Hi,
I’m trying to change the used size of channels at layer4 of resnet 18, I wrote something like the follwoing piece of code. but when I run it complains about the size mismatch at the fc layer, I don’t know exactly how should I realize which size should I use.

class Inception(nn.Module):
def __init__(self, in_channels=2048):
    super(Inception, self).__init__()

    self.paral_0 = nn.Sequential(
        nn.Conv2d(256, 64, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(64),
        nn.ReLU(inplace=True),			
        nn.Conv2d(64, 64, kernel_size=3, stride=1,  padding=1, bias=False),
        nn.BatchNorm2d(64),
  	
        nn.Conv2d(64, 64, kernel_size=3, stride=1,  padding=1, bias=False),
        nn.BatchNorm2d(64),
        nn.ReLU(inplace=True),			
        nn.Conv2d(64, 64, kernel_size=3, stride=1,  padding=1,  bias=False),
        nn.BatchNorm2d(64),
  	
        nn.AdaptiveAvgPool2d(output_size=(1, 1))
    )
    self.fc0    = nn.Linear(in_features=256, out_features=83, bias=True)
    ....



def forward(self, x):
    y0 = self.paral_0(x)
    y0 = y0.view(y0.size(0), -1)
    y0 = self.fc0(y0)
....


and this is the error:

Traceback (most recent call last):
File " generated_train/0.py", line 471, in 
summary(model, (3, 224, 224))
File " lib/python3.6/site-packages/torchsummary/torchsummary.py", line 72, in summary
model(*x)
File " lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File " generated_train/model/net.py", line 533, in forward
x = self.layer4(x)
File " lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File " generated_train/model/net.py", line 303, in forward
y0 = self.fc0(y0)
File " lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File " lib/python3.6/site-packages/torch/nn/modules/linear.py", line 67, in forward
return F.linear(input, self.weight, self.bias)
File " lib/python3.6/site-packages/torch/nn/functional.py", line 1352, in linear
ret = torch.addmm(torch.jit._unwrap_optional(bias), input, weight.t())
RuntimeError: size mismatch, m1: [2 x 64], m2: [256 x 83] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:266