mobilenetv1的pytorch代码

### MobileNetV1 的 PyTorch 实现 以下是 MobileNetV1 在 PyTorch 中的一个简单实现： ```python import torch.nn as nn import torch class DepthwiseSeparableConv(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super(DepthwiseSeparableConv, self).__init__() # Depthwise convolution self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, groups=in_channels, stride=stride, bias=False) self.bn1 = nn.BatchNorm2d(in_channels) # Pointwise convolution self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.depthwise(x) x = self.bn1(x) x = self.relu(x) x = self.pointwise(x) x = self.bn2(x) x = self.relu(x) return x class MobileNetV1(nn.Module): def __init__(num_classes=1000): super(MobileNetV1, self).__init__() layers = [ nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(32), nn.ReLU(inplace=True), DepthwiseSeparableConv(32, 64, stride=1), DepthwiseSeparableConv(64, 128, stride=2), DepthwiseSeparableConv(128, 128, stride=1), DepthwiseSeparableConv(128, 256, stride=2), DepthwiseSeparableConv(256, 256, stride=1), DepthwiseSeparableConv(256, 512, stride=2), DepthwiseSeparableConv(512, 512, stride=1), DepthwiseSeparableConv(512, 512, stride=1), DepthwiseSeparableConv(512, 512, stride=1), DepthwiseSeparableConv(512, 512, stride=1), DepthwiseSeparableConv(512, 512, stride=1), DepthwiseSeparableConv(512, 1024, stride=2), DepthwiseSeparableConv(1024, 1024, stride=1), nn.AvgPool2d(kernel_size=7) ] classifier = [ nn.Linear(1024, num_classes) ] self.features = nn.Sequential(*layers) self.classifier = nn.Sequential(*classifier) def forward(self, x): x = self.features(x) x = x.view(-1, 1024) x = self.classifier(x) return x ``` 此代码定义了一个标准的 MobileNet V1 架构，其中包括深度可分离卷积层[^1]。