lijin118 / 3catn Goto Github PK

Hi,

I run your code and get the following error.

Traceback (most recent call last):
  File "train_image.py", line 465, in <module>
    train(config)
  File "train_image.py", line 272, in train
    loss_G_s2t = criterion_GAN(pred_fake, labels_source.float())
  File "/home/red/pytorch4_python3/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/red/pytorch4_python3/lib/python3.5/site-packages/torch/nn/modules/loss.py", line 421, in forward
    return F.mse_loss(input, target, reduction=self.reduction)
  File "/home/red/pytorch4_python3/lib/python3.5/site-packages/torch/nn/functional.py", line 1716, in mse_loss
    return _pointwise_loss(lambda a, b: (a - b) ** 2, torch._C._nn.mse_loss, input, target, reduction)
  File "/home/red/pytorch4_python3/lib/python3.5/site-packages/torch/nn/functional.py", line 1674, in _pointwise_loss
    return lambd_optimized(input, target, reduction)
RuntimeError: input and target shapes do not match: input [36 x 1], target [36] at /pytorch/aten/src/THCUNN/generic/MSECriterion.cu:12

Can you please help to sort out this problem?

Hi,
I run the code and I got the following accuracy. Can you please sort out the problem?

{'output_path': 'snapshot/webcam2amazon', 'cla_plus_weight': 0.1, 'output_for_test': True, 'num_iterations': 20000, 'optimizer': {'optim_params': {'nesterov': True, 'momentum': 0.9, 'lr': 0.001, 'weight_decay': 0.0005}, 'lr_param': {'gamma': 0.001, 'lr': 0.001, 'power': 0.75}, 'type': <class 'torch.optim.sgd.SGD'>, 'lr_type': 'inv'}, 'torch_seed': 13945466295723118204, 'out_file': <_io.TextIOWrapper name='snapshot/webcam2amazon/log_webcam_to_amazon_2019-10-03 04:39:12.278936.txt' mode='w' encoding='UTF-8'>, 'method': 'CDAN+E', 'network': {'name': <class 'network.ResNetFc'>, 'params': {'use_bottleneck': True, 'new_cls': True, 'resnet_name': 'ResNet50', 'class_num': 31, 'bottleneck_dim': 256}}, 'prep': {'test_10crop': True, 'params': {'resize_size': 256, 'crop_size': 224, 'alexnet': False}}, 'gpu': '0', 'data': {'test': {'list_path': 'data/office/amazon_list.txt', 'batch_size': 4}, 'target': {'list_path': 'data/office/amazon_list.txt', 'batch_size': 16}, 'source': {'list_path': 'data/office/webcam_list.txt', 'batch_size': 16}}, 'loss': {'random': False, 'trade_off': 1.0, 'random_dim': 1024}, 'dataset': 'office', 'test_interval': 300, 'snapshot_interval': 5000, 'torch_cuda_seed': 42, 'cyc_loss_weight': 0.05, 'weight_in_lossG': '1,0.01,0.1,0.1'}iter: 00299, precision: 0.02662 iter: 00599, precision: 0.02875 iter: 00899, precision: 0.02946 iter: 01199, precision: 0.02911 iter: 01499, precision: 0.02946 iter: 01799, precision: 0.02911 iter: 02099, precision: 0.02911 iter: 02399, precision: 0.02946 iter: 02699, precision: 0.02982 iter: 02999, precision: 0.02946 iter: 03299, precision: 0.02911 iter: 03599, precision: 0.02911 iter: 03899, precision: 0.02946 iter: 04199, precision: 0.02911 iter: 04499, precision: 0.02946 iter: 04799, precision: 0.02911 iter: 05099, precision: 0.02911 iter: 05399, precision: 0.03017 iter: 05699, precision: 0.02840 iter: 05999, precision: 0.03017 iter: 06299, precision: 0.02840 iter: 06599, precision: 0.03017 iter: 06899, precision: 0.03017 iter: 07199, precision: 0.02946 iter: 07499, precision: 0.02982 iter: 07799, precision: 0.02840 iter: 08099, precision: 0.02911 iter: 08399, precision: 0.02911 iter: 08699, precision: 0.02875 iter: 08999, precision: 0.02946 iter: 09299, precision: 0.02911 iter: 09599, precision: 0.02911 iter: 09899, precision: 0.02911 iter: 10199, precision: 0.02911 iter: 10499, precision: 0.02875 iter: 10799, precision: 0.02804 iter: 11099, precision: 0.02875 iter: 11399, precision: 0.02840 iter: 11699, precision: 0.02875 iter: 11999, precision: 0.02911 iter: 12299, precision: 0.02769 iter: 12599, precision: 0.02875 iter: 12899, precision: 0.02840 iter: 13199, precision: 0.02875 iter: 13499, precision: 0.02840 iter: 13799, precision: 0.02840 iter: 14099, precision: 0.02946 iter: 14399, precision: 0.02840 iter: 14699, precision: 0.02946 iter: 14999, precision: 0.02591 iter: 15299, precision: 0.02840 iter: 15599, precision: 0.02911 iter: 15899, precision: 0.02733 iter: 16199, precision: 0.02840 iter: 16499, precision: 0.02840 iter: 16799, precision: 0.02911 iter: 17099, precision: 0.02911 iter: 17399, precision: 0.03230 iter: 17699, precision: 0.02911 iter: 17999, precision: 0.03230 iter: 18299, precision: 0.02911 iter: 18599, precision: 0.03230 iter: 18899, precision: 0.03372 iter: 19199, precision: 0.02804 iter: 19499, precision: 0.02982 iter: 19799, precision: 0.02840