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D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\rnn.py:51: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers
greater than 1, but got dropout=0.5 and num_layers=1
"num_layers={}".format(dropout, num_layers))
D:\Anaconda\envs\vp12\lib\site-packages\torch\nn_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
warnings.warn(warning.format(ret))
D:\Anaconda\envs\vp12\lib\site-packages\torch\optim\lr_scheduler.py:82: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you s
hould call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedul
e.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
Traceback (most recent call last):
File "train.py", line 133, in
main(opt)
File "train.py", line 120, in main
train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)
File "train.py", line 40, in train
seq_probs, _ = model(fc_feats, labels, 'train')
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\video-caption\code12pytorch\video-caption.pytorch-master\models\S2VTAttModel.py", line 28, in forward
encoder_outputs, encoder_hidden = self.encoder(vid_feats)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\video-caption\code12pytorch\video-caption.pytorch-master\models\EncoderRNN.py", line 53, in forward
vid_feats = self.vid2hid(vid_feats.view(-1, dim_vid))
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\functional.py", line 1369, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [12000 x 2048], m2: [4096 x 512] at C:/w/1/s/tmp_conda_3.7_055457/conda/conda-bld/pytorch_1565416617654/work/aten/src\THC/generic/THCTensorMathBlas.cu:
273
How to resolve size mismatches?I can't find a place to set parameters in a convolutional layer
The text was updated successfully, but these errors were encountered:
D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\rnn.py:51: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers
greater than 1, but got dropout=0.5 and num_layers=1
"num_layers={}".format(dropout, num_layers))
D:\Anaconda\envs\vp12\lib\site-packages\torch\nn_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
warnings.warn(warning.format(ret))
D:\Anaconda\envs\vp12\lib\site-packages\torch\optim\lr_scheduler.py:82: UserWarning: Detected call of
lr_scheduler.step()
beforeoptimizer.step()
. In PyTorch 1.1.0 and later, you should call them in the opposite order:
optimizer.step()
beforelr_scheduler.step()
. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
Traceback (most recent call last):
File "train.py", line 133, in
main(opt)
File "train.py", line 120, in main
train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)
File "train.py", line 40, in train
seq_probs, _ = model(fc_feats, labels, 'train')
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\video-caption\code12pytorch\video-caption.pytorch-master\models\S2VTAttModel.py", line 28, in forward
encoder_outputs, encoder_hidden = self.encoder(vid_feats)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\video-caption\code12pytorch\video-caption.pytorch-master\models\EncoderRNN.py", line 53, in forward
vid_feats = self.vid2hid(vid_feats.view(-1, dim_vid))
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\module.py", line 547, in call
result = self.forward(*input, **kwargs)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\modules\linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "D:\Anaconda\envs\vp12\lib\site-packages\torch\nn\functional.py", line 1369, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [12000 x 2048], m2: [4096 x 512] at C:/w/1/s/tmp_conda_3.7_055457/conda/conda-bld/pytorch_1565416617654/work/aten/src\THC/generic/THCTensorMathBlas.cu:
273
How to resolve size mismatches?I can't find a place to set parameters in a convolutional layer
The text was updated successfully, but these errors were encountered: