From a2c845a398e81e72c9de20517cd4d4812c412118 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 7 Jul 2023 16:45:42 +1000 Subject: [PATCH 01/10] Converting numpy arrays to torch and all network operations to PyTorch --- VPRTempo.py | 10 +- .../TranSalNet_Dense.cpython-311.pyc | Bin 0 -> 9910 bytes src/__pycache__/blitnet_open.cpython-311.pyc | Bin 0 -> 24418 bytes src/__pycache__/data_process.cpython-311.pyc | Bin 0 -> 5479 bytes src/blitnet_open.py | 242 ++++++------------ 5 files changed, 80 insertions(+), 172 deletions(-) create mode 100644 src/__pycache__/TranSalNet_Dense.cpython-311.pyc create mode 100644 src/__pycache__/blitnet_open.cpython-311.pyc create mode 100644 src/__pycache__/data_process.cpython-311.pyc diff --git a/VPRTempo.py b/VPRTempo.py index 19460bc..7281d7e 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -215,7 +215,7 @@ def set_spikes(): spikeTimes.extend(spike_neuron) yield - spikeTimes = np.array(spikeTimes) + spikeTimes = torch.from_numpy(np.array(spikeTimes)) # set input spikes blitnet.setSpikeTimes(net,0,spikeTimes) @@ -313,7 +313,7 @@ def train_output(): for n in range(self.epoch): out_spks[:,0] += self.output_layer - append_spks= np.concatenate((append_spks,out_spks),axis=0) + append_spks= torch.from_numpy(np.concatenate((append_spks,out_spks),axis=0)) # Set the output spikes (spike forcing) append_spks[:,0] += self.T @@ -324,12 +324,12 @@ def train_output(): with alive_bar(self.T) as outbar: for i in train_output(): outbar() - blitnet.plotSpikes(net,0) + # Turn off learning net['eta_ip'][oLayer] = 0.0 net['eta_stdp'][ex_weight[-1]] = 0.0 net['eta_stdp'][inh_weight[-1]] = 0.0 - blitnet.plotSpikes(net,0) + # Clear the network output spikes blitnet.setSpikeTimes(net,oLayer,[]) @@ -364,7 +364,7 @@ def set_spikes(): spikeTimes.extend(spike_neuron) yield - spikeTimes = np.array(spikeTimes) + spikeTimes = torch.from_numpy(np.array(spikeTimes)) # set input spikes blitnet.setSpikeTimes(net,0,spikeTimes) diff --git 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exactly 0 -# inhib weight means normalising on the post rate. -# - Removed erroneous normalisation of the depression side of the -# lateral inhib - was causing too little depression and the lateral -# inhib was building up too strong -# -# Changes for version 10: -# - for fast inhib, don't inhib if time diff = 0 (calc_spikes) -# - code for init weights as normal rather than uniform (addWeights) -# - add x_fastinp field to keep fast inhib separate from inhib normalisation (calc_spikes) -# -# Changes for version 11: -# - allowed set_spikes to set spike time with sub-timestep resolution (setSpikeTimes) -# -# Changes for version 12: -# - fast inhib spikes are all propagated with value 1 (calc_spikes) -# -# Changes for version 13: -# - improved weight decay, but currently disabled (calc_stdp) -# - used tiled matrices rather than loops for STDP calcs (calc_stdp) -# - removed the spike shift of +0.5, but may put it back (calc_spikes) -# -# Changes for version 14: -# - changed spike forcing - no unforced spikes allowed and added specific new STDP rules -# (calc_spikes and calc_stdp) -# -# Changes for version 15: -# - don't prune weights that hit zero, just set them very small (1e-6 or -1e6) (calc_stdp) -# - new function to plot spikes in a subplot (subplotSpikes) -# -# Changes for version 16: -# - reinstated the spike shift of +0.5 that was removed in version 13 (calc_spikes) -# -# Changes for version 17: -# - re-removed the spike shift of +0.5 (calc_spikes); it helped signal propagation but -# hindered feature extraction; instead, make weights stronger -# - added wght_norm for weights (the total weight value to normalize to) -# -# Changes for version 18: -# - changed max spike amplitude from 1.0 to 0.9 (to avoid confusion with next timestep) -# -# Changes for version 19: -# - BIG change to propagate spikes up thru a hierarchy in 1 step (calc_spikes, calc_stdp) -# -# Changes for version 20: -# - STDP was broken in version 19 - fixed -# -# Changes for version 21: -# - BIG change to remove normalisation of exc weights to a fixed value, instead normalise -# so spike amplitude averages 0.5; gets rid of normalisation 'fudge factor' and creates -# homeostasis between ITP (intrinsic threshold plasticity) and spike amplitudes (weights) -# - Also limit any synapse to max 1.0, and fixed a missing STDP component of spike forcing -# -# Changes for version 21, PART B: -# - BIG change to STDP which still wasn't working the best, see Chap 14 of full report -# - also increased learning rate for inhib normalisation which I set too low in prev version -# -# Changes for version 22: -# - totally rewrote spike forcing, can now easily outperform linear decoding -# - removed dec_stdp term from normal STDP (now just relies on the (0.5-post) term) -# - removed wght_norm field and norm_weights() function (no longer used) -# - removed all spike 'shift' code (also no longer needed) -# - removed limit of +/-1 on all weights which was sometimes upsetting balance -# -# Changes for version 23: -# - ensure weights stay negative during inhib weight normalisation (norm_inhib) -# - re-instate weight limits but this time set max. weight to +/-10 -# -# Changes for version 24: -# - modulate STDP learning rate by firing rate (low firing rate = high learning rate needed) -# -# Changes for version 25: -# - clear predefined spikes when they are all used up -# - fixed 'x_prev' to work for recurrent connections (calc_spikes) -# -# -# SOME IDEAS: -# - build-in learning rate annealing (pass: start rate, num timesteps, annealing power) -# - build-in analog inputs (pass: min and max value, num input neurons, bump radius) -# - allow thresholds to go -ve to allow spikes to occur from removal of tonic inhibition -# - calc_stdp still needs work for recurrent connections -# -# -# FULL REPORTS (latest first): -# https://docs.google.com/document/d/19uybCShRo5kUJXRgVOEnH5GoLQy-RgeP7cqhvw7XYwM -# and -# https://docs.google.com/document/d/17H_dOlwNNLmLUdHQfqvUQ8cC0IBmLunbq-CYumH8vMY -# and -# https://docs.google.com/document/d/1JcQHpmTQNsP9a2tcIwsjg_Fwj-2FSAmOAv30uWOg7mA -# -################################## - +#MIT License + +#Copyright (c) 2023 Adam Hines + +#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 numpy as np -import matplotlib.pyplot as plt import pdb -#import warnings -#warnings.filterwarnings("error") +import torch + +import matplotlib.pyplot as plt + ################################## # Return a new empty BITnet instance @@ -175,34 +95,26 @@ def addLayer(net,dims,thr_range,fire_rate,ip_rate,const_inp,nois,rec_spks): if np.isscalar(const_inp): const_inp = [const_inp,const_inp] net['dim'].append(np.array(dims,int)) - net['x'].append(np.zeros(int(np.prod(dims)))) - net['x_prev'].append(np.zeros(int(np.prod(dims)))) - net['x_calc'].append(np.zeros(int(np.prod(dims)))) - net['x_input'].append(np.zeros(int(np.prod(dims)))) - net['x_fastinp'].append(np.zeros(int(np.prod(dims)))) - net['mean_rate'].append(np.zeros(int(np.prod(dims)))) + net['x'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) + net['x_prev'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) + net['x_calc'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) + net['x_input'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) + net['x_fastinp'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) + net['mean_rate'].append(torch.from_numpy(np.zeros(int(np.prod(dims))))) net['eta_ip'].append(ip_rate) - if len(thr_range) == 2: - net['thr'].append(np.random.uniform(thr_range[0],thr_range[1], - int(np.prod(dims)))) - else: - net['thr'].append(thr_range) # xxx can't do this? - if len(fire_rate) == 2: - net['fire_rate'].append(np.random.uniform(fire_rate[0],fire_rate[1], - int(np.prod(dims)))) - net['have_rate'].append(any(net['fire_rate'][-1]>0.0)) - else: - net['fire_rate'].append(fire_rate) - net['have_rate'].append(fire_rate>0.0) - if len(const_inp) == 2: - net['const_inp'].append(np.random.uniform(const_inp[0],const_inp[1], - int(np.prod(dims)))) - else: - net['const_inp'].append(const_inp) + net['thr'].append(torch.from_numpy(np.random.uniform(thr_range[0],thr_range[1], + int(np.prod(dims))))) + net['fire_rate'].append(torch.from_numpy(np.random.uniform(fire_rate[0],fire_rate[1], + int(np.prod(dims))))) + net['have_rate'].append(any(net['fire_rate'][-1]>0.0)) + + net['const_inp'].append(torch.from_numpy(np.random.uniform(const_inp[0],const_inp[1], + int(np.prod(dims))))) + net['nois'].append(nois) net['set_spks'].append([]) net['sspk_idx'].append(0) - net['spikes'].append([]) + net['spikes'].append(torch.empty([],dtype=torch.float64)) net['rec_spks'].append(rec_spks) return len(net['x'])-1 @@ -222,18 +134,16 @@ def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate,fast_inhib): # Check constraints etc if np.isscalar(W_range): W_range = [W_range,W_range] - nrow = np.size(net['x'][layer_pre]) - ncol = np.size(net['x'][layer_post]) - if False: - net['W'].append(np.random.uniform(W_range[0],W_range[1],[nrow,ncol])) + nrow =net['x'][layer_pre].size(dim=0) + ncol = net['x'][layer_post].size(dim=0) + + Wmn = (W_range[0]+W_range[1])/2.0 + Wsd = (W_range[1]-W_range[0])/6.0 + net['W'].append(torch.from_numpy(np.random.normal(Wmn,Wsd,[nrow,ncol]))) + if Wmn > 0.0: + net['W'][-1][net['W'][-1]<0.0] = 0.0 else: - Wmn = (W_range[0]+W_range[1])/2.0 - Wsd = (W_range[1]-W_range[0])/6.0 - net['W'].append(np.random.normal(Wmn,Wsd,[nrow,ncol])) - if Wmn > 0.0: - net['W'][-1][net['W'][-1]<0.0] = 0.0 - else: - net['W'][-1][net['W'][-1]>0.0] = 0.0 + net['W'][-1][net['W'][-1]>0.0] = 0.0 setzero = np.random.rand(nrow,ncol) > p if layer_pre==layer_post: # no self connections allowed setzero = np.logical_or(setzero,np.identity(nrow)) @@ -263,8 +173,10 @@ def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate,fast_inhib): # FOLLOWING training ensure that: forced spikes array is removed, ie: setSpikeTimes(n,l,[]) def setSpikeTimes(net,layer,times): - - net['set_spks'][layer] = times.copy() + if isinstance(times,list): + net['set_spks'][layer] = times.copy() + else: + net['set_spks'][layer] = times.detach().clone() net['sspk_idx'][layer] = 0 ################################## @@ -275,7 +187,9 @@ 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]>0.0)-rate) + tensor_gzero = net['x'][i] > 0 + indicies = tensor_gzero.nonzero() + net['thr'][i] = net['thr'][i] + net['eta_ip'][i]*(net['x'][i]-rate) #xxx net['thr'][i] = net['thr'][i] + net['eta_ip'][i]*(net['x'][i]-rate) net['thr'][i][net['thr'][i]<0.0] = 0.0 #xxx @@ -305,24 +219,24 @@ def norm_inhib(net): # net: SORN instance def calc_spikes(net): - + pause=1 # Start with the noise and constant input in the neurons of each layer for i,nois in enumerate(net['nois']): if nois > 0: net['x_input'][i] = np.random.normal(0.0,nois,int(np.prod(net['dim'][i]))) else: - net['x_input'][i].fill(0.0) - net['x_input'][i] += net['const_inp'][i].copy() + net['x_input'][i] = torch.full_like(net['x_input'][i],0.0) + net['x_input'][i] += net['const_inp'][i].detach().clone() # Find the threshold crossings (overwritten later if needed) - net['x'][i] = np.clip(net['x_input'][i]-net['thr'][i],a_min=0.0,a_max=0.9) + net['x'][i] = torch.clamp((net['x_input'][i]-net['thr'][i]),0.0,0.9) # Loop thru layers to insert any predefined spikes for i in range(len(net['set_spks'])): if len(net['set_spks'][i]): - net['x'][i].fill(0.0) + net['x'][i] = torch.full_like(net['x'][i],0.0) sidx = net['sspk_idx'][i] if sidx < len(net['set_spks'][i]): stim = net['set_spks'][i][sidx,0] while sidx < len(net['set_spks'][i]) and int(stim) <= net['step_num']: - net['x'][i][int(net['set_spks'][i][sidx,1])] = np.mod(stim,1) + net['x'][i][int(net['set_spks'][i][sidx,1])] = torch.fmod(stim,1) sidx += 1 if sidx < len(net['set_spks'][i]): stim = net['set_spks'][i][sidx,0] @@ -346,9 +260,9 @@ def calc_spikes(net): # Synaptic currents last for 1 timestep if layers[0]!=layers[1]: - net['I'][i] = np.matmul(net['x'][layers[0]],W) + net['I'][i] = torch.matmul(net['x'][layers[0]],W) else: - net['I'][i] = np.matmul(net['x_prev'][layers[0]],W) + net['I'][i] = torch.matmul(net['x_prev'][layers[0]],W) net['x_input'][layers[1]] += net['I'][i] @@ -418,7 +332,7 @@ def calc_stdp(net): # Remember layer numbers and weight matrix shape layers = net['W_lyr'][i] - shape = np.shape(W) + shape = W.size() # # Spike Forcing has special rules to make calculated and forced spikes match @@ -444,8 +358,8 @@ def calc_stdp(net): mpre = net['x'][layers[0]]/net['fire_rate'][layers[0]] else: mpre = net['x'][layers[0]] - pre = np.tile(np.reshape(mpre, [shape[0],1]),[1,shape[1]]) - post = np.tile(np.reshape(xdiff,[1,shape[1]]),[shape[0],1]) + pre = torch.from_numpy(np.tile(np.reshape(mpre, [shape[0],1]),[1,shape[1]])) + post = torch.from_numpy(np.tile(np.reshape(xdiff,[1,shape[1]]),[shape[0],1])) # Excitatory connections if net['eta_stdp'][i] > 0: @@ -464,20 +378,13 @@ def calc_stdp(net): # elif not net['fast_inhib'][i]: - # Pre and Post spikes tiled across and down for all synapses - #if net['have_rate'][layers[0]]: - # # Modulate learning rate by firing rate (low firing rate = high learning rate) - # mpre = net['x'][layers[0]] #/net['fire_rate'][layers[0]] - #else: - # mpre = net['x'][layers[0]] - #pre = np.tile(np.reshape(mpre,[shape[0],1]),[1,shape[1]]) - pre = np.tile(np.reshape(net['x'][layers[0]],[shape[0],1]),[1,shape[1]]) + pre = torch.from_numpy(np.tile(np.reshape(net['x'][layers[0]],[shape[0],1]),[1,shape[1]])) if net['have_rate'][layers[1]]: # Modulate learning rate by firing rate (low firing rate = high learning rate) mpost = net['x'][layers[1]] #/net['fire_rate'][layers[1]] else: mpost = net['x'][layers[1]] - post = np.tile(np.reshape(mpost,[1,shape[1]]),[shape[0],1]) + post = torch.from_numpy(np.tile(np.reshape(mpost,[1,shape[1]]),[shape[0],1])) # Excitatory synapses if net['eta_stdp'][i] > 0: @@ -581,6 +488,7 @@ def subplotSpikes(net,cutoff): n_tot = 0 for i,sp in enumerate(net['spikes']): + sp = sp.detach().cpu().numpy() if any(sp): x=[]; y=[] for s in sp: From 59f6d6881fb4536fe86258539b2ea462196fd88e Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 7 Jul 2023 16:49:21 +1000 Subject: [PATCH 02/10] Remove weights From 9e2ef22d37bab3e07267f418e5e2efea7d89521a Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 10 Jul 2023 11:06:22 +1000 Subject: [PATCH 03/10] Checked Torch with GPU and CPU, trains well but does not support recording spikes currently --- VPRTempo.py | 18 ++++++++++++++---- src/__pycache__/blitnet_open.cpython-37.pyc | Bin 8354 -> 8693 bytes src/blitnet_open.py | 1 + 3 files changed, 15 insertions(+), 4 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index 7281d7e..90bc49d 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -26,6 +26,7 @@ import cv2 import pickle import torch +import gc import sys sys.path.append('./src') sys.path.append('./weights') @@ -55,7 +56,7 @@ def __init__(self): self.intensity = 255 # divide pixel values to get spikes in range [0,1] # Network and training settings - self.input_layer = 784 # number of input layer neurons + self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons self.feature_layer = int(self.input_layer*7)# number of feature layer neurons self.output_layer = 400 # number of output layer neurons (match training images) self.train_img = 400 # number of training images @@ -81,7 +82,8 @@ def __init__(self): # Print network details print('////////////') - print('Running VPRTempo v0.1 - Queensland University of Technology, Centre for Robotics') + print('VPRTempo - Temporally Encoded Visual Place Recognition v0.1') + print('Queensland University of Technology, Centre for Robotics') print('\\\\\\\\\\\\\\\\\\\\\\\\') print('Theta: '+str(self.theta_max)) print('Initial learning rate: '+str(self.n_init)) @@ -90,6 +92,9 @@ def __init__(self): print('Excitatory p: '+str(self.p_exc)) print('Inhibitory p: '+str(self.p_inh)) print('Constant input '+str(self.c)) + print('CUDA available: '+str(torch.cuda.is_available())) + current_device = torch.cuda.current_device() + print('Current device is: '+str(torch.cuda.get_device_name(current_device))) # Select training images from list with open('./nordland_imageNames.txt') as file: @@ -335,7 +340,7 @@ def train_output(): # Reset network details net['set_spks'][0] = [] - net['rec_spks'] = [True,True,True] + #net['rec_spks'] = [True,True,True] net['sspk_idx'] = [0,0,0] net['step_num'] = 0 net['spikes'] = [[],[],[]] @@ -423,7 +428,12 @@ def test_network(): spkforc = 100*self.numcorrect/self.test_t print(spkforc,'% correct') - blitnet.plotSpikes(net,0) + if net['set_spks'][0] == True: + blitnet.plotSpikes(net,0) + + # clear the CUDA cache + torch.cuda.empty_cache() + gc.collect() ''' Run the network 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a/src/blitnet_open.py b/src/blitnet_open.py index 9efbf5b..28b75f3 100644 --- a/src/blitnet_open.py +++ b/src/blitnet_open.py @@ -317,6 +317,7 @@ def calc_spikes(net): (net['x'][i]>0.0)*eta if net['rec_spks'][i]: n_idx = np.nonzero(net['x'][i])[0] + print('Woof woof woof') net['spikes'][i].extend([net['step_num']+1-net['x'][i][n],n] for n in n_idx) From 83a7553a20afa3985d964d4ac553d5aaa47d5e1f Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 10 Jul 2023 17:18:22 +1000 Subject: [PATCH 04/10] Adding P@100R and sum of absolute difference cross-validation --- VPRTempo.py | 98 ++++-- __pycache__/VPRTempo.cpython-37.pyc | Bin 12232 -> 10803 bytes nordland_testNames.txt | 348 ++++++++++---------- src/TranSalNet_Dense.py | 171 ---------- src/TransformerEncoder.py | 137 -------- src/__pycache__/blitnet_open.cpython-37.pyc | Bin 8693 -> 8693 bytes src/blitnet_open.py | 2 +- src/data_process.py | 112 ------- src/densenet.py | 287 ---------------- 9 files changed, 251 insertions(+), 904 deletions(-) delete mode 100644 src/TranSalNet_Dense.py delete mode 100644 src/TransformerEncoder.py delete mode 100644 src/data_process.py delete mode 100644 src/densenet.py diff --git a/VPRTempo.py b/VPRTempo.py index 90bc49d..f5ce607 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -1,6 +1,6 @@ #MIT License -#Copyright (c) 2023 Adam Hines +#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 @@ -35,6 +35,8 @@ import numpy as np import blitnet_open as blitnet +import validation as validate +import matplotlib.pyplot as plt from os import path from alive_progress import alive_bar @@ -55,11 +57,16 @@ def __init__(self): self.num_patches = 7 # number of patches self.intensity = 255 # divide pixel values to get spikes in range [0,1] + # Select training images from list + with open('./nordland_imageNames.txt') as file: + lines = [line.rstrip() for line in file] + self.img_load = lines + # Network and training settings self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons self.feature_layer = int(self.input_layer*7)# number of feature layer neurons - self.output_layer = 400 # number of output layer neurons (match training images) - self.train_img = 400 # number of training images + self.output_layer = len(lines) # number of output layer neurons (match training images) + self.train_img = len(lines) # number of training images self.epoch = 4 # number of training iterations self.test_t = 200 # number of testing time points self.cuda = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # saliency calculating on cpu or gpu @@ -79,6 +86,7 @@ def __init__(self): # Test settings self.test_true = False # leave default to False + self.validation = True # test this network against other methods, default True # Print network details print('////////////') @@ -96,11 +104,6 @@ def __init__(self): current_device = torch.cuda.current_device() print('Current device is: '+str(torch.cuda.get_device_name(current_device))) - # Select training images from list - with open('./nordland_imageNames.txt') as file: - lines = [line.rstrip() for line in file] - self.img_load = lines - # Network weights name self.training_out = './weights/'+str(self.input_layer)+'i'+\ str(self.feature_layer)+\ @@ -207,7 +210,9 @@ def run_train(self): # Guard function to prevent training network if user selects 'y' or net doesn't exist if retrain != 'n': - + ''' + Training network functions + ''' # Sets the input spikes into the BlitNet network def set_spikes(): spikeTimes = [] @@ -250,7 +255,10 @@ def train_output(): net['eta_stdp'][ex_weight[-1]] = self.n_init*pt net['eta_stdp'][inh_weight[-1]] = -1*self.n_init*pt yield - + + ''' + Setup & run the network trainer + ''' # Load the network training images and set the input spikes self.loadImages() @@ -356,7 +364,9 @@ def train_output(): ''' def networktester(self): - + ''' + Network tester functions + ''' # set the input spikes def set_spikes(): spikeTimes = [] @@ -372,25 +382,51 @@ def set_spikes(): spikeTimes = torch.from_numpy(np.array(spikeTimes)) # set input spikes blitnet.setSpikeTimes(net,0,spikeTimes) - + + # network testing function def test_network(): # Test the output self.correct_idx = [] self.numcorrect = 0 + self.mat_dict = {} + idx = [] + for n in range(self.location_repeat): + self.mat_dict[str(n)] = [] + idx.append(n) for t in range(self.test_t): blitnet.runSim(net,1) nidx = np.argmax(net['x'][-1]) - if nidx < 200: - if train_ids[nidx] == test_ids[t] or train_ids[(nidx+int(self.train_img/self.location_repeat))] == test_ids[t]: + if nidx < int(self.train_img/self.location_repeat): + if self.train_ids[nidx] == self.test_ids[t] or self.train_ids[(nidx+int(self.train_img/self.location_repeat))] == self.test_ids[t]: self.numcorrect = self.numcorrect+1 self.correct_idx.append(t) else: - if train_ids[nidx] == test_ids[t] or train_ids[(nidx-int(self.train_img/self.location_repeat))] == test_ids[t]: + if self.train_ids[nidx] == self.test_ids[t] or self.train_ids[(nidx-int(self.train_img/self.location_repeat))] == self.test_ids[t]: self.numcorrect = self.numcorrect+1 self.correct_idx.append(t) + tonump = net['x'][-1].detach().cpu().numpy() + split_mat = np.split(tonump,self.location_repeat) + for n in range(self.location_repeat): + self.mat_dict[str(idx[n])]= np.concatenate((self.mat_dict[str(idx[n])],split_mat[n]),axis=0) yield - + + # network validation using alternative place matching algorithms and P@R calculation + def network_validator(): + # reload training images for the comparisons + self.test_true= False # reset the test true flag + self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' + self.test_imgs = self.ims.copy() + with open('./nordland_imageNames.txt') as file: + lines = [line.rstrip() for line in file] + self.img_load = lines + self.loadImages() + # run sum of absolute differences caluclation + validate.SAD(self) + + ''' + Setup & running network tester + ''' # Unpickle the stored network print('Unpickling '+self.training_out) if path.isfile(self.training_out): @@ -417,23 +453,41 @@ def test_network(): # load the training and testing IDs for correct matching with open('./output/train_ids.pkl', 'rb') as f: - train_ids = pickle.load(f) + self.train_ids = pickle.load(f) with open('./output/test_ids.pkl', 'rb') as f: - test_ids = pickle.load(f) - + self.test_ids = pickle.load(f) + + # run the test netowrk print('Running testing dataset on trained network') with alive_bar(self.test_t) as testbar: for i in test_network(): testbar() - - spkforc = 100*self.numcorrect/self.test_t - print(spkforc,'% correct') + + # store and print out number of correctly identified places + self.VPRTempo_correct = 100*self.numcorrect/self.test_t + print(self.VPRTempo_correct,'% correct') + + # plot the similarity matrices for each location repetition + for n in self.mat_dict: + temp_mat = self.mat_dict[str(n)] + reshape_mat = np.reshape(temp_mat,(self.test_t,int(self.train_img/self.location_repeat))) + # plot the matrix + fig = plt.figure() + plt.matshow(reshape_mat,fig) + fig.suptitle(("Similarity VPRTempo: Location "+str(int(n)+1)),fontsize = 12) + plt.xlabel("Database",fontsize = 12) + plt.ylabel("Query",fontsize = 12) + plt.show() if net['set_spks'][0] == True: blitnet.plotSpikes(net,0) # clear the CUDA cache torch.cuda.empty_cache() gc.collect() + + # if validation is set to True, run comparison methods + if self.validation: + network_validator() ''' Run the network diff --git a/__pycache__/VPRTempo.cpython-37.pyc b/__pycache__/VPRTempo.cpython-37.pyc index 20ec1101d1b8baf0885d6006f5c8c38471fd1e9f..2de2883f0aab75e94e191d564cf5791856d1bd19 100644 GIT binary patch literal 10803 zcmds7Ym6LMR=)M>clWes+Vk?Ooj6X~NoKqzERom5aqMK1CCp}RXAPq)rE=ezp6cnY zZr`f0XIech>^OL`Y>0OWkAwg{{6JdmKZL|01s2c>1+=VI2q95_Kp-LfiwJ&z_6N&% 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images-04927.png -images-00310.png +images-04945.png +images-04963.png +images-04981.png +images-04999.png +images-05017.png +images-05035.png images-05491.png -images-04477.png -images-01102.png -images-01012.png -images-01868.png +images-05509.png +images-05527.png +images-05545.png +images-05563.png +images-05581.png +images-05599.png diff --git a/src/TranSalNet_Dense.py b/src/TranSalNet_Dense.py deleted file mode 100644 index 8904460..0000000 --- a/src/TranSalNet_Dense.py +++ /dev/null @@ -1,171 +0,0 @@ -import os -import torch -import numpy as np -import pandas as pd -from torch.utils.data import Dataset, DataLoader -from skimage import io, transform -from PIL import Image -import torch.nn as nn -from torchvision import transforms, utils, models -import torch.nn.functional as F -import densenet as densenet - -from TransformerEncoder import Encoder - - - -cfg1 = { -"hidden_size" : 768, -"mlp_dim" : 768*4, -"num_heads" : 12, -"num_layers" : 2, -"attention_dropout_rate" : 0, -"dropout_rate" : 0.0, -} - -cfg2 = { -"hidden_size" : 768, -"mlp_dim" : 768*4, -"num_heads" : 12, -"num_layers" : 2, -"attention_dropout_rate" : 0, -"dropout_rate" : 0.0, -} - -cfg3 = { -"hidden_size" : 512, -"mlp_dim" : 512*4, -"num_heads" : 8, -"num_layers" : 2, -"attention_dropout_rate" : 0, -"dropout_rate" : 0.0, -} - - -class TranSalNet(nn.Module): - - def __init__(self): - super(TranSalNet, self).__init__() - self.encoder = _Encoder() - self.decoder = _Decoder() - - def forward(self, x): - x = self.encoder(x) - x = self.decoder(x) - return x - - -class _Encoder(nn.Module): - def __init__(self): - super(_Encoder, self).__init__() - base_model = densenet.densenet161(pretrained=True) - base_layers = list(base_model.children())[0][:-1] - self.encoder = nn.ModuleList(base_layers).eval() - - def forward(self, x): - outputs = [] - for ii,layer in enumerate(self.encoder): - x = layer(x) - if ii in {6, 8, 10}: - outputs.append(x) - return outputs - - -class _Decoder(nn.Module): - - def __init__(self): - super(_Decoder, self).__init__() - self.conv1 = nn.Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv2 = nn.Conv2d(768, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv3 = nn.Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv4 = nn.Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv5 = nn.Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv6 = nn.Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - self.conv7 = nn.Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - - self.batchnorm1 = nn.BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - self.batchnorm2 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - self.batchnorm3 = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - self.batchnorm4 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - self.batchnorm5 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - self.batchnorm6 = nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) - - self.TransEncoder1 = TransEncoder(in_channels=2208, spatial_size=9*12, cfg=cfg1) - self.TransEncoder2 = TransEncoder(in_channels=2112, spatial_size=18*24, cfg=cfg2) - self.TransEncoder3 = TransEncoder(in_channels=768, spatial_size=36*48, cfg=cfg3) - - self.add = torch.add - self.relu = nn.ReLU(True) - self.upsample = nn.Upsample(scale_factor=2, mode='nearest') - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - x3, x4, x5 = x - - x5 = self.TransEncoder1(x5) - x5 = self.conv1(x5) - x5 = self.batchnorm1(x5) - x5 = self.relu(x5) - x5 = self.upsample(x5) - - x4_a = self.TransEncoder2(x4) - x4 = x5 * x4_a - x4 = self.relu(x4) - x4 = self.conv2(x4) - x4 = self.batchnorm2(x4) - x4 = self.relu(x4) - x4 = self.upsample(x4) - - x3_a = self.TransEncoder3(x3) - x3 = x4 * x3_a - x3 = self.relu(x3) - x3 = self.conv3(x3) - x3 = self.batchnorm3(x3) - x3 = self.relu(x3) - x3 = self.upsample(x3) - - x2 = self.conv4(x3) - x2 = self.batchnorm4(x2) - x2 = self.relu(x2) - x2 = self.upsample(x2) - x2 = self.conv5(x2) - x2 = self.batchnorm5(x2) - x2 = self.relu(x2) - - x1 = self.upsample(x2) - x1 = self.conv6(x1) - x1 = self.batchnorm6(x1) - x1 = self.relu(x1) - x1 = self.conv7(x1) - x = self.sigmoid(x1) - - return x - - -class TransEncoder(nn.Module): - - def __init__(self, in_channels, spatial_size, cfg): - super(TransEncoder, self).__init__() - - self.patch_embeddings = nn.Conv2d(in_channels=in_channels, - out_channels=cfg['hidden_size'], - kernel_size=1, - stride=1) - self.position_embeddings = nn.Parameter(torch.zeros(1, spatial_size, cfg['hidden_size'])) - - self.transformer_encoder = Encoder(cfg) - - def forward(self, x): - a, b = x.shape[2], x.shape[3] - x = self.patch_embeddings(x) - x = x.flatten(2) - x = x.transpose(-1, -2) - - embeddings = x + self.position_embeddings - x = self.transformer_encoder(embeddings) - B, n_patch, hidden = x.shape - x = x.permute(0, 2, 1) - x = x.contiguous().view(B, hidden, a, b) - - return x - diff --git a/src/TransformerEncoder.py b/src/TransformerEncoder.py deleted file mode 100644 index f2e8e07..0000000 --- a/src/TransformerEncoder.py +++ /dev/null @@ -1,137 +0,0 @@ -# coding=utf-8 - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import copy -import logging -import math - -from os.path import join as pjoin - -import torch -import torch.nn as nn -import numpy as np - -from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm -from torch.nn.modules.utils import _pair -from scipy import ndimage - - -ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu} - - -class Attention(nn.Module): - def __init__(self, config): - super(Attention, self).__init__() - self.num_attention_heads = config["num_heads"] # 12 - self.attention_head_size = int(config['hidden_size'] / self.num_attention_heads) # 42 - self.all_head_size = self.num_attention_heads * self.attention_head_size # 12*42=504 - - self.query = Linear(config['hidden_size'], self.all_head_size) # (512, 504) - self.key = Linear(config['hidden_size'], self.all_head_size) - self.value = Linear(config['hidden_size'], self.all_head_size) - - # self.out = Linear(config['hidden_size'], config['hidden_size']) - self.out = Linear(self.all_head_size, config['hidden_size']) - self.attn_dropout = Dropout(config["attention_dropout_rate"]) - self.proj_dropout = Dropout(config["attention_dropout_rate"]) - - self.softmax = Softmax(dim=-1) - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward(self, hidden_states): - - mixed_query_layer = self.query(hidden_states) - mixed_key_layer = self.key(hidden_states) - mixed_value_layer = self.value(hidden_states) - - query_layer = self.transpose_for_scores(mixed_query_layer) - key_layer = self.transpose_for_scores(mixed_key_layer) - value_layer = self.transpose_for_scores(mixed_value_layer) - - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - attention_probs = self.softmax(attention_scores) - attention_probs = self.attn_dropout(attention_probs) - - context_layer = torch.matmul(attention_probs, value_layer) - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - attention_output = self.out(context_layer) - attention_output = self.proj_dropout(attention_output) - return attention_output - - -class Mlp(nn.Module): - def __init__(self, config): - super(Mlp, self).__init__() - self.fc1 = Linear(config['hidden_size'], config["mlp_dim"]) - self.fc2 = Linear(config["mlp_dim"], config['hidden_size']) - self.act_fn = ACT2FN["gelu"] - self.dropout = Dropout(config["dropout_rate"]) - self._init_weights() - - def _init_weights(self): - nn.init.xavier_uniform_(self.fc1.weight) - nn.init.xavier_uniform_(self.fc2.weight) - nn.init.normal_(self.fc1.bias, std=1e-6) - nn.init.normal_(self.fc2.bias, std=1e-6) - - def forward(self, x): - x = self.fc1(x) - x = self.act_fn(x) - x = self.dropout(x) - x = self.fc2(x) - x = self.dropout(x) - return x - - -class Block(nn.Module): - def __init__(self, config): - super(Block, self).__init__() - self.flag = config['num_heads'] - self.hidden_size = config['hidden_size'] - self.ffn_norm = LayerNorm(config['hidden_size'], eps=1e-6) - self.ffn = Mlp(config) - self.attn = Attention(config) - self.attention_norm = LayerNorm(config['hidden_size'], eps=1e-6) - - def forward(self, x): - h = x - - x = self.attention_norm(x) - x = self.attn(x) - x = x + h - - h = x - x = self.ffn_norm(x) - x = self.ffn(x) - x = x + h - return x - - -class Encoder(nn.Module): - def __init__(self, config): - super(Encoder, self).__init__() - - self.layer = nn.ModuleList() - self.encoder_norm = LayerNorm(config['hidden_size'], eps=1e-6) - for _ in range(config["num_layers"]): - layer = Block(config) - self.layer.append(copy.deepcopy(layer)) - - def forward(self, hidden_states): - for layer_block in self.layer: - hidden_states = layer_block(hidden_states) - encoded = self.encoder_norm(hidden_states) - - return encoded - - diff --git a/src/__pycache__/blitnet_open.cpython-37.pyc b/src/__pycache__/blitnet_open.cpython-37.pyc index 98fe314f9b695c6ed1ad2b221433d70942b1ea5d..98be91db93af059fd4cefdce55d4d501d1afbfda 100644 GIT binary patch delta 740 zcmYLFO=}ZD7|t}=O*U=PNKL_{O%u$)R4hWF4GPkl3T=*)^t$ zN)P=5bI^l@9t%CmUKRg@9$fJ5LA>?k#d$Xx=kPG^=kq-8MeU-NzMD!VBl4X+IyV;| zen_7vm$2rx8DYBJvX~}ISbS6{TJ~?K-zLgf>*+&_5y~u_{QGsdi{tyFi2zY77PWj1 zNB8@-1a9Iux7XG@f!Tn>&oO!ZzVg-a5whKgb`+G&y{j|jyt;Y z3am+lYXa5qTt^C5Y~sp#U2GKh(2;ms+%3L@S+&_|dCX!*5X5-f8B``lcX%b#(o+5y vcFhEFLQKaXguRgQh-FXgmdaIF!b$+k0BZo70AoY!zsUg*yQQ`5?qp_*mPGVV zAbAIfkwC)5gp=8;#(zQ&zQnr+|gz&)-Y$>{KejQ#wyIt#fk_+kK$lq_ELbNvf6UYR@RXaO_ ziOr6$LAhutFWmKHfJwpqT?B4(R)$aZH?|%fjaX~~$3o9nf#ln98$_eaY%BUXnq#aA z>GaCPhL$0xNK2yKlL$}JcbF5sO_waT0beebC#%#jLy#r71vfG|vqIm8P|aLp58!oX z#(YePr*M*4xQR&>Haq>DX6!?u?FL+lX3JB^o}p3KR0_w0wx@c&uCx@w?@V4(&Q71O zz3Ag~(KKV`68xCG%mSF4yY#9xP_e9kXx3EN6P~=HgW|Z%QFUNP?of-N8a3ge@xpD^ z>zM4&Z4(4Z;zp}c+i?Ql$5hjIxC&i?MklVMT<>2(E364QKQsjI<__6DtmITaRs=lj^TA zoo*Ot;fBvcUEhFv`A6&+4)T@!A cols_rate: - new_cols = (original_shape[1] * shape_r) // original_shape[0] - img = cv2.resize(img, (new_cols, shape_r)) - if new_cols > shape_c: - new_cols = shape_c - img_padded[:, - ((img_padded.shape[1] - new_cols) // 2):((img_padded.shape[1] - new_cols) // 2 + new_cols)] = img - else: - new_rows = (original_shape[0] * shape_c) // original_shape[1] - img = cv2.resize(img, (shape_c, new_rows)) - - if new_rows > shape_r: - new_rows = shape_r - img_padded[((img_padded.shape[0] - new_rows) // 2):((img_padded.shape[0] - new_rows) // 2 + new_rows), - :] = img - - return img_padded - - -def postprocess_img(pred,img): - pred = np.array(pred) - - shape_r = img.shape[0] - shape_c = img.shape[1] - predictions_shape = pred.shape - - rows_rate = shape_r / predictions_shape[0] - cols_rate = shape_c / predictions_shape[1] - - if rows_rate > cols_rate: - new_cols = (predictions_shape[1] * shape_r) // predictions_shape[0] - pred = cv2.resize(pred, (new_cols, shape_r)) - img = pred[:, ((pred.shape[1] - shape_c) // 2):((pred.shape[1] - shape_c) // 2 + shape_c)] - else: - new_rows = (predictions_shape[0] * shape_c) // predictions_shape[1] - pred = cv2.resize(pred, (shape_c, new_rows)) - img = pred[((pred.shape[0] - shape_r) // 2):((pred.shape[0] - shape_r) // 2 + shape_r), :] - - return img - - -class MyDataset(Dataset): - """Load dataset.""" - - def __init__(self, ids, stimuli_dir, saliency_dir, fixation_dir, transform=None): - """ - Args: - csv_file (string): Path to the csv file with annotations. - root_dir (string): Directory with all the images. - transform (callable, optional): Optional transform to be applied - on a sample. - """ - self.ids = ids - self.stimuli_dir = stimuli_dir - self.saliency_dir = saliency_dir - self.fixation_dir = fixation_dir - self.transform = transform - - def __len__(self): - return len(self.ids) - - def __getitem__(self, idx): - if torch.is_tensor(idx): - idx = idx.tolist() - - im_path = self.stimuli_dir + self.ids.iloc[idx, 0] - image = Image.open(im_path).convert('RGB') - img = np.array(image) / 255. - img = np.transpose(img, (2, 0, 1)) - img = torch.from_numpy(img) - # if self.transform: - # img = self.transform(image) - - smap_path = self.saliency_dir + self.ids.iloc[idx, 1] - saliency = Image.open(smap_path) - - smap = np.expand_dims(np.array(saliency) / 255., axis=0) - smap = torch.from_numpy(smap) - - fmap_path = self.fixation_dir + self.ids.iloc[idx, 2] - fixation = Image.open(fmap_path) - - fmap = np.expand_dims(np.array(fixation) / 255., axis=0) - fmap = torch.from_numpy(fmap) - - sample = {'image': img, 'saliency': smap, 'fixation': fmap} - - return sample - - - - - diff --git a/src/densenet.py b/src/densenet.py deleted file mode 100644 index fb9cde6..0000000 --- a/src/densenet.py +++ /dev/null @@ -1,287 +0,0 @@ -import re -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as cp -from collections import OrderedDict -# from .utils import load_state_dict_from_url -from torch import Tensor -from torch.jit.annotations import List - - -__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'] - -model_urls = { - 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', - 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', - 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', - 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', -} - - -class _DenseLayer(nn.Module): - def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False): - super(_DenseLayer, self).__init__() - self.add_module('norm1', nn.BatchNorm2d(num_input_features)), - self.add_module('relu1', nn.ReLU(inplace=True)), - self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * - growth_rate, kernel_size=1, stride=1, - bias=False)), - self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)), - self.add_module('relu2', nn.ReLU(inplace=True)), - self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, - kernel_size=3, stride=1, padding=1, - bias=False)), - self.drop_rate = float(drop_rate) - self.memory_efficient = memory_efficient - - def bn_function(self, inputs): - # type: (List[Tensor]) -> Tensor - concated_features = torch.cat(inputs, 1) - bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 - return bottleneck_output - - # todo: rewrite when torchscript supports any - def any_requires_grad(self, input): - # type: (List[Tensor]) -> bool - for tensor in input: - if tensor.requires_grad: - return True - return False - - @torch.jit.unused # noqa: T484 - def call_checkpoint_bottleneck(self, input): - # type: (List[Tensor]) -> Tensor - def closure(*inputs): - return self.bn_function(inputs) - - return cp.checkpoint(closure, *input) - - @torch.jit._overload_method # noqa: F811 - def forward(self, input): - # type: (List[Tensor]) -> (Tensor) - pass - - @torch.jit._overload_method # noqa: F811 - def forward(self, input): - # type: (Tensor) -> (Tensor) - pass - - # torchscript does not yet support *args, so we overload method - # allowing it to take either a List[Tensor] or single Tensor - def forward(self, input): # noqa: F811 - if isinstance(input, Tensor): - prev_features = [input] - else: - prev_features = input - - if self.memory_efficient and self.any_requires_grad(prev_features): - if torch.jit.is_scripting(): - raise Exception("Memory Efficient not supported in JIT") - - bottleneck_output = self.call_checkpoint_bottleneck(prev_features) - else: - bottleneck_output = self.bn_function(prev_features) - - new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) - if self.drop_rate > 0: - new_features = F.dropout(new_features, p=self.drop_rate, - training=self.training) - return new_features - - -class _DenseBlock(nn.ModuleDict): - _version = 2 - - def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False): - super(_DenseBlock, self).__init__() - for i in range(num_layers): - layer = _DenseLayer( - num_input_features + i * growth_rate, - growth_rate=growth_rate, - bn_size=bn_size, - drop_rate=drop_rate, - memory_efficient=memory_efficient, - ) - self.add_module('denselayer%d' % (i + 1), layer) - - def forward(self, init_features): - features = [init_features] - for name, layer in self.items(): - new_features = layer(features) - features.append(new_features) - return torch.cat(features, 1) - - -class _Transition(nn.Sequential): - def __init__(self, num_input_features, num_output_features): - super(_Transition, self).__init__() - self.add_module('norm', nn.BatchNorm2d(num_input_features)) - self.add_module('relu', nn.ReLU(inplace=True)) - self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, - kernel_size=1, stride=1, bias=False)) - self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) - - -class DenseNet(nn.Module): - r"""Densenet-BC model class, based on - `"Densely Connected Convolutional Networks" `_ - - Args: - growth_rate (int) - how many filters to add each layer (`k` in paper) - block_config (list of 4 ints) - how many layers in each pooling block - num_init_features (int) - the number of filters to learn in the first convolution layer - bn_size (int) - multiplicative factor for number of bottle neck layers - (i.e. bn_size * k features in the bottleneck layer) - drop_rate (float) - dropout rate after each dense layer - num_classes (int) - number of classification classes - memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, - but slower. Default: *False*. See `"paper" `_ - """ - - def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), - num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False): - - super(DenseNet, self).__init__() - - # First convolution - self.features = nn.Sequential(OrderedDict([ - ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, - padding=3, bias=False)), - ('norm0', nn.BatchNorm2d(num_init_features)), - ('relu0', nn.ReLU(inplace=True)), - ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), - ])) - - # Each denseblock - num_features = num_init_features - for i, num_layers in enumerate(block_config): - block = _DenseBlock( - num_layers=num_layers, - num_input_features=num_features, - bn_size=bn_size, - growth_rate=growth_rate, - drop_rate=drop_rate, - memory_efficient=memory_efficient - ) - self.features.add_module('denseblock%d' % (i + 1), block) - num_features = num_features + num_layers * growth_rate - if i != len(block_config) - 1: - trans = _Transition(num_input_features=num_features, - num_output_features=num_features // 2) - self.features.add_module('transition%d' % (i + 1), trans) - num_features = num_features // 2 - - # Final batch norm - self.features.add_module('norm5', nn.BatchNorm2d(num_features)) - - # Linear layer - self.classifier = nn.Linear(num_features, num_classes) - - # Official init from torch repo. - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight) - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.constant_(m.bias, 0) - - def forward(self, x): - features = self.features(x) - out = F.relu(features, inplace=True) - out = F.adaptive_avg_pool2d(out, (1, 1)) - out = torch.flatten(out, 1) - out = self.classifier(out) - return out - - -def _load_state_dict(model, model_url, progress, flag): - # '.'s are no longer allowed in module names, but previous _DenseLayer - # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. - # They are also in the checkpoints in model_urls. This pattern is used - # to find such keys. - pattern = re.compile( - r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') - if flag == "densenet161": - state_dict = torch.load(r'densenet161-8d451a50.pth') - else: - state_dict = load_state_dict_from_url(model_url, progress=progress) - for key in list(state_dict.keys()): - res = pattern.match(key) - if res: - new_key = res.group(1) + res.group(2) - state_dict[new_key] = state_dict[key] - del state_dict[key] - model.load_state_dict(state_dict) - - -def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress, - **kwargs): - model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) - if pretrained: - if arch == 'densenet161': - _load_state_dict(model, model_urls[arch], progress, 'densenet161') - else: - _load_state_dict(model, model_urls[arch], progress, 0) - return model - - -def densenet121(pretrained=False, progress=True, **kwargs): - r"""Densenet-121 model from - `"Densely Connected Convolutional Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, - but slower. Default: *False*. See `"paper" `_ - """ - return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress, - **kwargs) - - - -def densenet161(pretrained=False, progress=True, **kwargs): - r"""Densenet-161 model from - `"Densely Connected Convolutional Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, - but slower. Default: *False*. See `"paper" `_ - """ - return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress, - **kwargs) - - - -def densenet169(pretrained=False, progress=True, **kwargs): - r"""Densenet-169 model from - `"Densely Connected Convolutional Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, - but slower. Default: *False*. See `"paper" `_ - """ - return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress, - **kwargs) - - - -def densenet201(pretrained=False, progress=True, **kwargs): - r"""Densenet-201 model from - `"Densely Connected Convolutional Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, - but slower. Default: *False*. See `"paper" `_ - """ - return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress, - **kwargs) \ No newline at end of file From 921ccce3175da6668cd18ffe170bb5f960e922a3 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Mon, 10 Jul 2023 17:20:03 +1000 Subject: [PATCH 05/10] Added validation.py --- src/validation.py | 73 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 src/validation.py diff --git a/src/validation.py b/src/validation.py new file mode 100644 index 0000000..f8110e9 --- /dev/null +++ b/src/validation.py @@ -0,0 +1,73 @@ +#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 matplotlib.pyplot as plt +import numpy as np +from alive_progress import alive_bar + +# Sum of Absolute Differences (SAD) calculation for images +def SAD(self): + + print("Calculating Sum of Absolute Differences (SAD) for training and testing data") + def run_sad(): + numcorrect = 0 + test_mat = np.array([]) + # loop through each test image to find the SAD + for nEnum, n in enumerate(self.test_imgs): + + # loop through each training image and calculate SAD + test = [] + for mEnum, m in enumerate(self.ims): + test.append(1/(self.imWidth*self.imHeight)*np.sum(np.subtract(np.abs(m),np.abs(n)))) + test = np.array(test) + + # calculate the precision-recall curves for each + # find the minimum value + min_arg = np.argmin(test) + if min_arg > int(self.train_img/self.location_repeat): + if self.train_ids[min_arg-(int(self.train_img/self.location_repeat))] == self.test_ids[nEnum]: + numcorrect = numcorrect+1 + else: + if self.train_ids[min_arg] == self.test_ids[nEnum]: + numcorrect = numcorrect+1 + + test_mat = np.concatenate((test_mat,test),axis=0) + + sim_mat = np.reshape(test_mat,(self.test_t,self.train_img)) + sort_mat = np.sort(sim_mat,axis=1)[:,::-1] + for x in range(len(sim_mat[1])): + thresh_vals = sort_mat[:,x] + + + + # store the output + self.SAD_correct = 100*(numcorrect/len(self.test_imgs)) + run_sad() + print('Number of correct images with SAD: '+str(self.SAD_correct)+'%') + +# Precision @ 100% recall calculation +def PR_calc(): + + print("Calculating precision @ 100% recall") \ No newline at end of file From 4e0703af1aee7e0b55f4500ef2afae99461547b5 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 11 Jul 2023 16:36:48 +1000 Subject: [PATCH 06/10] Working on PR curves --- VPRTempo.py | 5 +- src/__pycache__/blitnet_open.cpython-311.pyc | Bin 24418 -> 24484 bytes src/validation.py | 57 +++++++++++++++++-- 3 files changed, 57 insertions(+), 5 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index f5ce607..5093de6 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -389,6 +389,7 @@ def test_network(): self.correct_idx = [] self.numcorrect = 0 self.mat_dict = {} + self.net_x = np.array([]) idx = [] for n in range(self.location_repeat): self.mat_dict[str(n)] = [] @@ -405,6 +406,7 @@ def test_network(): self.numcorrect = self.numcorrect+1 self.correct_idx.append(t) tonump = net['x'][-1].detach().cpu().numpy() + self.net_x = np.concatenate((self.net_x,tonump),axis=0) split_mat = np.split(tonump,self.location_repeat) for n in range(self.location_repeat): self.mat_dict[str(idx[n])]= np.concatenate((self.mat_dict[str(idx[n])],split_mat[n]),axis=0) @@ -473,11 +475,12 @@ def network_validator(): reshape_mat = np.reshape(temp_mat,(self.test_t,int(self.train_img/self.location_repeat))) # plot the matrix fig = plt.figure() - plt.matshow(reshape_mat,fig) + plt.matshow(reshape_mat,fig, cmap=plt.cm.prism) fig.suptitle(("Similarity VPRTempo: Location "+str(int(n)+1)),fontsize = 12) plt.xlabel("Database",fontsize = 12) plt.ylabel("Query",fontsize = 12) plt.show() + if net['set_spks'][0] == True: blitnet.plotSpikes(net,0) diff --git a/src/__pycache__/blitnet_open.cpython-311.pyc b/src/__pycache__/blitnet_open.cpython-311.pyc index 95f65830a7d4c249a87d0afa890c5fb5e98176b3..882c1d0c587b70f3cf6b5af4ca84f88d33b44c3b 100644 GIT binary patch delta 287 zcmaE~k8#OFr+c1$V}d8s>;40g&~+hQ+D!G z(_VopzVQ6~G=*{)F}cX>9;4&rK=Twv@yXlF>zRr&CmUI;V3gc^+rpfkjg!^n0|Vz| z35O^>7EY79B4Sg#W)v?lo>Q@caZdF`5$!7?+AAEdi0E~=-sKUzAf$AWNBIhm@&z8{ z$rBy?*aaclH$QVo=4bTS>=j_l!FXwNU4#lVWc>V_G0O@uA4%^QP>K(vz>7sl$p$I);oED**!*w$@S(bjAE0YnAcCv zuvp5-wVBV-oSlt>)#L*M$7FAZC`RVV%N*n;Uv}_g=ZA=H)^JSbXLR4(6JX52cyaTA j2o+|==bN8K-(zI_x%o(J1EWw1BO_2d5*ac%EZz?Qv*ti` diff --git a/src/validation.py b/src/validation.py index f8110e9..d46220d 100644 --- a/src/validation.py +++ b/src/validation.py @@ -55,15 +55,64 @@ def run_sad(): test_mat = np.concatenate((test_mat,test),axis=0) - sim_mat = np.reshape(test_mat,(self.test_t,self.train_img)) - sort_mat = np.sort(sim_mat,axis=1)[:,::-1] - for x in range(len(sim_mat[1])): + # create the similarity matrix + sim_mat = np.copy(np.reshape(test_mat,(self.test_t,self.train_img))) + sort_mat = np.sort(sim_mat,axis=1) + + # perform P@0-100%R calculations going through all the threshold values + tp_corr = {} + fp_corr = {} + tn_corr = {} + fn_corr = {} + for xdx, x in enumerate(range(200)): thresh_vals = sort_mat[:,x] + tp_corr[str(xdx)] = 0 + fp_corr[str(xdx)] = 0 + tn_corr[str(xdx)] = 0 + fn_corr[str(xdx)] = 0 - + ap_pos = [] + for m, mdx in enumerate(thresh_vals): + temp_row = np.copy(sim_mat[m,:]) + temp_row[temp_row>mdx] = -1 + nonzero = np.where(temp_row>0) + + positives = np.argwhere(temp_row!=-1) + ap_pos.append(len(positives[0])) + negatives = np.argwhere(temp_row==-1) + + # calculate true positives and false positives + if m in positives or m+self.test_t in positives: + tp_corr[str(xdx)] = tp_corr[str(xdx)] + 1 + fp_corr[str(xdx)] = tp_corr[str(xdx)] - 1 + if m not in negatives or m+self.test_t not in negatives: + tn_corr[str(xdx)] = tn_corr[str(xdx)] + len(negatives) + fp_corr[str(xdx)] = len(positives) - tp_corr[str(xdx)] + fn_corr[str(xdx)] = len(negatives)-tn_corr[str(xdx)] + tp_corr[str(xdx)] = tp_corr[str(xdx)]/self.test_t # store the output self.SAD_correct = 100*(numcorrect/len(self.test_imgs)) + tp = np.array([]) + fp = np.array([]) + for n in range(len(tp_corr)): + tp = np.append(tp,np.array(tp_corr[str(n)])) + fp = np.append(fp,np.array(fp_corr[str(n)])) + precision = (tp)/(tp+fp) + # plot out the PR curve + array_pr = np.array([]) + for e in range(len(tp_corr)): + array_pr = np.append(array_pr,np.array(tp_corr[str(e)])) + x_axes = np.linspace(0,1,num=len(tp)) + y_axes = np.linspace(0,1,num=6) + fig = plt.figure() + plt.plot(x_axes,np.flip(precision)) + plt.ylim(0,1) + fig.suptitle("PR curve SAD",fontsize = 12) + plt.xlabel("Recall",fontsize = 12) + plt.ylabel("Precision",fontsize = 12) + plt.show() + run_sad() print('Number of correct images with SAD: '+str(self.SAD_correct)+'%') From 443b9de148fcc730350b63d046c7ea8ba5908069 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Wed, 12 Jul 2023 15:33:06 +1000 Subject: [PATCH 07/10] Working on the files to load --- VPRTempo.py | 53 +- nordland_imageNames.txt | 9453 ++++++++++++++++++++++++++++++++++++++- 2 files changed, 9303 insertions(+), 203 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index 5093de6..33c122b 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -27,6 +27,7 @@ import pickle import torch import gc +import re import sys sys.path.append('./src') sys.path.append('./weights') @@ -51,22 +52,17 @@ def __init__(self): # Image and patch normalization settings self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' # training datapath - self.testPath = '/home/adam/data/VPRTempo_training/summer_gamma/' + self.testPath = '/home/adam/data/VPRTempo_training/testing_data/' self.imWidth = 28 # image width for patch norm self.imHeight = 28 # image height for patch norm self.num_patches = 7 # number of patches self.intensity = 255 # divide pixel values to get spikes in range [0,1] - # Select training images from list - with open('./nordland_imageNames.txt') as file: - lines = [line.rstrip() for line in file] - self.img_load = lines - # Network and training settings self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons self.feature_layer = int(self.input_layer*7)# number of feature layer neurons - self.output_layer = len(lines) # number of output layer neurons (match training images) - self.train_img = len(lines) # number of training images + self.output_layer = 400 # number of output layer neurons (match training images) + self.train_img = self.output_layer # number of training images self.epoch = 4 # number of training iterations self.test_t = 200 # number of testing time points self.cuda = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # saliency calculating on cpu or gpu @@ -74,6 +70,25 @@ def __init__(self): self.annl_pow = 2 # learning rate anneal power self.location_repeat = 2 # Number of training locations that are the same + # Select training images from list + with open('./nordland_imageNames.txt') as file: + lines = [line.rstrip() for line in file] + + # image selection, accounting for number of location repeats + bound = np.linspace(0, len(lines)-1, dtype=int, num = int(self.train_img/self.location_repeat)) + self.img_load = [] + for b in bound: + self.img_load.append(lines[b]) + + # save text file here for testing images so its the same as the training + + if self.location_repeat > 1: + for n in bound: + filename = lines[n] + res = re.split('(\d+)', filename) + res[1] = str(int(res[1]) + 12837) + self.img_load.append(res[0]+res[1]+res[2]) + # Hyperparamters self.theta_max = 0.25 # maximum threshold value self.n_init = 0.05 # initial learning rate value @@ -86,7 +101,7 @@ def __init__(self): # Test settings self.test_true = False # leave default to False - self.validation = True # test this network against other methods, default True + self.validation = False # test this network against other methods, default True # Print network details print('////////////') @@ -413,6 +428,17 @@ def test_network(): yield + # calculate and plot distance matrices & PR curves + def plotit(netx): + reshape_mat = np.reshape(temp_mat,(self.test_t,int(self.train_img/self.location_repeat))) + # plot the matrix + fig = plt.figure() + plt.matshow(reshape_mat,fig, cmap=plt.cm.prism) + fig.suptitle(("Similarity VPRTempo: Location "+str(int(n)+1)),fontsize = 12) + plt.xlabel("Database",fontsize = 12) + plt.ylabel("Query",fontsize = 12) + plt.show() + # network validation using alternative place matching algorithms and P@R calculation def network_validator(): # reload training images for the comparisons @@ -472,14 +498,7 @@ def network_validator(): # plot the similarity matrices for each location repetition for n in self.mat_dict: temp_mat = self.mat_dict[str(n)] - reshape_mat = np.reshape(temp_mat,(self.test_t,int(self.train_img/self.location_repeat))) - # plot the matrix - fig = plt.figure() - plt.matshow(reshape_mat,fig, cmap=plt.cm.prism) - fig.suptitle(("Similarity VPRTempo: Location "+str(int(n)+1)),fontsize = 12) - plt.xlabel("Database",fontsize = 12) - plt.ylabel("Query",fontsize = 12) - plt.show() + plotit(temp_mat) if net['set_spks'][0] == True: blitnet.plotSpikes(net,0) diff --git a/nordland_imageNames.txt b/nordland_imageNames.txt index 0fa8846..a87a9bc 100644 --- a/nordland_imageNames.txt +++ b/nordland_imageNames.txt @@ -1,400 +1,9481 @@ images-00202.png +images-00203.png +images-00204.png +images-00205.png +images-00206.png 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-images-16140.png -images-16158.png -images-16176.png -images-16194.png -images-16212.png -images-16230.png -images-16248.png -images-16266.png -images-16284.png -images-16302.png -images-16320.png -images-16338.png -images-16356.png -images-16374.png -images-16392.png -images-16410.png -images-16428.png -images-16446.png -images-16464.png -images-16486.png -images-16504.png -images-16522.png -images-16540.png -images-16558.png -images-16576.png -images-16594.png -images-16612.png -images-16630.png -images-16648.png -images-16666.png -images-16684.png -images-16702.png -images-16720.png -images-16738.png -images-16756.png -images-16774.png -images-16792.png -images-16810.png -images-16828.png -images-16846.png -images-16864.png -images-16882.png -images-16900.png -images-16918.png -images-16936.png -images-16954.png -images-16972.png -images-16990.png -images-17008.png -images-17026.png -images-17044.png -images-17062.png -images-17080.png -images-17098.png -images-17116.png -images-17134.png -images-17152.png -images-17170.png -images-17188.png -images-17206.png -images-17224.png -images-17242.png -images-17260.png -images-17278.png -images-17296.png -images-17314.png -images-17332.png -images-17350.png -images-17368.png -images-17386.png -images-17404.png -images-17422.png -images-17878.png -images-17896.png -images-17914.png -images-17932.png -images-17950.png -images-17968.png -images-17986.png +images-12824.png +images-12825.png +images-12826.png +images-12827.png +images-12828.png +images-12829.png +images-12830.png +images-12831.png +images-12832.png +images-12833.png +images-12834.png +images-12835.png +images-12836.png From 1a22723400f3c366679011a78ab1f9af3719e772 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Tue, 18 Jul 2023 16:45:58 +1000 Subject: [PATCH 08/10] Added PR calculations from VPR tutorial, fixed colormaps, combined training sets in similarity matrices --- VPRTempo.py | 181 ++++++++++++++++++++++++++++++++++++---------- src/validation.py | 55 +------------- 2 files changed, 143 insertions(+), 93 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index 33c122b..f758e62 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -28,6 +28,7 @@ import torch import gc import re +import math import sys sys.path.append('./src') sys.path.append('./weights') @@ -41,6 +42,7 @@ from os import path from alive_progress import alive_bar +from metrics import createPR ''' @@ -51,7 +53,7 @@ def __init__(self): super().__init__() # Image and patch normalization settings - self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' # training datapath + self.trainingPath = '/home/adam/data/VPRTempo_training/training_data/' # training datapath self.testPath = '/home/adam/data/VPRTempo_training/testing_data/' self.imWidth = 28 # image width for patch norm self.imHeight = 28 # image height for patch norm @@ -80,7 +82,8 @@ def __init__(self): for b in bound: self.img_load.append(lines[b]) - # save text file here for testing images so its the same as the training + with open('./output/images.pkl', 'wb') as f: + pickle.dump(self.img_load, f) if self.location_repeat > 1: for n in bound: @@ -161,7 +164,12 @@ def patch_normalise_pad(self): # Process the loaded images - resize, normalize color, & patch normalize def processImage(self): - + # gamma correct images + mid = 0.5 + mean = np.mean(self.img) + gamma = math.log(mid*255)/math.log(mean) + self.img = np.power(self.img,gamma).clip(0,255).astype(np.uint8) + # resize image to 28x28 and patch normalize self.img = cv2.resize(self.img,(self.imWidth, self.imHeight)) self.patch_normalise_pad() @@ -173,6 +181,11 @@ def loadImages(self): if self.test_true: self.dataPath = self.testPath + range_pop = range(self.test_t,self.train_img) + for m in reversed(range_pop): + self.img_load.pop(m) + else: + self.dataPath = self.trainingPath self.ims = [] self.ids = [] @@ -187,14 +200,6 @@ def loadImages(self): self.processImage() self.ims.append(self.img) self.ids.append(m) - - # pickle image ids to keep track of shuffling - if self.test_true: - with open('./output/test_ids.pkl','wb') as f: - pickle.dump(self.ids,f) - else: - with open('./output/train_ids.pkl','wb') as f: - pickle.dump(self.ids,f) data = {'x': np.array(self.ims), 'y': np.array(self.ids), 'rows': self.imWidth, 'cols': self.imHeight} @@ -400,27 +405,52 @@ def set_spikes(): # network testing function def test_network(): - # Test the output + # set up variables to load output into self.correct_idx = [] self.numcorrect = 0 self.mat_dict = {} self.net_x = np.array([]) + self.tp = 0 + self.tn = 0 + self.fp = 0 + self.fn = 0 idx = [] + + # reset the output similarity matrices for n in range(self.location_repeat): self.mat_dict[str(n)] = [] idx.append(n) + + # create a dictionary of locations, with location repeats + match_dict = {} + for n in range(self.test_t): + match_dict[str(n)] = [] + for n in range(self.location_repeat): + index_add = self.test_t*n + for m in range(self.test_t): + match_dict[str(m)].append(m + index_add) + pause=1 + # run the network for t in range(self.test_t): blitnet.runSim(net,1) - nidx = np.argmax(net['x'][-1]) - if nidx < int(self.train_img/self.location_repeat): - if self.train_ids[nidx] == self.test_ids[t] or self.train_ids[(nidx+int(self.train_img/self.location_repeat))] == self.test_ids[t]: - self.numcorrect = self.numcorrect+1 - self.correct_idx.append(t) + # output the index of highest amplitude spike + tonump = net['x'][-1].detach().cpu().numpy() + nidx = np.argmax(tonump) + + # if output value is 0, classify as false negative + if np.max(tonump) == 0: + self.fn = self.fn + 1 + + # evaluate if the output is TP or a FP else: - if self.train_ids[nidx] == self.test_ids[t] or self.train_ids[(nidx-int(self.train_img/self.location_repeat))] == self.test_ids[t]: + index_lst = match_dict[str(t)] + if nidx in index_lst: self.numcorrect = self.numcorrect+1 + self.tp = self.tp+1 self.correct_idx.append(t) - tonump = net['x'][-1].detach().cpu().numpy() + else: + self.fp = self.fp+1 + self.net_x = np.concatenate((self.net_x,tonump),axis=0) split_mat = np.split(tonump,self.location_repeat) for n in range(self.location_repeat): @@ -428,26 +458,27 @@ def test_network(): yield - # calculate and plot distance matrices & PR curves - def plotit(netx): - reshape_mat = np.reshape(temp_mat,(self.test_t,int(self.train_img/self.location_repeat))) + # calculate and plot distance matrices + def plotit(netx,name): + reshape_mat = np.reshape(netx,(self.test_t,int(self.train_img/self.location_repeat))) # plot the matrix fig = plt.figure() - plt.matshow(reshape_mat,fig, cmap=plt.cm.prism) - fig.suptitle(("Similarity VPRTempo: Location "+str(int(n)+1)),fontsize = 12) - plt.xlabel("Database",fontsize = 12) - plt.ylabel("Query",fontsize = 12) + plt.matshow(reshape_mat,fig, cmap=plt.cm.Greens) + plt.colorbar(label="Spike amplitude") + fig.suptitle("Similarity "+name,fontsize = 12) + plt.xlabel("Query",fontsize = 12) + plt.ylabel("Database",fontsize = 12) plt.show() + # calculate PR curves + + # network validation using alternative place matching algorithms and P@R calculation def network_validator(): # reload training images for the comparisons self.test_true= False # reset the test true flag self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' self.test_imgs = self.ims.copy() - with open('./nordland_imageNames.txt') as file: - lines = [line.rstrip() for line in file] - self.img_load = lines self.loadImages() # run sum of absolute differences caluclation validate.SAD(self) @@ -466,10 +497,63 @@ def network_validator(): # Reset number of epochs to 1 to run testing network once self.epoch = 1 + # get the ground truth matrix + self.loadImages() + # Set input layer spikes as the testing images + print('Setting spike times') + with alive_bar(len(self.spike_rates)) as sbar: + for i in set_spikes(): + sbar() + + # run the test netowrk + print('Getting ground truth matrix') + with alive_bar(self.test_t) as testbar: + for i in test_network(): + testbar() + + # plot the similarity matrices for each location repetition + append_mat = [] + for n in self.mat_dict: + if int(n) != 0: + append_mat = append_mat + self.mat_dict[str(n)] + else: + append_mat = np.copy(self.mat_dict[str(n)]) + plot_name = "training images" + plotit(append_mat,plot_name) + + # pickle the ground truth matrix + reshape_mat = np.reshape(append_mat,(self.test_t,int(self.train_img/self.location_repeat))) + boolval = reshape_mat > 0 + GTsoft = boolval.astype(int) + GT = np.zeros((self.test_t,self.test_t), dtype=int) + + for n in range(len(GT)): + GT[n,n] = 1 + plot_name = "Similarity absolute ground truth" + fig = plt.figure() + plt.matshow(GT,fig, cmap=plt.cm.Greens) + plt.colorbar(label="Spike amplitude") + fig.suptitle(plot_name,fontsize = 12) + plt.xlabel("Query",fontsize = 12) + plt.ylabel("Database",fontsize = 12) + plt.show() + with open('./output/GT.pkl', 'wb') as f: + pickle.dump(GT, f) + + self.VPRTempo_correct = 100*self.numcorrect/self.test_t + print(self.VPRTempo_correct,'% correct') + + # Clear the network output spikes + blitnet.setSpikeTimes(net,2,[]) + + # Reset network details + net['set_spks'][0] = [] + #net['rec_spks'] = [True,True,True] + net['sspk_idx'] = [0,0,0] + net['step_num'] = 0 + net['spikes'] = [[],[],[]] + # Load the testing images - with open('./nordland_testNames.txt') as file: - lines = [line.rstrip() for line in file] - self.img_load = lines self.test_true = True # Set image path to the testing images self.loadImages() @@ -479,12 +563,6 @@ def network_validator(): for i in set_spikes(): sbar() - # load the training and testing IDs for correct matching - with open('./output/train_ids.pkl', 'rb') as f: - self.train_ids = pickle.load(f) - with open('./output/test_ids.pkl', 'rb') as f: - self.test_ids = pickle.load(f) - # run the test netowrk print('Running testing dataset on trained network') with alive_bar(self.test_t) as testbar: @@ -496,10 +574,32 @@ def network_validator(): print(self.VPRTempo_correct,'% correct') # plot the similarity matrices for each location repetition + append_mat = [] for n in self.mat_dict: - temp_mat = self.mat_dict[str(n)] - plotit(temp_mat) + if int(n) != 0: + append_mat = append_mat + self.mat_dict[str(n)] + else: + append_mat = np.copy(self.mat_dict[str(n)]) + plot_name = "VPRTempo" + plotit(append_mat,plot_name) + # pickle the ground truth matrix + S_in = np.reshape(append_mat,(self.test_t,int(self.train_img/self.location_repeat))) + with open('./output/S_in.pkl', 'wb') as f: + pickle.dump(S_in, f) + # calculate the precision of the system + self.precision = self.tp/(self.tp+self.fp) + self.recall = self.tp/self.test_t + P, R = createPR(S_in, GT, GTsoft) + # plot PR curve + fig = plt.figure() + plt.plot(R,P) + fig.suptitle("VPRTempo Precision Recall curve",fontsize = 12) + plt.xlabel("Recall",fontsize = 12) + plt.ylabel("Precision",fontsize = 12) + plt.show() + + # plot spikes if they were recorded if net['set_spks'][0] == True: blitnet.plotSpikes(net,0) @@ -510,6 +610,7 @@ def network_validator(): # if validation is set to True, run comparison methods if self.validation: network_validator() + pause=1 ''' Run the network diff --git a/src/validation.py b/src/validation.py index d46220d..3a33909 100644 --- a/src/validation.py +++ b/src/validation.py @@ -56,62 +56,11 @@ def run_sad(): test_mat = np.concatenate((test_mat,test),axis=0) # create the similarity matrix - sim_mat = np.copy(np.reshape(test_mat,(self.test_t,self.train_img))) - sort_mat = np.sort(sim_mat,axis=1) + self.sim_mat = np.copy(np.reshape(test_mat,(self.test_t,self.train_img))) - # perform P@0-100%R calculations going through all the threshold values - tp_corr = {} - fp_corr = {} - tn_corr = {} - fn_corr = {} - for xdx, x in enumerate(range(200)): - thresh_vals = sort_mat[:,x] - tp_corr[str(xdx)] = 0 - fp_corr[str(xdx)] = 0 - tn_corr[str(xdx)] = 0 - fn_corr[str(xdx)] = 0 - - ap_pos = [] - for m, mdx in enumerate(thresh_vals): - temp_row = np.copy(sim_mat[m,:]) - temp_row[temp_row>mdx] = -1 - nonzero = np.where(temp_row>0) - - positives = np.argwhere(temp_row!=-1) - ap_pos.append(len(positives[0])) - negatives = np.argwhere(temp_row==-1) - - # calculate true positives and false positives - if m in positives or m+self.test_t in positives: - tp_corr[str(xdx)] = tp_corr[str(xdx)] + 1 - fp_corr[str(xdx)] = tp_corr[str(xdx)] - 1 - if m not in negatives or m+self.test_t not in negatives: - tn_corr[str(xdx)] = tn_corr[str(xdx)] + len(negatives) - fp_corr[str(xdx)] = len(positives) - tp_corr[str(xdx)] - fn_corr[str(xdx)] = len(negatives)-tn_corr[str(xdx)] - - tp_corr[str(xdx)] = tp_corr[str(xdx)]/self.test_t # store the output self.SAD_correct = 100*(numcorrect/len(self.test_imgs)) - tp = np.array([]) - fp = np.array([]) - for n in range(len(tp_corr)): - tp = np.append(tp,np.array(tp_corr[str(n)])) - fp = np.append(fp,np.array(fp_corr[str(n)])) - precision = (tp)/(tp+fp) - # plot out the PR curve - array_pr = np.array([]) - for e in range(len(tp_corr)): - array_pr = np.append(array_pr,np.array(tp_corr[str(e)])) - x_axes = np.linspace(0,1,num=len(tp)) - y_axes = np.linspace(0,1,num=6) - fig = plt.figure() - plt.plot(x_axes,np.flip(precision)) - plt.ylim(0,1) - fig.suptitle("PR curve SAD",fontsize = 12) - plt.xlabel("Recall",fontsize = 12) - plt.ylabel("Precision",fontsize = 12) - plt.show() + run_sad() print('Number of correct images with SAD: '+str(self.SAD_correct)+'%') From 3e84fa08468d2270ea071d7b083937090dc8b8ff Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Fri, 21 Jul 2023 17:00:40 +1000 Subject: [PATCH 09/10] Improved validation with SAD, updated image loading tools, and added capability to do winter dataset as well --- VPRTempo.py | 109 +- nordland_imageNames.txt | 18111 +++++++++++++++++ src/__pycache__/blitnet_open.cpython-311.pyc | Bin 24484 -> 24597 bytes src/blitnet_open.py | 31 +- src/validation.py | 81 +- 5 files changed, 18243 insertions(+), 89 deletions(-) diff --git a/VPRTempo.py b/VPRTempo.py index f758e62..5a99a2b 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -27,7 +27,6 @@ import pickle import torch import gc -import re import math import sys sys.path.append('./src') @@ -52,9 +51,16 @@ class snn_model(): def __init__(self): super().__init__() - # Image and patch normalization settings + ''' + USER SETTINGS + ''' self.trainingPath = '/home/adam/data/VPRTempo_training/training_data/' # training datapath - self.testPath = '/home/adam/data/VPRTempo_training/testing_data/' + self.testPath = '/home/adam/data/VPRTempo_training/testing_data/' # testing datapath + self.number_training_images = 2000 # alter number of training images + self.location_repeat = 3 # Number of training locations that are the same + self.locations = ["spring/","fall/", "winter/"] # which datasets are used in the training + + # Image and patch normalization settings self.imWidth = 28 # image width for patch norm self.imHeight = 28 # image height for patch norm self.num_patches = 7 # number of patches @@ -63,38 +69,36 @@ def __init__(self): # Network and training settings self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons self.feature_layer = int(self.input_layer*7)# number of feature layer neurons - self.output_layer = 400 # number of output layer neurons (match training images) + self.output_layer = self.number_training_images # number of output layer neurons (match training images) self.train_img = self.output_layer # number of training images self.epoch = 4 # number of training iterations - self.test_t = 200 # number of testing time points + self.test_t = self.output_layer # number of testing time points self.cuda = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # saliency calculating on cpu or gpu self.T = self.train_img*self.epoch # number of training steps self.annl_pow = 2 # learning rate anneal power - self.location_repeat = 2 # Number of training locations that are the same # Select training images from list with open('./nordland_imageNames.txt') as file: lines = [line.rstrip() for line in file] # image selection, accounting for number of location repeats - bound = np.linspace(0, len(lines)-1, dtype=int, num = int(self.train_img/self.location_repeat)) + start = 0 + end = start+(self.test_t*8) + bound = range(start,end,8) self.img_load = [] for b in bound: self.img_load.append(lines[b]) with open('./output/images.pkl', 'wb') as f: pickle.dump(self.img_load, f) - - if self.location_repeat > 1: - for n in bound: - filename = lines[n] - res = re.split('(\d+)', filename) - res[1] = str(int(res[1]) + 12837) - self.img_load.append(res[0]+res[1]+res[2]) - + + self.fullTrainPaths = [] + for n in self.locations: + self.fullTrainPaths.append(self.trainingPath+n) + # Hyperparamters self.theta_max = 0.25 # maximum threshold value - self.n_init = 0.05 # initial learning rate value + self.n_init = 0.01 # initial learning rate value self.n_itp = 0.25 # initial intrinsic threshold plasticity rate[[0.9999]] self.f_rate = [0.0012,0.2]# firing rate range self.p_exc = 0.025 # probability of excitatory connection @@ -104,7 +108,7 @@ def __init__(self): # Test settings self.test_true = False # leave default to False - self.validation = False # test this network against other methods, default True + self.validation = True # test this network against other methods, default True # Print network details print('////////////') @@ -180,26 +184,35 @@ def processImage(self): def loadImages(self): if self.test_true: - self.dataPath = self.testPath - range_pop = range(self.test_t,self.train_img) - for m in reversed(range_pop): - self.img_load.pop(m) - else: - self.dataPath = self.trainingPath + self.fullTrainPaths = self.testPath self.ims = [] self.ids = [] print('Loading images and patch normalising') - - for m in self.img_load: - self.fullpath = self.dataPath+m - # read and convert image from BGR to RGB - self.img = cv2.imread(self.fullpath)[:,:,::-1] - # convert image - self.img = cv2.cvtColor(self.img,cv2.COLOR_RGB2GRAY) - self.processImage() - self.ims.append(self.img) - self.ids.append(m) + + if isinstance(self.fullTrainPaths,list): + for paths in self.fullTrainPaths: + self.dataPath = paths + for m in self.img_load: + self.fullpath = self.dataPath+m + # read and convert image from BGR to RGB + self.img = cv2.imread(self.fullpath)[:,:,::-1] + # convert image + self.img = cv2.cvtColor(self.img,cv2.COLOR_RGB2GRAY) + self.processImage() + self.ims.append(self.img) + self.ids.append(m) + else: + self.dataPath = self.fullTrainPaths + for m in self.img_load: + self.fullpath = self.dataPath+m + # read and convert image from BGR to RGB + self.img = cv2.imread(self.fullpath)[:,:,::-1] + # convert image + self.img = cv2.cvtColor(self.img,cv2.COLOR_RGB2GRAY) + self.processImage() + self.ims.append(self.img) + self.ids.append(m) data = {'x': np.array(self.ims), 'y': np.array(self.ids), 'rows': self.imWidth, 'cols': self.imHeight} @@ -343,6 +356,12 @@ def train_output(): out_spks[n] = [(n)+1.5,n] append_spks[n] = [(n)+1.5,n] + if self.location_repeat != 0: + base_spks = np.copy(out_spks) + for n in range(1,self.location_repeat): + base_spks[:,0] = base_spks[:,0] + self.test_t + out_spks = np.append(out_spks,base_spks,axis=0) + for n in range(self.epoch): out_spks[:,0] += self.output_layer @@ -429,7 +448,7 @@ def test_network(): index_add = self.test_t*n for m in range(self.test_t): match_dict[str(m)].append(m + index_add) - pause=1 + # run the network for t in range(self.test_t): blitnet.runSim(net,1) @@ -477,8 +496,11 @@ def plotit(netx,name): def network_validator(): # reload training images for the comparisons self.test_true= False # reset the test true flag - self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' self.test_imgs = self.ims.copy() + self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' + self.fullTrainPaths = [] + for n in self.locations: + self.fullTrainPaths.append(self.trainingPath+n) self.loadImages() # run sum of absolute differences caluclation validate.SAD(self) @@ -496,7 +518,8 @@ def network_validator(): # Reset number of epochs to 1 to run testing network once self.epoch = 1 - + self.location_repeat = 1 + # get the ground truth matrix self.loadImages() # Set input layer spikes as the testing images @@ -519,7 +542,8 @@ def network_validator(): else: append_mat = np.copy(self.mat_dict[str(n)]) plot_name = "training images" - plotit(append_mat,plot_name) + #plotit(append_mat,plot_name) + plotit(self.net_x,plot_name) # pickle the ground truth matrix reshape_mat = np.reshape(append_mat,(self.test_t,int(self.train_img/self.location_repeat))) @@ -548,13 +572,14 @@ def network_validator(): # Reset network details net['set_spks'][0] = [] - #net['rec_spks'] = [True,True,True] + net['rec_spks'] = [True,True,True] net['sspk_idx'] = [0,0,0] net['step_num'] = 0 net['spikes'] = [[],[],[]] # Load the testing images - self.test_true = True # Set image path to the testing images + #self.test_true = True # Set image path to the testing images + self.test_true = True self.loadImages() # Set input layer spikes as the testing images @@ -581,7 +606,8 @@ def network_validator(): else: append_mat = np.copy(self.mat_dict[str(n)]) plot_name = "VPRTempo" - plotit(append_mat,plot_name) + #plotit(append_mat,plot_name) + plotit(self.net_x,plot_name) # pickle the ground truth matrix S_in = np.reshape(append_mat,(self.test_t,int(self.train_img/self.location_repeat))) with open('./output/S_in.pkl', 'wb') as f: @@ -600,7 +626,7 @@ def network_validator(): plt.show() # plot spikes if they were recorded - if net['set_spks'][0] == True: + if net['rec_spks'][0] == True: blitnet.plotSpikes(net,0) # clear the CUDA cache @@ -610,7 +636,6 @@ def network_validator(): # if validation is set to True, run comparison methods if self.validation: network_validator() - pause=1 ''' Run the network diff --git a/nordland_imageNames.txt b/nordland_imageNames.txt index a87a9bc..01543f2 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norm_inhib(net): # net: SORN instance def calc_spikes(net): - pause=1 # Start with the noise and constant input in the neurons of each layer for i,nois in enumerate(net['nois']): if nois > 0: @@ -311,15 +308,17 @@ def calc_spikes(net): # Finally, update mean firing rates and record all spikes if needed for i,eta in enumerate(net['eta_ip']): - + if eta > 0.0: net['mean_rate'][i] = net['mean_rate'][i]*(1.0-eta) +\ (net['x'][i]>0.0)*eta if net['rec_spks'][i]: - n_idx = np.nonzero(net['x'][i])[0] - print('Woof woof woof') - net['spikes'][i].extend([net['step_num']+1-net['x'][i][n],n] - for n in n_idx) + outspk = (net['x'][i]).detach().cpu().numpy() + if i == 2: + outspk[outspk<0.05] = 0 + n_idx = np.nonzero(outspk) + net['spikes'][i].extend([net['step_num']+net['x'][i][n].detach().cpu().numpy(),n] + for n in n_idx) ################################## # Calculate STDP @@ -489,15 +488,13 @@ def subplotSpikes(net,cutoff): n_tot = 0 for i,sp in enumerate(net['spikes']): - sp = sp.detach().cpu().numpy() - if any(sp): - x=[]; y=[] - for s in sp: - if s[0] > cutoff: - x.extend([s[0]]) - y.extend([s[1]+n_tot]) - plt.plot(x,y,'.',ms=1) - n_tot += np.size(net['x'][i]) + 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 diff --git a/src/validation.py b/src/validation.py index 3a33909..9e98e30 100644 --- a/src/validation.py +++ b/src/validation.py @@ -26,46 +26,67 @@ import matplotlib.pyplot as plt import numpy as np from alive_progress import alive_bar +from operator import add # Sum of Absolute Differences (SAD) calculation for images def SAD(self): - print("Calculating Sum of Absolute Differences (SAD) for training and testing data") def run_sad(): - numcorrect = 0 - test_mat = np.array([]) + + # create dictionary for SAD calculation for each location repeat + self.sad_correct = 0 + self.sad_mat = {} + for n in self.locations: + self.sad_mat[n] = np.array([]) + + # create range for splitting out SAD matrices + z = int(len(self.ims)/len(self.locations)) + zRange = [0] + zAdd = [1] + for m in range(1,len(self.locations)): + zRange.append((z*m)) + zAdd.append(1) # loop through each test image to find the SAD for nEnum, n in enumerate(self.test_imgs): - - # loop through each training image and calculate SAD - test = [] + temp_vals = [] + # calculate SAD for each test image and store in dictionary for mEnum, m in enumerate(self.ims): - test.append(1/(self.imWidth*self.imHeight)*np.sum(np.subtract(np.abs(m),np.abs(n)))) - test = np.array(test) - - # calculate the precision-recall curves for each - # find the minimum value - min_arg = np.argmin(test) - if min_arg > int(self.train_img/self.location_repeat): - if self.train_ids[min_arg-(int(self.train_img/self.location_repeat))] == self.test_ids[nEnum]: - numcorrect = numcorrect+1 - else: - if self.train_ids[min_arg] == self.test_ids[nEnum]: - numcorrect = numcorrect+1 + temp_vals.append(1/(self.imWidth*self.imHeight)*np.sum(np.subtract(np.abs(m),np.abs(n)))) - test_mat = np.concatenate((test_mat,test),axis=0) + if np.argmin(temp_vals) in zRange: + self.sad_correct = self.sad_correct + 1 + + zRange = list(map(add,zRange,zAdd)) + idx = [0,len(self.test_imgs)] + for n, ndx in enumerate(self.locations): + self.sad_mat[ndx] = np.append(self.sad_mat[ndx],np.array(temp_vals[idx[0]:idx[1]])) + idx[0] = idx[0] + len(self.test_imgs) + idx[1] = idx[1] + len(self.test_imgs) - # create the similarity matrix - self.sim_mat = np.copy(np.reshape(test_mat,(self.test_t,self.train_img))) + yield + # determine if correct match + print('SAD: number correct '+str(100*(self.sad_correct/len(self.test_imgs)))+'%') - # store the output - self.SAD_correct = 100*(numcorrect/len(self.test_imgs)) + # run sum of absolute differences caluclation + print('Calculating sum of absolute differences') + with alive_bar(len(self.test_imgs)) as sbar: + for i in run_sad(): + sbar() - - run_sad() - print('Number of correct images with SAD: '+str(self.SAD_correct)+'%') - -# Precision @ 100% recall calculation -def PR_calc(): + # plot the similarity matrices + sim_mat = np.zeros(len(self.test_imgs)*len(self.test_imgs)) + for n in self.sad_mat: + sim_mat = sim_mat + self.sad_mat[n] + sim_mat = sim_mat/len(self.locations) + sim_mat = np.resize(sim_mat,(len(self.test_imgs),len(self.test_imgs))) + plot_name = "Similarity Sum of Absolute Differences" + fig = plt.figure() + plt.matshow(sim_mat,fig, cmap=plt.cm.Greens) + plt.colorbar(label="Pixel intensity") + fig.suptitle(plot_name,fontsize = 12) + plt.xlabel("Query",fontsize = 12) + plt.ylabel("Database",fontsize = 12) + plt.show() - print("Calculating precision @ 100% recall") \ No newline at end of file + # run PR calculation for sum of absolute differences + \ No newline at end of file From bc619102bacbc854456fe2f9d84f995949083048 Mon Sep 17 00:00:00 2001 From: Adam Hines Date: Thu, 27 Jul 2023 14:01:47 +1000 Subject: [PATCH 10/10] Finalised the SAD, validation, and torch integration for GPU accel --- VPRTempo.py | 31 ++++--- nordland_testNames.txt | 200 ----------------------------------------- src/validation.py | 42 ++++++--- 3 files changed, 49 insertions(+), 224 deletions(-) delete mode 100644 nordland_testNames.txt diff --git a/VPRTempo.py b/VPRTempo.py index 5a99a2b..172e55b 100644 --- a/VPRTempo.py +++ b/VPRTempo.py @@ -54,11 +54,11 @@ def __init__(self): ''' USER SETTINGS ''' - self.trainingPath = '/home/adam/data/VPRTempo_training/training_data/' # training datapath - self.testPath = '/home/adam/data/VPRTempo_training/testing_data/' # testing datapath - self.number_training_images = 2000 # alter number of training images - self.location_repeat = 3 # Number of training locations that are the same - self.locations = ["spring/","fall/", "winter/"] # which datasets are used in the training + self.trainingPath = '/home/adam/data/hpc/' # training datapath + self.testPath = '/home/adam/data/testing_data/summer/' # testing datapath + self.number_training_images = 300 # alter number of training images + self.location_repeat = 2 # Number of training locations that are the same + self.locations = ["spring/","fall/"] # which datasets are used in the training # Image and patch normalization settings self.imWidth = 28 # image width for patch norm @@ -68,13 +68,13 @@ def __init__(self): # Network and training settings self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons - self.feature_layer = int(self.input_layer*7)# number of feature layer neurons + self.feature_layer = int(self.input_layer*7) # number of feature layer neurons self.output_layer = self.number_training_images # number of output layer neurons (match training images) self.train_img = self.output_layer # number of training images self.epoch = 4 # number of training iterations self.test_t = self.output_layer # number of testing time points self.cuda = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # saliency calculating on cpu or gpu - self.T = self.train_img*self.epoch # number of training steps + self.T = int(self.number_training_images*self.epoch) # number of training steps self.annl_pow = 2 # learning rate anneal power # Select training images from list @@ -123,9 +123,12 @@ def __init__(self): print('Inhibitory p: '+str(self.p_inh)) print('Constant input '+str(self.c)) print('CUDA available: '+str(torch.cuda.is_available())) - current_device = torch.cuda.current_device() - print('Current device is: '+str(torch.cuda.get_device_name(current_device))) - + if torch.cuda.is_available() == True: + current_device = torch.cuda.current_device() + print('Current device is: '+str(torch.cuda.get_device_name(current_device))) + else: + print('Current device is: CPU') + # Network weights name self.training_out = './weights/'+str(self.input_layer)+'i'+\ str(self.feature_layer)+\ @@ -482,7 +485,7 @@ def plotit(netx,name): reshape_mat = np.reshape(netx,(self.test_t,int(self.train_img/self.location_repeat))) # plot the matrix fig = plt.figure() - plt.matshow(reshape_mat,fig, cmap=plt.cm.Greens) + plt.matshow(reshape_mat,fig, cmap=plt.cm.gist_yarg) plt.colorbar(label="Spike amplitude") fig.suptitle("Similarity "+name,fontsize = 12) plt.xlabel("Query",fontsize = 12) @@ -497,7 +500,7 @@ def network_validator(): # reload training images for the comparisons self.test_true= False # reset the test true flag self.test_imgs = self.ims.copy() - self.dataPath = '/home/adam/data/VPRTempo_training/training_data/' + self.dataPath = '/home/adam/data/hpc/' self.fullTrainPaths = [] for n in self.locations: self.fullTrainPaths.append(self.trainingPath+n) @@ -555,7 +558,7 @@ def network_validator(): GT[n,n] = 1 plot_name = "Similarity absolute ground truth" fig = plt.figure() - plt.matshow(GT,fig, cmap=plt.cm.Greens) + plt.matshow(GT,fig, cmap=plt.cm.gist_yarg) plt.colorbar(label="Spike amplitude") fig.suptitle(plot_name,fontsize = 12) plt.xlabel("Query",fontsize = 12) @@ -616,7 +619,7 @@ def network_validator(): # calculate the precision of the system self.precision = self.tp/(self.tp+self.fp) self.recall = self.tp/self.test_t - P, R = createPR(S_in, GT, GTsoft) + P, R = createPR(S_in, GT, GT) # plot PR curve fig = plt.figure() plt.plot(R,P) diff --git a/nordland_testNames.txt b/nordland_testNames.txt deleted file mode 100644 index 15bd288..0000000 --- a/nordland_testNames.txt +++ /dev/null @@ -1,200 +0,0 @@ -images-00202.png -images-00220.png -images-00238.png -images-00256.png -images-00274.png -images-00292.png -images-00310.png -images-00328.png -images-00346.png -images-00364.png -images-00382.png -images-00400.png -images-00418.png -images-00436.png -images-00454.png -images-00472.png -images-00490.png -images-00508.png -images-00526.png -images-00544.png -images-00562.png -images-00580.png -images-00598.png -images-00616.png -images-00634.png -images-00652.png -images-00670.png -images-00688.png -images-00706.png -images-00724.png -images-00742.png -images-00760.png -images-00778.png -images-00796.png -images-00814.png -images-00832.png -images-00850.png -images-00868.png -images-00886.png -images-00904.png -images-00922.png -images-00940.png -images-00958.png -images-00976.png -images-00994.png -images-01012.png -images-01030.png -images-01048.png -images-01066.png -images-01084.png -images-01102.png -images-01120.png -images-01138.png -images-01156.png -images-01174.png -images-01192.png -images-01210.png -images-01228.png -images-01246.png -images-01814.png -images-01832.png -images-01850.png -images-01868.png -images-02045.png -images-02063.png -images-02081.png -images-02099.png -images-02117.png -images-02135.png -images-02153.png -images-02171.png -images-02334.png -images-02366.png -images-02384.png -images-02402.png -images-02420.png -images-02438.png -images-02456.png -images-02474.png -images-02683.png -images-02701.png -images-02719.png -images-02737.png -images-02755.png -images-02773.png -images-02791.png -images-02809.png -images-02827.png -images-02845.png -images-02863.png -images-02881.png -images-02899.png -images-02917.png -images-02935.png -images-02953.png -images-02971.png -images-02989.png -images-03007.png -images-03025.png -images-03043.png -images-03061.png -images-03079.png -images-03097.png -images-03115.png -images-03133.png -images-03151.png -images-03169.png -images-03187.png -images-03205.png -images-03223.png -images-03241.png -images-03259.png -images-03277.png -images-03295.png -images-03313.png -images-03331.png -images-03349.png -images-03367.png -images-03699.png -images-03717.png -images-03735.png -images-03753.png -images-03771.png -images-03789.png -images-03807.png -images-03825.png -images-03843.png -images-03861.png -images-03879.png -images-03897.png -images-03915.png -images-03933.png -images-03951.png -images-03969.png -images-03987.png -images-04005.png -images-04023.png -images-04041.png -images-04059.png -images-04077.png -images-04099.png -images-04117.png -images-04135.png -images-04153.png -images-04171.png -images-04189.png -images-04207.png -images-04225.png -images-04243.png -images-04261.png -images-04279.png -images-04297.png -images-04315.png -images-04333.png -images-04351.png -images-04369.png -images-04387.png -images-04405.png -images-04423.png -images-04441.png -images-04459.png -images-04477.png -images-04495.png -images-04513.png -images-04531.png -images-04549.png -images-04567.png -images-04585.png -images-04603.png -images-04621.png -images-04639.png -images-04657.png -images-04675.png -images-04693.png -images-04711.png -images-04729.png -images-04747.png -images-04765.png -images-04783.png -images-04801.png -images-04819.png -images-04837.png -images-04855.png -images-04873.png -images-04891.png -images-04909.png -images-04927.png -images-04945.png -images-04963.png -images-04981.png -images-04999.png -images-05017.png -images-05035.png -images-05491.png -images-05509.png -images-05527.png -images-05545.png -images-05563.png -images-05581.png -images-05599.png diff --git a/src/validation.py b/src/validation.py index 9e98e30..85c34ff 100644 --- a/src/validation.py +++ b/src/validation.py @@ -27,6 +27,7 @@ import numpy as np from alive_progress import alive_bar from operator import add +from metrics import createPR # Sum of Absolute Differences (SAD) calculation for images def SAD(self): @@ -46,20 +47,26 @@ def run_sad(): for m in range(1,len(self.locations)): zRange.append((z*m)) zAdd.append(1) + + # split out the fall training images + self.ims = self.ims[zRange[1]:len(self.ims)] + # loop through each test image to find the SAD for nEnum, n in enumerate(self.test_imgs): temp_vals = [] # calculate SAD for each test image and store in dictionary for mEnum, m in enumerate(self.ims): - temp_vals.append(1/(self.imWidth*self.imHeight)*np.sum(np.subtract(np.abs(m),np.abs(n)))) - + temp_vals.append((1/(self.imWidth*self.imHeight))*np.sum(np.abs(np.subtract(m,n)))) + + + if np.argmin(temp_vals) in zRange: self.sad_correct = self.sad_correct + 1 zRange = list(map(add,zRange,zAdd)) idx = [0,len(self.test_imgs)] - for n, ndx in enumerate(self.locations): - self.sad_mat[ndx] = np.append(self.sad_mat[ndx],np.array(temp_vals[idx[0]:idx[1]])) + for e, edx in enumerate(self.locations): + self.sad_mat[edx] = np.append(self.sad_mat[edx],np.array(temp_vals[idx[0]:idx[1]])) idx[0] = idx[0] + len(self.test_imgs) idx[1] = idx[1] + len(self.test_imgs) @@ -75,13 +82,13 @@ def run_sad(): # plot the similarity matrices sim_mat = np.zeros(len(self.test_imgs)*len(self.test_imgs)) - for n in self.sad_mat: - sim_mat = sim_mat + self.sad_mat[n] - sim_mat = sim_mat/len(self.locations) - sim_mat = np.resize(sim_mat,(len(self.test_imgs),len(self.test_imgs))) + #for n in self.sad_mat: + # sim_mat = sim_mat + self.sad_mat[n] + #sim_mat = sim_mat/len(self.locations) + sim_mat = np.resize(self.sad_mat['spring/'],(len(self.test_imgs),len(self.test_imgs))) plot_name = "Similarity Sum of Absolute Differences" fig = plt.figure() - plt.matshow(sim_mat,fig, cmap=plt.cm.Greens) + plt.matshow(sim_mat,fig, cmap=plt.cm.gist_gray) plt.colorbar(label="Pixel intensity") fig.suptitle(plot_name,fontsize = 12) plt.xlabel("Query",fontsize = 12) @@ -89,4 +96,19 @@ def run_sad(): plt.show() # run PR calculation for sum of absolute differences - \ No newline at end of file + GT = np.zeros((self.test_t,self.test_t), dtype=int) + for n in range(len(GT)): + GT[n,n] = 1 + + # invert the similarity matrix so that smaller values are the highest + invMat = sim_mat*-1 + convMat = invMat + (np.min(invMat)*-1) + 1 + P, R = createPR(convMat, GT, GT) + + # plot PR curve + fig = plt.figure() + plt.plot(R,P) + fig.suptitle("SAD Precision Recall curve",fontsize = 12) + plt.xlabel("Recall",fontsize = 12) + plt.ylabel("Precision",fontsize = 12) + plt.show() \ No newline at end of file