前言

深度卷积网络除了准确度，计算复杂度也是考虑的重要指标。本文列出了近年主流的轻量级网络，简单地阐述了它们的思想。由于本人水平有限，对这部分的理解还不够深入，还需要继续学习和完善。

最后我参考部分列出来的文章都写的非常棒，建议继续阅读。

复杂度分析

• 理论计算量（FLOPs）：浮点运算次数（FLoating-point Operation）
• 参数数量(params)：单位通常为M，用float32表示。

对比

• std conv（主要贡献计算量）
  • params：\(k_h\times k_w\times c_{in}\times c_{out}\)
  • FLOPs：\(k_h\times k_w\times c_{in}\times c_{out}\times H\times W\)
• fc（主要贡献参数量）
  • params：\(c_{in}\times c_{out}\)
  • FLOPs：\(c_{in}\times c_{out}\)
• group conv
  • params：\((k_h\times k_w\times c_{in}/g \times c_{out}/g)\times g=k_h\times k_w\times c_{in}\times c_{out}/g\)
  • FLOPs：\(k_h\times k_w\times c_{in}\times c_{out}\times H\times W/g\)
• depth-wise conv
  • params：\(k_h\times k_w\times c_{in}\times c_{out}/c_{in}=k_h\times k_w\times c_{out}\)
  • FLOPs：\(k_h\times k_w\times c_{out}\times H\times W\)

SqueezeNet

SqueezeNet：AlexNet-level accuracy with 50x fewer parameters and <0.5MB

核心思想

• 提出Fire module，包含两部分：squeeze和expand层。
  1. squeeze为1x1卷积，\(S_1\lt M\)，从而压缩
  2. Expand层为e1个1x1卷积和e3个3x3卷积，分别输出\(H\times W\times e1\)和\(H\times W \times e_2\)。
  3. concat得到\(H\times W \times (e_1+e_3)\)

class Fire(nn.Module):    def __init__(self, in_channel, out_channel, squzee_channel):        super().__init__()        self.squeeze = nn.Sequential(            nn.Conv2d(in_channel, squzee_channel, 1),            nn.BatchNorm2d(squzee_channel),            nn.ReLU(inplace=True)        )        self.expand_1x1 = nn.Sequential(            nn.Conv2d(squzee_channel, int(out_channel / 2), 1),            nn.BatchNorm2d(int(out_channel / 2)),            nn.ReLU(inplace=True)        )        self.expand_3x3 = nn.Sequential(            nn.Conv2d(squzee_channel, int(out_channel / 2), 3, padding=1),            nn.BatchNorm2d(int(out_channel / 2)),            nn.ReLU(inplace=True)        )        def forward(self, x):        x = self.squeeze(x)        x = torch.cat([            self.expand_1x1(x),            self.expand_3x3(x)        ], 1)        return x

网络架构

class SqueezeNet(nn.Module):    """mobile net with simple bypass"""    def __init__(self, class_num=100):        super().__init__()        self.stem = nn.Sequential(            nn.Conv2d(3, 96, 3, padding=1),            nn.BatchNorm2d(96),            nn.ReLU(inplace=True),            nn.MaxPool2d(2, 2)        )        self.fire2 = Fire(96, 128, 16)        self.fire3 = Fire(128, 128, 16)        self.fire4 = Fire(128, 256, 32)        self.fire5 = Fire(256, 256, 32)        self.fire6 = Fire(256, 384, 48)        self.fire7 = Fire(384, 384, 48)        self.fire8 = Fire(384, 512, 64)        self.fire9 = Fire(512, 512, 64)        self.conv10 = nn.Conv2d(512, class_num, 1)        self.avg = nn.AdaptiveAvgPool2d(1)        self.maxpool = nn.MaxPool2d(2, 2)                def forward(self, x):        x = self.stem(x)        f2 = self.fire2(x)        f3 = self.fire3(f2) + f2        f4 = self.fire4(f3)        f4 = self.maxpool(f4)        f5 = self.fire5(f4) + f4        f6 = self.fire6(f5)        f7 = self.fire7(f6) + f6        f8 = self.fire8(f7)        f8 = self.maxpool(f8)        f9 = self.fire9(f8)        c10 = self.conv10(f9)        x = self.avg(c10)        x = x.view(x.size(0), -1)        return xdef squeezenet(class_num=100):    return SqueezeNet(class_num=class_num)

实验结果

• 注意：0.5MB是模型压缩的结果。

MobileNetV1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

核心思想

1. 使用了depth-wise separable conv降低了参数和计算量。

2. 提出两个超参数Width Multiplier和Resolution Multiplier来平衡时间和精度。

• depth-wise separable conv

Standard Conv

\(D_K\)：kernel size

\(D_F\)：feature map size

\(M\)：input channel number

\(N\)：output channel number

参数量：\(D_K\times D_K \times M \times N (3\times3\times 3\times 2)\)

计算量：\(D_K \cdot D_K \cdot M \cdot N \cdot D_F \cdot D_F\)

用depth-wise separable conv来替代std conv，depth-wise conv分解为depthwise conv和pointwise conv。

std conv输出的每个通道的feature包含了输入所有通道的feature，depth-wise separable conv没有办法做到，所以需要用pointwise conv来结合不同通道的feature。

Depthwise Conv

对输入feature的每个通道单独做卷积操作，得到每个通道对应的输出feature。

参数量：\(D_K\times D_K \times M(3\times 3\times 3)\)

计算量：\(D_K \cdot D_K \cdot M \cdot D_F \cdot D_F\)

Pointwise Conv

将depthwise conv的输出，即不同通道的feature map结合起来，从而达到和std conv一样的效果。

参数量：\(1\times 1 \times M \times N(1\times1\times3\times2)\)

计算量：\(M\cdot N \cdot D_F \cdot D_F\)

从而总计算量为\(D_K \cdot D_K \cdot M \cdot D_F \cdot D_F+M\cdot\ N\cdot D_F \cdot D_F\)

通过拆分，相当于将standard conv计算量压缩为：

• 代码实现

  BasicConv2d & DepthSeperableConv2d

  

class DepthSeperabelConv2d(nn.Module):    def __init__(self, input_channels, output_channels, kernel_size, **kwargs):        super().__init__()        self.depthwise = nn.Sequential(            nn.Conv2d(                input_channels,                input_channels,                kernel_size,                groups=input_channels,                **kwargs),            nn.BatchNorm2d(input_channels),            nn.ReLU(inplace=True)        )        self.pointwise = nn.Sequential(            nn.Conv2d(input_channels, output_channels, 1),            nn.BatchNorm2d(output_channels),            nn.ReLU(inplace=True)        )    def forward(self, x):        x = self.depthwise(x)        x = self.pointwise(x)        return x    class BasicConv2d(nn.Module):    def __init__(self, input_channels, output_channels, kernel_size, **kwargs):        super().__init__()        self.conv = nn.Conv2d(            input_channels, output_channels, kernel_size, **kwargs)        self.bn = nn.BatchNorm2d(output_channels)        self.relu = nn.ReLU(inplace=True)    def forward(self, x):        x = self.conv(x)        x = self.bn(x)        x = self.relu(x)        return x

• Two hyper-parameters

1. Width Multiplier \(\alpha\)：以系数\(1,0.75,0.5和0.25\)乘以input、output channel

计算量变为\(D_K \cdot D_K \cdot \alpha M \cdot D_F \cdot D_F+\alpha M\cdot\ \alpha N\cdot D_F \cdot D_F\)

1. Resoltion Multiplier \(\rho\)：将输入分辨率变为\(224,192,160或128\)。

计算量变为\(D_K \cdot D_K \cdot \alpha M \cdot \rho D_F \cdot \rho D_F+\alpha M\cdot\ \alpha N\cdot \rho D_F \cdot \rho D_F\)

网络架构

def mobilenet(alpha=1, class_num=100):    return MobileNet(alpha, class_num)class MobileNet(nn.Module):    """    Args:        width multipler: The role of the width multiplier α is to thin                          a network uniformly at each layer. For a given                          layer and width multiplier α, the number of                          input channels M becomes αM and the number of                          output channels N becomes αN.    """    def __init__(self, width_multiplier=1, class_num=100):       super().__init__()       alpha = width_multiplier       self.stem = nn.Sequential(           BasicConv2d(3, int(32 * alpha), 3, padding=1, bias=False),           DepthSeperabelConv2d(               int(32 * alpha),               int(64 * alpha),               3,               padding=1,               bias=False           )       )       #downsample       self.conv1 = nn.Sequential(           DepthSeperabelConv2d(               int(64 * alpha),               int(128 * alpha),               3,               stride=2,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(128 * alpha),               int(128 * alpha),               3,               padding=1,               bias=False           )       )       #downsample       self.conv2 = nn.Sequential(           DepthSeperabelConv2d(               int(128 * alpha),               int(256 * alpha),               3,               stride=2,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(256 * alpha),               int(256 * alpha),               3,               padding=1,               bias=False           )       )       #downsample       self.conv3 = nn.Sequential(           DepthSeperabelConv2d(               int(256 * alpha),               int(512 * alpha),               3,               stride=2,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(512 * alpha),               int(512 * alpha),               3,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(512 * alpha),               int(512 * alpha),               3,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(512 * alpha),               int(512 * alpha),               3,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(512 * alpha),               int(512 * alpha),               3,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(512 * alpha),               int(512 * alpha),               3,               padding=1,               bias=False           )       )       #downsample       self.conv4 = nn.Sequential(           DepthSeperabelConv2d(               int(512 * alpha),               int(1024 * alpha),               3,               stride=2,               padding=1,               bias=False           ),           DepthSeperabelConv2d(               int(1024 * alpha),               int(1024 * alpha),               3,               padding=1,               bias=False           )       )       self.fc = nn.Linear(int(1024 * alpha), class_num)       self.avg = nn.AdaptiveAvgPool2d(1)    def forward(self, x):        x = self.stem(x)        x = self.conv1(x)        x = self.conv2(x)        x = self.conv3(x)        x = self.conv4(x)        x = self.avg(x)        x = x.view(x.size(0), -1)        x = self.fc(x)        return x

实验结果

MobileNetV2

核心思想

• Inverted residual block：引入残差结构和bottleneck层。
• Linear Bottlenecks：ReLU会破坏信息，故去掉第二个Conv1x1后的ReLU，改为线性神经元。

MobileNetv2与其他网络对比

MobileNetV2 block

• 代码实现

class LinearBottleNeck(nn.Module):    def __init__(self, in_channels, out_channels, stride, t=6, class_num=100):        super().__init__()        self.residual = nn.Sequential(            nn.Conv2d(in_channels, in_channels * t, 1),            nn.BatchNorm2d(in_channels * t),            nn.ReLU6(inplace=True),            nn.Conv2d(in_channels * t, in_channels * t, 3, stride=stride, padding=1, groups=in_channels * t),            nn.BatchNorm2d(in_channels * t),            nn.ReLU6(inplace=True),            nn.Conv2d(in_channels * t, out_channels, 1),            nn.BatchNorm2d(out_channels)        )        self.stride = stride        self.in_channels = in_channels        self.out_channels = out_channels        def forward(self, x):        residual = self.residual(x)        if self.stride == 1 and self.in_channels == self.out_channels:            residual += x                return residual

网络架构

class MobileNetV2(nn.Module):    def __init__(self, class_num=100):        super().__init__()        self.pre = nn.Sequential(            nn.Conv2d(3, 32, 1, padding=1),            nn.BatchNorm2d(32),            nn.ReLU6(inplace=True)        )        self.stage1 = LinearBottleNeck(32, 16, 1, 1)        self.stage2 = self._make_stage(2, 16, 24, 2, 6)        self.stage3 = self._make_stage(3, 24, 32, 2, 6)        self.stage4 = self._make_stage(4, 32, 64, 2, 6)        self.stage5 = self._make_stage(3, 64, 96, 1, 6)        self.stage6 = self._make_stage(3, 96, 160, 1, 6)        self.stage7 = LinearBottleNeck(160, 320, 1, 6)        self.conv1 = nn.Sequential(            nn.Conv2d(320, 1280, 1),            nn.BatchNorm2d(1280),            nn.ReLU6(inplace=True)        )        self.conv2 = nn.Conv2d(1280, class_num, 1)                def forward(self, x):        x = self.pre(x)        x = self.stage1(x)        x = self.stage2(x)        x = self.stage3(x)        x = self.stage4(x)        x = self.stage5(x)        x = self.stage6(x)        x = self.stage7(x)        x = self.conv1(x)        x = F.adaptive_avg_pool2d(x, 1)        x = self.conv2(x)        x = x.view(x.size(0), -1)        return x        def _make_stage(self, repeat, in_channels, out_channels, stride, t):        layers = []        layers.append(LinearBottleNeck(in_channels, out_channels, stride, t))                while repeat - 1:            layers.append(LinearBottleNeck(out_channels, out_channels, 1, t))            repeat -= 1                return nn.Sequential(*layers)def mobilenetv2():    return MobileNetV2()

实验结果

ShuffleNetV1

核心思想

• 利用group convolution和channel shuffle来减少模型参数量。

• ShuffleNet unit

从ResNet bottleneck 演化得到shuffleNet unit

1. (a)带depth-wise conv的bottleneck unit
2. (b)将1x1conv换成1x1Gconv，并在第一个1x1Gconv后增加一个channel shuffle。
3. (c)旁路增加AVG pool，减小feature map的分辨率；分辨率小了，最后不采用add而是concat，从而弥补分辨率减小带来的信息损失。

• 代码实现

class ChannelShuffle(nn.Module):    def __init__(self, groups):        super().__init__()        self.groups = groups        def forward(self, x):        batchsize, channels, height, width = x.data.size()        channels_per_group = int(channels / self.groups)        #"""suppose a convolutional layer with g groups whose output has        #g x n channels; we first reshape the output channel dimension        #into (g, n)"""        x = x.view(batchsize, self.groups, channels_per_group, height, width)        #"""transposing and then flattening it back as the input of next layer."""        x = x.transpose(1, 2).contiguous()        x = x.view(batchsize, -1, height, width)        return xclass ShuffleNetUnit(nn.Module):    def __init__(self, input_channels, output_channels, stage, stride, groups):        super().__init__()        #"""Similar to [9], we set the number of bottleneck channels to 1/4         #of the output channels for each ShuffleNet unit."""        self.bottlneck = nn.Sequential(            PointwiseConv2d(                input_channels,                 int(output_channels / 4),                 groups=groups            ),            nn.ReLU(inplace=True)        )        #"""Note that for Stage 2, we do not apply group convolution on the first pointwise         #layer because the number of input channels is relatively small."""        if stage == 2:            self.bottlneck = nn.Sequential(                PointwiseConv2d(                    input_channels,                     int(output_channels / 4),                    groups=groups                ),                nn.ReLU(inplace=True)            )                self.channel_shuffle = ChannelShuffle(groups)        self.depthwise = DepthwiseConv2d(            int(output_channels / 4),             int(output_channels / 4),             3,             groups=int(output_channels / 4),             stride=stride,            padding=1        )        self.expand = PointwiseConv2d(            int(output_channels / 4),            output_channels,            groups=groups        )        self.relu = nn.ReLU(inplace=True)        self.fusion = self._add        self.shortcut = nn.Sequential()        #"""As for the case where ShuffleNet is applied with stride,         #we simply make two modifications (see Fig 2 (c)):         #(i) add a 3 × 3 average pooling on the shortcut path;         #(ii) replace the element-wise addition with channel concatenation,         #which makes it easy to enlarge channel dimension with little extra         #computation cost.        if stride != 1 or input_channels != output_channels:            self.shortcut = nn.AvgPool2d(3, stride=2, padding=1)            self.expand = PointwiseConv2d(                int(output_channels / 4),                output_channels - input_channels,                groups=groups            )            self.fusion = self._cat        def _add(self, x, y):        return torch.add(x, y)        def _cat(self, x, y):        return torch.cat([x, y], dim=1)    def forward(self, x):        shortcut = self.shortcut(x)        shuffled = self.bottlneck(x)        shuffled = self.channel_shuffle(shuffled)        shuffled = self.depthwise(shuffled)        shuffled = self.expand(shuffled)        output = self.fusion(shortcut, shuffled)        output = self.relu(output)        return output

网络架构

• 代码实现

class ShuffleNet(nn.Module):    def __init__(self, num_blocks, num_classes=100, groups=3):        super().__init__()        if groups == 1:            out_channels = [24, 144, 288, 567]        elif groups == 2:            out_channels = [24, 200, 400, 800]        elif groups == 3:            out_channels = [24, 240, 480, 960]        elif groups == 4:            out_channels = [24, 272, 544, 1088]        elif groups == 8:            out_channels = [24, 384, 768, 1536]        self.conv1 = BasicConv2d(3, out_channels[0], 3, padding=1, stride=1)        self.input_channels = out_channels[0]        self.stage2 = self._make_stage(            ShuffleNetUnit,             num_blocks[0],             out_channels[1],             stride=2,             stage=2,            groups=groups        )        self.stage3 = self._make_stage(            ShuffleNetUnit,             num_blocks[1],             out_channels[2],             stride=2,            stage=3,             groups=groups        )        self.stage4 = self._make_stage(            ShuffleNetUnit,            num_blocks[2],            out_channels[3],            stride=2,            stage=4,            groups=groups        )        self.avg = nn.AdaptiveAvgPool2d((1, 1))        self.fc = nn.Linear(out_channels[3], num_classes)    def forward(self, x):        x = self.conv1(x)        x = self.stage2(x)        x = self.stage3(x)        x = self.stage4(x)        x = self.avg(x)        x = x.view(x.size(0), -1)        x = self.fc(x)        return x    def _make_stage(self, block, num_blocks, output_channels, stride, stage, groups):        """make shufflenet stage         Args:            block: block type, shuffle unit            out_channels: output depth channel number of this stage            num_blocks: how many blocks per stage            stride: the stride of the first block of this stage            stage: stage index            groups: group number of group convolution         Return:            return a shuffle net stage        """        strides = [stride] + [1] * (num_blocks - 1)        stage = []        for stride in strides:            stage.append(                block(                    self.input_channels,                     output_channels,                     stride=stride,                     stage=stage,                     groups=groups                )            )            self.input_channels = output_channels        return nn.Sequential(*stage)def shufflenet():    return ShuffleNet([4, 8, 4])

实验结果

ShuffleNetV2

核心思想

• 基于四条准则，改进了SuffleNetv1

  G1）同等通道最小化内存访问量（1x1卷积平衡输入和输出通道大小）

  G2）过量使用组卷积增加内存访问量（谨慎使用组卷积）

  G3）网络碎片化降低并行度（避免网络碎片化）

  G4）不能忽略元素级操作（减少元素级运算）

• 代码实现

def channel_split(x, split):    """split a tensor into two pieces along channel dimension    Args:        x: input tensor        split:(int) channel size for each pieces    """    assert x.size(1) == split * 2    return torch.split(x, split, dim=1)    def channel_shuffle(x, groups):    """channel shuffle operation    Args:        x: input tensor        groups: input branch number    """    batch_size, channels, height, width = x.size()    channels_per_group = int(channels / groups)    x = x.view(batch_size, groups, channels_per_group, height, width)    x = x.transpose(1, 2).contiguous()    x = x.view(batch_size, -1, height, width)    return xclass ShuffleUnit(nn.Module):    def __init__(self, in_channels, out_channels, stride):        super().__init__()        self.stride = stride        self.in_channels = in_channels        self.out_channels = out_channels        if stride != 1 or in_channels != out_channels:            self.residual = nn.Sequential(                nn.Conv2d(in_channels, in_channels, 1),                nn.BatchNorm2d(in_channels),                nn.ReLU(inplace=True),                nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),                nn.BatchNorm2d(in_channels),                nn.Conv2d(in_channels, int(out_channels / 2), 1),                nn.BatchNorm2d(int(out_channels / 2)),                nn.ReLU(inplace=True)            )            self.shortcut = nn.Sequential(                nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),                nn.BatchNorm2d(in_channels),                nn.Conv2d(in_channels, int(out_channels / 2), 1),                nn.BatchNorm2d(int(out_channels / 2)),                nn.ReLU(inplace=True)            )        else:            self.shortcut = nn.Sequential()            in_channels = int(in_channels / 2)            self.residual = nn.Sequential(                nn.Conv2d(in_channels, in_channels, 1),                nn.BatchNorm2d(in_channels),                nn.ReLU(inplace=True),                nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),                nn.BatchNorm2d(in_channels),                nn.Conv2d(in_channels, in_channels, 1),                nn.BatchNorm2d(in_channels),                nn.ReLU(inplace=True)             )        def forward(self, x):        if self.stride == 1 and self.out_channels == self.in_channels:            shortcut, residual = channel_split(x, int(self.in_channels / 2))        else:            shortcut = x            residual = x                shortcut = self.shortcut(shortcut)        residual = self.residual(residual)        x = torch.cat([shortcut, residual], dim=1)        x = channel_shuffle(x, 2)                return x

网络架构

class ShuffleNetV2(nn.Module):    def __init__(self, ratio=1, class_num=100):        super().__init__()        if ratio == 0.5:            out_channels = [48, 96, 192, 1024]        elif ratio == 1:            out_channels = [116, 232, 464, 1024]        elif ratio == 1.5:            out_channels = [176, 352, 704, 1024]        elif ratio == 2:            out_channels = [244, 488, 976, 2048]        else:            ValueError('unsupported ratio number')                self.pre = nn.Sequential(            nn.Conv2d(3, 24, 3, padding=1),            nn.BatchNorm2d(24)        )        self.stage2 = self._make_stage(24, out_channels[0], 3)        self.stage3 = self._make_stage(out_channels[0], out_channels[1], 7)        self.stage4 = self._make_stage(out_channels[1], out_channels[2], 3)        self.conv5 = nn.Sequential(            nn.Conv2d(out_channels[2], out_channels[3], 1),            nn.BatchNorm2d(out_channels[3]),            nn.ReLU(inplace=True)        )        self.fc = nn.Linear(out_channels[3], class_num)    def forward(self, x):        x = self.pre(x)        x = self.stage2(x)        x = self.stage3(x)        x = self.stage4(x)        x = self.conv5(x)        x = F.adaptive_avg_pool2d(x, 1)        x = x.view(x.size(0), -1)        x = self.fc(x)        return x    def _make_stage(self, in_channels, out_channels, repeat):        layers = []        layers.append(ShuffleUnit(in_channels, out_channels, 2))        while repeat:            layers.append(ShuffleUnit(out_channels, out_channels, 1))            repeat -= 1                return nn.Sequential(*layers)def shufflenetv2():    return ShuffleNetV2()

实验结果

参考