自学教程：Python深度学习之使用Pytorch搭建ShuffleNetv2

def channel_shuffle(x: Tensor, groups: int) -> Tensor:    batch_size, num_channels, height, width = x.size()    channels_per_group = num_channels // groups    # reshape    # [batch_size, num_channels, height, width] -> [batch_size, groups, channels_per_group, height, width]    x = x.view(batch_size, groups, channels_per_group, height, width)    x = torch.transpose(x, 1, 2).contiguous()    # flatten    x = x.view(batch_size, -1, height, width)    return x

class InvertedResidual(nn.Module):    def __init__(self, input_c: int, output_c: int, stride: int):        super(InvertedResidual, self).__init__()        if stride not in [1, 2]:            raise ValueError("illegal stride value.")        self.stride = stride        assert output_c % 2 == 0        branch_features = output_c // 2        # 当stride为1时，input_channel应该是branch_features的两倍        # python中 '<<' 是位运算，可理解为计算×2的快速方法        assert (self.stride != 1) or (input_c == branch_features << 1)        if self.stride == 2:            self.branch1 = nn.Sequential(                self.depthwise_conv(input_c, input_c, kernel_s=3, stride=self.stride, padding=1),                nn.BatchNorm2d(input_c),                nn.Conv2d(input_c, branch_features, kernel_size=1, stride=1, padding=0, bias=False),                nn.BatchNorm2d(branch_features),                nn.ReLU(inplace=True)            )        else:            self.branch1 = nn.Sequential()        self.branch2 = nn.Sequential(            nn.Conv2d(input_c if self.stride > 1 else branch_features, branch_features, kernel_size=1,                      stride=1, padding=0, bias=False),            nn.BatchNorm2d(branch_features),            nn.ReLU(inplace=True),            self.depthwise_conv(branch_features, branch_features, kernel_s=3, stride=self.stride, padding=1),            nn.BatchNorm2d(branch_features),            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),            nn.BatchNorm2d(branch_features),            nn.ReLU(inplace=True)        )    @staticmethod    def depthwise_conv(input_c: int,                       output_c: int,                       kernel_s: int,                       stride: int = 1,                       padding: int = 0,                       bias: bool = False) -> nn.Conv2d:        return nn.Conv2d(in_channels=input_c, out_channels=output_c, kernel_size=kernel_s,                         stride=stride, padding=padding, bias=bias, groups=input_c)    def forward(self, x: Tensor) -> Tensor:        if self.stride == 1:            x1, x2 = x.chunk(2, dim=1)            out = torch.cat((x1, self.branch2(x2)), dim=1)        else:            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)        out = channel_shuffle(out, 2)        return out

class ShuffleNetV2(nn.Module):    def __init__(self,                 stages_repeats: List[int],                 stages_out_channels: List[int],                 num_classes: int = 1000,                 inverted_residual: Callable[..., nn.Module] = InvertedResidual):        super(ShuffleNetV2, self).__init__()        if len(stages_repeats) != 3:            raise ValueError("expected stages_repeats as list of 3 positive ints")        if len(stages_out_channels) != 5:            raise ValueError("expected stages_out_channels as list of 5 positive ints")        self._stage_out_channels = stages_out_channels        # input RGB image        input_channels = 3        output_channels = self._stage_out_channels[0]        self.conv1 = nn.Sequential(            nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=2, padding=1, bias=False),            nn.BatchNorm2d(output_channels),            nn.ReLU(inplace=True)        )        input_channels = output_channels        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)        # Static annotations for mypy        self.stage2: nn.Sequential        self.stage3: nn.Sequential        self.stage4: nn.Sequential        stage_names = ["stage{}".format(i) for i in [2, 3, 4]]        for name, repeats, output_channels in zip(stage_names, stages_repeats,                                                  self._stage_out_channels[1:]):            seq = [inverted_residual(input_channels, output_channels, 2)]            for i in range(repeats - 1):                seq.append(inverted_residual(output_channels, output_channels, 1))            setattr(self, name, nn.Sequential(*seq))            input_channels = output_channels        output_channels = self._stage_out_channels[-1]        self.conv5 = nn.Sequential(            nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False),            nn.BatchNorm2d(output_channels),            nn.ReLU(inplace=True)        )        self.fc = nn.Linear(output_channels, num_classes)    def _forward_impl(self, x: Tensor) -> Tensor:        # See note [TorchScript super()]        x = self.conv1(x)        x = self.maxpool(x)        x = self.stage2(x)        x = self.stage3(x)        x = self.stage4(x)        x = self.conv5(x)        x = x.mean([2, 3])  # global pool        x = self.fc(x)        return x    def forward(self, x: Tensor) -> Tensor:        return self._forward_impl(x)