用pytorch写一个resnet50网络结构，实现对维度为[1,28,28]的图片的特征提取

当然可以！以下是一个使用PyTorch编写的ResNet-50网络结构的示例代码，用于对维度为[1, 28, 28]的图片进行特征提取：

import torch
import torch.nn as nn
import torch.nn.functional as F

# 定义ResNet-50网络结构
class ResNet50(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, 3)
        self.layer2 = self._make_layer(64, 128, 4, stride=2)
        self.layer3 = self._make_layer(128, 256, 6, stride=2)
        self.layer4 = self._make_layer(256, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):
        layers = []
        layers.append(Bottleneck(in_channels, out_channels, stride))
        in_channels = out_channels * Bottleneck.expansion
        for _ in range(1, num_blocks):
            layers.append(Bottleneck(in_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

# 定义Bottleneck块
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = None
        if stride != 1 or in_channels != out_channels * self.expansion:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * self.expansion),
            )

    def forward(self, x):
        identity = x

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)

        if self.downsample is not None:
            identity = self.downsample(identity)

        x += identity
        x = self.relu(x)

        return x

# 创建ResNet-50模型实例
model = ResNet50()

# 输入维度为[1, 28, 28]的图片
input_tensor = torch.randn(1, 1, 28, 28)

# 特征提取
features = model(input_tensor)
print(features.shape)

这段代码定义了一个ResNet-50网络结构，其中包含了Bottleneck块。你可以通过创建该模型的实例，并将维度为[1, 28, 28]的图片输入模型，即可进行特征提取。输出的`features`是一个tensor，它的形状取决于最后一层全连接层的输出维度。在这个示例中，我们没有使用预训练的权重，而是随机初始化了网络参数。你可以根据自己的需求修改网络结构和参数初始化方式。