Python Resnet50中的张量大小不匹配，PyTorch与CIFAR-10

我的代码出现以下错误，我不确定我做错了什么。我试着改变了一些事情，但似乎找不到错误所在

转发函数的定义是否存在问题？我假设这部分是好的，但我不知道还有什么可能导致这个问题。希望这不是一个愚蠢的错误

谢谢

line 52, in forward
    return out + x
RuntimeError: The size of tensor a (5) must match the size of tensor b (4) at non-singleton dimension 3

import torch.nn as nn

# 1x1 convolution
def conv1x1(in_channels, out_channels, stride, padding):
   model = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=padding),
       nn.BatchNorm2d(out_channels),
       nn.ReLU(inplace=True))
   return model


# 3x3 convolution
def conv3x3(in_channels, out_channels, stride, padding):
   model = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=padding),
       nn.BatchNorm2d(out_channels),
       nn.ReLU(inplace=True))
   return model

###########################################################################
# Question 1 : Implement the "bottle neck building block" part.
# Hint : Think about difference between downsample True and False.  How we make
# the difference by code?
class ResidualBlock(nn.Module):
   def __init__(self, in_channels, middle_channels, out_channels, downsample=False):
       super(ResidualBlock, self).__init__()
       self.downsample = downsample

       if self.downsample:
           self.layer = nn.Sequential(##########################################
               ############## fill in here (20 points)
               # Hint : use these functions (conv1x1, conv3x3)
               conv1x1(in_channels, middle_channels, 1, 0),
               conv3x3(middle_channels, middle_channels, 1, 0),
               conv1x1(middle_channels, out_channels, 1, 0)
               #########################################
           )
           self.downsize = conv1x1(in_channels, out_channels, 2, 0)

       else:
           self.layer = nn.Sequential(##########################################
               ############# fill in here (20 points)
               conv1x1(in_channels, middle_channels, 2, 0),
               conv3x3(middle_channels, middle_channels, 2, 0),
               conv1x1(middle_channels, out_channels, 2, 0)
               #########################################
           )
           self.make_equal_channel = conv1x1(in_channels, out_channels, 1, 0)

   def forward(self, x):
       if self.downsample:
           out = self.layer(x)
           x = self.downsize(x)
           return out + x
       else:
           out = self.layer(x)
           if x.size() is not out.size():
               x = self.make_equal_channel(x)
           return out + x
###########################################################################



###########################################################################
# Question 2 : Implement the "class, ResNet50_layer4" part.
# Understand ResNet architecture and fill in the blanks below.  (25 points)
# (blank : #blank#, 1 points per blank )
# Implement the code.
class ResNet50_layer4(nn.Module):
   def __init__(self, num_classes=10): # Hint : How many classes in Cifar-10 dataset?
       super(ResNet50_layer4, self).__init__()
       self.layer1 = nn.Sequential(nn.Conv2d(stride=2, kernel_size=7, in_channels=3, padding=3, out_channels=64),
               # Hint : Through this conv-layer, the input image size is
               # halved.
               #        Consider stride, kernel size, padding and input &
               #        output channel sizes.
           nn.BatchNorm2d(64),
           nn.ReLU(inplace=True),
           nn.MaxPool2d(stride=2, kernel_size=3, padding=0))
       self.layer2 = nn.Sequential(ResidualBlock(in_channels=64, middle_channels=64, out_channels=256, downsample=False),
           ResidualBlock(in_channels=256, middle_channels=64, out_channels=256, downsample=False),
           ResidualBlock(in_channels=256, middle_channels=64,out_channels=256, downsample=True))
       self.layer3 = nn.Sequential(##########################################
           ############# fill in here (20 points)
           ####### you can refer to the 'layer2' above
           ResidualBlock(in_channels=256, middle_channels=128, out_channels=512, downsample=False),
           ResidualBlock(in_channels=512, middle_channels=128, out_channels=512, downsample=False),
           ResidualBlock(in_channels=512, middle_channels=128, out_channels=512, downsample=False),
           ResidualBlock(in_channels=512, middle_channels=128, out_channels=512, downsample=True)
           #########################################
       )
       self.layer4 = nn.Sequential(##########################################
           ############# fill in here (20 points)
           ####### you can refer to the 'layer2' above
           ResidualBlock(in_channels=512, middle_channels=256, out_channels=1024, downsample=False),
           ResidualBlock(in_channels=1024, middle_channels=256, out_channels=1024, downsample=False),
           ResidualBlock(in_channels=1024, middle_channels=256, out_channels=1024, downsample=False),
           ResidualBlock(in_channels=1024, middle_channels=256, out_channels=1024, downsample=False),
           ResidualBlock(in_channels=1024, middle_channels=256, out_channels=1024, downsample=False),
           ResidualBlock(in_channels=1024, middle_channels=256, out_channels=1024, downsample=False)
           #########################################
       )

       self.fc = nn.Linear(1024, 10) # Hint : Think about the reason why fc layer is needed
       self.avgpool = nn.AvgPool2d(7, stride=1)

       for m in self.modules():
           if isinstance(m, nn.Linear):
               nn.init.xavier_uniform_(m.weight.data)
           elif isinstance(m, nn.Conv2d):
               nn.init.xavier_uniform_(m.weight.data)

   def forward(self, x):

       out = self.layer1(x)
       out = self.layer2(out)
       out = self.layer3(out)
       out = self.layer4(out)
       out = self.avgpool(out)
       out = out.view(out.size()[0], -1)
       out = self.fc(out)

       return out
###########################################################################