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my_test.py
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import os
import numpy as np
import torch
import argparse
import time
from config import cfg
# args = argparse.ArgumentParser()
# args.add_argument('--net_name', type=str, default='', help='name of net')
# args.add_argument('--resume', type=int, default=0, help='whether to resume model')
# args.add_argument('--resume_path', type=str, default='10-31_23-16_image-clean-low(128, 72)_audio-wo_AC_CSRNet_1e-05')
# args.add_argument('--ckpt', type=str, default='')
# args.add_argument('--settings', type=str, default='')
#
# args.add_argument('--is_noise', type=int, default=0)
# args.add_argument('--brightness', type=float, default=1.0)
# args.add_argument('--noise_sigma', type=float, default=25)
# args.add_argument('--longest_side', type=int, default=1024)
# args.add_argument('--black_area_ratio', type=float, default=0)
# args.add_argument('--is_random', type=int, default=0)
#
# opt = args.parse_args()
#
# cfg.NET = opt.net_name
# cfg.RESUME = (opt.resume == 1)
# cfg.RESUME_PATH = os.path.join('../trained_models/exp', opt.resume_path, opt.ckpt)
# cfg.SETTINGS = opt.settings
#
# now = time.strftime("%m-%d_%H-%M", time.localtime())
# cfg.EXP_NAME = cfg.SETTINGS + '_' + cfg.DATASET + '_' + cfg.NET + '_' + str(cfg.LR)
#------------prepare enviroment------------
seed = cfg.SEED
if seed is not None:
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
cfg.GPU_ID = [0,1]
gpus = cfg.GPU_ID
if len(gpus)==1:
torch.cuda.set_device(gpus[0])
torch.backends.cudnn.benchmark = True
#------------prepare data loader------------
data_mode = cfg.DATASET
if data_mode is 'SHHA':
from datasets.SHHA.loading_data import loading_data
from datasets.SHHA.setting import cfg_data
elif data_mode is 'SHHB':
from datasets.SHHB.loading_data import loading_data
from datasets.SHHB.setting import cfg_data
elif data_mode is 'QNRF':
from datasets.QNRF.loading_data import loading_data
from datasets.QNRF.setting import cfg_data
elif data_mode is 'UCF50':
from datasets.UCF50.loading_data import loading_data
from datasets.UCF50.setting import cfg_data
elif data_mode is 'WE':
from datasets.WE.loading_data import loading_data
from datasets.WE.setting import cfg_data
elif data_mode is 'GCC':
from datasets.GCC.loading_data import loading_data
from datasets.GCC.setting import cfg_data
elif data_mode is 'Mall':
from datasets.Mall.loading_data import loading_data
from datasets.Mall.setting import cfg_data
elif data_mode is 'UCSD':
from datasets.UCSD.loading_data import loading_data
from datasets.UCSD.setting import cfg_data
elif data_mode is 'AC': # Qingzhong
from datasets.AC.loading_data import loading_data
from datasets.AC.setting import cfg_data
# cfg_data.IS_NOISE = (opt.is_noise == 1)
# cfg_data.BRIGHTNESS = opt.brightness
# cfg_data.NOISE_SIGMA = opt.noise_sigma
# cfg_data.LONGEST_SIDE = opt.longest_side
# cfg_data.BLACK_AREA_RATIO = opt.black_area_ratio
# cfg_data.IS_RANDOM = (opt.is_random == 1)
print(cfg, cfg_data)
#------------Prepare Trainer------------
net = cfg.NET
if net in ['MCNN', 'AlexNet', 'VGG', 'VGG_DECODER', 'Res50', 'Res101', 'CSRNet','Res101_SFCN',
'CSRNet_IN', 'CSRNet_Audio', 'CANNet', 'CANNet_Audio', 'CSRNet_Audio_Concat', 'CANNet_Audio_Concat',
'CSRNet_Audio_Guided', 'CANNet_Audio_Guided'
]:
from my_tester import Tester
#------------Start Training------------
pwd = os.path.split(os.path.realpath(__file__))[0]
cc_trainer = Tester(loading_data, cfg_data, pwd)
cc_trainer.forward()