGD/llTUx6l3bLXi5etHvHz9Bnk6U18T6cmNC38Vu2OtIWU61BoMuTdM8yQ3Y1fKJ418O51y96aCy3XDcqooU6QyA+Ap9SQwXZsqIVJG9oR0nhF9aRg4ZPTN2TyuOwWvgBoHHhZXkJ1IJfRCnPNTmbAtK1o2TPOE63UhiIOxMkt7TCr01qljo3z/2GIJMmDWcZoLkKiHAii4XKMMuDqmiQt8bRsj2JSU7iIoVYNZCuojOFAaMKktBwUPkL7NsbnSP0bDNBvnBSDTIWhocyqBSVHXWndHC7E+wmhOmonONg1JjRupJeH7EoziTac4PqeCtW/ssCxwUNQyobYKc6ahM9hXKe6bDd0GN9cBoB3w5tJQECDlcGuOYMe4T9TWR/AU0aF3pxWBG1L2AfhCAOu9YZoK2kYgh2SYrABGMSx7vxkBeM7ovWLbNgJwkDWx1JFLxjxPfO4GAYU2NDZZzzhYqBHAqXLSUsJuMsjfRafhSEFH6q7rulXDc7NOoLuqFff1I0TVwTzzNwFc4j4rG+BKWVmHujiMNhKh58kJKhHHADcf9PWhBihVZGxsAAagGwcuUXpQE0BAgiMw2DdiMi+ASrVUZhplnA42FqyVIbMKL1ARfiPRdHBhZOEdnhzTzN4++5+G2JU8JhPC8I3/ZYq43NuIfHH4rx2YRG4xaNHbvfwIZFwb8SiBDq0DzUYG7RnfFP164j7Fb5hvV9R/O6bj3VrwdC7yMOC//YYx4F/yLMAOFhwYFRRxRTaAgAPdD/AqEH0A0gOoO7zs/aeJfqjauflIVGAdhGr9MKO6EeAddS6OEIMShMUjitEVnw+w52CVzS0I2fUoYzGyyBXHs9iNlfp4Bsd0zr75ZU/InmDdYC0x77EAxTOsnJFm6iyGQFmU72TSQwgk9d7grSFPBfNE9qJ1aqMeHjY8PizIlnGeJ0wTF9taNzQ1YESnWLYkbtglZ7IOWviZNj3Bp47rdYE3ikZ3I8WMT3zik6jbBu8N67JIr0LxKdNALHUiyKzwBlH6yq9nMgRmtHm3wvcmaSrWbUWXVUFzplhyyUi5oEi309GQTqq+6WQi3BvmuYDL6DHV5+joyD2j9KzNrA8r9976WPgBoG8VXoxpDQO2TVFt7eit6hmzwi2ZEXg5S5W3WtHAtDIsA+aYxDocNV3sZrwB2HVTW63IEq2zqodl3vM8ocwZ67ry2WXOwVJctvemIoAyig1YEtwwXFXFHk9lEhMh48TEz7kbkpjslBKsyh4BjtpmzMiAFfS2Sbclo7TuFBcf7t9opNegPjcYcyjlSBPu4AZO24eUIGHzMXbJMDTUtiIjY5oniXnBdg9mOJ/vgJ5xuXTUtgJm6J6wrQsc7KEDZ/q1qQRXZVdgqjLJuRyobRuakdZZ+VXKxI7jSg9HsUDvJmFyVCsZYv3WwrkzJ9JkMfidEAZuI9aC4ptkw9x3rEfONKYjsYRaAC4YFpYz8x4XAfiUM5vOdrpc72tzR247SIweUh/09aEGKLEYxKujj6i86SfAcXt3BJvCjXT/rBpxs2Z7LPwAxKAQFYvK4jqAec78BjckzEC6AwLLZzI24/caTKOEC13sTiBgoUvIv6AfgQKV9yQb2n7atl/HkTwOd9EABrBDhI5IOWmLjf4ZMXRjE/UdBjT4zYIqPD/+FemJiAiJY0y6APA+OEBtiCEmarAuAaAG0grmxXe9iktvM7Zr17keWRPsDM/xbQNUjbPf9SIcDUE9jRia1u16d7fDzw9DKTYkWAh1fZjuAaEXEpjQ5/X1XDgOhkW7/XOcn1wZrOP9QCbAw/G52ABHcNDLv2fUpSI5Bb0zaLvfQf3Hqq60URaacsL5MvE6q+FUDL1l9l1Cgsk9tNaOZdnQGhmFMk+4u7/HNClloOqdDKAmmggWY3dZ+nyweqqBtP35dEZOBDnLskhgPClybSpFLTCbWTmxrgRBTUZqzWFWGanLsDlnw1obzLhpTpnl7ebU3Nxd7tDR0L3hUk6oNQO2wToFmlNh/5lsjnWrEh8CsIZpNupzepgbSkTsDrc+yk4tU/ORE1NYOSdqymZqSULU6FDMkBLQ2K7gNE0wI5toOSNNjJpLn9BqQ9tk8mgFhjBq5Dzk5scqldbJFNTakdyBgpFKpL8Ib1jdKmqt2jzZobgkx3Ri/6O6bQhROqDouTnWhWXFuUSEzM0pGdeQWjfaIhjZnZxMjtAUbbp3tt1AQVcn87o1LJlVjxlFovUd5LOaNdarFBOKKcXuw9k0pYSSJ+SsFS/EwF3MWE4sTAAzcOZR1WVwb6zOaqxoaV2NUp2aGFyA87nhcWnY2oru62Djmnf0jSC6Q2y95j6LDrJiY1VzIgKLI4jiep0iePDQcvA9WQ3/HIB7Gi1BYp3vkbYJp/NxdK5brEKml8xYWewQkFro8YyGpz5WsAFAeT55Z/sTW7GMflGW4Ejq3qwSkUP15wd5fagBCnx/mHuYfLsRGYDw8yM8OQzuEYSSf2NOlQNJvjLjQTHHPfZXoU+XQTsnLktMJV40NsMLcyDvUoQrMnUjwmZEbQgNhQ9HQmkhsFcGcPNxRHMoc78Z3OO5K+KDPhtsAy/ZRykeq1l3BipeNn66//eBwxmUo6D2oFwRxznCdP21A6iGKGvEzTj1mG0EHgbqgaiwHe+iJkNwwXxsBtCPb9NcBIX7iff9+fgO0o5alv1e7XqOY172GG3dnH2wGFwJxz2yA/AY9x4Hbcv4/Fv3Qcc82m0dxcs8pb18NZ5Y6glt7UDlxr2twJToxFlSwQYHOns4RVfa1hwlc7M0pVpKNlie0Dswz+pi29lN+Lo0PDwsaEg4nU64v7/H+XzWwkjQE2XdFi0DEkfSuq6Ypol9eQzwXlHNMJ/OyDnjdDphXSVkB+l+fu8GN8dpmnG+3LMiYN0EVLjRVTEkOWfOK66WMGD3t4ADicJApn1jQy8jxWSGERWmxM0XUFTp7GtjSisBGJ5FZNL4MOP5JiRMp0ns1oSoquhOF+CpZDhMjsEVS19RpoRpLiirY02skkky3Wq1YVW0m1PG3flMQXBbARB8EfQYkhXU3OF9E3HrgGWyBqZy70x/iy4fiw6ZUgKj2oT3w9Aqxan0cGElHzU4hX4/OQ/30NDFrOuGVFhlM5VZ4tcIgGLjc4HKgpQKgZruX5eOKgDdVPauusfKltjw92PFetSxbS7dUhob5b5fcAwkMaO78aINUzamMlREkTOuj1emTNU8sbWG8JEaZIYZu4QriDCNBlZ70SBv8iKtVRv6GiSm1LpXCodzpibIj/Mfg+mDANBQOsb9cFfAG2vSLloNRjc6gXOwBsOxr4muQg5zh2WmlrjuYqTsFAXvu25ipWispK7u2/sydgwWf/fXhxyg+M217qZdXGDGpjHeH2/0MZiYPmhIHsYiNo7Fz8a/Jaj0Pfr2tKeGLNgAkHGIhSi+1s1hvSoaEHgagy42LTIGUa1jTt+KrlxqgKiwcE6ayJGLN/cRrR+/O7QtROUhrI3Uyf7+4WtyOPc8PEJcyD9y6QFQoHVB56TLGeIsfbclHFwQd5ZgnGSCrKUF/jTEY1N3RRFsiELWJlJ0t+BKsudA/KTBaLCVQ/onO+4YG6KMd+Y4JjBuju/u6jMciwNGeem4h+BCkDSRb56EYzhZHs8X49lgB2jjLPZziLQgqzB2DgsSExYU9MWRN0PqMx4erow4S8a28a2reg7RWIkNMV2bbXY2g0wAzZ0MWpyB6Ei6bBWvHxa8fvOAlCdc7u6Ry8zTkFkhG08egSwGVb1uG+bTpCo6Rv3XhRvA6TQj54TTaULvGY/XBdfHR0znE6bTTNHstgIOXE4nnO/uxCawJcO6rNh6h09p5OJTUirHbGw2Zts4J4IqIGdHLoYoHGXqlCmd82UmY9VYpQIJf7s6aLvoTasMbopJ+CkresvsZDuCJqOgNhmrKJj6SMBm2FpDtoTz6QzcJSzLglwKEgq6O5ZtxTRlnVtCKkXA6LKPJ9+DlWyGyRJSLmLlwvfCVH3SAQE6uLHX1taRnL48rBaUmNMNyzXK4Z3eI8iAs0LD2BUD3YMcjOult0ykNmtnABUdLRymNSTJXsBHBSOcdvrhtGzdUQTQCY5ocBj28CmFjCeabgo8ekLotThI2ScmZqGjY7g7C6TGysxOzIBl6kbee/5CzEpDc46lJsI5pVtha3jVmKJb72Dnae+otYzUR4iDGTh0GU4G2NOJeEKkea1z//EBuiTq7lH+jrGmxUpys6OJLUE4oR/W/Aissow6A9yUqEw1kxEh72eHhLOtAcZGhb3t2kvutTZY2l1o8bu/PtwA5bhRRlQ5EJ02fbCT6XhQUsT6+Az/7qp+uVGER4Tkom+H9kCbRY8S1nifouYRqe2sRQyK+E4/bK7HkjF3KbT1cFukqeJYnUCKC1EaExADdOjaiA5GhDK2e6WELLQfOocj7WYcvfyOiPgNYJi0RyaKCXaxxdtPx1VuDaXfDltuMC1HkBIUJo2RsAMyvSEa6I3MyA1VuI8BltkKHCUb7QTMTczVnnZJh8Oktw93PFe9svL3gxfRfY4S6g6Tjf8NOnlf4DA4myMrEguJjxEzwLBBugMYigPorMrwluFNXbQxYVsdp3zCslVsq3L/pwmmbqotzJ4A9GrwSlfgYhnn0xmlzHCVf9KHx7FVzRE31C0hIeMyn3E6zbg7z8gJEtpqLhivpWR26g02IecMbBuWZcH5fMb1eiXV745luaK3ivl0kp9ExuV8wrKsWB5X9huaZ5ymhG1b8fh4VcXLhPP5jDPOaHeN/aPWTZuXNA8uMzeVYNbqAiwNvSutUfaO5sAumu3NkSyLPTUka8hW2fAsG69P43EqilodjK4bm/NF5+qY5jllRPdzRuAS4rfGMuwyIVuBO3A6nTmPLAMq2T6fz2SFOsfJ6XRBcm4oVUh0p/uBlmlEFwxA6w2wDVvbdK+ZPkqzLPiTI2W60OaJhnk1Nyyo8FWlouhhv4Nov+FgiqRtlU1VLSwNHDmbetM0bbJsuZHBYKxpfW1dRmfSxORkSJtjW1l66/NEQIhgpTnm6KytVcaTAj1HrXsA51oLw0TO4KqwiXnIaH+Ai9alsRH7KTfl1ujnEUJQ6gXjPBJK+Ab5ralcpK8LEhoojL1exfikhGnOWrL3Iomj2SXXkOOaTU1kGz56se5JQCuWnjkxCZihvcR9nI95GuOFL67tU2J/h6Yea6EtqN5UjdrhN5YOOq8WjS1N3loENlkmcM1+hw3jK7w+1AAlNnoPO3LbK0bSjb5EG65hMBHAYSNHfG7fQ7ifyAnQCQnGZjF2F2pe9j16QN0R+Vucpz4SOcc4/x2c7DvYYDAO1zCQNLpcaHGwaN5PLEylblItXAYIU3TMJsEasDMnOz8Z92c/l9iIA9rF799mbOIXXWxOa1Vamn5jZHWsnHK4JhFwZKhuNnSBytY6joDu9r75zblrlUA3Pr90uKVDbHrICfW3jjruyeEl+6cdgPjxvRjfMSKwUMQPfYy/dUjecJa9HsXLOotGYJWtIFthw0BnMJi8oG1aYLoDlmHesGwVy7qCLrR0b3T1s4mUkSOCJtP3Mx/eG59N78ByrcPV9Lqs8G7IZcY0FZxOE07nCaXQr8eyKi8qXUVr6zidM9r1Km0AN41SCq7XK548eQp/vCLycmbyRXHH5XxBLpneGymhlIqHhyuWx455njFPZ3ghgLouC+rKiprLmZVEdabZ2bruVgNdXiBR2skNJQGe0bvBN5cWh4uqd1WKWGzA0QcLyJ41puksGvP7yByUPKFum5x76eTq7ti2hvNZnrYSjjZ1fHV3TOUEg3o/SQhca0Nvj6OFgVnW/A0mhaFCtjTGZVQWAgRitVedQyeYyezYbdC1pAlsCy1dkSpJSgbKnDEVCjtPp8IUXHPY2rGsVWXjxoaQMwgMDHLEbqT9sxhFTRYSogoWemyOZBhqJUhh12SOXfcMb5WlyRPbPVDIrCnqztk5Fld+yd4Qku+JtB5Zl71PWawio4ISdnBwZQNVB/D4+AbzdIe7uwu8Gh6vD6gbV8IUDIqZii0IbbT9UOzKR0U2LvrHTUWBHNfvaZqoeZJvi99ozfb1K9bYAGoOB1SGPaok5AXmh3UlWdHYEog1PZAwALQQ50apNX1szNSJ2/sAeQPo6P6F9ilYwdEhGSH0bYcd5Hd/fbgBCgKgcENMKTowxjIcZbW3G/YtrxExuh12msjPHUd7/O2HslM+jCNw2TftnVnYIcmRNUnKd0rfYWGQ8350aWYjzI9y2b2mXucleDwefqBu8H5EAy4/XE7vhzTTQaoRlN4urMX4dwzKcZY3t0131gTQgi7tRwM4P7wzvi7O+X2XjtjAd7CkDT6uBft3xSrDJ8poKikt5AC6LBXSzbcfvv/tb/X9X/vCH/09jvf3/Z/FAKWu/z8I1Q5fl6TV6KBOYiozXH2jzEj7u4MsByje7FvHtlSk5JhswjSf4CDbFnbhb968ppregK0+SgCZpU9Qf5BGZ2CzCiBh3VZs24qS2SCvVmDbFPmBUVBrdCeeL2e1IyA70bVRh16E3a4JKFtraLWMTS7njIc3D3jy9Clev37FSA1A+Hcs64JLOiOVPAzWYIaHxyse3rzBfJoJVGYyiG1b8fjwgHVZcP/kCc7nC0qpmKeJVQWtii2h46/BYZlrw7DrBrsXM9h0bYAA0IYXTO9dQs98MDvU1JPRWm0dKU8UmpbCakL1f4E8LFrrZERcYkmlglg0rLhHOrHemiJ5ngcyVGHG0vYsLUB3NgINg9CuCjmYwY3tKrjZkQUsJSNJvG4A0vkEZPmUBOMDR0kJecqAHFXryt4561oxnxLydcW6bDidZpxOE1LZNWnujrpsNAQEGYOSaIXvlWtadz/02yLa8KZz7xSc0kKBaQMDkNOEPCVZ6FOXwxJfVVNJqU+nXBx0X1w/shskMNS6IhbMY00OUW0EE3QXbgqezpcT7u+foW1Af69R1J2yNvG+p3hzpNpC7Mo1CGAaiq4XaraZIn0Tbt5HrUc4BR/W4fGKERgsEoOdrv0kKtwgDdnxU9HY1UfgvqelqcEK0MF7xFYFTfYPh/0EgHuFo6tqakKtTeyTJALxxhDyf8DXhxqgACxhhENOpb6zANBmLz0HTKVmsSEfw14e6HDUWw0A91W6WQYIMRzYCsQEOB7X9v/VxNgpuhAT7rQaqfG3wMlNtK+N2qu8XhB4aUf/AyyEODhyvHEefvgHz657O3wiwEPoTrTpaQMNcHSzAXucu27j8fJNYsBx3yOffdBPxP0bQNwk8A00zyjSenwZGZEUi8u4VQcAajvHsU9mB2RQp9M5fH88l/j8ze3ndaXD9UKdj8Hoev9cGtdGVqqPWwEI8GjBi4HUrGuHK3h8eUW5n5F8Yo6ZazOmXJj2ahV5pmrePGO9VmzWYevGTSRpQeuMnouqF9aVQGWaC5KFuypL4cvkjPyyw6yhVldzPucCHN2f2VsBj9cNU0+43CUkm7gJKy3X5cjaRPH2Hvefd4AeGIZpOuHN60c8e/ZRZLuOPkUmlmlbmaY4nbhpmSXMU4HhhMe04np9RG0V5/NMgDRRWLltG7783nOczifc3d0hTTNTpbUi9Q2tbqh1JT2PwlYFUKQKUuXUOxFkt3VTYMkIEqBTdbeEutG2PZdoNEjwVmuD1zrEqgFksxVYZrfi3nxUeCVVepg75jTDeyMVniIF1QDb2yBYJtgNky5HGlV3tR/mt+1BzGhJkHeNR4KRCWqdZdgFYJfyjmAxDR5WPqrCSfCidU/A7nS54PpIwFamaHfBFE5v3IB77+wIrdDBiNV2AODOaqNoPJiMTfHaHlglUZOWTaJhoPWKvnLt8p7FKnQJTstgCKPMuoXIuXecLAHFhsAZkNZE/h67Dk831FUFlQylJJSS2AbAElKiVocsKfUuEcyNnk1wRCUO++gk6pmso/sG9DRYh6iqc62tOScgSoZjX3M+Qx7Lx5ISvm+pm2xvaGqXTkzzta2iVbJpyNRQdhi8HFis8BeKvapJ3qB9rJQJkVUoRSXsTrDb3JFUJj/aoYDjkz5J5Uaz97u9PtQAJXlwJIbwwDiKTuMVm/ngSoIOO77n5p7tNzWOxRxysCfaHm1nGkbI7QFe9uMMatMV1wtNBH0HQNbtGMgZwWC48og6ydCHHJHzDi2O/wulNo6b8fG3eg/6YEEiWug6d0rZfHQi5lpy/HyAGCZQbsGW/m77O/u4DcFd6XfuFNDq3oVwLiKRYHIGCNfZOW5ZmXFNkZayvWInnlNzH711TA/nFpq+/x9H9i3+++htMiYhGH1B+pxR8jVulyF1G5Ft2Ov3xo7WuRe0xwpXVUTb2M8kTxI2Opss5hy+EByDpRjWumHrG++X0hNToqV8zvTDSGHu1FgRYVpsDcy3P24VrfaoEkY0hGzueNxWrOuG5o67ywWpnGB5Gq0XCNA5hghQxJp1/iGNH/eFpbzP33uO+yf3ePXqBbUipuffOpbrgt4c58tFFRmsxLicT8gpYVkXPLx5jdOJWpik9FGtDcuyYFmeY55nnM9nTNOE1rh6j4q4bmIZor9Q3hk/iKZujaDv4PbZYvq6qXrDxRQJlMmsqzngYbLljmoNGRlMQ3TNNZqThRallALPhilnTCXTTt6pa6JhNUt+W0i13fbAqu3PIJ55pCiC6em1kZrPoWuTURnofDtNE9Z1ZXqp7wGT9x51ishZLR5UiktX18vY3HrrmEC9QesdZeK6EA0DAW5e9M9Mgy1xqDmnA1bkHyUvGOKCgjwVdtOF2GY3IKkgInMMe2sKFJLOTyn4FhNY4zNLHJ1Y8s11Vz2kalMJf9BVMb87WZ9WUdcFj2/EFEpgCzQ2dZQ1Bdd2zseUCKzgjgY1C1THV6Z69jROAKoo34717u11Lq4HERg5xbVlKiL/5UBs0sVlG1VFvTPVF9xwrLEcTn3cp3iF6V8EuS3WZHWV92bwSl1UQxjWpTjB0U7DbA8AP8jrQw1QGNNE9L5HaqQYtYEAQq4CGvo/TWe+4ytR/Lzr4xiGADGxsfngFdIAPNgdHpP6aUB/HBy8GMv5iGoiT2ljsQdiCwakW3Eb1xqbPQI4WEQtB/Gmheok0O8+KnZm4QBtDr933yd5bBq34OYIFQjkgokZG3Yfvx5VTQEUIl0SqZYAUrqkAZTcWW3QfV8k3vqL9+ktkBIsxu7A2G/Ov/n+nrcfPTf+eG+AkV0Q/NXmVlQfJXWODnaIljR7F1MWTmRYc2ybem+gI3uBb1rY5PBoydFbZ98n44hba4WhyN+iAduq5mhJAJiixElulq1t6rHhWOsi6pYbTlelA8tMHZFO4rn66J7qDqSccZpnvPvRj+J8uej+hk5rp9C5wLfdYMo71pXls0XdngHH6zev8eTJEzrM1jo8KPhs+BmY4e7ubmwgJQM2AVPOWFayKX3rON2dkXJCKaZrrljXDS9fvqTvyd0Fp9MJLRFYcOSb+rBsBDCASj0pHM65xOgdY6fWCrO2A14ZlbXGslPTeLWwORc9HkpGs73MduigwubdXKW4NNwq2YFSCE4Qc8KxQWBqbPAEAAgRuMZiFsBvShkOpjKqM3RdOXPj7K1pMhrQga01YKb/U1LQES6yrELh8UoqBzNDR7EElOjngwFMqY1g3qChy/RvQ/RRgkN+MRhscG8u/5KOOU0gRuhAJmCNdgm6PUxZpkSQ7V36DN7b0NrlQuDBqqIQyQLBcLhK8JniU1NEPjxtF45tjXFDB1hHkyjWEelQWB/VLClnOBK6N7S2d353d8xzYaBg4SCbdcy9RDknG3PypmrwGOSa7X+05ofg2GINhpxqk1EHU9mYEtIxJZiWrmjlsbvaApzLbdt4TzL1W6029tlxCX5zZgl9um1+S9nErcnl7/b6UAMU0nnHLSO2/r7vPEKSDKr3njyAHigi8n2r9Mljfw1Gw8eeFQfgJthHiR7za50bmfvucqt304rAhFQjymf+/ZiKcPiwcd/ZFFX0hCAp2BVAEMj1HfrcwGv9hgHYv0OMkt9uujsbcLhXN2DmFghA17QDnT1tNcCEztNsB046s8AAb704GXisNs5XBxO42zmYACjCNaMKpre6p2v8Vmi2AxMf1xngyOGjMgEATE6QN7Bs0D87+0RCo4DlmuyYO08TDOyp0tUDJXlis75uTM+0TLJ+lI0rYgHYldYS+taxbhtSotW224a10Ydh2zbMpxnTPGOeJ9LWbSPzYA2GhpQLfcBaQjZj19bOhXvboheKtFy6rh556QRMyDjNCZe7E+4uJwQwiefJiK8rGqzKf4dLqaLGVumVAbqJptrw5uEN7s/3eP78PZQzl0h3aUS6k0npjru7i4zFEmwytFYxg52WHx6uWF5ecf/kCeZ5VspoQs5ZjMoVX/7yFWelfkpJTJdZhk279qv3PrQ5KanHSg8Gzgej0LVRmJHiPpb9h57BVUrrPdaFHWB5zAkJQLIliUh3oas7kJAxT0WOoEFFkoWjKRy1BiwK2NOXsflyLhRs26rxLS2IKyVeJmklEhsDbo62NbJtudB1WF5NWaFvB3s6kf0zmGc6vDpnNAWWrgaFjU6ksMF0h5NtShrvRhdutBBhdvTGZog5M03jThmFsYEOjRMTpG9SkJhZacU68URHcUuofVNhAnUk+3ogbcso45OfSefYcnempbT09Vh3nL2M7u6e4nxOqJU94KITsSsdklJCmQvgFZECXVY2OIwKRTiQMkHVVPJgciAQcQQesfJGZeJNVXD8HASQ+xorHZDSrbEXJOMcbO5oFajVUTJTkINdVIBOt2C+LwBKbw4rLC8Pdp+sLuQdZIMRSnAUpaBkyIubLft3eX2oAQo8tnNo9+iinNlYC8rvm4ys4o2x3bnv+wxfhzJdAKEc5aGdCBumSWhxAgBYLtfHm7nLjD3MOKn4zqBhbfwuzqofNAs7u8P33uhc9J4W/3H4+W7stV8v4MiHKw1KFYf7cLil1HhEjtyPv9lBCP9u+08PQCDuZbw/rLH38wxAYftGiB3w3BjBHd6N8fdtqiuAZACx/fb6uDbSrOGOeDhFHFJpseH67XXGug/47vwIDxoLKU37YgY1Y+ug/bpn1HXDujUtDGAJaQfaxo64st1ESgWtOtpaYXD6XRh1Ls181wO5Y902wEnltyZfglrRFLW01rCsi8zFOk4zWbi6dZhneGXfYwDD4pxCzQZYoV9GCv4PMOkh6pYRFtohRahtZ8fcd/V+KVQCWmbH7dEtOjEv3nvBw8MrPHv2FJYMtXZu+HqOLBHvWJYrcko4X85qDqfy4WRAMVwuJ1yvC968fIWr0jqlUDDJvj0XLAtLkx8eHnF/ucNpnmFa/ahD3Ouzoqqkt9AOaMz5QVYfVXBdmx84SNZtYxlu2qPZPNm4z5HaCWbJnNR/bx3o3CxYCRIMo7iu4QxNbVGLnkM2a9OSODMl9LaRwcpJx56wbTG+gbVVaVGo4WNQxcqMnAybGgOmKcOQaa4Wdu3uY0OnBX06jBuhCYGJ3mIXFeA6AIRsXPFm0NadLNsmk76KUyb4nOcTWZXElpPJDK2vTNP2hhrpE1Mj1kbgkdialddh0bspghCHJ+ncPGGaZD+8VbTNFDT6MC0LcAJzGeYB82VCaxMeHh5gVgDf4J7Qo3+bA6mSMRrW842VSewNxd5UXoF1i5LcrvWQTAzXq716IZmJpTBV10RVqMZGpLQc8J5oyOc0Ykw5Iacy2KAmJ2AyX0lrtErfjde+rl0ibBtVn/RDmVCmguaNfb2M1UsOpdl6rCsVMEdOE78fkZH84AjlQw1QmFpIN+xHbHLNgrAiZWljYdAgPRQGc4E5gJEBVPqBQYAWZMMhkTK2x2ZANMPbCYvbnT/y1cEAjK3OWePR4aPpl43Px3v4/U0oPvHrb8FFDN6bmwQEa3P8ERSBAftwCYR+3LT3LOLhcMfDG/bS3cPnIwd81KzE4hz37v0/e/tscPN5xHZ5ADkYTEp4x3AhiTQe2TNGHEGZjt8KUSobc9gQPMxq9zMxNg1OGk+9urqtZiTnAt5rkwkTUwWtO9ZeZehFlmSe2NuDHiNq1taB02nGPJ+xWUV92JCniVw32PumiyaG0bq6SxCZChuTlakMe/Tr9QHZmFVorbLKYRMj1tJe/g5uUK0zZzwu1MBoKpipRsvyy90Fz54+wZSjFf1ApQitVHKoqRy7GntXwFDSQfjn8kdgU7c3r17j3Y9+HL/9xd8cpmoxFCiGdDxeH9B7xf2TJ6NyiM63ezS+rhvePD5iW1c8eXKP0/k8/BjmeZZGpeK6bLguNDybpyKBLsdLzmXocvjsm3w/wNQIMI4J4IZu3/P9CfN0lm9PlIuGD41GlAWQ7CjA8AJBb0jORz94h9iEiBQBmFJlhhzif4ELjn12us05M/efgVPuqI2W+BQ2M01UK8EplFpIKSv67QQFicxDVK9F2wuCbTKcrmDA0u7WmtwBTBKRB6Tn+WXdp5zUH0bGcnPJWKcN3k8SYjqmeZa5XlLVi6Gp/XvvHdtKZ9uUgS08S5zfyGqY0D4odRsAxcE0EA+q/kBAr10GaQwA2U9npUh4yihlRplmnO/usT48ICosg2mDUS/jTtflI1O7L09ptzyQqdlWgaJue703lVBnpEgJ9mDc7WaNZ8C8VwARTOxjsnW1MslsCBn+MmQ8HVPJQGFlVVGlV+0O3wKsx3grI/1apiLgwiAr9jQWkXG855yp1+oV1ZvGuPbdW8HnV319qAEKEXuD2Z6HS+F37pAXSo//13oaeg5u0MSoipBs77XStXEkRdWD9z5CD7e9Etkw2JCYkGS27GZScACG09+ebhoipRHHAfBds9H5T0QembjjFoy8XYIWzqhjsdX373COHTBDkX+QjYwU1MBnBwD2ft7pwOjoUx2bIr44nwCAb6Pn0GhA9uT77weTIVHd3v+BT4wliOr07B1D0GbxPYZI4HukumBq6KhIxUzFXgdhMzHI/komjwND6hMjung2Ts8K36iQhzvKNLHkVOWppWR09W7JIC26bZtocpfAc+ainzrSREEgNScJqzQd7KCc2Z8lyYYbpFUZkdeRr88loXaCIDiwLQtSLrBOt9BIIXFQRYO9hJzUL4UDCt2pb7i7u+Dps2e0tM+inxMYRXmDeUOrbLqXU1cHU47v3S68IisdELqilAzvvfccH3v3EyjTCbU2zJO8OWQoU0pGaw0Pjw9wKN2jfjPZElLnuVsiCHq8PuLli5c4bxuePHmiZ9vVYyZhKuxYu20bHq+PmMqEu8uZfYKUGqAPRYLZhKVfmRLoTF2wDUaMK40pnScATNOELIEygRwBb5Ul/16ZUtB6x9YohjY4UDtaRODTzDlRI21goylizPcB2jV5A1QmI7gNFgApwdqGSGuYsXzZ4MiD1SDrle5O8K1K05OQ80R9ihmqV6QEdnTvHVAqIkTnUSoNExOUZAQ3gjwGPTR+tbFmEcYUnO9nJEin0sjqbFvFqORxwASk4MBUFFApRcdrMyQQnLkrVQf57GhjhsnnpRQBSKA3pp394IOTBAaTGUoi87htrIKp3lSVNmF7uMLAVF3KSSldV2BhI/2WjJFOpOQsk2Ek9BOTkbPK1CM81KYeDKTFDqN1M+3CXsDkHaPwTFWGC5hajvRcMnoHTTk8shpSUhNLJ+DtOQ8zvvAwYiqnSWCuOZ0TQVLnmEFJsET2pfc8gmEzLtOtvpWf+iqvDzVAiQZeZhkuND4ennQRZpCXGtMMwVCk2O20f3btkGN7F85JOmZHUH18cRJqSzbj4BppnT5otyPIOCYOufHvjQgde125igcHO9MjJIhF0Q7lvHqFSx97VOypItP57d9qyFpYXcDL414eN/aIeqyP+zSu/i0A3L7Cz+La9xBCJ/4WQwJAeXoxHv72gfZnCrAiBOmoQzl+jQ3KeVQS0daVnWB1XWNhtzxU7eN56z6y0kfiWCcblz0jtUKL6410dlCkpzyjOoWGFe1mgagV6BvH4LKstLEuBVPO2BwSiTb0tgLgglcbydj7+wn94Y2qYhI8ayPOho4NQ5znwFozvFdQEEhPka1WZLCfh8HYJHDbME8zLBU0lcR2uCod/DZK8w7LbJCWi6H1CsvT8LmI1gesRBHoUCSWQxDrPvrxeI50WsxXA9zxhS/8Jn7Pxz+Jz3/+14GZWgO5qzOKBxmD6/Uq181C2jjzvKhPoRdELrSIf3x8xPJ4xd2TJzidTohOu7kk5HLCPM9Y1xXLcsXzly9REnsBnU7ziOBLyawC6hUJhnVZB3tF8AFpKRwFzOU7HL1tCH0ZPNIbTY3TmB5plexWbx1zpjYoqiPayvfNZVZk33A6nRCC/+jLw2oQiUW7o3lDrQ1losZpU6lu9BHi4+jDa6MUNsRLpUg8SrdZK2TrhrEda5nhnR2oY51xgFUtzVAFjiMog9xtcylA0+8y379J2zL0EgIMKbreurNM3jHWq2CwvIpcVLohVodgegL8jfUBLDFOlcFEbxSfl5IJKFTyG2n4EUwakOeMKRogiHGqW8Xj4yPXzWRoiZqqbIWBmT470vopURuCiAX377ES+g3Ol5QNQFdqMg2A4cAIaI6sczSsbNJ/mZURQkepM5xi97apVxQSYJleKSAwzdID1coS/5INngGrsS4YLFXsTsC8xmkicOnuu2eS++jYnXMacoHW2uH8P9jrQw5QoAZGAG/6zmCEchjY92c3DOfQBhuoDtg331Fpok2sHtDqV7qxQzfhTo8ESGQYehg78i4Yi7IdPhsnufMTktfyTeP7BW/jCsf5AoxIj5qJwczE9xo/E0DoSKPz9/EfR50IhNr5bz+w+u9/udI9+3F00oDK2bioOw5Nb8SIBALR9w4go2dioof1HUGH7eDLQ1cr4IUBOBjJpcPNCLGbek3Ee1PUZPHEknel/ciSoPG59M2BntCWTflkNn3zM/OvTY34uHlqgc0ZqLSPd6G5KSVM0wx3YyO8DgAV80Rvj761sUEAKgtMGbCMXAq29YptWzCXCdu6SWnPlI0ZUA7XXHvnZmYUzNVaKRjULem903zMYnwC7FrsqL1hPs+4e3KHMk+ASjElQxAT5dpYMRipLovu1g/VW1GeKm1CSgYX2Hj95jV+zyc+gcvljLqtKEVzOIaoGA0YsCwLIzqkoR/JKSFN9PXJOeFyuSDngjcPD3j+/Dkulwvu7u4O3Y85B5j6ySyh3jY8PLzBuq64f3KPaZrJFLjBjLT2fDJYJqPTwJRJ3egz071gntJgBygSTUBnhVVJExdvcy3q4fJL4WSIFAE2+mu1o2WO3258FsmorWC/Ft9H7ACMRmp9rUipj/sfuqHo79JqZZrImP5OMsSLlIBlGxU7rW8AgiVxPXfR+lKxhv7FtQmxNUNG9oTUuLn2poZ4WuVYcXZbCdnRQ389UjtkEkzHdaQCBA8c61GyhNOZ5cnu+5xPRgHrVjuQMloW4wlnGx89q0gDkakCUhG3LvY2SoIdBKZ1WZFBxvFheyRrmZNs4xtZl7inlRs7mTQCPgjcb2tHzs7xqpi2H7jsIeCH0lIC96KbFSRx3uaUFTCx+jEqM5O1WGgROrDeAVRHLk7WLGte90JDQH1/yWlsAa03sVkEsblkTLN8gFoFRbNa11W95gfPH39rXf8grw81QMGRBYnr5mrGX48b4/tGqLdVZx199JKxdBCginaMJEdE9uPwb59GAJGo4LGYTBif3SNTPqhyOLdoMHdjG6//jGgkuJXjJnp8HV0CuVEERth1KX742c1X+f6eveHb298ghseihPt4jNgM925FN2MwAE+oiA/4x3IATN/POb78xoNAC9sAH8dnbAJWkeJRGkfVE2nca4zx4iDwMXlswIDUDDhEgN44kVNziIOGVxoVJXUdbRuNtNa0apHOYk0IVHLKWOuGXvfknSXDtlV4x9Ci5Fy4gfV4nhXzRPGnO8BuuxL7mYvWoQFYM4oCaXPNEsVhNZ0LIystDtkyelIayDLOdydcryva1pGNKRBLZH7WVnE5X/DkyRPk+cxFPKupZmfK40YjFBHe0bFSBofAsd+HKaInMMyZkeKLF+/hnWfP8Ftf+AIjdxegUflnPPOIYC92QU6s2Ij06ul0wrZW1NYwzxPM7mHpEY+PD6i14nw+U6uDnYVMKeN8zrDzCdu64XFb8eL1a0yl4O58HqCx6rrKlDHNBd5n1Lrher0S+G0bQlacS6YbZ8oqPwWmVDBNGbVVsUwEB3SW5XKWpNyNjbe3TrBQJlZdbBvn33Gd7xly0x9aiK2FEWTMZQF8sKcSN17pd3LRuK2DFdrTd6pGEiAJTUKshA4DEkGTtwZk09CIuZjBclcxjrXLgDuASjTzE1McY9WpB7FEM7ycyWj11iOJtC9BCg5HxSKAjnaI3g1NXgYpPG7Q92DLuA/MckaOdcWQ0Wq4/PIenmbqdygtIFs3zzNgFU2+MObUYFwuF1hOePnyFda18rzMI6PO+ZhmTCVj9ABWIDmAoNF7ph0XTUAsWgLUmHMsyb0KdPoILh2+M+we/eY4NnoK7SPXK5dQ271LWJt2EfLGdK4wJeNMD4E+A1gKcp1B5SFDwKvl+dT6/ycAxUFraopgNWNH+a6Nx8nnwZtjCq35szY2vtx3kMFc5g5m4H6Ykvu3v72J2+Hn/NNv3q/CWkQlBsyomPa9PPhWVBqVPfvg9OhZczyyh3od47xHZ2IA0KCNyOJYdnwLvrqGs8NEqQ+sMFiZHh8CkMCGifquAD/HexLg4AD69+/d740hbLd3caaBzdjIguys1tvXflOi7codS0XMPGn8+lDxA05Ub50UZyloI8oDWZK1w1tH32zQr24J63UBGjDn07DPhyKYeZqGWVhXoUAdLcdZ/koVf5dR2oli1q7meu6Yc8Y0zaRt1fwtS3MxzzM62rhXpHV3l2SWD0IpHdrje6pc4DoZhkhjNfVyypbQw1zKmR6yNOHJ0wuePHkKSzQIY98PRWOQQDK0Su4DJEfUOaUCqHEbAPol5C5aPuaaoaHCkuHFyxd456PvIJfMzcm6NpjoMbwHA603PD48onf25wlLfO+OMrGCpxlZp1IKzqcZy3XB69evsJYJ9/f3SOV2+cspoVwy0mnCuixYlgXvPV9wmmdcTifMpxMCSEfVwzRl5DLLqG3Dti6kya8uUSUbGppFCkIVOZ3Cxjjv8HlhKXgdYzq0BTFvt21DBAtM7WTAmFYMoa/B4M3Uq8tGAJIPZewG9SACjf5SAtalsW0BCAIBFyuncl55yMANOZ+Y9tFGb4DAa8JWG1LHiJwbGquFwO/11oEUrE1Wtce+FmXfx2ZCQq8N6C5nXlVmJsPAOJpbKAR0vauUP37NhwvroMld6gePO157soTJJpQJbHZXnc9jZtqotYZt3ZCSYZoztnqF9xlAQy6GOU1gks+RUsN8mllt1dmTKucZtVY2eNTGbmC6jk7GVefC54KuyiLZWySw6aQr6OqHvSwWVOrWVTXU/bBm6z4c/jvgQsl0PN5WNQfdurR9YNdxLW6xMwDEPQ6mu5q8Xnqt6LqmVttYh13tFJqqBk0g+oO+PtQApXeHV5Xw+rEgSxPGlLdLFFDGBLHh4oqx2cdvO0jvp0OxNqPtg3gVx40xIrxDlO6GW9t6bfvORkvBlJDsIVL38Z5jRMo3BV2GiIRMugk1ieNnRNG12/Z9gxI83DMicwEZfQ/TM3s6Jwb+2Ntjc4/vDPMdVXNE7fvxezl/oiQ4kLvdnAtpXJK749kdIoyocuE1ht6hY79Nu7g1GIys7yF7dKxaCf+Arms1VeKIvWgNXiGjpj6Eo+aGru6l3juubxbAMinwypLDlB2tV0w243I+oa7sy1J7w7KsMJX5hZ16646SaNe9LpVW876iTBM26U9qq0jJxLAkuFe0xm60rUZ/kkgRdPSeQF2UD+qaD43Ufa2kmUvJsMwy0Ot14b3Q80uWUOYJ0+mEUmbS4gCsN9HVDSnkVdi1J73W4IdAM66EXGbePyQ9O/43N0z6XDDnzee6rStevXqFj777Lr7w+c9jzhnWVKqcbIzbImCxrSse3rAXyuVyh3LivSFzBB2/6rvuUDLb29PA7QWm0wnn84UpuJi7Rk3H6XxGmSa0tWJdrnj1asV0vWI+nTDPJ6TsY/yyeWKB94K5FHapXa7wvldynFPC5h292RBIx3ftugsKcEfvGPiouOCz7ixh1/yaphlpStTHRUfk3jHPbNyY2u5cTeq/wXqSdoTHs8TqEW5mTgo/S4Ddq4TOXd2hITaEvjARBOwRcgQ3DCpMAQI1Dlzj4Iaq9Tfpmi3taexoIEgBNBk0CqwJtoYuLOU9jZbIHt4I2x0sjc57gJQtoUtvlZTm6r0DaaLQXACMrRPIShpYGpyLMWWr9WnbNpScqM86G2afBrHUsbJk2DoSEs7nAp8ZmKzrJov8qBzb0LaFDyHPWmeLHDKk8xHwT2M+G7p0ZbGP+QjuDMM92ILNLxQAj0yCgmfTM2tO1+K1ojUWfkwTBcat9fHfg6W3HfTCWKwQ1hQ5qSP2sOQwpr2d7Qz4ueNO/dVfH3qAAtiQnoQHiBhKTlwctBNj0z0IiOJYgxngX81jkRBXEIPgcAz+h4/JvYcB+3FNA4oTs1NhDUVE0IJue9XMAA+BnuOEYjDCh7nQAENiOsSM7jgGAZj2AeFym2S0zRhl+IeIhoX6n/D8fWcg4v6asbKgs+22WUG0mfe33zukQXG/YzEzRYttRI87+ou/bAc0bmh9U9QOROnlfp5QRZaNRTTuu8FgngYD496RO2nd1DNSFQBpRuOpTnCSVKge3FJ3h1famQOGVKI5VsZWH7C1VaLNCWYF6NsQQSbL0gmw/Le2jjVVgiLnphTVASzzTMNcqfcKgNH4tq3YeoUrIjd9traGhHBwTbDuSNbILOo2ujQOFYxSkyqBomXEWACj3FSgnFStaazvzOFYAKPMEjEXDGYskezqrxJg20HdRvQjgbq/IiX0AnzpS+/hG7/pm/HF9EVsaxUjAlWlyE9Dz7ykrFLTDSktShftbAs76XLudjhOM7vE5rzier1iuS7Ytg2Xyx3peC3cUX2XckGemYLa1hXbVrG8fo1cFlwuZ5zPJ7g2q9ZM6bGMU5kwTSe61LaK3hpWGfV1VQLpQjRHEnZmM9w3cSNs5c9sb/rm1PDA9z4zeipc7xKYRuq7dqU5U4rMDpKaLzMriazLA8NZKu5Oqp/POxq/EVi6sxLDJeikGFptJWo9rEkEcTnSuMSoBNsCMrEatFh/9aySxQZ7uxRyvewyk1N5LZyW9Yhbqjnb2YWYQmzb3XbBFgNm1FeEgNUgXSKMlUKxJlVpYTRfoO8xdOTEsZ+ySZDvgJWxH3Q4prDGdZby1lJhqaDkgto2PD4+AkaWAj368VCIH/2TWqsj2EtK7XYFpEjq+qwRsKepMMbPDi70TFMT+KvooFfKGJYpq6w4AnCxPsY2DehpBAy9udZYBjmhuzRQe3c5XWAAlnXFmzcLavuqQsb3vT7UACUozbEx+/jFSIwcjWECpcfgj2ZIA9JYgBIbPzd3iiVTPOT3a1GO2pXQOIwYcwAORoHZytiQQxvjsYFDFzN6HhwSJvoOtzaQMpz1+qwu6oer5IXzatJOg4xD2c2PIv0CGXOxwVX80g7gBSyJS6Ik434N8VMwMOPI+ufe0XLkns01iMu44zecJLgp7SCOuVtVCCpNwWtNoI+NMUgEsKfo0PeFLpiu7IkdTbvBVEbXNVYssd5nr68DIjVUtwqv+2ZZt4o8lUEre2Nevy0N0RuGEfYEpMLcfAhWu6NVilNzZv4/IqwAz/N0Qu8V0SMEBqzrguqMapLZiLCSH6LtYipx5pjo2EtyAUZCtTbM+UwTrsoF0yI8tAQrBTkXRLv5sKEfhqZagDmPgtnan7xpPoxn66TO+8Z0TjUHEE6tZAhKzni8LvjSb/82Pvmp34v/13//rzz7rLSdjO7CgTUaCbbesK4EKPM8K+0htiVltZhgN9aUE07zjGSGdduwiLXZthWXy2U0+Ys5TfExq12m2rFtK2pteP36FR4f3+B8vuB0muEAloUGYj5lVsUkQ68JyarmaIZ7Q4+hFSXIvYma530qM+XZETm7Oxs7lolamFp3AA4KH3cvKI4DU7sFRtSAW2d6AY6StMk7pGMw1I1AuTf2mmld6RLfNSLw6PkCRLfMrnJjNrTDYEmZxt4GcD2uCxwPzvmnKrsk/iUbGzKmFIqFPWU/7PRd41uANNgoi/SZ0iBwAxrT6N3lv4Jd7FtEBRpcPkA+xhZXkWB1XekTrSWhyejs7E2/viRWiYLbkAq41TgCAGq8ynweVaSpgvYAkE7EVAVnhq70LpxrV1SsAvQameaCum3whVVa7aAJi5T+TQp8AD4bIL9S6Y+6UZ8STyuXCUCnU26rw//LtL8FO8Ju2/vc90jFG4Hr+TSPFOb1cUXrTO190NeHGqBsq7w2lBMbUQVif4/oY9+QzZpskCOIGeUfY9HzQ5motz4WO7gPkWo70gWhjVDOiOZUO1oKAW6HjwlJpbqjJ4e10HpoSnpVrpGL1OiPAFCAhBCiqQTN9xLlSPkcN4t4jZ/rcj18QHQPTa6YXqX6H2JHxRdm8onavRwMQcHL5VQ+AzvLwfcLrqnqSvfW9+/fb2MwJg54G02+wlTRrQBw1FWCO6d+Zy5FlShS5HfAxvcAlpmqoQcOeM91nr0FQjMUqMR3Y+QYqKe1jrU2eJOtOOhX0GpFBVX7pZyR04ytbiMPm1LGWhuyA2G8FYZlrQHmrpRNxWniwlHrBssJ52fPcH1sY2HsrXKxyEAKQaA5ppkR2rLsHjvM9XI8RnRdsipH2oZ1qdhsYSVHB2jtXrBer7hLCXfpyWDskMSiHJg4BBfjDvfKaDKmwUhf2Jh/KRG8OYApTzK0kzZCQCiB/YN+6wu/hf/D7/8Enj19hjdvXqD3pMZ9dlORldQEsTu1OMt14RgsfVh3x/AtuUj7s8GsY5oyylRQpgmP14WMyrLifD7h/v6JUpYBEBiVThM3mFor1pWpt1evXuHNm4zz6YTT+cx5WR3ZN11bpt9Sd3R5S5jV0VhwiIehah3vLIU1MXyhKasdCU3dkx3dmDqBa/PVcWjEVemT44fI2alfscw2C7Wz1DyDuph1Zaotegv1FsDEb4K8rj5dxCEJm6zxRyNIzTmDTOQklqRweFJnXK11FtReRyp8lpZwq8eLihetF/va4WwncZpQRsFmhnlHhipbYrt1V4VaJXvFb6dA3yA3Xd0mG5eKcF1NEuBqTz4uieid+ppsBeOjBxYn0uZc1qjjQQpJAS9v1iYO9+HYmkqSP9QetHkIf1NBykBRz6kQotPB9ndgJ2xPxSVLcIilaTJu8z0lVEqIWRs2NlMCzOTmW0bQmC2hemXXYgG4ulWKmksajTpfv3mDVy9fSqzvkG/+B3p9qAGKpzQ6hsYGGIsjTA/C9w0cIH2eFZEOWq/H5NImDoY4HBxg1ANTRMJBGnbYRKp7d1K+DLt40Magjvdxg5JWQB+J5lHAftze/aajJMxgRf64DrRD7heDasfYeGMCBjvhUXmhUz129IXuBTECF8p6GJwpZXXFxShZhBNw5MxjhSV07z7uLQBVBROdr53iu3ACFa0iC3MfUWRK9CipyteXKXFxNh8UfIlI1x2oPiZt8YyoDuhKq1k7aIg6I4c9R8toqPcG7xV1rUh6bmQQEinQSrtqKwYkw93lglevXqL3NMSO6HxfrXJoBSc2oCZg7kp/kP+jjqrDKrC1DTmxv8fl7owCx1QKtnUdi/95nobYKqek9JHBUsE0QwtuXCk3rHWj1wm7GBdqYZxltYtvcKf3iqHh4fE10jwxLaCy4DCCs2BXUkdVl9ke91HnMnQp+j8K0Un9HpmJNIAqATxTUYYyZdRe8YXf/gLe/djH8Pr1S5ZvwwCvMJvoPaE8+8DhzrGyJMPUCzABc56xVUZ/DUoteD6kBxznRMblWjKWZcWDhLdhlx+AaLcfkLlXpJe2Fct1w+vXj3h8XEcHZQMrrpDUewWG5obTPNMAa6sj1VPDT8UMFdHVVz4dSJimMgSMWRFGkqlerDvd94gZgFyNd41QSgToxQp6W/msyr7RRylsdGUebOdhjo1IPJG1613rnGeYvueGMTaavFG/kmj13mJd5had1TvHAribjwaIo/w3MTBKRtOvBNlEgBVJCXSvtU7+l4yeAiUxtXvVX4plFrEQhss0gwf9zG1UxhCY7Gtz70BvFSmzrDl8aXjoAGwK0DwpUAHLmuH7qLVYS5iqTjBYr4BM+U65AD2LocpYtkd+LGv+iAmZTwW5u4IOH3vTnj634VkzGCABH7fD3qF73ppjWTbAO+qmAgwzpNSQRYC35tg2ro/J92yAmdGiwRK2bcGLdcWyrNRdqdzebtSiX/31oQYo59PM6FH0IEm0gML7oByjTC9TnnEg83QoCea04fv0L07CuKkRFSpnjBj4O6HC+Rzizx20wPM+yXF8ryPnPdo/RglEs/t5D+m6u0qVgzHpVKrnYGNMg2pnKLwdKi2CSpHY08YUjcNnAJMGHsFa+FLs7IqrDE0iqnFtPiZ/XDpTKAcgtO+hg6Hq3pkmYTEgYAlTPvEYKUSwAX3CeE8+BVCaxxJz6OpIimbqjsbFoCtiwIjeqbfYKqPHbXkEYLjMp8Nz4L10kC42o500rcS5YRooRNt6A4XEKm0EmZPQyrDiip/NKRY6AozeK9CrSgW5ySQ4JkU0uRRtsrxPKVFwyA6pLn0FGRF0blqtcrHgImI0CGtASjPKuahagz9nx2GalNVa2fsm0bZ+uV4pnvQuEIxhqR30e9O4qL2NSiUMNs8Gy9cUUcfimaQFSPH8UsXz58/x7rsfxeXuHsv1jd7fkFEQVHMsquGKG40T67bh9evXePfdj2GaphFFmwBQSqwC6o2b6FQyOuZxjuu6orWOeT7jIqDSehtBRAQZ+x825NvWRnO4ZcE8z7h/csdUXEpDDG5pAn1OAYtNIdIB8iwZgIBDidU4mivSpMMMalGgDT/ZWEvM946xXQK83jumXFTpZBpLFK5SfNvgYrWiaZ4NDyEbPkvU+IhdasacTjYAZEBaa7eluokVRZZ2QbcrUGRVX9JcSFpDO3oiw0TxbAAHBVgKRrNS196Uzve0r2kA32G8D91C46cUkmBCBKGmwDMWv6OuLTZ7mJrhCWBQmFy55+QsN1gfrNPYG5QSgUBqCLhNgNSN9Tuxbsb6Fl2N4YD3hNrYzZlzJlhqAu2SM7atom4sN1Y9Hq0ExFRZ2sv8W+u83xK0Wg99EderutF9Onot9aFv2tk1zqWCaZoUSHNOt85KsPN8RraMdVtRG3sEZREG5/MJH/T1oQYoybroKh/UfezGbgfKcTAE+t/BuADhhDl0HzgCm8NnkKCVH0FCDme/TlVabLoBKWJhPmKStxXMMX4PoeD43Q5mItLfldMGDlwuHKSKj2AkcoZJYCs0Kq1HPjmOy83PxrDeRWX86h1kJJTBKDC9FCzEjr7j6ilm3WHfAJGed6YrImgj5EjIBIseXxsT9vAkjNFjgknspxRF1wLYG6olhAeUbyxxy1NGKYalNtK7VlT66NQZ6X5dr49sXtb39FkqrM4wbKhORXoyH5UNcEML8WJn3xl3w2k6S0DZRsUDF6UOimUy4B3zTP1Hqw7vSZ1c6Z+BTjGoe8dZ5xUt3M33iqWIhpKYim6Ngr+hPeH39w6sy6YFHVjXDVPOWGuFe8fH3n0Xz549YYmsUllta2J7+nCDjKHKlF4FusoNuxxDjawMBF6mQTHzs1m28NHE0X1n9kopWJeKly9f4WMf/z34b//9FXN8FQCqWDv1iVG6gRtmQ13I3NTOTslhdZ8sIRVpYhLTDwZtbOiYjN2Pp6lg2yq2bcX1+gbbuuFyOeN0OsPRBsBnt2syffM8sZx4YinqulY8PDzg8fERp9MJz955Qht9dFSVCeeUaeHfOzUoUa3y+CgPH80IdbmN+0JmlaBy3DClg00ah1YbWt8OpfkCFAKBOc1cvxo9WCKyrlsEMFxTu6ovSikUr8pozVQ+Oyq4UlcpMfFKbGDuVSyPU9uVEra6jfHjDrI6qahbModysmmsFxSyBqAlm8C1PaHWhtQLIEuE7gt2QWcWU0n8VBt7wtCrKsJYG+cRMVWK4AlKq8e9FaNkmr9M+qhNgEW6nQcxLdC9ywYj2271H/aPHhobgq2RMhfojc7LZCOj4/Wk59/FKMW5M5AthdKD4iE/tgGiY77Gc6uax7017m5pXyOQfJwHNDayHJUBwMsOVLlGcx4kgcySE6b5LGDOo/aS2Qi1N7nlfrDXhxqgwLoW+0DEGi1GYWBschKPY1D8CBGtwyV4Mux78fHlMTBTRIIxrLixwXahFxDRTVQ/HM+Vg/Nw5DERjoBoF4zubIq7IzpNRqoETkbAFRlb6vtX2WHz13tDL5/V52K0D5eFdToCKbE0g1MxUa5o6luhunjf76cNEBWLk7rCIhJHPAdzRt57NYjv1SNia8KHKpxHzWxXPQMooGNjABO4odcOWieQpvXu2JaNTJDAlvckMagBoCGU14atBy1KJiPys7G5MoXGyL/MM70onN1XI89PYyRDzlwMeycdTwGkxLO1YioFc5lIqcvsaT7N8N5QsgCjk/FJaV9st+pAo0iVzdU6UmfOmRF+H+XUwmcEXdlQ1zZy780dW++kg7cNvTvmuzvM84SPfPQd3D0561mFgdiBGtSCV2sdbEKAkhwDPvCsJUQn14jpuvx4MMbkAYzDVX2iUkVUfPFLX8Q3vvtNuL97gsfrA38XKYzeB1XdPZRhGExmnOe6rpimia0BJCxtjeO4lJMEzmSqWDExowiorOuGbW14/foNtrXifDchPGTcIRCraFKMRM4ZU+lY1pWNCa9XrOuKu8sFd5cLcgkwZhJ8J+SJMXnKCRc7a4Pn+4ocPqtSVTH/e+uqCmyqqIj2AkXPehc0egQSiEBFDr9wpCEi3xcwIz0jhqmhp9C36HNG1i5E39TO6LkEOFGlYNc8j8Z0hqYAJdbjaOialPbM6JnaraHFa4fAUdVIGKl6iXM7qxuT0l4c7i4htbPbeJRipxDnR5B6WJV13i7gbzJMcZDJysGKwGFZWkZVUcU9Hmkk7Oyim6oCk2FMAQcbjSodDQgg9T0NMwzllLpvYiWZEsu8pxbpvQ5LTb14gKTmiVxDCeJSayNo6o16olwKTI0k3TuKRPtkWgAvHBtNPaOyAigeYx3jfj6X0Y8qZzUNnaKTNg0e+Vw+eIrng78TwI/8yI/gD/7BP4inT5/iE5/4BP7sn/2z+JVf+ZWb97g7Pve5z+Ezn/kMLpcLvu3bvg2/9Eu/dPOeZVnwfd/3ffj4xz+O+/t7/Jk/82fwa7/2a1/LqQAgc9/g/NvptleJ5dG9oTrbcUcZXpUPSe1VHWKb8veKQJW7dP0BAnAogWQ0jwomhc6PNijKnMNQa6evCW54DLMONtAi6rQEuUKqyVUG5GauplPQpANRbdbfCQJnDZaawAmPb5mtuqHjujW2Fk8NZg30bOd/W0TypkZ0STlJJ93Lko1Nf/dxD1iBExs374mlzuMm3avUUCZ2rGV3S/X1yI5cgFyAaQKmApTsSKkjF/aAyMVQJraVn1LCKU845xknmzB7QakZUysordCQqfMUkyckK/BmWJeG9aoN0QxrXeUrQtU/y+MEZNYVy+MVcMM8nxi1asNIWSxC63Kz5IJcygmW9tJXd0cuaXSuNXOs28LoI2WKouNP54Lae8U0ZzXWc5zOBedzwelc2DDPD9bkG/u2cAHkAtX67uyZrLCtucrX9xQeN7G2VfRKYHJ9eMSyXFEbK2ru7i/4+O/5GJ4+uRcz1wmgokQYuImmIioLwDycekFtj6tMum5tgNTeO+eocyGkkLgOtgVQ6nDoDzKu1wVf/PKX8enPfJal2b3L74Wgwp0CRUsOS45UNMZALRIArMp/12gUJ30FgZ5jPrFzbs4J2YBs3Egv5zMuF/6ZpoxlYQPCdV3FnKTxOYqATT/LmM8T7u5orX8+M5J88/CAL733Hp6/eIXrspFxc6j1QBf7B8zlhGli5+Wc1DQxcV0JwSngqG1FbRvWbaUpoFO70FsF0Ph+KIAy6jia7hdF7RiRbKTJ+J3RRJJBRhN931ROCk9AJ1CuVfbKivQDyO6VJNSsteaotQ+TsoAmjr3KqAkQDfWaJVVtJUx5QjF2xzZXx3chHMusaIF1CZkngcUyKv2qPINKYTNIBniHVIzY0/Bq6j3mKedVVFjF3MW4hkPUFKmgFJpErcmJzyEAxK41hCqc1DsIeQRqtN1XX6SUMKWEkpjEtqhOAv2bcuYzyylhnieczxPmE9lCxs2udFKs01o/vGMqGfNMd2MGdZzvpSS66uZgTtjCIScC02VZcL0+ImfD/d2Ey4V/6KfTULJzTc8d85xwvmTc3Rdc7ibcP5lxuUwfeI//mgDKz/zMz+B7vud78O/+3b/DT//0T6PWiu/4ju/Amzdvxnv+1t/6W/jbf/tv4+/+3b+Lf//v/z0+9alP4U/+yT+JV69ejfd8//d/P/7ZP/tn+Mf/+B/jZ3/2Z/H69Wv86T/9p28FoR/gVWPgN244Ub7JPhV0AQ1vD6ZiXLlk5XzdURtz1s0rN3S5p5i5gEgb/zbRLJHKCbYk5cTmXBljsRwbeWqD0XBrpIm9ofXKjqp1I5XaN7S6qoEY//QWgivfr0OUcOQwYiMa9tc4iNn2GAGKUfd/W/xe0WCwKo7hGnhU0NyUqwFgSiuDJnPgDTkMJx+TiO6YUX9vnYTsnCf+KRNKLphywikXnErBlCnknHzC1CdMbcJUCUxSzcgtM/JT5NQkcs6WUCKd4cA8n8bm55DeZDw3kTYbPU+YjnClphpgBUCB+YS6UfTKHHvfF1mnpiVqslOKqgbXghMOoYxks6hblhRTLDxNCalwoUUCU1zZgMSFdV0bvBl6S1iXRYxKQXd2TN3Whu1akY2+LAMkl6gIaah1G2wYPUMyTvOMZ0/u8PGPvYOPvfsRXC4nOFpsD7wGgQ92ASblvDf8OqT1DNhqGx1cXVE1X5wH13XFddGfdUWrnOutEaxQU5SwrPQmSeoT89tf+ALKNOGdj3wEtXW0XlH7NtJz0TE57n/MRyiq3Db6TFyXBdu6qUqCxnW8psTxNza2aIzXkI3i7MvlhPN5Arzj4fVrMirbNgKQJJFjAJec2V/odJpxd3c3gAoAPD4+4vnz5/jye8/x+LiCPX5UYaT1aWijchrjt3UGVuErM5UJk0qi6TRL+/1UoODHyXK0queRJTLd2ZIdVu5CUpYNm3QVaYAI1bOoDJn6GGNZ3dijh+6lO5iFUlo6Ab2xj0vvhi7befr3VBoaquVD9KvyRjDElEhhCwAPIBMaDLHAGWIzstadxGqlAR4zwowxujw3V4O92m5WxthHWqu07u8C4bB9KY1ZIvbW3chsaD9gxNJlUcH1LlqgsAlglgBd+0iSm6+ZKg2dgl8HktNhJo6TU8ZUpsHYdbCrckdDKYbTqeB0mlAKWYws8Eyxu2HK9GJJOWE+zbi7u+A0TyiZAWFJGPM9JwaK53PB+Tzhcik4nwrmkjHlhJIN58sJd3cnXO5mnE4ZUzkIjXuDoaEkuuDOU8JpTijlQFn9Lq+vKcXzL//lv7z590/8xE/gE5/4BH7+538ef/SP/lG4O/7O3/k7+Bt/42/gz//5Pw8A+Pt//+/jk5/8JH7qp34K3/3d340XL17g7/29v4d/+A//If7En/gTAIB/9I/+ET772c/iX//rf40/9af+1Ac+H4LUA4I3YGdAHDgohgNUxMSIqBfoSMYSQEu3eC0+46Kux2Z32KzNooV62rdzD8muxrOY7+Yu65BA8aoU2mEAgpoNVfRRiHuYIeM8+OpDLX64O3EVN9fjb322A3uPEYsF8fDGEdH44ThK2hxSU/s57eWlcR08igOydU6WBl19uByCjG4wj94UfH69duTOCpnwWEA8v870FHt5yf0SwGmeAQDLtg7q24oqOBIAJNrYU2EHiyhfIi+A+dMm8WwYE+0gLPrtsEII5lj6hhD7clFzwIL+9tH+/XpdUKYyUlEAF5+g0ZP6a2wr9UUlEcQt6yOsFJj0LnF/t7ogVSBPaYDmVrnoR6v6OI+UEi7nM/JUcH9/RikZZWJV2ejfo1yyqUQWZljXZVRpANJggKCrVYEZVSn0zbVhk62KiHc3UaOhmVlBOkgEKToO91dWsWy14jd+/Tfw6U//Xrx48YKdoM3giUDISuFzbw2llPH57kBX17nWGPW5O87n82AMAGBdybCUwrE/TRPqVrFuKwBqRSxnFPnVPF6vQwh7Pp9xuVzG/Yp5cjPukyHnGdNUUKZEH5vasFXa6E9lwv2TO5xOEzKMOiQQaIaLqwOYZgoLvffRrdg73T9dAc00nSm8ltOwJilaZ7fkMs+o2zaATgWYhtQzbQcAnhNnn2oNkXIaVUFKkNIsrFXU2tRPyQcLAXRgw25eBglOO3/XXR0BK5CMpfMGwFR6D4DVb4lnEGLNAUCwg+RoBcC+MBjAeWStBKJSKShg6o9WBNTOuEdLABN9EmO8A6hMacFlZwGEjXzIEpkVsl3AjKyyXbKJOSWUFKmPAopcu5aSfW1P4WDdgeqV0EyBJAOrjhTXop5Y7k3OrUpRmoEf4thlV3S5aSdTGpMgNmfDNMkZ/DQjF+p6LFh67UVTKRSXa41kU1SaVKZMNjzr/V2SA8DRWtVclp7Fk1i74z711V//H2lQXrx4AQB49913AQC/+qu/is9//vP4ju/4jvGe0+mEP/bH/hh+7ud+Dt/93d+Nn//5n8e2bTfv+cxnPoNv+ZZvwc/93M99TQAlKQdoZizXQkcwjVwktDCOPCfFjciKBDwAAY+3D+x94z1u+u/vrRNgBRQJWn//rT/8IBw5U+YmSwU5qVBg3+8tko7jZ/FQfVxbl/fA8EkISnGIVHFzbbfXsovnklOEGfgt9CM3H7RQsAQo2y/ubSHsXs63L9IjFaCz4zNr/JexFM/AyV5qgXmmuHfQsNR/pJRZieTBjDGtw5vi474lS1xAc8ZSF4pHs8F7k29GIwhwR86TJmRCb5xMLAvOXPyl83DpArr8LQgiuCDvHXsbmRjXRi2VPQAaW2Uq5FtvOJcZTB1isGQcsIZuNBFbFzrT9lYZ+Sltwo2LlRjJGpZ1wbosyD1az5vKRXmP+IhYnZFTwt39He6e3CMcK2OR772xuiQzwlyWZaQy9ufK8eCVwtlWHduyEtgo7QSPiJybUs6JPjWlKO/NW5ISF7TeXOjZaawGCZGVrnnv+Xv4zO/9DJ49e4ZXL76IIi1XMKGcIkwnJJVFHxoSMTLufbAefZpwOp3GPYogIJc8RLClFIJWzfluhpOdYJZwvS5Ytw1v3jygbhXn8x1O84yDFnmM2wBCuWSkfBo+KttWxR6tePFiwzRNZGpOZFqaDLCoH0goeRrpgpQ3DRVWUnRtTly7CkpxVFXk5FKQR9WUA4ksROsUejZpGwzQZpOxLRtT4x7arKZKMr6i71eUI1OuZWLFQidj0mxEl236k0BMLVzdjDvt+7NCNUsJFs+kGNdrMb4lT2RWzJBNVSopjeDRUIChB4t1EwpYgV6d7Q5mztluBisZUaQSYLx3MhjRm2scSkAkNIAJYfe/s468JlZKWWLHXxiDaYOz2zP2YNdwDHhN2rNY4xuq74xvik7MDqDv9hpmCU12CjlxXwkn2Nzo3VQFqthLaMJ8KgoSOf9ymrFthtUABOOSwh2ZsoVkwKlMKGXCsqzUnmUgmcPBdamM5pBMXY4qwZzRSyII++D45H8coLg7fuAHfgB/5I/8EXzLt3wLAODzn/88AOCTn/zkzXs/+clP4r/9t/823jPPMz760Y++7z3x+bdfixp3xevly5cAROmKThoEiYVN/Q4C8vsifVfZKhCD2N73ntjQj8Bg/93ObuzHVEk53iawduZFoirZLMeACb+W2GgRJcrj8Mcj7mXE0SJ878RsNxMTCKByZD+OPxdrcXPtqjTRL+k0+ZV8Xm7v0/FeAtiFZ4P21DlzjWS5nm6WNQc8g9KVpIhH4MR4na13WZ/rHteDd40Ek2G5PpWM8Jkpc0F2QykAG+24WIcKdGDKM1Jig78a/gkwFAszNUqsKUbsg7mgp0ceFHtvnQLITi1UpB7iWSelDsqUkRfD6TQLpLFkONpFtt6AbqO6olWKS1MiK2hy2Rw9laQZuC4rsFSK1U4zxZKiw4PWL1PCdLrgyZN79htBgIhD2kobwi0TsPdi4ebDXDYErMIZ0nIGWkUIpgMdRTTrrs3IE1pK8DJRdDelQdnHhurSCJVsaK3ixYvn+PjHP4HXL55jVIqBlVRkTqYBNmi9Tq2XAxIWcpzGdeScMc+zSjmNQHOY6xmyTYeyd3b2NRjmaWJEvEzYtg3b1tDbA+q24XKetJEe7QR4/+ifQhFuiApb60Mfw2NteJwW3F3OuDtfmBoRqwFv7C6bMYS6yAnnuwuK+rw4pIlLE8XRlU6knD8Ol6cQg5p9Y3eTVwp23VywgzGHCSIPc9wA9ChlJTO5Nyzd2Wg+H82lSgYkNlWXn5L1KGPYdRIxr7vpezuQwVQwBJK3tgGNLQmOnk52OEeDAT3zcjvbQ7SuTs2Q6WKK4IZj1gVMUJWucgFFB9cV3xkbDnEfbRe8Non8OaaYsuG1ImPMpwhOOwgQKS52lQMLCEkMb0mViwZUsPlp8gBoYV4o0NX3KsykwLBZR1GxAaukTG7FLN3eeuX6k9iDJ017RWgaGppdj0bfpITeE+CsFoOOH4C894RcDdVB2wHvMC9sRdA/uLLkfxigfO/3fi/+03/6T/jZn/3Z9/3u7c1+L0H9nV9f7T0/8iM/gr/5N//m+z/TN/XkEwAwlgxGuWvcXIsNPsI6BLu2i7ZicOvI+7XofwPFjt8ac4Xct338IjbrXfi3p4Oi9Tcnxg6qbsRWfgRG+6H3+6TzOp6rJuduOX979uP+HOjcoV05vtsMw7vFgoYHXCzFQZHyFrMUCof4W+p3ULlOHtRAjlTKcjekRsFd6gmpaTIj7ZulAGh3x7bJIt4VOdcNOayuewcakEDdQu11CC6L9AgpszGfd1dZX0fOBR/56LtoFXj+/DkMGV618BVS6AFIuEgrzxz3rzUKNxtp9eQJtRFIW5oOFHRVmS3PhW3gITAKrFsbwuo2hIdcGNu2MfpNsknvlRGKh2ssrzUZSwxra+iPC1IqEvg5aq+YSsb5fIfL/T3KPA2tQ9jjJ7nwxlbBVA+/8/HxYb/PCNFzRm0b50E+eCVY5NrLgXVJo3plmmaELTvB9XE8SsNR2Dm6bhu9a9aOL3/xi/imb/xm3N89wbI8aqx2MWsdOVOAHo3uAMA7S3R9yIR8AIJ5nvVs8zBtg4t9cYlwO+9rVKhQP8Zxc7mbUdaEdKWObL02tLbgdD4TVKt8f6RSYxyBjFIpZ7hz3CzLQh+Luo1g7HV5g6dP7nF3d4+c2e/HWkM1wzQVTHOBoyEXHy7J9HbpI23YRhpQ1LzK0Itl9EzBo7sEtSWr9MXZWqAn+vEoGp+micJmj4og9afqncaOcDSvqlKR7kQOv2F10NTVOx9F1mIeHC5L+i6TxEgBdwU7CT3x3nWPBH5WN28uK+H8Gm0uLNZapefMyFD0lfMuG0XIrWIv81VFZ04AMlM8kD6IFWP7WplTgbAAyNOQnTVww4aZUiv7uimicIj0u0P+VGBhQ2vsGWQRgBgshLXh69IarfUTKysYBEEu3ft86lFZpJOMprSW0hC2mwBLE9CgC6zWAqf8IYU7GwRYvElfFwrNqAKN7yQga6qEND17Ez1kN5mIr/76HwIo3/d934d/8S/+Bf7tv/23+Lqv+7rx80996lMAyJJ8+tOfHj//whe+MFiVT33qU1jXFe+9994Ni/KFL3wBf/gP/+Gv+H1/7a/9NfzAD/zA+PfLly/x2c9+liLYlHbiILE9d7ID4Bilx7cswgAQ/fZm3bIq8d+mTUI3W8lGt92munun5sAVC3s4q4otgUVjTQwhFbQwJz8Ak/czMxyAIQDD4T1dp+oII7lIAembAr+M1+itoTcnDda9t42Pv5kOCWCzAygcoIouFUctDdM5IAhxQBaK/F03ZGSknpiS6HEZZKBEDMiWxtGrY1k3LFsDTNqMhUZmW2u4JEZ7rXXMEwlYsoqcaFnnuyx1n/S6J5Yyttrw3vOXuF7XscAhFnvWgTJv6wnNwgOEv/fqqmTQvVelihkoTFUZMbraHHhlarFLDOhkPNa1jt49KdHwbb1SzLpiibVJG2cfDEp4jqRUyMYY79e1boBvKNNE8JoycimYzieUmZU+A6inNsY7nyUfFAEJ8+rhqMrSay74rW6ieCekTmAEV9QrAeoYz5DWSjuJWVIaqaF46F+kleky6csJwITeG7at4eHhDV69eoWv+72fxS//5/87JmdlmJmj5Qbf+F1hae89Irosep/mU/Ddz6XWimlSFFnb3sG1VkzzPFINLAHPQKGIt3tFtoL5xLLjvCZqWdYN21Yxn0643F3GJnyYfQNYAvRymabozbWO+Uv33w1ffv4Cr9484HK+4HyhXoiNBysdcmWbnmXrn3xCSx1uKyp20TDHJgCLsvnQnBW4dW26BlgW2EsUwaoaJkr9k6XhdzJsG5LBk1yFQX8SZFNasY3CgRhLKRk73yZtzAN8R+Qfeh4MxtDExLnWB26eXF96JeviHokcoKQydBkEwXKphQ+NFQ3TMs9zziMtFOO2tqYWEPRTQa2DoXLweIaMkljdEtWhlB3s8ylKyWM9RXdY0Z6hcRDFCcmZou+NgTYAZE+7Dtm4ZrmJpQXZwQwBq8O+EujHcoIZdS9ciQkXsyl4NqaiUgpJgSo0IxAjXMOUd22hQ+k9saxd5c+hUYFXpqKajDxzAWrF6Kb8O9nxf4XX11TF4+743u/9XvzTf/pP8W/+zb/BN3zDN9z8/hu+4RvwqU99Cj/90z89frauK37mZ35mgI8/8Af+AKZpunnPb/7mb+IXf/EXf0eAcjqd8OzZs5s/42VUNiTGzwOcGDoMjQuw7fSULgSRg2AsEBa8tv9tx5/toGLfqF3IFjr2vjkf84uhuxDxBUiUFaVojPD475tz1FUQlOTxPWEpP/60LmGY1PYunVffGRP3EO0eyp8RdHAS4o3P2sg9cyF3Kfehlt38rpgQcRwb1z6WLpgn9GawRrYk18Ty4Fow9Qm5sxonNrUm1F17x1Y7ttECnFE+c+WksEueKDxzE0Ch18fWOrbu2FpjdUBjE62IkADDWlXa2YEvv/cCbx4esW4rgiMKQay7D5YmZ4KsVlmN0DYem/0sJDpsVf1yNlYBuEtQJzBgzKlTwMuf162hbk0MkaM3w3rdKDxMeVQ6bJVVP6WQ1aq9Y2sNW20DwNWmXhxuil4d3RPm0xmX+yfD/K2N8cbqpaBlA4jEmImqunAKDhzjjdboTdUXXem3VqVJUCn2KAVFRPeNBlEazdHkzfXfjggeCPCaenaUKaN7w2/85q/j2TvPcP/kCbbaJEpkJRnHKMdMiPLCr6Ho+aUU4xO4Pj7ynBvvR2sbuw9vm0AYFPlKt1AKq/VKQSlkhHI2nM4TzpcTLpcTykTB7nK94vWrV1iuUXVlI1XTD2tGVBKWknG5nGQIN2GeZ8wzHYrrVvHq1Wu89957eHh44OaY6aa7bQ3bUrEubXTfnhINvXKZcb67x3SayQolG+tbbyZ6nqW82SK9uAtOcwGvM7OJY5QmszMxCxEaKtJkgMzyzLRR1+jpQy+F1riRNiP7kk3CTO+7fkvaIUb3/MOBYXzGEiJ3BU3R7K61hrquKh8nCGjbvmbEmuwytKxbHd3ImdHVek3PCDFBAtwQoxfCcR0rDPAiNgz20UBWpqSi9amodJudlLMCVaipaDCxTHU1jIJAZ6AdvavgxpRnlx5JrRZMlVkupspZiw9zGbupxYaBmjmDgKOlAfQGk5JNf5hGLCUjG8+75DTAbqS3qV/Z7wd67BOqpqUp1cggJEusNPS9x9UHeX1NDMr3fM/34Kd+6qfwz//5P8fTp0+HZuSdd95Ru3LD93//9+OHf/iH8Y3f+I34xm/8RvzwD/8w7u7u8Bf/4l8c7/2u7/ou/OAP/iA+9rGP4d1338UP/dAP4Vu/9VtHVc8HPvmSRxdNYGdGyDiE2RFTCrtV/b7QBtq8EXSC0Tzxu9A7cGAvfDjHdictmcPs7aAA30HKES1G+gJ4v1LFb/4V1tJ75Lm/5Sh2JdW5l9bGefI6uj4PIWiT+6DpvxNc+dHRfRPyYsEOPjrIWMYrUh77uQZrlAbflFuCdVMO3/ZSu6575QImraF51WQFamcDKkdoiQ7MUmMpHZLU67oho/meRwTaIC82tM6ce5T3LeuGbXVMaUKtHQ+PVzpShn9ICFbTweG0N0QfHUbYpKpLomtniOOY2lDeGEoJZR/lxuzQWpCngtYrb2rf1f/b2rChYV1WgpfW9F0NaZpQ5owoU6etO1XySOG6Sd8GN1K1l8sdSiq43F0QQrqI5jgCOQZTybDOTsDrej0sRntKMKWE7Fn+QbsWJ/oiTaUcPEzUJA92M1aqAMc0nbXgsz9L1T0MQXHYBHSd7zTRkv/Vyxf49d/4NXz9/+7r8Z//8y8j7Lt7d260Su8YknyEsvRJUd4N0uRGHcK6btxIDZimPMBCyiazPd7npHJkuOF0Og0dS90qcp7pGSFh4ZpXeq+sm9iOFXd3F1mCd401jFJMagY41k4nAlf6jhS0FiBwQ60dr1894M3rR5WHshFb1xhZvY/KjthQp1TQPMMnprbW627Q1TpQkxFoyVxrXR/hvdEZdyrSdB3i6JyQpwxTl91cZuSJLRKYUi8csxI9R0DQHWgKkMgyTNqJKaYcFSEjRbFrGViph1FV0iVspiGY7Wu3OkJzU9yAzvJxzrtdcyKiBO6G2nsUgY7NNzZXpmDYJoIg2ke/rU3soZkrQDlwyiLs02Cd+GP2xZHQXSLlfrOW81736OsjoMJCAr1D68Q8T4DS2dSEqSwa1FuGE3fId9hiZTdrtMxgnlGsNJp5HmARUHNFbgDjGsZaAMjBHWJETUGI9kaYnsVO8NNJmZmIHaT/7q+vCaD8+I//OADg277t225+/hM/8RP4y3/5LwMA/upf/at4fHzEX/krfwXvvfce/tAf+kP4V//qX+Hp06fj/T/6oz+KUgq+8zu/E4+Pj/jjf/yP4yd/8icPZYgf7NU7ads9aueLEyMoZo2YEd1jLKIuvn8vS94TFTscGd+mH+wDKprdBZAw7B+8pXbHmY33H2n15nvKxESVud7HyL/voEJIfrxfQMk86EcxN5x1UNr1cEXB3RknhO+N/0auvdueHUMbC824O2yiMXhH62Bu0TJy479zT1E7RfErWCbsTqwYTfy22kYZrkN+D3FO4zl3uZpmVtOMTZqphD1dkblAhhg5npcTRLWl4nqt8GooJxuGYikrStf3mLHcLxraMZI3mVXdjgMGX1zY5pJRitMpF6yOOk0zRnpRWLljYzM+S2NMtsYFFEEv6+A5JeW/MfwL1nVlWiexLfso5UNCN8dpPuHy5AmePn1KhgTA9XpVGs8H2Oqy7Q8tyvV6JZDpu0dEHuWM3HQi+mndR6TbWkcuBe4VdV1Jlad9LhtCU0AtU/j7RJ+cqEoabpyJ+q7JyBTAHblQ8/Lrv/7r+D998pP4yEc+ipfP35PnCY0GSzmpr5HYjyPNbxwfXYDFu2PdVrQrn0PKwDzPaG3C6TyNVA8MAjssF465HW60rS1kLJ2l7UWl0st1YXS/rHjdO+b5JNO3aTAnu3h+X5NYXaSeRjWTmatld/+sBHqPj4+4XC643J1ZHeUsibfoNE6IPNi/03xBvzQ8Xh9wvV7RHjcsWwU2qFGkYVkWGOgflPKEbh1WXJVekOaC+qK4j3E/tlZBDU+Ir9NIS7D5qkTfvaHLFp3OquwlU3LmOAZgmHQMVYXBKaQfayIgIxat41BDS9kBgCmS5pVpWE/8XgTDCLg1AXRDuM3CuHYFq4He0cMKwMUyJLIHrbK7NnJCa4cV+RBIxjUctV6msBEj+HXEYjsqv7gRcCNvLtZKiprqtM+XgzrnE5laWOhJdsFwF6VeW8PWGyY1XERSAKm0U4hcoTTstrIlQYJhdAj0XetoYsFiHUzaVyoQgi8Gt0ZnX296TmawrwF2fE0A5StvurcvM8PnPvc5fO5zn/sd33M+n/FjP/Zj+LEf+7Gv5eu/wnelHalF3o//wi3EiCoTIDxIopPo8HywOMZhI9ffQ1B6SN/wOzEi/7EhHl43ixB8iMEC0OwtvfbrASCBp6A+V9bxnhRfDC66fE8eULUIeIXvyH5vYjH0UebHcjCQQUEbiJmone9Llnc2SGmgIKEGaHIM06LUyJaUnoefgUs1D2cKKQmo1MqeKW47IAmaOc479ALmCWXihs8UCjezrXXV+ps0fofIxeOZJNS1Y7lSA5IsY1s30snJYDma8PmoBuJFJjU4i3QgSDU7PQzMbTTA8sQoHMZOyA7gdJrH5tZ7x5vHN6R+pRlovaufkCGEMS3M+QCV5KqUtDesK515XakN+rlRU5AsoYuuni8XnM4nCgBbZ/8T7CA6AfBEz4gEGyzZPv4c67rQ+E7RNdMgO+3N5yNRbmuKttWPh77egwWM3iFZC2bbKhl1OJKa6ZlBZd/RoEwbWqOwueSELSVsreLLX34PH//4J/D8vRdwEVHV2R14nme5qArwYK/EYH+TtIvbERucTNLUyZd9Tcoo92aUTuDOqp/Esstk6IkbxLZVwGykzJI+W7eq9N0jtq3icjkrAnalQRyRYh0sW9opdaCgFDIGLE2my2/vjseHKx4fF8zzhCdP7uStU1RNo3neuUhRK55wPt0hp4KUrjhJtFqlaQkfmTIRoBHsu5o7AgWNJoo5D1k/fTECt3MhTGL0HPscHAJWYx8v7u2hBaEYvtUKV2k8lDLpUUHofvPfTem62tXLCJxDJp8UuETjznQsAOSJaxSFvW3MCbI8zmobEACaA9HCpIe4N1ItzkWteRvi0BGo2s44jXETgMG55lsPH5coj2BKke8NF9o9XU/Gjak1uKFtDjdW8BBIM0D1BDQRLkneJ2R3K5DI9HtyiVoxPFXI3qiCTWL21htTRaq0I/MRbyeVk3RNHvtrAiYkVIljw++qKZ1HC38Cow/6+lD34rlJnwRcv9mUI6+9C4gcNqgvHwwI9BBiEw92w48H1+dxOD4A7KKucSojXfT+apf9dH38bM/tZQ2EPZI8Cho5O/c0kak6xkRT7Dl/fgMv+W2gFYDE4LYDjkD0pg0LFuXAgHkg6KR91IEWZYlJmyyFpOF+WHpGU9TUncZZzV3aD7Zp7a6cMarKan0seknmRtTDAFNmdPnk/h4vX7zENM1qXxB9O2TWlTOymyp2WD63mDQqVTRtULmo7EORTJt7AjVC3Hy4cR/DIcing4u6d/qKTCf2yqFMRdGO7Z1zI1Jat4Wgb87juRnI3sATem1odUNQLSyZ5Tjozelou1E826Hy3oUMT8osf81Twd3dhRS9O43HwConYVilaASYnTqV8BSptWI+sxR2EnDbthVQaXE2VckI0E8lY+uOBlXmpDQsy4fux0FBu747CUDEd+ZcBAAqvYQEZgOAd+/DrG7dOr74pS/hm/7334T7J0/x+tUrtAYByz4iurFpaDaY7ifLQ8HSSmd03FUFQR+bgut1xfkElImbdddxa2WK0WCYphnrxq7FDFAMtbpSiomdoHNGzRW2bljXDetCbdFpnnG+nNgscICiPcUxxr/6rFC4WjBN4bjK49WNfkDXa0OtK1M/93e4nE4DGG61EXzkSWORTNLlfEbRfb8u1xHhB7vTW8d1ubJKTc8xJcN5PiG3NHruANIS6f633lS0Zxrj+iMQmgWTXYARBqW9VCUj4b+7M3hp+wyMJpkcZ6YARc0HLaOo3PVYTt68Y1OLiyRzRLIM+7LOe94AVJBQqLCeMEXHZ4EwWNKWEOumQLQ7vFHkTcZFZndlFymzQWEbbHcAPNfYjK0sejBFNSgUeI1/C1OwxJlVkSEIDhZGFpGAXJ0NTEcjmVJaaVxDkCApMgTN1T+Je0GJlgGd+slAVZbpamuxh4FKyW4O5EhpqRpQa4ZlejdV++CO8R9ugGL7ABiMAvYSzI5dWyDOYQz+cEbl79IYq0S+b7NFB7DzFc/jVvgTrE62fHsc7RABJHp3TAd2JKjwGyW4zvGokRnHAcjDRB5wDPIdubsGIMa9EVIHTZoCrLFfx27Lj7gPAG3le6bAKY65EvKXKfxeDZOVwYBs4Sni6sEBpg2W1VGbQBkASBuxbZueixT9WcxR+IhI87FtG1a9t3klBWrMo448toMRAZj/XtcNDor9kipakqp/xgbalaaICE7naylSU8EGaMRwtUVKQJkSYNFdN6OlpvLmJhaAnylphin1GJwWQSY1LoaME04HoSrXhVbJDmSI6Un0rog+MHM5Yc4THOwiuq4buhnOM2nznNJgY9wwBMXjOtsyxvDd3R3HkYzbeofYDB8i27ouYsPAYDUZSqJBHYH+DjpiBEeE22vTuc1jjLdOBsYc8MoxAyQ1QCQQzmaYy4S6LXj96hVevH6Fz3zmM/iVX/5lyMJ2APlt28jQpDxA/TxPrHzJ4XArl2FgpLyimWNvHQ+Pj7jgREHuyhRa9PGJMugJ8yi39M6eSrV2MUGmLsdFYl0jqKgN176gthXn8xnTNO8aqhYeJTLVy8FUUUfB9W4aeqo1rcPpuFXHY7vi8XrFaZpxf3+P8/mElCCt0oKUMrsul4Iip+XWGso8YzZqK9Z1pYFcpxi89l27QTBRmZqTjsLM4EUg2gx92aRDANJUUMSIkZzVvVKAlMtEoKOtuoP3Nk8lllw4RXNDkxQl1BYl4Y1BWtN8Z78cimEDPEQzyW1t7HOWbKQkKFCtEuGPSmuB1y52tQzw2/flEV47K5iSDX2IqbVDQ4Mnmv2llBjpdRYOuAmAg6JR/YNMnsdQ3r2m4u+keU+yYt/3kgIYh4/UagQfbWtq37Az494dW6eJnhu1PCOVBgKcbHazRzYx5NxnY3+IddyU9XdqDcuEakcDy2AFd6H2B319yAGKoqP01gW7g3QY2ShPjqQZ5h76DjoJBrDJos88ZgZutRvdmQOMcTGAR4TB42Mh3OID1afH54YplhMYmSdECdgYcqKR2UW4B/dHwIE2BgU8jHf2+xF7gsf/xAZ8iCxdLqjsiEtQQK+fAGIEHwMiyZ9kxoRw7nTV9hckRkywQStSaV9HhNC7BmZnYosWAUU3uAOV/Wm8A/Npoj+BAJW5Yy5sGLZuFVjrSJWdphlbv8JHhM1cc936AHtLbejNBigJNfl0vjAv2+pwWL2/3OERD1iWLuFl0iYd/VHYdt5MugRNvJyN4KCQyTGlQkJM3eRCe76c0Vqk+qLrq+hb72T05N7YOsWiW62sTMFOz3prSDKNSznh7nRiVYMVTDOZhvP5Mkzkmqy9ORyot2hw9aoi8KA/CaO00OGwnJnnvq5XpXh8r5AwQ60LenecpknMuuaO0kusWCM7lXOW9Xsatvbh4eApokeg1455nuAAthqbB51PT6eCZVvxW5//PH7/7/v9uHtyj+ubNyN9VGsDG8cZyiTQn+RzkRhl5gSc5gy3aSygTU37aquYpxnTPDMl2N/gfDoPgWQwDMkmzOczlmVB3SpSmnRNTr+Qtu3VHjYjhMPrdcNaN2wrUOsbTNOCy+VCx+DewDjUEL4y708r816yPBlohaLRujEV6tVxrSuW64ppLrhcLuoDFF2oWWUTwvF4VhHUxXHdCa7qSifSWN/WRgEwfXBUGt7CyhzcAA1oWwV6QytqDpQ6ZnYGRUkZZZowzezqXRsQJfS0bqCglLYOjqimZClx29dJMyCnkSrJxvRM27axzrpprDuZiKwgLZqAsv8R9TPwjOgFm8Sujf5wAvYx/3PKBG/uShlKL9hjT7Ix78dcF1iKBp8AVIVDUMPgLyqP9j2NTGpCCGLh4gYtGBPtgcfAmiiGej+IuRKrRVYlhP0U7kavuZyjWoqFCL13+Kr7E0wIwp09w5Qqw+F8smUk6ZRq3QO7CPKiQuqDvD7UAMURVF88mNhgdcuUb7TuCB1TCvMxAEzPmI416nVGnXbnSLgRcY5hc0i79BrsSdoHokpib6uDIpbU4EzxX7solock+xCgRF8zjjBoFBMrFNc83h+DPya1BFjdEY2/fHxPXIqiikC4augVbojFCFDYUI6J0aGwHzHQrkL3lsViQJNa1HieSEFbIYW8LTQ6UwlOBmlFbx1eaWV9kWjv8fFRm3JGyYyKk8AbcaIzxZC4yS4bN/dSCoryofT3YI61KL1TQZ1H6FDinuSc0GR/P08TNwQ1uYvbbAZYpvYnFWOberEOYXrXahslrmadJYamxdcpxKXIUb0rdD/DVyKZvCKQsNWmFFxWdVGwUw52jk6j0V4XE9V7sDVhgU+9BDfv0E1IqNqYghvulKGDUclurQ0lFXQ0VmA0tmcPV1SKEXkOrYf1OYamgqyA2BmllrIYhCgH5vv3QGJPXyZMZcK6Vrx6/RKvXr3EN/xP/xN+6Rd/AeF/dAwcRhWJEvlRUdRdehqZSiVThFs4f5Z14bqS2FzRex/aDIyNfGIkum5w72o2mWAZOOWE5dpk+98FYvP4LluAdeXiv64benfM84zT6YSUJMZGMCdRdWUDPAaQDNDEFJCrMSSvLbrOrtuGZV1wOV9wOs1K2bEqaAR4xjEQFZC5sNlhzgnzw4SHx0d5P3G+8/j7PEGnhkErIMwSGhK2ZUOrdHO25Fjain5OuD9dkOfCq8rs0OttVdqx09fG5QCMCLx8uC3ESgXIA0aBmCOaekqFZ64ANVIyzveZ/EkAuO/3F+aqTiHzmrrz2gyDYQltioNjxnWwHXKQtQldHgOJYK77CBaHSWCJVPxx58K47hEg9+Nare9RoFDUfLAHSAp2OnFdg1JskL6OLDCZn+SAWQQLPgB9SWCxhMv6X+tFlHcH4AhfpnFiJkgZ3japw1uF+3YY04eL+F1eH2qAAsRmwUntop944zBKRpMiXJNJkFnU5DBiCZQL19AXANgptvhvpzjS9jxqAAPmGHe2gmPR4XbwZtGmxEnQBiMTwILlukGpH1+ksM0A5DiWNBtummSHh25ALOqu82xKtQS+MeLfgap18FE2aoLe2TOKJxTLXNB1rZaYmnGnYVp0sHU7UJKWWQ0T59k5mXMuQMroVzIYdVPTPVIzMBiFr61jujCSvl4f1fCtq5PrmTbjwoYl2bhebmkDhWKSM2JKZVRI6cEimhaaGV69ek33UkVkEb2XkqkXqNSdTPOEle1aRyRmBvoQgIxAk3Avooak3DLBCo+XbPdwqYfeOblkeIt7ktS5dELKmdU4h8olWpJTl5JyoTdCZm4/GTfZ67oOkBFeHAEaCMrohptSsARNCwmFmSkZpsIme6Vk1OUBSSmYXhtSIRMRt7Wr5FXL4GDSMBa43cxtRN7YmwnSi0NaLA38rs7iychYRUXPt3zLt+DJ02d48+YlSp4okHXHlGNpC5DD+8h7VqkPSADU0JDvJFW+VbrNXi4XzBKMrsuKoOsdwMPDA+7u7nSfo2KpqaopBKIKGvpur8/2HAlmV6YqO8uI2StoxeVywTTNgKL7UsLEjGd4rABig8Nds8BjVKxLxbataJ3i6Ov1iuvjI0opePbsGe7u7uDeZTJIqn7bKnLmM5/ziR2mPeM8nXA5n/Hw+IB1u2LdWJrbmzZuUGsxlh9nKocl0GTokqq31m2Bp4S78xmOim1LEid3pRQxxMCW2gCZIawFxEz0aAyxC1MtUc+zVXXPTQlbozIqgWOstYO27ejUOirpyNTFd8HSYDhLtuHTZE79HiuwtVjYLgFgRZp+Fj4rvgNLOBQ0OHpqI8XHLJJp7eb6QpBCNeAe3LrmLI/ZbF/H9EB575z7T7QyiTFCR+59vbYcJqLSY1WBI+f6EVolA5mnHsIVd4RTmIVYVnswGvcQGuAl0NV5X+M/6OtDDVBqa7CNQCBsyFkyqAZVQ5iVEGZCQ0Q7Eji0nR/6inH0SPbYvvjq+Xd5awSHONItIBOWhEKhb7ipMDLAIRMvfs3Ni5s4KVGdBjeTHEh/X6wCdY/CY+OWECjEsWtR0EzGcgJJifnI4wCzyBd2Cl6TJ0w9oyBjAvO9CgJQ016hESVpOSUJYZsiMojlYSO3Zbliq8CyyamTrCGZEwv7a5m2iR2AqFU2ndITc5Ynt8qKhnmesLWKdV2RS0Hrpq6hii5T2Grvos3wU2DxQce6rFjXZTAPrfcx6akjoDeGW6P/Q897BU6juNYy1ThudYhGIxdspjRWbNVJLEkn89DrPhCWZQHcMMmboAOo7piTYTqdYMZNtnUfAsPTnJHnmaDKHWtdcTqpA6433henCyiZIQGpRhaB7/Xx83VdDmwHyzOTyqmZOiK7M59mRBodxmZmAO3ja21imjhX9i7XeWy8QXcbotqGbESaMtrSdttyjbve2ZMGW8Wr16/whS9+EZ/9+q/HL//yL6K1hrkwMu+twRqZHllZ8DsOlDjngPo8bRta5fdNpQwa+jRxs35cHlE3uvPmqWBW1+NhdOcRvdL+IDQTHRXTdMK6rmTDckKymUxFTli3DVvrIxpu7Q2maZWvFOdMiJ1js47Sbz7b3a8HYCUZBbYJdZtQ+yYjwY51XfGlL30JL168xNOnT6Q3iuaFG9aVa87d3YW+FYXj95IMp/OMJgfkulVc14rrcmUPoaoKpjLRjbcRfNrJsK4ru5FPe0l9b07vkLrJTJBpkjLNKPM0jBupYYuQ47hGknHcto09sKaCnMlYlWnCXNimAesGO/rrDIt2QwhMHWNLYGB0mKMBtponoNfBOIdotWdXnydH6mIJsYOB4Qcjqb3xgKxy8xjPDKA7gil8O/0h3Q640YfYOMDJ8c/bqcB9y9rBHHcInkPy8DJx9J4E1oiUonO0e5LtbBrXNeQH2oPQJGkwh+V4H4aej+OxKPDe/as+yOtDDVBac5Si2hOjkOzopRKRG1F0UBAjUTJe1kdNjR62CRhwYQ1hFpxaFICUuplFbzUAN9XAh8ESi3IcQg6wEfoHKDCa53hyeJjMAWImdsDTPVqOx2NWXhL7gI3lOKnmLFBtCmQtap+MESMM84wMl8FPQvaC7BmT87yyJ1SAVtfoaNVG1Ud4htSmslOn5iduh7vO0BxVlOSmxmIZCc1s0LmOht6VUlCE2JTHZBM4eh5ExUFsMK1TgZ+lp+GETTLvYlkj2THe+yphcZjBoSsKSYlMiTtyYQfZ7g1VjcMiRzvPO9uUsLssGgBkiuNCjJptn7Q56NiIfiS8POoAyG5kTKcZuTmu6hxKwWuWj8cGwDGVgvPlgvv7e7x8/QaPj49IKeEj736EaQhdZ6DhkqfBhBydTLdtHWOWDpRNBlwY9zGqI8gSbOi1Ic/qd6No2SwqTxJa38bqn46TA3TrDKV/XEfzPiI7wJGnDBcysRDzNUfxjKWtqL7it3/rt/CN3/zNePbOO3j54jlKT8iJa0MWyA/x9KhUcR8GbiFkTpbgeRcZdmf6ZskLJnU/3raVINiZ8mmN2hNW4RT0bYNl9jspKp9et4WbdofWKT6KeZ7QMwWhaZE4Vc84xsX5fBKbws2YjNeeigydgPueDuLfqkLLCakC22bSnzDlVmvF8+fP8fDwiPv7e5xOJ5xOF/QeFUIrQqQZPZk4RlmGfDqdcHbDuqy4XinMZfNN6Zrchxtp7/TBQDdV7dA0LicC/A6CdepS6IfielbhO7SnXU2gAdjkrmzG+ddjI8w07CPb1ZF6JeubMqYpcwM1AFpHewt0susnGtpgy0IcHRVEhhDh7+aMx3JuAOhRpeKHSrb4jgA3nYy9Bft03Eh0rVGJymWLqaIQS3OONAWgYo/HUvZ+wDKOGfcRYBUnJAXALm0YwEOJq6OLd6SyGOxHqszJksd+CAU22utSIsuWnMD0/2tOsv+/9soqAwMw2IsoVo1KGUADAhGnaVPxt5iNASS6OIY9H5jkDRIVLONTfvyk2IBDyuYIhWLwuGg0td/ZhyRHjsbjAQknCphc1+FRZtyNM1UsCj/CRSXKgj1pErhJsX0ASx4AhV4eWYyJwVA8IVH+qhbp9BOIFuwAUHvGslbab7vj/u5ugDuTmKoLHA0L+7YCaljlSq9EZN1F+1EjxEWnOB1Oe+0j0ojywrptooST0L7LA4OmS2wjT/YAnQLUJoHZUlf1nyBr1LTpWgq2TCZkKaoO6CxpcEw5yrEhsKeHK7o3FqSknh4lmpQpnx108yiZ9P1Y/Cus9dWhNbOn0JQKat1oc50TgBOBg2j71hqW5Tq6fj9Z71Fnskuw6Cpb2OlZmorHZVH6iJttgMAQrwIR2Tf0rvvcolrCsa4bklc6qF6voD9IppwjZxi2wxxg7MbUDhm1js4Fd1DXvA+R888pI6nPCp1eG1Iq2Lyi5ITa2KPn9es3+Mynvw6vXr6WoV8azyzrehHPwciQtEpH3Fp9RHhe4/0EKstK47Kzn3GaZ6XkqBNalgX5chlOtKvs1o+6DgCYyqw0XR6eKk3i0mkqsMbS1ZxY5bMMgafjzZuG06lJRLuXJJvdrDoC60wVTFMZaU96qqixY2VFVxwDILuxrityznjy5AmePLnHPJ9GQ0V+T0YqE9cGAb5lXdF7Q5kynpQnuL9/gt4bHh4emVoS+5SzSfdCp93aKxrYgbhtDWkqyHKtjuolbooKzhzyYto3zm67LokMSKwbiaBQI4lr0e7o7N3ROkv/85RlLdDHOmRQKXDiPHWoDYgDamCsY+69t1LnRmxOvUw3R8pyfHEKSV3BS1eJUDgoD2M4bRrJsjoVC1THpnDoIReqNMjI0n3Xuo2Sb0SRQjQlZToKAlLHKqFYdm5gTAdZk1jXPGO3oNhZyOEGG3FH2oMwUyDaoiRK5EG0UVCH3w/0+lADlIhGArySruNi6O4HEHB8yA5YBv38BG5ik4i/4xPCGJIhkr73eKiKmN0HDe3RtMqP38cTdFGV8RvX5jcowfE/ACyNwRBLu3cw/ROfd9DbPT6CnS3hvxV19EjjZISUNWg6Q8YEU3VNQgFLiTOgsjoyDNpH5YfAe1C747pWvFkqSs6Yaod8BNVDRmyEetXQvrvDrahcsI1Ikfv77jvAvGWHOzUsdWt6xuoo5F0+B8xt0rRM4lyPu6FoIcnMSaBjqas2awI8CmY7XFU5vTUao2kx6k6GMwRryRKpy/FKCFdeICYgQRWc1vNkR8LAis9o66ty4qraaXsUnBKrpba6wNIEQ8L5dMLVyVDNSk/kXHD/9Anm04x1XTFNEx4fr4qIz0Pv4J3+MzkzwukSyk1l2sejFrvYCOM5jyHcD+6vrY/utjnZiPoNKon2Pa8fjGED04NwH9U9+2KNkRLh+utIVki3d+oj2rUOl9XaGkqZUNuK6/URv/X538A3f9M34+nTZ3jz+jlby4MCXDRTQzRWarXGtEw5nbCsK1BppjYJ3FZF5blk5Eawcn18BNxxPp1RSsHDmwd4o8BVBIcqwXZmb+hpIpod1Uum1NwKk01+yRknB5ZpQV5pld9k3b4sV7RWMc8nnM/UAW3bhrBY2JkUzqVpKgD8BrBE1dQ0TVjX9QaoRJfqFy+e49Wrl7i/v8f9/ROcz7Oidq4ZppL2aL64LAtcaQ16wfi4f+4y2Gs037mcT0g5C8SxW/gwAksxzjK8Q4Z3hzRBd3gG/LhOBgsMC7J4aJko0oaAoDQnoJ6srhXdJ5xywqSGhizzTzKSCyZCLOuWkJPDm8NbpGZckhCmSKJDepxwiG/hDHAt+a3vSrDfRja8WBnrawR4oV+ENSDLdNIAHCpNvXLzT8HCytsk5k/zBvesYEeVUKYUzsgUKHjufL5xHiPejYB33H2M8dZVSZkkFo6CEqaiuP6O3a7xunsyWGGw/EFfH2qA0sEoPW700GI4wJ6NIQJLh58z93jUGNAiObr5HvCkG6L8Krb8IDqSJknKoZ0AGiqi+ubmpXxdEqhxiwobbQyISPrAnAT7E/8bnNo+VPbrTtHQnFdsumbmAPkZs8z+C70zheT0FskelvTUqNCCOoAJJ0xVvwwqttmgr3XHum0ypoLq6vvIfTZFdeGvQT1CREYOb2RAqprgTWUakXvOGegEdB2N7d9drc6rSuQaYMlVbTFg/KC7TaZEvXICRhtxbD5ErxE+1NZZ3CkAMcTVUIRSDFNJw2xs6EgAOMSGjfsM9Lp3PQ6UG8+T6ZGogtjHVNCeAXBalWBWkf/j42OEL0OXcHpKFoUW9VDPlxnvvPMMORdqZrR4UsOQ8KiIOUTNvfH+m1gLRvcaSwezJXKKYSVOMDdjYuRpxpLvrWJbeTyKbXWfTNUDqhTqjfT4JADbTfnwsboDln1sxtpJsCzs+RI3P0s/8fLFczx/8QKf+bqvw6/80nNsySmMFsM4JV6va5zUbZNrKn8erQXKIXVYcsb5fGaDw1rxeGXPJpbtEtysjwEeZoLAviFP7IW0afOPaDWYsZQg0fF0cy0pGX1LjGxKbdRFsSVEw8PDA7Ztw+VyVrqq7vfW97Jw+glphRA7Gem1qCbaNjW3rBEk1DG3X716jTdvHvDs2TMxKhmtEcRR/0OgdTqdaBQnYBtsBACKh+HotaLJJM7MUFJHA8ddq3UIUGurAxjCGIbknHYmXGxAVqWJJ6CnqhRt9HnheazbCg9xsRmiNWXXmuq9o60bbDowXfu2McAJ5znZnJFOHhHrcR2WdYU3VophfzXvu2ldfBdsfEc/pK+CTUUEhMH5E2Nr8QiwmySE4ZqH3gfAGCCoQ8Jy3tOSfDh7x/o+1mcHoGrYaP1i7kg9wVQlGOmhAH+Woa0ugB0/bzI8td6RncZ1wbg4N+DBiH2Q14caoBxR4A1XotTB2NMjmvZj4sb2zcwCnMSxRI2lAYfVMdjH5g/pFagVYYQ46LmgJHVutEffabjYkHbVtn56oPNuII7d/DWOE9PBnOVisMPnHNKSCEjh/03ev8TalmVXoWjr4zPnWnufE5EZmTft/IbT3PuM9RJ4ElhCCPEXFySE+AhTtUTRWEI2FUBIUECWqFBwgRIyAoSghAAhgaBgEKKWhYsBCSGETX4iMh2RcT5777XmHGP0/gqt9zHXMX4QySuFvOzIOHHOPnuvNeeYY/TeevvEvzPEKO2jSod/KYhqUM+K8OvWXGHCHAt20lCg9YLdCZlqA6MLCaS5QEAXSABTbjbGQBgFTTdaxCxV/dA0INHkajhEz84szRGt2cH/CfdfkuYGRGgQFteo906TNjMsOQMJqEv1eyM3ngR86GfpF/NmN+1iIKVvdHZcf3YXhzw3utFQ7phhEkOjAIlDl4dH4bE/N3cnm40oZhOg5DMoBoZ2KhDGQK0Laq3zoOqdSp77e5Jd931zwhrfd8gIx+g3I5w0Dy9xEy2ReCbSJLIyD4i+Hr13bvLOPRKhIVO2MhESERaDMQePrmvO7wPlHCxORAwoUSCwmBxz5u/fP1f00RA24KPzsEwQtG3H+++9h6/95t+Mbz57joeHl8hpQXEYf9t2SDGs6wpzsii8YCglAuV4eCylMifJ+RwdgDg3ZOx8xp+d77AuK/Z9Q98bTIH1vJKzstHRdUpAS0ECZl4SPVoykAy1rETwXFUhoA9QcqVKThwLBSdkjI7Hx0fUWh1NwVxncd+4TG/5B2Hol3z0E6Ptikijbg3zGrA7Hnj16hUeHx9xOq24u7vjtbOB1nfnbTiC7A1iFGDz+4SCJ9HcD4Z5sJrRBTm4Dwme/eSNFAsT5SGdvCAR7t/Fk5k11q0naJsRkQgLAevR8B1FsjiHrTV6r4RPzVTYIJDQcPDGVG0OG3PP+dV97Nyvzde/oxlmA92c45SSo4ljIl6xH8zn34slc1Iw9+NjT1fxPT+yd8zPmmge5VB1cZTOv6vDybxq3H/5sdiY3YQ0csvnmWd+3umIvLM3z6Q4qmwCBMGbPOYVsTaDbsEvMzeF+3ivT3SBwks6HC0JCNAPr3kb4As+CEKYFeHty8wmYTLFaRhzvXnYACJ2w7gWDLv1OjkuvPyqnzFnhOYSvV8Nskj8Lbv5Dv7eNN4PIVEoRT6Hn4bQK8N1+goniJqDaQbIcL4JOA9ESKf932F+JgJEqJaqYu/teHjgRb4YmqoXCpmzaeGGIiVPc6QEOjwynZYbZUR3z1m6YQKIgYaMYRNBMPjhbtzs+AnCOC2SmPlAsnrnyGV2PcYMCr9BM6xteNcsAizrgpLLG11g8XFTl3bjHXBwi7hZYb7HlBI7Ind05KapECdRErKlDXzA5By/qEP+HYJM50yE9frBcTF1ImphR5cyk2ilVFyfnqBDcXc+YXfZclkXHzmMo5AYwws4my66ZiRgLsvinzlkqzqLR7hvSFzrMYYXfuqcE2HibmvcoG+6dovO1NdxzokFWEqURYP3Z13I1eidAaC9d5RcsK4ntCAtW+F6UiKSpVQAgt4GXr58ge9970N89as/hP/wi/8PnU69u0+J6GAQTcMLJoovjgd4OJsaFpdNhxNmfBaOT4CHpyc8u7vH+XzGZd+gY6BtO3LJWJc6C1yiczsLJdNZkGRfE7TOL7DkVurw8ag7FEsQdn0tR/EQUvFa61TLHQVRncTwuP6qLEBTCuJ49jEUfASQPNl5zAMunhEWRU+4uzvj7bffxrqcKedt2xxlRWESxSgQ8veBxSMpQqFXXbYdqcopZSwlY8hh/qfG8MGmA4aONAakJNSUIWlFSZmNlOS5PwRrMIkACRhKfxkRYfaQCCDkLYVCKIjEXpIce66PzxSgGVtSIvVqWIrMvTBGsubPJ99Mwki+ESdAneAffJXbcwo4Er5jH48/O1rZ4ySwwb2dnD0eJmFXpWOgSCjlAuswb8SEBw+id+fZKKbHOQB4CCGcg2MeOhj8k9v3fwhHhH0mr74Th+FrIaYbR/NFder3wZH9ZBcobwJtvL1FvJObiISTicZRuQswLYNtPlQy/5kX8CYzQPItUXWeSgDsRsZ6PKC3BYo5mhMYjyEcI/17T03yTYFz87CH3j6DG63cjBNgbgWuOfARTE8XSXT8k4QBtyEWgXiBFHUPEAesuTX0cGWMuhU1ESWiJQoTSugYhEefEdpbiIdDBeQbHzqUKWluWAJC7SmTec+9mAOq0Qe6G4CllKBhUf6rIPHo3gHvJAyYZk/+M2IEp44ClVy81iN/qKY0VR6t6bR8ju5BzVAk1BF5lpBRoFANwg2O7zsOBhoxBQrEexrs+sORVH3cGAjLnPm7cialw5eD3X5CLhWlciS260BJGblW5JSx1HRIaMEiSLUh3GHXM0MEE1hxk9TYgbTGwpv24wLgum0omYTOIODmlFEDbjeSk3ftMzBPhLECRDtsPhtREOVMeS/dejHvo2lHcLjmvVdDrQWjD5Qs6J0HTU6dzqww9N5guuO9b/0yfvNv+v/gnXc+gxcfvUSVIP2S/Lg5TyfnA9UjOuYH6sRWybcwJ9ymMofH8+89Pj3g+fO3cFpXbNuGtu3oLeHu7g5XV/OICNq+YxvM36m1oDe2p6mkWbwDmO8JYCgmYCi+bnImkhI+QCKJP7M11ErX4FoXJ4sr7A2fiVt1GDkwc9yYwtGXTsJjdLfF75OnEsX4w8MjHh4ecD7f4e2330Ktq3PLBsz6VLzEc1mXCrFC1aF25LISnbB0rDEvhpL4vjCGX5MBdelz08b3m0i6ba1hPZ0gmpzk3OdngdyMpceRx1OSYakVEBL2u3Y/LwxiMbYcc40KqC6MqJIg28bzESPLo5DALIAEBU03qKPCkqLwPc6SW0WbDoXKmGcA90z35bpBf/iHPD+G+vsJ4zVH9ifCDe7tccZlc4feebC9OXlQuLHcLJBu9rjgV87P6u/JfzY3XUzD1OSIfUzOqJgDkRsBTAU3l+J/+fpEFyg8stKBAhhw5DXYVNXAAkHh1x6/D4SaBcA8rA9I8uaHedqs/8cxZhGZN5T/4xUmjupxQt1vfM2x8AJaBXCzcHwx+dwy9F1BoooxCU2DEsRNyPj7hNqIoLB6Ds17yMTCTrGHlb7IfBiHh/vxB/Lnj/haECZMIlgqf05JcAWDW92P4QS5MDHLKJWbY8qCvdFrQBKQM5AyvADC3ExVFUMERcimB5yo6ReRrPjjXtDkTgEVFgleQL4p303o1o+i1uh/s+TCubx7gBydVHCF2JmERFBwO0cVPviBkKkeChz/b6JvecKdkzDsfZ+qTYlejDAQTY8pFU2lzs0iDu6IfM8lU35cMmSMY8YPm0VN8jU/VFFSmp1nEoE5crLvO8wG83KCE6DMI+F1dmddw4TryTFiEdFagW0NeeH7jVFR8CDM3CxLMtR4uJ3PJ4xu2GyHiPt9IAp8oPcdqhwtQQjBmycfWvDIDJOn8dGLF/jil6joieDHGF+13tDREFwAKo2c6SRuvJdIlMaNXUFKCSgF+7bNgriNgcfLE87nM9ZlwXVQQfXyxctDTWaMAFCPWSiFhFg6IgNLrbhu2xtIa5CRyaMqqKWQn5UzkhCWJ1pCrlDk+9yd7/xAPJ6TGO0AuJEZx7jRZlHBcV6a6+uWM6NvHGrA5XLB9XrF+XzC8+dvYVnOMKPJnFmbqqFaK5AUSRZcn7ivINDenFnAeEO3tx2K4RYK5Py0vc3UZhOBJS86RsO1sYBIEGgbCGm+v0tIIv8jSP3dDNaHR1foROCO0FibezmvoftnSYyWj9gBmMvTk3nTizdeHE3x/1TAkD74XmVv7vEy91zzexOjOXMU3AULmMeYPxheFNwgOdxXOB4PhMNoyjUbY3hDBDv2MY7hjgZaprXivO232yFiBBGEdkGMlPj9Tfh8x/djkrH/HBFI9jH2x3x9ogsUVSdBTle9QyUjQilxzmkaaN2SUAO2D9g6yGY2Dwkg3XQi4vAoDiEb30PA70pVENGSMTfBuBlcd1GhJ7zBN7lZD7eaefiNlah4Y6xjrFQDKRHJkCFz4UUhpNAjsfkNiM9HC4NdYxyc8XCRtMsTUiwqe7txWASAPu3PdTTCtcIMkYCjszCAT5vNDJw+dkdLOKwB/FKIoxpO9GNkd1Ty0R2oj6Ccc0KHluP933Q2kbtjQhmxdlr0S8rzwAriFiHr3b+/TFVW9wybcGC93VjGGLw2xu4/pXjw/WDIHndut0gaSaht99wUwVTZJPHDwHwvGwPoAwsEec3Op3CPGTDwkDbhrmqpdUL/sUn1tiMJx1VeVUw3VXIjhAR7o8phDOauxKimFJffggdTfI4WoxyheV3JhowyN//AmcZUO8mxyQnHlKJwZYgXOskt/YWhkLGGZtjnVCM1P3i9IHd/i23vGE3x/vvv40f+r/8Xnj27x8sXH7rs1tOASwUSP18utAAfY2BvfrCWAkkJ+xYKlKOD5NpMzEESmdfxerliXaiaaq3j8vjkktaBkgtyKTitJ7Te8PjwgLv7eyzriuvlSqSolEOWmzJgHQOKvTWs63mOm2stgJywtz55R2FqaGZ4fHzkzzqtLsk+TPBSytOcj4cgZrEoEshKnuaGtdpEUN6UJpsXpJQUXy5XnE6nGUx4PEdBuk1YFqp8YkSYU6ZiyhFbjllYPKIIkjHjx66+p8b+6kQUdXI+mwQn9DvJN0SOlNf6fuvE9z4GEugSq8YmK1BCFrxCErVQtRf2+PMaq6vwxPlCNyq1aJK4N3F0NCkF4VSbmE9keowM4xV7yxGmB4TbqgbSDwAu8Jh+JTdNbSB7mN4sCgj9a4YNFxIc50A0KOyivHsxJ8f6M2cefBlv92gOXakqYEjrdPF2rxSXQauvNSJm/t92vO+P8/pEFyht26GDnU5U/WluKr5oXH52gBu3N/OoZn/NIgHzLx2/tqhG4ZXhUUVH900EI1jNMv9e/Lxfq+qefzfhONgUPt9JdIEN2N7N00RdGtaFahf/kLQwPtAbAEcxg+N6GNil9zH8ABeE50s4oAIuWVPDEL0p6pS8BUnYEMUhQ+5gCTUldg8GRMDXdFUN2NDHH1IcGXALd26ocdmOQ35ZKWdsrd3cm+MB1SgUU4YZuSwJhBQvlyt9G+6fAWAHENK47pwBknSBnIrPx49Y+fAtySUUAzbNyUJZEAdJ5NjEREpNZ4bPGB3XvR2bkptK5Zwx1HkPN2swSLfBvRBJqGVxF1D6JyDzHofza4wBUsrY94b7+xV9+Jgs0UMmkKWSmRw7Rkf4K6SUsC4LQtWj2t2ULDv/ZMfd3dlJyANSmbUShX6S4mgLDyU1QUrsmJt2dlmJXirWY8ziRGUnp9r00vBAQQDwUVNrYdVdCGFn+mm01vHq1Ss8PD7iB7/4BXzvxQeQoUhpINfi647rjwaDntNUeL+Dy8DvmVlUl4x929FHQ3AOSi5UoLgaatuuOK0nPHt2j9evH3B9ekLaC8ri+T1e3LQ+pjInlzzHnZfLxdcWHXjTICmThz1HyESkspuUsbDbth3D35eaQtuO3hvWlesjwh6JtiREOnKa3JYxn3Heu6MAj6Rsjj7bHEFtG9VMy8L7dLlccLk8odZl2uj3PshJ6h2U15vb3xfADoNAjnh5T1P2tGiEmWHCw+sHXK+73xt/b56QrKokM5urRIbOEZ2ZoZYUYO+BVMC5Nd7cpAQgmcviHa0QcHSdzFEnQxvMjVpSnpbwnOgoD3EHUFMQ7aOr8rFPyQnWFQNH4TZPF68Wxc+F7OtLbpR+cXiICxXgqJJ/WNb/EqWNj5QcfQUcXfYUZ3EC84h7LwLoQB6MGkhzPGyurhN2TMlxIY9xSP6jzQ4Juhn303l8BifSm1/CwsdZ+HFen+gCJWZkkhK7stuZzKwxDka7+RzIQFKnTFhdIK7Y4U0OtcGbxYo4JB/fyzQufDwELg2TNH8v3kMstEDD4j3GgibciaPwMcxiIoEQffbFnCKiMlQTMGhSX55pqomO9xjfk9crIQ48Wsa31mnmJsJNXwQDnhMiBylPhWTIMRQ1CVAMrW1EE2b1z4cgZprByaBKwbtQf/DMSJpc/DDsowNCB8rb+Sft3PkQtObkOsmzq4nvDdCfpfocvO2UR8IMux9qbWVxE/bsknmQ9lDz+PcJvkkfDaMDJcLU1P08lL/ubo9uI0LhyvxsgdQkCLoXUOEZYjA/VEgUhMDNjTj3D3iexZFOGebbn3qLhxV4wNelwFLyeIHjcU4p4XQ6oTW6wdKDJooewxgNd3f3UB3IwiRd8ga4qdCzRRkB4cDZwUHx7JChWOuCZV3Qt45DnRFqAp0LPCXQjMpHDbVUFntesEaxbGpEvVTRW8e6LvN5YrdNPob2gzAMwP1FBrbrFe+99238yI/8CJ7fv43HxwcirYOqH8kyDQnNeAitpxOvkVI5lnxb7KOjeU5OvGJMEyNREUEbHU/XC07LiufPnmHfNuyDYwqql+CFCn/G5gZ567Jiu26zyBIRd19lVERvChhHdSZhcMcsq+GGhuFoSwk23x+9TgYDAk+Lr0cWrTFKiIPodsRwm6SbUqgcwyvKx66j3qDD3P9Syui94YMPfgXLsuD587dc9cOf2doG2IDpglKOvbGUiqG8xrFL5lyw5Oxo0AkPDw94fHzEdetozr0ohQGB+96OcbwkyBiU9gowRvIxrMy1ONUks6nk064CJAOGCiAMu+xIgIXzsBf8/n3e2CMICXiTEvlTRxAjXC0HcxR5zdNqIJxv1VVxNqjcKtkJpTooFb49h3wfJ4XRHAGJPzpUc3OE4/euK5+/7Nf+EEXw12P4M5r8mpjPChxpCv+K2OfN2KB1M2Q50J84d7IIJNNdHDejRY6TPj5L9hNdoJDgxQc/SDrzJUAwmQEePRZf5xXu7PgS/ztQjEAYBLdqhKO7gJBgyY42T8j0pio63oYEVOhViTpcHCMc2KRPUzMfhEKapolhJkiaGdKgR0jEXsN7PjN2c9nGLGE5FvFnxBnbCsbWm7DS7m1gtI4iifw1PzyHKjNCMLC3Prvb3QP0RCq68XvVupBcqphz/bCLh3cGBjLaZYzZJSdHS4YOH+kcD1i+CdMLe3k6XDZnmXsxmN2VMbHa79fueUwZW7tivzaUXKHDcBkNSJRO+s1BzTRpupXFBlFudCJLtrhSAUDvgZ55sTlHFy5L7d03rjRh5Nj8D9ibY6lpbR5EtxQFYvJ766GDg50YUkKuFafzCd34W+LX6JbvwW6O73lZKlojVF8EqKUiJcUYLOx2zzMppULHQM6CmillppHd8UCFR01KFWOSE4m+9FgXs+qOazQwU+UKn4XwZwkyrQiwLitCEpyzTJ4Al9DwgomFaU5EGICQS5sfSh3dFB+9+AjXbcMP/fAP4z/+h190E19uqFw79BoJI7vhY5tuHAeWtQAScnEeaClniAax0wnNExkTR1J2nE8nohfbhut2xYw7MD7XOVHa3FrD64fXrmo5uCIQQ10ybNfZUAAG1Y5SFo4b1WZmUBwKy3IoeQDMpqKP7t4pt2yGo2E5TrijrY2iI2eOwFhUkPRca50BlyyMwhSOBdO27WjtQ9RacXd3714qJ+zbhm1rR7MhHKmVxOysoTRF1G5QdCSh4u75s2c4rSdcLhuu2zb3ZRHBeRr30RUYnqAdpHNxrhZFCT76zQk18TMY1Emqvv86rtz2gSW727gxgR2WYMOAkEE7msZa9eALigDQPJtPqsFcsVjo5bLkwvGaUX3ZzVwxpEiqQA7SrfgeHk1MfFO8Afkn4b2H3D6vVIuK/x3zRjYlqizT4NqdSkEF4EUYHPWypK7MIb9vAgCBSkU5poAho2Y53iO4v2efCPDpVT8PPj6E8okuUCQlJKomMTM//M94JnuFL/Du8U1k4xgB8SBVdTRFDhhKfTOAV5oQzAJHRG64BsfKuL0B89de7EaHeuziQKQIZ2WHJwLKgl2Jk0j/xHXfsF9o811LonMt3GbZK+ZQofBbJ/cjoJkYf6ojJRGmJ0CWkFtyI9+7og8WQc0ANb4HMUr2aGVMxUpJGZmcsfmAjtHQ1R0hE2E9ATtuKfIG1KwwjE7OSco+U9awQM/zXpFY2hGZFABDHpHpExIOlmaGp+0J5wTsrTlHoiJJxr5taKnh5LH24ugLACx1cV4SVVF9dOzbxo4VZboliXIVUVR9pBSnG3luRLhH6FkcHKEwCL8AAdywK7oupkaL0JE1HmYzkkfPz+5wurvjNfVCiNdmTJ+T+DnrWqE6sG0NIhnn85nXenTkXLCumcoV5yWUnHjoq06zv5QOtRHgXBYLIjCLQCg5Q61tEzKeCjTxkSIoBV5SoWJjWY7nA95oVH7v6/UyR1wsptJ0VTUFNh+PBVKl6sVp5niktYF92/BL/+2/4bf8lt+Cb/z3X8brV69c/cO1VmpGXo78JB0DUih5TuLhbUqUdV3OjFUwQ67ViZbDk4g5Kk65oC4L2nUHtg3ACed15YG9cwzDjj65Bw7tyXtvLhXOjjR0CLih5ZwdQQnYXknmVRYu2Yv8nBKWWrH37oXicLWPISXF9XpBaw2n02kWKkGSjS7+lsh/i/rG10b6dnBSUhKUskKEWUGtUaK8bSGZzT5ue4nHxwfc39/h2bPnSAlo1ysTuUVQavV7nIHMsa6qoqQKmHNBfPRXlwXPxj3opGp4enrCtpNYnTOfS/LkvDAefP4GhJwsgWfcBZKt83Nr8AGVycDaOXaukVuVE3SIIwjJ+z/za5zmGqJjcQhyj8YMwNRh9NHd2bdwX7SG4A0KjGqfHCaDyYm+QTI+kD9V2mHE3eLvk0MTztUaIxx/RZaSDsNIhuHvUZxvaL6+gos0EgA3HzX3ChNgIrH0QOHzLX7tidCk2chn3wuSGZJG5tqvEwQlZGXiFvNE0wJ+PLpc48ryB5Cy2XB5DLTEfAYHDHbkCPIP/2xCZ16wiIWpUXfWPeCuPvPtmXdNAn59uVEbiXnwnQEzEyflIw9GDuQkCL9JEyFM/7+BsGofiITlMULFdMCyMHj8/E1VPtzpEHRiZMV+eFy00d2NlV29KTDkkB8n764HFObW22Tzb3wwzNGgdBgBZeeWiJsdxSEdHagJnQeDY5LdgZJqJfMZALCuJyarWodJmaS0qAL3rQGyuzV1Rttpb3652mSfZ+/mtQ8stRI1EMMeuR2do6R1XVwya8c9TQmtHxt85HoET6RWpnDsrc3EVYCbU1jZG+Iw92IlFSSH2lUZcV4KUTSF4v75M3z6M++w++7BB+nzYAn7/4D9497f3d1h23a/dPaGxHZoR4RIam8Qs5nNQgVHY7KvO68KCu9/6xhd0dsBZYuIy88HqneXsPCuYYdMJQ7XP31O2GmOMXC9XF39UtwzxLk+puQgeMc2eketi1vNs5SK4mypC0bboQp876OP8OLlS/zQV7+KX/x/fpE8isK1WGvFsixojUncrW24c/M1YIGKzHtL59XmqOCAykDY1/O+Hc97KQW9DzzpBenuhLVWFAg23TC2HWlZPRSwo9SM02mFWsO2GdbTGSkJLk90DK614nyqCFMvNd5b23b3CxJQzJS8eVJgMO16XVdcr9eJalFmPtBaw/39/VwDfF5vNZ8HIZKeNKGaw+SVkIR9SMCzy8l5Hp1mQXv4thg++ugFHh4ecX//DHenBefTvXMggD4M1uluHRLwcPINn5arF1zrslIdJIZcEsZHL7D3i6MMBjWiidrGDSKZuA8I5jPKc8AOJ3E4v054T7MfzDoMRTIscR8vbi73xhEUDZRRvSheJMQeyJgHQxVxxJNIKPuiaCh8tAaeBdzvnLqAaJzD4JJNYPa9KPAh7uuD0RreLAWnyIxNDx20C7r16QcV50PwR3pXJBl02gYReIF7PiWnwUrwKI+CVnw0hFm8eUMO7mV9UNqdHFn5uK9PdIFi2mNy4aWqTATkMPC6BTb9a/xX6sUCwk1VY8Ml62nMcQwrxdlhGBBEUf65V9V2Y7DjEliIm6OZIGn2b53m+AYx1pHkVvQc2Qw55qQwokFFMvMMHJlN5jr4YY5OFNA8zhf+4AeUlD22R72fLd7dYyqQIgRrqGLrJOcly46CMD/DTL0DyFiW1av67A+koo/w8/Dhk6NX4vwR9e48bP4B+hP03qfxUE5BU8QbHV7NGXlZOI5YCtq1oashq2EY59jBcQGYhVOk4mF/4IOSE4Y1YBrLySQG58R7ALPpzmtDsS4Lzqez33//c+/UpYeElWsklA4DAdOSm0CI2f1aJGE9nf3AUURm0753ZCN0HGhIrRmnZWVXUhjmxmyjUF0AQHYCY5oMfm4shqPQpilbHx2nuzNJj/uObWtUYU0/A24c67qAIxM6PkaeSfJRTHt64jOndK/NzskQocIgNj2qPdwN1JuDoc7uD1KLOEFZFbX8Kq6YX/NwI+WmO5BLJkk1A61f50Yvon64spAY2fDLv/RL+NEf/VG8/am38dFHH8BaJ2fHiMi1vcGs47SefITp4z0T1FzcPtz9jTzrSU3nyCCkuGMYLk8XiFE6PEKCvJ6w1AKoo5/7RtWRuxnHvS41s9EBOU8xbhtK99tSKtq+eVduE5EL8muti0tk6S/Tx0DxXJzWWGQaEq7XDaMPrKfVvy/5Kmk2EUcRHny64JHELhqH3IG0VIxRnAMjvp43XK/krlHtRJTtxYuPcKmFhcr9M9oHGMdQfcQ9pEpojIFcEpaSpudOymm6CZeSsJ4WKHQWHjYytNHLyQaJ/SpKfiEchdVYzxSv5CwQR4d0sKBI1dEIcwWoCeApyCaKMD0zSY68q48N0zxX0mySaRPAPfFAyC1xzyUXzcnwTtK3znDWlDgCitF3JNwP92eKCV042k80MpNTpo5UGNx1W4IniXmWxXMK38eI+L6JgpKgrMxsk2gGOZGIZeDYCotFi3M03KnZfBPZwvf1+mQXKLPA4AEfXVuMbuKxikMYEqm5/qDfVC4JONAL84qRP4XVoCjCZAjg3wsPwqAHOdgV2JcfE/CCBu7mSn19soix9m8mPl7IGUHEPYouokOiCTk5VVfNO31KaA3hj8LiSCd8zwKpGUl0SWjfzaJHZwUv3sn21jgLTcmN2Cj7E4HDf/nwqkjcfLrq4QmQMm79E+bmF7Mtz1zh6AyHOiURKRBL0/Y+SFx0oiyEnMfmwxVHDtze3tKAdkOt5Gn0nbNzUx9b7Z2d1LxfCbfW+HG9sxdSay1MsM3uxqpuGe6+IqUeLqMhO47XdOKFIdXkaEx0heKqACffgV1ZJNwm74bNlLbnuWBZV44XUsLlcnWnUHPfEkOtC8hF2bG3HffP7rBvO+/HRsfPUjhe2bfNuScbAJq9ZZdkRwet6jLT3mIJznRbUyp/zDf9g5R3zOHT3PgCSuc6iiBCA0ejp9OJjrRjzO65LhW9HcnABo63iA7yGnM9Jldc+eZvfC+lJu/2d7x8+QqXywWf/8IX8OrVR57Cy/vWGp8bEi4T+kZJNg95L8BdNSHCa7Kuq/NOdmyjTfRqmuL5flJqxugNl+sFcr5DyQU1Gy7bBgN/Zq5HLEL3ZGiLcYqjSJfLEwTAuq64jIF9u/L6ZoH2zoMrGQwuh/cxWfcQx2WpXlyT2wFResE8drS243Q6+biNhRbgIX9BDlUWk3SnZegcCdZ97reBSKkatm1DRBSE/06MUSahuA989PIlXrs77bO7e6zrCdguU04cGTWqimSFSjNHMZLQR0kEuLs7eRAhHZp7J3H2et3x+vVrH/F60TyUDacXoKG+SykQ5ExCvQA1V9RUIWowcMxSPKNmcp5AZCLQm3gWRASLlOh5oX5IM1vIUQYn9gJM6U4C7GKw4QWOo+Fsto5iXiTIzZnKSHFzzc7Pkh3xCwQF0Dkqm2RY5flmOjhGjobbjzeYoncWfCnWdvJxkY+BDoMIzM+tptNVlnu7j+DFT0f/Xtmbg4/7+kQXKLyAAIxmX+I1o+Go+rXrXEDHTfMOzaEIboReq9xA1phVoRcpAX/G/8bXmiApC48YNsaCmKFLEGQjkzpcEI/vBR812WFsg1gQYw4yqa3PUCcZtt6wN6IPqWBap8MiMdb8wB1uMCU+ZjHffNQZ1h5kRtgDEkRbv4bsdMiTSTnNalyE/gXmxEOIF1/e6QOGoR0p4Q3774ATyfUAJo4Mjqhu5Yb+walSKbSfHmZIJVNiK0Au3JyHcDPF04Zt3/ggzJGWqzNc5hbBfzLvNXDccmVw28wR4veRUqg8GGNKUGMmHCF/kdWhrjgIbw0kjuy6kybHHHfILOZkXoVA7gR3d2cYki83OoiaOEteoqNlSi1ANdtoJPqxS17Q+za9KHbvqIMgWsJ6vDNTXnvwfCLM7FArmPnnITCDDB4MM3pd3OMkHYcX3CsmGPwpJ5yWOnOUzqcTiamleNEJ53UdKi4dnss0mwVD71cW2N4QhNxW/CDZ2xWLVnz7vW/hN/yG/wvvv/8pvHz5YjYrakHio6xzgDyZ1lgoBk9mGLvjWivWU/UxR0JdMlpzBM59Wdp1p1GaKXKp6K3h6XLB+US04j4Jrq3h8nTBYiuWtdKQbDBBO/k4ae+GkgqePXs2N4d1qRCYq+J4HVKmdDf2C/7acE5EhJJzdfZ9x+g74KRPM8F2bWit43Q64XQ6uXrIkbTodi14XYrgTqWkuE1S7r3NYmV1G4AYS4VKat+bOxYT/YwR36tXr/Dw8IC7uzu89dZbWAs7f+boRMYX9y1tREJCzafq7ruJh/myENXpreLZszs8f36P3a//5XIlT8qzlHjYcn0VKVhyBTKw5AKikAVZEnLNrpwiCTc5shjFB4xNj93s+TR6qy68MKibCk5E0Mz9WsyNK5lblWtG24miJJRjH7UO2M5n0gzJSGgXpwP03pGTenAjScJJ4/74aAjcf6GK7pOA4QhwkkTztHB6jbGTDlfr8eeEIornVDTyh+qLe6gd4YjmQwQ/G27VsIE0fZzXJ7tAcde/ePWJSBhEePCyevYLdDsvE0fq/JA6WNP2P/ycKFZkHh3++8DUekMBS0EKBNBZuFQRJ5j613mRFAsHwPEzDTDrc6Pw33L43BePcYZNGJRrREDEZKAxwXM4O10H4HCsOpRD9QIh5bCWzp6nMsCHR4pX4okP7PHgyaEoUiNcCXq3CMjNONQwztYWhoU1n0EGXwMAKqp/Pvi9cra3dxgGL/rADY1mWwsG3J7aTdh6J+Eu50x4f/BQiUJI1T5A1pIAAQAASURBVAPiHHoMZvokSftdzSJQMSRP/mVx0lkIZzc9ciIiu6I8OyERccOv5L4MJJcJDCULxEhcG0ydBPya6jD//oeseFlWPHv2DHfnMyRn7FtjEZozSs3YWoOAmTEpJYfyWdSVQqdSIhJlqivGUOSacT7RNOzufObPjw3M7wNXHQ+bbXvyt5owOkPrTucTbBi9QXr39cfv33d/n941mca99fVs/HeQpVtvWBz9CTJxLcULDjrt5szCBebqHx+rmtLXIzxselcfjblEuA/0tuPDDz/EF7/wJbz7lXfxH//jAwa6e1mQNBg8Jwb4JQxlV9lao81/KagLOUPbZrher1y7pR4bsx1O0AGTqyiW84r9csX1SvRlqQvOOePpckHbd9Ac7ngmUqK8tpSCpTKpmgGEG681PEtqjOkaG/uHgeTx0bnGS+b7Sp7XsywL9q3NJGNufAWXyxN6bzidzkxl9jXP6xPqwejq4+cd3k8hUQ6nXEAm94XfQxx9Eez7BtUOQv8y9+fXrx/w9HTB/f0d7u/vUesCmCKNDLN+hCmqkQ+SjtGCKp/HdWX6tKBBbcfpXHA6FZxPFduzM9GUZuhNcb0yJkAS0S4ZzjEpUVgIJHP8mTNjEnR0hJdUFCjkuPiebwm5JKTCpjmlhCwJaqH68eY50CS4mZx7mpijHOJ7KUQpifdCqnXzz8vrv4Siy1HJDR3N06Vn9lcounyPonKzzxFtoIMioJdJdnqBN9ZhQiomUElw/0eiSG8UKDGCyt7gmWu3+fXduY7mrrM9GpqP8fpEFyjBfQacJDVszv9iRmPAhAdvqzh28PFrwiOzA5tFytHZi6Tp4jrLFfdTwCBsFlCWqMyCJCyZLWaMwM27js3leB8xp2eqpx+o5nCeciMmETaAoOSbkxFNgS9QAyILBnAZq28K+9YxRsPeOfbJN/PqGAfkknCQoY5XcEJa6ziVCssGdWiUB6LbksMm0kJ4j0st3F0BmYZJHB+NA26MTqMrLFNWXVxumXKwxumxMgnIKBjoeLo+0VulFiQpTJv1a5zMo9lNfITlkZKSAZMjVPDm3oxBUy2Dd/Iax/lR1Io/+L11SHAWlPPWITSpU3MfD4f8aLLH0WRduLnF3P98PuGddz6NJBmXvaP3Czd9v49xUyKkLazmaaJlzhGit8y2bRjWnaNQASvzwFBVaCcZcHSm8IplwF2ZSz74GkOpVtFueHj9dLifCt97KcUN/1wFkMQPe5uIiirvn6qhbw05ZYw8pgU2vLkI06d92/059kTlESnTB7kW8IPCOQIQcNQBrqnWGr717W/jR3/jb8T9s/+Ojz78kOhbwiyKghQv4N4xPUvG4HitcMzJgETMAznWMyX25v49vm6UfJl6WtGuG/RyRXh/nNcVry9PeHp6xOm8+giDRdGc/cNQ8koyMTgmHZ4kLSlhXVekBEdUYhxGp1SYeAE10Bwp5D0fGCo3xlk60YzHx0cWoI72xHtggZsnmkV07SDPsnkJdCUyq+oc6fAakzNSa/bx2+GtFCMy1YGXL185mfYO93f3OK3M7xnuewS4EMI9rJJ3+NyP2vSSCddcNYZqnvKCuhTo4O+f705sZDpVfuoNzfCReMqJDQUCGY5GxomqN+dCycXXuz/PXrSkRDQzRVMiGUX82vsJENw/FmB8TnQouu4oblPgP4ZnExKc9oLI1orRZox98eYR5yhJnJGhXHJPJCECKaBkPQd3JVDdgJhvuDX86P5nUWgk37OFI/JoBDPyfD9eXuGNN/gxXp/oAoUfWyenQcPrWKK4I3phoVP31+345vZ7HYdxXFV948+hAKcfNKpKMG7oXowEJ8VMkMzm7/H7ym0TifmwWRQtRA5u1SLh6kkI8WDFj+FdpLkqJcZQroQJZEA9/ZK5NYaSgkTWeZgMdhCcJjknIlEVkTxELd7brflb6w3rcsL5fIeHh1eAz0upCuAGxg3EDbjGQbgz8SIBQHfSau98SJMnCg+XDOfwEknibqEeQOfyuoIEFG5gzfgcCdhlZikwFexpOEyrHqa3uHsoN32ZSdgHaWzeA6Hc0HxsQK6YQ87B0wl3Uh+JhWJnKhl2nd+3lIpc6ixYnp4eDwWYHNkhy3rCtjeYbhiW8PR04dekhHUl1A87AuboEyNozS3s/YC/Xjc8e/YM5/sVr1+/hurA3d0zRwOu098DOiA5eS4MHJUZCDZUqIMECX33A9Bl2Yvn+MAM2Hi/U47O20dEcw5Nn5xhirGP2URMLyNH0eaG7Ijc0MGtLRdkbyDKVBqZF+uusAPXrypJsPu+4zvf+Q6++IUv4t1338XLFy+J3CzFoxkUvRtSNogf3sXcNM29Q2opLu/tOJ/P06yQYLc3IjljH2OOVkOCDAB1WbBdNzxerrg/8/o+O9/h8XLB5eGKulKmHEF74cJaSmd3LtlTjm1Knsk9IooUayB4FZGpZMbmSQDUkiCy8Hq6h0lrbEpKqcgpYTcGR55OJ6zraaIjzDQ6PFl+9etNefKBvMReypo6+dqkxPYoUgbC+TTW86uXD3h8uODu/oznz55h9THg6PRHEfdksYnS8DMROSRqWEo63r8kVBH01tEam7/1/g466AK87Y4GzsKB46Vkhj7UvZYK0WcvjFtndpOB63QtJxcK9LmXwJGSkm7PoJsmw8LDSm7O7igEb7g7EOf9ZR/tJucZqrt0iyPAGd1duwUy0ewETA4dfD9WM6g7T89CyMfQyY6EZvNChJYWVOH4/x9lWgCkSpeteI4Z4HrkJMVZ8v28PtEFClEmm1D+nG2JTBiTpKjDDj/+7aiXb4YGWLhfTuwSMjA3VnIX/EoPODM73SwOl/qF5M5dYKcLpf/12xt0W5CENwEiQnzoRDCi+DITtE70JGe4DNDhWvihKFSNmDOv1S2TxRJ9FMz/GYOwoxlJscKskpSTm5Lxc4ciIyeOajiiAEpNaH1Ht2DqB3kx3ovw4Z5QEdUoHCsohrIIGZ05FwH922DRp0ORqs+CMw+qIGDN9Gg5FEviNv6n9Yzh3jht39H3DNXdfSwyJAtKWXxrSXNNGAxd+MBHJwRwZKLj6GojbTgKEBfeAQDWZUEbRI8y6PhJpYgiZUpoS1m8ywaAJx5I2mGNyMO6LBiquG47ciYqknN2PgnVMjSiE+cQrNj3hpSImizL6j4YNwiDIz2XywW908NETckXgTAaAOoeF4lyd0mAOrzuJMPeO0iJGjOvp0MBodmWAsDNqIqL3s2yhCqx1ncMi/wRxdCEfef3Sr6GdNBjpy4LJIfPys6iEuxO12WhIql1dLD4VDkUCpHTYjrQ2hX/9b/+V/zm3/Kb8al3Po1f+e53uB6SkLsm6t4qmUiNjxpFBNt1c5JrBsz5WLkAoAw9OCixlkpJUE1TadM9iXu9P2G/bni8bOR91AXP7xKu2xV9NPR99xC9ZZKes6OFfVB2n3JCQUXrO7R3PD51LLXy75XqhVeESvKQbHtHKZXrQRk22HubIYMRy8AxMKWyrT2g947z+W6SyJnJY15ksEAgCTdh3/ubh5DvaaFujP+mwizBLKF3dcdbgGPtziLAr+2wgVevH/Hq4TXuzme8/dbbWNeFxbIZSl0wRgOcdI3wjwJHLEHajvcfAorY3kn+LcjlztHfw9JBVdGCM+dIQip0rpalYi0Jgh2tK7oKn4nWZ0hqXVjQmDCtO0mC+ngjwyDmvEgzTyL3+I44K2L/zn7IC91ng3MXTt0mieeSmlsUuOghBUmZ+3gCYJmFtqqgC8f7WdjssX+2ic5lcbsLWDg7zP8Jxaz5+xeAPkRezJsjQSVQtzhTjaOeUEN+3NcnukBR800RB8s7rqCvRwBHYcJfA290AQG5mb3x+2aAqMAiDtKPITGQCAQ2gOy/zQtROf4yvEKOhxW84XYDEToyjOCdiC/mY8zhkGoU3EFsBdNU22iY+SEB4wHs5JUQdxxSwR0J11YTRXViWaAxkew89Igejw1s/jngLq373Hwj34EbkiEIXaxVEiTbnFGHX4dBYR2wcltMghc0rkUWWGRAMBCCowph8rEkwb43n6O61K8m5ERS7BDBaV1wvVyQUsKyVh8NcMaeM6WNwDG6is44afCbHPmJ+5p8PTn6ZVNGzPFcLgU5Acu6IEkidN52iBQAyWXx/J61UtKbNQNJsK4n3N8HMVI81XXzLBYWbU0bllp8hOPJr62j1sNjJ+eKMTpqLbhcHjFsxVIrtm3D9Xql0+kNWtTbzjGQIzGqg4oGGJ4en3C5XLCsK31C3HSP14T/7GOnn4UXjXwO+QgcB5SvIXUuTTlg47EPrMsCJJtI2SoJuXrIIYh6reuCtrVZdGeJHBdzE7dtFovVwxPDt+eDDz/Aixcv8MUvfBHf++AD9DbYjQ5BfsN+nejL0+XCgq9Uju7cRJD29d71q0KTc9xA9MvAQhg3Y8uQhq+nO+xXOqImAEspuFtP2Dqw94697+it4Xx3x5HO6O7c2p2o7kWnAXVZkUsh2rLxXre9TdQ1bgBTo8mFb5N3kNzp9ezmcBx9hFIrlzTt8peF/JVa+bMO+XEomyr5OtcrRKjMYmHtPA9Hw+hOm+ZBFhEX8ezRQ6bfjN1i9AM8PDzgerngfD5Tonx3JoJnmOhZ7MGq8Hwcj8TIRJ8URPJKdvfqrmhjP1Bi4zirVrrz5t4cZWoc4SdAMlHpVBLWu3vIvrORSnny/g4VpfLvJHujcKaPSEIJg1FHVMwKtDdHqhIivyyeydsokalw5Dv1vdEwQLt8GwoVz/QBo00gGaZcU4GMGFikSxQdZp6B5UKIyk5qODLId+sViyMsIh5rIkB4UcWUgJYAeSo1dRgR9u8DRPn4dFoAP/uzP4sf+7Efw/Pnz/G5z30Of+yP/TH85//8n9/4mp/4iZ+YkF/889t/+29/42u2bcNP/dRP4bOf/Szu7+/xR//oH8U3v/nN7+etAPDFiTzVOEDYU/MwFbyZYBwXzywKGU6dIzlyuDvqaAobhCVFaZBWusx/smYkS8ghWY6SI2qcyaKy+Klzc4b/VPN5JNEE36h99scFS8RE5z9k8BPxGDPISoRQXsCLIYMzY3HC/2QHrcYDTgoliKVkeig4EZHqkiMvZhY90zPFuTjuB5FLdqtu+GiAKzNIvYCTa+lQhAjqGtpvnF/V/24+CJ3agRSmWCSkJr4RjxhgETScJSwBUxoh8IDIeW0Omd9tSFokA89RjD9AOswfNCpJyGty3xJYTIOI5gAO/fOAaZ6qGjkyZka3zLoilwVqTA3ujVwG2okXSCpYljNqPSFGhGMog/7u7nB3d0cC34ReGIQoknG9XjHcb6XFzywFp9MZ63rykUGfctDTidA9Q94uSEKEIkvGmheuzZAZh29EpvdFKq4qANE9RPFhNu+TOqHRh2IcxwmIV4XSKaU37kcoiqg2c4SuZMAG2k6CKCxgah5wjD3g83p5esTl8oh93+bBGJyqfd9nkf7ee+/hU5/+FD796XdIX3Zelymm8qy1xuTbnPmPc19G79O3ZlmW6QVySI1lPhdDY61QDbQudJVVG6jrgtY7Lk8XcmxMsdQ6RzXbtvOffUNr7HRLztjbjuvlCdt+dfM/jrmYZzOY6ZNuLBQGHWUpRVUMbagZOK8V53XhP6fVFTxlol5D1RPMuS6eni54fHzEtm1vEK8Z4igeTXAUJqEOzO6GGghsrYsXOsv0+VnXgru7lXLh8xl3d3c0RnREdvTmTrksZB4fL/jwg+/hux98gI9evIJaQiknACw49p3Xbt+aF1zDr+Xu+4yr3nLC6bRgPVWs54r7+zuc1tVluiRFr2v1tSaT3wgjKX/fNifdR9FhE0sPqFxALmLsKUTUuF8d2LuPYtypl8X5TaacjznDeyklEKVXBUTnuRJjxSjqps8WZI5so0GN7wuA62USvUFl4yAvRlx+XUtGda5bICGTx+Iy+Wi8o9ozoxcNm0hK9kOqDr9eH/f1fSEo//pf/2v85E/+JH7sx34MvXf8pb/0l/AH/+AfxH/6T/8J9/f38+v+0B/6Q/j5n//5+d+31tYA8Of+3J/DP/2n/xT/4B/8A3zmM5/Bz/zMz+CP/JE/gq9//etTj/9xXiaHC5+ZEfYCboCQgxjqFQP/cZ0kZ5kuXYRAhiEPN3sDQ8WSCu3NhWRMeOceN4pqktt3Rc7C7CBxW8Lkecj1YRNGDPIj+UWHdDWqZC5CjnkEhu5ulDlHyik/q+pAcfInD+80megQutCmkpx/IQc3wMm40Rmnmw/EmSq7R2ruB1IhOeywd6cypY/uMKQTDjMhdHM31IjoFic0ih1QKw8odmeROstQuQwgrOITcq6YMljvYm2+Q8Kd1vk75PUMSIY/+CQG9oHZkbplzRH658WruuafUOtxd2e6ccBaYKFRi7miRDGGF0puLiCpkg/kHhKt7dMcDAA3zGVxmbhLSU1xdz5jqQt2L7a6c3sAw+XyNA3acq7++RaHtLnuSy0wUTQvOM/nM/NQGonD8OK21oq704kFbd9casvNkKoYjjb2bfP8DjiaE1keXC85ZVg6yLvcNDFjAdZlwdPlgrF37NvOjbQWLI5axPeJcdoYHYqBdTkjvFlsuFeQDmxekHTnUTgVeo5WJAk4FDKYdbx69QKXywVffvcrePnqJdEjcGOuoAmYOl/pdDq5P83wmTvtzCGYz8584v3XyTf6dPQiU6J5yDcV67piv27AdgWwoq4Zp7qyCBtUHyXNVEwZc6JyKRi9Q3Rg3wdev36Nd955B/f3Z46itg01hasuC7zWWDjVSjnz9brPgqH3I7k6RgL39/fufzPQG5+5XMQLpUcGEJ5PPgo/8p9EDKVUz5gKwmrC3d29jxudfOq5JLEuQh0YTrcAyd4i4ggOFUdRFPF5Njw8PuDh9QNO64p33n4b5/t7LMuC6/XiBakiI6OU1TltA00UXufMRkhccSciOKcz2r4jSL85Lxx1ArheiWzpYKAmUsLl8RHJkSixzGLcOL4BKuamYVyVQwfPk6AXzOUjRwMWsl7ILOBzuXGG9UbJAJfpJ0AUHRQkHEnEgPg5SqrBcUbF6FCSzrPQEDXL0YDWEui6Pz9gfhAVQkThUkmOxMYoL65vOPgquiqquJrQMrL0I4n5Y7y+rwLln//zf/7Gf//8z/88Pve5z+HrX/86ftfv+l3z99d1xQ/+4A/+mt/j5cuX+Ft/62/h7/7dv4s/8Af+AADg7/29v4cvf/nL+Ff/6l/h//6//++P/X7UYfVQcpjfbMyLfTjnxUI4qkB45+cjmCFU5ZgrO+DOr+5MK4iNiGz5GN28IfxIsZHxIYzFGJCWCaFhFiTHgT87ThGI0WPD3PYt+Y0nN5R/N7r24gTF3ocf9BnLkmkmBsooibqAkjWH7ti56vRUiJm9VC8sEjedMXZ/gOlsqHKMoZJrzvjfPhZSIEJ/om7juGfQWk0FUjNnnJYg4+De9NGpfHBtWrhsJkeHxhjMAUo6nwRV+lZE+vHwPBKSpdmNS8o4nRYkyShVmN0khDwVNi2HwsmRYzzx7AkvqHDILWN6l6XMdaAYKDVjRiP4e0tSyFcQShm3fWM3pYptbzitd6hLmQUjUR0mBANGXsHo2NuONPhZU0rQwp8byAgQIyp+jloruSuF3Vf1tUvyJEcsp8zDb/dN+XK9ou07DJRtllLQlYXMup7Q9p1/biQvL+uC7IfRvm9ojSM4i0PPDpKfQygun13QoNiFJnAlF3qrwN/XemKnqhsgQJXszre7oyiKlBPuzhxxAHBfl0Kky8nLS6brMOTIZLper/jWN7+J3/ijP4q3334bLz76kM9+SbCaAXP/Hs+ayjfjWRGXq7vSJnKCggzZWmO+Ffg5bfQpgSbfhY3Dtm+wbFhPK/q+4fH6hJNW1HXBnaNbfXR6BHWiSSMPjgNLoTLCuhvmZSy1ot0kbUf44bbtRFhc2bIsC9FXN+fKmfEXWQoUMrvwMQau1x2X6xMLshG8GsP1SjXZ6XTywjUKSr7XWo8C83qNPKDYIKMoyQDaG+M0IjJjyvdDLj6GTjSsd09hloSSKlSoUHvvu7+CWl/grbee4/k9OTPDJdkACdi1gHvQ9NIZh6Iu1lRJWMsKbeGGTTXYs3KP013Fdu3YNz4PGeJkVcftB0cz4fRq6hldOEziuC/4fhJohO+PcbZwnTiSnBJmBlwg8IHYOAE1CZtodRO4W1VhFGDx7M1mSsLz60BUJo9OAFrL+fM4i8mEpSbGAaQ8far443ws7F8buU1RBFLR1qiOKxlDC/Z2xcd9/f/FQXn58iUA4J133nnj93/hF34Bn/vc5/CpT30Kv/t3/278tb/21/C5z30OAPD1r38drTX8wT/4B+fXf+ELX8DXvvY1/Lt/9+9+zQJl27YZ9Q4Ar169AgCv7rxiTOlQZb3R3dzciBiOOSQlI1EWGtwKE4a1uW/JtNA3o1IFvwqeSj7zdLgkeUU6bDgCEuFOOtODB6jWgCX0oWh7n1V2uHnShc8fdhlO0PXNUmx6PbDQSFDd/SBZsZ4WGEKdMKaDbkKMFDgf7532+Cm7SdsEHOG+DGSqizO9h5JvQTTGP2dAi3xj9EVxkwl16+rhB1RKTPIcRklykvnTYFB08xRSAQYMuRas5xUCw940wEOIQ5zJu8B4gLTT1GkMv7de2S814/mzT3EMs199BLJgOtqG06RGrHoUbJgbhPjaibDIlBNKypzta1wDc0WUH3IGqBUIsneeZ5zOJ1yeLtwQpQCpwJAxlETZ0+KeHCK4bpsTheuEVANV2HvjpjVoDZ4LN5eJHNx0/rkQUQgvDY6RnC9hhsenJxRJaNuOWg9lTHS69L+I0daBuo0+IJ44nXOBJiJt5vB+rIt041lx3TaYd3BLrehjOEKnQMn0fGmbfw9DXVioqTLteOYeDZ3jyXjeR1fnUzjMjYNkquD4a6jixcuXuFwu+MpXvoIXLz7iGKGwuMvJ05HToZLrvhmvywKYhzs6ehojK69qJyJAAzG39h9Uxolzm0gUp9Q+LwW9dTw8XXGC4YTFfWo60U4DRutofo3jUMuZ4Y+SSDalGzGfHYEgSUZK3vSYm2dBPE8J3gxRFm2mzK7xZ5kIoaIkgaZ0HI7+DASyEePCQMdjjQW6GCgYDzCuARYZwWdImPEQY0D1IKpydM11F+O01rqPHbm3Zo9eMGV21wcffoSH169xPp/w7P4ZzutKC/0+3B7fsNQ8R218BhMix2uaUFbBdu1voAGlVJT7ivP5xH3TJeUAoOa8igHUIghzmhi3zIgP/4YR/neMa+OIcSQawlwuL37j+ieEmZpM/xukufQAFw9Ul+CTW3KTku2I71DimbdNRJqW9LFv4s3m3f83S/bzwS0R5MbDCYBOmfmN+MAMo2v4Y/qnfVNV+z97/W8XKGaGn/7pn8bv/J2/E1/72tfm7//hP/yH8af+1J/Cu+++i//23/4b/vJf/sv4fb/v9+HrX/861nXF+++/j2VZ8OlPf/qN7/cDP/ADeP/993/Nn/WzP/uz+Kt/9a/+D78vak7QMUiYd9n/SKsxhyDicpvSiZCwMlGFDLpKZiRgJGin3FVwhGMl8IFQxEaNCXkZGHgrkmh45m444ggMvFBRHylpN7TNvTPCJ8K8ABoGSYM+FDAkGA4tV8gyWS6NMTBax/35jLv7O0AEeycRdh8NEHZasVAjswXmBYu4iZxD8ewA/MBGpOsKcjZodSMrkDUeh6JkHi7W4cgK4l2zcxBmlEhKUB8v0ELZJdPCDsDMKD2GYb1bkQrIBzLyLcTTj8UEYxgfmOKF37RPTn5I+dghC1TdZTTulP98tRuJupkTWB3uFJ1OtubQKnLMeeEM9ezSVEXJVDREroUlbpy6bdhbw2gNn37nHYTcmI89CWm9dyyro0U6sO0XRAaOtubrI+Puzl1fnStRS8W6LDTn0xg3pWkmB3C8qkmxacQUFPpKJODydEG94eWUvELy7bUMYzqXopaCImkieMk4GpjZIP77qxcWt/PpoQM6rjwkc0ZdCiqy79GGZeWYKgtHgJKiazQsdUVag8TcHIYuaL15ke2w9FI5gnQUgV3hkUMyRsO+X/DL//2X8bX/99fwzjufwXfef4+FbfIE7fmsEm14eHzEuqxY68k77+M5upXX9t7RGw9USQnZDmSOXiljjldKdRl0V5zOZ1zMcL1swDCsJ8YPeNwPuhdWqoq6LFjXFeZcudcPD1TxpITtSiQsSKG1CAmyjiIW8VHpLHYPf6XWmL21LAtKLlgWoOaM6nycMdQ9bhjSp9BZqCzLgvv7e+57gwgmxxO8jtFJ01iSewe9UPos5miWFwhLQs42CfW8DxzzMPBwd/Wi76Omk79z3Tds+07Tt7sznt0/4whG4MgRuRxICar0FQqCZxRKpRQ6Brfh79+LYiXhuFZvCr3gVC+WtNssxAlaGPOcHF1OXgzE2CqyraYLM4BpgJfKbFSGsoCAONqSE5Jnt1Ga7FWaNwQ58TlH0BCY58x7IhnSSZo2M0qfXTUa5QOvf57v5+jHg9QexeygLNljSSQJivNfYqcNVG8M5gqFijW4Zx/n9b9doPzZP/tn8e///b/Hv/23//aN3//Tf/pPz19/7Wtfw2/7bb8N7777Lv7ZP/tn+BN/4k/8//x+t6TMX/36C3/hL+Cnf/qn53+/evUKX/7yl13CBYfNgIARY9OLTifgMQAQ5OlPMpIRNYEgW0JGAYagbySdQYFlzW5pzgJHxQ8yrysHjgOO0iyPRJ+yd7LkhWQMkmHVeQohZfOhJOVoBwGJJl/EiY7ugl1bHAYJCc/un2E9s5PZW0fTjq5twt0Kd0K8QWHIIckQU4zeoCEJm/cD07Cp9wHzLkhSguSEbuSI0IHQWSvi3HMhbB5ybjNl2GBXN9Wi3FcbHQ9Nu9uG+/Mm7Eyue3MkhrBujocSOr1d1PkoCPg4HnKHXLuS1HY+nZByxj4j4fkZb1ExFmfEdJCIqMkANBFpClTUoDAZkzMS14hFTcJSVpg2XPoVgOC0MoX14dUr6Bg4LdVTVQ25CNayoubkSbeRTcTcH7qrcpM/nc4cySR16N6dRY3eNFlINKXsmG+2tx05FSxlYQK0F5jaKSl9/vw5HApCYH5k3hOdOZ1Ok5Ctqtg9l6lk2tVTKs/PrQhZeppqjN4HakpAhK4ZO6hQzgy3sd+vG3IRnE+Lm64lbNsVbd+wVKJZ/NoxDa2CYE2VS52F+OhhLz6mOoVmXCxMv/fBB3h8eJi+KPu2oSUqZWgxDsAPydNKgivER5U6iKKAo0fRkHQeida7Z/4UJxfP1FhgKlUC/m6jY1kXPI2OFw+vsVwS7p8/pwrMibtb29lwOA8iJ3rppFKwu8InDgQRoVTbFAPDD4+B8BcizesYQTMlmWP5dV0ncrvUisv1in3f6VKshqs1iAmWmdsz8PT0hDEoSa7T4K7PPSSMH4miDN9XDtsHEp8Po7HknT/37dtU6YQxeKinRAO+QBbpIkyPIxZ+Hd978RIfvXyF+/t7PH/+DMuyQtwJNoGuwYaBNoaP0bmfmRGh5s9jsb6kiroe1gYll2N/j+KmR1jqmJJ8fu3RMAeqC+X6ze57Q98ofl0uBXkpnj4PYDhL0K9HeJVQvh9xJpTYC4CSThPZGxguRmjcG+H2+okO3kg20fLp6yNpNmYUb0QkiEuY3arCzJimLW6r4fya4BySmAvKv92Mkzlt4Wr+8V7/WwXKT/3UT+Gf/JN/gn/zb/4NvvSlL/1Pv/bzn/883n33XfyX//JfAAA/+IM/iH3f8dFHH72Bonz3u9/F7/gdv+PX/B7x8PwPr6hnLLp2TPOe+fsqk5XuNf2EsTIAmCFrENyY5MjRCAClT4dW5oiow/ycmrBaHCoT0g/yZsB5h3EbK1ybfhpC7bqIx8j7Qe5noBrmYTg8K2dKON2EK35GqQWlJDQdzuNQ7H1H68OzJPJU84hfM/F5eLjm2iRz8iqJpYn0bMMRniwYg1Hv9E4+5u9d2cV0GFEtfyjDlyYIeapK7oK7wyo6JPk1VnX/Dc5it60hiZJY5j4dakBNeULnmPdD3XafVz26K6JlLNJIFGWQYEgTU06Meg8EBTI7opBqe73l93hA4ERWNywbQ72TJ/R+f3+PZSm47rt3OhwX1UK/D6KngyOhTEn0aA1QFih0WqXzZs7J1QTFvSs2925YYa4UUqVHg4yByFABMOHlODjpnFsnH8JwOH2WdLj5jtnpAmM0J+BSUZNLxdavXjLz8Gn7ztGfZOTKTV+COBVjItAbISNgb/osNFdcJfe6sdFnU6Fj+IGnM98npwRZ65RI85AH9kaDsX3fSWIUAKbIlfenJHFTKiBMFn/5l38ZP/IjP4JPf/rT+M53vsORVTJylMBxpFmfhN8gcWYPvzTzMSYMlnwcpRyjDO0QUVfHRRc55lqEOTERzjtSw+lMIvDrh0dIWVA8IJGH7sAwFjyPT4/IqSDlgvV0nu/v+fPniPDB63ZBa5t324nry5GsUjJKrbTsF/qEFOWzamoYPiffG9OXA4nLJaHYwQ8LMniS5OPDgWVZcHd3csnwIErhz1rxaICA9yd3wYu3GBerHmgvcCj2wqSSPLuKMZY5+t89DmBd16nyEiEa+fDw4NL6M+7PZ6znM4iADJiFcmyfIx2YYleq7CA8T3oWFOEZ1FrzfLE4U8SNH4EyBnp3F1tHOcfwMUvstR51ERET8T9ElZn1Fcgfr7sr3Qy0J1AfH3ljFaPcWxUPwIImQSA5Cg0iQFM1lAsiC4hcn+GFUrypoEPg8D6J3/X7yH04kESDpchgoyGjuj9KSsWbT36ucGr+OK/vq0AxM/zUT/0U/tE/+kf4hV/4BXz1q1/9X/6dDz/8EN/4xjfw+c9/HgDwW3/rb0WtFf/yX/5L/PiP/zgAyv/+w3/4D/jrf/2vfz9vB2GGFiBEoF3BWk4ejnU7YzOEbpuVrI0x549i7AhFMhizwjmbGvXhk0ymApEBgaKNA14b8Nhvr5Fs/k9ItGIE4AQnyRjWKdFMTlDFIceEUU9Pab3nK+CYTSZJGAPYdGBA6XiaACkVi1swJwiGa/rNDggveAKqnVW581ls6FRg90EfBwl78jFQhLb4uVTfgFlVIx6cODjUpp9KHMg5050zSXICJubGFEmcpRT0yxP2vuGuLvMhDoWPqUL3sPIPqbagJoYHMuvD/xJ7CmfrK0TTfJjhD3O3Mf1oJCAcgUtM3U7fu++h6sZQUQAcc3SvIwFkbPsVvZOkOPoA0sB5WZAMaEPR2kZELBEBvO5XXn85umDO3kPOqbOLNBiaS1BPJ8qSY20XD1vLOeD7CpGMSJ+ta2FB2TtMB+7OZ3omJCKP2Yvl4dlVYxhau6KUiufPniOnhJeOGEXhqwakVNG9C4sRC7x4yAH5esGSS0gpBaoJKa1TRmqckU75N/NGCmAeMNd2mCrKsmCpJ/Te5kYv4DgviPEpCXKqWGqCZsXYKEvWPjBSxwcf/gq+8Pgl/OAXvoAPPvyAzqaqSEoVB7xwj6A8ncVF8I44Rux9QFwKDB8HZDnuZRSNeQwM+P112WfvDXtjCuwigudvPQeEUvTtugOrOLH4hKfrI0bvyImS42IKbIc762XbXPFG+N1soDgHIUak3TiCEX/eI4AzeEoY3dWEvI7n04kogt/zuvZJyCfvwhwRlRueU8P5fHZH17C8T3PNcL0e/kpRhKTk2WGQWXQB5KGYFYTyJz7P4eScUWuQ43WiffRW4UEYhcrT4xNOpxPu7u/J4ZGM3sU/nzovzr//UBLyBZ6rRRSU3LeOSFxObqGQXXqdAt0DjSL3/bBsCGl+kji4WdADx3jHlHt4IJgxcpTkIo5yjCwD3UAGzyAv9MULBWU1AxnHyDIa3lRoHpkSeXOoCb3LLHINPK+iVgklHj8Aiyzxpo6fj75XkchtgDs/KCQS7b3b+36Uut9XgfKTP/mT+Pt//+/jH//jf4znz59Pzsjbb7+N8/mMh4cH/JW/8lfwJ//kn8TnP/95/NIv/RL+4l/8i/jsZz+LP/7H//j82j/zZ/4MfuZnfgaf+cxn8M477+DP//k/j9/0m37TVPV83BcPUpIcaEGfZyBfFvd6TWnyQAhVAcnIl+B/E6614VVmpFt7FyjZo6QV8L+NcBANPkBAY4G6iGEiEIdU2C3TfZbo/aUnWx6Vb5BKbShG44af3W6dBw3lesnJrgZDMxYVOWekklHTMVcUAJIFucs81LPPOE0NiIp9kHQqkjFAUpP6qEUGnV8DsSA5yrtI5xcIBosrh3OR9OgSbqWWrUP94YKBRE/nDWVx75HOnyOV15sbBr02YANoLrcVzMMsuWsi4JW6F61MQ+XaMAj2bce6nrCsC1NWRycC5F26DMKtxfN3+nZ1YhtmNxdmegBRmCVV9O6jqFLYvYJwbW8dz+/u8cNf/WF89OIFXnz0Eq8fXa2QU1TUSAmTlAZwdBN8ktiMgyDYld1fzMz3fXdEhIdTSkGqDJid318Auta6PHj0gbbvWE+Lz9qPDTqlhGU5UY2hhI+ZiSKTM8VDp/BSDM7WVYgyBKk35QPJCTUcu0JDRZm/z7GUK+T04P8cY98g7Q5cr1cff4QXUAIwJiE31BS3Kr9aqD5Q0EkVSfD+r3wX/+dXfxif+vSn8eEH38WwAQxMeF9SRvLNPw55g05eyLLQZ6btO8rd2dGxyKqJAzRNnggdcZvzuxThWWFe5IoI7u/ucXl6wrZtE6kpJeP+fI+Hpwekkt9wUo7rQ1uB6ICj4XDp982a6q25wodS4aHpRgVns3fOKTkfi6gomzTuaXHve28YOlDT4l0iYwMeHh6xrgvO59N0xSU/ZbwhQef9fXNPZ5FB59xYX7EOiLREY9PdCVewrhVjpGmfz9Ei1yMzi4Ifo3h4fGTC9PmEt956C6fTmetOjaNn+Kgnj6kKhHGUtbh7Mc9ZHyH3xuuY3B8mZbeEoE9UGh373jlaSkcDEioc+N8h0kRqABHMQqQyH1+PFAnvMnOpiIRHM8m9NufsTeUh3Q/ys+nAgKG4w7N4A67+jAe6E9SIcfvrEEWoN8izITiG5OZn58RgPAblNtT3zSiE//nr+ypQ/ubf/JsAgN/ze37PG7//8z//8/iJn/gJ5Jzxi7/4i/g7f+fv4MWLF/j85z+P3/t7fy/+4T/8h5x1++tv/I2/gVIKfvzHfxyXywW///f/fvztv/23v6/KCgCghjF4GEXVn7yzSRAP7PMngOcqBHliVuIchKGENEOeJvDiRJTue4CHNPHQHRN2jBwJA9ABJ/1FNRxkIfjMGeXoJmNcFMx/JgyzE08poe3jplJPVO9KqGEyxNGRXBLzSXymb8D0G4kHIkkCaj0CDH2EMwBUl2Pue3Nz1EFX0IAkYVBjei75KoqcKyJdVYTjKqRE9ZKTMeMzmfMG4IWaioO83qWmlHnPhEViazt0J8dCfa6pw7sswGFSRb6BM5MbbgWzUBUwGW4cpfPANaMChImw6ggA102tdY4IxQufgI8VOrs/LjsevhhksOdUoGngdDo7Kc8lwEa30OfP30LrimfPnuN8vsOnL2/j9etX2FpD7zzolqVg2/epiojAv8vlMt9fuHuu55UQdhQ0IlhKnXD56ezeGNdtfv4xFNoTdk/jhdIUrNQ8xzKjd3gl7rkmK3ISqAqTip0bEyOP+HnNOR4pJaAPzwDBAZMb798AYNZ4yBaBuHFRbPKsLhJqpntpH+PoFt1mPZe4p/zZKSeUcownc864buSA6D6OkR04LjED2qC1/ovvfYTx5Y4vf/nLePG9D9EH33CghTUxlI8HL8dREG8WuqJLw7Is2Nw7pNTia5rLIzgtT/t1kmUBJ2yCBUOtFaiurnH11P2ze4xGMiOsIQtQasWzu7dw3S/sZI009PNpBUTQx47W6S9TxHB/f4fr9crn179vThnr6YTFzLkdHJdSap5gA76eg+82/LO6ZFqIUkWWVc4LtuuO3jj+NNOpFNo87fp8PvuaZsbQGH1yUqJIKW6rGgF4wXcix+hAfHvvjkjEMy0HauFfH7yR3ikrjxGVWfjzELV9errget1wOq149uz5pBBwaOf7Y6faSCDQrriMKyTTW0qMGUvLsqD3fRKEUSrXoo9sVucwjTEH+Yj8J0CQK5sCjs7JH4KfKZYyChjcCglRBsegYzg64vDpJGLHONRLhm597uPxYqMgXkjR1Vu9qBoaRqd+bDoSHh5Ot4WxWEJO0ax68eIUhGiqzOgJFTEn5iPcj/v6vkc8/7PX+XzGv/gX/+J/+X1OpxN+7ud+Dj/3cz/3/fz4//HVozBJM3sjO2cAGngHIbIAqABHLmxANEEjTdLlhPCbSfkVHMoi7K1RoEzol90en39xWBdU45h6sq3OylodRYlpHgupwiwUv7aRgxOdURQYyaFBwsjsooZvCrkUQGSGqgUSJ3BVTTokn1ByQsCzgIZbvbmEjfNTk+M+JyOBEsAsqCB2zB5znsZqvXc0HTRtWlcy14P06Z/FghcRY4sls0MbGXvvGHtHBvMgAqEJQnK8B5jNiPIgH+bCTn3fB9DVRzbDizWhc6gqlvWEJBmXpw1BmgySoZkiV8bdb9eNviyZFyqg1vBtUd+00khIKx/48+mOJlz7lR1xzp6Nc8UHv/IrhJ1rxr5tPAR3QupB8DMvutb1NKXkcZAELL6s3KhL4eHZe8dSnVDZNsDdTAWcL/dpN86iY7TmHCGXgQ868dZa0ZwEx0aMHfZ+3SFIyAsPrZLzNLcCeKChwXkS4khAoFnHOgnUMIlArhu5SCVGZDoPLB2McRBhB1tqhY6OvbUpac6lzA56SXx+amHYofnhywLak6THAEAFx2j8eSQkv8Y3v/UtfPWrX8Vbn/o0PviV7wI20J1nlTMcUZN5CPbRIdFQ+Ea83KiW1H0tVBWWjcTk3mdBH0Z/0Yyp0riNh49CO+HzstRJHjYDTn7A3q8nbJ12+ykBpt2Jjuqj3OaqmOwFZkVHx+gN+2iOoHI9l6WiGjk8bW+4bhtqLShWfdzKrzvN4EDzETiRnaUqag7X2w3a+hzBmRcCj49P2PeG02mdaEjw3mKMEwdfkI3Dqj/4ehOJWioiMDQaRCYtYxbhKfG6pFQRyerhigyEx8oh3b9er7heyV959uze84cy1MeiEPgex41VEl24A83Lmc/uZptfswpVQBOR2Jx4Lm1t9+I1uWO3+z5ZPoIEBbgNWJ0CDwCWQw3lZFWV45wQf2+xR3qDfDhk6xxFx5kyuYewOa7S4PrMc965mikBxrNlItY+sokmVSBTzTPl7X5+DhuwDqh2pImufrzXJzqLR4zJiwx2dm5J/J8XLDR1vYG64CqcYYCGZp+mRRBMSV8s6ICLxxhzrjf8AocJGeDmZ+JyT7bifOgyCXe0f48HHEcgm2NhQ83hu+RjFEEqrNYXJ0oZfEQkysUyBnpTFH/fIgJxuJZQ3fCcED9YVGF9YHinCHATGVCGYQ0DEmVtyhQviNvbc4Tg+TROQDNwlEYilk65Yk48GLNQrphLQVf1DprXRm0guWkb3A+jtx067CBSutNvEvH8Guc/JKJkcUDUhaiF6SE95EYnON/dYQygtUFvjVp9Q20+C6aZGfNyEtbTSjt6J4yVpSKLy0h9I50zaqOEWy9XpLRgXU7obWB0o3LGfU32/YrWKJGM6jElwd47lrLyGsC8S+T9qZUpxvu+z+4urKLhY8GcMoYMnJy8p75BqClKYqbNvjXUeoyHIESJloXz+UAFICF/dbllLViWFW1raFsD1APNHM2prvBaRNAzQFDA3BgrT3VVHwMl0a22+piKXTA/D5VsQLRsuzEY8O58x3FC7mg7jdaWpWAMHlJ0aPWUX39eVBXXy+WmI3epMQQphdumRzrsGxYpeO+99/CVd9/Fl778Zbz86AWvoylUE/Z9YF2T5wa5A6cUpEK1iE4kIvHa3ThDE4XlXpGcUJ+SG6bNQ8ZHR45YiG/8qgPiRX3KGZfrhiwGESqVTrWidQG6Ynu6ck/jI49aaK1ffMRHtUhhppVHTEjlNWQz4zYKJ6oVI+ZiyqhhABqRChH0AXRt7L7d+CylRLM8MbQ+gMHRS6B3wQ05nVacTqt/Rh9ym81RTc5HfEAYqcVBuu9OXAUQfBTuX336IZk3hix4xL9ngghl/TFiY7GzIPhZkYR8vV5xOj3i/v5+5v2QFDtQ/PmLfKDkTSIA1MpnYds3EsZLcaSm04U8wUe/HnEhCfZrTAskUZU5nPM2ixQYitVZoJgZ90afY89xiu9ncVb10dF0RxiBsrFTwEfBYSQXsvNAUIjQkZaQ5x5ZfMRP2oEVuEdWPLlsnlMptBjVYzw7+TZ2jN8+7uuTXaDIYcrkPqkIhmpKR8GiFk6JSnmlm5GKuDGOOEchUBPADwFuTKqG7tLigV8lk7LDlEb94EpG8iMd9Q5WtXlnGw8XZZ3U5YsNjpTMOJ/ONsmEIkfmSyppHkJ0/MzkiAyaqhX3RlWDbyCUdG6jeVHmo654z0o0Ainz57cBbezacioT8ocBlrxIcofRnOiQqjC6ie5tdoYCboxJnKAqrhIwPqSmJAemypTQdm0Yu/rYLXlxEuiX82cMTP90bxbTQ4LYvHvLkmHCTI2lLljPJ4wBvH71BGgH1LtYODHaSW4pUfp8bbtHqQMZAsrzfBw0lQaD6Fiih8lohvv7FaUWfPDBB6iFeR7mo6Hz6UyuUsosOOoJBqAYZbwidOtMIjDjeOJyubqzZsblsqHWE5alIqyun56eAO9u+oiRAUMz975DrfvBpLhuVA/lzGsdB0Mpec77g7sxRvNDV5Al4Z1PfwYffvg9JrNWHlLlZnMNdIxRCCTa5pInqbnk4oeNy8sd6WOoYvaxivl6o5dJLQvO5zM/lwHlXNzHwrtveIEPesqQC6GopZCz5SgP1U44itYkRJ20Y6ihJiqnvvGNb+A3/PBvwHfe/w6++93vwLRjwDyNO9AOgSrJ4SUXdHQS7CdpM/hJcbBi7i2mOtGfIxOmTPnztjdeU4fIxU3MMMxDDweertdZnBaPB2jdOS3m96bQRTrXCpOEkiu2vs+fPW33vVAVSWj9yj0mZSckH10y1WQkQ7etM+ytK0qlIkeSolRxBDhBtSD34aMcmyPwiAy4XC5orc38H459YjPlfhRy41ti6ZEv5iiTb8ClsIimvX33AqKg1mWOibqncXMU06crbRRgM88s89eXC8Mx13XF8+fPcXd3BxmJ3A0lR9CcCF587xEUrKc7Gm+2bRbxOVMtpH1gKQlJFhrOGZPL6ZAN38O8CU1Oyna3YHJq1L1gMg4Dt4E31Ku+p+vwcYwO7GO/KWgMboUz+TmGAxmPotQM3gzSL8xyQoY/197khoIyA8hgMUbldqZFvt/NWEe3hRXmyPHjvT7RBUpGQvFQQEyMxLXySgVPuIX23WHWYciFoVUqmLP0A1I8ILJkYLiduYYmHl7Ay0ZhpQmHuUji4E0mQENCI4yHrt85dTfZOctzh76uBwSXs0z+TPxMyQnJCYaqirpmkj1787wgVyf76EN9jmOeUKzdkEHyW4o3k926fDlBfdbM9xClQUh8+5SQAYKa64SKxQa08fAvS0bNNPTC4PvuuDGog8KGHkXSALJUwDp6G6ipIEt2j4aQfwNAQhJ6Byy18gEbQPXRgymQUGhpX9Jk0+8bJdd97ERzkjihNWbSnidR6Slh4O+PaQYV0sbF5+rDu/+ChARLQFkzPvuZz+J73/ueEzzD4wUYpmhJkOsCATeuVDOubqwV0vGynnC5XBCjgtbc2jvlGRqX6hk2Bq5Pr9FawzoOfk2tFX24p4QXwrHxtLZjXRa6BicWDGEEF++h945t23G5PCEOVgzBs2fPWdB7scXN8diAArmjvTtXOguE8B05CmEpXqSEzbhgemdkH7WVnB0NGm7dze+xrCeMPrDv2/zMMZ7UmzHR6excAi8gSTqkymyMAfUmQIx8pyI0j/zKV97F57/4RXz40fcwtqNo62NgbwO10vl56A5Z8s3BltxhOSOniqE60UoRqsCQBEuOJG3uMVSZsNChHNXlvCKORgiaEYE6n8/Yrhds1w3ug4hSOapQNOzbTkoX+J62bUM+U4hafByVLGFrjaNtEYjR76N1Bu2VnJBFgOojAEfjgmxryriK5CTMKAJE7IYzQl7Tvmf/TO4F4vda1bDvDPKjE+3q9z/It458JzvGEzfdIFOYM0SWia4AMsPo4iAU58qYGWoNvqBNBRIVR/tEVAAW97V4aOrQKWFe1wV3d3fu3BvjJRZWw0eI+95QSwG9l4oXUhwR8h6zSK05Y1krrq05Aps8Pdh5OGYYCndLhquuaKw5vbQtGtfkvEhDcCpNvfi4QXmHEq2cmilj8YLhkwUJ1eWN3JvVq4+JEszNFaNYt+pjnpyQM5wIz+RiEuC5f8S6EODw/AJ+/RQoFRnJCmBO0hHjrNALE5sPDuEzyutI8JIUZmdR5fEBAHx0owNNFU2ZXWF+0MMyoDJn9UEo6hrsfXP31TyZ1ZIwNyyITeJZ/GEgJ4KQ0rnjoUT3x44nlTQ3/AhaixjuWtyBUoQwq3iaj4jbuZsjGM6+RsIYPKhO6wLrA31nx5qSy41BkliSBKTMRE/AmdkC+Lw0p4RTXVHTguW0QAS4Pm2AGNYTMCSs/zEPHBvmRUXC82dvwbrg9YvXPmd2jgQECY7UuEuw+EYMPwjDCMmESh5yW+ikaGbMYfFsmbsTUY6A/7OPktTlseKuu6b0wzC3VD+dTsiN0eUXI7pkSKiF3IFQRbXWUHIhaXSSUwFAYBstt9eTeyn0DkmZ/B8zbIMjJo6iWMiQa5Kxrm9RQjz5HOR7xUbjK8I9TGiiBhHsfUMSqhyWhWPG7CgEk165OdPlF8zv8RkyjE64r1+/pry18PNVLw4V4Bo2d/1URcEhO4SjZMFxaY5GMOTs8L2py0JyujmCowOPT49IicWLOPS8LAsgwLbvkNYo34YXGaVMeWyugtF0Ko2YwfPkB98xMuzaqdoRQdsKvv2tb+HzX/gCnj97C6/6Nu3EzWiCBxQfxQ7KxCut71V52C7LMuf8YXaWckKRQHqZiyVmczRg/uua80xdViHKkW9Q2b131OUEHR3b3lH5EDphtaIqDd+gbmzGbz4NJnlAuB2CiBNceaCs9YRNdx6uGSipYlnqdHsFDvUaDdXqvO4xjmmtz72PSkDcmNcNNB2el0QUVMfA1aH+02l1dYz4GOcYg3Kfi735RunoPiy3RNuDzzImN0QmefPYmw+uH0c/kWUW141rjq7gqorrlUT5p6cL7u7OuLu7x7KslLj79xxdkYRy2pKFoZNjh/p4G2CjEuGb62lBd+v+8IMZSkPP5JQCjk8SBD6KMZ3vPc6ryQl1ziAJrVEEOA/FCxF6XemkKUSrSX6Jj3+Mo58+bI59EzLG4BlTcpqFdazdlG+s7UXmGQpEwen38YY0i49fn3yyCxSG+wmSkYAq0IliDDNWiRYyKSAJq7vuMkCom6ghbjYfjIC7moJGas6biO4x0ABunoDagOmAjSBsgnNeifwMpvnCojoec2zBhTG8MIlRRihofO0AvjnI7DJUxf07FFK5oZmQlMeYe8Ippl6xutopTNfUN9eSCmHm68XZ48GcL1NBIQmAZFSraK1haztnnQ6XLpXclLVWLCuljG1rSDXRIjt7sSGed6LKTJ6Ye7rrIjv5gerXAGpOWnUY3WKj8gpfDSWVeRjN+2jwkET1kURBSRmlcqygVQ8IfrIU4C6ivG5jcKOoObwUDCVXnM8ZvXXyKnLBttEcjKZqK7b9gqQAJZ+8/9frFeKFMTdRqkQyPN4+CXprqHWle+npPDdV+lzcEuRswsH7vmNdnDPTaWwWnhSR1EqlyEpzud6x9YbFUYvHx0d89jOfgUEnDyCSukNKHwZcp/NKwucYaD4yCuQxrk/sO4c02CXJPnNkVk6MP8lZALiG9+uOkY8MkuLXvTt0H7EERyHOezYRGHEPkiToLjmutR4FlN2ghsGP6B7kpopvv/dtfO4HfgDvvvtD+PcvP/CdQADwazo6So7VAt5HVWS3PW9eYPTgbvk6LTls0TkOGd1zW5Y6u90chVvh996u16mMCHl8a0xYHvuOp8cr7gg/IaWMu+WMa7ui9YatdUrfa0HOZwDMzwHIfRi9QxOwrvRJyllAipNgKTKT0LtLbEOxtbfmScPBV4CjcB4CR24vO2q3945C+HK5oDtJNA7k7FyJINGez6c53jkkxMnzocJk78aITIks3B6IMQ4Pk8JlKfP7sRDRqRQiUZbb5BhKb6E91DjqSGBBzkB4vFyvV7x+/XDjTnuC+jMx3EyJe3kYH9qM9hjetOz7Pi0EyC18c1xKJIn8se58mYP3Nubz9maB5qs1YkYm9+dWianzjArlokKhMxfId0OhN0uQuSWzVbQkMxR5qid9NBVrfZLhZwEyd4RZzAJwUcXHe32iCxTtDqM79CjAUXyr56fguGg8lDuh2uMqzhsj/gAH38NoK+kFhc3uVX2wfRDd1NUl5EWMnLH4PJDurg7r5uzko9si0rxjT76B8hPNjJ6ckRcerIGoDIfPRwryk3uKKE3LWHjRvwBG6TXcYRGJ5Er+0qB9xx6z/sxwO7q30jsA4qoe4WGtpigW1tVAKGViJEQkiLRlgJ2jJB4OpgPZaLWsxYmwAlyuD4CyMtdO8mm8v+w2+vRgGJBcJ+8ouC23B2ISFkXBPQCikgdHfmGRbjHC4lgEyEiFfJnRFfDZb9vbDH+MUcZaF2Rzw6ycmY9iiqenR4i4Qy3gEt0Y2VFySEtI7ozhkKxmOC/nyY1aloVjmfXkneWOWhcWrEGE9VFCcBTCBdZMsK4LyBVSDAPcN2xer23fp1Prejrh8eH17NBCDcFCwo9o41oWA9HBGn4VRD6C2KsGcjTKEWwXxR9vUXDG/Hv75wh5MMDN63w6U566VlwvT1P6KGBY36FoWqbh2bbvyMiz4FtKRa1EZ06nlffU3OMiucMq2FX20fH0+IRvf+vb+OoP/zDe+uXP4OWLD5ET3IiL83tJRE+HDoyd+8LwkVRxyXB2lcaMC8h03WTScEGuPHj6ODbsuKdilBNvIYFXRcmCJAUQphyXZUHRgcvlijGUuUc5Yy0FUEXfNjQdeP3qgZYAiVk7SdyLwoC2Nx+XVJTKYp95ShliR9hfCAUOq3lFziQe1+Q54A77EwXxYsJYQPTR6Z4sK/ZNcHm6eDhj9uLRnzEfca3rgnVdkPOClHw0EQGnSNHoI1Q+01TMYmwWh2V2lKgiJXW0gVtgNJOxMvkZBVhWJL+XYs2VL92LlCDHcs2/fPkCj4+Ps1BJqaC1nanjkrCsZXKkkrCoCPNDgAGQmlyufeOqmlMUFAoI3b51frbYkw+PmIOWECiSF2le0BVV2BB0A2C00zD180uEyL0K4I0vUqG5aXbnYxzutOmmsKQsGq5ayr62DnnxBAViXQtAS/yQjv86GfHECAfwdFgESsAxgCGKBy84jFAoeQQKONdBPN56qKFNORZHCjohsQP2iqJACZ/AlwrMme07ussF3XvPCw0DOSAQwbCO6YHp5+tEWyZCQ5lykEuGY3bDO+Tex/R/MPicH8d4yxAy7DyJgyJCHopR5ZMAZmsE9Jm4CBOhIjRng0MAWeYlQCCD4VMwF2VnzguUIxwBia0sOliUZMlALXMji80X8JGU0qZ5qRXZR1TMeyEsnVw8jhjvqHcJ/vBxnHVsXGMMpGWZEGhCQko6odIoTlVJoh5dsbv9fkq8DtEZW0rICzfSfd+wrCtqKXi6XPgzxTCiy0nc4GuthORr5aEsA3JO7IiNhmDPnj2bI5eAfknO1bk53rp8ZklYzicstaKNgev1grbvyLng/tmC/Xp1jgBTfgEeTIvH2OdSkdrAq1ev2KV6J9dbd47AChlGLowBlyfO2UsNRr4fYq7UMd/s35CET9iYLLFcjmTbQEpIVoSPXH0NObwepL0YNQTZMjJbkm/y5sjatm1Qow15TglJyzFXF3/afH35O4TawPX6hPX8DO+9/x6+/JUv4913v4r/+PoVYG2aDLJIOHhLZoBJml1tSuQx5Zwx9h0dNosvRDGt7g6aGFYH+PoCpvoiyMDxeWstyKVOoiTMsKwn7LvzhXzEVCsL5wTBw3bFftmADtw9O3HkeXOQQQ37tuFSClU1jirvYwDN6MGiB7IcCrAoWKgA4n2rhQVZb1STzdQyNYze0LYrwkK9LmxA2lBsPi6ccRuqRFp6mzJ7Pr7B6QtS65toN6/drWzWJe83slqbyI1hqgTCLtvXQVyblNLMnwoL/Vqrj6z8syuJwC9evMDj4wPO5zs8e/YMd3d3My2YVvMs2nkvmeQuKdESAdzWxTgaCr+eiLpggaDOpTlyd6Ko5+F/a5cfPB4vGASu5DKIurGfZQxr8K6J4xkf9dFI092fEU7lcrO/H2h0ICa3HL4YCw+hbQdRG2PwbZwZQuO5XzcclGHGDyCcsyXL/iB5ZWnCC2QBERqt1oszxeWYkfU+sPuDmcBuQ4VoBcymN0jccMLX/vcNMBNYxjRjap3+BJREulOqK0U4XgoIOTp8c1hQJz/FTJDK4r8/JsxN9Y+zoQ1IFhZA5MlEoQMJxvSA6DF/1VwPozPL6KNPi30eHErylnaY0mHSQLQkl4xUSbzb993dIsucSZecMXYy0LWzqMhROIorrm5mkwYeinu4w8Kc6yJvQIE5s8hLoCul4LARDyj5QEyO8DRVokNQ8yBGXpPWPOHYCWmqht6YHzJM0TuvKa2+CcWr0ZJbanXpKQ8RMtttyhJV2bJ14XtdpLBTBdCVUtx1dZt65Wjlet29i1wPp9RxGMQFMXQ9rXh6fAJAbkTKaSo5uD4V18cLWrtyQykZl20j+jcUHc2vAc+sp6cn3N+fEVLAlDPWRD+Hcd1ncZ9LmvD/baekju1LzlNeeshAea3J/fLkbBzrfjgCaqZuLS7MnCkG1cqsIdP5M0MhE2gMxxB+3wV8HwDVPGNg3x4ZnDdCiWZTip4TyX/mY8UE4Hp5wnvvfRtf+cpX8f5738SHH35nDgEFjIxQkFzKrBx2/6kkN9MiX4oSVm7+ydGJ0TtKTgytE0c1UvHR23DiKcc+PKiIchZHZTiRZjFIF9sK04WZOsZ9J1eiIs+S4OHxQvRvLCi1uNy7TPWQ5IR9p+OxAfMamXH8qEp0xmD0iUoJOQchmsTW4Ty1nAWtDWzbFZvL/TkPYIbZ6Ep+R86QsiCPQe+V5lbxcWAClLU32uWv63muJyDW1PE+Az29tb8PEiwRk0gMVm+oiKaE+uUYGR1J37E/RFFyvYoXjg1hoR+HM0c2Da29wuVywel0wvn+DnWJ5ksBVbcXIIIv/jyYh+6xQWOaexeZaI0vaC92gG1n8OgRUQJXEsLv/zHeGd6sAbSXiPFKTgk6bgJhMy0b4NeEz9gcGHE07orEJAfyGa/JL7kZ3wCAFKHaCW3yH7mHx3X7dVKgBFeDAW7wPJpgH4OSUgMEdHTMDll1GfQS8UU9umJ3SRqdKX12acPRA4ECnsYqSHZUs7FhRufHwkhRUuVsVgkrwzvnjDDJEsyHzuIwtlm5i9BCPUsE991Cmc4N8YOLm64jL+6jMkcajsTAe1tznFTN4NaxLCK8sApFhBqvZc4Zd3XhaCQbwwmloPcd57sTclkwemPRooqaE7Q4vF0jTl0AUKqYiqckA6y4e6esV4BSk78lHvi95YMw7K9bslhIrVm0TRgKuTC9t7fuJki8fjM3R5mGStknvWTMgnxHMuswIl7k7DEozwQY28COhrHyPfQxoBvdWUWiQGTxRIVL9o7ZcL1uqKeTS975dpcTnSYvl+t8j9FNH14R3OyenZ/7iMoRoz4gqQECnE8nut660qU5T6i3Rpmrkxafnq4QSSgZENCQqndDSsxLGmPAhmE0ZrmUSmQlyKy3+UNvvBz1yyWk5TfwcBgjwKZHiUJ95OCcqmTTtl99TEPUj0Ubs5qIYvTeSZp15GR4ei+Wgpzp7rntNB9LkjlnTxz/qduh51IgPraoZ1q1JxF86xvfwBe/+GV88Ytfxkff+wiqOzUURv8IEYF0xdB2HHAGpMjZckJjSYXjF8At+Vmsszt/9PXiyKgyzG/yvpwLNAbdW9ugqVdKAvWojeu+YVmYDdM610CxSnfTWnF/J7hcNogmnJczemnYGxG2ZVnYKIEkcvXog3VdoRpcBwACnNYTQjIenT2t8Tla660TGU5sAIcXCblkLEvB3T3zfFobTkQnoTk4HUEYJ2m3IIugqeLp6Yp970fKcqJxYCQhA7HPH3wfeq8khPInRkExzql19QImCLMceYbVf4wZwzyQo6vqyqQdYTcwPA6Bvi1cG63x+j48PeB8PjM0dF24J3R1t2QgJcXeBKdThZiijR3hgG0qvu6pKGJqto/xsTDxXbh/DmFjYL6n+6DK11Vcn/CDcnzIhM9nNIianLdIVKmIzLEuQP5lnDMl8QydZwccrY8K11hOQXCMuM0AawiLfKpJZRJ0P87rE12gDDUkddd0CQJrEITgHRUJpNlJndOi29SJXozKVv9aHjJk/7cRkq2BuMvkPvAQTYOoSeQlSFGfz3nlPobLvMyt5A574qQu5ZTDmbE3xeiG3gmdplRgliBDvdIVqISbqyBbVKrshoOxDT8cDCxSWNDwQTVVaHNfDBy21oDzCkY8wPAsEoa7qRujtUbzn1KoMqJzpdvpN+/ETLHUjLIuWEoFMz8M2njAmSpGcCkcnTIQ+s7ZoVqRafbFbsznrCmCsgwYw71eWIQNjVm/oJ4WHFHh4tbSVBu0vvMzAi6P5iOpRlne3poL+0gwLI4YQeBW+CwoGJ7onZKFaqRxDUgFTFCW4ocoP2cFXA1DD4eUEi6XC0pKqPU0jaTC2Aign44qU2m3bZ8jkciLym4Bn/sAskJHn/LJ8Jvgnp1cUuqBeEnIcxGO4PZ9R9uad5CFXC3nqjw8PMBU8dZbb91I84FA+iIzSAfneuFeSZSFKbk1F1eWCLv3BE/rZTYNcBD2Zgc6kSH/cT4OMKV8WlyaLMYmpZmPg1JCKixAuhoqksc3HOPenDLa7l0eDLUsuF6u+Pa3vokvfenL+NQ7n8WHv/IeyMMhB6uWPA/iyCWK8ccYHUsFSqlMidWAxTN0qUwqH51kcX+uYPAUawGEpo9xQjgIBUBcds8DQDyXqzXF3emE3jO2y/Uwa0yZyiwIWtvx+AhIIWIVviTJifCwG4J14mfLJUMbD+i7u/vDCdcMuzdRwjfi+0xkwiQWsq42m+TWxINxjrSH0Ql5IZ+hgQW8GkeP2VHJ8OsYo3t4po+15ejeAyWJFwm6eRb70xTP1AuYceP9kyc/DQi6gPOU7PBnyd605mzoPU0vlVLgRUpG+GyZAZfHC9pG99zz+Q53d/fovbmRpav7umFZFlThuM/iCVB1I7xB1DgllJxwWleUQisGAxFAdS4d5CYPx2KfNA/rY2EQKGzIwlkbk4+YxGkQAiAS2FNGCWRkDHRT6OT7mf8cH2FKKI6o+uR4CNAUSfQyCynTiBD4eK9PdIFiQ2GJwXYQjllGoBuI51xmsdCdST1U0XWgdx97qM8K/cDcfQH2IFT6Ax3EytG50XVTpFhImZUqg6cw58k9FCoAUl0OiCthdqFJeGC3RldEys08ZVKjQuaDaTk5CYrjjt53mCOCArjvy3DEB9MHgZXvoUW/dUKcD42pp11i6t4hkc/gnh7+83IuM2CvloSMhG5CDgoMZcm4Oy8oRSBW8HTdMbpCSkcaiUTd5A6cZkhF0NzVVhDvOTExNbsTgGEa6QWMnJ14vG37lA6TR8KOrQ2qVsi54f3sjhKIc4T68NRl5T3vylEOHCWrNXuBxAc3l4zr/sRrqp5eagP398+Q5Ihup++I5wkVAXxsE66Zqopt3zBGRy2nyT0CeMjlzPvCmPeO6/UCsyg6fJzo44ruB60FEjjHdaH+id/L8zBI0e04oXH0gfu7e2z7BkBw3S/MNNk2z1U5ze8XmTgxyx8eJBdr+rZLtVnUC/a9O0zNsEK4Md1ExvzQa61h24arLzgu3Xe6YpJ8HsjZIdttrQM+dskpzVTYKNjfSBpXAyTQqYF9cLz77L7g/fffxxe+8AV86UtfwEff+xA6GlhCp1kss2ghVDvJj8PQpWPx/BVVpj1DvHs3o7pNEkqCK5zIo4m4BXVvCqK/xlGr3ydxyTZyQkqVSJcZiwIzbNcN2HeksiAljuVM/Lo0kPvkRZE1ZerwskTLjRgj1rpMk7B933Ab2Bcp1nFPJNHLw8yAxAYrEF8d5NttbXix63C/DpI9jXtjXTKum2JrHRXiMQ/G/X3wc/UeaMrih6xONQkNlg8SLT8LEC6xRH9sfobIoAE6SmFqeHBtVJubx8UI6Rgjxe+VcngTmbU5HgUO59sxBh4eHvH0xNHP/f0dTqcz4MV5Hwo0l19njqJL5qHOBnswtgPuEVTc38ubYSozbSZKv4Gmwxw9d7Qjin74ZMHCPVaRNU3bCQZLBrof5FaOUtFtqiWTZ2ix4MlIQq6b3Pwdhe8tdoRbMmvOR4Af8/WJLlAE2f9xua+y640uQbxmZ0HSWXh4N0/TpeSNuR0ZNv5ghmsovMrM3tn01glr+qwSaUwvjsN+PTZmg0gswgwIpW4ka8K7ZcqfxzD0ZjN0TbyD5uHizqaZiyGUNd39AJIVd4I1JFN0PyhExNVBOhcvHRBjLonZYbyhhAkZr4/AiN5whJVrdmKf0gE3saPtURi6fwuE4yABM2faTu+K1ARSaV1Pvprn7iRBk8aCzGmmQw0FDpOaPxTDK30jUdSi82/DjeMIPz89PWHbd6SM2ckDmDNqmEyunAhm0YJEh8iw787RMTvbP1fQ5dEloKMPQq/J/WdwEMhyySiVHIC1LhgjHC3puNvHIGE3ZzxeniBGRCGkh1HM9j6cM0M1xbJQ7i0B4xo77GWpGCM6wjrRmShWcs54/vw51xjgUm+GeV2uV+SU8Oz5c4gItn2HAR7Ql1Ftwbos7Pa8AWDYIpGIUhjUVtxGHY6WpVB7AE4c5CvlPF0t+eh5fABnBVREJLpB7/uOiJEP7lDycaypcybGQGsd57sV5vwcuGlWylT8jNahEocPHUJqJV/loPcarpcnfOc77+NLX/4yvv3Nb+LDD37F0UJWNzKcRxCjBOdHZSlQMEtokTTNr+LgB4DrvC4J7kQ4D8jgr7EIiENBJnqX/XsGCG9Jj+ekVGglMb/v5FwEeVbcTTVlD6NLMSbmePru7g4hh1/870Rzdb1c/WASP9z5HCQvVESmEQ+Ag0M3xnADL7r7qvGe5yKwIT6Gkcn1KDV5OOcGvw2wzsZFAH/GG1pb6EuUmdUVTWfOZSYnh2Q+io4xCfhORE0sGAGZNvn0ySGhOxy8GUSY3fvnGHNxH7HJgTpGngdRNzvZVVXx8PCAp6dHnE7naaN/JC4bbRqQAaEbMmDQURj/EVJ0AGFCV+uCWhJab+jSmULtqAfSwTVMIL+HSdeYz5AOQXhgjdFdaJH8GgYhmG62UeSxaYZ7aY2JyiQxmHiSOFECrm1v6i3ZvGZhJfH9vD7RBQoXoaI7KTG8GMw7UUNyJ1dCv8PYHQ91Iu0NyzuyXkZ3SBkcGxXf2Enmsgkxq2JuvjYOFcnQMW9SKXmmg6p2XC6UY4qTWQGBWMKI8QRwsKpdJkM40tw6mcUBkzZZaJG1LV4vFIw8fKHLZHHPhEq4pt+h5+RkMRpcFW6IUgAMn0UTEsxBDEsGs4hpV8gg6qAmJB8L55RNjvHD4+vXaJth7B3aFDgxxMySuASbxUJyjkEHH1rrwFLTJNQyc8UhTU//HGponppa6wnr6YTWnfwqiSSv5cY4LIrKgNF9PgtLSMbfF5f2LZmGSiknbG2j4knYMfTW/fsQjek2YLuh7Q3r6YT7u7vjYK8Vw0JpEcx3Xzsl43J5wu4pxtkRpbpUbNsG9EOdUEqdRUbOxQ8Km6oCejtktGbz7wAyw9HCCIzcDe/8gbl5RJdzvV6xXa+O3CyIcK/WOvZtQz6ts4sOnwQRgWiGapvvEXCEIHMdJ/jYZcLWeULw2RVuow8/sAug5pv8mAdSSoS6cy5T7QBf3wLgrbd4v3bnn0hJznsQ79QXh73dmMsMaWGuTRD9zBU/77/3Pv6Pz/4f+OoP/TBev3qF637Brh2LVCe2BfciOYjkYxgYnh4foctKfxsnvFe/Dzl7Jou7kNaFOTFBLL7Nj1FPvI2inByU7imyXD8cX7Kwp2EeC7ZQ+5WlIsjk5DnwXpZS0EtH9YyY7PvUtu9OzM3Yxw7L8Oyr5OgBcL1eACOBl6TeNJuIUhKWWknmHSzc11xgcgQBZhGMfgZuxijLqTjfo2DfiLYagtdE24auA/vepjcPxz4sQpurz45YAlf+5TCM01kY1nok64r0uTfyv33fTgKZ7/kIc401S9lsmQoXGh8yBJRrOiEMJ2PffXoKG/0F9/fPHJF0359a0fYNNsTH2H7dKw/7UsIBOgoG3xOd3xf3JxRRQdLOqvSlcp4Siy6u30A+YfSlGs7NW2rFelqnWeE8I11YYvDi3ABzD4NUyWHhte+Anwc2KQQcT+YoZD7m6xNdoOg4xjOx2ephAUm3yEGyJ225+bWzQFEn9hhoRBOchRSjBFceGA80n/IhiEdRbapDls1nsykn1KW4JGxHH4qkaY5VzI7u65gdAhErvtTqCAZlxNmlxMQt4f92W/NU4CUw2BnanF1PIqMcB52pzm6MDxiJedbDWddn9E4whpvf5RTGcn64JszRVRYemmGal+RQJOxbx2iHYqOWQkQmOawoglEMCSxAeg7CW3IDNP6skGfHGMDvuBcZ+Y2DuJwXFDPIJsiZZl+x2cUoQjyQXJyglhNvetwLIArB5jk/xsC1CSFzFsucFdCLYllx/+ye+TrgZzRxwyYvBOgO67yU3vD4+IhaiicS03K7LAWPj49eIPJhrjVMBY9ZOWFYIfm2Hm6xIpjKEHq0gGoPf0Z4z7hpt94hrlrovaPt+1znMOB6ufjYBfP9ixykwugeex8uTY3N3jDTr4EJ64ZcOlQ5gS4QDr6BpOd3Ss7BYZEdHXCQuWN0N00F4UaIxvHA8OINrVGc7gXKfI8is5tNKaH1HZIrXr9+hfe+/S380A//n3j70+/g4VvfAJR8K4EASkdmADxMFTNryAbVUcnHKeEmys+asS5nPF2eeL37oINrvg1U4wFXanJeWbj2eoK5dkhekIXy49Y47qqVDVH3dPaRiAiuq2cgdUOWDA3puVznc9PaERJ4vVyRc8W6nrmfpeTIH5Gs8/k8c7fiQEQyTDUNIsOII80x/5zvo+SM5XQCjEX93jrMOpIYahFAM7pQBRc8tKVUQMMVm1lVkesTqqlbcms0PHy53NaT5M3MP280s+QZsulwYYFzffjro4CN9Xwrgw8ye/gp9Y6ZAVRypimf0wXGoDttawPXy4b1tLBQScwR2q5X5xnFWMkVZIMjeCibrDH2+RmpqoqXc0wQ69Gf+XEUYiz8BUOY0wawQVP1Z2Z07Bum+jRJgsnxrME8KFMHVBr365ExxDmNnJ4iZ7e5cGKzRCEov07CAqnW9Zln/B5sLrrhs2pNhV87/NAZhJPNq07uWnoQ/+b3U6hW3igVjgUSu+wEgacRcsRnmOqZXGisFKSoWiuW9YQwUDOY/+xwSQDgI4G6lEkugx2bsek4KljvplKmEkED8jW6BXYnSKYbJCauCV8kNkkif4NBeJhdFgCM4CeKj29UaMOdaL5WvJAocDdfNyYajeMzVUHbDTpI7ux9OCFLfJ5vSEIjq+wGUZYLhisfiEAlzktVJt8lpMKEF9NxYIAQfClMKW3tyodzADroJdA9Vl7VSHCD4CDfhmTcORm9O3k3IFKbNLB5CPsBTMh1wdtvvY1SC4Yq9sYAuJwKMI5iMbgnaorrtuG0npyAlzFGw7ZdqY7o3Ud/kfSaobOoZRFZa3YovnmhTElj7x1QZfyC2cwgEh8PJi8okwEPjw/0wllWD19bnP+xYd/3OfuPzrEZDZ7CqdUU2DadhUDyLm6a48Vc2gvaaeQkNBHrncVjyRl7372IpDwzOtlbxUVrXug5bJ8TXWa3tmGMgfP5NKW49A5hQZXcx4GrXzFdS9WccNtuuBgFy7rg/e9+F5//0lfwxS99Cd/57nexXTaapRW3/QZHimo+wvD9oeTComXwoIi164MsGunJCY8Pjww3TAKDm16lOjOfJGX0bUPJ9UClLGNo51gx5ymbNrD4TiXDrN+MIsjZySJAAvOaSoHuO7RTGhzOryHPfXy8YF3UYycKgA6AxNHrdWORdTohJ6Il1+16MxI/OG3A0cSYo10Gw+6bS3cUbvSBbd/Q++48OUykg8UFn59aCpp/NhGiy09PNken4bQd2TtvOq3yuY3fCyFAzKei6Rzzc7AouOVyvSlpPng7x2iHf76u5Kjs+w7tHafTyb9HhkjH6Gyun54uuFyf8PhYcO9eKuv57ChRQ1KSTUWIeCJcekXm+DAlu1E28UXUTufnpY+S8foOde8mBj6O0b1x5iguxuFeh8z9MeWj4BuD/B4dQDNaHWQYLGeeTd5MSwIqfJyfjqDKXzcyY8rwzC3m+ZCZIx2qYbpmQOmwwTyKIBLOibN5ZRqpmuIbS1SwJebNfKiSZOSCiTaE1G6oQjxDhdDuDhgYfV4LDj6JjxLgDPT5aQx1ye6vwUM49PKmB4eCCI9LoY3vY5LSzBw+xgzySwDMnXPNjLHZALsRHTAZ/j3gFJxjjgofRaTMzY3mQgwhS5pQE/NpYmZrQ2E9+AA7ilSICU61YlQ64oZzYoRg8fuRxFtywd095+ECmZsrwHFOynmSoIFj84UI1Br2je93v16Imqi7uqpO0yVeH5LMSk1ISi5JyjxciOQY+tiJzIUPgxetdGBdsCwsXMcg8fDufH/MYR0hqnVx7lGee6SIYHfnTFObtvahhIiNu1R2p1mSy2837xjPPocmWhKKgtZ2tLbher0wiK0ulKYKcD6thIa1M2hxDHbnWibqkNwAoY9BzkcpEHdIPZ/PeHh45NrNB8xN2JlI17DgVB1chSDjFu+awy21NyVh09fbdt2wiUyvIaADwzBGhMOZS/b9GZoOpy7LhOcp6cB128DARnaJDLkj+TrUK9AOszzX7RG2yGDQph1VFrx89QrvvfcevvrVr+IHfuAH8c3//kh7e02UDoo3LRKZL1y3dV0hdYUZFWMAlwRTsBV9kEc01hOu20bXUz9EmL20uuqjO7HXmPeUE3kuQ91WoXupf+wN8T0Ab9A670nIysUU6mPQsHBXpY1+Tpm8fOcxdG/wwo01Dvdt23A+l2nTECOQW7LwFCrITVaL70HqTq172yEQ3N09Q10KHh7MAy8j8gCA8WDcto59Y05P8meR5oBvpiQvS70pIoiSxAgK8NHebEz5mZblOKzf8NbB0QykGVVxWEtEkOLcwW+uQTQO2+XiNgrJlVsFScjVCQPK7XpF23Y8PT3hfD7j7tkz1NNpJiGz4BtABwvjSIyEF3e2zWY0snJCTh1I6t3dHS6XJyKdlyeq0bypjM2pVHIlj5/JQoXqTkVxH5yhOq/T3hUp049sqKKoxx8krlHkDEGCWCDWMi0tPs7rE12gXJ1kasrCAYGmmGGYFySmTsryoKQbhdNwj4iDfYxjYcJlxwh7e/OOKEEcIoRDxMx9qI4QUK56Wk8OnxECHl4sJRHkknyh3UBwErP7g0Og6rb4g9U8YVNxZ1aOgGj4ZujG91xrmRAffBPi+nOpsbdbIavOhTAq0gFdx6EsRCznGIy8FSB1AzI5Aww2S3OZ88FNc+RTM30JRQx1ORJH1TMr6F5I6FIApiBzZ0JzcpfBkN2P49Z4iwcwO1Z1v5nuwWXTPEi8CB1MKoYwCHFIB3ry5GPn9AxFcXfG6rN7QEAtccDwBZ/+9Kfwmc98Fs+fP8e3vv0tPF6eYMqNez2d3APC76cIlrWy8+00ZVLrUAOt7/14oQS0c8TjyaatNU8AFlyvV2Q32lK9UaQNV3cZFRdU/2TabecVrTdc9yvUxrRiF/AeTZdMOaID1H1/aiGa1fedGUUiHoh5EN76GyRZc85BOq49v3L2r+IHaHSbdM8sXGOmk7ypqsiVPBUSSOEFueK0lmlYGIcNzI21KnOemipSzjivxfkQJANHjlHS6q67GXUheb31gn1zBUfi4Zay4Jvf+AY+//nP48tf+iI+/ODb2Lfr0XnzYfdgP/JldCBkJa7gCTKxc0h0oE9L9OXG3oBft230qYln+NmzZ8gi2F1eCg9yi+BPwJAykHCE0mVgcjOGNwumAtNO1GsH9r5jWVdHeiiNP5/vXVXHZzmaCbMdvXXUpeLZs+dMS3Zjs8vlgn2n9H1Zio8jIktHputzFDjBhzOYj+4MkhR35zNKJTn4GLvwoDdVrEsF4yQujopR6ab+fPfe8fT0hN4r1nWdqAL/bCClw2QtkMhQWrUG90IB1rWi9+TvI06Ko408PFfSG4VO/Fn8TAY5EpXJTvDtfQcLnOxIOwAUqLLRue4brvuO148POJ3OeP78Geq6eDEQhYKhSKJkP2WoAb0NwIvOXLKPkJOPraLAEkrGB/dHNkJO4EaCSAdMSJsQTLItUXs+b/vNs0+0ls+LGHlYKbPBqTlBCmkEQ4SjHRlAynNNftzXJ7pAab0jl4yIuAfgEjKOJdQfBO0OWVlwORyac6gx58SOEU5yBTeBLG7Ypo029mqzMtehyJ73sNTiUkf+eUlkpZt4cdI95C3RcZKW3wYD0YFcImGUHXHMWaOoimycXDhaMZBfE1wMEUp9CfMSHoyDCD6GYBCeqwOGS+iyF0BetAQsb77xTqkxL5grbc2JbsndZz0uwLhRq8tyc86w7oqoNmbWUDj1OrsS8KJIVX1k412JAblwYaeUSCJrzXkS7mOQMta7lQnCrc3K3sD3wwj5hGs8vF7IGEg6vDUTa615xc+Nc6nnSbCsUjlDlxhrGPM4nh5xeXriuosE1exEUGFhEUhdb85zKBzllEpiaPBMhnY/nEj2vbXql0yUwpSky8fHx8mZWBamz5rZHNOIeDeUBI9Pu5MgwzSP7227XicvJeU8iXW1VOREp8/dESd9+dLNnI45fEoJl+0KHUcnGejeXDM3fJR932GJIyRLNrt+8rXonZFTwroS+TkvhMVj3BXrJIy7np6emInTma2VsqMgPt5k02JIVVAXjnibx9yXurrXDaaTZvJ/G9iItG1DXRe8ePES3/zGN/DDX/0qPvvZz+Jb3/qmjwGBVDLE4wxSLmg7Db1i4zdwpFZydZQq+zNNZCVJRS1Mh04puZXAUbBnYdJxqQUGdvdwgnHHAFMpvYnyUdUYAwNGrlfN5H85WhcBo6VUFiFdUTJdZvdrw7oqzqcTcil4/fo1ni5XPHt2D0PFvm2wveF8LljXkx967MxJ1O1e5LqiyFUulvjfbJjgKhVgb7ujWPAQyStHD86F4nsWpCV58i/5HFSqRQGUkeuCMJw2pYV/7805Wat/vY9sTedzEPtm9qykbaN6iMikIKImQuHIQiY8q45nMyz4o1CJ4kr8+c8lIY8EszJHSAfR1vN45oiX5PvWOlp7hev1gru7Ozx79hzn8703JIcTdPDAUnKrA1+7mtgQBvJ7i/bk7C7HQoO/3sNITeeo8A06gLCZDL+pUHbCC42uHdIzi2U36+vFfcFygg0c18cl0KGG+zivT3SBwofMN0Jh1PfssJMgiJG0L8gwSSiKOW4pTlqttWKIMqBPQDTBF/0MKnPwITIqUiE5iYcxYTuNDjKTKzFax75zVr44t0KSAV6YBNqwOskrutMDQWFwXilUd/Az+5gouQKFtAnOl1tzq/Xg0nDEQ+Rc3zz03GQnfs+6wlIgDm9e5+QddHZpb/aZZSz05DJXVSCBhLZhhqZH7oMpoB1Iwefi3s0Hg80zORuuotGhMyAxYMvWKVW+XnekTLVL6y6qkMxxn5CMlQvRpgiPK1Jho9Egz8MScyJ7Pgonh6NIAnQ/B461ZPIT2r7jxb7P95WdZAiTiVDQnIgFlI6Bbdv94RSMplMxEX4l62mdai0Gcwm62SRdZv9s3YY7Uipizj4GC54xOg+LWLdj4OlpewNVEe+8r9t1qi/CibOrG4+J4HK54OpKHgEJtiLiiA/I2o7kVOOhCCGC0PeOJIFohbeJf52O6b6s7i8EAVVwgxJ2Jf2EaroCtE75cM40T7xcLlBl9hDlt+Jv6ZB11lo5jtMxw82u+87ZvwmuY+fPGGMWSiOItX5dQ96Z8oL3v/M+vvilL+Hdr3wVv/KdD6CjQU3gYBMi0DJnOkeHl1CYK/LgBkRZ5PbRAeswc4XhRufV892Z3aqjCAGno5EDQkMvJ9s7n+XgJfE9J2QSaZv54cegvd42TrElodQF93eMVDAdKDQaQtt2lJTx7K3nEEl4/ZoW7pIEbRt43C+4PDbcP78HpffD90jGcMAJvaVQzk6FGp1q12XluAo8sGol6dac4LztV6ip75PBIaQMdnqOKDlHQdLd2w6F4Hxaj+bSuO/23p0IezeLjltp8LIsOJLbiWi01g8jRDk4OTkI3BrcqoM0G4VGSnZTpITPiCGXhGocHwIhhCgkou47Lpero6YMt4wcoYjjePWK9+D+/h7P759hqQuRy9aIoBtRk1wSwizRGu0VxLlfcFTrkFMnmCTkTI6RuT9Wb2PuvXw2cKwtbW6wdmQBDR2eu9Rh5kxA4YF73XcgJWgmwupUXIIHbpT3cV6f6AKluacAwI0skAceBTdzLiXZLgefQg3q/A+mcCZc96tXm3JsnN6VxGu6FLqyYF0q1rogOBsx++zxYHl+w7quKHVh5DgOtQ7HPcU7xcPltnslnYVqoJjxqlICSV5IhrmJmg7nLzhKU11d0bremrD+KpLqDWHJMD+3JK98vTjKiYdplozsX5flIPeqKiw5nG1uH5eEzuaVxUJTumcmRyGSd3zA4YA4nVodVgxzsSBAJx0OByeUsvpmkHC97jC9IiUWOEspSKn4KMsA+IFlhgFFMQOMG1J1mDrn4v4jPkLyfweKdBCvufkw8It+KyGBruuK0/kOXXltyWdgzHpJdDW9bhtN7HJGXRaY7dOkqxsPqdP5PO91cTQpl4RtJ8+pLivu7+8niTWcMUNRFt3P9cqQttviBDjQjEiJLUU8p0mR6wrmi+xEAmuFub9IPCv7vnMNaWS2GEqmVFZ9lBZGTwCIUiZx9CljqQUXJ7EmAUQTmneFY1TfZDsuo6OOOnlGAvKtaJF+cWl5nUnSwRsI91aiPQBHL8C+EYXJufq4kh2yuVkfycNwJFTd8LAjl4qHh9f4zne+ix/6ypfwhS98Ed/85n+HDkOzhlwdjQQPumLgZp3dayWMwgb9SSQRBk/SkRIdgCnjVtzjDorMwgPdSSW8jiR10qcj9hrI0WzN/UcNtBRXmDVYyVjqAhkGHQ1NFWhXnPMJ63nBdr0AAAQJl6cL83T2Dc/feguLxxsIIn3YsO0b0hObOkrvDw6MJEfgckIpFSU/x+V6nWaVJGB3TNt3EdT1BCB8Tjaii2X1/Wl4eOVhvZ8yFUXresK2XXG97mjbhlzKNJQMrkxrDa9fv55Kn1IOeb7ZwXUhuZbPMhs8m88gkaJbbslhNx/Bj+5hzg0Qt7wUPuurIznb1v2QTzCrM9ohmspal0mIT8kwBpHg0TpevniFx1dPOJ1X3N+dsawrgidE48TIOfP4ljZw+LnwM/bW0ccOcZL8RKvB83E98SyLuBUWS96E+h7FkduNEMXvvi9LkpTbgAgtB2xZYBBkp2JMMt7HfH2iCxTyS4IgizkXdBD/OHgRGv6MmivUH5BaClaXuxk868JHcMMMA27g5BAVNyJFhuBUF9Qb9nSQbVuntHm4AdW6LCg5o+07xBnXQYqLDpmBdjo5BXx4aKudgh8Ad270UQ2lX4DaAPxBqiVDvTAzAKmpj6bc4Ccfs0mf5TBwCqxiko8oVODcHEOGITsSU/PxeXkEOSYYRVxU6EiufC5Q6Z6j4kWZoxcieR4ODLRy0y2HT2dx4oWGKTeUWld/CNlhJZBAu/cdmgg/s+OJxOo3Z8OA+744KgPfYPP/l7x/Cblt286C4af1yxhzzvdda+3r2vvs7ONHPhKFJAZEQY+VCOaikAtYSEEQBQsBIRCMKGolgokxBS+kcErBhAQJBLGQQoJJIakcAnKIoBYs/Pjl3Pb9stZ63znHGP3S/sLTWh9znRiz9//xffybzLBz9l7rvcw5Rh+9t/a055ITD7bi8Qd8EJN1Ox4UxkMuDL8az3Rxn5rom87KjXU+TIgxGZIRjVjYMOVks3kiFq0VUyLEYS0+TRO9JuxAVzAnKMaI083JCmbFNKXxvY52eOAilWQk9nI8RaRwmoMd5ByVwtZ9V6XqIMbhvjx3P4A4FoyWSOok72uIO0/5isNkvi826EqJ8nnySfiMuudDrQ21n8fa8kM2xogES00NdP1kLsp+T11mqc5NETUEiy2kj9sgQJ5ZDK+XAsYTwNCcwPGeUDIZlfb3wWDrt976Or7lc6/j9c+9gXfffQ+lXDiqBxuD0gsOE9OBayexHeLIohUotWCzFN+GhnlmcXo4HMb40jOT/BkutULUgyptTDqeH4yxEccFmd1vZUI4nxFAM9GUDifBm5mZjZ/8QI4SUNYNT5Ynhibb+LOTLH1zczMO1ZEwHQJyDnZgs+D3Ri3EiJubEwP17HuIOEW4VN55RM6ZqbWgVZo8xhQwy4QJXjgS8fOLczwccJkvON+fOaJVd3+V4evTejcSbcU0ZUtQDuMzcyTqqh83EqPv0PF4a4W+Fyhu/EY0az4a7zCQAO7Kl9aCBWRyhMTrMuN0ZKiiexIRNTF5v2UicYzk1gl7M9eMt3d/f4d1ueB4OuF4OAyekJtEws6wrkAvHGuFEFikjs+Hq6LW+TXc0YMEk20Ha1QqE9xjREzdzPKYfcVziectxSlAC8azbO5g3dHdZRZ/wozaALWQPTMA8lk4QEWAq3FgRNYUkUNAVcLvaUrImaOJac6QwA2hmha8tv4coiJiY4HATVphwXrmRMn5Mh8gN+kS8RlqxXxMBgfbyKh320j5rrloSPqMKSGNmHSQD6Mcl0QL0OtNQB8PQyBs1MLxCTvJ0iq0dQZuyZ5kSSQDGAy2FLm4e0NpDdI75pyRxA3Yrhey8Q3EggC1DZWTj69UqaDwMcg++32+ho4xALKPtDgHtZ7M35v9vpQiDgc6spatoWqwh5tBXmmiL0TXlcQsm++Jnb40puN4ISh4kJjUMVmeDWCQtSlB3FXTod1gbozJuq+UKElNib8bYp/BDuhpmtAa+SXTTJi79Yrz+Q4pzSbpXXBzczKYmcRtN0Lz8LyUEiST3Ek5cB7qruHhYTk+Dx8+xLMnT3jd+oaq1fwS+PnmeR75Pfd3d7iYHXe39Xg8HskxWc6YY8Y85wFpR8tVIEyfUGxU0+1ZYQKto5c72Zq9k6KUlcVyZmBkmia6I1uGVDC/nRizrWXKowmKhH2s4zC8km8hQVALJY8QQ/bs88RI9ZjCuSoRtSXUzdaICpybTTUTx44RLAIggo8//ghvv/023nzzTbz88st45+23DOlzYi+LucOUMaeI3il/rnUfg/izI2OMaiaEhvptW0VW3RPPzQLB/S9izkgxYh2ZLn2sM5gAPuUIaIJHDYhwxKPCYhswrkotjOq46qZ7b5imGdu2Yrksgx+Rc8K2LggxGGKlg9fENXVNnuS9ZLMlZlDI9OPlsoymw1ObWZTU0ekLSGoum41xczZ+XoT2naPnaFlOVJmt28Zi3JRYRMYTQmt85krBpRbUbIGK0wQPDfVso9HABPejYVBl6+Sn1WoZVXVHetXIpHRzDhZMCQDRuDE8X6AeTEjEligF3ZKZy5VH4ecoE78+I6bINVkbahestWB79hT35zPmecbp5shnOka0QkRduMlDDEEstZJ0PmwOmvE1jfQrXCMIYlyweaxVb2xCWIxgC2xWEO5HsXFyGouVJh1roDBBE4AozKxTypQ/6euzXaCIICTLrMFAQwck5eMcqNIQvzaEJMhBQBfghnXjBe+Nu2hXi9DeSKJLVx1NEEGaIuV4qqhlD9cb78e6HKa6CraF7O2Y0pCRcmGDi9rSX6u2K78E1gtRBOoVau8DktxHSRW9cQyTwr4pedGklZu0B8uJLb5e3XjNHCrNXrx05tegG9cEVK1E6wTRWc1z8fOdUXdE9YBRgQjdq/FnQrCxi4Xp1WYxfNaxgAhSLeak69D1uMW2qRtaE43cRuFoB91LgdPpFsEe/LIt6NWdIwUSZuQp4Xg4goGADcHvtfN1TK6qys8ZQsDhcNyhXBEb7XCun3MCQkDMGXk+ooMHa7VNB8DoJkPImOeDfS4eGpfLijzxM82HA9NeW8XphmvkeDwixoj783lEpve222q746sbVAHcNB/c3qK7/XfiM6BdxmEWzTjKC8YxGzfkcRyorQ3VR0oJ84HjH7kI1qUYysQRz5QnBvdBSBgfyGDDWjcbEcpQs8TItahNKXk1dZRD0t14ZN3uvww55z766o2weOg7x6WUMvJCFERhhidQYKS9qqI3EuCrFGaiqCIijCKLSptGUrWNoXoHvvKVP8DnXn8db7zxOj768D161hSODFUF21qRckPMXHfaZDyPelWgcLkFQ/EyVIWHWVFclgWHw248xqwpIzoK3UEnzVjW9Wo0G3a0MLBIadUM05QZXL0D2hjLYdsQau9owBhDhxCGp8hlWdj8VcU0J6pbNnoLOZ+u9YIQE1KaWFRXN+DajfV2TxzyvVzB5WvMRyYACbshBuSQiahVT3mm46p7aTTo4Dh1ZdF5OMyI7iJcCmopmGaKD4IhWK1XlG1FqwXaGubDPNCJUZxetU8sIMxJPAI5sCCNcTIJuLlpCx3IQzBbhmt2QdPhrSLi5FAZIzu/fy7XnqZgzUY1nsrKZjdlorXmadMa5fqlXLAuC+bDjNPpFsfTCSlkUy26PFwBEfQQMCd3ht7MTM6SvkMcyif6v3QrLHlOhswi/3A4YFtXbFu1/DMW0r53h+AO4Q1agNI6etpQYsWcWGAPxP0TvD7jBYrBfvYAw294ZN4MLZUrYHM0BUj2UVaZfZjHWGftKEitqJ12wlAzWwIscpqdRjO0pJZii8AeQoP5YbN+t3vOOZqcky8FZVjJipKcE1J8viIdfiQWirebiZF/M2DYYUgEblLASLmMIZqiIwzeiAR2aWx8XOLYIa0jQSAhGbRtXJHgBkgYowBY98DSQQdxKsBlwmI/w/JYBhJFZIUPjSmuihPuTOosMqzyR2UuPr/kRpCnhNYpL725uUGKEcu6IseEhkLr5tahGpF6Z8HlGRNWyHUoIhJKa9YtZnSxomGacZgPQ0EigehJTBHTPJODEiILDwhKrUSlgsXC5z3NNcZk6pSO+ZBxd3ce9xE2/rgsdGztDTgcZoNQgSlzY8op4f5MKLu2hmzQfhyIQYNIwNkIriEE5HjVXdaK8+ViPj17iOBhnjGPEMuGu2fPDEHi/LyUhuPpCNlo8uSjiF0KSj8LplhzrTpxdnSZ6KiqqDEgu3RbA9b1ApFiGSoAuPpQm0Lb7jbpLrGqHmZHLoFCEVUHT2FZtr0YlwBIYPijkf6YXtxR4mpuqywcmBRM/55wJYVsTVGUUPw8H3F/9wxf//rX8Mab34JHL76It996i4RmC5frrSIsKybNUK3Gg+HBxP3En4NgLtNmphWAupE0y0eL4xF/pIPzYmyfOxwPaNqwbiwKSXbm16YcqBTzrJbSTf1DbktTQIzgLs0JvR2tRUMVaHCXU0avDUg8CI+nA/KkOC9nuBpw2+jdcXt7Y0F4sGsoRgTldWGxwTGic+6mnFG2hstyJicFVEemRJ6cO+uKkPzfzUrCc6l8HF5r3wugxH1cJKJsLOJ99Am1Zs/W5uVyRqmVPlXTPvbh9bZr6Y7PrSGbY+tWCmCFrHb2xjKQkqs9GxjPh5NefWQG6NVIypFojt+dw0WUpWAzZVgpFZMh0SEEMLqHHmC1NpS7C873G043C25ubjimAdWB7pKrpaNAkROb7CZmzVA7QmjoLSJltWeA79PR5TYQ0slUg/RgIerjmVTRbBCouqrVokzERl+1/SFe5x/3+kwXKKqenumx5iRpJWPVt87NSZugiSIGzqe7wiSKHKlQLdIHvAYAk0njYoxIISIFXqra6pAvOzHPD14/hDl7q8akDiY3M4jruREcu1tu7mJkKyevgkQ5AEHiUFiobcjFlCQka+4dshMAWc0SdTnMM0ccISBIsHAx5gGpHyzBacViBj5WGPguaaMXHqiex9DRhxGvKXU4X6J/RWfXJzat5z+F1ZMVJ27q5F0VE2IJmV5fqmbGXvxKMWg0IOUjLe7XzTZ+L2P2r/MCMgQxlj4RNIggIOIwkRshiIhxn8cjGDRvZLhgks/5cOTGNci8jDmY58MYo/W6349aGtbVsjDSBFUm0Doq5gRcDPY/W9yUE8P97PQ5zDO2lY6pKZjRWDDeB4Sz4dasc7KrZahPbQ3LutAnwwzeLpcFh3m27pcKNM9Q8YDLUivksqCWYO6sLITYtQsEBdtmEm0IYjLCbN9n6cGKzf2/LVHZNr0WWMxsqytV9sJUFPvGLtjHFLa511YHkqgK418IQlR46mop2yhuRAKiBiyXlc+lhPHMh2Ho5Q6xfNa0K05HrrO33voGHr/2GN/yLW/ivXffh3ZTzQmAEA0RCECoiCGZvF/o9WKLM1jB33vDsl4QhIVgLeZp0/0AJMHYn0mAhdOUMw/4UkdQIQ+yFb1HTPOEqAGyAa0uKHVDyvM4bEOIyOZdo4X7px+iOdPorJUGFbr7Xi4X1FYYOWFkfFd69N5HIUA0yBR4NjbyPZSu3hVBWVjX3tmRywG1JgYMWmGMQG5IDmz2emuUhevzKJT4aN9yb1KecLlc4OrNbSu4nC/DIdlf7rVTrOieDE0JxhfxHWSMVkWwrgvc2h8ixnva9y4nhj839vhD55XCVWYcd7vbr/+ciNaIYhFREUQBUmQRvG4rYoiYTdgRDK5udTdmu3t2h+WyIE8Zx8ORXLUYaTxZK4vuWM2WIyJFYVNmVvYsZvkeeebtpOBm/Eoi/YJ0mDgSXDfUVncRigTMMwnHzdLtr71tmv4JUfGEYM9vB6AG+7WGorQ4rzbqYEUq6BogzTkNvFi0qdZh/hUhCEbmc4UGDwJDE2wW3swN1L0lABghzuaxkciGJDGGNTAG3d4p2bzWHwb3TvDZNq30bd6urJhbpUcFAOsMuEkTGXF3RPM9iRkxBzNI2+HW0Pz9iDn9GSrjnBgLBKy1IUdA3GZ7HP06vgedhGLBjnr4tb1+aClr5oMg/mCrjzyEkjS1v4MCzYsbcE9qViDZ89+VVXma2CVAdjMviCmlgltU7xJUH78oqKYotQx1CroOVQ0SYfvpcKASyjbDlPLomNgddjQBxGSwHDmwQJ3nGyg67s93WJYVDx7e2uzebKV7M96LYp4zYGTubVuRcoabz/F9MwNl2zY6NprFeq3b1divD7VFjE7I7SaZ5fpKSkLx+e6eh0bZyPhPCQ8fPsTDBw9wWS7D4bZWc3ztsC4wQiSa2zKfhRgCCZhGar0+QtgR78Rk2NdBiEZ1iBFDA9aloMO4NOgcRVlh4vLMHDlyi4ZobeuG1nwUlQdPy/NjFEQyxUZwrZEELFbECATnu3vQR2baR4r+/Bvs7Q3C+XLBO++8g89//vN4/NpjvPPW17n2FQgzifiKBjRyP2LKaFJsvOrcLD44bsuuJr+MHqRonBo3YnPjwaYCMYfeGDNyLMN3p1YiAOfzxcYwFvgmAU+ePKPdfTZOi8mvQ8rIxuWAAtoqE94DuSOX5cJCQwRlKVi2FYfjhAcPbxHTAdM8mVszC85SCgMmhSaAPNgyQztTwrauuNT7MaoWOMeB41cJ5Fwty4JmWWOerZMUWNfNnoXEINSsV50+my5K7Vk4B0fQKh2GuUc5qkvkvfaKujSUWjHNEw6HeexbtdJ3hAhTNyWYF8wROZskupOUfE0A/cOFijd8Ztuf6aHlz4MXKdySA1qz/T1H9EqfoK0UFgPbghDJC+PYWRFU0JugdqBU2k1cLhfc3d/heHPCze0NlVeNzud0cg6jcSUKa5wUCGrpCBPRk9UUgdfotkfEiAif08azTe1e8PnJKFLQjRrgis3WIj7p6zNdoBA+Bh8uVZTaUbZuKgJT5IhgmqMpD1gBinVxKeRRmCQncXYLElTOE11i2o10qgozuKnGddn5KYNMa66FTmYdWIDYmx1YwF6pd33ewKarmDx4D5pTd/pUPmQ+P3UyaohcOFF4YGg0qWpT9GC+In3XwrPw4XurzT+fQsH5pVf4HHAZNCGyH0CKQViDzXAFe6BXdDgf+wPr14j3jHyPAWXL7jfAcsRURsbdGOZI/vMUaFvEsK2mmzWrVloiQGz8sW17BoSqmgKKBRml3Q1q7zvFhJQnIBBOV9n9DiDuhaPjc6sTJe1a0EGSyBbHfN2Ip3FAu/R/oPIqhITNoN6uHcf5hK6K8/mM081xwLpDyRE4wmyN1fnu/5EsB6YZn2AvZlVJkpxSxkfPnmFdF6SYcLksgAI3OcMJmykmpg134HBgQm4x7wIZ987GN9oHKugjTlz9N9cTr5tiv/4An9MowfgP0UZvgpQD4wRyAjpl9ySOm2ICIP8pML+KkfGCbBs+gnfWvJ7jnitt1lsvCGFPAr+5uUHvzbyGmH8y5OSdHW2pBQISdN95+208fv11vPmn/hS5KBczvOMQgXsMnMy7E+2DCFKasKwLoB1JEqQD+cCu3UMvnUzeTe7MlGdba0qpr6f0OmLhdv3buuFyXu2AJw9umidsa0GCqdYcCYv76FgdFekVCILjfGDOzrYhDjNAfl/ZCp2WJaCLkZGFRez9/T1OxxOCCD+nP/PWwHTjWuU8DQdoFspmxxAnHGfmDZG7U1Er97uU9rTuaGaQjlgPvosEHCZBkIqa6+BKbWWzJoQHf05MD+/qkQCV1v7WsORMxdi+J+95PICPpP0+WACsdmsEd9NO90RRc84e96zve30wTyvVPYXZn7UQiXapKqacseWMrRZy1nqH9G68q4iUA/f6tifYb9uK2jZcLnc43dzg9nTD8U4rg7jNZpNkXhKG+7imrSUoojVKdYx7QnDOCXdkUrmsgIOnlnOtVZArRKmP4pOXJ5/xAmUrlbbv3qmDngLa9sMwGHqiQtlwSGF0csAO60bjXEgEJFKKB+ts3IdBQkCvbZDBqE4x9CS4g6aRRO0w8zCvECNE97m6GHem1Wod1F5hqgK90DMBAuSJ6EI3dYEaelELZ7z0byAc6C6mQ5IX9oArsWsio3vh+/Uih5/DQrJEqBoQZim4WmD4RmBQKCBmZx3Az+sVBxEnHV8odtC2bvGGI3tlvyrcBPgjtHtStR8uDqcqR1sQszGncZhASNa11owPmVixaR3KMOHbPUyqzUbZDXU0aejAIPcicM1wjJit8AnmwtiQ53mMMIYzqQiW5WKdeR6Be45CZXN6ZQFqCBqAWioO82ThdcDpeLRcD1p+A+zAgjiywHXkRauPGwYPpHv8gXWi5r9D1n8aHicuj14uF1uLvCMxRiDusmEoD6Kx8dp9UzP+Y2F2ZVjVO7pwA4zCglW7w74AJZ6OEvFn0d+EbrrrtqGZ4kNwHTgG1Lqa34/5Bh1YFGhzR+H9c/DA6EN2DYABgvYzMcYf+0HULbuL5N5u/KAj7u7u8P577+GNb/kWvPTyy/jG177Oa98AjeY0imZrb29gVHV48qzbeRS9vTUSoePe2fthq4aiXneg67KCh6OFfdr1EESkRGv0sla0SMQ0mUcImxKlek153Ym6Elnw7CStwIKVHkFBsFzucTgeMM00S6TMGwiJ4yS6mMKUKCsuy4LJRkVOpCSaSRRsGPiBKIkqUbPt2crmSWhMGSSiarN716z4T6iloZQVIR5wnG9sDfBZ7DZ2D+KjHEYBzIEk4LKxuUwxk0Ru90jBIqKsK/f4qTGfyKIWnJvDcVI0noYrpZzT1YyrsSuB9iLFEUeuxT2UENhrFRmo5Z6H5llgGOslRYsxqCSdM2KBKOiUEzQZObXtVg1lK3hanuByf8bNzQmn4wl5OsDN3Zwsy+d2b8qdS9arjn3FGyKqFLMJQKjaKiZtVrUzIUZOJYTuy/VKLfVJXuGP/5L99cUvfhHf/d3fTSj44UN84QtfwG/8xm+Mv1dV/NRP/RTeeOMNHI9H/JW/8lfw3//7f3/uZ6zrih//8R/HK6+8gpubG/zwD/8wvva1r32at3H1+8CkRAR0paaE3p+UxqYULd+hYds6tM8QTITCakdrK1QLVDe0tllHTXKdd+wcD8XBplfwRmZTcnhc+U7qorQr2Lx5jG0MbfB/SCKiqVspjbLZCrSto212YNqmLuIoAwuxVhvKWkfwYQg8TA95wiHvHBwAVrzA3F/Nr8A7W3UuDbNK/J8UI+bEwLmUKPcUvVq42tD1eeMoqKuH+iBCUXVC51M+tVRPRIkQJIi3haAYeHBgJNjPc4mmUv5dea1q66ilY9saio1KvNhwO/oQgxXs16S18f8AJaE3xzQ8Xhw58Xl9kDh4SiEkcgpSBkl4G5blMqSWKSbM04QH5sJ53T3l7OQ2X0vByLf8/LtSjB3Ytq0jZfb+fIfz5d58UDCI166aqENN0+EmfwLfSPZgtGBruvaO7MRBqG3AHINczmfcPTtjWTZcLivWpdK112SHQcYdsm7T3kdvz6XZAhhme9cI5M41MqO3WlEK4+eXZU9xLoV2/ufzGevKBOFaCXEv64p123BeFtyd77GuC7qRHu/vn2G5LDifL7g/n3E+X7BcVqxLsXTZOoh93BM2+4eqvXXZsC67S7C/d1VFKw3rekHv5KB99atfRWsNr7/xJubj0VBbDGWE2iildHJkvBmorQ7EaGz+vdkB2dGVRnW9FRtLcb/pyvvdQNnxWlazl+c+c22f7h5L08RxS5CIw0wicvLMItCefisVpe4Orxz1NMqQS8VhnpFzssKVGUerjWHKVtBrGwqgEAIePXo0xiwOlS7LQhI4FClNxnOREQ/hZmlELBou6xl3d89MAmwjRWWCbq0FkI7aCu7v77BtKxxlLtuG1Z4dxhowd6ZV5uq4sV/OGeu2YV23Qdh1V3GAv2ddLjjf35HrB4zP4uvTzwUA6H0f/QA6DOf250Hs61h8uN09Az49p4f76e4XxbOEYoxO1MQKIHRFjCREp8RzDtKxLGdczncAGuYcMc3udL4jN+u64uOPnuC9dz/AkydP0JsixxkpzhAkqO7oozf4U6Jrd0z0hCqljMKGPJMKxq1Q5UN+nYVGmkJKAsel80zTuk/6+lQIyptvvomf/dmfxbd927cBAH7pl34JP/IjP4Lf//3fx3d+53fi537u5/Cv/tW/wi/+4i/iT//pP41//s//Ob7v+74P/+N//A88ePAAAPATP/ET+PVf/3X86q/+Kl5++WX85E/+JH7wB38QX/7yl8cN/6SvGBIC9ll7b84mNhg80jSqdboyEoYHFDtE5d0SCUL00+BLBhrgJFh37WNctSlC9Hr22AZ64hWzKhcROyojRVnHY2wLSAvPbe6cjweETPKXmkKm9466MmE0xYiQOd/POZrfgGncK1NTo5Fig6kGBrQo+4wdnaMGjj9IfyV/Yy+MxJkh6ix1P2gs1NU8LLwg8xc5I+T+BO8wUcdn8c86Rjeqlrq8u+kSZaLNtUMrtQmaQecw07CgiqDW4aQZzeasKaYrlQjssytCSISU5wnteMDlfGEwnqlcmvKAWbcNKuzeaZa0js92ur01zsDuKqv2HnKeh0zQo+G3bcM8H7AtZL7f3EwMqwMRjARgU8VaLmZc1bCuZmcdCM+79M+hbZjleMppeFM4CXCgHtAxOqD8OA357WGesRjETbVMAlQsO6gg1YCbmyMUMsY8gKBZwJwjDUSp5LlIBlUjO7u3iB32RBYFrQg6MAjfgr6vUycKQi27ip2jG0RJ2A/8bNyTZV0RQhtoiAiJk0mcS8Y1VreCbrEGnkLODB27BuFgZF5/fpQW9DYWTCK4u7vDV7/6VXJRHr+Gt77+NSsSAPIs+WyTlF5RGiXQtW44HmeT8p5ZeCgDLLVxzu+fMWWxTJ44UqS1FCbXRprdQTrJwNg5QK6UyTnjUjiuzZMpiADklKly8yJSO0Jgk5WRUJUGbw0N8zTh4e0j3N3dYds2TNMBSYjIZFBNWOrdcPQNIeLhwyMulwWyLug9ovd1ePWQY5Ht93K/TnbwxWxZO51k164FKRysIOD+fT6zUJrnCTEGlLai9YhtK5BA08KW4ti7cze1ZbU0a3NQXjcWYPd3dyS9B0HUPWBQla7FboufjJjqyE+tzUa5wRCWdoUE7oozR2uJArFo84JMJA5khntqGEWUoxeUMxdIY/BikGAeNnXc896AeSbpvpSK5XKBCO0EpphQA++nI8q9Uc6+bBfcPbvH6XSDm9MNUs6QzmKHe3cfBZJ0MblzxFa2geoO4zsbJYs1iIeDGVZuHA/TniINJ+BP+vpUBcoP/dAPPfffP/3TP40vfvGL+L3f+z18x3d8B/7Nv/k3+Kf/9J/ib/yNvwGABcxrr72Gf//v/z1+7Md+DE+ePMEv/MIv4Jd/+Zfxvd/7vQCAX/mVX8HnP/95/PZv/zZ+4Ad+4NO8HcRAwqIa+9sdU0XikJTW2hAizJuAHaqPOAY/ojPUr9TG/7ARg8ALnTi+OqVk8KX9BL3mWHhFrTZT3BM0fZZJktw+X4apVbzr9ZNUEg/S2ioNudrunupIDpQdTXROCWDwdUOMMpxqiSqqGVep+SnYSCrY5LwrR2NXRFsf3RDe7s8VEs5E8YMCA+y3f5UdnKOrb7M0224jAiCENCRq/m0+FvA6sXcAEqHKEYdLuMe16p12y5uiiG0Y9l5TSsjRGPM2OnJuzPGWGR3FxgeAz1SdsJjgPg0pJ5M3VmDp5vDK9bVtlMl64bYsDdNEc6llrXbvq8G4inmesGwLbm5m61ICpeqQcQWnPCFPeQ8AU8WyrMhTxul0ggIoC+FwCBBMnu5dNLkaPmZhkcc1S5daGtDRqCokIIUbqJKzcjiQs0Ci43mM0krZ0GqzcQENrt311h12c57GtRwbtIUXUvEjtjasaIoRDTIkv6amtTEmAww1uGdI3NOwWzNH5oBSVkYctMmKKj6/KSUq+nJGDKZkg+ISAzYbc6mywDnMJM3GFgcaESO/XzsskFTHc9Ajcdp33noLn/vc5/D41dfw3jvvDsRPVegDNJ4RmiL6oeOp18MLw4pfz8UKMdg1tu7eeCKroUfio6LaBio14iMCSavOQWq9GcpKsn7v5MeEGDkONb5d8bF1jEgTAMs4c5uEeZ4hlfvElCcqPazblxDGml2XhV2zwsiwinl2OStThV0F4g1Qa3WgrzEyk2jKkx1sLMrdXOx0OvkGQwJ2iFRwdUVVc4uNHCewIQvICOiZPDOQT4/Tic/3+XyPZb3jWjF0uQ2kT0fAZKwNOSfkaTZZN+MJPCWZRNpmqGUchGeuhzrer6sWnQR83VD49VaL4iBKEyCSgZBMZszE7q1uNFvrjeZsxq9jQOKErZA8XrQR/b36PSJKibIRm58+eYr7+3scj0fc3t4imQnkUFWBxqJdzfB0mnA4HA1JaWM0uo+IXJLMVOm4peEQ3FodeXaf5PX/MweltYZf+7Vfw/39Pb7whS/gf/7P/4m3334b3//93z++Zp5nfM/3fA++9KUv4cd+7Mfw5S9/GaWU577mjTfewHd913fhS1/60h9ZoKzrOtImAeDp06cAYBC+jlEHoSTjmCjd7npryEgM6TN5q3MbSEqy8UuFGVr5gdwGtA3ADjzKTLl099GNkzK6owfSaUgV6F+h2hAlolq321vnaErNHMpSTGGH7h5ZzzljqwVBEjhqgNdVRIyiyYXV2f67ft+RHe8IRMhDCcaV8Hn3UDZ1Bd0CZIxcFDIOviAuFeYrWDUyZqxKFRRkx6H4+zva4Nzy2khI4/DcERmFO4oy5eHKD0Vc39ONGOnzUXa2rVMaGyOD+jxBld9hidUhQCuQJi9eCdcnK17cNtrlyWUriCkjAFi2jc69QkKxxA5pHtsOeCKo85ZaA2rd0HoBJKJ39zZoNkqixTUkodZu604Bk6W3yiKg1opWlcW4jYhSjDgcD6a0uAchbu+iG6ISAaswHoYREbeNBlXdErt7r4g543A8YFvMnMxMoVwOOR/ojbEVJkLnnFEvexQ9w9b2gmTnd1mBElwWzLGoOz9zc87MnjGoWEIYxXfOE7RXiLBb3h1JYRwCysxjcMUASaGz8nD3xiJEK4ayreGkyOBnhDkR+8+dQaStWhGnykM9dIEG5VzffCVijLgsZ7z77nt44/XX8NLLL+NrX/+qjQ4FKe6oqEPl5JIQ6ZmtgG6tQhusIOFT4+nl/r000eOYNqcE2HOmttbVipveGlqgIZojbCkmFKUMnIW/jt8TQxic8kG0Ttn4XDp4b7UQzk+ByMmyrJgP9KyBEaWbedHUUlC3YuhmGAftNM02SjYuhcJGEJSll3I3CrWwRqppQsC6bLbHukcShpyZI1MiXK0J1ksd6zaaKlBtHbrzcwhiiEwwU8GAZTmTO1EUYoRUwDl3RFJ102EgOB9nQyPFnnvgcGAGkTs/O1roRQb3qw53z3VlUIzR3GmLjYA6ROpzDQb5SGIgtSKkiBxmlFaAHpCsAXbPITdjrKb66V3RnMAa+Ow5KZdjGJ7nd3d3LJ6PM1U/00ySeqHlfTNqhGrCNAXkNCOlNj4nn7e9OBbxZt0ccaN5o6w7F/OPe33qAuW//tf/ii984QtYlgW3t7f4j//xP+I7vuM78KUvfQkA8Nprrz339a+99hr+4A/+AADw9ttvY5omvPjii3/oa95+++0/8nf+i3/xL/DP/tk/+0N/Xqu5wA6Cp1nAq6LaQrGnD2jC4DLwQWVF7uQzQsAKd9Dc7c99jDOIW5EOkb37rNkRAO9/yaGYjJdQ2zbeb2l9JEKKG1rZhuYPhEQgZRlpyK0WdCXJM0AsRdlMl3pBKUwgvSaeiQA7LuNKCrVrZGOjgWYYMQ5EItjc6jgoWsPeyXM7BAmRXilRqgfQGpxuvnz/jqaoYpD8GthhiBlhOdGSmyZJrk0bE1nVHTmvCkL/geC8XoQuuqpU4eQYUVTRK8ly153MLjWOY+M6TpTw5WlCWbkZxhDRLIX6OM34+NlTtK7kn8DtuZshBk5OLQNWX9fVvB/6CLR0f4x1W3kFbf1cLheiBzbKY/quqcdaY2YMgDDvpLzpcMCUMs7395j6NEZ3Hp3Oz9p23wbQE2JZF2h1KJqz5EcPHzHOINAwMGjHti1jBg2F5W8wj8X/PJs/hZMCYc8OSbIeqka0cIzXdCdsR8+Zss/bpmjjWI55bm5O4Mz8gjmR+0PZqP1cBPQWoT1zndaNzUkMhhIGRt0HoeOwdFStKL2iqwC9IaiOkZLzIkIAtgq0wvUYQiDaaV26BkHplZlVqvRFeeUVvPHmt+Cd997Gcj4TIQ0ZEcH8gTqqtjEqFliRla4Ooc6DkCm7Op7TEKlUWi4M3QsQxBxHUwR4cUPzwtIKWwxx1DbaIZghgc8fYGGgMSICJDEbYupPuMSAaHEV7gyaYqIT90Qeihes3ijElIzczfVBojJMvRStIGsDyRVhIR3miEePHuF8Jqm81oJSuI4U/QqBBFJOcNm1KlElH/nNMx1ey9bQI4zQypE/72Uy23geBRIC5pljky1tKBuVLVR6usBiD2rtpQ9C7jTPI/fLuSVeSBNZ6FdjX7Wxlu/Bgn3sw6YvJfraDBNJJzVfnS7F0BLK+n38bjEAWlF75a4beSdTnBAONKIUkx635hQI57m0wRVRKLZyQW0blssZh8MBDx48wDTPlLE3R/vE0CPh+BN2zAaO2r059sbW6Q4cYU3QTw6gfPoC5c/8mT+D//Jf/gs+/vhj/If/8B/wt//238bv/u7vjr+/1oIDGNXV/+71x33NP/7H/xh//+///fHfT58+xec//3ns8IXNbVWHY6FD41EjNFLfDWNr0y+FlaMvhJD2nBp1DkgktBtjQI7mIVA589sLAgDQYcomIoTTJQy0pDamQ7aGsYnCRlJRdgliMPQkBBkEL5GIZGTZGBMDzTK7u3peh0SuFOdC6JDrCvieHAmBjUBYHBHJ8BRlbnJWzDRFUyeo7shQtyjCAIz3G0SQvNtTL4hskzOSV28wuadwjg+bbzrLXJxTYXnt6uZFZiIH19ezJGoGbScJgLAoba0ZysIDQQJHJTHwUHNOwzRPCMmupWCYYuWcUbZiUmOgtIqHjx7hvC5oqnj46CHvdA94+vQJUt6JbPuBEodrJrsHoiDd1mWICWKFmxdPTlButRqBj6MXiGBb6SHAjYpfq0qflG1ZUEsd2SjuH+FESUf8AMv3MTNDWps3GlqpjdSgIDdcd+M+IWxdKg+iw8yOlkgHu/pt3cZ97V0NWfJi0JBJpd67G7nP5j1GYhb0VkZRCJhU3bp/dnpCPyPd5dIcFUQU82bovZq3h3G7ekfpiq7MOKHyOWArzQ4BQaumOoIgpW7Ij9goyopfUIGgHWaAxxeLL0qf7+/u8N4H7+ONN97Aa69+Dn/wB/8f7jGutOKHQjO1lKvC2Bnz+XS+DBEbFm7dYHI3Okw507FXATQZHj9eXE0TJdWlbtYUmQurOtEUyHlG7+tAJHlqBKBVIloIaNpQ6ooI8wNSa2CsC4+SkNME7TLQjZQi1mVFSo2FWzfpqyFvSJH7l4WkduVooWwVNTdDhGUUZ9cnwbAlsFevHEu6GWbvdYxmg6HCtVg2GDJCIEciBI59Wu9UDaCbOzVH4Rl5jHX53jZoEMQ4UZ/GhQVXx53PZ3hWD0nvHb1v1tTG54qLGJP9r32G7nllbFS2zdf67nS7Uxb2D39Nmr1+Nd1HMa5iVTWlnPgozIIfC00lK9w9OyFGJ3f7+J1Izt1dwbJccDyecHNDRIXqxQ2tU1llGisi7x1oyZy7DUlxafrY2B0V/4SvT12gTNM0SLJ/4S/8Bfzn//yf8W//7b/FP/pH/wgAUZLPfe5z4+vffffdgaq8/vrr2LYNH3300XMoyrvvvou//Jf/8h/5O+d5HqOW65fPuoCdbHktz+LIx0cddAYVNcIqYA/3NDJTmkGPXYhWiLIrSSntlbwq3D6VFABPbMV4H601lF5s87Rsn1KhKggG2UUhbpLH+1ZojOz4GuXEwTIt3B8hhAiVZllA7FwjwFl07wiShoxWr2b9IgJRc7FsgMoVFtLVveKImnRmoAQ3lVKqWboquT4Y5pY+MXuu81I4f8WVHhXaZMidDWOCBhIoORIyoyDraEX3h1sEgOhIJ2URo8a/sJFcYxGUMh9ABR1HDwda1a9n2lof5gPlqwDU5aPWTYgTebUPVVBIEev5HofDkYVDY2qtomOeT8+tu9Yq7u7ucDweoEr3Uk8JZgfl5lTyHEfE02E59iDJ2REYjvsoAaWlfGFY3LqirmWMJlQ7rbvNY8UzbYKZUnUNNgKZ0HNG2eJ478fjCaLAZb0MC3FeBwzi7DRNlNhbIWv7DN9b38ekKSXrcA0hUiYCU9XFtdOMiLojPR21tCsXV3JQtrIh54iuFaHHgd6JyHB+FQ1mx194UBlaFgOdWZlB0+jiXDec7y9YVj6HIsL4ihjRGw8ssSITUkaBHIWKsIiE3sjVUO3YWkM+nQAIPnj/fbz22mt47XOv4Z13v4GyLghWcDn6SMX9Nx0sXa0wckdqyrAlxEGIdNTPjepa9cbFxkaAqWHoVj0fb3B/p9jKNrp/VXpmxNFU7VL0kLwJASRylIAm2ErBlDKScDxSeiHyYMjYPE9Ylo6y1YGQreuKkCMyYBEghh5VHcjbzlECzndnpLTi9sEthuTeGjrpuyx+jMUNcUYPA9kDGlKazSOl2tiHcQnbxvDAPE2Yp9mahh1pbsbr61cS/mDFeErR1EAbpsMJiIGf3fbqrp2uztV+/jyhd4wRT4xu6rgfxru/SdjPKf6NcVLMu6crqC7dm3eS5veRYe99t0ZQ4HrU7s0aCddtfB85Nvz+1SwF5nlmrMc0sXA0t9je2ajU2vDs2TNczgsOhxmnmxMOh9kQ+DYIvqpET7ruYymiSzo+x3iPeP45+N+9/m/7oLh06Vu/9Vvx+uuv47d+67fw5/7cnwPAm/W7v/u7+Jf/8l8CAP78n//zyDnjt37rt/CjP/qjAIC33noL/+2//Tf83M/93Kf+3Q4leYIpV2DYEQm74TAYt9V9oQcJiFMk+1mBVs0+3jrbbPLhmNlVuKEb1DkKNm8P2Bf3FTHNDybO/us4uPdKkiRBUW7G7h4ZY0SpBb0ooEAKCdM82yFFm2sSFY3caQ2vdh1BfQC9UcR4DTzjbYsnCcUq7B2hCMLYcXFUyB4szzDaJz+7MkNGl+BsD/67ghtzKw3dPWlgo7LEAzfAfDG8QOOFhwZCw9Tw+z0ORrBl0CCBEwZmdVVDiDjiq6VaSF4YBVLrnoc0wbv9tW3w/B8AmPJEdMyuy8GCBWurSL3h/v6MeTpgXS+4ubnB8XgcD+L9/R2ePn2Kw+EwuqBaO6DBigQZs/beG6TtI6/a9g3ho48+wjzPpvaZbPwhI7BQPZMJuy9Ja7TATmYxv23bOOi5JunUiZxQtgr0guPxgONxhkBwe3ODjz76COfzmUWUFYutNtRSkacMCMb4aOfawN5LMF+8jnma2SF3zuWHkSLcHNAKgxRNBWDjNlWqMITFcCkdiB2tOX+La7ibMm34NqAPaFqVtvQ5Z7o4Bx6SZVtZDEYiMfCiHERKJQR7Nqh+C60iiKmJbAwyzTOSZgQ7fFrXIbNMMeHpkyf46IMP8PLjl/HK41fx1te/vh8mEPPoqONwGN21PZMhRtRtM6SJBPvauxlpgeOEacI8TViMaNu0ovqIysjzIfJASjmN72U2Csdvl8vF0CtfGwHzNCNIwrKsVCilbATtFa1XiETkSGGAc7ZIZlUc5sNuWhnMcn/1XCbdD9VOc0sfE/mOMc8zVSfLYk2ijSSbjK57v15Xh1onNyhpRtkqlmXF4eDclEr1Y9ptHbZtG89LcYSklFEURlsLaiToECNOpxOOhxlPnj7F+f4e82E2qwk+A8Gaz1rLiICYptlQbJiNAN+Tpzm3poOA7QUsn6c2/t6t9lnIySgE/HmD7LJmdy73/Q8hIgWaLarSRsEL+jqMFjMOMVDt1Ng8r+tCuwVDVILYpEBJHOzKQrTWgstyxjTNuLm5xel0gxQFW1nheT8Ojni6t6fUA87FCvgU9cmnK1D+yT/5J/jrf/2v4/Of/zyePXuGX/3VX8Xv/M7v4Dd/8zchIviJn/gJ/MzP/Ay+/du/Hd/+7d+On/mZn8HpdMLf/Jt/EwDw6NEj/N2/+3fxkz/5k3j55Zfx0ksv4R/8g3+AP/tn/+xQ9Xzal5jnhxcMXq86SU4CZ9Kl2IxO2SWknDFlEgDLug6SUUoRx+OBNtFBh7R4eIqJYwCUrDXdfQ7cFZEL1O2LHUjWQYIUJ2dALfukw55bIhjGdk9pQoosSPgG4kB7IIrDPKOGanb+PLjVaiCHjDlK4qY0DKmsi2i9syORAASBCKWOaoWfF15q6IlaMeBEKBnXQsf/V5giqjR7IFk1SVdu/M26xAjyeRQ7OXlc12DGdlb89WbXjdwdt+0f1ush4HSY7cA3ebdtHr3x4PSU21rb6IKdAKgAtloIwwuza0JOuCzr6IJEfLQE3N7eDjTn2bM7nM/3ePHFF/HCCy9YB8UD2YPKUmI6K4RdMwnfVwcVOHevrbHzyswOcT6Gd3Rb2b/Hi3OSaPfIBR/5eIS9iKfPCj7+6AnKuuLhwwc4HA5EkSrHJFOeTJ1Eq/x1XYlEWeFTa7PQtm7qMJobeiHeNQyvluCR8wOdZ1drkCZcMuxclGgW585HIdqovG6Fhbxb/6t2ZOFh4bydAMYW1FLNHyJjmiZTwJkBVRDLxhRo53WJ4iZ3fL9dWcyiuaweUDRAmSETA7llvSsKuB5y4nv/yle+gldeexlvfO4NvP/u+2h1A3qH1ELIvSkQE+f9vDRWKDXfVhg4WeMgfbetGgK7oR4O9DUJwbKHjLtlDQCRpIpSt6EOA6i4qU2xrcX2JqK4ITJvy9OsnS/UTc4vgXbnCBE50fEXCePrye8LFhPBBi9lwZx43dd1Ne6ejakk2EFpig+h31QQoX/JerGDTMazsSv8WOBrZwORYsLxNAOiWJeM8+WMbdtwPFKUUFtHhOUGqRM1+bwkCQhhYlFYqK65ORwHolRrHYnIOUU8eHCL8/lMA7fIkaFckT/d4sIN3vKcMU00QVQlYuhqoxj94HbENNrfWf6RjfXdDmDQD4LzoyLFHspgW5pSBmMD2j1lRzSKghjpPdJqM+uGOpQ3ObNIrLWjrhua8YcQI2KGoaOW8h722IxaK5bLgqf5KR48eIjj6Tg8bFqrQBfGrCQx/6LL2I9yTruY5BO8PlWB8s477+Bv/a2/hbfeeguPHj3Cd3/3d+M3f/M38X3f930AgH/4D/8hLpcL/t7f+3v46KOP8Bf/4l/Ef/pP/2l4oADAv/7X/xopJfzoj/4oLpcL/upf/av4xV/8xQH9faqXHTpjxMJyE0z/pfTNlRDozHmJYjPHaLb2pYyDYEoZ8zwxsjpGbH0dnYjPzdRmb4jRwggr0YGmgLZBuhsdpMJm0Xa4B/+Hc+QkcU98tI4854QuBtWHDg0dMYipVNhNJ+XChDJDxx9AGJw8fp51l1Bhl+DzS+9M7L12iMMQCFbhu1QPZiIVhBvVKAQVNHE1p1kVGZ3ftQsh5VYCDX6YKdBMhSQyNhGo5ynxdyEAKpzfd+Wc3glvqoopJ8zzkWZyMeLu/pkhNAoERcyCnKYxUqiGnvVa0XS3uW923eecMc2zdUQcHzK3JWBZVqzLPUKknTcNh2bEeMGLL76EV155BYDS/0Fg3Ac7jFPEsm0I3gEZ3JkjA/mwuRulMftTGmOqbPJgL0ioauItHAz/EMbmTpXCBhErMtQcPbcNW9kgQmg8pYRe9/HBPE+AhF1yXcrOQbiaefvheg0tdyi6GMlZFBrAzdPGjEGAKFSOGUXluc/BLr8h5YlFf6+QGHF7c8S2CKKZexElYSdIS3523FGAYwjDudedPunRwLUlgUVHiJSlus1+bbRB58HAxNpWNpvz29pszJLqhsSJWPZJpycRVPHs2TO8994HeOWll/Hq49fxjW98BcGu9W7cx3HXcl4HWTOa0ijGiGAorhpPItges9nYsrXGIMAQ0dRTg90CQYAuZum+jn0xRap13CqhN77nCB/j8pmLIRpBtMG7MfePqhrpwxPoxeHKSQ979G6/bAU5CfdWANLN8dl+f87JwhxtBG3o7RhBxDQKeNrRJyuGreHqRBwG5yQKWk6YG2W12jvmwxHBEo9JlPYAR8HxOKG1jsu68H0HutIu64WI+TQhBD63ZavkVEmwmIoVl8sC1T5QIHJEAqLttaUUFDMym6Z5BC96XhafWXdOxji0Q4h0rY3MkyIPhqpV/7yA2MTeR+8sBohI0nVXCNENtGhwJAOQpohQgWKOsAgK6UYot/DI2ljE1O5yeX67u6XTS8VHXA3rdkH9qOJyOeJ0OuFwnBFDxFoK1kInYheNjDFU3xHMT/L6VAXKL/zCL/xv/15E8FM/9VP4qZ/6qT/yaw6HA37+538eP//zP/9pfvX/8sVxh81Zex//xJDM0jtwTmb29CKUtoltmk6c9ANlsgNKArCUC6o26wjND8QKEmbsJLCpsHC2sHMI/GHxzTKaNFACLOSMMsQg3CyT7sxt7ewWg2Xn7FI660W1A9YBphDRYqSRmMhepFgyJesN89fwUUpXIMqQYneDItHZTUWxLJ9+vRgNorNOE9hno140cDaOYTXtx9h4KUceEJquNfv7EM363GD2agWKeEGlff95I5BLkELC6cEj5lOUgrWWQSwETDkQqBJIOXMGX12WLNDKjk4DuUYxMbAs5oTLcjGUy+3PgXVdGLx1pP7/9vYGp9MRrdF74XA4GHwbRlqpF2i1cvOSym5K0BFTtgc62dpklxhkL7iD/SwiQx0eTulFp7PjsxEog4SR/TPNNGPj5rxhWzccj0f0WgfPhY6UHpwXMey3iRcDLsPvz9tTO1eLtaf47A8pEw1pzUixo5Li+pMrTpj7xtRaIMlgfwdZIs0NQ+CYli7NXGuw9UjEkL9XBIhJqGIpbawBMd6TOPzpPCcomxP08Xvdkn+eJyMKd4TAcY7p2agGMj4HjK9UyoqcqQT72le/jpdefAlvvPk5vPfe26il8DDjHMeQxOtDB7tiJNA2nHEIgLYOSUSJKPOtaF3Qe7L9YefNBHuWQ+La8PwZIl/mlZSS2Z7Tyr5pRwzk241zTKh4ckQ6BKGhW+sQsVgAK4ZLreTJhYiOPQvMfYVySkM+PkeidUCwUUkaDr7k6YF7lghyjgAmnM8XtNZwOh6519o4ztOjS62IKmZkxj2nlIKYEnKahw1BRDQ+yYreszUcwPl85vg0ZHOoplHfNM9ACEjzhNjcmyYAs5g3UBlE8d4YR+IEVJgAo6yb2eXTdC0cZht3YOwrfRQB5JsU4wxRGfg8B8+LmOvxYO8Ks4TiszWUSjLQF9iZ5X8eUkQGR/RoREi4h3R0ROTAvTyaZw1asxgFNU5NYoxE38MNS91Q74iYHS4HHA4HzMcDQiQqHVTHOgliPJS+e1/9ca/PdBZPimkUDr6xRgs6E2MQ996h0iCJviRp4oYx4EKT7ebJLKC1Y90Kw8FiGIqY3nXkoeSUBzwn5pw5CgybkzeLACfMz5/LCHgzfzPiaesVMdOPofaGUMOovJ3PUEpFEG6sGnePkyBk2pdBRNFB9PPuJoB5HuN8EaILHbtWf2SvqEnyDJp0N1Aayu18EP9RwcYKQWFzeZJV/XeNs85GQM5UIfve80k4c21CjkprzeZURKBYRCogiqDGDO/82vVywd3dnZExWUChs7CbcoaHpc2HiRBzrZZ9pGit2EYaR2J1yBFbpcOmGCmaIXYKd5Q8HGacDNKkZBjw7oZFxoRpygZn0w132zZ0VctUKog5YMosQtZ1xbKsmOYJg3AdWDDEEFAVFvhH3kPOedwXd630omj41VjHSovvPXE0SEDdqFAopdgIzQ7gTiv1pVIJA1BOyOKJ3CG34Vb4yMCqbgA7LyU8d/+9G/cCIMILFBb1ZfMDUBAbixyOrgrO5zOL8N7opOmcsg6SQi1gsJQV7uBb6472jDTs4GMGFpy18x8RFsGlddQOTMG6eCkWQc9Dp3YOH2lTTm4UvBg2wjgAfPzxx3j/w/fx6uNX8fjxY3zj61/nOo+CKU2gfk2vro2bA+WBtIVAlK35s4CdtxJE0GpBnIKRkTnqsTILbjAYY0JTjjEp6wWcQEl0+Voi7sikkFjcG5LZNcScoKWgoSMJhikfp8IMaazbBlQS1PNkFvb2s1PiqGK5XBCSHzWKPE9G4HUzAsssMnJoShkPHz7Euq7Ytg1JdaRaSwB66bicz/bM0P3YCcHLUtAy4ERS8rlmpvKal1Y0iW1vbGanmwDVA0p1NQzgmVLdih9IRE5HpDxhXTcSZE2qHoSKQH6P5TdZM1QqkZic016ASzDvE97/ka7cu0VcuCJJBrkYxuPypnee/Trvza2P9nK+kkZ3R2xk/O6UAmKO0EJFHLl+zdBwnp0hRsaIrBvcgM6b7Qm7+hOgEKDWirv7pzhf7pDuM25uHuD25gYpBpSyITSSAOiJ9f8QgvL/by83nPIODo6kBLOHN0jcVROD4dxB1COya51mS9ZUxVabkfEixPIZiiVhio0VjnO2WWRABzscJ8f27p0AHSBjcvMpdnnRZZTKAinEwINX9ywV78Rd8iZiQYAAPI0zhoAe+tgsBrPbVDfo5AI4muKXCRBz6jQZG4gmqbLgQDelisHiY/8Vr/4BN+UhssN5effN1tJN++g8SdyVwKKi1zaKDt94xf5R+9nuBcev85k3u1hK4KjEqLUN7V40cpq2jqCOBhRmSdRsM/UNtTT00oDGkU6ep0GwjSGgBbXMCUv6NJ7H8Tjj5uaGReec0FFRVgbCNZORe5ECmHV6pdV9KRWPXnoBtbJrPOUTSYv1gmXZoNoxT4Sf53lCrc2KpgTV1XgUMKt0QzPG+MAQMlXjInkBEMxFOYwiq9bdVAmVn7PWimUlfN1qA5SF8zwdxswZUBxSRprI2WqVMHYtJJFKiKi9Y9J97DPGe4aOcaFGpCEDC2hdadk+JMlOwCV3aV0LkAVtuyBFRjm41DlnZjpp6yT4JvIDtAdIskO+Ew1QVXp9YFfVtdYQEn17StmoNAmCUgqWhdLyYMWW2s8RTjt3FCeQ8FfW1QieHW9/4y28+MIL+Pybb+Ljjz7CsydPEbpAcjIH1V1CLzYakMDu1OXpBhbSyweKw4FFL4skkhW9WOTMnz+vWvifQ+t+2SGGUCrHxxCu2daYa8VRUMaEjNbCGCXSK4prYMqZcmlHf1QHgb63iibBGr0JoVS00lC1j4KydDdpq1g3jjzCZIg1mNvSQBKMasWD21ucjkcs6zLQABa1HN3VKjC1rh3K+/6+bduQ1vZO8jT5Irx2XbzxpLtsTOQkhhzRilpjUY0PB/ROPxV3uj3MM4LQg6WZT0yWbMhHQg99FDa9NVzOFxTjlaVkidlGnr8CJrGHbGLsj2LmmF7UO0E8pTjGe/6sNUOvPR9tb0CfZ6UG5XQhBIWEbgrPTpRLjKvYaYKYs0uvV9RaEELElDLpBiGgVP4+V3ICfB4+3jbc3z3Fw9sXcDodECczJeweCvrJXp/pAsUhbsoY+yBlAeBIw+f96twHjg7EDp5sZLpk1tKt2lwP7t7IULjW2FVMmZbZPq+v3Uilxs/okYtuChF+tEfL0IjmyyGDmMcMHA0dUehhII2W9lupiEiAGpkpRGjg55Ho0LsXIAYxGpeDTVIfA5YB6/Z90fr8X202KxB0MT5GozeLuy/SYh5WyDinwtneGIUWABYWtoH7NSEHIezvpQPudFB7o6GQcXRg7wXdEYLKzRX0ZZEuHA2pmTAlJ/caNmOHhqqOkK8YI8JZgMBETyahKqB7jszp9gYPHzwEoFi3gqfPnuL+/gwAhoA13NwcOSKxXJXWO1IkUtLskAScT7FLGQFhYRMitkFObjauICF3Cm6stUsC+d53P4wYycW5nC8kjabdS6Ns5nNi6JkHka3rZmRRsvBbbZimBI0BvRE2Pp8X3J/PKIWeFhTfCnq/8J41OpzSgRW2eTuPCaaiwSiUEWgi6DbuqiSxdu0IRqQVL0gGFG2y9FJNpigQZBYp2tF7QZGKbWOxRdO4iLLS08Mt7re1IAhJ7tUPKiN0e8DbKOT54WgM2On/sK2dOScb5dqEzY2UDGXhb6MO9zqplfLb+XBAiglPnnyMp0+f4OUXX8VLL72Cu7s7qFbUqghpMm8anqw8QDl+3oDhySOQ8Qwq7DAyM0aPGeCaMCNJ7/qveAe7rT0L2wDsJNloPLPmKhKiqNlQYYBxAjFGU7Kxg57zjLUxVwc2qgYE0sNAP7N1+KJ0Q24gp0rQsC6L7VWcTWzbNkLoJCRs6wo11Kg2pgnPgPFJhM86MPYgfr6ALnu6rp0MlpWTkN16QC2zSBViIa90gmVBUk0cECQNgu7gcnSMUE4W0mqqwAypVGmVymYh5QQZmT7dRuXM1Kq1YppmU476mPP51+CaOe9G1UaUbryntk/v5neuHt3REox1cD0W2guWZuNz+ywCICYIKsTunyrIjQsZqglRdEwWtNahTqJ3jdj5CyuU2KRu64qPto9wdx9xOh1xPJ6Q8vRpRDyf7QIFTn6zTb+pDmKVQ2a1GaTfeXNduhlDsDwVYNtWVJubK0ziZfAweQAHpCA4zNOQOfpDmjyIrj+/QbCSJaLB2WJEnmj8ptWm34EoL2e+EQF0nl37itAakniX0qESuGGOAsyKDYB8FpdNWAcr+7/ayEQwBi59X6z+Of3AEDBMzA2jau2QwEPGocmckiP7cMO57t2zOuXAIOnOjVwMqpQYaH6lCmn8OjGGuoqyKDOVztaY6szqnA9NgJMuk/07cd/SKrsA6Pj+UZSuIL+lVONH8MDXrkACTqcbM4uLaH21iHhukC+++CLEisBpyli3C0rbiErUHYY/n8+2YUTUWsbmMzuc3Rh30CPdaKNEk11G62iviKi2iSwLU1GTweN1JXSeR3fI67csi/2uecygXc4oErCcLzbys6Kn7cZog8sCjitiFEA5YiHSRz+RWhyKN1+GnEHGgt3vvit+vC0MIXDdqiKYd4UttP0ZgcXSwwi7uneNRq0Yv7d1tffDHBRtFVOekPJkyALVby4jXdcNPZP86OM6Hs4WugYS6tldO3+Kz1ztHaHrIHa7gWIMvE5Bo3XxXGM+Aty2Dd/4xlt46cVX8fi11/D+B+/ifP9sDDjpt9SsfdkRuto8zyQMEzaXp/qY2fcW8lSsMPc0ccDcsBW1u5R1D3GERzD0Boku+w6juHHuC9GUYAiz8Z063aIPckSeMi6XC6QnrkMRtCaAXpmL2agSwiYEWuh2nSKN24IAQufudVkRT+Tn5GkyK4g61maKE7QBIXmDZQ0dOlAbi/yQqIwRW3OWK+VINDkoRP1KqJhapvIks6PSHsczU9CMQxZGMrsEQeiC7sTuupt0hhiRwPHXZblg6hYs6GRsoTCjNdpcrEqiLdGUOJ5Xsfd+7ZviSiz3+9pVf2JI8h4Cyj/zVHr/fv4sN01rzRsHP6MMgRfytIIkPqu2V6cUB0rVDxNKY45UKeSwlWVBts8hQRDUESH3crH058qomvv7M3OW/heeZn/U6zNdoHRzvdyMIBlzRswJSIEqDb0aAcFGIzB2u3FH3GWUclsjA0UfOwiO8WhGTUQCmvEKaFZ0gCqhaN5oIho8OK4loRx1dO2k2sWOuvLnQP0Bl8Gnaa0haUGIeSiVYF1TkF3JocbiJmGXBYHn6XD7i1C4i59L+AArdYmKqJHPAqA9IBlZ2Ds2dAv5046cleTFZD9Snf9zXfQIoHy/XpwQNmRx5UxzXh+L3bYiDEryXmmUKFcFtJtI295vs63eeRY+honThNbIu+itWsgc556tW4JydVkyXSu7dvRS8ezujuOqEHE+36N3Bv698MIj3N7e4Hy+YMS8l8oH2ezCd0IbN/3N0Ixd5htGsVBrwzQdcDreWPF8YWZOowReREa3WsoeeOhE4K7N3HHzQAHKtuHu/g6H+YDDYYbLH9d15QYYw/DYoNmckeZiQIxMdC61oBgKlVM25Vo3aS8Nq9w9lsUTkZYY6bvQagOiYIoJWQJHDi4ls/tFxMs7fjeS4mgOMDMuR9d0J7YOcqSOUmhs5DFGTBb0V00B43LMEDrylDFP8yjwgo1SfOOex4iCknTEOA7Jy+UChWKy0UsM9N4YoYeGgIQezT/JUA0J+PC9D/Dk44/x8OEjvPzSY9w9u0ON4DW1M661DappXCMWhjsc74F6gJiRWiPx2wn3IoPPwuvUIcKxK2XSGPJ21Q4YqThGV+HUcaA7ktXE2hjnmSml2JoSSqlYy+Z3cxRM7svD3ylo1dBhdKQcWPS3BpWO48xsl1I4lpJMNGyrxaz4A1JixlBtFaGQA1JLw4QD5kMErKC1oxetsICb8gG1bTT8s3UD5b1NKUEOAdsaUGrBZVnsAGYBWCp9qPIU0WrHttDALE9MaM45obeM1XxwaozwfKtqMuKW+Fno4UTH6pQSz5beEYIRQ7Wjrit6q0gm7ed40CcCMta9bdZQKEol7cALklqvVJL29W6778WZn0Ec7XFEqkreliNnHGH1MWqH4YMAkNMMyRzNl8aYFUZ5lMEvKqUO76VrMz63gqAJIdHhy+WCZVk+lWL3M12giOxknWQGZrtixg5o9YebYG1MFkeeInpt47CLnZBuivvYwLkTMTAht6uFkE0ZQQRbIXuZc3puqiwUwhUXwGDZqpAYKclUWglTcsXNPoWIYgdxbw1NxDJ6Et93iBhGRt5sK5nx/KyeK8RN3sceXpz5i4vGDdfdvdM+qxGGh1mXmYgBtLOe5jxIjL2xACm1Yzgy6s6taQY32htCD93GGoKReun/WCVPVjjtyBU6DgKXOrufDBEeS/Y0uFVMjSNBUWz+m3IeplSMsOdhmFOEmDMk3wrfIwO7/GHmrPv+/h7nM1U9rVknC36sPCUIMtZ1g6uotm0do7KcCUMfDvMYRU6JhlubbfZb2Uw6S3jYnSgFatkeE8c/tWI26SKLG0L9l+VicQzRDmY1O/duqJx5y0RuTLVxjq/W6aackYfjJdd66BzvDS8RtcRWEcDhY/UCxMsGGb9rX2d2/IY9tXWgnsJxGIwfw02tAIjD7dnfEzu8aAVNGoVSEHKhRIAU513tY7yz/brsm75zAPw5CEFwe3OLDz78ACEwPVhtfNB7H142vTeEKMiS0IrJuyUgJ7fz3sPfWq34+je+jgePHuHV1z6Hd999B+fzHQRADaauKQ0avWDya8iF5fw5J4lzzRmdNNJU0H1gAIE0Ix6KjkaHEQoR1x34XpwRZfEYDw8b1a5jXEIlUB3E5N7V4gSi+RfpeH5DisOgq1bLMgOgyPtYuzXjNs3QbTXTN8qpL5fL8Kbax7WCdVsZeLcVXGTBsU4WueDuzR3dpLKH+YDDaUYtBeu2jV7MSf0hBByOB8gKlK2bcRyL9pgSVuOtzNOMlC06AySUSlBMiTy2ZV2QDSEQU5ntfiVh8NHO5zPJudG7OeecmJ/ISqVMq0xDZ1BpMKQvjJgKXO2UrWH8Hhq6+bXaG/DrgsV5KW5x33unq3IICCOWZHef5frY0ee6LeP3eSMowv3XjepyyvSNsYgPqu1gk4MwGhKRndi/rgWf9PWZLlB845wiF1m42qAEnK917VYw2CEfo23WfXeHhdhCZAekJiHsUITG8QqRaY6QAMFaCi6XC2V84t4bsK6GhQMhapK5Ooiu5JSxLHRY9dLBM3iiQc9BAtDozhmEyELwB9fGGlyIlgjcwHyKUYUDPHQNwRmHqr+3aBtZMOMzsWRo2+OsCNpsFp+zueq6fbcdwMUY4ApC5PQaoSR2dw+0rhjWFewIptVO+tx/Dw6GF5b2wdxLQwCT4sXdS0PB2HhD1EJIttmb/4wEt2LhWC2SC5Fz5gGdPYejYprI39hzJCo8pbiZKRaMV5NiGnPh4IiTfU5XYvnBUErB6XTioW8p2z4qdBdK2kvruE7FUBgfxU15ArNptr0wA4YSbYeAq3Weiggxj4U4eDIi7MqhRDIodTZDM0MofLbta947MfepuX4GvUwhTWDf4Pzvhgyz8xkQQwTH9wuQEgm4aYQCwg5GQNQ9PaIZjIE/xypFEcU0sUNsvY6CN0QqS1Tp2GuP+pWKpQGdxXi6Gkm49DdGujufTkeSk9GpiOt1SO+HJb3w0A8pIqSEDz58H0+efIyHD1/E48ev4St/cIGAslgSHDPHSoZzGgQ77g8RALVmwAywoJAW6MKcTP1m1uRe0FOFx5GBW7/7i7wGwvDJig40QyNkP/RjUMYbwGWqVNG4vDqmMByaVb2I3G0OWgW0tRGVQP+kCE+JPuQZRQIuZUNpFSp0HRdQReWorPOFWqvQoDjflyHrdjOzUtwwLWGKB+5P2lHKVZHs1xWCw+FoaGaxEQS5Nv5cruuKlH1s0gexvwnHsfOcsa18voKN3UO4UmdGKvnc7M1HRDrWPjvloCCatG1otSJ3GrxROaSoFch5v3duPT+8V2IYiil/jrwY8c/siiu/Dr13tM1UW+LZV2HP/7JXNIm4nzO90cHbN++cEqKty951jIWquQqPfaKnYbnA91eHu+wnfX2mCxTvIrpa9LYERDGCVms0fAomh5XdV0F7Ry0k2IUYLYrbvRieRx7U1A9cHexMaqG5k6tJYCZQRDQETemy2VXG+ZtzxGE6WKW54wMelOcjHIcmCT1z/6ytIVbOhSXtJjwkru6psx5mBYBeKcFUDOOK8TRXxQ6D+sKz/XF0UbUPeDN7fLvuiZ697xkeqjzw6pUVuto1VNscXObpyoDR4Xh3CxgfUbyqwZR4ILfKuTMRgf3zNjPJ4ljLCqRaELIbKTUEWCdiBco8z5jyjAYg5TgSeke+yZj/T7YJZGxbGxu7r5EAMUjX7fK/SZlkRbFHw/PwTWMjqKVREm3jKVWaWnXrZNxwbbPspTxRwuhzZN90U8rgBOOKIBm56QQBDscD3EPhcJjh0QvbsiIEdriQhNYKegeCMfeH02tMmCY+J+uy2gjTx4XOD1KLIKA7KK/9TpD1eyXKA8gJ7c4RiCEi8OGCOP8p8jOF0NGrjHXsQWkCtWKaXITD4YDeK2rdxhqb8jQ63GZp0hoNiQgBMHRiWRZoYNGpYGpsV7WsKUXOHIeNwtBGrd7kUJkg8Jl7DBFlrXj7rbfx4PYB3njjDXz4wQfYtovtPZXydu+A/Ryy++fW8SIM4mRz4jJuejC1to9ynSDq/8QUoVUQxFyq406oDuGqyI7ct6R7cGUzJSN5T/M0kVjemyXo7sgRkaI2iiM+32kvyO3aFKk0QUsRwZVlSazhCbjfFub9SLBRe4GYBLl3xXw4EoUQR8yJqKS0h2KqArUVbJuP/tIoPoB9tLcrfBJSCvxsrVijK+bn1AxpSM/5kag2XIpZE0wTgpgMd5hKGlolCREyVHjbtpljLd8/Zby++WG8TxZidE/2JpjFEzD8f7pz3na/k29GTBzN2Ys8jD2ZTR2dvrtcnTdw7o4+9zOmnNFbQ/ExIbjXe+FMHhTH0GLPQDD5eSmVyHBn8+VNznA4/4Svz3SB4htFELEFFa1bYiotwk5u8+wY7QHaaXHMGX20h4sISdO+s+klggax1uWFgNI7tsqNKlgX75uWmHxXA9DgShbmXeSU0CuwrbQCJgdGR2Hg7PyQEoITeq1QqVuHoGCaElKO7LwUjLZGp9eJeZUAV8ZmVYkJw/NGxBQ39uBCjJhqRQ/4/XxILYBsELCcH2Gbsv0ZaSrPPyysuv0aAvCNvxQjCMpAk/z3Vht9OfRPFQPtzSXxM/JTyXMP48jPsAet945tWWxEkZEif0ezWfTpdMLxeIP7yz15RiI0bRIxkmkYfideUE1TtM9KT4woJFTe35/hVtI6IFPP0fD8mjaIaar0PRmpreKEUEGtprixhOJunAtFM6RIh5IA1imPDJSwe+b4c3E8HoYc0gPdppRROrAYobS3im3doKDBG1EaJuOGEOnZIgLnHADkRlRh8acw4VYnZF5avfLkII9FYCRLQwGDoSoeJghNzN70brmWfZwEntnNuq/o0l7eaCJrgd4gy7Ky2LgU8i1EIZND4EbibmXvpq3ghgJa7LqFXZrro01FhyxEgWp1xMtiJXof0QncW9hth5kH2Hvvv4eXXn4Zr71CX5Svf+0r8LKCZHw62cZuhYI9b54h5TN9Ztq0UYgQjeroAVa8cuznVRx5O4IAOtF6Fw+oFbc7GVOi8X3ExoBmZX5/f4fJSc/WREXjk3R0a4wCOsy/SQSoZioZiCxU46CFwDaBGT/8M/pJJdwejlfFIAv3qEDKtKxXAKcHD3hTekOpEWUzZWF01OaqOYkehBmGgtIPXh/vrSYLz9MElM14W56PY41nLYAmsy8Q+EjRi0gJCRLJivPzIkqCGtdEWschcOy4LMvYO4HOoNirdUiirg40RVNGmqfRMNoXXhFq/7BSR8bzsjdJXviIFUJuS/DN/7Cxwlhv1y7gQlgUQX1/UeOoKTTwfYcocNKzRl6ilIAmdC3WbWP45Mzx9jWq98e9PvMFim/yOSVaz5ukzOE0z32Qq/+DO7SGvXKs5jmigEHVEa3vsraYKLlx+2c6YoZxQEKAYJ0EZP+9h9l4A61jK9Vs9Z2jwt/VW4O2Dg1ASoGbdqcunUWK5c6EwIWgCpjLbbdq1POUB2XTZsZDIXN13QKscBi8DgyYF9cPBa8yf55alL0aGa55NDdQm/93f65Q8VdXoJUKN38LwVnl7pRiuxMIy08hGbzITdGVFFAd8HYzjwimnzZkk8Y1K466NIhMOBxnOhlKgIYAhIgnz57icjnjwYMH2JYVWy2Y54N1HoRs1eDUbdsQY7burY6H9LKyU88mWXTZ47JcBm/h6dPNZr8N2ZQm+1zWRgRCQrcz5+d5RsoZtWwIOQMmQd+2jQWwrb0YIqpWUw6RbNx7G11KTvwaOnau+8jCfr92YFlX7rd28PBedUxzRtc4ZvckA3rkA39+M1JvN/SkKygzFi/UeO+bj4DUCgDokPprBxoa1PgO3h2qYiBvXoTuG+9+YGhviCLQVvHs2VPEGLFtRHnmmSM253EAwLptKBaAeJjnYVZGgj3jBWpjiCFjEWhAWIv5EvU6pKZ7c9NGwQURbCbdn5Kgbhvef/c9vPLSK3jl1dfwwYcf4NndUxZV3Y0SOYkUgxBDCJijES5bs7Rjyt2deB3tupOcvIfI+QigG/oQQsScs1m2y0B5HH1lcyAcUVij4GO51hsu20oLAENxgshw/WU4qXD8ZvtTaRVAghMlVfnMlFYhLUA1kvvXee9aqaYGU/StIM2zhXyy4Ishjb1vmhIEdo+qGtLo657vv9s9IyptQoe+jxN89NpaM3SCSCOzn3y8yMaESr4Vk5I+wOLOUAhuiHx+g6Be8bJk/JXtxzHSPbdUbEaiFcCQ2zD2AfV8sdpwqRWhFpyOR/ObIqJUrxVXVwW8yK4ACuNsEduznndtvSbiAs4TccRll+IDHnjo59TzaIv7fbFIE6QkCBUQ7Qg5oadgIY2NY9+uWC8Lurm/f9LXZ7pAoXzXoKNgMKjouGEhxf3GifHPrTujZNE9GhRbrbSot4WXU4ZoRzJ4MI1iR1CvUJPWAhrc1Cfw+1IixJciJpupesgS1y6hMQliaiPb5nW3mUZX9p+qCEkRc4LEhH6lDjHTEci1Qsc3eBs3qX0+YDxTGJ4ONppQ+ylWnwzjNn7PlekW/ODei5Vu1/Ha+2NHTvjTPZ3Y5ZUcIwEAM0FEBIgkPEJJ4qQPTRiEztY8StCKIWUBtjXOeeMo+PwwCzgeTzgdbrCuTKkuXfHk4ye4P9/j5ubGugyDs2cG6vEZlAExkx9yYBKubXgKdoPJHGivZcVOsi02AuQm4N4kBY8ePbIO3RJJQ8BpPsJDLWNMaHUjYuAeB+ZgGrwYlR3RYOZMsm5JbTrJ+1WwoZiLbVCXszK7yTs6VdcJ+Fix4rwsqL0iRxlmbtUyZbw7h9036+Hgzspjb5Z9/UBhOTxhHIxRdh4YCb8CKI3/StnQtGNk2OD5Tdb5Pb1WNAGCqaYG10WdHEv34FoKajE0y8z+DqbAc8VDh5O+K0rZES/mFVnEgDZbHzx8Y0hQuVI+iUCDDmOzIAHvf/A+nj57hkcPH+Lll1/B3f0zjiUhRGdBeWewxsDjDJy31AxNcZ7U8zJR72YBJ/TyP67C+6YZUgotGIy7wSaOc14/wHyPVCuUtRpJXoCoakoUc4u1fBu3xW+qkNqHssRHKjnLKAaAAk2Wsm4Ez147elWSw/uGsq6YjkcEBbbzhjRxJIsOLEtBwO53orb26Qi78zBKrZAYMM/Zuvt4JWtnIUPieXWQDq4ycYO3aZoRhPlVpVbzcWHW0NpXTNMBKeZx3UcR3TsHnmpMRBMSuDlZzsmQ0Gr3nQozNpMmARaugbIVXIS5RpTSp6vx6vOSZNW9aPH/dTRkjNX7Pv4l78RDQMtVY7M/1yHsRne+5q59lnYE2J5nI/amGBkPocwaK5Yr5ohOqRWX+xWf9PWZLlB2hrF1WtChGEgjv+F5+Ku2Db2pmQMxrbFVdiO7NJTVeICgglVh7LxpKWZzmBwnODDxBrlHgqr5bzTBUtehbOkc/BDOFpgc86rCt4o2INCXBRxdhDRx7pkienGtO+c8buC2Ix026RGSvHZughcwYf9XmEIC7File6miJPwq0HrhwRF8rkt5tHcJ2gGt5h6rbv8tRiZk91q2OjpGboTX5VRAiOQyBLBDj+L5K9wYqipgvh+D46JuEKbmZOnci4xpZq7O48ePMc0TIPfoS8f27Ezk5PYWjx49GjkvHYKcSJjdtp14V2zmzM2c3Aw3/MopQZLgGjVyJ1gAePbsjsXH6QaqHR9//PE4QDYz//N1+uDBAyzLilI2dC1YlwVdG2Kgw+aYDSvn/xKo6unakYOPsCoOllq8LJx7T3miG2kM2Mz9FWag5YcIlJPArhhKn8tyQSkBp8MRE+yQbw1sfNrY5NRUWwDJuHOekLwIgXW26FaQ+6YJ0M4n0vTJvDM8byVG+sMw3VhGt+UFYM4ZxyPRrnW5AKqYIuWiPnYFeP+2wowTvz/TNOEwH8zozTlnlLIuxUiN4go+59NU1KUiREWebOQBGXbpIUekJogaqbyB2DNCs7RaCr76ta/g0Xd9Jx6/+hjvvf8OFnvfLsfm5+cD5wZhMRkB3hU1VoD4MyjWxaawR23UVoesmuhiI/9OFWqZW35wretCJZHaARR3ojURKmbQ5JyRY2SRf/Xi894ggRyWEAOy7KR7H5HTFwiAmgmeNSApJyAK3W8ricsKxbauVmQ2rJcLWi2YDweO+Wrl6DdhpOfaUB3LZTHJb0Jskc62OQHRIkUM3ekwpDHuakuihAHrumFZFojtP3R1JuIxTRMgHAev6wVyBGLMtg9axIR/blNFicWdQIi0aFcsQbBuRGhKKZjnGfM8D6TM0cKuHWUr6IGoXW4ZabIkewedwx4/4ff12v4ewODe7CoaBdAxz7y38zxR6r1tuEZL2Gi6bf6OsAI7id65mVx75DrFKSOoAl1s9H2yyUFDyoroxfEnfH2mCxQ/8LiBW5hVvEJNbBECnhVjBNHgaMFOLPQZqMAZ8gDcuh1AU8ZOiwBJ8uA8RFQaNimGMoMbZISGbuZq/GkSnQuiVgzwM2hV9AoEtRh6Go/sc2UrtAb7WfhYckrD4DSYz4g61gtgH/jA6C5cnIJ9xOTYsNVL8A1Y1eY3uhd4/stVFY3QzFUH7DBg8F8G5wir7P4OMi4GLzq74wDp5A2EuPu51L5BOwwSDuOg8Q1Qlcodp1DmeSKPRPJQb5zPF9yfz/AoggcPH+B0OiFE8jRyTihbZbaJoT1OjvOutDXCtzDmvKMG46PY5yabPzIfqDccDkfc3Jzw7NkzuCzQ5X6E4GH8Ah2W+PRAYfbOUOY4JG+/SzvnyYxwp63/tm30mQDJdbWSyyLiZF6OUEIMLLbmPSG4270nfF7RqqUxByJavomPA1N1v7c+Mghc5/vzKACCuXM68gKD5p6Pg79OeQ4RyIhQYeZQjF54RyPsTtb9AafjEQJBNrmuj8IG58RVFCCPIxjKI+LyZK6bmGboIle8D88TEsDInCnRbNGzS1zR5mMkcloMIRDzDQpAToKPPvwQH3/0BC++8CIeP34dX/mD/2neQjo63OroaTQkxe7XIFc3kmudjB9AknpQOsA6B6bUavyrGdu6YtsKg/uE3A+OLClTVS1sICpN7VJK5suxk03dNj+kALiZpSEVvbkrEdEusebK98brTl2USdQ+wgGAbEaCpdCTJcaIoJ7/xBG8I5EeJaAxkjgcPCeHz0nKyWIK6P9TG11bkyVat9ohaOhiooMQmXxuaIEqzBagsXiwApkoHMMBc0o4HA5YlguWywXz0QJrHVUYDd4uw+7YC/NWGw4ICJHXets2FiHdCN0hoKldN3tGtDN6pNSCqc6YJvdO2cd1XqCwcODndbNCvVpj/sxxFE0vmGmaRlbRsqy2x3rIab26Pr4PXJnUWdPIIoUk38GRMl5ZgOAQGN+xWZTDtWLoj3t9pguUHTXwomCHoJ0DAvimacY0skfKX9+IbeMYwGf63eE6YUGTYrBMH0JfPjLqjV3WIEJ64QDsGzv8gFEI2A01M9QCMA73JBExCqo2hEiVSZCINGWoCKvU59gkXmw5r8EKFXiZ4eoiwKUCA6aXvTjjtXBUBOb5YsIyr6gNvWjdYUsrIkxqRsfGDBWg1ALP/lCr/LiRw4ojPxxgc3MA2mivnTo09JFrBFi2kIUK8jPsRlEO207zhBdffBE5Z5zvF9Ta8P6HH9LgrHccTgfMhyMPXkNOTqejFXfusMjr4nwS70aq+Wk0gyhTItzK8RM70Nq4OXmWi48hPDTQU7d7d56FF23u58BEVfKgmJAM7G6e0cZfUPIkSqk43UxjA/VxBYAxK9erz1AKvQf854VIXxtXZ2yloJifT0qzjTUjoJ1GcTC1QaNE3jkO3s653BbYbXpIYIyW3M215GvMn415moEgZk2/sgFIzLBinDxIdkfAlGdI4AEFgMGfXLwk6pkrLITIzmg+QkCM+3zevoXPqdCme7IkZtrK05RLAUgUTPk0EJd+PkOv9tdgyiBVhZgMPUiANiX3KUZspeLtt9/Go0cv4PXXH+Pdd97Ccr7sB8xAiYiowD+fFa+E1rOR0/tYR83kxPMckGO0A6+imUldTHmsPRjK5ihDShlUXPSBwLl6w9fqtXRdYNcUCm3kgg00wkysRTDGkGqohRsHcvSRjBwNbCY59sKyGj8tZ47H2QRxdKvQEXzZSgUkD5UNDzzF6XSDeZrx7O4O60o3aEYdJEzzNBCQEIS+MYZgArCUdj/c5bmiXm2kDWzIp2w29STa1lIgoO3BYZ6fG+V7s4ywe02l2DGljLCS+zLPBxaRpWCzNGN/Nr6Z+Nq7WiIzSblpSmPcsq6roRU6vj+bL5jz5jzAkQUNv84LkMPhMGwFlmUdLtLcSxS9uxEpm1pHlfk8eeOvY+/xdTQc2R1xFkHvEdqeP8P+d6/PdIESItDVugrYjNGMhYLJmbRzocPRib4rTOhdwWreHfr88OEBz84gBXZUZdvQTTPPh5PEMDez8st+vbi8WBkHtjqj/CpDA5arYDM/CVT5OHGvK2W/MTJ1dMwa4d2po0nghqs2++97l399MKhZErP6dbJet0KBZN8xqur8Obu53HUlzmIlhjAImDRAugoL7IooCWmKhCXFCZSu5uDPVNv1YwsDEQkxEkFh7O5wy90/Bz9UniY8evQypnzAsiw4ny9Ytw21dRxPRzx64RGmecLd3RnH4wE3pyMkKGpbIeIcEqCrmy21sUEBgKSMbhC6+zo4suCeMH5YNAsmOx6P9IopxRQhgmlK5jnQn1sjQ3pcKxQNN7enQbyN1ilmg8BbpZKHSczZHGMTjsejFUMsVuZ5HrNfcSW0XffeO7oRIx0C5meuNoIJRs5ucLWJ1P291t6gaAgSzV+GY8IYxRDHXTrLNRsBy7raR7JqDQHNFWNtaFtFkN3LSM2kDtBRVLZO0l2pBTFORowtdNqMxumyQ1Ei17WIQCzaoBlfYF0ZD+ASaWZyUUETRFArs4HEoP6caa7H7/cOWanIsA3DGyNVyqHdQThIwAcffoCPnnyIV156GS+9/BjfOP8BdxjLmIqGCqEz0yQqVShcjwUiHTG5UzHHxL7vtLlx3Gh7S2sNfTX5J3beAuzfS6nD50NkJ3pjTIKvD0eM/YVqGzZ5VOYkNFRKzJWjVgJPAtHdVl2EfMCUIh2SO31mXP0UckK0ooucMEOmGpgkLXTaVYWZmxmCAljDyWd4miY8ePAAIQgul2WMo7sJE1KekCOLszGyEFcg7givwHLTIJYkzuuW84bT6TSs8Bk6aUaGczDJP4sCbwiG8zessQoBs85YrRgPckAwpU+rRMw7gHgFhIdI1RaRVu4nudIFepomoAHbspmqic9XsD3Zx8oiRFZ95Onuyv4z2bjF0Ri5QtCTkUlO34UCOxk3DEdeL3j83NiRd55pU4zoHnnxCV+f6QLF3f48F0bVMj8CrIOCHfjswPg1tmEFsrwDIuY0Y8Y8fgadPAUAfVa42WyAUhoXQBa2b1I2LxoQfAqEg4G9ouxG+osSME2c37XGyHuIW1179xs4WxZudJ5y6jNO6u6DqXH2lczOH3unczWa2d1bvZjbr6MqRhdPaMCMuSrlfDycd44OADPBsw0fQC8btNCqHqAnCl9GoHJjNchzPhjuEQAAIQqatmFW5+ZEfoyIRhIKI43sIgJ6iOaB0Wmc1xqWUnC+LDgcDnjw6BEevfACai24Pc148YWHyDljWc5G6HOyKDfMa36IOzaGEMaMNmUiE+taUCO7PkkBc8xUYrWOR48eIUbGu7NDDkigY6h3qjHGIWWuJutWdJyORxwPB97vQNSseECf7i6xVCPw/Xqux7asqLVhygk3pwPD2XJAjwmKbjJZyntbrSjm8LmuG03chJA1tENFUdWJ2zb7djQO4xwzV1GTc9o8Gp2jNx8HBSXE706r3Z6xWisulwXz4TiKVzdMg6GUnIXz94REyX1tHR5aeDweIQC2raKFYMUxr08OHNupFeqt70TtdV2tow578rgdFCFGxCQohZEDY7x11SW6XD6Yq2atDc2kwGonPQmiRADruuK9d97Fyy+8jM+9/gY+fP893N89G5EStXDMGMx80JuMAZmD+5gGSnGdJO7EXoenxPaia7LwIFZCzIRSLQyQqjVv1mjG5jwFDBQRgAUjVggS8mGCdMuPkTRQSihQtZLEDeyFnIKcvhC4b6LCuSO1VfK/UkLZuhXzDKNLMZgPB0dDeaZsdysFdSMfqYHP6mweP703K9gPgy/mxdc1gTemnaQdYgQaeTXNCOQhBByOzB5a1w3ruuLp02eoteHBg1scDjSFI+JAcQUdfrMpfRRNeV9iYnMGI6XHFBCbk1gVc2DBQ9v4Fb1WwPZACSzCEDj6UzF1oakIW20kLtueGIy4P86dvjcFAEbB4snHwagDblfviNd16KkY8nRNkh17e9xRSUdW/Ay95s6JmNJUYbrRT/b6TBcofSg7dHhqiMO7XTAUPSY5Y0w1XfQU5ssQIxRiB7F181cy2zo0+3F0WSF4UbD7bxDCC9R522bRbUP0MUQY0mS+d3SYwyCVKbS0l93UTOkPEAOt5Z3wKxCD1DnzBlgt02/EibiKIFc8l+uKxP6M/2soQGcYWwwBbrvamg5lQbF49uEwqn0UTwB2t0SH2M1unvulksQqCTFQKaCtPTdGMsgKzh3y0VuUSPqsUF3kro/0JOFnbb3hfL7Ho0cvACYDjTHg0aNHeOmlFzBNGaUsOBwnzvixEwZba7bBKnoPNqNPozhhvs7GrsX8JmqpaPa5gyEZIoLS2iC9kXDJcUyeMlrtWJfVUBeaF03TjFoZl3C53KO1gpiOUJNi19awLBs2s9KnE6OHyskoXN1bxTeQyUIDYfNeEld5v5sHoElDQcHaGratoNaCnLIVyeaVYeFjquYGatekXBoYG7DbvHOzSjZy6sYzMBdOQ+VUO1I35VznMb6sK5/FGDgODOD3FHqN8LAFpAGlAK1YQVw7JLTnNk0qWqgWEKEE2NUn3dYbwI2yXaFYwWTsy7rSdt3Gfykl3N1d0Gob3BUqqpx3I6g9IMfENV9Z+EH2aIGtMPUaAnz00Qf48OMP8fDhQ7z0yss4L/cjDb0WJp7LlQTTFRTXkH9O2fxWihV6FZfzGeH2djh/ckui+zEfcv6P82Va56HvCkh/BUNoyVciguT7E8m2bEDcTLBc7XEeRhngyEbEdbp57RwLpUCvnWrv3wmdGQkpzUQkuhVDmfk4feNeEyVgPh7Q7+8H+hJhgYMhIBup1XkZwE7o9D2vlD3lHLrbTHjDGtWNzhp6j3j48CGgwNOnT3G+kGTfe8PxeIKTur3YVlXEUEFPFe7JITLRPsWM3pw7xrEpC1h6o+TE/BoRKohK9XOD1IIEIp3u3cMmo+HSLiit2kgnIqRgruX7fu+ILZ//ZpyVNBAmb16b7fED9QoOAggbxMbNOkgcKLgXKztyshc1/uf+HjjJ70N59klen+kCxeenhGQdtgSCRgjCbogjtj0HRyHAji2wWyASsvsAPDdPgwW2TQdqvQH43J1f3xFEMc8HHI+sqpvlRzDx+Cqh0jkeXSHM9CXhTXz2LAYZK2AdGnsxVqo5Joh1teQO8m99wY0F7A5vO2bL32ufa3BfHBmx+SQMWXIDNgEJld2SSsegGQBgAW21ABKIZnT6tjTr9ll80AyuF/oXeB4IUSlCh+QbOhwf9hGWFStelCUjqR0PR5wv5wF1l1IwHU8DvjwcZuQp4dXXXqGsstXBgwlGdtOZcPH5fMblspA3oWT5T9lGfRAs5pNyOBwIl1/Nc0Ogaidls4YOEdmQk1KKjQE7pilhW8gv8XwephwHLMsVgXUUdjzkyYva18/uS+DyP14vcgvoxpkSRwqtNSNA1vHvMSbUouitwNODXcbatCMFruqmDZGmK9aYM4iOPAzBchF4COK6bkQfzP12IC0wvo02EsNFAHfbDc4HYZG51YKoYUDIZaPSzvlf4er58XFK0za4Uu4RwhUXx0Zbyt7ZESFkAe7I4v4+gvEKFqwbi8HJRi61VFRU1ErulKtsVBVBA1qwEL8YUZsR8ht5FrDRKWY6hN6f7/HW29/Ag4e3ePGVl/DBh+/j/u4Z8pzRqxfscVxzVSI4MblEez9suQZ4H0spKNuG2bp6PufsXNmIXcH+ycngxZSH8WoMbITTdBijBHfuzTnx/itHYtWQotrqcF/1UZo/+4FGU8ZdYkPnqeYpJRRr/tiMNMxTQswzSlutGGfRPOWM2on+zRA8ePAId3d3LERBD52npeDm4S2maeb7bg1u4kYUgYactbEw94M1T5n8GhMk+HkiZg9xf3/Go0eP8Oqrr+Lps2d49uzZ2Dd81AOkwc9SpeRaQhhBgL01NMDcZcUM6NpQdz7Hpws27rN9zRHmaeI+4wR70X28vJpbbU4JbWKqdohm0mcRLNf8K/8+34uccO88TRYYtDwgEkgPK4jY2ZUGquIvN3u79sEa+VvYEUGFQuOfFATFNmkFo6T5AFu6JqjcAXQQtyCADvTDxxy+OPafq7ZBxBgxpzSyGXozozXpwwk2QHG6vcXheARszLCu63gAwlWV3WtD7R0QhrhFETSbgZJAa2S5PrZ4HnwpmoU/D3/PO4F1sUBE00bTKyUhl2j8ztP4ZkKs8ye07865brHMw4UZLISP7fLJflh4teyqKQWQzPTIyZ/8OoGKgE4XYWx4fJ/+fmDza3YW2k1OazPVGBggF1NEaRvWpwstpG0jcEl5qYVkOYEVWoq7uzvMhxk3N7dIgRswDcMET57eoW7siOliK6OgnaYJ21axLCvm42H8PJeF+hx2PswQL4yNqHj37H4cnNPk/JXnvQZUQYfJ4dobkXJ8riMT8HMEi3531Y6PEZws6Z0xogNYXM+KXaaaYqT/gwbcGU+lO6Stjqpw/NZqQw9EG2PY0UbyGQCCtAFlXVDXgnxMtsnvI6BR7JoSxUM0+RgaUTcIQof5WLgrrKWLt2aoBJU9YsUx4w64vFpn6JoI+Ri9NYiQWNp6Nw5HHOo99Q3bEm9VOybk5+bmztHZtqs8pt4BRMQEO1jMRgARQEJtDYdjQmhWAJQCNbt8DzIFiOq9/8F7eO3ZYzx8+BCvvvIqlvMFUQRpsvtuhaofrBzVWvCh6nDM7qqYAUvXtlBUO2TVxq4SBGL8MYhAercChC6tVYEphfH5XSEWE5N+c25DXlxrG4RWAANBVVX0xnH4ruQCKEdn6ne1vCreFyVpOkYkkeEqq0pl0DxPyJLHOgTYeOYY0Trw7Okz3Jwe4OHtA1zWFZ7W7kUD917PR7v6XMoDNyVy5cYe2K4csPuOCDgyt20b3n//PTx8+BAPHtxCteN8f7amrhrSEBAj9wbtHb5At3VDg13vroAht04gZuPQxjiG5pPcZ6NxIetWjE/TcDwcbFzU0BEGLaAqHdBRy0Cz9oImEKkP7bl93D/nNGUjxy4DpQN21GWaMlqnVHzP/XH6xD5S8vt7rfa7drrelZFsVD7p6zNeoJDYScZ0AEwhAoBzdLgUFwb57v4l36ygAXRUwAAzTjzBUkDyY++E9mPkgoxBkOcZh2lCs/BA5x34IuMIyd6rJ/KZiVqUSHQBNoZyeN2gSW0OSZtHQmcRpoqrDJ9dssnDYc/p+ebi5BoZcgmjFwpjdDPgeldk6IAyxaFrpYTTzfBgn1WhzHjoALz7UrXRk8V228EbRJCTs/BZBKWQ9k7syr+ma0dQjNTffTbOjfx0OtqmF8hTqIqb04lIhxJRmecJUfieLuuG83nDuhY4YRjKWWyIgbP4pmN0Mk15OE+qdfHJSH+tunU3k4WdhxQQBun6cjlDQFkz7e8rluVsxQYLOhpbOYm2mmTVZrxlv/6+CUD3JO/j4UipqXDDkCD0P/EDMiUcjyeWwCEDrVtIG68lC5yO4+FALoQbB3YAnnZrh4BotOJXx3UgP8KI0hIBsWA7K1I43mRSeEoZUcKVhNELv45ghmcKR1lcuWQeMFGGGijEiCkRtYshMl8pOhtYILVCtSGnjJxnU5txnNLdmt0OFydDxhhxPJ7MlI5KiMPxsCO1CUC0bhFUweSc7Vp3xAAaOuZpGL8F2UmBIQSUbcW777yDhw8f4pVXX8V7772PYoRd92RpTggPjOVQO4Baa4iGdFYj4CqrJhYp3QnXfXwuVbXMGR5Qh+PR1Dk83MTHsGLmlfAmLBoRO1nBIwwAbJW/LxoyYM9rNMVPiDtqGxNHgrGaeRcUpbFBG0ZiQQfYK7Y30YImkCe1rBYEySC9IGw63FIgpomjw1ipCRBBrSu26kGJu3pLlUjQnGZICGZc6IaJnrbd0cQl3m2QyD/66ONBkJ0PB6gd6Ly5wcz6PFxUByLRKkwFaqaLNtIjDtWg0il8cBdm9euh0JjRUkRcybs5n884HA9D/g8QpYqqw1Or9joKrVnpYh6MW7I7DHMc6+gJ/VDmIUsGHKHrwzNonucdba1WPA80mXvhSJcXF3LsuWHXxY/HSHyS12e6QAEwtPpe8Tq8CZMeQwS9B8TY9/GEj3zgf6QDakMQY3onJAiadb9qlT7lxwJ0QYoZEQHnuzNKLWTVd1MS2SiDfgXmcSDM6CE5QG1UQ6kWfT/64JyIEsHwanQnutk8VcixUfcSsYNTrUhxQp+nFjtpGBCIWhdo1yDEBChzQaDX7ik7SztGNbdToJnqJedMf4TE4mCrBXEOSAgsHJsOuaXCRnL+k2OkTbJYARbYYYruG5zaphYk0pDODqvW9s87TRkPH96ye46CUpttXAF1XZGnCSlN7OSqYlkKLssFy1ptw6DygJmPimkKyFGwbevwUiCPYc9QgVgibldcLhcm2ZqC5ng8Itjm5wcEIe1oRDgfrVQ6S2K3cPfwyzLydtIglEbjPtW6kzbdBdRNnvg8uO+PGTb1jjhNlApvhYf5NKGt/Tk5qxO/e+sIEz1IfMymKqMYohMz70NKCcEkqQEyCJ9EJzirluwYn3sU7V/X7SBu5uabUkavhciZJL4f+5y1FRZs/vtsnOUjn5SfNySsldbiKU+YDwfEuqFWl/pHI8iHke3DbpijnRAYnRET/URqqRCJkASoFtp5m/dKSgndRpDO0wgdtK+3YrybwitJQEXAhx98iGevP8OD0wO89MoreOurXzE+xI5OoitCDoDYSAfGh2OFzE1erajxA09lrJdkyioWrBV1KcbZ2rvmaMom59x5cQiQ/MpGiRw9EeP8KUcPwYq2GAI0NiuOjf44ikiSrnsFLsY9ENWdlxfYUKaUoLWM0YOYkIHeHBu2jc7VOc/m4Npxvr/Hslxwc3OD+cCDWKHI5pOzLJRxNysevfhbV+6Dt7d0ktbqiPLeiEUbj/CALUPheblc4ARsJ6B6KGgIAUFJNF7XFdpZEHK/ZKjpNM1G7DXH4BCviNcNrRLBUtufoUAOAfN0QKkV67oMUcKeF0ccTwM9V7SbgmzbjKfSkaeEmN3fp6BWHedkayyarlOHrwmuXlCJGUSmOEF7Qb+qMTzOw7PM/EWZ/PMZYdeFyid5faYLlBQDDvN0xYsAdpMCO9QVCCHb1+iAqr32FRF0EUA7JJHImZNlwRgM15qSWW3IRLANqXVFN9WHXkGDU8pEDdTfJ91Kt1LMDY2Lzy3daw9YyjrQjKBA91koMGBAHhbWHQkPAREzfMJua3/No5FRblyjLcLhlDRIiNZJ+wICD6AI7GQzfr8GmzE2IE6J89sINJN9VnRIYpE0pcyRAgRta9hKs5l2GzBoADviIAGIRgJWRW/Xh28y2HpItcA3R9ThT735Jh4+eojLslC6t6z0gAh0ok1GVmwg456s+z6uV2sYgZI5C0nOEegrxy4SA3ptgLm3qnNjAtENZpkAZSuE962YYSfV0To78Sp13EMaxOWxZOmZQh6Dm3RBudlsZb2691dkZ1vyDqGOSwOqZfY5cBoF07JcEMPhapOQ8TUxcE17xEAvBb2S3CxQ86roCLmb9LkTbjY0iygJRyDqz4iNy/zp5J/t3kSMI5hRCkcUKQokZpt/s8ivvSFsYM6H0MK81YbW9xm3qg6XVVXvohsQvIiIEGTb1BtSTCb/pSmiBNrAt1qJzAwTN1j8AiwczszGjF8wUAAzV/P8mV3Hs/O1XHKcg2BbLnj33Xdx+3/e4vFrr+LjD97H/d0d5Js2c9iz4SM2oA4XWdjhK0EhkSRGApbduBkyzOS8e0dvKBtHNvM0cSQYk5nC0e3T0YbeC3QAoDZmtfRjyC6nn/KMkiq074ZeLLqBruSfHU4seC+XZXT3Egn1+3qIaUJrxXLJmjUBEYcD5fNl2yAIxvMCdIq4LAuePut4cPUM9aY4nY4IQawgJ8fOkfSmFZdzpfDBvEvcWdYLFPW1BB2SYY9VcRdrIisYhRSUe697f5RCXy0Y+tR6x6aK+XBg0ngIRI4hgFoQZVArPOnSy2sZ0IMiWwM+/Fk6m9NvPvB9jAoAZau2l5I/djgcn+NjOZeumSrOvVOAPWSR/05SvfSdCD9GzTY6u0ZjdqSXa//6/fk1/qSvz3SB0hXY1g0IlAqKz4bFuhCD750It99MN5rafQsgnfLgkEk+Nw+L2q4ZyeyyxwUXMcMyzmCnKBbvrmi9IpvtNABc1rNxB4DhRSrXhl0grNcVahBe6ztxNdjfMwRPBkkKCmiwhQpY5b0fZDyzrqB03WeFHB3ZR7ED0v+AC3B3mA1Ce361zjllKhMaLOvEeAToNvuO5gyLAMSEydJPS11p5Na7uemaT4Bw06XpGU3T5nkmdF8IlQaww3B79dc/9zrefPNN3N3vycS9K8rWOGYQSofPZ8bcb9s2rjUPMff9yMgpIuduHXREzgIY4iM+17f74f4h+UDZ4/l8GaNAXjv6oPTeEboM9U/XRrKtvc9tW607C2PkVlul1DWk4QgbYrCxzU7kS5GEYU/yhsu76x5I6IqOWipzRLYNk3VSfAp4f0dGSK2DIwTsckZ6TlRyQ8QShU2OWB3ZMxQNEDSXM/cO6c1CGmWs8dEhiIXuhXnIJnOmsZub+XmRzD7RYHE4suNNgSDnNDZbemMoso3hHKWiRjOCx7cgTLauDeXKUx5ogqutAKJ98ILORsUpmguvKSG84bF54eB3AUSQOCI0+boqPnz/A7z2+DU8uH2Ax48/h69c/i+OpVXG95P3wt8XwH2gbCtdfx0gVQU6r3kP++911NURgWma7BnBKAp47QKawJDKMCz8XcLsY7hrf42cFSWWIdHNkYWB6vXPZqG9gtlUeUqoJVIOrh3aKrqSKM3iB5AoaGVFMe6C71PTPNMYrW4IAYg5Y4oRmCYspWBbzhCQn6HoCClgnkkYXteOqCY/1zbQ1/PljFi2wQ/qtUFigqSEbBb1HgypWmwtRKQUBlfputAnJ8y4fGZBUWsx1Mkzthq2ZTVjtAyAKdVqI7OYxJDYCukeJGgj/yjoFr8gMaKUzTxxOpIpKBXmRQRuB/uYZk8nzomoN9+3E8Rh+1EZo+zRAY1T5NpjJe6fW/1z72j/7nXUn7tG4zkc84s//vWZLlBaa9gEZunNoqJj7x4Bs6QWA+etw+JFjONh4iKk/DhYkVCtq6DVezT5rcHx4wBwkzEfMov5EriXB2eKVHVsVgixc0CgyRWVMERnYEZSoXV0UOZlvDPai6sOtOi62PBRluIP802c70KAR+FMbVjRoTYWoqOqd2o7VMxFfDUdC4IkiYTc3s24iaFW3YfJygO99EKpaud8MsZEPwVt5gnAUQa9GGwTafydN7e3ePjwIcpacFfuWXSJwEMVj8cjTqcT3nr7Xdyf7zHNk4VzMcMINlvetg0XPQNdjd/hsriImDJpxokuoqqFA4CYINIArfa+xaLKuTmJBISYBiTqcK1DodNEme/9/TOiEcqCOMaMeaK88P7+HqpUf/ncet3WK35NQFcbubkaDfz3ZG6bAJDEs0T+MNm7e3fUzOOgdTSh86fYcyCdOTq99+FnwaIsMI5eYVC57mM6CZZkGoAC4xdgHICq+/p8rlnoO8GyGkfJ5aN8vw1hyvb9XLp10/H8Qjpa9ZDPZJ/1muxNRYf6RmojVlrfm8ePEer5XuhR4xumRLuH14iU/YzaK9wFmXyxQNK8KaSCBHTxe9DHCNK3YyJjbRz2y/2GD9/9CI9uXsALL7yA99874f7+GZ9jq+H8OfVCLcUITBm1dBMAmAdN5WcPhnB48jS7/gznjkW7BinGYTBH5IfPJQLoxRIjYotYdWOheLWfsNAjKtOsUCSyYbiR33tDkVhkd0SJyPMEBEEtNO3rWiEKVBErTCPvgWU/UfkSjPCZ0bYVrW7YwPF3igFJozmsdtzc3EABFCP2uiP42ujA6kUzoIgSza+H96TVirV3pHnmeM/QOffwqBbYCSjYc+4eL+7M6uveM8EAYG2rWffzGSbPg8nJOSVgjJYEMSSkEFBjouFl9fT0gG7InPFibT8WK9yqFRrdmm4Bgu7jcitU1mVFTXW460p0HTjg1gbXvJHdjE3G5/Miw43b+Of7tEBkV7n1qwLXR2F/ohAUgAVINqh5T2h1V1ULoTKvhmt5LV9GtLQFKyCr28lO6GLqGd5IHQe4MeZN0QvwgO+doUmtKWFfK062bcWYj5uc2Oe11yMZJ5WOilODTawUJCqK1+oYQypVQN0YaR9BKYxXI3wgPXKeb9bQR1z/kRgAonagmp+IyBXHwTapAH4O8eLEkjbHtVOg+cjJrpMECBpCB9AapNdxEAAw+3QWJ/M848GDB8gp4XJ/4YYfONIKMWI6ZDx89ADn5d4C54AYJ3O6ZbeRIhUHallBPpaotRlU2xBjpgrICL7SadynGrCa3XPKHBHFzutfFqIQEJIP3WbeSXiOyl3P2X1kkzPD/OiZIOTGSBjz3Mtlwbatw+TPHVFDkPGgu4yYDz8GoiMx8uAvSm+FRmm4y8N9g6itmmsnnT8BoFr+zzCjSxEpCpLznTpdd8N1x2SFvBev7n0hiOh9s2KN6JSagVm3n+VoU69EdsK8myT6S5WjotquAjzhAAUNrHqHQdVmsGdutTFZWCgMzbAxrcJGZa0xBbmx44bsBYFawQ34+Iy/oywF1UYeCMm6cQ/B3OWi+3Mi4zmD3bve6ePC5lTx/vvv4bXXX8fp5iFeefU1PHt2h96rQePkakjjnsFrEJHSBGhF74WoBdiotQ5ElZ3Ub/4+MUbMduiO4tcCRge/KbFoqo2J7lETPXFELOscw1zLG5Jgz1NvDSEnk+nvRQrUcGIvcHXFNE84nCa0oljXDdXS4xmN0YGJyNTxlLAsF0M7zA8pJcT5QLS20qU1W8q8BmBdF6gAx9ORXZ2N4mIgr0vL9dpyyT4LsoFiq6LZgR9jtCT7CMGEIJXpxia/ZhyArRuT2V+v4RgCNCW0VrGulpRsHLNaTUFXK2IOyNPM8VhlkT/NaWQLFRvxBuX97QhISVArME1iRc9GAYE91yTXP2+0BmsUStll4WmeDGG7OtMgbFbtVLiWEuso8lnoO8pyPWEAHPkxryPlSWP9PdhQ/AnxQUkpDZa5yzVdnhiMlKijoNCxaV7LPQUBSTjuYcNiG1ptgEZI2qs/+9vhjRBiQB5pojLkkWJujcu6oBsL3ztLiDPGneXcRxcOKEKK1om6dMsRGs8ZMdOdsSF6GQvTgMqwGefIS5mcbI2idlMwjU6N6MsYEVlXe03C8k+uaKPiJ2nUMlZgskMRqER0WIaFUt0iqnwohbNf5qIkmxmbVNFGFCGwQAGAZ0/vsS6rXQc+IA8f3uL04JaFAxTbUpBSZg5JbYOP44oh2BUKIaCUbRBjERKijc/yNKFuG1onhL9tdbd93wpaL0Yo5Cbmm7MIRpfjxZZvBh5p7wFtcz5ArOMpZZfftbZLslvtKFvD7e2trXASeN3DQcwszF1UCa+Yukp4Lbt2wsPNURtK1D0TSMQSjCvt/CUGbMuKbWXqdoyRRRpYfIcYCJuDiMkoCrrafN0PT+bF8FkrEJj9uV0nPwwdAnYeVzVvGBEz+sNOzusKtEbuAYKr4bgBN+3GriJH5HI+j46t1waZODKQAErJNxqDwWb/qjZ1Cj7u2uXOjji07qogclE8EqFpg9jM3a0MrqErvx7eZfuz1SxgLyaOCi6XM955521867d+G15++RW8887bePbkI6ip3tzqHXEfKydJY4Sw6QZtGC69UKFLaXJ32oJiIx7fK1trWKu51iqgtWOWTFWOZflIKMh55yAAgIo7YTsBn5Xptq3IdjFLrYiyWxVAd6PMpm5jnzAfMmKKWBZB2QrQKrZiHj/TjJiAeZqxrAvc/RUg4XaOAduyjCIFgAUmBiznC1GRQ0dsMw0LcximasvCZtGdVlvrWNcV2aIsVJU2B6q0MZCd0yG2L9UQmNOknpMFQ1dZ2PM+7EUK9yYq96q9V09i7l2x9o5YG+b5gByoSNJOUjbJuHVHI21thfG7fONng+kBkwAwRRaZ7vAs0egEduZs22acGSKyHHU5KufP4T4u3lEUz5DT8fVjjairY2WcW9+MpgD6J0fFIyGgQQf85iOQ0W01BVB3hYi4vl0gZkSl9mCnmPiQ9d2ciFIwEjlhWu9u8HAURos7691hNFb9NAlC2m2kB4EvyE5EKm08KOyo0y7VhLH/0W0n1b04wW6I4/Cvv7zKdSi2d0Xwrlu5WIP611r6KoTCDGf0dxZuYg6uaosxiEJCNOm0oyxknXNOaooIuCeGdaSwjltZvM3ThK6KbVvgJDP1z2HdmdtLK7npgLg3Q2ZWRZ7QymrIgeLu7hlinpBSHpk0TKDNxibnZw5RsKwX5EmgGhHEZHhKU75lXXE+n1FKRUoB27qiKY3aOHMn4RQSkFMYXhAphOFG2c38DsCAYE/HGzCeXkan4TNiHfeDhkx5yuYeS/SFhzrXTK1lt8ZXQJNxQbSjboVjhGhyU7tmtTWUuqJqB1rBqkCvhHKT8CCurQ0I1jkyEjBm7ilnsPI1dBFXagy77w4nqhpS2XWXhKmObnyw+QM79Fbb2Obc26hVfn03VYN05cFtfjX8QbZmwWK/2X0RU7SkLNAW6Efh0fBGZG29IXbysK6foW5+Mj4qcUQl5gQpjZw3NJq1d+bmOPvICaQiYo6k9CDpNgKslWPNSWbEOEEhePfdd/H6629gPh7w+LXP4e7ZExafMdhki77HyVRjpVREc7UGgM3UaLF3OMMtBiJIw+XZr7cdEG4BL0nQRbGVyjTdGIz7QDQPsEPWCkixxo/fz0MvJD5/Qa3g7NXGUldawAhEJCNqL3ReNkxGACs4+T4kRBwDP9s8MVuLJM3dmynljGpmmAp9bt8opULChgSurymxUJnnGcfjhqdPn8KN21Q7ylboHn21B/n+XGuBoHP8a2hATAmHEIzP1vavbc2k9mGsxeuCnGOQbgW+cwI56ixlQSkVN8cbjoqbR0zsRT2bWSv0dS/ifaQUwoQaI5rtfefzGcfjcSBnqiTV+r5PzslmjXJD62pE473ooJvutX+TiTK+qXn1h1EEO3Fc9v3NybiAfqrxDnCtKP0Ery9+8Yv47u/+bjx8+BAPHz7EF77wBfzGb/zG+Pu/83f+znMPg4jgL/2lv/Tcz1jXFT/+4z+OV155BTc3N/jhH/5hfO1rX/tUb3q8+X2FIyYSyoahVtuJYg4tUXnhszMzD0JHymLKD8/cYFEQg9AkKuwQPhAQJdHKGT775/vprZuBWKHFcXACKHXuYlCgBEHpDcWhZ6XEMyRaZg9EwjZjhbm7NnIBtnUPsnO4mNp6WNe5L+pg+T/QnbQHuIeMFxhuFGQhf/Y5vav1bj3YNSe/hhb0AcFIpgdMacacJxxyxmFKOEwZsylEPHE62X0ikU/NRO0GD25vcTwcMecJooJWfF4fQbJgxvF0HP4vJJOuuLm94TUz/xJVxseHQOfdw+FAgqAI8jRh3VZ7+Hfp7LO7p7i/vzdC2WbOig6fslNzya3A+Ch2Df3mO+zNu8H/15XW1sz+MCQictGqdb1Oju29I08Rh+N89eCbksBHMbUOJMTn6a0z76b1nXvgHAgvVIeiBmpyRbrjUsHiypFdZmltLzkoPoc2MlSUYJJpzuhdej7Ib1b4d1Ns1UpyLdQgYaEigA7KHCEG6ODS9KbozaByHQv8auzI9eoy6tb5s0T2oqK0irVsuCwr7s9nrNuK0pjW3Lo5eHojoi4Dv3JoBUi8t1DQpWyAwEi0NNSqraJ2dsHNnlX3qdthbRt/wjwotOzyb5DQuKwXvPfeuxAJePXxYzx4+IicHlsTEC9yKNdvtdPuX6lcoXdOxjTNptaxA0Ce987Z5fHfhMIpxu9KkerFaB2y2ti8OlmUFexA/oKE55Jwo60B+vjsGVvedXuQ3vl8weWyoFSabEpMRLZUR/Cji9On6TA+d61EYiVOiPkASERvglIVm/FyFELFYG2oW0VZWciI0C/pwYMHmOcD5nnC4XCkLYA1XD4aU/XDOoyxIYt8y8rpzbxZqFKMybk+vid0dK1YtwtKWRAjTMZL1Njl2ykmpJgtn0hxPt8TyTRC77KtWLdtJI3XWkzFw3969yR55pHd3JxwczpSoaWK+/s7LOsCtcKAIY/RfnccBUTZCtbLgnVZOCrT3b7+Wshx7X3i6iZPb/b3VpzHZuvPFT8hyPjefnUO/XGvT4WgvPnmm/jZn/1ZfNu3fRsA4Jd+6ZfwIz/yI/j93/99fOd3ficA4K/9tb+Gf/fv/t34Hq/0/fUTP/ET+PVf/3X86q/+Kl5++WX85E/+JH7wB38QX/7ylz91dSWy+yJ40JN2+k/EGJi7Eyn7TWl3tHPDpgCmBpPp78RQPsy8mYTricZUVGfrG2QdusWOC936mt3E6TDZz8SAwMdmrkCpnv/SEFJADgcE2U3aADxXTDiF4D/qAAAtWElEQVSa1m3Moa4usQKr25zXDwntrlIKluNTDYomVwb6zaiLjkXpPyfYA+sHcFfQ/rybeiJEk2la9yscGwX0XfLZgRLqcOWMIZrVPVmat7cPcDrd2MZV7H016zQVDx+9ZOO2Cgj9B8g5mrCuG25ubhGEAYnuyVJbwzQlzOb94Yd/SpHJ07XhcJjhUeqlVNRW8PDBQ8L6K5n9Iua2aD4ygghIx7JcUFs3Ah6xJZJYfQRkvKicUMoCiIwCyfkCzxOcTXqt3ayx5Qqp6YNcWaMbSglqI3rhfiLaG6F3QzKqIRI5MxTND+DSG9S4EyEw6TQbkbALyZM5sNCbZvJlvHh1yDYE8rIgvL/O82GBE6hq23FF7GPIgCAMwoQoYgC6jU7TMMnie8dASITo1u7YA1fXaHNvC3a40zxDlbwWR0Dp9klkSEfV2BlaNtb9FapjvzMmyssFgtQb1o0hjDFEolsbxxHBOSbAGE9BxUa07GzneTKvGH5d2YpdK9j3K9595x28/NLLON6c8Mqrj/H02VN0reaGymLBYfGxLjcgJsE0T/zc7YqQbKRiWs2Ledr08Z4mjeY3Ys+4wGzl47Cgp7y3wFN+6f8EG3GKNQnNssksu0avfq/vfspRVYxEY3KasW300ag+trCRNsnTbeQz5SsFEuMPGgm14LqYphmXfkVChQxjwFYbNNOfqBhqOU0Zp9MROSesK+0h5sMB0zxhWRaiZ70DhoyUEKFax2re1Xc2qprnUbDmPNkzX9GxWxG4pDenad8HdD8TnNvTtaMXCjOEPIBRFDVHVIbRp08KuM5CiEiJMmvJNLBLOeHpk6e4v6clv6MpMKRNbS/p3ZAcI+X6+DDZmemf2//XR9Pk8+0InT9H3tABGOc5uXz0FeJI/P+hAuWHfuiHnvvvn/7pn8YXv/hF/N7v/d4oUOZ5xuuvv/6//P4nT57gF37hF/DLv/zL+N7v/V4AwK/8yq/g85//PH77t38bP/ADP/Bp3g6JUwa3KXHbUdGmFDBP9MFwKScAwsWqtPrIMzuKQLlgayaBg9UEIaAatOdGZzFEplT2jikksznvA2mI1ilAPP7c0JCukGAWwa1xTi5ikkoj1gIWkKaAOROrwbw+w6fz4/PqAJ8Ja9+N0NyFlHNHvYJTbdF1h9uc3+GPoR0v4Xq6KAgq6MoHqvcKLX0UcPw+khpVduKkCiAm06b6hGZAUCClCfNETwCoy9Pi4PC89NILuLl9aMRNoG4bi4v5iKqKnGecjjd48uTJmLEfzNdAVZHN1bCUwlGCVe+3tw84N66LXdzAQunmZoxVkuca9d2Rk91Ux7bxIXZLcvJPrFi2A4kcG165wzyPzpFqGA/kMi5UIFqEcSazw3LCNjcPHd0Io+I7qu6z6W+GuHvvmFMevJg8ZdzEG8gi6A2oXZHjRAfalLiWWjd0xNRj0tAKRybdrhPvE4y3oYOPdE3E9esPKOWjYf88Eq6UNTEgWiE9Oq4AK0ZJJoR5gKD5KmRXVqr5y5gjakoR82SHhRriabNwrkXnc9mI8moPUbgRns/a7c97BwLdcT0MEtj5ayx6aRoGAN2eYR+HurV/TCQsugkiFAP9oiMocH++x3sfvIc/dft/4KWXX8aHH36Ajz56f/B9YnJOUef4TtmcoPGe55zQxEdUDTEHQ934nkut6GphhFBk8yASbWOUK+BnyjkZ3ylZyGbZ95jOSoN8HEpmewhD1ZhitPe5o43wDrzSRC1FoEYYP8z3Ljg0C3SifCSpsvlMMUKMyI7eUMHYDNhYnAKDBpGOENQMEM1t1RSZIbKTZ1Gv46B3jss0Tcy9EVfsXMj18PsuttfplQuumLN2J5eF9yLDKH4DORoZaZ3JzdEzdax49v14N7rbRzdEp2y032D7WEVTjP09pThQUomCHDIexAdIMeHu/g6lMAbgMBuabIgaEd+O3lxU0bGVDa1XaK7QaeJZFmDvx8MlxUbnTFTfDR11L6DhBVQY/jmiCgkZ0v5fIMm21vBrv/ZruL+/xxe+8IXx57/zO7+Dx48f44UXXsD3fM/34Kd/+qfx+PFjAMCXv/xllFLw/d///ePr33jjDXzXd30XvvSlL33qAoUVILtWV6mIGWjN1kXTZyFCux0MI2X3ymvBEjm9u+1KOLQW45tEC9QSEuRab4z6E0BBVKE2j0yfoMIud3SSVjF63DQLCR0bp8ebB3P0dK+PYKe8e5uYEngUE4SAYVXN7oESQoDC4bQdKaGKwojCkZu/X7fWqFLiE0MypXsREJoPQBeM+rt30+VT+gvQ5p5fygRP78Apd5zo22EL/KBAAEdl3ExNKaWK4+nm/9ve18bYVVXvP3vv83LvTIehQ2mnBaxVCVgLiK0vRSICiiGgUQyg8QWjX2oEIWCi1JjiBy3xgwaJoiJBiSb1Q8FgUKBEKBqjmEJDC6bWUKSQjpVC23m755y99/p/WGvvc4e2QPnlTzu4n6ShnXuZubPOOXuv/axnPQsDg3MiPUgEKSXw5F2vFI6dewyfNrRGERKUTocTGnnYe70KkHhZazE0NAQAmJqaiqcQYzQ6ZUf+2ep7rExV1SaMp2fr97LsSGeOAxn2kWl9ItoFqa5rKM0j251zsE0TO4mceB8oiKhMhzKMuHZyASk+8KzIF4YLbUmC73/RcGS8sFZVxUxIkYtmoUHRKVF0OzyDxEvJzOQo8gKZ0UDZASSZ5nsOMpk3lKS4dsHPD5/YvCTePpyCQ9lENpxYVqB2Uw8aHS4DhXuNohOl0vzsGfDhg4hgkIkPCjMBec4Cy6qq0OmUsmkbLkcB0FnO7CQRsqwT54EAYSiaRi4+MEoFUR8nAuwVoqSrScT2ykhLqAGbg0qXnJSJTbj2BDSah+IVphBdAGLsgr9KlnN3mbVWWtOZvdyz53ksGF2AvCgxd2QE4/v3Svs5m0fqTJg2fqpEn+BgLeuM+HQrPipKieaFmTLvHBrr4TMtTsB8KAqTjYNztrU14CCbUtiICt5wHPu9xCF8YU0hgvLEjJNW8JWNLFRoLyUiKM/3VWAWQsk8tMeGeyaUtysxOuPxCCauwaERgrxDMGLUJY8yIFgYJSMmTIFutwulNaarKUxNTnIZomDL9kaez1DGjVXVkNQ1Nbdgi1uw0Sw+BlR7XXTQ6DFzMD09zbOEcgO2TmjLviF5r5sGnajpIC6ryxobrAOYVeW1nTVgYpCGMCSUhyf2u7aGa9LPCpadEoQwgLNB3dRS7mnnirWMuUwTJ3ZVruQwYo3leWNSpuf5txmyTEu3YS6yhtaIMpRytOgtmQVFbL44HBx2grJlyxasXLkSvV4Pc+bMwV133YWlS5cCAC688EJceumlWLx4MXbs2IFvfetbOO+887Bp0yaUZYmxsTEURYG5c+fO+J4LFizA2NjYIX9mVVWoqir+e//+/QB4UWG7dM7eea/2KAtOUPJcbgTPZmI+CMm8bAGG4DXgvIVt+hw4lQFPweb2LnIOJL3uLnabSAknZI2WBU8m4wurdXshlNR/g1A2bPrcfskXPIwHJ8cdSdz+yJdHS+kJ/SxHuBFJgZTvY0Dk/UJpO+m0CEJdzx8IGRRIuhmiqt17dnjUbKQECyjiwV7QBAceeAiloGTeBz/ZIkBkJh9GscKcb1jCnDnDGB4+FmXJJmUgBe/FPbFp4P1UTJQyk8uAPQdXtxb/jeVauCWHY+cei05RoraEgU4H+ycmkOe8MTWNFUEuPyR5zqfXIJZtZPMK8QsPedM04s3g48CtQJsG9gWKdRmhQye0tM6kO+vY9lqUbORWy+kq1G5j10q4ZkRxKGCoRIQTWqiFRw8H2fB5siykZNOeYPje1CDFQtCmqVHYAibjhb4oSjSNA8/MQXx/p9PlJEG+7G07wC92EPC2iNo2kqT0d3vJZoT2cyiI9kVeJ5B4BFFfWzngIXqpwHDIZmTF8p9HLbRMKdPQLhrghcWek5cCSleo6or9LDSbHGqtkYl3g1Fh6KWC0rxJc92f2CJeaXjXsO9EZGIyFAWXRggENJyAKU1RV6CUATK2NnC+iUmvQkvNh5hY65AZJ9oNYHJiAnv27MHo6CLMHTkOe57fg/37XgBPjQaXnTjTYV0b3zax/p+ZAt5LKc82Um4QgzHF+jVYAhlhthTgiTcuLzYARq6pdRbaZzLioe145DXXM0snJ3xmxpi95bJHB03DTGS/5o8cp1V8tvFxzQj8axRnOxeZZ/gKNSBt0jxckMuI7JOiyEPrUmaeaenCaw+Y2hgMDAzA5Bp79+3F5MQkqrxGlhk+cDgXWWR+Jviu1VpzF1GvFzVkAXwYyWd03/H6aeAs6+Kc446YcLgAXBzN4JxDY2suC2uF3JRRUxZa6g24acE7LzOpmLXO85IrAQSevxOviYul+HC/hYNMWRTIhAWc7k1HgT3QsvBxWrjcmwqsPaybGsqKWWJuoog2dMR6H+aH8doa2O5QVm8a3l8yiL+O4k6v4FPzanDYCcopp5yCzZs3Y+/evVi/fj2uuOIKbNy4EUuXLsXll18e37ds2TKsWLECixcvxj333INLLrnkkN+z1U8cHGvXrsW3v/3tA76uFGfYjgDyFjxLJUNeZLLwCGviPLxXCGSCDg8V+ARiHWG6trElN140FmEIUyFdONRScYEyJqFHnfVwGaFo6x6yB0itV4fTgkOuNZTOYrISjLOC+ru/Lh9uxfDfaIccFlr5f4XrgBXFu+5LSEKcyfOi66iJlDnXhD3IiHeBZzMlPp1qZOThJMNmLwn5HBQ6P/pqqsIkWDlxGZ2hO1Ci0+0gjOnmOS1ALpqCyakpOOfR6XQwMDSAbmcASmns27cPYdgdM10enZJFYCxUK7D3xf1oqhqVbEAhSw9tgE6SQwCYnJxkRqZPGR+GCvLDJHoPWZSKsgDJ/JDQMqw1d1NoI8PMao/K9wCw1sT7gsuIWqNTlsIY8HWyco3C4K2o8BcjqizL0Ihex7tQx5bZM1LPLoQZCV1Z7TPEgs+Q/DQ124Z7x6JhqvlU6DxT69RXriGZgk0gaN9XZuy7fwkkow7k1BxSZWFCtM7RGh/KfaHDgq7iPdJYK7R5aMXlIYDtvA8drdWdYrZSIUeYGus9u/3mOS+8kAnDSnGpTZNCboAGIoAF4kbLZottBwJrofqF444Ta2J9DbyCzrl045RD1slQdkpJzMLMEg1m/9ibhV1/NahhttEoEjaTfUsQryknAoUuEHQ1u8fGMHLc8eh0BjDv+AWYmNiLYELnSYtBnIKKnXrBBdQDGa9RICA3XLYzWcZttVkGJ5uw8YTMkJQh+9g+pTHQZS2ccw5W/GGst+2cJEkoA/vUX8aBIljvUHY70unTyPvFQr3k5M42bDPgZKptEOUro2Aog22sJFWE2lpYR6isQ7dTolNwwqkU+9CQ47ZmJcJt1XdYIA9ow34ine4cjGQF9o/vRdWbjqV4gohu4zovCb8FdFmi7HZRVRV6TY0iz6FI98WeS7D9ST6yvlEVCsIes8bR2gZa3LGbhl2TQyJA8twKqQsS9tzL9wpsU1iXlNIos5bhbqyOSbaWC6SE2YIiKTkZdLvd2MQRmywA0RMqua6B2WrFsVVVwzgWRvNzB7Z4ED1bEEkXRRHbnIP+JJTZPXmZxaVnJHyvhMNOUIqiiCLZFStW4O9//ztuuukm/PSnPz3gvQsXLsTixYuxfft2AMDo6CjqusaLL744g0XZvXs3zjrrrEP+zOuvvx7XXntt/Pf+/ftx0kknQRtECpo8kGmNvGDzpcaSzH3wciINdJiJRmmkeLO3lkT4ygyL0goQES2XMQlwXmi+DKHlCqKzCOUNZYCizOSEQ5HGD+cEXrj7Wv4I3NIrFLGzLDgMpQ2ANR2kuB2QzbXkJB3LAq3WJZzIo7BNfmOFoBNo665eZsQ4KX9AKW6b9tyR4ILjKjR3KCDc7NLCDWmlNko6WGZ2DJBCFG8REapeTxKUVp/D9UqLxlZQChicM4C5IyMwJsee5/fEjJxv6Lbjp6oqdAdK9OppTE9Pc+lNHngvp6J4spafE0z8BkQERsSUfp6XsZvHe4fGNsyU5LkUWoCgOQACK8QxbRoe4KWkmyHQ+t5z90YQiOeiQeEaf94yDJLUcW0+JCLS3o22/BCSWCNj6r237f0TrznES4Y7AGC53dMTd9H0pnuomxpNHbq0COQdvMx/YdbIQWdM93ppJYZWTM8LmCULrEhYpBUPcxTTLBYPe0DxomQM35Nc59bw0WWZP4eSRFUZE+8jSBmSE13HJnoxoSAxuTOyGLeHHCIWVCsV2E5uYc0k5iYyAvIMag2ykmx5fo69+JV4x+6/obuENSdsPqe0FZsBSFmlfd607DTO9bXxS0mI5DOQaCRYG5KB4DE+sR8v7Pkv5i8YxfDcYzG4ew7279vLSYCzbBxmQkIUrklIlPj59uSZTdYKWaYxNW3ROAfyrKFCTcwwl1yGMhDBcyjxaQPSFO97a1lIXpZFvBcDM0C+7+AlCavznp29iZOb0NIamsOC7o48b/ZOiZBUYpUZg9o61vR4j9p6SewJUAUGyjKWkJqG71knDBo0ly7hnIiRp6CUwqBi0fecOXOQafafsa4WdggAwsGR4+q8xeTUFLKyQJZn4g5uRT/X+v7ExV1yNK0NdMYbddM04nnkpOxTyDrjURQdHlngPaBcXKmjrxfYq0krjUrWawUuvYTry7O7MkkKWDvC3Uv83IbyYdiqiDi2JEwGgBmdXCroNFtiJq7/AA88JBERO+laMjDClLQDFfvLWmGN895L67Tsw/4lP+Rl8H/2QSGiGeWXfuzZswc7d+7EwoULAQDLly9HnufYsGEDLrvsMgDArl27sHXrVnzve9875M8opY/9pWB7ds42dc6ugkxrW37AxLFMQxZhF07/fKKD1P8ctQ+7UsSdLzZUe1vavD8rhGo3fvIsAjPxlNZ/+mz/poG4CMjeyacicOYNsoBmxbMOxlQUBjqxAyDN+L78mUOmGz9bjI+KmzH/3cvP18iEXbCi6dBGShneSTIim6MsvFqSjsAmQfMwNqa4M0CLTqP/ZA3erKpeDe8UANOWrTQbHxEIeZmhLHMMDLLCvqlt32emOIRqaM4QelNT3F5qBzE+PoUo3pLMP0yYtVa8DpSPp6pQBiQpqYSFpK6beDonIhQyAdd7B1+3XTc8oj0MCvNRE9HpdtGV+RrGILaRBj0TJwBeauMmdj8ElkfHU4ZMsHZs+W/kRMYLe5/dfUhcJOGEnMC57suveCJpY8zgncfk1BSs81CQWjqxpsY7Je6cnKTlWS5zbYL/CuurgiskyeJnxMgsbGwvZUDjIo62nMb3LgtcdRRoy4KvjdDhPC5COWmJRVhI+YbKci1D7QAntXkonmFCIla33rKexZPU8CGaLu56CJu5dR6aZI4NHFSec2ca8cnVOo9ghGethakq+XnsS1L7ltIGgkcFj0FoHWYhlgShxOuZhYy+NoFuV/DWYvfuMYyMjKDT6eL4+aPYt2+cy60g1M4DlEObVrcWlheS5BTiAaOVkY2IvX08cjmfyLyYIoNRMl0609C6LTtquW5hPlcVBOqi8SKIQNObOAMG4FZ/vnZcUnUV3x9N9AwJYmPx2MkNQiVcKfGuKTLe2GuLxnFykpkMsBpZw2xMLmLUUI4lKReanGA0u6N68DM1NTUFD2CQBmEyjaFjjkGWGfR6U+0GDkkyAYTxFbbhqefBe4mHjhKiQ7I4tQb2myS27GdlUJYFyqLA5NQke7/kRfx/nGuneMdpzuGgSGw5wQMFG/B4BxUPUN571ORgvZXW+RwDA3MwWAzATPcwTWLpL1YKAYH1A3G5sFAFtGZmJ+wvSuXikCvPiJdqg1Ki0fR8ABAGJpfPCUDW0D7dSV9psPWhemkt4JVxWAnK6tWrceGFF+Kkk07C+Pg41q1bh4ceegj33nsvJiYmcMMNN+CTn/wkFi5ciKeffhqrV6/GvHnz8IlPfAIAMDw8jC996Uu47rrrcNxxx2FkZARf+9rXcNppp8WunsNBKGPkhhMOT461Co5iiSbUfAMV6n0D7w2AAgSZZgmAdHjAVcwCKZRJ0QpaFQDq2yzYsZFb0TJxG1WhY8gT4Dm91lkGr8R5EwCU/FdO/cGB0BgTO1CY3fGiXegbUR566VXoPPAzTvltfGQGCUFOr6IMl8XI2bCp6bjgKdUyM3zCVPGUzmWk4DtBMIr9SMgRvPaA9zHOvEjyKHrveuj1GgAaPCBMY3BwDrLMo8gzDA0OspgzL0CeNQdDQ9zBMzU1haLIMXdkGJnJsG/fHngLvPDCC6hEABq6uRRB/Ai0zOThlYMpX49ycLDP3CiH0kbcQCUGsqmXZcHul97KZ3ZQOuPFjLjbyHtCpyxRFKWMrzfSLSQD7JRi+3XnYokhnFxCQqnFZK6p2WY+z9n50buGNzFZ7JxvHXe10txO3UfRhiShaSwPA9QyCbbbRZZbVJVF1avgPdeClRX2CgBJgtY07Cw50C2lxTDMouLE01orCYa0ERO1p0dAWAvflsPCvejDQMFwbxKguYU0LOoAPzck6n9OEDyKkh1ObePhnLQvyiwcQoOmNmBjNgLISfLBDrQegNJetE4OUF68aNju30lpQUNKO+RAXgEqg84KKG3hxW03+H70qgpZFowVxUvHWThy0GCzPLIEpYV9IgdFQCFCfS2fh0XNnDD164YUFMb37cMLL+zB/NGFGDluPubsGsOLe/7bWtiTh3NaXHBb2/nApHjipIsPDZyE9+oGABspskswJ19Zzve7Ek8OLW6jTDwx6xPKq1VdIxdLhrBuAMwGOfnZRil4zdoJIwc6K+UO7i6jePpXPuiTgFwFZ1ceSTE4OIi9+/ZJIkcg5QDFz4q1DlkRdCJFFKCDPJRTIM3C9uDz0TQN/NQUAEJZ8uElMxkGpLTrrKzdGRtk8qBQh7rmtnzXf7hRzCf4sKb0lXuYteXDycTEBMqyRKfTwZAZwuTkJHrTvVjG5d2aO8TigEuto90FO01LguuJBccyUJTXV3Godg62qVHXFt1ul+0B8gxOEgi+lqLjIxUPS+xMDWSGE01nmTmuqwrOssBea640BIsBrbjEH0ZLOGnh57JPLslX6MpzyHOSJKst/ffvY68Wh5Wg/Oc//8HnPvc57Nq1C8PDwzj99NNx77334sMf/jCmp6exZcsW3HHHHdi7dy8WLlyIc889F7/5zW9i9wQA/OAHP0CWZbjsssswPT2N888/H7/4xS8O2wMFANOqnuAU9amDFbdOUvsmFayWw4ROk4GIXQ+9bL5wUpvm3ZwvqtTjc50jDhwMi7PAOTas0Yo7VVTchBAX8Agp+fAfUaVTS8/meR7LH2GRDy1qqm9zYzZTz/zaSxC+b3h/OC0Yk0EZDet52J+WuIeSF0eM2zW9c7zgSlLllYfOwJOKjUdDNSxZNA2bwSkRvPDH4SSqUUDWABBnFaU46w4nhqJgtmhwkE/uYbqom5wUIyCHoaEhGKMxOTkOa2ueA1gDWVHEhyO0mwNMrXtpjXSubmfxOBdr494DTV1HRX7Y7oPoKzASoVbMQlXLAjtibU5RdOMCzqUdO0NYaOXnhVk54ZrGhYKL3qIBcvCGhXbahJOudKAFuhyEQhdtR0iWgYRxCGPgAR6CWRRykvQEa6clwVBQrk1kNWz8TDw0TsFkKrJ7xmRSfgylO8lGwlgHEef1z+sIz0RkKXwTvx8QqkNyaIB0S4ETB6qD94e0addSpvVhgJmGslzq4lbQOrJIRkoo7LMiZVnNBwsWV0M6Swyca+LE7FB352ethvOETqeUhCKT2VKh3dtBqUZOp8KwxsRejMKIOIlXgCIPlWnWfGQawXMCSnQe3kamrShKsADUYc9//4u5c0eQ5wXmL1iAycn9cHIfsaeJA4zmUpIcIGJCSMGPiQWWXMZsYEjF6eqaLPLcoMjzeJiNLAhaTRJbH3gobaTl2KIochHFcmLnwVqXQEWEclZw4fUintWZeA41jWgReF3W4VoRu/42dQPT6WCOd5ia7rWzgHTr+Oscl4x0X9LgrEVja2Z9FLfYs88Isai2ruCbCka8gYIvlpbhnWHxC6XfoigwJeMfiALTBeS6LWMYk4t+rZHnkTUWTVNjapKZ3sCGENgDxzpOdiDlDiPO1N43YiUQnvmWXWQ2uB2y573n9UrWdda0OH5eVVCH8WvasOjcOg9vKTJFSvyMlFbMmjoWufaqGq7n0O10pUmA100vXV9KE0A8365pHLyv4axHnns+KJrgq0IA6mgBobVBUXIHHnr/nxKU22677ZCvdbtd3Hfffa/4PTqdDm6++WbcfPPNh/OjDwq2FgidGEa0AfJHtRu3907EVwSteMGvRake8gijuc2LKWUnIqt+34PIfcTMmb932PiFpiUF7UMnQ9vFADBd1vImUqdTTC+HtjxyIakQOk7z5FEnpj1MZYBLF1Jyid1HIHFk9HFUOi+i4jMhmbgCQC6wQqFpP7joOkn8nCwyvhVQZcwo8K8TOoR4I7WeACdxIcWZHXnZz4z4bRiJCVBVPDrdWhY1l52OnCga9HoVer1pWSyYFp6cGkdvmgcHZrlBlivonF0vC8PmUk0T6Mr2AWD/Cf53Je1wtbT8Ethgiu8ltpPn4ZLiHRE28rCgIBBqfOLKCy4Z8JA0fjXGSmt4YVBEpcYnYElKIAyGs1xHJyIZzQBoGJkUC7nHfNTUhA0xPPhW1YBt4IQVgbj8Qhs4D1jrhT1hVquhpl3s+TLyVqshQ/Y4wYldTpAOEN+KMlmN36ARVkMpBU18f2sFZhSjv0nQS3CEgmgWyoNghfEgMIHIMTLGQOWQ2UIK5Nm/RSkvHhgKzhMaz3oU8uy4zC7dzGA6IsAFYzp+j5bSA7Mo0pIbn0hG09g+XUVfBxcCjd9u5UQe8OE5pmgQF8qpgIdWLNbXQQAsYmF2aeXv6VBDBTdSrzAxMYH9+/fjuHnzMTwyF8e8cCz2vfifeLAhcBk/RJZFpax/IkBMKx2XebRBkRdoLA9sywuNHCWL1A2bHAajQEBFdtZ4nqDugkEYOOnnhDu0acvmSo1oqxCTeCiSMSJ8j2fi1F3VNeBYlxFZWo40yAAoMjir0O12QQBqG9iouIyK5sIjLzIUxoCUhoVCJRu1dQa5VuJ8Leyvs1B9jA6BtRzG6Fh68T6Iv7msVGQ5pqselLDOBIIVpoifdQ1TcNLQ1A2c4nvXGGaZqqpGbS3yomCNhzzLrVDdwXtJ0jzHzhguI2VZATamoL7yrXjrGB5oGkqGYc3jvaivAkDCsBkDbbhkZ62bIYhtJ6UX6HRLFL0KExNTGJ+YQFmUzMwYHpjrlYxtgMxc854Hfsr4l0wOWXzI1LLGa+Q5P+dKzCGNOVCucSjMylk8YZFomgYZmWiUFSgL0b7xw+tYhezEsyE6tqo+LxLZpwPb0FjbR1EGU62Z9HwAO7ZmADTqMASuvx6vEEsC8QlTAEG6hsiBrAcyTl54LklQUEM0CNyB5F2fnoR/OlN+zsebVZlQ2hHtAknXD8Cmcp7gbLAjNjELZ60JSSnIcQeUb6lsoxUyTSySajzIOjbgUTJl03nYuh0rwMFiNqbMc3Q6uXx+OUlZrmcSAV0qUGQ5JppJ1ko0DnVTc2lFKdSNZPaWN74s43IMOU6QGmcxPj4hnTheaMcSUEDdhBZf0fGEUkmhpQTjZeNUUODr3Yg9O1HwDGAFfb8NtnVeTOjkBG4liYCYOHmeLVJVFWtTJLFw/er5WHcW7xqq5b8cI2cUqqq1tNZaoarYdj3UdZlyJXGcpHi/orJQsKime5iampYTIHeFaU3RTLBpeLHMi0zKXEpKPo08ZxZV3bDg0wRRKLM+Loq+Cc5xYlJXDWsOLJsRhiFzJM+eJi6ROm0l6eaOr5YEVJJsaXjNvzcF1kcRt8pKCcdax15E8jwaAFZ5GORiJ8BdOeG+0NrASRybph0EGOAUoEMJ0xhY1875CutL/+cMc6tIBd+ioD8Lb/KoPJeeotOtJ0kKSLQxnNg31iLL+fmcnp7GrrExDMwZgjasm9i3778IXRpBeMoVaElGhFkKnj1Ka55fRMxuWuLNhGwGneUAeTTWRerfWp5ro2VtCfe/I54fFGLIbrqFuLUSpqd7UJrQHRiQLjaCbVjUaRuZRuxdy76B2aOqYs2CybhMx7orJ2sqp2JFUQLBIr1xM8ZusAeP43Icgu2DQt1YGFKI89DkalgxE/SOWDSdZ+IOrBAMIoOIva49KqqhdM5dcHUjByVOsIyU22rToNPpwpgcNfHBKtgCBMbEKaDw8ryJK7XzHt5ZEDXwjrNKT+xIC0ly8pzZ3HCwYCaUhxIabeRw5VsGlHPUyDKFTkZu+uCvMyMi4mwR3gYmXimS0iCPcNm/bz8mp6bQWC4ftfo9RAaYKw2A91wmLgoWFTfWCiPIwySrmpDXGfJKWDu0e+krQdHhFISOEjz77LM46aSTjvTHSEhISEhISHgN2LlzJ0488cSXfc+sTFC899i2bRuWLl2KnTt34phjjjnSH2lWILRnp5gdHlLcDh8pZq8NKW6HjxSz14YjFTciwvj4OBYtWjSjJflgmJUlHq01TjjhBACIk5UTXj1SzF4bUtwOHylmrw0pboePFLPXhiMRt+Hh4Vf1vpdPXxISEhISEhISjgBSgpKQkJCQkJBw1GHWJihlWWLNmjUHdZhNODhSzF4bUtwOHylmrw0pboePFLPXhtkQt1kpkk1ISEhISEh4Y2PWMigJCQkJCQkJb1ykBCUhISEhISHhqENKUBISEhISEhKOOqQEJSEhISEhIeGow6xMUH784x9jyZIl6HQ6WL58Of70pz8d6Y90xPDwww/jox/9KBYtWgSlFH7729/OeJ2IcMMNN2DRokXodrv44Ac/iCeeeGLGe6qqwlVXXYV58+ZhcHAQH/vYx/Dss8++jr/F64u1a9fi3e9+N4aGhjB//nx8/OMfx7Zt22a8J8XtQNxyyy04/fTTo7HTypUr8Yc//CG+nmL2yli7di2UUrjmmmvi11LcDsQNN9wQB9+FP6Ojo/H1FLOD47nnnsNnP/tZHHfccRgYGMA73/lObNq0Kb4+6+JGswzr1q2jPM/p1ltvpSeffJKuvvpqGhwcpH//+99H+qMdEfz+97+nb37zm7R+/XoCQHfdddeM12+88UYaGhqi9evX05YtW+jyyy+nhQsX0v79++N7Vq1aRSeccAJt2LCBHn30UTr33HPpjDPOIGvt6/zbvD74yEc+Qrfffjtt3bqVNm/eTBdddBG96U1voomJifieFLcDcffdd9M999xD27Zto23bttHq1aspz3PaunUrEaWYvRIeeeQRevOb30ynn346XX311fHrKW4HYs2aNfSOd7yDdu3aFf/s3r07vp5idiBeeOEFWrx4MX3hC1+gv/3tb7Rjxw564IEH6F//+ld8z2yL26xLUN7znvfQqlWrZnzt1FNPpW984xtH6BMdPXhpguK9p9HRUbrxxhvj13q9Hg0PD9NPfvITIiLau3cv5XlO69ati+957rnnSGtN99577+v22Y8kdu/eTQBo48aNRJTidjiYO3cu/fznP08xewWMj4/TySefTBs2bKBzzjknJigpbgfHmjVr6IwzzjjoaylmB8fXv/51Ovvssw/5+myM26wq8dR1jU2bNuGCCy6Y8fULLrgAf/nLX47Qpzp6sWPHDoyNjc2IV1mWOOecc2K8Nm3ahKZpZrxn0aJFWLZs2f9MTPft2wcAGBkZAZDi9mrgnMO6deswOTmJlStXppi9Ar7yla/goosuwoc+9KEZX09xOzS2b9+ORYsWYcmSJfjUpz6Fp556CkCK2aFw9913Y8WKFbj00ksxf/58nHnmmbj11lvj67MxbrMqQXn++efhnMOCBQtmfH3BggUYGxs7Qp/q6EWIycvFa2xsDEVRYO7cuYd8zxsZRIRrr70WZ599NpYtWwYgxe3lsGXLFsyZMwdlWWLVqlW46667sHTp0hSzl8G6devw6KOPYu3atQe8luJ2cLz3ve/FHXfcgfvuuw+33norxsbGcNZZZ2HPnj0pZofAU089hVtuuQUnn3wy7rvvPqxatQpf/epXcccddwCYnffarJxmrJSa8W8iOuBrCS1eS7z+V2J65ZVX4vHHH8ef//znA15LcTsQp5xyCjZv3oy9e/di/fr1uOKKK7Bx48b4eorZTOzcuRNXX3017r//fnQ6nUO+L8VtJi688ML499NOOw0rV67EW9/6Vvzyl7/E+973PgApZi+F9x4rVqzAd7/7XQDAmWeeiSeeeAK33HILPv/5z8f3zaa4zSoGZd68eTDGHJDJ7d69+4CsMAFR9f5y8RodHUVd13jxxRcP+Z43Kq666ircfffdePDBB3HiiSfGr6e4HRpFUeBtb3sbVqxYgbVr1+KMM87ATTfdlGJ2CGzatAm7d+/G8uXLkWUZsizDxo0b8cMf/hBZlsXfO8Xt5TE4OIjTTjsN27dvT/faIbBw4UIsXbp0xtfe/va345lnngEwO9e1WZWgFEWB5cuXY8OGDTO+vmHDBpx11llH6FMdvViyZAlGR0dnxKuua2zcuDHGa/ny5cjzfMZ7du3aha1bt75hY0pEuPLKK3HnnXfij3/8I5YsWTLj9RS3Vw8iQlVVKWaHwPnnn48tW7Zg8+bN8c+KFSvwmc98Bps3b8Zb3vKWFLdXgaqq8I9//AMLFy5M99oh8P73v/8Au4R//vOfWLx4MYBZuq697rLc/yNCm/Ftt91GTz75JF1zzTU0ODhITz/99JH+aEcE4+Pj9Nhjj9Fjjz1GAOj73/8+PfbYY7Ht+sYbb6Th4WG68847acuWLfTpT3/6oG1lJ554Ij3wwAP06KOP0nnnnfeGbsf78pe/TMPDw/TQQw/NaGOcmpqK70lxOxDXX389Pfzww7Rjxw56/PHHafXq1aS1pvvvv5+IUsxeLfq7eIhS3A6G6667jh566CF66qmn6K9//StdfPHFNDQ0FNf5FLMD8cgjj1CWZfSd73yHtm/fTr/+9a9pYGCAfvWrX8X3zLa4zboEhYjoRz/6ES1evJiKoqB3vetdsT30fxEPPvggATjgzxVXXEFE3Fq2Zs0aGh0dpbIs6QMf+ABt2bJlxveYnp6mK6+8kkZGRqjb7dLFF19MzzzzzBH4bV4fHCxeAOj222+P70lxOxBf/OIX43N3/PHH0/nnnx+TE6IUs1eLlyYoKW4HIvhz5HlOixYtoksuuYSeeOKJ+HqK2cHxu9/9jpYtW0ZlWdKpp55KP/vZz2a8PtvipoiIXn/eJiEhISEhISHh0JhVGpSEhISEhISE/w2kBCUhISEhISHhqENKUBISEhISEhKOOqQEJSEhISEhIeGoQ0pQEhISEhISEo46pAQlISEhISEh4ahDSlASEhISEhISjjqkBCUhISEhISHhqENKUBISEhISEhKOOqQEJSEhISEhIeGoQ0pQEhISEhISEo46pAQlISEhISEh4ajD/wO+W/M2eMzZYwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load the input image\n", "raw_img = cv2.imread('./mats/1_BasicDemo/summer.png')\n", @@ -95,21 +101,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load the patch normalized image\n", "patch_img = np.load('./mats/1_BasicDemo/summer_patchnorm.npy', allow_pickle=True)\n", @@ -137,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "2bd6ae95-2a79-4b45-8a60-503079339739", "metadata": {}, "outputs": [], @@ -155,29 +150,18 @@ "\n", "Our network consists of the following architecture:\n", "\n", - " - An input layer sparsely connected to a feature layer, 784 input neurons to 1568 feature neurons\n", - " - The feature layer fully connected to a one-hot-encoded output layer, 1568 feature neurons to 500 output neurons\n", + " - An input layer sparsely connected to a feature layer, 3136 input neurons to 6272 feature neurons\n", + " - The feature layer fully connected to a one-hot-encoded output layer, 6272 feature neurons to 500 output neurons\n", "\n", "Each layer connection is trained separately and stored in different weight matrices for excitatory (positive) and inhibitory (negative) connections. " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "6d98749a-8f28-477b-871c-93626e96786c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load the input to feature excitatory and inhibitory network weights\n", "featureW = np.load('./mats/1_BasicDemo/featureW.npy')\n", @@ -196,7 +180,60 @@ "id": "826213d7-7721-440c-b1a8-47fb613339eb", "metadata": {}, "source": [ - "In this case, we have more inhibitory connections than we do excitatory for the input to feature layer. Let's load the feature to output layer spikes and visualise them." + "Whilst it might be a little difficult to see, our excitatory connection amplitudes are on average a little higher than our inhibitiory. However, we overall have more inhibitiory connections that positive to balance the system.\n", + "\n", + "This is because when we set up our connections we use a probability of connections for both excitation and inbhition. In this case, we have a 10% connection probability for excitatory weights and a 50% probability for inhibitiory. This means as well there will be a high number of neurons without connections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94acb2f4", + "metadata": {}, + "outputs": [], + "source": [ + "# In this function, we will plot and visualize the distribution of weights and connections.\n", + "def count_and_plot(array):\n", + " # Flatten the 2D array and count positive, negative, and zero values\n", + " flattened_array = array.flatten()\n", + " positive_count = np.sum(flattened_array > 0)\n", + " negative_count = np.sum(flattened_array < 0)\n", + " zero_count = np.sum(flattened_array == 0)\n", + " \n", + " # Calculate percentages\n", + " total_count = flattened_array.size\n", + " positive_percentage = (positive_count / total_count) * 100\n", + " negative_percentage = (negative_count / total_count) * 100\n", + " zero_percentage = (zero_count / total_count) * 100\n", + "\n", + " # Print the results\n", + " print(f\"Excitatory Connections: {positive_count} ({positive_percentage:.2f}%)\")\n", + " print(f\"Inhibitory Conncetions: {negative_count} ({negative_percentage:.2f}%)\")\n", + " print(f\"Zero Connections: {zero_count} ({zero_percentage:.2f}%)\")\n", + "\n", + " # Create a bar plot of the percentages\n", + " categories = ['Excitatory', 'Inhibitory', 'Zero']\n", + " percentages = [positive_percentage, negative_percentage, zero_percentage]\n", + "\n", + " plt.bar(categories, percentages)\n", + " plt.xlabel('Category')\n", + " plt.ylabel('Percentage')\n", + " plt.title('Percentage of Excitatory, Inhibitiory, and Zero Connections')\n", + " plt.ylim(0, 60) # Set the y-axis limit to 0-60%\n", + " plt.show()\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " # Call the function to count and plot\n", + " count_and_plot(featureW)" + ] + }, + { + "cell_type": "markdown", + "id": "f180220e", + "metadata": {}, + "source": [ + "Now let's have a look at the feature to the output weights, and see how the distribution of excitiatory and inhibitory connections differs." ] }, { @@ -207,24 +244,18 @@ "outputs": [], "source": [ "# Load the input to feature excitatory and inhibitory network weights\n", - "fo_exc = np.load('./mats/0_basicdemo/fo_exc.npy')\n", - "fo_inh = np.load('./mats/0_basicdemo/fo_inh.npy')\n", - "\n", - "# Create a figure and a set of subplots\n", - "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5)) # Adjust the figure size as needed\n", - "\n", - "# Plot the excitatory weights\n", - "exc_plot = axes[0].matshow(fo_exc)\n", - "axes[0].set_title('Feature > Output Excitatory Weights')\n", - "fig.colorbar(exc_plot, ax=axes[0], shrink=0.4, label=\"Weight strength\")\n", + "outputW = np.load('./mats/1_BasicDemo/outputW.npy')\n", "\n", - "# Plot the inhibitory weights\n", - "inh_plot = axes[1].matshow(fo_inh, cmap='viridis_r')\n", - "axes[1].set_title('Feature > Output Inhibitory Weights')\n", - "fig.colorbar(inh_plot, ax=axes[1], shrink=0.4, label=\"Weight strength\")\n", + "# Plot the weights\n", + "plt.matshow(outputW)\n", + "plt.title('Feature > Output Weights')\n", + "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n", "\n", "# Display the plots\n", - "plt.show()" + "plt.show()\n", + "\n", + "# Plot the distributions\n", + "count_and_plot(outputW)" ] }, { @@ -247,7 +278,7 @@ "outputs": [], "source": [ "# Calculate feature spikes (positive and negative weights)\n", - "feature_spikes = np.matmul(if_exc,patch_1d) + np.matmul(if_inh,patch_1d)\n", + "feature_spikes = np.matmul(featureW,patch_1d)\n", "feature_spikes = np.clip(feature_spikes, 0, 0.9)\n", "\n", "# Now create the line plot\n", @@ -267,7 +298,9 @@ "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283", "metadata": {}, "source": [ - "Now let's propagate the feature layer spikes through to the output layer." + "This looks a little homogenous, but this is the feature representation of our input image. \n", + "\n", + "Now let's propagate the feature layer spikes through to the output layer to get our corresponding place match." ] }, { @@ -278,7 +311,7 @@ "outputs": [], "source": [ "# Calculate output spikes (positive and negative weights)\n", - "output_spikes = np.matmul(fo_exc,feature_spikes) + np.matmul(fo_inh,feature_spikes)\n", + "output_spikes = np.matmul(outputW,feature_spikes)\n", "output_spikes = np.clip(output_spikes, 0, 0.9)\n", "\n", "# Now create the line plot\n", @@ -315,6 +348,221 @@ "print(f\"Neuron ID with the highest output is {prediction}\")" ] }, + { + "cell_type": "markdown", + "id": "0c8c82d7", + "metadata": {}, + "source": [ + "## Quantized model example\n", + "\n", + "Now that we have seen how our base model works, let's look at how our int8 quantized model performs by comparison. Working in the in8 space has a few benefits, like faster inferencing time and smaller model sizes. There are a couple differences however when feeding spikes throughout the system that PyTorch performs in the backend.\n", + "\n", + "Let's start by converting our input image into int8 spikes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c893e76", + "metadata": {}, + "outputs": [], + "source": [ + "# Converting fp32 spikes to int8 uses a learned scale factor during quantization aware training\n", + "spike_scale = 133\n", + "patch_img_int = patch_img*spike_scale\n", + "\n", + "# Plot the converted int8 image\n", + "plt.matshow(patch_img_int)\n", + "plt.title('Nordland Summer Patch Normalized Int8')\n", + "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n", + "plt.show()\n", + "\n", + "# Convert 2D image to a 1D-array\n", + "patch_1d_int = np.reshape(patch_img_int, (3136,))" + ] + }, + { + "cell_type": "markdown", + "id": "9f68d3dc", + "metadata": {}, + "source": [ + "Now we'll load in and plot our integer based weights, as well as some scale factors which will be important to reduce the size of our spikes after multiplying them with our weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70c99f47", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the scales for the feature and output spikes\n", + "feature_scales = np.load('./mats/1_BasicDemo/featureScales.npy',allow_pickle=True)\n", + "output_scales = np.load('./mats/1_BasicDemo/outputScales.npy',allow_pickle=True)\n", + "\n", + "# Load the int8 weights and plot them\n", + "featureQuantW = np.load('./mats/1_BasicDemo/featureQuantW.npy')\n", + "outputQuantW = np.load('./mats/1_BasicDemo/outputQuantW.npy')\n", + "\n", + "# Plot the feature weights\n", + "plt.matshow(featureQuantW.T)\n", + "plt.title('Input > Feature Weights')\n", + "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()\n", + "\n", + "# Plot the output weights\n", + "plt.matshow(outputQuantW)\n", + "plt.title('Feature > Output Weights')\n", + "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n", + "\n", + "# Display the plots\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f185e3ff", + "metadata": {}, + "source": [ + "Now as above, let's propagate the input spikes throughout the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddb1ef65", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the feature spikes\n", + "feature_spikes_int = np.matmul(featureQuantW,patch_1d_int)\n", + "\n", + "# Now create the line plot\n", + "plt.plot(np.arange(len(feature_spikes_int)), feature_spikes_int)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6e9189aa", + "metadata": {}, + "source": [ + "Those are some big spikes! We're going to have to scale these spikes back down before we forward them to the output layer, otherwise we'll have some huge activations. Let's take those scales we loaded in earlier and apply them to the feature spikes.\n", + "\n", + "We have three things to consider here:\n", + " - A slice scale factor (per neuronal connection scale)\n", + " - A zero point (a factor to change where 'zero' is)\n", + " \n", + " Let's print out these three factors and see how they scale our spikes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95032ac7", + "metadata": {}, + "outputs": [], + "source": [ + "# Print out the individual scales\n", + "print(f\"The slice scale factor is {feature_scales[1]}\")\n", + "print(f\"The zero point is {feature_scales[2]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9f62b909", + "metadata": {}, + "source": [ + "Now we'll modify and scale our spikes to then pass them on to the feature layer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a7dd83d", + "metadata": {}, + "outputs": [], + "source": [ + "# Scale the feature spikes\n", + "scaled_feature_spikes = (feature_spikes_int//(feature_scales[1]))+feature_scales[2]\n", + "scaled_feature_spikes = np.clip(scaled_feature_spikes,0,255)\n", + "\n", + "# Plot the scaled feature spikes\n", + "plt.plot(np.arange(len(scaled_feature_spikes)), scaled_feature_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2a767c92", + "metadata": {}, + "source": [ + "Now that we've scaled our feature spikes, let's pass them through to the output layer and get our match!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7519c07f", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the output spikes\n", + "output_spikes_int = np.matmul(outputQuantW,scaled_feature_spikes)\n", + "\n", + "# Scale the output spikes\n", + "scaled_output_spikes = output_spikes_int//(output_scales[1]) + output_scales[2]\n", + "\n", + "# Plot the scaled feature spikes\n", + "plt.plot(np.arange(len(scaled_output_spikes)), scaled_output_spikes)\n", + "\n", + "# Add title and labels if you wish\n", + "plt.title('Output Layer Spikes')\n", + "plt.xlabel('Neuron ID')\n", + "plt.ylabel('Spike Amplitude')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3cc4588a", + "metadata": {}, + "source": [ + "And once again, as in the base model, we can see that output neuron 0 is the highest respondant.\n", + "\n", + "Let's prove it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67e457e4", + "metadata": {}, + "outputs": [], + "source": [ + "# Output the argmax from the output spikes\n", + "prediction = np.argmax(scaled_output_spikes)\n", + "print(f\"Neuron ID with the highest output is {prediction}\")" + ] + }, { "cell_type": "markdown", "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5", @@ -324,7 +572,7 @@ "\n", "We have gone through a very basic demo of how VPRTempo takes input images, patch normalizes them, and propagates the spikes throughout the weights to achieve the desired matching output. Although this demonstration was performed using NumPy, the torch implementation is virtually the same except we use tensors with or without quantization. \n", "\n", - "The purpose of splitting up excitatory and inhibitory weights is to allow for extra hometostatic normalization of inhibitory connections, which has proven to be critical in regulating overall system activity.\n", + "We also went through how the quantization version of the network handled weights and spikes in the integer domain.\n", "\n", "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo." ] @@ -346,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/tutorials/2_Introduction.ipynb b/tutorials/2_Introduction.ipynb deleted file mode 100644 index d57c263..0000000 --- a/tutorials/2_Introduction.ipynb +++ /dev/null @@ -1,734 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", - "metadata": {}, - "source": [ - "## VPRTempo - Introduction\n", - "\n", - "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", - "\n", - "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", - " \n", - "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", - "\n", - "### Introduction\n", - "\n", - "Traditional methods for visual place recognition (VPR) tasks typically employ the use of convolutional neural networks like ResNet to train large datasets for feature extraction of incoming query images, rather than specifically learning said query place. The networks are extremely effective at accurate localisation, but are are slow to train, inference, and store.\n", - "\n", - "Spiking neural networks (SNNs) by contrast are more energy efficient and have low latency computation, meaning their deployment capability for VPR is extremely promising. Specifically, networks can be trained on the exact location you wish to query which takes a fundamentally different approach to the VPR task.\n", - "\n", - "VPRTempo uses a temporal encoding scheme for spikes, where the amplitude of a spike is determined by an incoming training or query image's pixel intensity. This amplitude defines the 'timing' of the spike, similar to a latency code. As spikes propagate throughout the system, spike-timing dependent plasticity (STDP) learning rules train neuronal connections based off of the pixel intensity spike amplitudes. \n", - "\n", - "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo. For more information how to install and setup the environment, please see the [README.md](https://github.com/AdamDHines/VPRTempo-quant/blob/main/README.md)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f846c03", - "metadata": {}, - "outputs": [], - "source": [ - "!conda activate vprtempo" - ] - }, - { - "cell_type": "markdown", - "id": "0928d7a4", - "metadata": {}, - "source": [ - "## 1. Get the Nordland dataset\n", - "\n", - "### 1.1 Download the dataset\n", - "\n", - "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", - "\n", - "Save the data in the `./VPRTempo-quant/dataset/` subfolder." - ] - }, - { - "cell_type": "markdown", - "id": "f0a607d1", - "metadata": {}, - "source": [ - "### 1.2 Import modules\n", - "\n", - "Once we have downloaded the dataset, we'll start by importing all the necessary modules.\n", - "\n", - "For this tutorial, we use [Jupyter Dynamic Classes](https://alexhagen.github.io/jdc/) so if not already installed please install. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca1aa5e3-4537-4e1e-8629-bb134e749707", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install jdc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9caff25", - "metadata": {}, - "outputs": [], - "source": [ - "import jdc\n", - "import os\n", - "import torch\n", - "import gc\n", - "import sys\n", - "sys.path.append('../src')\n", - "sys.path.append('../models')\n", - "sys.path.append('../output')\n", - "sys.path.append('../dataset')\n", - "\n", - "import blitnet as bn\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "import torch.quantization as quantization\n", - "\n", - "from settings import configure, image_csv, model_logger\n", - "from dataset import CustomImageDataset, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "from torch.ao.quantization import QuantStub, DeQuantStub\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "markdown", - "id": "ffac2f0e", - "metadata": {}, - "source": [ - "### 1.3 Prepare the dataset for the model (optional)\n", - "\n", - "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file.\n", - "\n", - "If you have already done this from the previous tutorial, you can skip this step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51f350d0", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from os import walk\n", - "from nordland import nord_sort\n", - "\n", - "# unzip, re-organise, and re-name the Nordland datasets\n", - "nord_sort()" - ] - }, - { - "cell_type": "markdown", - "id": "f4d2f885", - "metadata": {}, - "source": [ - "## 2. Set up the network\n", - "\n", - "### 2.1 Define and initialize the VPRTempo model class\n", - "\n", - "We'll first define the VPRTempo class which handles the configuration as set in `./src/settings.py`, determining which images to load, and establishes the layers used for training. For this tutorial, leave the settings as the default.\n", - "\n", - "`__init__` is where we define the layers used for the model. In this case, we define a `feature_layer` and an `output_layer`. `dims` represents the number of neurons in the input and the layer itself, which in this case is `self.input`, `self.feature`, and `self.output`. Note that the size of the input for each proceeding layer is the size of previous layer. In this example, we have an input of 784 neurons (for 28x28 images) connected to a 1568 neuron feature layer which then connects to a final output layer of 500 neurons.\n", - "\n", - "The other hyperparameters for each layer are set here as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99b5130c", - "metadata": {}, - "outputs": [], - "source": [ - "class VPRTempo(nn.Module):\n", - " def __init__(self):\n", - " super(VPRTempo, self).__init__()\n", - "\n", - " # Configure the network\n", - " configure(self)\n", - " \n", - " # Define the images to load (both training and inference)\n", - " image_csv(self)\n", - "\n", - " # Add quantization stubs for Quantization Aware Training (QAT)\n", - " self.quant = QuantStub()\n", - " self.dequant = DeQuantStub()\n", - " \n", - " # Define the add function for quantized addition\n", - " self.add = nn.quantized.FloatFunctional() \n", - "\n", - " # Layer dict to keep track of layer names and their order\n", - " self.layer_dict = {}\n", - " self.layer_counter = 0\n", - "\n", - " \"\"\"\n", - " Define trainable layers here\n", - " \"\"\"\n", - " self.add_layer(\n", - " 'feature_layer',\n", - " dims=[self.input, self.feature],\n", - " thr_range=[0, 0.5],\n", - " fire_rate=[0.2, 0.9],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " const_inp=[0, 0.1],\n", - " p=[0.1, 0.5]\n", - " )\n", - " self.add_layer(\n", - " 'output_layer',\n", - " dims=[self.feature, self.output],\n", - " ip_rate=0.15,\n", - " stdp_rate=0.005,\n", - " spk_force=True\n", - " )\n", - " \n", - " print('VPRTempo succesfully initialized')" - ] - }, - { - "cell_type": "markdown", - "id": "d9e3c15b", - "metadata": {}, - "source": [ - "### 2.2 Dynamically add layers\n", - "\n", - "As above, the only thing we need to do in order to add additional layers to our model is to include a self.add_layer(args) to the `__init__` component of the script. The actual handling of the layer generation is done by the blitnet.SNNLayer() class from `blitnet.py`. Here, hyperparameters are stored in the layer information and the initial weights are seeded and normalized for training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dabd3c7d", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def add_layer(self, name, **kwargs):\n", - " \"\"\"\n", - " Dynamically add a layer with given name and keyword arguments.\n", - "\n", - " :param name: Name of the layer to be added\n", - " :type name: str\n", - " :param kwargs: Hyperparameters for the layer\n", - " \"\"\"\n", - " # Check for layer name duplicates\n", - " if name in self.layer_dict:\n", - " raise ValueError(f\"Layer with name {name} already exists.\")\n", - "\n", - " # Add a new SNNLayer with provided kwargs\n", - " setattr(self, name, bn.SNNLayer(**kwargs))\n", - "\n", - " # Add layer name and index to the layer_dict\n", - " self.layer_dict[name] = self.layer_counter\n", - " self.layer_counter += 1 \n", - "\n", - " print('Succesfully added '+name)" - ] - }, - { - "cell_type": "markdown", - "id": "e3b92db1", - "metadata": {}, - "source": [ - "### 2.3 Set the training regime\n", - "\n", - "Training is also handled by the `VPRTempo()` class and recursively runs until all the defined layers are trained. The initial learning rates are copied out so that they can be annealed appropriately for the defined number of time steps. Training runs for the specified number of epochs and the total number of timesteps as set in the train_loader class (more later on that, a simple [PyTorch DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)).\n", - "\n", - "Once a layer has been trained, the learning for that layer will be turned off and training deeper layers will propagate the input spikes through each trained layer until it reaches the one being currently learned. Learning involves spike-timing dependent plasticity (STDP) rules, firing threshold adjustments, and inhibitory connection normalization." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "623595aa", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def train_model(self, train_loader, layer, prev_layers=None):\n", - " \"\"\"\n", - " Train a layer of the network model.\n", - "\n", - " :param train_loader: Training data loader\n", - " :param layer: Layer to train\n", - " :param prev_layers: Previous layers to pass data through\n", - " \"\"\"\n", - "\n", - " # Initialize the tqdm progress bar\n", - " pbar = tqdm(total=int(self.T * self.epoch),\n", - " desc=\"Training \",\n", - " position=0)\n", - "\n", - " # Initialize the learning rates for each layer (used for annealment)\n", - " init_itp = layer.eta_ip.detach()\n", - " init_stdp = layer.eta_stdp.detach()\n", - "\n", - " # Run training for the specified number of epochs\n", - " for epoch in range(self.epoch):\n", - " mod = 0 # Used to determine the learning rate annealment, resets at each epoch\n", - " # Run training for the specified number of timesteps\n", - " for spikes, labels in train_loader:\n", - " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", - " idx = labels / self.filter # Set output index for spike forcing\n", - " # Pass through previous layers if they exist\n", - " if prev_layers:\n", - " with torch.no_grad():\n", - " for prev_layer_name in prev_layers:\n", - " prev_layer = getattr(self, prev_layer_name) # Get the previous layer object\n", - " spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer\n", - " spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]\n", - " else:\n", - " prev_layer = None\n", - " # Get the output spikes from the current layer\n", - " pre_spike = spikes.detach() # Previous layer spikes for STDP\n", - " spikes = self.forward(spikes, layer) # Current layer spikes\n", - " spikes_noclp = spikes.detach() # Used for inhibitory homeostasis\n", - " spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]\n", - " # Calculate STDP\n", - " layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)\n", - " # Adjust learning rates\n", - " layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)\n", - " # Update the annealing mod & progress bar \n", - " mod += 1\n", - " pbar.update(1)\n", - "\n", - " # Close the tqdm progress bar\n", - " pbar.close()" - ] - }, - { - "cell_type": "markdown", - "id": "bc5e068e", - "metadata": {}, - "source": [ - "### 2.4 Create the forward pass\n", - "\n", - "Layers in VPRTempo are defined as an [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layer, with incoming spikes being linearly transformed with the layer weights. The forward pass simply takes incoming spikes and caluclates the transform with positive and negative weights and adds them together, returning the transformed spikes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68a22d64", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def forward(self, spikes, layer):\n", - " \"\"\"\n", - " Compute the forward pass of the model.\n", - "\n", - " Parameters:\n", - " - spikes (Tensor): Input spikes.\n", - "\n", - " Returns:\n", - " - Tensor: Output after processing.\n", - " \"\"\"\n", - "\n", - " spikes = self.quant(spikes)\n", - " spikes = self.add.add(layer.exc(spikes), layer.inh(spikes))\n", - " spikes = self.dequant(spikes)\n", - "\n", - " return spikes" - ] - }, - { - "cell_type": "markdown", - "id": "9fb8e16d", - "metadata": {}, - "source": [ - "### 2.5 Learning rate annealment & model loader/saver\n", - "\n", - "Finally, the last thing we will add to the model is the learning rate annealment regime and the functions for loading and saving trained models." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e99f6d13", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def _anneal_learning_rate(self, layer, mod, itp, stdp):\n", - " \"\"\"\n", - " Anneal the learning rate for the current layer.\n", - " \"\"\"\n", - " if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps\n", - " pt = pow(float(self.T - mod) / self.T, self.annl_pow)\n", - " layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate\n", - " layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate\n", - "\n", - " return layer\n", - "\n", - "def save_model(self, model_out): \n", - " \"\"\"\n", - " Save the trained model to models output folder.\n", - " \"\"\"\n", - " torch.save(self.state_dict(), model_out) \n", - "\n", - "def load_model(self, model_path):\n", - " \"\"\"\n", - " Load pre-trained model and set the state dictionary keys.\n", - " \"\"\"\n", - " self.load_state_dict(torch.load(model_path, map_location=self.device),\n", - " strict=True)" - ] - }, - { - "cell_type": "markdown", - "id": "a4d65918", - "metadata": {}, - "source": [ - "### 2.6 Initialize the model\n", - "\n", - "Now that the model has been defined, we can initialize it and start with the quantization process." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55aa0e9b", - "metadata": {}, - "outputs": [], - "source": [ - "model = VPRTempo()\n", - "model_logger(model)\n", - "model.train()" - ] - }, - { - "cell_type": "markdown", - "id": "a88d4a18", - "metadata": {}, - "source": [ - "### 2.7 Generate unique model name\n", - "\n", - "We will finally set up a unique model name based on the network architecture so we can save and reload our trained model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc786b3d", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_model_name(model):\n", - " \"\"\"\n", - " Generate the model name based on its parameters.\n", - " \"\"\"\n", - " return (\"VPRTempo\" +\n", - " str(model.input) +\n", - " str(model.feature) +\n", - " str(model.output) +\n", - " str(model.number_modules) +\n", - " '.pth')\n", - "\n", - "model_name = generate_model_name(model)\n", - "\n", - "print(model_name)" - ] - }, - { - "cell_type": "markdown", - "id": "17640d20", - "metadata": {}, - "source": [ - "## 3. Define the DataLoader\n", - "\n", - "### 3.1 Set the DataLoader\n", - "\n", - "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", - "\n", - "Since we present the network with one image at a time, the `batch_size` is kept to 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6714bc35", - "metadata": {}, - "outputs": [], - "source": [ - "from dataset import CustomImageDataset, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.training_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_training_images,\n", - " test=False)\n", - "# Initialize the data loader\n", - "train_loader = DataLoader(train_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" - ] - }, - { - "cell_type": "markdown", - "id": "2f3f4bdb", - "metadata": {}, - "source": [ - "## 5. Set up and run the training \n", - "\n", - "### 5.1 Define and run the training regime\n", - "\n", - "The training will loop through each defined layer until every single one has trained. In order to propagate spikes throughout the system, trained layers are appended to a list so that they can be re-fed back into the network to calculate spikes based on learned weights.\n", - "\n", - "Run the below cell to train our `feature_layer` and `output_layer`!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0075638b", - "metadata": {}, - "outputs": [], - "source": [ - "# Keep track of trained layers to pass data through them\n", - "trained_layers = [] \n", - "\n", - "# Training each layer\n", - "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", - " print(f\"Training layer: {layer_name}\")\n", - " # Retrieve the layer object\n", - " layer = getattr(model, layer_name)\n", - " # Train the layer\n", - " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", - " # After training the current layer, add it to the list of trained layers\n", - " trained_layers.append(layer_name)\n", - " \n", - "print('All layers trained succesfully')" - ] - }, - { - "cell_type": "markdown", - "id": "5e6daea3", - "metadata": {}, - "source": [ - "### 5.2 Convert and save the model\n", - "\n", - "Now that the training has been completed, we can convert the QAT model over to be fully quantized. As the layers were trained, scale and zero-point factors will learned for all the elements of the model and can now be applied to the layers. Once converted, we will save the model for use in inferencing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53ededaa", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert the model to eval mode\n", - "model.eval()\n", - "# Save the model\n", - "model.save_model(os.path.join('../models', model_name)) " - ] - }, - { - "cell_type": "markdown", - "id": "c0d69843", - "metadata": {}, - "source": [ - "## 6. Inferencing\n", - "\n", - "As in the previous tutorial, inferencing with a trained model is quite simple. The only additional thing we need to do is reinitialize the VPRTempo class and convert it to quantized before loading the model. Without pre-quantizing the inference model, state dictionary keys will not match since all the layers and associated components have new parameters such as scale and zero-point.\n", - "\n", - "### 6.1 Add the inference function to the VPRTempo class\n", - "\n", - "We will start by adding in the inference function to VPRTempo. It is similar to the training regime but omits the learning components `calc_stdp` and simply runs through all the layers until it reaches the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a368c532", - "metadata": {}, - "outputs": [], - "source": [ - "%%add_to VPRTempo\n", - "def evaluate(self, model, test_loader, layers=None):\n", - " \"\"\"\n", - " Run the inferencing model and calculate the accuracy.\n", - "\n", - " :param test_loader: Testing data loader\n", - " :param layers: Layers to pass data through\n", - " \"\"\"\n", - "\n", - " # Initialize the number of correct predictions\n", - " numcorr = 0\n", - " idx = 0\n", - "\n", - " # Initialize the tqdm progress bar\n", - " pbar = tqdm(total=self.number_testing_images,\n", - " desc=\"Running the test network\",\n", - " position=0)\n", - "\n", - " # Run inference for the specified number of timesteps\n", - " for spikes, labels in test_loader:\n", - " # Set device\n", - " spikes, labels = spikes.to(self.device), labels.to(self.device)\n", - " # Pass through previous layers if they exist\n", - " if layers:\n", - " for layer_name in layers:\n", - " layer = getattr(self, layer_name)\n", - " spikes = self.forward(spikes, layer)\n", - " spikes = bn.clamp_spikes(spikes, layer)\n", - "\n", - " # Evaluate if the prediction is correct\n", - " if torch.argmax(spikes.reshape(1, self.number_training_images)) == idx:\n", - " numcorr += 1\n", - "\n", - " # Update the index and progress bar\n", - " idx += 1\n", - " pbar.update(1)\n", - "\n", - " # Close the tqdm progress bar\n", - " pbar.close()\n", - " # Calculate and record the accuracy\n", - " accuracy = round((numcorr/self.number_testing_images)*100,2)\n", - " model.logger.info(\"P@100R: \"+ str(accuracy) + '%')" - ] - }, - { - "cell_type": "markdown", - "id": "5e841fc7", - "metadata": {}, - "source": [ - "### 6.2 Define the inferencing DataLoader\n", - "\n", - "The only difference between the training and testing DataLoader is the directory with which it will import images from." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4a1adc8", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the image transforms and datasets\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.testing_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_testing_images)\n", - "# Initialize the data loader\n", - "test_loader = DataLoader(test_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" - ] - }, - { - "cell_type": "markdown", - "id": "018de09a", - "metadata": {}, - "source": [ - "### 6.3 Re-initialize the model class, convert to quantization, and load the model\n", - "\n", - "Now we will re-initialize the VPRTempo class model, set to eval mode, and convert it over to quantized so that we can import our newly trained model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d51e96", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the model to evaluation mode and set configuration\n", - "model = VPRTempo()\n", - "model.model_logger()\n", - "model.eval()\n", - "\n", - "# Load the model\n", - "model.load_model(os.path.join('../models', model_name))\n", - "\n", - "# Retrieve layer names for inference\n", - "layer_names = list(model.layer_dict.keys())" - ] - }, - { - "cell_type": "markdown", - "id": "472d24e8", - "metadata": {}, - "source": [ - "### 6.4 Run the model inference\n", - "\n", - "Now we are ready to inference the model!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37aa84e1", - "metadata": {}, - "outputs": [], - "source": [ - "# Use evaluate method for inference accuracy\n", - "model.evaluate(model, test_loader, layers=layer_names)" - ] - }, - { - "cell_type": "markdown", - "id": "26f46e43", - "metadata": {}, - "source": [ - "## 7. Conslusions\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "763e86c5", - "metadata": {}, - "source": [ - "This tutorial covered how we can convert the VPRTempo model to perform Quantized Aware Training (QAT) to keep the model size more lightweight. You might notice that if you compare the system between FP32 to Int8, the model works equally as well with a reduced bit-depth with the added benefit of a reduced model size.\n", - "\n", - "To read more about QAT and quantization in general, PyTorch provides many useful articles;\n", - "https://pytorch.org/docs/stable/quantization.html\n", - "https://pytorch.org/blog/quantization-in-practice/\n", - "\n", - "The key benefit to this is being able to perform fast training and inferencing on CPU architecture, which for resource limited compute scenarios is critical." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/3_Quantization.ipynb b/tutorials/3_Quantization.ipynb deleted file mode 100644 index 790dff6..0000000 --- a/tutorials/3_Quantization.ipynb +++ /dev/null @@ -1,493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "70df0e83-9a35-41b3-81ec-cd12538045ed", - "metadata": {}, - "source": [ - "## VPRTempo - Quantized Aware Training and Inferencing Tutorial\n", - "\n", - "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", - "\n", - "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", - " \n", - "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", - "\n", - "### Introduction\n", - "\n", - "In this tutorial, we are going to take the base VPRTempo model to train and inference a network with PyTorch's Quantized Aware Training ([QAT](https://pytorch.org/docs/stable/quantization.html)). Functionally, this tutorial is similar to the previous one but will be simplified. For a more detailed dive into how VPRTempo works, please see [Tutorial 1](https://github.com/AdamDHines/VPRTempo-quant/blob/main/tutorials/1_Introduction.ipynb)\n", - "\n", - "**Note: it does not appear that Apple Silicon is currently a supported backend for QAT**\n", - "\n", - "To get started, please ensure you have installed and currently have activated the `conda` environment for VPRTempo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f846c03", - "metadata": {}, - "outputs": [], - "source": [ - "!conda activate vprtempo" - ] - }, - { - "cell_type": "markdown", - "id": "0928d7a4", - "metadata": {}, - "source": [ - "## 1. Get the Nordland dataset\n", - "\n", - "### 1.1 Download the dataset\n", - "\n", - "Please [download the Nordland datasets](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) (Summer, Spring, Fall, & Winter). There are two datasets available, the full size and downsampled versions. Either will work fine but our paper details the full size dataset. If disk space is a concern, please use the downsampled version.\n", - "\n", - "Save the data in the `./VPRTempo-quant/dataset/` subfolder." - ] - }, - { - "cell_type": "markdown", - "id": "f0a607d1", - "metadata": {}, - "source": [ - "### 1.2 Import modules\n", - "\n", - "Once we have downloaded the dataset, we'll start by importing all the necessary modules.\n", - "\n", - "For this tutorial, we use [Jupyter Dynamic Classes](https://alexhagen.github.io/jdc/) so if not already installed please install. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7bf4ad2-755b-40f1-8bd3-5b98376ed5df", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install jdc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9caff25", - "metadata": {}, - "outputs": [], - "source": [ - "import jdc\n", - "import os\n", - "import torch\n", - "import gc\n", - "import sys\n", - "sys.path.append('../')\n", - "sys.path.append('../src')\n", - "sys.path.append('../models')\n", - "sys.path.append('../output')\n", - "sys.path.append('../dataset')\n", - "\n", - "import blitnet as bn\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "import torch.quantization as quantization\n", - "\n", - "from settings import configure, image_csv, model_logger\n", - "from dataset import CustomImageDataset, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "from torch.ao.quantization import QuantStub, DeQuantStub\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "markdown", - "id": "ffac2f0e", - "metadata": {}, - "source": [ - "### 1.3 Prepare the dataset for the model (optional)\n", - "\n", - "The datset seasons are downloaded in .zip format and need to be extracted into a single folder. The `nordland` function has been provided to automatically do this for you and to re-name the images to match those in the nordland.csv file.\n", - "\n", - "If you have already done this from the previous tutorial, you can skip this step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51f350d0", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from os import walk\n", - "from nordland import nord_sort\n", - "\n", - "# unzip, re-organise, and re-name the Nordland datasets\n", - "nord_sort()" - ] - }, - { - "cell_type": "markdown", - "id": "f4d2f885", - "metadata": {}, - "source": [ - "## 2. Set up the network\n", - "\n", - "### 2.1 Define and initialize the VPRTempo model class\n", - "\n", - "We'll now import the main network model class `VPRTempo`. Please see [Tutorial 1](https://github.com/AdamDHines/VPRTempo-quant/blob/main/tutorials/1_Introduction.ipynb) for a more detailed look at what this includes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99b5130c", - "metadata": {}, - "outputs": [], - "source": [ - "from VPRTempo import VPRTempo\n", - "model = VPRTempo()" - ] - }, - { - "cell_type": "markdown", - "id": "a88d4a18", - "metadata": {}, - "source": [ - "### 2.2 Generate unique model name\n", - "\n", - "We will set up a unique model name to save and load for inferencing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc786b3d", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_model_name(model):\n", - " \"\"\"\n", - " Generate the model name based on its parameters.\n", - " \"\"\"\n", - " return (\"VPRTempo\" +\n", - " str(model.input) +\n", - " str(model.feature) +\n", - " str(model.output) +\n", - " str(model.number_modules) +\n", - " \"Quantized\"+\n", - " '.pth')\n", - "\n", - "model_name = generate_model_name(model)\n", - "\n", - "print(model_name)" - ] - }, - { - "cell_type": "markdown", - "id": "17640d20", - "metadata": {}, - "source": [ - "## 3. Define the DataLoader\n", - "\n", - "### 3.1 Set the DataLoader\n", - "\n", - "Now that we've defined the model, we will set up the DataLoaders. These utilise a PyTorch CustomImageDataset and ProcessImage to import images and process them for training or inference. In brief, images are loaded, gamma corrected, resized, and then patch-normalized before being converted into system spikes to be propagated throughout.\n", - "\n", - "Since we present the network with one image at a time, the `batch_size` is kept to 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6714bc35", - "metadata": {}, - "outputs": [], - "source": [ - "from dataset import CustomImageDataset, ProcessImage\n", - "from torch.utils.data import DataLoader\n", - "\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "train_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.training_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_training_images,\n", - " test=False)\n", - "# Initialize the data loader\n", - "train_loader = DataLoader(train_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" - ] - }, - { - "cell_type": "markdown", - "id": "a3067711", - "metadata": {}, - "source": [ - "## 4. Quantization\n", - "\n", - "### 4.1 Model quantization\n", - "\n", - "VPRTempoQuant makes use of Quantized Aware Training QAT and has a few simple steps to prepare the model to accomodate this. First, we will get the default quantization configuration for `fggbem`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36244802", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.quantization as quantization\n", - "\n", - "# Set the quantization configuration\n", - "qconfig = quantization.get_default_qat_qconfig('fbgemm')" - ] - }, - { - "cell_type": "markdown", - "id": "47a292cc", - "metadata": {}, - "source": [ - "Next, we will set the model to be configured for network training and add our quantization configuration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a884ad96", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the model to training mode and move to device\n", - "model.train()\n", - "model.to('cpu')\n", - "model.qconfig = qconfig" - ] - }, - { - "cell_type": "markdown", - "id": "ed34b802", - "metadata": {}, - "source": [ - "Now we will convert the model over to QAT." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "087f3b36", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply quantization configurations to the model\n", - "model = quantization.prepare_qat(model, inplace=False)" - ] - }, - { - "cell_type": "markdown", - "id": "07f74ad2", - "metadata": {}, - "source": [ - "At this point, we are ready to start training our network!" - ] - }, - { - "cell_type": "markdown", - "id": "2f3f4bdb", - "metadata": {}, - "source": [ - "## 5. Set up and run the training \n", - "\n", - "### 5.1 Define and run the training regime\n", - "\n", - "The training will loop through each defined layer until every single one has trained. In order to propagate spikes throughout the system, trained layers are appended to a list so that they can be re-fed back into the network to calculate spikes based on learned weights.\n", - "\n", - "Run the below cell to train our `feature_layer` and `output_layer`!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0075638b", - "metadata": {}, - "outputs": [], - "source": [ - "# Keep track of trained layers to pass data through them\n", - "trained_layers = [] \n", - "\n", - "# Training each layer\n", - "for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]):\n", - " print(f\"Training layer: {layer_name}\")\n", - " # Retrieve the layer object\n", - " layer = getattr(model, layer_name)\n", - " # Train the layer\n", - " model.train_model(train_loader, layer, prev_layers=trained_layers)\n", - " # After training the current layer, add it to the list of trained layers\n", - " trained_layers.append(layer_name)\n", - " \n", - "print('All layers trained succesfully')" - ] - }, - { - "cell_type": "markdown", - "id": "5e6daea3", - "metadata": {}, - "source": [ - "### 5.2 Convert and save the model\n", - "\n", - "Now that the training has been completed, we can convert the QAT model over to be fully quantized. As the layers were trained, scale and zero-point factors will learned for all the elements of the model and can now be applied to the layers. Once converted, we will save the model for use in inferencing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53ededaa", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert the model to a quantized model\n", - "model = quantization.convert(model, inplace=False)\n", - "model.eval()\n", - "# Save the model\n", - "model.save_model(os.path.join('../models', model_name)) " - ] - }, - { - "cell_type": "markdown", - "id": "018de09a", - "metadata": {}, - "source": [ - "### 6.3 Re-initialize the model class, convert to quantization, and load the model\n", - "\n", - "Now we will re-initialize the VPRTempo class model, set to eval mode, and convert it over to quantized so that we can import our newly trained model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d51e96", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the model to evaluation mode and set configuration\n", - "model = VPRTempo()\n", - "model.model_logger()\n", - "model.eval()\n", - "model.qconfig = qconfig\n", - "\n", - "# Apply quantization configurations to all layers in layer_dict\n", - "for layer_name, _ in model.layer_dict.items():\n", - " getattr(model, layer_name).qconfig = qconfig\n", - "# Prepare and convert the model to a quantized model\n", - "model = quantization.prepare(model, inplace=False)\n", - "model = quantization.convert(model, inplace=False)\n", - "# Load the model\n", - "model.load_model(os.path.join('../models', model_name))\n", - "\n", - "# Retrieve layer names for inference\n", - "layer_names = list(model.layer_dict.keys())" - ] - }, - { - "cell_type": "markdown", - "id": "5e841fc7", - "metadata": {}, - "source": [ - "### 6.2 Define the inferencing DataLoader\n", - "\n", - "The only difference between the training and testing DataLoader is the directory with which it will import images from." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4a1adc8", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the image transforms and datasets\n", - "image_transform = ProcessImage(model.dims, model.patches)\n", - "test_dataset = CustomImageDataset(annotations_file=model.dataset_file, \n", - " img_dirs=model.testing_dirs,\n", - " transform=image_transform,\n", - " skip=model.filter,\n", - " max_samples=model.number_testing_images)\n", - "# Initialize the data loader\n", - "test_loader = DataLoader(test_dataset, \n", - " batch_size=1, \n", - " shuffle=False,\n", - " num_workers=8,\n", - " persistent_workers=True)" - ] - }, - { - "cell_type": "markdown", - "id": "472d24e8", - "metadata": {}, - "source": [ - "### 6.4 Run the model inference\n", - "\n", - "Now we are ready to inference the model!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37aa84e1", - "metadata": {}, - "outputs": [], - "source": [ - "# Use evaluate method for inference accuracy\n", - "model.evaluate(model, test_loader, layers=layer_names)" - ] - }, - { - "cell_type": "markdown", - "id": "26f46e43", - "metadata": {}, - "source": [ - "## 7. Conslusions\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "763e86c5", - "metadata": {}, - "source": [ - "This tutorial covered how we can convert the VPRTempo model to perform Quantized Aware Training (QAT) to keep the model size more lightweight. You might notice that if you compare the system between FP32 to Int8, the model works equally as well with a reduced bit-depth with the added benefit of a reduced model size.\n", - "\n", - "To read more about QAT and quantization in general, PyTorch provides many useful articles;\n", - "https://pytorch.org/docs/stable/quantization.html\n", - "https://pytorch.org/blog/quantization-in-practice/\n", - "\n", - "The key benefit to this is being able to perform fast training and inferencing on CPU architecture, which for resource limited compute scenarios is critical." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e6886b77b98be9b428f9fbeb16aef0d7ab0235be Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 30 Nov 2023 17:19:23 +1000 Subject: [PATCH 64/69] Working on modules, current issue the second module doesn't output for some reason --- .DS_Store | Bin 10244 -> 10244 bytes dataset/.DS_Store | Bin 8196 -> 14340 bytes main.py | 51 ++++++++---- models/.DS_Store | Bin 6148 -> 6148 bytes networks/base/VPRTempo.py | 95 +++++++++++++---------- networks/base/VPRTempoTrain.py | 91 +++++++++++++--------- networks/quantized/VPRTempoQuant.py | 6 +- networks/quantized/VPRTempoQuantTrain.py | 6 +- src/blitnet.py | 2 + src/dataset.py | 9 ++- src/loggers.py | 68 +++++++--------- tutorials/.DS_Store | Bin 0 -> 6148 bytes tutorials/mats/.DS_Store | Bin 0 -> 6148 bytes 13 files changed, 186 insertions(+), 142 deletions(-) create mode 100644 tutorials/.DS_Store create mode 100644 tutorials/mats/.DS_Store diff --git a/.DS_Store b/.DS_Store index 2e3e8215ef405c688d92e1b551ce3ba13b846760..b9183bcce3be679eb9994ae6b0c29813116d3f44 100644 GIT binary patch delta 112 zcmZn(XbG6$&nU4mU^hRb#AY4=0j9}o#7a5Lj7)SC3@yzk>qyvdzAog?Gd3@lY;K~XU}S2!SxV(ZFfa%J@p>Sh_a6*4Caz@L%&zd8 NWiqp<3e;3ZCIFS^C941c diff --git a/dataset/.DS_Store b/dataset/.DS_Store index a321f0b86a27a4ebfe9a8a908a70828e9d4c93f7..ea74ea872093a8a770552a84473018d23402d961 100644 GIT binary patch literal 14340 zcmeGiZEPGzb>1en_mXYAZfxQNZ*%gcYT7ui<9yg@8ufg3n!5RLnzQ3`;^eY-ySBI4 z+r94Y-NkW@OjRn;Qc!+T6^g>IwzRYe2vqO`BuEKJ{3!t`e5(k=$B$NmKZT<3W@hhh z?`#VpxwL6_PBU+2-kUc&Z{Nqh+a-iRqM+>{q=yh9Vx>@)uz7-zD20JS@w6@~t8fOe zZpMmznGlnh#iV1Iwl%$odhvvSQ*`}mR;P$sw;t>aVvsF)6>IkmRb4$D}`|%E^x?P2R8H(p5o~vJS*6z z5A1X<>chAX7dS)=7T`+}@}=OX7{Hg~eAK6l`*49nzMKHt8Tt4-gP)-Q+a2eljGRE6 z!>|n{5K3UN1X^)QA{McULp034RqSh|n`DVgio>Rz&6t%ed;n>KS$Gp$m0g+|lsf80 znDLNXpZ8KButCqtP(k<(n<(6C5x3spjwS8)57w%ytdWu&s$n7iB%{>A7KR%EldwDCdzpMY$(*$3?hk zP3p9%W^{AJO4y!f=O$FgQLSk`F`F{7w#Bc!nX$#;F-NB?WB0wyMH!19DtET>j!a^_ zLr5(CGX1{@A84sk2Ma_-E!)y#@nd{8g284R86Zc<3385HAQ#DVG?~@qLxSCZ#jDHlz)Nb@TU`}))ZEHs9@;H3OUrV>)sO4Q z%P8tY$kDAB)M9gZW!;9&TefcR?YlRAVED*G*O;e~+N6`{`Yg|wGIVD`b+fvqp-UMn z8JagUUNl@IW9p-pn$sgK&=tAX+>Sn}Ls2+EbwQalRmd2R6bx@xj)@}MBdT;tlNw&! z5nH3XXrg=>`JkV>UFlR#QOuyDW2^ZK?o_r(qb>?YlvPgHE%ivF=g?e<6P-?*Kp}y00~`7FuBwY=JJ=4SmoLdm#ZMa10$;5+-2=UD?Cn!g-j5 zId~L41y90L@HBiDJ`c~qm*5h78NLEvhi|}T_!fK*uELMuC-5_P6aD~ygg?Ps@K5*` zyel*a8-#XYqtGdI3EPEkp-<=+?h*D0M}-j~C8)x~f-NxDhnYgzWu7h{kV9b>VZFKe zu~B$sX>;p1_`vXk(&3xN+^+Lb-imQNdiz~>@9N(_P*Fs#xn*SyngG-L8f#&C&-9{B zXRk3!ly2KxHodAba^oCBgVE26)NbV_pE|ZhM1zqV{NdI+ad=2>DS4d@7@-Axn zI#>_wa0m3DekU+YA7vVzgfTb+X*dfR4B0ubz=lUq*B^y>_yjD#W2ot$L0$hGT!d%g z3-CqM_vhgScoANLucEep8(xF&zz^Vua0Pzk>;11$>;DRW^EI}t$wz868U9hP$-&_M zwSx_h2ZDPg_~h(ra9@rl7~C(UqjcLEi&+%hOYAAzTO8cUb$m)S0#DkMS6D&`gcA5@ zN?;8hJA!_qh}9zA(z*PS@88+m`r6cidY78ifxf3_bRUwmp>m!ue}7N%_m`OwzR&r4 zW3v*E z;LMifV}33ez*qCYrSQQVu+$=xn*XeLvK{9C^dGJ;|8FE={!im)SV9Se5?EdW4II@& zzyCY1{P+LMGYB!E1VRbi^b&y8L&>289;5tXqy9wnTe~RMvsfw3_p#D~eYOM}_BkFx z_BkF7=;wI-jL2^g7pGl>3mjq(%m4jDK>Ah62mGDzieyGN`#a%EW`v(#g!%u9=l}l! Du1P2i delta 201 zcmZoEXmOBWU|?W$DortDU;r^WfEYvza8E20o2Vx_*}#NF63A!Z2VzE`ID=bA(wb{wTZEgf{CHg&>MC-~~m16_|I9V^t diff --git a/main.py b/main.py index 0459fbb..cb2945b 100644 --- a/main.py +++ b/main.py @@ -25,6 +25,7 @@ ''' import argparse import sys +sys.path.append('./src') sys.path.append('./networks/base') sys.path.append('./networks/quantized') @@ -34,14 +35,15 @@ from VPRTempo import VPRTempo, run_inference from VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant from VPRTempoQuant import VPRTempoQuant, run_inference_quant +from loggers import model_logger, model_logger_quant -def initialize_and_run_model(args): +def initialize_and_run_model(args,dims): # If user wants to train a new network if args.train_new_model: # If using quantization aware training if args.quantize: # Initialize the quantized model - model = VPRTempoQuantTrain(args) + model = VPRTempoQuantTrain(args,dims) # Get the quantization config qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name @@ -51,33 +53,45 @@ def initialize_and_run_model(args): # Train the model train_new_model_quant(model, model_name, qconfig) else: # Normal model - # Initialize the model - model = VPRTempoTrain(args) + models = [] + logger = model_logger() + for _ in range(args.num_modules): + # Initialize the model + model = VPRTempoTrain(args, dims, logger) + models.append(model) # Generate the model name model_name = generate_model_name(model) # Check if the model has been trained before check_pretrained_model(model_name) # Train the model - train_new_model(model, model_name) + train_new_model(models, model_name) # Run the inference network else: # Set the quantization configuration if args.quantize: - # Initialize the quantized model - model = VPRTempoQuant(args) + models = [] + logger = model_logger() + for _ in range(args.num_modules): + # Initialize the model + model = VPRTempo(args, dims, logger) + models.append(model) # Get the quantization config qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name model_name = generate_model_name_quant(model) # Run the quantized inference model - run_inference_quant(model, model_name, qconfig) + run_inference_quant(models, model_name, qconfig) else: - # Initialize the model - model = VPRTempo(args) + models = [] + logger = model_logger() + for _ in range(args.num_modules): + # Initialize the model + model = VPRTempo(dims, args, logger) + models.append(model) # Generate the model name model_name = generate_model_name(model) # Run the inference model - run_inference(model, model_name) + run_inference(models, model_name) def parse_network(use_quantize=False, train_new_model=False): ''' @@ -90,10 +104,12 @@ def parse_network(use_quantize=False, train_new_model=False): help="Dataset to use for training and/or inferencing") parser.add_argument('--data_dir', type=str, default='./dataset/', help="Directory where dataset files are stored") - parser.add_argument('--num_places', type=int, default=500, + parser.add_argument('--num_places', type=int, default=1000, help="Number of places to use for training and/or inferencing") - parser.add_argument('--num_modules', type=int, default=1, + parser.add_argument('--num_modules', type=int, default=2, help="Number of expert modules to use split images into") + parser.add_argument('--max_module', type=int, default=500, + help="Maximum number of images per module") parser.add_argument('--database_dirs', nargs='+', default=['spring', 'fall'], help="Directories to use for training") parser.add_argument('--query_dir', nargs='+', default=['summer'], @@ -106,10 +122,10 @@ def parse_network(use_quantize=False, train_new_model=False): help="Number of epochs to train the model") # Define image transformation parameters - parser.add_argument('--patches', type=int, default=15, + parser.add_argument('--patches', type=int, default=7, help="Number of patches to generate for patch normalization image into") - parser.add_argument('--dims', nargs='+', type=int, default=[56,56], - help="Dimensions to resize the image to") + parser.add_argument('--dims', type=str, default="28,28", + help="Dimensions to resize the image to") # Define the network functionality parser.add_argument('--train_new_model', action='store_true', @@ -127,9 +143,10 @@ def parse_network(use_quantize=False, train_new_model=False): # Output base configuration args = parser.parse_args() + dims = [int(x) for x in args.dims.split(",")] # Run the network with the desired settings - initialize_and_run_model(args) + initialize_and_run_model(args,dims) if __name__ == "__main__": # User input to determine if using quantized network or to train new model diff --git a/models/.DS_Store b/models/.DS_Store index c57311ea80e8b15f67695fdfd2c7bc0a5de4731e..28b3c56f896fa8c0c5ff67d4e21152c0cdbb6e3d 100644 GIT binary patch delta 194 zcmZoMXfc@JFUrcmz`)4BAi%(o%8<)Yz>v>i%wV|Lk$E|zI!H=_A&en_A&4OaSsJLs z3v>i&S0^5A@g!ZMwZDjOo5Z*nba6fCtEP7 sPyWlKz-q)`z+gIAiCG;et}xk 0 self.havconnCombinedInh = self.w.weight < 0 + del self.exc, self.inh + def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0],device=None): # Get torch device diff --git a/src/dataset.py b/src/dataset.py index e51034e..9ed9112 100644 --- a/src/dataset.py +++ b/src/dataset.py @@ -159,21 +159,22 @@ def __call__(self, img): class CustomImageDataset(Dataset): def __init__(self, annotations_file, base_dir, img_dirs, transform=None, target_transform=None, - skip=1, max_samples=None, test=True): + skip=1, max_samples=None, test=True, img_range=None): self.transform = transform self.target_transform = target_transform self.skip = skip - + self.img_range = img_range + # Load image labels from each directory, apply the skip and max_samples, and concatenate self.img_labels = [] for img_dir in img_dirs: img_labels = pd.read_csv(annotations_file) img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(base_dir,img_dir, row.iloc[0]), axis=1) - + if self.img_range is not None: + img_labels = img_labels.iloc[self.img_range[0]:self.img_range[1]+1] # Select specific rows based on the skip parameter img_labels = img_labels.iloc[::skip] - # Limit the number of samples to max_samples if specified if max_samples is not None: img_labels = img_labels.iloc[:max_samples] diff --git a/src/loggers.py b/src/loggers.py index 326eee7..6401e91 100644 --- a/src/loggers.py +++ b/src/loggers.py @@ -4,57 +4,49 @@ from datetime import datetime -def model_logger(model): +def model_logger(): """ Configure the model logger """ - if os.path.isdir('../output'): - now = datetime.now() - model.output_folder = '../output/' + now.strftime("%d%m%y-%H-%M-%S") - else: - now = datetime.now() - model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + now = datetime.now() + output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) + os.mkdir(output_folder) # Create the logger - model.logger = logging.getLogger("VPRTempo") - if (model.logger.hasHandlers()): - model.logger.handlers.clear() + logger = logging.getLogger("VPRTempo") + if (logger.hasHandlers()): + logger.handlers.clear() # Set the logger level - model.logger.setLevel(logging.DEBUG) - logging.basicConfig(filename=model.output_folder + "/logfile.log", + logger.setLevel(logging.DEBUG) + logging.basicConfig(filename=output_folder + "/logfile.log", filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s") # Add the logger to the console (if specified) - model.logger.addHandler(logging.StreamHandler()) + logger.addHandler(logging.StreamHandler()) - model.logger.info('') - model.logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗') - model.logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗') - model.logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║') - model.logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║') - model.logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝') - model.logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ') - model.logger.info('-----------------------------------------------------------------------') - model.logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') - model.logger.info('Queensland University of Technology, Centre for Robotics') - model.logger.info('') - model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') - model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') - model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') - model.logger.info('') + logger.info('') + logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗') + logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗') + logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║') + logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║') + logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝') + logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ') + logger.info('-----------------------------------------------------------------------') + logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') + logger.info('Queensland University of Technology, Centre for Robotics') + logger.info('') + logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + logger.info('MIT license - https://github.com/QVPR/VPRTempo') + logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + logger.info('') if torch.cuda.is_available(): - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - device = "cuda:0" + logger.info('CUDA available: ' + str(torch.cuda.is_available())) current_device = torch.cuda.current_device() - model.logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) + logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device))) else: - model.logger.info('CUDA available: ' + str(torch.cuda.is_available())) - model.logger.info('Current device is: CPU') - device = "cpu" - model.logger.info('') - - return device + logger.info('CUDA available: ' + str(torch.cuda.is_available())) + logger.info('Current device is: CPU') + logger.info('') def model_logger_quant(model): """ diff --git a/tutorials/.DS_Store b/tutorials/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..6cbab197bfe01d7a1bba7c4bacd81a6ed4589230 GIT binary patch literal 6148 zcmeHKL5mYH6n@zq?bJo6P+>1Zz-w9C?y|CY8OM6qqmJl7rOxaW9h{w%&eXD73Uk&U zOHy%LiwGjF!+**m_fdTd_S9bnolU*_^$CjZ_$vDr-s@wf6wzj=1R~vrA-|=5YZ`3TR;%Zuk z@#H!8o+y>n-`P5Z=2lP62=()LPKyT!MY1 z#lT{15F-#_T!F?_*c3w;cj%?H3oOzN4v literal 0 HcmV?d00001 diff --git a/tutorials/mats/.DS_Store b/tutorials/mats/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..b2b0f537229c8fdbfc2772807bd4fdbbb908cc6d GIT binary patch literal 6148 zcmeHKO;5r=5S;~-5@NzZ6ONmBB?@RT#!Ibu@M?`7)L<(_8*A54kOLv zT)wcfT6Bs|*}3-b)yN+NgJJ3g{YyGJQz{OpaS)zIy-}yKex#B?5GB37CPdvHmRwy# zNmq?LHB7px=K5yBDLJK1r8*wBn!7dG*x#Gf%SOHdGc@(fKm{VS!Tjo1u1z3RvDM0Ik zL??7DW(M`rfrD-V5DS>rhB5st5=UBeEoKIB22B_$qM-`gVhBUWytH+$#mu0ggRsqq zuvZqgLlOGwcz&tNLAVBaWCd7(p9)a#hgPBe|9F1?uZwtK1z3UqsemZ7{kDhCX7|>a xr=-1BqTisCQC(*6i-LjPim{fq;wri}%u5OoU5lAP%%Jg)fR=#=R$!qDd;$SiTIT=& literal 0 HcmV?d00001 From a937ff16fc691439bc26e8f470cd0e95dc37aa37 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 1 Dec 2023 13:52:53 +1000 Subject: [PATCH 65/69] Completed modules for VPRTempo and VPRTempoQuant. --- main.py | 31 ++++--- networks/base/VPRTempoTrain.py | 5 +- networks/quantized/VPRTempoQuant.py | 88 ++++++++++--------- networks/quantized/VPRTempoQuantTrain.py | 103 +++++++++++++---------- output/.gitkeep | 0 src/loggers.py | 67 +++++++-------- 6 files changed, 160 insertions(+), 134 deletions(-) delete mode 100644 output/.gitkeep diff --git a/main.py b/main.py index cb2945b..a8f745a 100644 --- a/main.py +++ b/main.py @@ -42,16 +42,22 @@ def initialize_and_run_model(args,dims): if args.train_new_model: # If using quantization aware training if args.quantize: - # Initialize the quantized model - model = VPRTempoQuantTrain(args,dims) + models = [] + logger = model_logger_quant() # Get the quantization config qconfig = quantization.get_default_qat_qconfig('fbgemm') + for _ in range(args.num_modules): + # Initialize the model + model = VPRTempoQuantTrain(args, dims, logger) + model.train() + model.qconfig = qconfig + models.append(model) # Generate the model name model_name = generate_model_name_quant(model) # Check if the model has been trained before check_pretrained_model(model_name) # Train the model - train_new_model_quant(model, model_name, qconfig) + train_new_model_quant(models, model_name, qconfig) else: # Normal model models = [] logger = model_logger() @@ -70,13 +76,16 @@ def initialize_and_run_model(args,dims): # Set the quantization configuration if args.quantize: models = [] - logger = model_logger() + logger = model_logger_quant() + qconfig = quantization.get_default_qat_qconfig('fbgemm') for _ in range(args.num_modules): # Initialize the model - model = VPRTempo(args, dims, logger) + model = VPRTempoQuant(dims, args, logger) + model.eval() + model.qconfig = qconfig + model = quantization.prepare(model, inplace=False) + model = quantization.convert(model, inplace=False) models.append(model) - # Get the quantization config - qconfig = quantization.get_default_qat_qconfig('fbgemm') # Generate the model name model_name = generate_model_name_quant(model) # Run the quantized inference model @@ -104,9 +113,9 @@ def parse_network(use_quantize=False, train_new_model=False): help="Dataset to use for training and/or inferencing") parser.add_argument('--data_dir', type=str, default='./dataset/', help="Directory where dataset files are stored") - parser.add_argument('--num_places', type=int, default=1000, + parser.add_argument('--num_places', type=int, default=500, help="Number of places to use for training and/or inferencing") - parser.add_argument('--num_modules', type=int, default=2, + parser.add_argument('--num_modules', type=int, default=1, help="Number of expert modules to use split images into") parser.add_argument('--max_module', type=int, default=500, help="Maximum number of images per module") @@ -122,9 +131,9 @@ def parse_network(use_quantize=False, train_new_model=False): help="Number of epochs to train the model") # Define image transformation parameters - parser.add_argument('--patches', type=int, default=7, + parser.add_argument('--patches', type=int, default=15, help="Number of patches to generate for patch normalization image into") - parser.add_argument('--dims', type=str, default="28,28", + parser.add_argument('--dims', type=str, default="56,56", help="Dimensions to resize the image to") # Define the network functionality diff --git a/networks/base/VPRTempoTrain.py b/networks/base/VPRTempoTrain.py index 9d2a6d9..4170407 100644 --- a/networks/base/VPRTempoTrain.py +++ b/networks/base/VPRTempoTrain.py @@ -152,12 +152,15 @@ def train_model(self, train_loader, layer, model, model_num, prev_layers=None): init_stdp = layer.eta_stdp.detach() mod = 0 # Used to determine the learning rate annealment, resets at each epoch + # idx scale factor for different modules + idx_scale = (self.max_module*self.filter)*model_num + # Run training for the specified number of epochs for _ in range(self.epoch): # Run training for the specified number of timesteps for spikes, labels in train_loader: spikes, labels = spikes.to(self.device), labels.to(self.device) - idx = (torch.round(labels/(model_num+1))) / self.filter # Set output index for spike forcing + idx = torch.round((labels - idx_scale) / self.filter) # Set output index for spike forcing # Pass through previous layers if they exist if prev_layers: with torch.no_grad(): diff --git a/networks/quantized/VPRTempoQuant.py b/networks/quantized/VPRTempoQuant.py index 53194cd..22cb13e 100644 --- a/networks/quantized/VPRTempoQuant.py +++ b/networks/quantized/VPRTempoQuant.py @@ -50,7 +50,7 @@ #from main import parse_network class VPRTempoQuant(nn.Module): - def __init__(self, args, dims): + def __init__(self, dims, args=None, logger=None): super(VPRTempoQuant, self).__init__() # Set the arguments @@ -62,7 +62,8 @@ def __init__(self, args, dims): self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') # Set the model logger and return the device - self.device = model_logger_quant(self) + self.logger = logger + self.device = "cpu" # Add quantization stubs for Quantization Aware Training (QAT) self.quant = QuantStub() @@ -73,7 +74,7 @@ def __init__(self, args, dims): self.layer_counter = 0 # Define layer architecture - self.input = int(self.dims[0]*self.dims[1]) + self.input = int(dims[0]*dims[1]) self.feature = int(self.input * 2) self.output = int(args.num_places / args.num_modules) @@ -112,7 +113,7 @@ def add_layer(self, name, **kwargs): self.layer_dict[name] = self.layer_counter self.layer_counter += 1 - def evaluate(self, model, test_loader): + def evaluate(self, models, test_loader, layers=None): """ Run the inferencing model and calculate the accuracy. @@ -120,16 +121,18 @@ def evaluate(self, model, test_loader): :param layers: Layers to pass data through """ # Determine the Hardtahn max value - maxSpike = (1//model.quant.scale).item() + maxSpike = (1//models[0].quant.scale).item() # Define the sequential inference model - self.inference = nn.Sequential( - self.feature_layer.w, - nn.Hardtanh(0, maxSpike), - nn.ReLU(), - self.output_layer.w, - nn.Hardtanh(0, maxSpike), - nn.ReLU() - ) + self.inferences = [] + for model in models: + self.inferences.append(nn.Sequential( + model.feature_layer.w, + nn.Hardtanh(0, maxSpike), + nn.ReLU(), + model.output_layer.w, + nn.Hardtanh(0, maxSpike), + nn.ReLU() + )) # Initialize the tqdm progress bar pbar = tqdm(total=self.num_places, desc="Running the test network", @@ -164,7 +167,7 @@ def evaluate(self, model, test_loader): table = PrettyTable() table.field_names = ["N", "1", "5", "10", "15", "20", "25"] table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]]) - model.logger.info(table) + print(table) def forward(self, spikes): """ @@ -176,19 +179,30 @@ def forward(self, spikes): Returns: - Tensor: Output after processing. """ - spikes = self.quant(spikes) - spikes = self.inference(spikes) - spikes = self.dequant(spikes) - + in_spikes = spikes.detach().clone() + outputs = [] # List to collect output tensors + + for inference in self.inferences: + out_spikes = inference(in_spikes) + outputs.append(out_spikes) # Append the output tensor to the list + + # Concatenate along the desired dimension + concatenated_output = torch.cat(outputs, dim=1) + spikes = self.dequant(concatenated_output) + return spikes - def load_model(self, model_path): + def load_model(self, models, model_path): """ Load pre-trained model and set the state dictionary keys. """ - self.load_state_dict(torch.load(model_path, map_location=self.device), - strict=False) + combined_state_dict = torch.load(model_path, map_location=self.device) + + for i, model in enumerate(models): # models_classes is a list of model classes + + model.load_state_dict(combined_state_dict[f'model_{i}']) + model.eval() # Set the model to inference mode def check_pretrained_model(model_name): """ @@ -201,7 +215,7 @@ def check_pretrained_model(model_name): pretrain = 'y' return pretrain -def run_inference_quant(model, model_name, qconfig): +def run_inference_quant(models, model_name, qconfig): """ Run inference on a pre-trained model. @@ -210,31 +224,23 @@ def run_inference_quant(model, model_name, qconfig): :param qconfig: Quantization configuration """ # Initialize the image transforms and datasets - image_transform = ProcessImage(model.dims, model.patches) - test_dataset = CustomImageDataset(annotations_file=model.dataset_file, - base_dir=model.data_dir, - img_dirs=model.query_dir, - transform=image_transform, - skip=model.filter, - max_samples=model.num_places) + image_transform = ProcessImage(models[0].dims, models[0].patches) + test_dataset = CustomImageDataset(annotations_file=models[0].dataset_file, + base_dir=models[0].data_dir, + img_dirs=models[0].query_dir, + transform=image_transform, + skip=models[0].filter, + max_samples=models[0].num_places) # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=8, persistent_workers=True) - # Set the model to evaluation mode and set configuration - model.eval() - model.qconfig = qconfig - - # Apply quantization configurations to all layers in layer_dict - for layer_name, _ in model.layer_dict.items(): - getattr(model, layer_name).qconfig = qconfig - # Prepare and convert the model to a quantized model - model = quantization.prepare(model, inplace=False) - model = quantization.convert(model, inplace=False) + # Load the model - model.load_model(os.path.join('./models', model_name)) + models[0].load_model(models, os.path.join('./models', model_name)) # Use evaluate method for inference accuracy - model.evaluate(model, test_loader) \ No newline at end of file + with torch.no_grad(): + models[0].evaluate(models, test_loader) \ No newline at end of file diff --git a/networks/quantized/VPRTempoQuantTrain.py b/networks/quantized/VPRTempoQuantTrain.py index 9ee3f87..c99af76 100644 --- a/networks/quantized/VPRTempoQuantTrain.py +++ b/networks/quantized/VPRTempoQuantTrain.py @@ -37,15 +37,15 @@ import numpy as np import torch.nn as nn import torch.quantization as quantization +import torchvision.transforms as transforms -from loggers import model_logger_quant from dataset import CustomImageDataset, ProcessImage from torch.utils.data import DataLoader from torch.ao.quantization import QuantStub, DeQuantStub from tqdm import tqdm class VPRTempoQuantTrain(nn.Module): - def __init__(self, args,dims): + def __init__(self, args, dims, logger): super(VPRTempoQuantTrain, self).__init__() # Set the arguments @@ -53,8 +53,12 @@ def __init__(self, args,dims): for arg in vars(args): setattr(self, arg, getattr(args, arg)) setattr(self, 'dims', dims) - # Configure the network - self.device = model_logger_quant(self) + + # Only CPU available for quantization + self.device = "cpu" + + # Set the logger + self.logger = logger # Set the dataset file self.dataset_file = os.path.join('./dataset', self.dataset + '.csv') @@ -68,7 +72,7 @@ def __init__(self, args,dims): self.layer_counter = 0 # Define layer architecture - self.input = int(self.dims[0]*self.dims[1]) + self.input = int(dims[0]*dims[1]) self.feature = int(self.input * 2) self.output = int(args.num_places / args.num_modules) @@ -129,7 +133,7 @@ def _anneal_learning_rate(self, layer, mod, itp, stdp): return layer - def train_model(self, train_loader, layer, prev_layers=None): + def train_model(self, train_loader, layer, model, model_num, prev_layers=None): """ Train a layer of the network model. @@ -147,12 +151,14 @@ def train_model(self, train_loader, layer, prev_layers=None): init_itp = layer.eta_ip.detach() init_stdp = layer.eta_stdp.detach() mod = 0 # Used to determine the learning rate annealment, resets at each epoch + # idx scale factor for different modules + idx_scale = (self.max_module*self.filter)*model_num # Run training for the specified number of epochs for epoch in range(self.epoch): # Run training for the specified number of timesteps for spikes, labels in train_loader: spikes, labels = spikes.to(self.device), labels.to(self.device) - idx = labels / self.filter # Set output index for spike forcing + idx = torch.round((labels - idx_scale) / self.filter) # Set output index for spike forcing # Pass through previous layers if they exist if prev_layers: with torch.no_grad(): @@ -195,11 +201,15 @@ def forward(self, spikes, layer): return spikes - def save_model(self, model_out): + def save_model(self, models, model_out): """ Save the trained model to models output folder. """ - torch.save(self.state_dict(), model_out) + state_dicts = {} + for i, model in enumerate(models): # Assuming models_list is your list of models + state_dicts[f'model_{i}'] = model.state_dict() + + torch.save(state_dicts, model_out) def generate_model_name_quant(model): """ @@ -222,7 +232,7 @@ def check_pretrained_model(model_name): return retrain == 'n' return False -def train_new_model_quant(model, model_name, qconfig): +def train_new_model_quant(models, model_name, qconfig): """ Train a new model. @@ -231,42 +241,47 @@ def train_new_model_quant(model, model_name, qconfig): :param qconfig: Quantization configuration """ # Initialize the image transforms and datasets - image_transform = ProcessImage(model.dims, model.patches) - train_dataset = CustomImageDataset(annotations_file=model.dataset_file, - base_dir=model.data_dir, - img_dirs=model.database_dirs, - transform=image_transform, - skip=model.filter, - max_samples=model.num_places, - test=False) - # Initialize the data loader - train_loader = DataLoader(train_dataset, - batch_size=1, - shuffle=True, - num_workers=8, - persistent_workers=True) - # Set the model to training mode and move to device - model.train() - model.to('cpu') - model.qconfig = qconfig + image_transform = transforms.Compose([ + ProcessImage(models[0].dims, models[0].patches) + ]) + # Automatically generate user_input_ranges + user_input_ranges = [] + start_idx = 0 - # Apply quantization configurations to the model - model = quantization.prepare_qat(model, inplace=False) + for _ in range(models[0].num_modules): + range_temp = [start_idx, start_idx+((models[0].max_module-1)*models[0].filter)] + user_input_ranges.append(range_temp) + start_idx = range_temp[1] + models[0].filter # Keep track of trained layers to pass data through them - trained_layers = [] - # Training each layer - for layer_name, _ in sorted(model.layer_dict.items(), key=lambda item: item[1]): - print(f"Training layer: {layer_name}") - # Retrieve the layer object - layer = getattr(model, layer_name) - # Train the layer - model.train_model(train_loader, layer, prev_layers=trained_layers) - # After training the current layer, add it to the list of trained layers - trained_layers.append(layer_name) - # Convert the model to a quantized model - model = quantization.convert(model, inplace=False) - model.eval() + trained_models = [] + for i, model in enumerate(models): + trained_layers = [] + img_range=user_input_ranges[i] + train_dataset = CustomImageDataset(annotations_file=models[0].dataset_file, + base_dir=models[0].data_dir, + img_dirs=models[0].database_dirs, + transform=image_transform, + skip=models[0].filter, + test=False, + img_range=img_range) + # Initialize the data loader + train_loader = DataLoader(train_dataset, + batch_size=1, + shuffle=True, + num_workers=8, + persistent_workers=True) + model = quantization.prepare_qat(model, inplace=False) + for layer_name, _ in sorted(models[0].layer_dict.items(), key=lambda item: item[1]): + print(f"Training layer: {layer_name}") + layer = (getattr(model, layer_name)) + + # Train the layers + model.train_model(train_loader, layer, model, i, prev_layers=trained_layers) + trained_layers.append(layer_name) + trained_models.append(quantization.convert(model, inplace=False)) + # After training the current layer, add it to the list of trained layer + # Save the model - model.save_model(os.path.join('./models', model_name)) \ No newline at end of file + model.save_model(trained_models,os.path.join('./models', model_name)) \ No newline at end of file diff --git a/output/.gitkeep b/output/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/src/loggers.py b/src/loggers.py index 6401e91..eace9ef 100644 --- a/src/loggers.py +++ b/src/loggers.py @@ -48,50 +48,43 @@ def model_logger(): logger.info('Current device is: CPU') logger.info('') -def model_logger_quant(model): +def model_logger_quant(): """ - Configure the model logger + Configure the logger """ - if os.path.isdir('../output'): - now = datetime.now() - model.output_folder = '../output/' + now.strftime("%d%m%y-%H-%M-%S") - else: - now = datetime.now() - model.output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") + + now = datetime.now() + output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S") - os.mkdir(model.output_folder) + os.mkdir(output_folder) # Create the logger - model.logger = logging.getLogger("VPRTempo") - if (model.logger.hasHandlers()): - model.logger.handlers.clear() + logger = logging.getLogger("VPRTempo") + if (logger.hasHandlers()): + logger.handlers.clear() # Set the logger level - model.logger.setLevel(logging.DEBUG) - logging.basicConfig(filename=model.output_folder + "/logfile.log", + logger.setLevel(logging.DEBUG) + logging.basicConfig(filename=output_folder + "/logfile.log", filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s") # Add the logger to the console (if specified) - model.logger.addHandler(logging.StreamHandler()) - - device = "cpu" + logger.addHandler(logging.StreamHandler()) - model.logger.info('') - - model.logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗ ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗████████╗') - model.logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗ ██╔═══██╗██║ ██║██╔══██╗████╗ ██║╚══██╔══╝') - model.logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║█████╗██║ ██║██║ ██║███████║██╔██╗ ██║ ██║') - model.logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║╚════╝██║▄▄ ██║██║ ██║██╔══██║██║╚██╗██║ ██║') - model.logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝ ╚██████╔╝╚██████╔╝██║ ██║██║ ╚████║ ██║') - model.logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ╚══▀▀═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝') - model.logger.info('-----------------------------------------------------------------------') - model.logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') - model.logger.info('Queensland University of Technology, Centre for Robotics') - model.logger.info('') - model.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') - model.logger.info('MIT license - https://github.com/QVPR/VPRTempo') - model.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') - model.logger.info('') - model.logger.info('Quantization enabled') - model.logger.info('Current device is: CPU') - model.logger.info('') + logger.info('') - return device \ No newline at end of file + logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗ ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗████████╗') + logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗ ██╔═══██╗██║ ██║██╔══██╗████╗ ██║╚══██╔══╝') + logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║█████╗██║ ██║██║ ██║███████║██╔██╗ ██║ ██║') + logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║╚════╝██║▄▄ ██║██║ ██║██╔══██║██║╚██╗██║ ██║') + logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝ ╚██████╔╝╚██████╔╝██║ ██║██║ ╚████║ ██║') + logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ╚══▀▀═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝') + logger.info('-----------------------------------------------------------------------') + logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0') + logger.info('Queensland University of Technology, Centre for Robotics') + logger.info('') + logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer') + logger.info('MIT license - https://github.com/QVPR/VPRTempo') + logger.info('\\\\\\\\\\\\\\\\\\\\\\\\') + logger.info('') + logger.info('Quantization enabled') + logger.info('Current device is: CPU') + logger.info('') From c8e32d839b03c516d3333c0b18626f953f9ed662 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 1 Dec 2023 13:57:34 +1000 Subject: [PATCH 66/69] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3a4b5ab..37ee3d8 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![stars](https://img.shields.io/github/stars/QVPR/VPRTempo.svg?style=flat-square)](https://github.com/QVPR/VPRTempo/stargazers) [![QUT Centre for Robotics](https://img.shields.io/badge/collection-QUT%20Robotics-%23043d71?style=flat-square)](https://qcr.ai) ![GitHub repo size](https://img.shields.io/github/repo-size/QVPR/VPRTempo.svg?style=flat-square) -[![PyPI downloads](https://img.shields.io/pypi/dw/VPRTempo.svg)](https://pypistats.org/packages/VPRTempo) +[![PyPI downloads](https://img.shields.io/pypi/dw/VPRTempo.svg)](https://pypistats.org/packages/vprtempo) This repository contains code for VPRTempo, a spiking neural network that uses temporally encoding to perform visual place recognition tasks. The network is based off of [BLiTNet](https://arxiv.org/pdf/2208.01204.pdf) and adapted to the [VPRSNN](https://github.com/QVPR/VPRSNN) framework. From edbc96ca3706b1638abf9a42a4d8d71e577e81f1 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 1 Dec 2023 14:07:02 +1000 Subject: [PATCH 67/69] Readd outputs folder --- output/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 output/.gitkeep diff --git a/output/.gitkeep b/output/.gitkeep new file mode 100644 index 0000000..e69de29 From 0d3b24776239aa4f8c0015603646df62ad8565a7 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 1 Dec 2023 16:17:10 +1000 Subject: [PATCH 68/69] Completed tutorials on using the network --- networks/base/VPRTempo.py | 4 +- networks/base/VPRTempoTrain.py | 8 +- networks/quantized/VPRTempoQuant.py | 3 +- networks/quantized/VPRTempoQuantTrain.py | 8 +- tutorials/1_BasicDemo.ipynb | 2 +- tutorials/2_Training.ipynb | 199 ++++++++++++++++++++++ tutorials/3_Modules.ipynb | 204 +++++++++++++++++++++++ 7 files changed, 421 insertions(+), 7 deletions(-) create mode 100644 tutorials/2_Training.ipynb create mode 100644 tutorials/3_Modules.ipynb diff --git a/networks/base/VPRTempo.py b/networks/base/VPRTempo.py index c201080..18f4d17 100644 --- a/networks/base/VPRTempo.py +++ b/networks/base/VPRTempo.py @@ -204,12 +204,14 @@ def run_inference(models, model_name): """ # Initialize the image transforms and datasets image_transform = ProcessImage(models[0].dims, models[0].patches) + max_samples=models[0].num_places + test_dataset = CustomImageDataset(annotations_file=models[0].dataset_file, base_dir=models[0].data_dir, img_dirs=models[0].query_dir, transform=image_transform, skip=models[0].filter, - max_samples=models[0].num_places) + max_samples=max_samples) # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, diff --git a/networks/base/VPRTempoTrain.py b/networks/base/VPRTempoTrain.py index 4170407..077ccf3 100644 --- a/networks/base/VPRTempoTrain.py +++ b/networks/base/VPRTempoTrain.py @@ -257,7 +257,10 @@ def train_new_model(models, model_name): range_temp = [start_idx, start_idx+((models[0].max_module-1)*models[0].filter)] user_input_ranges.append(range_temp) start_idx = range_temp[1] + models[0].filter - + if models[0].num_places < models[0].max_module: + max_samples=models[0].num_places + else: + max_samples = models[0].max_module # Keep track of trained layers to pass data through them trained_layers = [] # Training each layer @@ -274,7 +277,8 @@ def train_new_model(models, model_name): transform=image_transform, skip=models[0].filter, test=False, - img_range=img_range) + img_range=img_range, + max_samples=max_samples) # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, diff --git a/networks/quantized/VPRTempoQuant.py b/networks/quantized/VPRTempoQuant.py index 22cb13e..f616289 100644 --- a/networks/quantized/VPRTempoQuant.py +++ b/networks/quantized/VPRTempoQuant.py @@ -223,6 +223,7 @@ def run_inference_quant(models, model_name, qconfig): :param model_name: Name of the model to load :param qconfig: Quantization configuration """ + max_samples = models[0].num_places # Initialize the image transforms and datasets image_transform = ProcessImage(models[0].dims, models[0].patches) test_dataset = CustomImageDataset(annotations_file=models[0].dataset_file, @@ -230,7 +231,7 @@ def run_inference_quant(models, model_name, qconfig): img_dirs=models[0].query_dir, transform=image_transform, skip=models[0].filter, - max_samples=models[0].num_places) + max_samples=max_samples) # Initialize the data loader test_loader = DataLoader(test_dataset, batch_size=1, diff --git a/networks/quantized/VPRTempoQuantTrain.py b/networks/quantized/VPRTempoQuantTrain.py index c99af76..6c2448a 100644 --- a/networks/quantized/VPRTempoQuantTrain.py +++ b/networks/quantized/VPRTempoQuantTrain.py @@ -252,7 +252,10 @@ def train_new_model_quant(models, model_name, qconfig): range_temp = [start_idx, start_idx+((models[0].max_module-1)*models[0].filter)] user_input_ranges.append(range_temp) start_idx = range_temp[1] + models[0].filter - + if models[0].num_places < models[0].max_module: + max_samples=models[0].num_places + else: + max_samples = models[0].max_module # Keep track of trained layers to pass data through them # Training each layer trained_models = [] @@ -265,7 +268,8 @@ def train_new_model_quant(models, model_name, qconfig): transform=image_transform, skip=models[0].filter, test=False, - img_range=img_range) + img_range=img_range, + max_samples=max_samples) # Initialize the data loader train_loader = DataLoader(train_dataset, batch_size=1, diff --git a/tutorials/1_BasicDemo.ipynb b/tutorials/1_BasicDemo.ipynb index d18bbf0..0aa5780 100644 --- a/tutorials/1_BasicDemo.ipynb +++ b/tutorials/1_BasicDemo.ipynb @@ -594,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/tutorials/2_Training.ipynb b/tutorials/2_Training.ipynb new file mode 100644 index 0000000..0479320 --- /dev/null +++ b/tutorials/2_Training.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2a09c9e8-68f6-4edd-a8c1-1cc7f6b8bb00", + "metadata": {}, + "source": [ + "## Training a new VPRTempo & VPRTempoQuant network\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "In this tutorial, we will go through how to train your own model for both the base and quantized version of VPRTempo. \n", + "\n", + "Before starting, make sure you have [installed the dependencies](https://github.com/AdamDHines/VPRTempo-quant#installation-and-setup) and/or activated the conda environment. You will also need the [Nordland](https://github.com/AdamDHines/VPRTempo-quant#nordland) dataset before proceeding, as this tutorial will cover training the network using this as an example.\n", + "\n", + "### Training new models for VPRTempo and VPRTempoQuant\n", + "\n", + "Let's start by training the base model with the default settings (if you have pre-trained a model, it will get removed for the purpose of the tutorial)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a757316e-4aa8-41aa-b03f-1ce4489d3705", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the working directory to the main folder from tutorials\n", + "import os\n", + "os.chdir('../')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eff21590-9d00-4d8f-b860-ca1c0c849187", + "metadata": {}, + "outputs": [], + "source": [ + "# Train the base model with the default settings\n", + "# If the pre-trained model already exists, we will remove it for the tutorial\n", + "file_path = './models/VPRTempo313662725001.pth'\n", + "\n", + "if os.path.exists(file_path):\n", + " os.remove(file_path)\n", + " print(\"The file has been deleted.\")\n", + "\n", + "# Run the training paradigm\n", + "!python main.py --train_new_model" + ] + }, + { + "cell_type": "markdown", + "id": "285ca349-6295-4f04-bbe1-360d43111f82", + "metadata": {}, + "source": [ + "Now we'll run the inferencing model to check and make sure our model trained ok." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1037e4ee-0361-4aa1-bfba-f979ffa51b88", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the base inferencing network\n", + "!python main.py" + ] + }, + { + "cell_type": "markdown", + "id": "cbf8cd37-1af8-420f-ad76-481157a2920e", + "metadata": {}, + "source": [ + "Great! Now let's have a look at changing a few of the default settings and training different kinds of networks. The default settings train 500 places, so if we want to only look at a smaller number of places we can parse the `--num_places` argument and specify how many places to learn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81daf9c8-ea06-4e69-82a5-9179a7c38ea1", + "metadata": {}, + "outputs": [], + "source": [ + "# Train a new model with 250 places\n", + "!python main.py --num_places 250 --train_new_model" + ] + }, + { + "cell_type": "markdown", + "id": "a46cde0d-6343-48a4-87f8-aa5c9f56b231", + "metadata": {}, + "source": [ + "And we can now inference using this smaller model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "559e2391-d0bc-4d58-bb35-8cf69ae08b5e", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the inference for a model with 250 places\n", + "!python main.py --num_places 250" + ] + }, + { + "cell_type": "markdown", + "id": "aa835469-90cc-4f09-b7b4-688465321b18", + "metadata": {}, + "source": [ + "Arguments for the base network work the same for VPRTempoQuant, we just need to also parse the `--quantize` argument. Let's now train another 250 place network, but also change a couple of other parameters. The default VPRTempo settings is a little slow to train on CPU, so let's reduce the image size from 56x56 to 28x28 and change the number of patches for patch normalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eee89998-d334-4cdf-ac5b-88aed367ceab", + "metadata": {}, + "outputs": [], + "source": [ + "# Train a 250 place network with VPRTempoQuant\n", + "!python main.py --quantize --num_places 250 --patches 7 --dims 28,28 --train_new_model" + ] + }, + { + "cell_type": "markdown", + "id": "42d5f3ce-acbb-435d-b197-f7e5841dcd70", + "metadata": {}, + "source": [ + "And now we can inference this model!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6a9e948-5349-4714-b7d0-03ce694409b8", + "metadata": {}, + "outputs": [], + "source": [ + "# Run inference on newly trained VPRTempoQuant model\n", + "!python main.py --quantize --num_places 250 --patches 7 --dims 28,28" + ] + }, + { + "cell_type": "markdown", + "id": "2648e38e-a0d7-481a-9009-3c4cf3e3fff0", + "metadata": {}, + "source": [ + "### List of arguments you can parse\n", + "\n", + "The full list of arguments that can parsed to VPRTempo can be found in the `parse_network` function of `main.py`. Hyperparameters for VPRTempo are hardcoded into the layers and are not recommended to be changed since they generalize fairly well across multiple different datasets. \n", + "\n", + "### Conclusions\n", + "\n", + "This tutorial provided a simple overview of how you can train your own models for both VPRTempo and VPRTempoQuant, and changing a few of the network parameters.\n", + "\n", + "If you would like to go more in-depth, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) where we cover how to define your own custom dataset and work with expert modules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2bdc6d1-4690-4b48-a75f-f9d716cb7154", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/3_Modules.ipynb b/tutorials/3_Modules.ipynb new file mode 100644 index 0000000..bef58f5 --- /dev/null +++ b/tutorials/3_Modules.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7b7b7556-f8df-4e50-801b-7e215e46a415", + "metadata": {}, + "source": [ + "## Using modules with VPRTempo and VPRTempoQuant\n", + "\n", + "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n", + "\n", + "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n", + " \n", + "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n", + "\n", + "### Introduction\n", + "\n", + "In this tutorial, we will go through how to use modules with VPRTempo. Modules break up the training data into multiple networks, which has been shown to [improve the overall performance](https://towardsdatascience.com/machine-learning-with-expert-models-a-primer-6c74585f223f) and accuracy of larger models.\n", + "\n", + "Before starting, make sure you have [installed the dependencies](https://github.com/AdamDHines/VPRTempo-quant#installation-and-setup) and/or activated the conda environment. You will also need the [Nordland](https://github.com/AdamDHines/VPRTempo-quant#nordland) dataset before proceeding, as this tutorial will cover training the network using this as an example.\n", + "\n", + "### Comparing results using expert modules for VPRTempo\n", + "\n", + "Let's start by training the base model with 1000 places, which is 500 more than the default settings. We will need to parse the `--train_new_model`, `--num_places`, as well as another argument we haven't seen yet `--max_module`. \n", + "\n", + "`--max_module` tells the network how many places each expert module should learn, which by default is set to `500`. So if we're training a new, singular network with 1000 places we need to increase `max_module` to 1000." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e0d1a7b-8788-436a-8dd5-27f60077c524", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the working directory to the main folder from tutorials\n", + "import os\n", + "os.chdir('../')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0981001-fa1c-49e0-bbe3-e5a628098eba", + "metadata": {}, + "outputs": [], + "source": [ + "# Train a single network with 1000 places\n", + "!python main.py --num_places 1000 --max_module 1000 --train_new_model" + ] + }, + { + "cell_type": "markdown", + "id": "e7d8287b-b47b-4f7e-9d1c-4b309746b284", + "metadata": {}, + "source": [ + "Now let's see how this performs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32435d47-e583-4768-86ed-2998ec78fdd6", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the inferencing network on the singular 1000 place trained model\n", + "!python main.py --num_places 1000 --max_module 1000" + ] + }, + { + "cell_type": "markdown", + "id": "845764ee-1492-449c-9816-a45ccfe043a1", + "metadata": {}, + "source": [ + "Performance here is still pretty good, but let's see if we can improve it by splitting up the network into modules!\n", + "\n", + "Now that splitting up our 1000 place network into 2 networks, we can remove the `--max_module` argument because the default is set to 500. Instead what we will parse is `--num_modules` to tell the network to split things up into two models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7cf368b-2f7f-4345-b882-7bf51622c0d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Train a new 1000 place model with 2 modules\n", + "!python main.py --num_places 1000 --num_modules 2 --train_new_model" + ] + }, + { + "cell_type": "markdown", + "id": "84ce9e04-ff62-45ce-83b3-89e4623f0fd1", + "metadata": {}, + "source": [ + "Now let's see how it compares with the singular model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6ec3ec7-cca7-45a0-906b-cc3e6d14b5f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the network with 2 modules\n", + "!python main.py --num_places 1000 --num_modules 2" + ] + }, + { + "cell_type": "markdown", + "id": "8da07161-81e9-4773-90f1-e9d675e8c071", + "metadata": {}, + "source": [ + "A modest boost to performance, however you have to imagine how this scales to much larger networks - especially when considering training times. Because the output layer is one-hot encoded, you need to increase the number of output neurons with each place you want to learn. Splitting up networks has a key benefit for VPRTempo to reduce overall training times with little impact on inference speeds. \n", + "\n", + "(Optional) Run a single network for 2500 places or 5 expert modules for 500 places each (reduced dimensionality to speed things up)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffd18263-190c-47f4-a37f-130a040d7a67", + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: run a 2500 place comparison for singular vs modular networks\n", + "# Train networks\n", + "!python main.py --num_places 2500 --max_module 2500 --dims 28,28 --patches 7 --train_new_model\n", + "!python main.py --num_places 2500 --num_modules 5 --dims 28,28 --patches 7 --train_new_model\n", + "# Run inference\n", + "!python main.py --num_places 2500 --max_module 2500 --dims 28,28 --patches 7\n", + "!python main.py --num_places 2500 --num_modules 5 --dims 28,28 --patches 7" + ] + }, + { + "cell_type": "markdown", + "id": "02edb3da-9d34-4709-950d-d202c5e902e7", + "metadata": {}, + "source": [ + "As in the other tutorials, parsing the `--quantize` argument will run exactly the same but for VPRTempoQuant. Let's do a quick comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e520f0a-7b6f-4546-8169-14f20f2f9d18", + "metadata": {}, + "outputs": [], + "source": [ + "# Train networks\n", + "#!python main.py --num_places 1500 --max_module 1500 --dims 28,28 --patches 7 --train_new_model --quantize\n", + "#!python main.py --num_places 1500 --num_modules 3 --dims 28,28 --patches 7 --train_new_model --quantize\n", + "# Run inference\n", + "!python main.py --num_places 1500 --max_module 1500 --dims 28,28 --patches 7 --quantize\n", + "!python main.py --num_places 1500 --num_modules 3 --dims 28,28 --patches 7 --quantize" + ] + }, + { + "cell_type": "markdown", + "id": "58581b5c-70a8-4708-bda1-1ccca1edc70b", + "metadata": {}, + "source": [ + "Once again, we can see that whilst there's a modest boost to the accuracy result the clear improve is the training speed. Because each network is smaller, the opeations on CPU are a lot less computationally heavy when splitting the networks up.\n", + "\n", + "### Conclusions\n", + "\n", + "This tutorial provided a simple overview of how you can train network models using expert modules. \n", + "\n", + "If you would like to go more in-depth, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0309b80b-05c2-4577-8ea0-2e45bf2d6aef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bbe4350b3610bc205b788315efafd81fa36ac976 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 1 Dec 2023 16:20:47 +1000 Subject: [PATCH 69/69] Updated release notes --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 85cc2ab..1252a7d 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ # define the setup setup( name="VPRTempo", - version="0.2.0", + version="1.1.0", description='VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition', long_description=long_description, long_description_content_type='text/markdown', @@ -36,7 +36,7 @@ # 3 - Alpha # 4 - Beta # 5 - Production/Stable - 'Development Status :: 4 - Beta', + 'Development Status :: 5 - Production/Stable', # Indicate who your project is intended for 'Intended Audience :: Developers',

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@@ -82,17 +82,20 @@ VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure. -### Custom datasets For convenience, all data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location. ``` --dataset - |--traversal_1 - |--traversal_2 - |-- ... - |--test_traversal + |--summer + |--spring + |--fall + |--winter ``` -Running `nordland.py` script will automatically do this for you. +### Custom Datasets +To define your own custom dataset to use with VPRTempo, you will need to follow the conventions for [PyTorch Datasets & Dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). We provide a simple script `./dataset/custom_dataset.py` which will rename images in user defined directories and generate the necessary `.csv` file to load into VPRTempo. + +To learn how to use custom datasets, please see the [CustomDatasets.ipynb](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) tutorial. + ## Usage Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operated either through the command terminal or directly running the script. See below for more details. ### Prerequisites @@ -149,5 +152,8 @@ python main.py --train_new_model --quantize Similarly above, if you wish to run the training through an IDE then change the `bool` flag for `train_new_model` to `True`. +## Tutorials +We provide a series of Jupyter Notebook [tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) that go through the basic operations and logic for VPRTempo and VPRTempoQuant. + ## Issues, bugs, and feature requests If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues). From aa664e31ce9393b463a43b0e530ccfb1277f7e43 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:25:56 +1000 Subject: [PATCH 56/69] Adding new weights for basic tutorial to git lfs From 046a1f051615ce47f20a68cf364a89017872e72e Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:28:30 +1000 Subject: [PATCH 57/69] Removed old files From a606c7b4203cace11c568c58901b4d3cdfdef543 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 28 Nov 2023 16:34:48 +1000 Subject: [PATCH 58/69] Remove old files --- tutorials/mats/0_basicdemo/summer.png | Bin 309004 -> 0 bytes tutorials/mats/1_basicdemoquant/summer.png | Bin 309004 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 tutorials/mats/0_basicdemo/summer.png delete mode 100644 tutorials/mats/1_basicdemoquant/summer.png diff --git a/tutorials/mats/0_basicdemo/summer.png b/tutorials/mats/0_basicdemo/summer.png deleted file mode 100644 index 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