ShuffleNet v2网络结构复现（Pytorch版）

ShuffleNet v2网络结构复现

在这里插入图片描述

from torch import nn
from torch.nn import functional
import torch
from torchsummary import summary


# ---------------------------- ShuffleBlock start -------------------------------

# 通道重排，跨group信息交流
def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups,
               channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class CBRM(nn.Module):
    def __init__(self, c1, c2):  # ch_in, ch_out
        super(CBRM, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(c2),
            nn.ReLU(inplace=True),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

    def forward(self, x):
        return self.maxpool(self.conv(x))


class Shuffle_Block(nn.Module):
    def __init__(self, inp, oup, stride):
        super(Shuffle_Block, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
                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),

            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
            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),

        )

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)  # 按照维度1进行split
            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):
        super(ShuffleNetV2, self).__init__()

        self.MobileNet_01 = nn.Sequential(
            CBRM(3, 32),                 # 160x160
            Shuffle_Block(32, 128, 2),   # 80x80
            Shuffle_Block(128, 128, 1),  # 80x80
            Shuffle_Block(128, 256, 2),  # 40x40
            Shuffle_Block(256, 256, 1),  # 40x40
            Shuffle_Block(256, 512, 2),  # 20x20
            Shuffle_Block(512, 512, 1),  # 20x20
        )

    def forward(self, x):
        x = self.MobileNet_01(x)
        return x


if __name__ == '__main__':
    shufflenetv2 = ShuffleNetV2()

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
    inputs = shufflenetv2.to(device)
    summary(inputs, (3, 640, 640), batch_size=1, device="cuda")  # 分别是输入数据的三个维度
    #print(shufflenetv2)
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----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1          [1, 32, 320, 320]             864
       BatchNorm2d-2          [1, 32, 320, 320]              64
              ReLU-3          [1, 32, 320, 320]               0
         MaxPool2d-4          [1, 32, 160, 160]               0
              CBRM-5          [1, 32, 160, 160]               0
            Conv2d-6            [1, 32, 80, 80]             288
       BatchNorm2d-7            [1, 32, 80, 80]              64
            Conv2d-8            [1, 64, 80, 80]           2,048
       BatchNorm2d-9            [1, 64, 80, 80]             128
             ReLU-10            [1, 64, 80, 80]               0
           Conv2d-11          [1, 64, 160, 160]           2,048
      BatchNorm2d-12          [1, 64, 160, 160]             128
             ReLU-13          [1, 64, 160, 160]               0
           Conv2d-14            [1, 64, 80, 80]             576
      BatchNorm2d-15            [1, 64, 80, 80]             128
           Conv2d-16            [1, 64, 80, 80]           4,096
      BatchNorm2d-17            [1, 64, 80, 80]             128
             ReLU-18            [1, 64, 80, 80]               0
    Shuffle_Block-19           [1, 128, 80, 80]               0
           Conv2d-20            [1, 64, 80, 80]           4,096
      BatchNorm2d-21            [1, 64, 80, 80]             128
             ReLU-22            [1, 64, 80, 80]               0
           Conv2d-23            [1, 64, 80, 80]             576
      BatchNorm2d-24            [1, 64, 80, 80]             128
           Conv2d-25            [1, 64, 80, 80]           4,096
      BatchNorm2d-26            [1, 64, 80, 80]             128
             ReLU-27            [1, 64, 80, 80]               0
    Shuffle_Block-28           [1, 128, 80, 80]               0
           Conv2d-29           [1, 128, 40, 40]           1,152
      BatchNorm2d-30           [1, 128, 40, 40]             256
           Conv2d-31           [1, 128, 40, 40]          16,384
      BatchNorm2d-32           [1, 128, 40, 40]             256
             ReLU-33           [1, 128, 40, 40]               0
           Conv2d-34           [1, 128, 80, 80]          16,384
      BatchNorm2d-35           [1, 128, 80, 80]             256
             ReLU-36           [1, 128, 80, 80]               0
           Conv2d-37           [1, 128, 40, 40]           1,152
      BatchNorm2d-38           [1, 128, 40, 40]             256
           Conv2d-39           [1, 128, 40, 40]          16,384
      BatchNorm2d-40           [1, 128, 40, 40]             256
             ReLU-41           [1, 128, 40, 40]               0
    Shuffle_Block-42           [1, 256, 40, 40]               0
           Conv2d-43           [1, 128, 40, 40]          16,384
      BatchNorm2d-44           [1, 128, 40, 40]             256
             ReLU-45           [1, 128, 40, 40]               0
           Conv2d-46           [1, 128, 40, 40]           1,152
      BatchNorm2d-47           [1, 128, 40, 40]             256
           Conv2d-48           [1, 128, 40, 40]          16,384
      BatchNorm2d-49           [1, 128, 40, 40]             256
             ReLU-50           [1, 128, 40, 40]               0
    Shuffle_Block-51           [1, 256, 40, 40]               0
           Conv2d-52           [1, 256, 20, 20]           2,304
      BatchNorm2d-53           [1, 256, 20, 20]             512
           Conv2d-54           [1, 256, 20, 20]          65,536
      BatchNorm2d-55           [1, 256, 20, 20]             512
             ReLU-56           [1, 256, 20, 20]               0
           Conv2d-57           [1, 256, 40, 40]          65,536
      BatchNorm2d-58           [1, 256, 40, 40]             512
             ReLU-59           [1, 256, 40, 40]               0
           Conv2d-60           [1, 256, 20, 20]           2,304
      BatchNorm2d-61           [1, 256, 20, 20]             512
           Conv2d-62           [1, 256, 20, 20]          65,536
      BatchNorm2d-63           [1, 256, 20, 20]             512
             ReLU-64           [1, 256, 20, 20]               0
    Shuffle_Block-65           [1, 512, 20, 20]               0
           Conv2d-66           [1, 256, 20, 20]          65,536
      BatchNorm2d-67           [1, 256, 20, 20]             512
             ReLU-68           [1, 256, 20, 20]               0
           Conv2d-69           [1, 256, 20, 20]           2,304
      BatchNorm2d-70           [1, 256, 20, 20]             512
           Conv2d-71           [1, 256, 20, 20]          65,536
      BatchNorm2d-72           [1, 256, 20, 20]             512
             ReLU-73           [1, 256, 20, 20]               0
    Shuffle_Block-74           [1, 512, 20, 20]               0
================================================================
Total params: 445,824
Trainable params: 445,824
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 4.69
Forward/backward pass size (MB): 270.31
Params size (MB): 1.70
Estimated Total Size (MB): 276.70
----------------------------------------------------------------
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ShuffleNetV2(
  (MobileNet_01): Sequential(
    (0): CBRM(
      (conv): Sequential(
        (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): ReLU(inplace=True)
      )
      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    )
    (1): Shuffle_Block(
      (branch1): Sequential(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): Conv2d(32, 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)
        (4): ReLU(inplace=True)
      )
      (branch2): Sequential(
        (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)
        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
    (2): Shuffle_Block(
      (branch2): Sequential(
        (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
    (3): Shuffle_Block(
      (branch1): Sequential(
        (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (4): ReLU(inplace=True)
      )
      (branch2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=128, bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
    (4): Shuffle_Block(
      (branch2): Sequential(
        (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
    (5): Shuffle_Block(
      (branch1): Sequential(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (4): ReLU(inplace=True)
      )
      (branch2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
    (6): Shuffle_Block(
      (branch2): Sequential(
        (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): ReLU(inplace=True)
      )
    )
  )
)

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原文地址：https://blog.csdn.net/weixin_43694096/article/details/126110274