ShuffleNet论文地址：1707.01083.pdf (arxiv.org)

ShuffleNetv2论文地址：1807.11164.pdf (arxiv.org)

ShuffleNetv1

简介

ShuffleNet 是专门为计算能力非常有限的移动设备设计的。架构采用了逐点分组卷积和通道shuffle两种新的运算，在保持精度的同时大大降低了计算成本。ShuffleNet 比 MobileNet 在 ImageNet 分类任务上的 top-1误差更低 (绝对7.8%) ，在基于arm的移动设备上，ShuffleNet比AlexNet实现了约13倍的实际加速，同时保持了相当的准确性。

逐点分组卷积

作者注意到最先进的基础架构，如Xception和ResNeXt，在极小的网络中变得效率较低，因为密集1 × 1卷积代价很昂贵。为减少1 × 1卷积的计算复杂度。克服逐点分组卷积带来的副作用，提出了一种新的通道洗牌操作，以帮助信息在特征通道之间流动。

Channel Shuffle

使用 1 × 1 卷积核进行操作时的复杂度较高，因为需要和每个像素点做互相关运算，所以这会消耗大量资源，所以作者提出将这一层也设计为分组卷积的的形式，但它只在组内进行卷积，为了使得信息在组之间流动，作者提出将每次分组卷积后的结果进行组内分组，再互相交换各自的组内的子组，通过Channel Shuffle允许分组卷积从不同的组中获取输入数据，从而实现输入通道和输出通道相关联 。这种相关联有助于增加网络的非线性，提高网络的表征能力。

def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.size()
    channels_per_group = num_channels // groups
    x = x.view(batchsize, groups, channels_per_group, height, width)   # reshape
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batchsize, num_channels, height, width)  # flatten
    return x

这一个过程很好实现，我们只需要先将总通道数除以组数，计算每个组中的通道数，然后使用torch.transpose函数交换第二维和第三维，以实现通道混洗。

ShuffleNet unit

我们关注（b）和（c），因为（a）就是一个将DW卷积替换3x3卷积的瓶颈结构，后面两种在GConv与DWConv之间进行了通道洗牌。唯一不同的是（c）满足的是stride=2的情况，现在我们先将其组合在一起。

class shufflenet_units(nn.Module):
    """可参考 <https://arxiv.org/pdf/1707.01083.pdf> Figure2 (b) and (c)"""
    def __init__(self, in_channels, out_channels, stride, groups):
        super(shufflenet_units, self).__init__()
        mid_channels = out_channels // 4
        self.stride = stride
        self.groups = 1 if in_channels == 24 else groups

        self.GConv1 = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True)
        )

        self.DWConv = nn.Sequential(
            nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=self.stride, padding=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(mid_channels)
        )

        self.GConv2 = nn.Sequential(
            nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        self.shortcut = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.GConv1(x)
        out = channel_shuffle(out, groups=self.groups)
        out = self.DWConv(out)
        out = self.GConv2(out)
        if self.stride == 2:
            short_out = self.shortcut(x)
            out = torch.cat([out, short_out], dim=1)
        return self.relu(out)

这里就能通过判断stride是否为2来决定是否进行下采样操作，并将下采样结果与输出特征图进行拼接。我觉得我这里实现的还是相当完美的。

ShuffleNetv1网络搭建

虽然作者没有提供源码，但是想要实现还是比较容易的，可以看到Stage2，Stage3，Stage4有共有的特点，比如在每个stage的repeat阶段都先进行一次Stride=2的操作，所以这里完全可以参考torch官方实现VGG的方式去写，只不过说这里的方式比较固定罢了。

class ShuffleNetV1(nn.Module):
    """
    参考 <https://arxiv.org/pdf/1707.01083.pdf> Table 1 实现
    根据论文所述 —— 组数越小表示性能越好
    """
    def __init__(self, groups, stages_out_channels, num_classes=1000, repeat_layers=(4, 8, 4)):
        super(ShuffleNetV1, self).__init__()
        self.groups = groups
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 24, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(24),
            nn.ReLU(inplace=True),
        )
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.stage2 = self._make_layers(24, stages_out_channels[0], repeat_layers[0], groups)
        self.stage3 = self._make_layers(stages_out_channels[0], stages_out_channels[1], repeat_layers[1], groups)
        self.stage4 = self._make_layers(stages_out_channels[1], stages_out_channels[2], repeat_layers[2], groups)

        self.globalpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(num_channels[2], num_classes)

    def _make_layers(self, in_channels, out_channels, repeat_number, groups):
        layers = []
        # 不同的 stage 阶段在第一步都是 stride = 2
        layers.append(shufflenet_units(in_channels, out_channels - in_channels, 2, groups))
        in_channels = out_channels
        for i in range(repeat_number - 1):
            layers.append(shufflenet_units(in_channels, out_channels, 1, groups))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.max_pool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.globalpool(x)
        x = torch.flatten(x, 1)
        out = self.fc(x)
        return out

cfg_with_shufflenet = {
    'g1': [144, 288, 576],
    'g2': [200, 400, 800],
    'g3': [240, 480, 960],
    'g4': [272, 544, 1088],
    'g8': [384, 768, 1536],
    'x0_5': [24, 48, 96, 192, 1024],
    'x1_0': [24, 116, 232, 464, 1024],
    'x1_5': [24, 176, 352, 704, 1024],
    'x2_0': [24, 244, 488, 976, 2048],
}

def shufflenet_v1_g1(num_classes):
    return ShuffleNetV1(groups=1,
                        stages_out_channels=cfg_with_shufflenet['g1'],
                        num_classes=num_classes
                        )

def shufflenet_v1_g2(num_classes):
    return ShuffleNetV1(groups=2,
                        stages_out_channels=cfg_with_shufflenet['g2'],
                        num_classes=num_classes
                        )

def shufflenet_v1_g3(num_classes):
    return ShuffleNetV1(groups=3,
                        stages_out_channels=cfg_with_shufflenet['g3'],
                        num_classes=num_classes
                        )

def shufflenet_v1_g4(num_classes):
    return ShuffleNetV1(groups=4,
                        stages_out_channels=cfg_with_shufflenet['g4'],
                        num_classes=num_classes
                        )

def shufflenet_v1_g8(num_classes):
    return ShuffleNetV1(groups=8,
                        stages_out_channels=cfg_with_shufflenet['g8'],
                        num_classes=num_classes
                        )

经过实验测试，下面是我打印出来的模型参数。

shufflenet_v1_g1 Total params: 1,880,296

shufflenet_v1_g2 Total params: 1,834,852

shufflenet_v1_g3 Total params: 1,781,032

shufflenet_v1_g4 Total params: 1,734,136

shufflenet_v1_g8 Total params: 1,797,304

根据论文所述，组数越小表示性能越好，就是说shufflenet_v1_g1的性能应该是最好的。

ShuffleNetv2

有效网络设计的实用指南

G1) 相等的通道宽度最小化内存访问成本(MAC)

目前网络通常采用深度可分离卷积，其中点向卷积(即1 × 1卷积)占据了大部分复杂度。而1 × 1卷积的核形状由两个参数指定：输入通道数和输出通道数。设h和w为特征映射的空间大小，则1 × 1卷积的FLOPs为。

这里可由不等式：

则=

可推出

论文中也给出了实验数据：

得出时，网络处理的速度最快。

G2) 过多的分组卷积增加MAC

FLOPs为推出，

同样的，论文中对这部分也做了一系列的对照实验，可以看见g越高，网络的处理速度越慢。

G3) 网络碎片降低了并行度

这部分没有公式佐证，但这种碎片化结构已被证明有利于准确性，但它可能会降低效率，因为它对GPU等具有强大并行计算能力的设备不友好。它还引入了额外的开销，例如内核启动和同步。在ARM上，速度降低相对较小。

G4) 逐元素操作带来的内存和耗时不可忽略

元素操作占用了相当多的时间，特别是在GPU上。这里，元素操作符包括ReLU、AddTensor、AddBias等。它们具有较小的FLOPs，但相对较重的MAC。特别地，我们还考虑深度卷积作为元素明智的运算符，因为它也具有较高的MAC/FLOPs比率。

基于上述指导方针和实证研究，一个高效的网络架构应具备

• 使用输入输出通道相同的卷积
• 了解使用分组卷积的代价，合理的设定分组个数
• 降低网络并行的分支
• 减少逐点运算

ShuffleNetv2网络搭建

下面是全部实现的代码

"""
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
    <https://arxiv.org/pdf/1707.01083.pdf>
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
    <https://arxiv.org/pdf/1807.11164.pdf>
"""
import torch
import torch.nn as nn

__all__ = ["ShuffleNetV1", "shufflenet_v1_g1", "shufflenet_v1_g2", "shufflenet_v1_g3",
           "shufflenet_v1_g4", "shufflenet_v1_g8", "ShuffleNetV2", "shufflenet_v2_x0_5",
           "shufflenet_v2_x1_0", "shufflenet_v2_x1_5", "shufflenet_v2_x2_0",]

cfg_with_shufflenet = {
    'g1': [144, 288, 576],
    'g2': [200, 400, 800],
    'g3': [240, 480, 960],
    'g4': [272, 544, 1088],
    'g8': [384, 768, 1536],
    'x0_5': [24, 48, 96, 192, 1024],
    'x1_0': [24, 116, 232, 464, 1024],
    'x1_5': [24, 176, 352, 704, 1024],
    'x2_0': [24, 244, 488, 976, 2048],
}

def channel_shuffle(x, groups):
    """Shufflenet uses channel shuffling"""
    batchsize, num_channels, height, width = x.size()
    channels_per_group = num_channels // groups
    x = x.view(batchsize, groups, channels_per_group, height, width)   # reshape
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batchsize, num_channels, height, width)  # flatten
    return x

class shufflenet_units(nn.Module):
    """可参考 <https://arxiv.org/pdf/1707.01083.pdf> Figure2 (b) and (c)"""
    def __init__(self, in_channels, out_channels, stride, groups):
        super(shufflenet_units, self).__init__()
        mid_channels = out_channels // 4
        self.stride = stride
        self.groups = 1 if in_channels == 24 else groups

        self.GConv1 = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=1, stride=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True)
        )

        self.DWConv = nn.Sequential(
            nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=self.stride, padding=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(mid_channels)
        )

        self.GConv2 = nn.Sequential(
            nn.Conv2d(mid_channels, out_channels, kernel_size=1, stride=1, groups=self.groups, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        self.shortcut = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.GConv1(x)
        out = channel_shuffle(out, groups=self.groups)
        out = self.DWConv(out)
        out = self.GConv2(out)
        if self.stride == 2:
            short_out = self.shortcut(x)
            out = torch.cat([out, short_out], dim=1)
        return self.relu(out)


class ShuffleNetV1(nn.Module):
    """
    参考 <https://arxiv.org/pdf/1707.01083.pdf> Table 1 实现
    根据论文所述 —— 组数越小表示性能越好
    """
    def __init__(self, groups, stages_out_channels, num_classes=1000, repeat_layers=(4, 8, 4)):
        super(ShuffleNetV1, self).__init__()
        self.groups = groups
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 24, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(24),
            nn.ReLU(inplace=True),
        )
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.stage2 = self._make_layers(24, stages_out_channels[0], repeat_layers[0], groups)
        self.stage3 = self._make_layers(stages_out_channels[0], stages_out_channels[1], repeat_layers[1], groups)
        self.stage4 = self._make_layers(stages_out_channels[1], stages_out_channels[2], repeat_layers[2], groups)

        self.globalpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(stages_out_channels[2], num_classes)

    def _make_layers(self, in_channels, out_channels, repeat_number, groups):
        layers = []
        # 不同的 stage 阶段在第一步都是 stride = 2
        layers.append(shufflenet_units(in_channels, out_channels - in_channels, 2, groups))
        in_channels = out_channels
        for i in range(repeat_number - 1):
            layers.append(shufflenet_units(in_channels, out_channels, 1, groups))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.max_pool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.globalpool(x)
        x = torch.flatten(x, 1)
        out = self.fc(x)
        return out

################################### 以下为 ShuffleNetV2 ###############################################

class Improved_shufflenet_units(nn.Module):
    def __init__(self, inp, oup, stride):
        super().__init__()
        if not (1 <= stride <= 3):
            raise ValueError("illegal stride value")
        self.stride = stride
        branch_features = oup // 2
        if (self.stride == 1) and (inp != branch_features << 1):
            raise ValueError(
                f"Invalid combination of stride {stride}, inp {inp} and oup {oup} values. If stride == 1 then inp should be equal to oup // 2 << 1."
            )

        self.branch1 = nn.Sequential(
            # DWConv
            nn.Conv2d(inp, inp, kernel_size=3, stride=self.stride, padding=1, bias=False, groups=inp),
            nn.BatchNorm2d(inp),
            nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(
                inp 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),
            # DWConv
            nn.Conv2d(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1, bias=False, groups=branch_features),
            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),
        )

    def forward(self, x):
        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_out_channels,
                 num_classes=1000,
                 improved_units=Improved_shufflenet_units,
                 repeat_layers=(4, 8, 4)):
        super().__init__()
        if len(repeat_layers) != 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_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 3, 2, 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 = [f"stage{i}" for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(stage_names, repeat_layers, self._stage_out_channels[1:]):
            seq = [improved_units(input_channels, output_channels, 2)]
            for i in range(repeats - 1):
                seq.append(improved_units(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, 1, 1, 0, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True),
        )
        self.fc = nn.Linear(output_channels, num_classes)

    def forward(self, x):
        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])  # globalpool
        x = self.fc(x)
        return x

#----------------------------------------------------------------------------------------------------#
def shufflenet_v1_g1(num_classes):
    return ShuffleNetV1(groups=1,
                        stages_out_channels=cfg_with_shufflenet['g1'],
                        num_classes=num_classes)

def shufflenet_v1_g2(num_classes):
    return ShuffleNetV1(groups=2,
                        stages_out_channels=cfg_with_shufflenet['g2'],
                        num_classes=num_classes)

def shufflenet_v1_g3(num_classes):
    return ShuffleNetV1(groups=3,
                        stages_out_channels=cfg_with_shufflenet['g3'],
                        num_classes=num_classes)

def shufflenet_v1_g4(num_classes):
    return ShuffleNetV1(groups=4,
                        stages_out_channels=cfg_with_shufflenet['g4'],
                        num_classes=num_classes)

def shufflenet_v1_g8(num_classes):
    return ShuffleNetV1(groups=8,
                        stages_out_channels=cfg_with_shufflenet['g8'],
                        num_classes=num_classes)

def shufflenet_v2_x0_5(num_classes):
    return ShuffleNetV2(cfg_with_shufflenet['x0_5'],
                        num_classes=num_classes)

def shufflenet_v2_x1_0(num_classes):
    return ShuffleNetV2(cfg_with_shufflenet['x1_0'],
                        num_classes=num_classes)

def shufflenet_v2_x1_5(num_classes):
    return ShuffleNetV2(cfg_with_shufflenet['x1_5'],
                        num_classes=num_classes)

def shufflenet_v2_x2_0(num_classes):
    return ShuffleNetV2(cfg_with_shufflenet['x2_0'],
                        num_classes=num_classes)


if __name__=="__main__":
    import torchsummary
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    input = torch.ones(2, 3, 224, 224).to(device)
    net = shufflenet_v2_x2_0(num_classes=4)
    net = net.to(device)
    out = net(input)
    print(out)
    print(out.shape)
    torchsummary.summary(net, input_size=(3, 224, 224))
    # shufflenet_v1_g1 Total params: 1,880,296
    # shufflenet_v1_g2 Total params: 1,834,852
    # shufflenet_v1_g3 Total params: 1,781,032
    # shufflenet_v1_g4 Total params: 1,734,136
    # shufflenet_v1_g8 Total params: 1,797,304
    # shufflenet_v2_x0_5 Total params: 345,892
    # shufflenet_v2_x1_0 Total params: 1,257,704
    # shufflenet_v2_x1_5 Total params: 2,482,724
    # shufflenet_v2_x2_0 Total params: 5,353,192

参考文章

轻量级神经网络——shuffleNet_shuffnet-****博客

【深度学习经典网络架构—9】：ShuffleNet系列（V1、V2）_shufflenet network-****博客

【网络结构设计】3、ShuffleNet 系列 | 从 V1 到 V2_shufflenet网络结构-****博客

轻量级神经网络——shuffleNet2_输入输出通道数相同的卷积-****博客

轻量化网络结构——ShuffleNet_shufflenet网络结构-****博客

论文阅读笔记：ShuffleNet-****博客