squeezenet是16年发布的一款轻量级网络模型，模型很小，只有4.8M，可用于移动设备，嵌入式设备。
关于squeezenet的原理可自行阅读论文或查找博客，这里主要解读下pytorch对squeezenet的官方实现。
地址：https://github.com/pytorch/vision/blob/master/torchvision/models/squeezenet.py
首先定义fire模块，这是squeezenet的核心所在，降低3X3卷积的数量。
class Fire(nn.Module): ???def __init__(self, inplanes, squeeze_planes, ????????????????expand1x1_planes, expand3x3_planes): ???????super(Fire, self).__init__() ???????self.inplanes = inplanes ???????self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)#定义压缩层，1X1卷积 ???????self.squeeze_activation = nn.ReLU(inplace=True) ???????self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,#定义扩展层，1X1卷积 ??????????????????????????????????kernel_size=1) ???????self.expand1x1_activation = nn.ReLU(inplace=True) ???????self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,#定义扩展层，3X3卷积 ??????????????????????????????????kernel_size=3, padding=1) ???????self.expand3x3_activation = nn.ReLU(inplace=True) ???def forward(self, x): ???????x = self.squeeze_activation(self.squeeze(x)) ???????return torch.cat([ ???????????self.expand1x1_activation(self.expand1x1(x)), ???????????self.expand3x3_activation(self.expand3x3(x)) ???????], 1)
可以看到首先定义压缩层与两个扩展层，压缩层用的是1X1卷积，扩展层是1X1卷积和3X3卷积的混合使用，网络inference的脉络是先经过压缩层，然后并行经过两个扩展层，最后将扩展层串联。
定义完核心模块，来看网络整体。
class SqueezeNet(nn.Module): ???def __init__(self, version=1.0, num_classes=1000): ???????super(SqueezeNet, self).__init__() ???????if version not in [1.0, 1.1]: ???????????raise ValueError("Unsupported SqueezeNet version {version}:" ????????????????????????????"1.0 or 1.1 expected".format(version=version)) ???????self.num_classes = num_classes ???????if version == 1.0: ???????????self.features = nn.Sequential( ???????????????nn.Conv2d(3, 96, kernel_size=7, stride=2), ???????????????nn.ReLU(inplace=True), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(96, 16, 64, 64), ???????????????Fire(128, 16, 64, 64), ???????????????Fire(128, 32, 128, 128), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(256, 32, 128, 128), ???????????????Fire(256, 48, 192, 192), ???????????????Fire(384, 48, 192, 192), ???????????????Fire(384, 64, 256, 256), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(512, 64, 256, 256), ???????????) ???????else: ???????????self.features = nn.Sequential( ???????????????nn.Conv2d(3, 64, kernel_size=3, stride=2), ???????????????nn.ReLU(inplace=True), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(64, 16, 64, 64), ???????????????Fire(128, 16, 64, 64), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(128, 32, 128, 128), ???????????????Fire(256, 32, 128, 128), ???????????????nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), ???????????????Fire(256, 48, 192, 192), ???????????????Fire(384, 48, 192, 192), ???????????????Fire(384, 64, 256, 256), ???????????????Fire(512, 64, 256, 256), ???????????) ???????# Final convolution is initialized differently form the rest ???????final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1) ???????self.classifier = nn.Sequential( ???????????nn.Dropout(p=0.5), ???????????final_conv, ???????????nn.ReLU(inplace=True), ???????????nn.AvgPool2d(13, stride=1) ???????) ???????for m in self.modules(): ???????????if isinstance(m, nn.Conv2d): ???????????????if m is final_conv: ???????????????????init.normal_(m.weight, mean=0.0, std=0.01) ???????????????else: ???????????????????init.kaiming_uniform_(m.weight) ???????????????if m.bias is not None: ???????????????????init.constant_(m.bias, 0) ???def forward(self, x): ???????x = self.features(x) ???????x = self.classifier(x) ???????return x.view(x.size(0), self.num_classes)首先依然是定义网络层，在这里有两个版本，差别不大，都是fire模块的堆积，最后经过全局平均池化输出1000类。这里对卷积层采用了不同的初始化策略，我还没仔细研究过，就不说了。
pytorch实现squeezenet
原文地址：https://www.cnblogs.com/wzyuan/p/9710565.html