Issue
Here is my class for cnn.
class SimpleCnn(nn.Module):
def __init__(self, n_classes):
super().__init__()
self.layer1 = nn.Sequential( # 224*224
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer2 = nn.Sequential( # 112*112
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer3 = nn.Sequential( # 56*56
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(256),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer3 = nn.Sequential( # 28*28
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(512),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer4 = nn.Sequential( # 14*14
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(512),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.out1 = nn.Linear(512*7*7, 4096) # 7*7
self.out2 = nn.Linear(4096, n_classes)
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = x.view(1, -1)
x = self.out1(x)
logits = self.out2(x)
return logits
And it returns such a mistake.
RuntimeError: Given groups=1, weight of size [512, 256, 3, 3], expected input[64, 128, 56, 56] to have 256 channels, but got 128 channels instead.
I've seen other mistakes of such a type but can't find where I'm wrong here. Thank you for your answer.
Solution
In your code self.layer3
is first defined but then overwritten (a copy-pasta error I assume?). The error is thrown because in the redefinition of layer3
you assume the input has 256
channels, but the output from self.layer2
only has 128
channels.
Answered By - flawr
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