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RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 484 and 549 in dimension 2 at /pytorch/aten/src/TH/generic/THTensorMath.cpp:3616

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images, labels = data_iter.next()

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数据集中的图像大小不一致.
需要使用`transforms.Resize([height, width])`把所有图像缩放到同一大小.

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RuntimeError: invalid argument 2: size '[-1 x 400]' is invalid for input with 719104 elements at /pytorch/aten/src/TH/THStorage.cpp:80

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x = x.view(-1, 16 * 5 * 5)

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`Tensor.view()` 相当于 `numpy.reshape()` 方法, 即重塑形状.
其中`-1`表示依据其他维度进行推理得出的维度.
这里的参数需要计算得出, 不同的输入尺寸需要计算对应的参数!
---
我们来计算一下, 计算公式见下面的图片.
---
input size = 3*32*32
-
class Net_t1(nn.Module):
    def __init__(self):
        super(Net_t1, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
-
1 torch.Size([4, 6, 28, 28]), conv1, (32 - 5) / 1 + 1 = 28, padding=0
2 torch.Size([4, 6, 14, 14]), pool, 28 / 2 = 14
3 torch.Size([4, 16, 10, 10]), conv2, (14 - 5) / 1 + 1 = 10, padding=0
4 torch.Size([4, 16, 5, 5]), pool, 10 / 2 = 5
5 torch.Size([4, 400]), view, 16 * 5 * 5 = 400
6 torch.Size([4, 120]), full_connect
7 torch.Size([4, 84]), full_connect
8 torch.Size([4, 10]), full_connect
---
input size = 3*224*224
-
class Net_t2(nn.Module):
    def __init__(self):
        super(Net_t2, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 53 * 53, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 31)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.view(-1, 16 * 53 * 53)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
-
1 torch.Size([4, 6, 220, 220]), conv1, (224 - 5) / 1 + 1 = 220, padding=0
2 torch.Size([4, 6, 110, 110]), pool, 220 / 2 = 110
3 torch.Size([4, 16, 106, 106]), conv2, (110 - 5) / 1 + 1 = 106, padding=0
4 torch.Size([4, 16, 53, 53]), pool, 106 / 2 = 53
5 torch.Size([4, 44944]), view, 16 * 53 * 53 = 44944
6 torch.Size([4, 120]), full_connect
7 torch.Size([4, 84]), full_connect
8 torch.Size([4, 31]), full_connect