如何修复：运行时错误：pyTorch 中的大小不匹配

我是 pyTorch 的新手，并收到以下“大小不匹配”错误：

RuntimeError: size mismatch, m1: [7 x 2092500], m2: [180 x 120] at ..\aten\src\TH/generic/THTensorMath.cpp:961

Model:

class Net(nn.Module):
def __init__(self):
    super(Net, self).__init__()
    self.conv1 = nn.Conv2d(3, 200, 5)
    self.pool = nn.MaxPool2d(2, 2)
    self.conv2 = nn.Conv2d(200, 180, 5)
    self.fc1 = nn.Linear(180, 120)
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84,5)     

def forward(self, x):
    x = self.pool(F.relu(self.conv1(x)))
    x = self.pool(F.relu(self.conv2(x)))
    x = x.view(x.shape[0], -1)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x

我曾如何尝试改变x = x.view(x.shape[0], -1) to x = x.view(x.size(0), -1)但这也不起作用。图像尺寸为 512x384。并使用了以下转换：

def load_dataset():
data_path = './dataset/training'

transform = transforms.Compose(
               [transforms.Resize((512,384)),
                transforms.ToTensor(),
                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


train_dataset = torchvision.datasets.ImageFolder(root=data_path,transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=7,num_workers=0,shuffle=True)

return train_loader

问题是最后一个最大池化层的输出尺寸与第一个全连接层的输入不匹配。这是直到输入形状的最后一个最大池层为止的网络结构(3, 512, 384):

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1        [-1, 200, 508, 380]          15,200
         MaxPool2d-2        [-1, 200, 254, 190]               0
            Conv2d-3        [-1, 180, 250, 186]         900,180
         MaxPool2d-4         [-1, 180, 125, 93]               0
================================================================

表的最后一行意味着MaxPool2d-4输出 180 个通道（滤波器输出），宽度为 125，高度为 93。所以你需要你的第一个全连接层180 * 125 * 93 = 2092500输入大小。这是很多，所以我建议你改进你的架构。无论如何，如果将第一个全连接层的输入大小更改为2092500， 有用：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 200, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(200, 180, 5)
        #self.fc1 = nn.Linear(180, 120)
        self.fc1 = nn.Linear(2092500, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84,5)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

给出以下架构：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1        [-1, 200, 508, 380]          15,200
         MaxPool2d-2        [-1, 200, 254, 190]               0
            Conv2d-3        [-1, 180, 250, 186]         900,180
         MaxPool2d-4         [-1, 180, 125, 93]               0
            Linear-5                  [-1, 120]     251,100,120
            Linear-6                   [-1, 84]          10,164
            Linear-7                    [-1, 5]             425
================================================================
Total params: 252,026,089
Trainable params: 252,026,089
Non-trainable params: 0

（您可以使用火炬摘要 https://github.com/sksq96/pytorch-summary包来生成这些表。）