Pytorch CNN mat1 和 mat2 形状不能相乘（（128x29040 和 2048x50）

Question

在 Pytorch 上构建 CNN 我遇到了关于矩阵维度的错误，并且无法解决它，因为我对 Pytorch 和 CNN 有点陌生，我知道这是我的层中特征值的问题，但不完全是这些维度的问题，这是模型和错误跟踪，我的批量大小是128，我的数据是转换为张量的图像，图像是100*100像素：

     def __init__(self,classes):
       super(roadModel, self).__init__()
       self.conv1 = Conv2d(3,30,kernel_size=(5,5))
       self.relu1 = ReLU()
       self.maxpool1 = MaxPool2d(kernel_size=(2,2), stride=(2,2))
       self.conv2 = Conv2d(30,60,kernel_size=(5,5))
       self.relu2 = ReLU()
       self.maxpool2 = MaxPool2d(kernel_size=(2,2), stride=(2,2)) 
       self.fc1 = Linear(in_features=128*4*4, out_features=50)
       self.relu3 = ReLU()
       self.fc2 = Linear(in_features=50, out_features=43)
       
    
    def forward(self, x):

        x = self.conv1(x)
        x = self.relu1(x)
        x = self.maxpool1(x)

        x = self.conv2(x)
        x = self.relu2(x)
        x = self.maxpool2(x)

        x = flatten(x, 1)
        x = self.fc1(x)
        x = self.relu3(x)

        out = self.fc2(x)
        

        return out
    
    RuntimeError                              Traceback (most recent call last)
<ipython-input-15-978518dfa1ac> in <module>
     12     for (inputs, labels) in trainDataLoader:
     13         (inputs, labels) = (inputs.to(device), labels.to(device))
---> 14         prediction = model(inputs)
     15         loss = lossFunction(prediction, labels)
     16         opt.zero_grad()

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

<ipython-input-13-58a66aa14326> in forward(self, x)
     24 
     25         x = flatten(x, 1)
---> 26         x = self.fc1(x)
     27         x = self.relu3(x)
     28 

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
    101 
    102     def forward(self, input: Tensor) -> Tensor:
--> 103         return F.linear(input, self.weight, self.bias)
    104 
    105     def extra_repr(self) -> str:

~/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
   1846     if has_torch_function_variadic(input, weight, bias):
   1847         return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
-> 1848     return torch._C._nn.linear(input, weight, bias)
   1849 
   1850 

RuntimeError: mat1 and mat2 shapes cannot be multiplied (128x29040 and 2048x50)