《MobileNetV2: Inverted Residuals and Linear Bottlenecks》

Myth丶恋晨 2022-08-30 04:04 16阅读 0赞

**温馨提示：本文仅供自己参考，如有理解错误，有时间再改！**

**标准残差块：**

1、使用1x1卷积减少通道数

2、使用NxN卷积提取特征

3、使用1x1卷积提升通道数，提升后与输入通道数相等

4、将残差块的输入与输出相加，形成short-cut连接

备注：标准残差块的通道数两头大，中间小

**反向残差块（***inverted residual***）：**

1、使用1x1卷积提升通道数

2、使用深度可分离卷积提取特征

3、使用1x1卷积减少通道数，减少后与输入通道数相等

4、将残差块的输入与输出相加，形成short-cut连接

备注：翻转残差块的通道数两头小，中间大，中间通道数多的部分采用深度可分离卷积降低参数量和计算量

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**MobileNetV2网络结构：**

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** Pytorch中MobileNetV2反转卷积源码：**

class InvertedResidual(nn.Module):
        def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None):
            super(InvertedResidual, self).__init__()
            self.stride = stride
            assert stride in [1, 2]
    
            if norm_layer is None:
                norm_layer = nn.BatchNorm2d
    
            hidden_dim = int(round(inp * expand_ratio))
            self.use_res_connect = self.stride == 1 and inp == oup
    
            layers = []
            if expand_ratio != 1:
                # pw
                layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
            layers.extend([
                # dw, 深度可分离卷积第一部，分组卷积，group不等于1
                ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
                # pw-linear，深度可分离卷积第二步，使用1x1卷积进行channel融合
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                norm_layer(oup),
            ])
            self.conv = nn.Sequential(*layers)
    
        def forward(self, x):
            if self.use_res_connect:
                # 使用残差连接
                return x + self.conv(x)
            else:
                # 不使用残差连接
                return self.conv(x)

**Pytorch中MobileNetV2网络结构：**

MobileNetV2(
      (features): Sequential(
        (0): ConvBNReLU(
          (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (1): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
              (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (3): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (4): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
              (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (5): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (6): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (7): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
              (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (8): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (9): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (10): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (11): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
              (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (12): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (13): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (14): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
              (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (15): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (16): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (17): InvertedResidual(
          (conv): Sequential(
            (0): ConvBNReLU(
              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (1): ConvBNReLU(
              (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
              (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU6(inplace=True)
            )
            (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (18): ConvBNReLU(
          (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
      )
      (classifier): Sequential(
        (0): Dropout(p=0.2, inplace=False)
        (1): Linear(in_features=1280, out_features=1000, bias=True)
      )
    )

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