yifanjiang19
commented
4 years ago The input resolution should be larger than 256 x 256, otherwise, the maxpooling layer will downsample the feature map to the size less than 1x1.
Closed ZaynF closed 4 years ago
yifanjiang19
commented
4 years ago The input resolution should be larger than 256 x 256, otherwise, the maxpooling layer will downsample the feature map to the size less than 1x1.
Hi, I always get this error every time I train,but I didn't change any parameters yet(only patch size & batch size). How can I resolve this?
model [SingleGANModel] was created Setting up a new session... create web directory ./checkpoints\enlightening\web... C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\functional.py:1890: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead. warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\functional.py:1961: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) Traceback (most recent call last): File "train.py", line 31, in
model.optimize_parameters(epoch)
File "D:\Low-Light_Enhancement\EnlightenGAN-master\models\single_model.py", line 398, in optimize_parameters
self.backward_G(epoch)
File "D:\Low-Light_Enhancement\EnlightenGAN-master\models\single_model.py", line 339, in backward_G
self.fake_patch, self.input_patch) self.opt.vgg
File "D:\Low-Light_Enhancement\EnlightenGAN-master\models\networks.py", line 1028, in compute_vgg_loss
img_fea = vgg(img_vgg, self.opt)
File "C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\modules\module.py", line 477, in call
result = self.forward(input, kwargs)
File "C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\parallel\data_parallel.py", line 121, in forward
return self.module(*inputs[0], *kwargs[0])
File "C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\modules\module.py", line 477, in call
result = self.forward(input, kwargs)
File "D:\Low-Light_Enhancement\EnlightenGAN-master\models\networks.py", line 963, in forward
h = F.max_pool2d(h, kernel_size=2, stride=2)
File "C:\Users\DCMC\Anaconda3\envs\EnlightenGAN\lib\site-packages\torch\nn\functional.py", line 396, in max_pool2d
ret = torch._C._nn.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)
RuntimeError: Given input size: (128x1x1). Calculated output size: (128x0x0). Output size is too small at c:\programdata\miniconda3\conda-bld\pytorch_1533086652614\work\aten\src\thcunn\generic/SpatialDilatedMaxPooling.cu:69