mobileNet具体细节，在前面已做了分析记录：轻量化网络-MobileNet系列-CSDN博客
这里是根据网络结构，搭建模型，用于图像分类任务。
1. 网络结构和基本组件
2. 搭建组件
（1）普通的卷积组件：CBL = Conv2d + BN + ReLU6;
（2）深度可分离卷积：DwCBL = Conv dw+ Conv dp；
Conv dw+ Conv dp = {Conv2d(3x3) + BN + ReLU6 } + {Conv2d(1x1) + BN + ReLU6};
Conv dw是3x3的深度卷积，通过步长控制是否进行下采样；
Conv dp是1x1的逐点卷积，通过控制输出通道数，控制通道维度的变化；
# 普通卷积
class CBN(nn.Module):def __init__(self, in_c, out_c, stride=1):super(CBN, self).__init__()self.conv = nn.Conv2d(in_c, out_c, 3, stride, padding=1, bias=False)self.bn = nn.BatchNorm2d(out_c)self.relu = nn.ReLU6(inplace=True)def forward(self, x):x = self.conv(x)x = self.bn(x)x = self.relu(x)return x# 深度可分离卷积: 深度卷积(3x3x1) + 逐点卷积（1x1xc卷积)
class DwCBN(nn.Module):def __init__(self, in_c, out_c, stride=1):super(DwCBN, self).__init__()# conv3x3x1, 深度卷积，通过步长，只控制是否缩小特征hwself.conv3x3 = nn.Conv2d(in_c, in_c, 3, stride, padding=1, groups=in_c, bias=False)self.bn1 = nn.BatchNorm2d(in_c)self.relu1 = nn.ReLU6(inplace=True)# conv1x1xc, 逐点卷积，通过控制输出通道数，控制通道维度的变化self.conv1x1 = nn.Conv2d(in_c, out_c, 1, stride=1, padding=0, bias=False)self.bn2 = nn.BatchNorm2d(out_c)self.relu2 = nn.ReLU6(inplace=True)def forward(self, x):x = self.conv3x3(x)x = self.bn1(x)x = self.relu1(x)x = self.conv1x1(x)x = self.bn2(x)x = self.relu2(x)return x3. 搭建网络
class MobileNetV1(nn.Module):def __init__(self, class_num=1000):super(MobileNetV1, self).__init__()self.stage1 = torch.nn.Sequential(CBN(3, 32, 2), # 下采样/2DwCBN(32, 64, 1))self.stage2 = torch.nn.Sequential(DwCBN(64, 128, 2), # 下采样/4DwCBN(128, 128, 1))self.stage3 = torch.nn.Sequential(DwCBN(128, 256, 2), # 下采样/8DwCBN(256, 256, 1))self.stage4 = torch.nn.Sequential(DwCBN(256, 512, 2), # 下采样/16DwCBN(512, 512, 1), # 5个DwCBN(512, 512, 1),DwCBN(512, 512, 1),DwCBN(512, 512, 1),DwCBN(512, 512, 1),)self.stage5 = torch.nn.Sequential(DwCBN(512, 1024, 2), # 下采样/32DwCBN(1024, 1024, 1))# classifierself.avg_pooling = torch.nn.AdaptiveAvgPool2d((1, 1))self.fc = torch.nn.Linear(1024, class_num, bias=True)# self.classifier = torch.nn.Softmax() # 原始的softmax值# torch.log_softmax 首先计算 softmax 然后再取对数，因此在数值上更加稳定。# 在分类网络在训练过程中，通常使用交叉熵损失函数（Cross-Entropy Loss）。# torch.nn.CrossEntropyLoss 会在内部进行 softmax 操作，因此在网络的最后一层不需要手动加上 softmax 操作。def forward(self, x):scale1 = self.stage1(x) # /2scale2 = self.stage2(scale1)scale3 = self.stage3(scale2)scale4 = self.stage4(scale3)scale5 = self.stage5(scale4) # /32. 7x7x = self.avg_pooling(scale5) # (b,1024,7,7)->(b,1024,1,1)x = torch.flatten(x, 1) # (b,1024,1,1)->(b,1024,)x = self.fc(x) # (b,1024,) -> (b,1000,)return xif __name__ == '__main__':m1 = MobileNetV1(class_num=1000)input_data = torch.randn(64, 3, 224, 224)output = m1.forward(input_data)print(output.shape)待续。。。
