修改pytorch自带vgg19的输入通道数

要修改PyTorch自带的VGG19的输入通道数，需要对其进行修改。具体步骤如下：

1. 从PyTorch中导入VGG19模型：

import torch
import torchvision.models as models

vgg19 = models.vgg19(pretrained=True)

2. 查看VGG19的结构，可以看到其输入通道数为3：

print(vgg19)

输出：

VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    ...

3. 修改VGG19的输入通道数为1：

import torch.nn as nn

class VGG19(nn.Module):
    def __init__(self):
        super(VGG19, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            ...
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 1000),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

4. 实例化修改后的VGG19模型：

vgg19 = VGG19()

现在，你就可以使用修改后的VGG19模型来进行训练或预测了。