圖像分類丨淺析輕量級網絡「SqueezeNet、MobileNet、ShuffleNet」

深度卷積網絡除了准確度，計算復雜度也是考慮的重要指標。本文列出了近年主流的輕量級網絡，簡單地闡述了它們的思想。由於本人水平有限，對這部分的理解還不夠深入，還需要繼續學習和完善。

最后我參考部分列出來的文章都寫的非常棒，建議繼續閱讀。

復雜度分析

• 理論計算量（FLOPs）：浮點運算次數（FLoating-point Operation）
• 參數數量(params)：單位通常為M，用float32表示。

對比

• std conv（主要貢獻計算量）
  • params：𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛×𝑐𝑜𝑢𝑡kh×kw×cin×cout
  • FLOPs：𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛×𝑐𝑜𝑢𝑡×𝐻×𝑊kh×kw×cin×cout×H×W
• fc（主要貢獻參數量）
  • params：𝑐𝑖𝑛×𝑐𝑜𝑢𝑡cin×cout
  • FLOPs：𝑐𝑖𝑛×𝑐𝑜𝑢𝑡cin×cout
• group conv
  • params：(𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛/𝑔×𝑐𝑜𝑢𝑡/𝑔)×𝑔=𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛×𝑐𝑜𝑢𝑡/𝑔(kh×kw×cin/g×cout/g)×g=kh×kw×cin×cout/g
  • FLOPs：𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛×𝑐𝑜𝑢𝑡×𝐻×𝑊/𝑔kh×kw×cin×cout×H×W/g
• depth-wise conv
  • params：𝑘ℎ×𝑘𝑤×𝑐𝑖𝑛×𝑐𝑜𝑢𝑡/𝑐𝑖𝑛=𝑘ℎ×𝑘𝑤×𝑐𝑜𝑢𝑡kh×kw×cin×cout/cin=kh×kw×cout
  • FLOPs：𝑘ℎ×𝑘𝑤×𝑐𝑜𝑢𝑡×𝐻×𝑊kh×kw×cout×H×W

SqueezeNet#

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

核心思想#

• 提出Fire module，包含兩部分：squeeze和expand層。
  1. squeeze為1x1卷積，𝑆1<𝑀S1<M，從而壓縮
  2. Expand層為e1個1x1卷積和e3個3x3卷積，分別輸出𝐻×𝑊×𝑒1H×W×e1和𝐻×𝑊×𝑒2H×W×e2。
  3. concat得到𝐻×𝑊×(𝑒1+𝑒3)H×W×(e1+e3)

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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

網絡架構#

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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)

實驗結果#

• 注意：0.5MB是模型壓縮的結果。

MobileNetV1#

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

核心思想#

1. 使用了depth-wise separable conv降低了參數和計算量。

2. 提出兩個超參數Width Multiplier和Resolution Multiplier來平衡時間和精度。

• depth-wise separable conv

Standard Conv

𝐷𝐾DK：kernel size

𝐷𝐹DF：feature map size

𝑀M：input channel number

𝑁N：output channel number

參數量：𝐷𝐾×𝐷𝐾×𝑀×𝑁(3×3×3×2)DK×DK×M×N(3×3×3×2)

計算量：𝐷𝐾⋅𝐷𝐾⋅𝑀⋅𝑁⋅𝐷𝐹⋅𝐷𝐹DK⋅DK⋅M⋅N⋅DF⋅DF

用depth-wise separable conv來替代std conv，depth-wise conv分解為depthwise conv和pointwise conv。

std conv輸出的每個通道的feature包含了輸入所有通道的feature，depth-wise separable conv沒有辦法做到，所以需要用pointwise conv來結合不同通道的feature。

Depthwise Conv

對輸入feature的每個通道單獨做卷積操作，得到每個通道對應的輸出feature。

參數量：𝐷𝐾×𝐷𝐾×𝑀(3×3×3)DK×DK×M(3×3×3)

計算量：𝐷𝐾⋅𝐷𝐾⋅𝑀⋅𝐷𝐹⋅𝐷𝐹DK⋅DK⋅M⋅DF⋅DF

Pointwise Conv

將depthwise conv的輸出，即不同通道的feature map結合起來，從而達到和std conv一樣的效果。

參數量：1×1×𝑀×𝑁(1×1×3×2)1×1×M×N(1×1×3×2)

計算量：𝑀⋅𝑁⋅𝐷𝐹⋅𝐷𝐹M⋅N⋅DF⋅DF

從而總計算量為𝐷𝐾⋅𝐷𝐾⋅𝑀⋅𝐷𝐹⋅𝐷𝐹+𝑀⋅ 𝑁⋅𝐷𝐹⋅𝐷𝐹DK⋅DK⋅M⋅DF⋅DF+M⋅ N⋅DF⋅DF

通過拆分，相當於將standard conv計算量壓縮為：

• 代碼實現

  BasicConv2d & DepthSeperableConv2d

  

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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 𝛼α：以系數1,0.75,0.5和0.251,0.75,0.5和0.25乘以input、output channel

計算量變為𝐷𝐾⋅𝐷𝐾⋅𝛼𝑀⋅𝐷𝐹⋅𝐷𝐹+𝛼𝑀⋅ 𝛼𝑁⋅𝐷𝐹⋅𝐷𝐹DK⋅DK⋅αM⋅DF⋅DF+αM⋅ αN⋅DF⋅DF

1. Resoltion Multiplier 𝜌ρ：將輸入分辨率變為224,192,160或128224,192,160或128。

計算量變為𝐷𝐾⋅𝐷𝐾⋅𝛼𝑀⋅𝜌𝐷𝐹⋅𝜌𝐷𝐹+𝛼𝑀⋅ 𝛼𝑁⋅𝜌𝐷𝐹⋅𝜌𝐷𝐹DK⋅DK⋅αM⋅ρDF⋅ρDF+αM⋅ αN⋅ρDF⋅ρDF

網絡架構#

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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

實驗結果#

MobileNetV2#

核心思想#

• Inverted residual block：引入殘差結構和bottleneck層。
• Linear Bottlenecks：ReLU會破壞信息，故去掉第二個Conv1x1后的ReLU，改為線性神經元。

MobileNetv2與其他網絡對比

MobileNetV2 block

• 代碼實現

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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

網絡架構#

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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()

實驗結果#

ShuffleNetV1#

核心思想#

• 利用group convolution和channel shuffle來減少模型參數量。

• ShuffleNet unit

從ResNet bottleneck 演化得到shuffleNet unit

1. (a)帶depth-wise conv的bottleneck unit
2. (b)將1x1conv換成1x1Gconv，並在第一個1x1Gconv后增加一個channel shuffle。
3. (c)旁路增加AVG pool，減小feature map的分辨率；分辨率小了，最后不采用add而是concat，從而彌補分辨率減小帶來的信息損失。

• 代碼實現

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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

網絡架構#

• 代碼實現

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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])

實驗結果#

ShuffleNetV2#

核心思想#

• 基於四條准則，改進了SuffleNetv1

  G1）同等通道最小化內存訪問量（1x1卷積平衡輸入和輸出通道大小）

  G2）過量使用組卷積增加內存訪問量（謹慎使用組卷積）

  G3）網絡碎片化降低並行度（避免網絡碎片化）

  G4）不能忽略元素級操作（減少元素級運算）

• 代碼實現

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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

網絡架構#

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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() 

實驗結果#

參考#

卷積神經網絡的復雜度分析

縱覽輕量化卷積神經網絡：SqueezeNet、MobileNet、ShuffleNet、Xception

CVPR 2018 高效小網絡探密（上）

CVPR 2018 高效小網絡探密（下）

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

輕量級CNN網絡之MobileNetv2

ShuffleNetV2：輕量級CNN網絡中的桂冠

輕量化網絡ShuffleNet MobileNet v1/v2 解析

Roofline Model與深度學習模型的性能分析