train_augmentation = net.tsn.get_augmentation() input_mean = net.tsn.input_mean input_std = net.tsn.input_std if modality != 'RGBDiff': normalize = GroupNormalize(input_mean, input_std) else: normalize = IdentityTransform() train_loader = torch.utils.data.DataLoader( TSNDataSet("", test_list, num_segments=num_segments, new_length=data_length, modality=modality, image_tmpl="img_{:05d}.jpg" if modality in ["RGB", "RGBDiff"] else flow_prefix+"{}_{:05d}.jpg", test_mode=True, transform=torchvision.transforms.Compose([ GroupCenterCrop([224, 224]), Stack(roll=arch == 'BNInception'), ToTorchFormatTensor(div=arch != 'BNInception'), normalize, ]) ), batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True, drop_last=False) print("Length of dataset is {}".format(len(train_loader))) ''' Start Testing Process ''' accur = [] gt = [] for epoch in range(1): for idx, (input, target, indice) in enumerate(train_loader):
train_augmentation = net.tsn.get_augmentation() input_mean = net.tsn.input_mean input_std = net.tsn.input_std if modality != 'RGBDiff': normalize = GroupNormalize(input_mean, input_std) else: normalize = IdentityTransform() train_loader = torch.utils.data.DataLoader(TSNDataSet( "", train_list, num_segments=num_segments, new_length=data_length, modality=modality, image_tmpl="img_{:05d}.jpg" if modality in ["RGB", "RGBDiff"] else flow_prefix + "{}_{:05d}.jpg", transform=torchvision.transforms.Compose([ train_augmentation, Stack(roll=arch == 'BNInception'), ToTorchFormatTensor(div=arch != 'BNInception'), normalize, ])), batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True, drop_last=True) print("Length of dataset is {}".format(len(train_loader))) ''' Start Training Process ''' for epoch in range(400): for idx, (input, target, indice) in enumerate(train_loader):