在我构建的模型中，我试图通过用全局最大池替换Flatten层来提高性能。
为了检查形状是否有序，我在网上随机抽取了一个样本：

test = torch.rand((1, 3, 224, 224))     # [N, C, H, W]

foo = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.MaxPool2d(2)
        )

foo2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(2)
        )

foo3 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.MaxPool2d(2)
        )

l1 = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(128,  1024),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(1024, 10)
        )

r1 = foo(test)
print(r1.shape)    # torch.Size([1, 32, 112, 112])
r2 = foo2(r1)
print(r2.shape)    # torch.Size([1, 64, 56, 56])
r3 = foo3(r2)
print(r3.shape)    # torch.Size([1, 128, 28, 28])

# applying global max pooling and reshaping the layer to [N, C]

flat = F.adaptive_max_pool2d(r3, (1, 1))
ff = flat.reshape(flat.size(0), -1)

print(ff.shape)    # torch.Size([1, 128])
res = l1(ff)
print(res.shape)   # torch.Size([1, 10])

这里的一切似乎都如预期的那样工作。
我的模型类也有这些相同的层，其中包含forward方法，如下所示：

def forward(self, batch: torch.Tensor) -> torch.Tensor:
        r1 = self.conv1(batch)
        r2 = self.conv2(r1)
        r3 = self.conv3(r2)

        tmp = F.adaptive_max_pool2d(r3, (1, 1))
        flat = r3.view(tmp.size(0), -1)

        out = self.linear(flat)

        return out

不幸的是，当我尝试通过（Fashion MNIST数据集）运行实际图像时，我得到错误：mat1和mat2形状不能相乘（128x2048和128x1024）
我的批量大小是128，但我不知道2048可能来自哪里。我的图层都不应该输出任何该形状的内容。
完整的错误消息如下：

RuntimeError                              Traceback (most recent call last)
/root/fashion_mnist.ipynb Cell 7 in <cell line: 1>()
----> 1 runner.train_model(epochs=80, batch_size=128, criterion=loss_fn, optimizer=optim)

/root/fashion_mnist.ipynb Cell 7 in RunModel.train_model(self, epochs, batch_size, criterion, optimizer, device)
    113 t_ep = datetime.now()
    115 # run train routine
--> 116 train_loss, train_acc = self._run_train(train_loader, criterion, optimizer)   
    117 self.train_losses[ep] = train_loss
    118 self.train_accuracies[ep] = train_acc

/root/fashion_mnist.ipynb Cell 7 in RunModel._run_train(self, train_data, criterion, optimizer)
    141 inputs, targets = inputs.cuda(), targets.cuda()
    142 optimizer.zero_grad()
--> 144 outputs: torch.Tensor = self.model(inputs)
    145 loss: torch.Tensor = criterion(outputs, targets)          
    147 loss.backward()

File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1186, in Module._call_impl(self, *input,**kwargs)
   1182 # If we don't have any hooks, we want to skip the rest of the logic in
   1183 # this function, and just call forward.
   1184 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1185         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1186     return forward_call(*input,**kwargs)
...
File /opt/conda/lib/python3.8/site-packages/torch/nn/modules/linear.py:114, in Linear.forward(self, input)
    113 def forward(self, input: Tensor) -> Tensor:
--> 114     return F.linear(input, self.weight, self.bias)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (128x2048 and 128x1024)

你知道这里发生了什么吗
该笔记本可从以下网址获得：https://colab.research.google.com/drive/1QGpSpUCbuDz-dktmLCv_YpG6LZjYZ1TM?usp=sharing