[Python] RuntimeError: Given groups=1, weight of size [64, 32, 3, 3], expected input[128, 64,...

I am trying to experiment with why we have a Vanishing & exploding gradient, and why Resnet is so helpful in avoiding the two problems above. So I decided to train a plain Convolution network with many layers just to know why the model LOSS increases as I train with many layers e.g 20 layers. but I am getting this error at some point, I can figure out what might be the issue, but I know it is from my model Architecture.

images.shape: torch.Size([128, 3, 32, 32])
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-80-0ad7109b33c1> in <module>
1 for images, labels in train_dl:
2 print('images.shape:', images.shape)
----> 3 out = model(images)
4 print('out.shape:', out.shape)
5 print('out[0]:', out[0])

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),

<ipython-input-78-81b21c16ed79> in forward(self, xb)
31
32 def forward(self, xb):
---> 33 return self.network(xb)

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
418 _pair(0), self.dilation, self.groups)
419 return F.conv2d(input, weight, self.bias, self.stride,
--> 420 self.padding, self.dilation, self.groups)
421
422 def forward(self, input: Tensor) -> Tensor:

RuntimeError: Given groups=1, weight of size [64, 32, 3, 3], expected input[128, 64, 32, 32] to have 32 channels, but got 64 channels instead


My model Architecture is

class Cifar10CnnModel(ImageClassificationBase):
def __init__(self):
super().__init__()
self.network = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 64 x 16 x 16

nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 128 x 8 x 8

nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2), # output: 256 x 4 x 4

nn.Flatten(),
nn.Linear(256*4*4, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, 10))

def forward(self, xb):
return self.network(xb)

for images, labels in train_dl:
print('images.shape:', images.shape)
out = model(images)
print('out.shape:', out.shape)
print('out[0]:', out[0])
break

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