I’m new to PyTorch and deep learning.
While reading resnet.py code, there was a line that I couldn’t understand.

  
      github.com
  
  
    pytorch/vision/blob/1fb0ccf71620d113cb72696b2eb8317b3e252cbb/torchvision/models/resnet.py#L111 4

    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7, stride=1)
    self.fc = nn.Linear(512 * block.expansion, num_classes)


    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.constant_(m.weight, 1)
            nn.init.constant_(m.bias, 0)


def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None



  
  
    
    
  
  


Why is fc layer not considering the size of an image?
Shouldn’t it consider not only feature size, but also width and height of output of the avgpool layer?