BatchSize: 1
Loaded data train: <torch.utils.data.dataloader.DataLoader object at 0x0000023A5D1D4B80>
Loaded data test: <torch.utils.data.dataloader.DataLoader object at 0x0000023A5D1D4C70>
Learning rate:  0.0001
total trainable params 65054464
Training From Scratch !!!
Starting Epoch: 0
QQQQQQQQQQ
save npy
torch.Size([1, 1, 80, 16]) torch.Size([1, 15, 192, 384]) QQQQQQQQQQQQQ
Traceback (most recent call last):
  File "1.py", line 465, in <module>
    run()
  File "1.py", line 458, in run
    train(device, model, train_data_loader,test_data_loader, optimizer,
  File "1.py", line 271, in train
    a, v = model(mel, x)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\parallel\data_parallel.py", line 169, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\2023project\project\0win_os\05speech_Pro\diff2lip\thirdparty\wav2lip384_my2\models\syncnet.py", line 79, in forward
    face_embedding = self.face_encoder(face_sequences)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\container.py", line 204, in forward
    input = module(input)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\BatchSize: 1
Loaded data train: <torch.utils.data.dataloader.DataLoader object at 0x0000023A5D1D4B80>
Loaded data test: <torch.utils.data.dataloader.DataLoader object at 0x0000023A5D1D4C70>
Learning rate:  0.0001
total trainable params 65054464
Training From Scratch !!!
Starting Epoch: 0
QQQQQQQQQQ
save npy
torch.Size([1, 1, 80, 16]) torch.Size([1, 15, 192, 384]) QQQQQQQQQQQQQ
Traceback (most recent call last):
  File "1.py", line 465, in <module>
    run()
  File "1.py", line 458, in run
    train(device, model, train_data_loader,test_data_loader, optimizer,
  File "1.py", line 271, in train
    a, v = model(mel, x)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\parallel\data_parallel.py", line 169, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\2023project\project\0win_os\05speech_Pro\diff2lip\thirdparty\wav2lip384_my2\models\syncnet.py", line 79, in forward
    face_embedding = self.face_encoder(face_sequences)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\container.py", line 204, in forward
    input = module(input)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\2023project\project\0win_os\05speech_Pro\diff2lip\thirdparty\wav2lip384_my2\models\conv.py", line 28, in forward
    out = self.conv_block(x)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\container.py", line 204, in forward
    input = module(input)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\batchnorm.py", line 171, in forward
    return F.batch_norm(
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\functional.py", line 2448, in batch_norm
    _verify_batch_size(input.size())
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\functional.py", line 2416, in _verify_batch_size
    raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 1024, 1, 1])  这个错误如何解决torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\2023project\project\0win_os\05speech_Pro\diff2lip\thirdparty\wav2lip384_my2\models\conv.py", line 28, in forward
    out = self.conv_block(x)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\container.py", line 204, in forward
    input = module(input)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\modules\batchnorm.py", line 171, in forward
    return F.batch_norm(
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\functional.py", line 2448, in batch_norm
    _verify_batch_size(input.size())
  File "H:\Anaconda3\envs\EDTalk\lib\site-packages\torch\nn\functional.py", line 2416, in _verify_batch_size
    raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 1024, 1, 1])  这个错误如何解决