def forward(self, x):
out = self.conv(x)
out = self.bn(out)
out = self.relu(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out) # <-------------------error
return out
Traceback (most recent call last):
File "F:\soft_xj\python\lib\site-packages\torchsummary\torchsummary.py", line 140, in summary
_ = model.to(device)(*x, *args, **kwargs) # type: ignore[misc]
File "F:\soft_xj\python\lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\xj\Desktop\AI_teach2\ResNet50\model.py", line 69, in forward
out = self.fc(out)
File "F:\soft_xj\python\lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "F:\soft_xj\python\lib\site-packages\torch\nn\modules\linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "F:\soft_xj\python\lib\site-packages\torch\nn\functional.py", line 1610, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [2 x 256], m2: [64 x 7] at C:/w/b/windows/pytorch/aten/src\THC/generic/THCTensorMathBlas.cu:283
summary resnet
I can print the model