Hello, thank you for the code. I trained the model on my on dataset of FONTS which are black and white means the channel is 1, so now where can I set the channel size also can I use same vgg encoder or I have to change the encoder according to my channel size because while training on the images I didn't get the error but while the testing on I got the error I am using the decoder after training from experiments.
(test1) D:\Coding\AdaIN_Code\AdaIN_exp1>python test.py --content_dir input/content4 --style_dir input/style4
Traceback (most recent call last):
File "D:\Coding\AdaIN_Code\AdaIN_exp1\test.py", line 155, in
output = style_transfer(vgg, decoder, content, style,
File "D:\Coding\AdaIN_Code\AdaIN_exp1\test.py", line 28, in style_transfer
content_f = vgg(content)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, *kwargs)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\container.py", line 139, in forward
input = module(input)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(input, **kwargs)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [3, 3, 1, 1], expected input[1, 1, 128, 128] to have 3 channels, but got 1 channels instead
Hello, thank you for the code. I trained the model on my on dataset of FONTS which are black and white means the channel is 1, so now where can I set the channel size also can I use same vgg encoder or I have to change the encoder according to my channel size because while training on the images I didn't get the error but while the testing on I got the error I am using the decoder after training from experiments.
(test1) D:\Coding\AdaIN_Code\AdaIN_exp1>python test.py --content_dir input/content4 --style_dir input/style4 Traceback (most recent call last): File "D:\Coding\AdaIN_Code\AdaIN_exp1\test.py", line 155, in
output = style_transfer(vgg, decoder, content, style,
File "D:\Coding\AdaIN_Code\AdaIN_exp1\test.py", line 28, in style_transfer
content_f = vgg(content)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, *kwargs)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\container.py", line 139, in forward
input = module(input)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(input, **kwargs)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "C:\Users\ak874\anaconda3\envs\test1\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [3, 3, 1, 1], expected input[1, 1, 128, 128] to have 3 channels, but got 1 channels instead