def load_data():
train_data = dataset.Market_DataLoader(imgs_path=cfg.TRAIN.imgs_path,
pose_path=cfg.TRAIN.pose_path,
idx_path=cfg.TRAIN.idx_path,
transform=dataset.train_transform(),
loader=dataset.val_loader)
train_loader = Data.DataLoader(train_data,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=True,
num_workers=cfg.TRAIN.NUM_WORKERS,
drop_last=True)
val_data = dataset.Market_DataLoader(imgs_path=cfg.TRAIN.imgs_path,
pose_path=cfg.TRAIN.pose_path,
idx_path=cfg.TEST.idx_path,
transform=dataset.val_transform(),
loader=dataset.val_loader)
val_loader = Data.DataLoader(val_data,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.TRAIN.NUM_WORKERS)
train = [train_data, train_loader]
val = [val_data, val_loader]
return train, val
def loss_func():
criterionGAN = torch.nn.MSELoss().cuda()
criterionIdt = torch.nn.L1Loss().cuda()
criterionAtt = torch.nn.CrossEntropyLoss().cuda()
criterion = [criterionGAN, criterionIdt, criterionAtt]
return criterion
#%%
if __name__ == '__main__':
sys.stdout = logger.Logger('./log_GAN_ep2.txt')
train_data = dataset.Market_DataLoader(imgs_path=cfg.TRAIN.imgs_path, pose_path=cfg.TRAIN.pose_path, idx_path=cfg.TRAIN.idx_path,
transform=dataset.train_transform(), img_loader=dataset.val_loader, pose_loader=dataset.pose_loader)
train_loader = Data.DataLoader(train_data, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=True, num_workers=cfg.TRAIN.NUM_WORKERS, drop_last=True)
val_data = dataset.Market_DataLoader(imgs_path=cfg.TRAIN.imgs_path, pose_path=cfg.TRAIN.pose_path, idx_path=cfg.TEST.idx_path,
transform=dataset.val_transform(), img_loader=dataset.val_loader, pose_loader=dataset.pose_loader)
val_loader = Data.DataLoader(val_data, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False,
num_workers=cfg.TRAIN.NUM_WORKERS)
train_file = [train_data, train_loader]
val_file = [val_data, val_loader]
# train_file, val_file = load_data()
nets = load_network()