图像分类丨浅析轻量级网络「SqueezeNet、MobileNet、ShuffleNet」

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 imes k_w imes c_{in} imes c_{out})
  • FLOPs：(k_h imes k_w imes c_{in} imes c_{out} imes H imes W)
• fc（主要贡献参数量）
  • params：(c_{in} imes c_{out})
  • FLOPs：(c_{in} imes c_{out})
• group conv
  • params：((k_h imes k_w imes c_{in}/g imes c_{out}/g) imes g=k_h imes k_w imes c_{in} imes c_{out}/g)
  • FLOPs：(k_h imes k_w imes c_{in} imes c_{out} imes H imes W/g)
• depth-wise conv
  • params：(k_h imes k_w imes c_{in} imes c_{out}/c_{in}=k_h imes k_w imes c_{out})
  • FLOPs：(k_h imes k_w imes c_{out} imes H imes 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_1lt 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 imes W imes e1)和(H imes W imes e_2)。
  3. concat得到(H imes W imes (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 x

def 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 imes D_K imes M imes N (3 imes3 imes 3 imes 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 imes D_K imes M(3 imes 3 imes 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 imes 1 imes M imes N(1 imes1 imes3 imes2))

计算量：(Mcdot 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+Mcdot Ncdot 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 Mcdot alpha Ncdot D_F cdot D_F)

2. Resoltion Multiplier ( ho)：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 ho D_F cdot ho D_F+alpha Mcdot alpha Ncdot ho D_F cdot ho 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 x

class 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 x

class 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

卷积神经网络的复杂度分析

纵览轻量化卷积神经网络：SqueezeNet、MobileNet、ShuffleNet、Xception

CVPR 2018 高效小网络探密（上）

CVPR 2018 高效小网络探密（下）

http://machinethink.net/blog/mobilenet-v2/

轻量级CNN网络之MobileNetv2

ShuffleNetV2：轻量级CNN网络中的桂冠

轻量化网络ShuffleNet MobileNet v1/v2 解析

Roofline Model与深度学习模型的性能分析