pytorch复现ShuffleNetV2

import torch
import torch.nn as nn
from torch import Tensor
from typing import List, Callable
#通道重排
def channel_shuffle(x: Tensor, groups: int) -> Tensor:
    batch_size, num_channels,height,width = x.size()
    channel_pre_group = num_channels // groups

    #reshape
    # [batch_size, num_channels, height, width] -> [batch_size, groups, channels_per_group, height, width]
    x = x.view(batch_size, groups, channel_pre_group, height, width)

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

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


class InvertedResidual(nn.Module):
    def __init__(self, input_c: int, output_c: int, stride: int):
        super(InvertedResidual, self).__init__()

        if stride not in [1,2]:
            raise ValueError("illegal stride value.")

        self.stride = stride

        assert output_c % 2 == 0

        branch_feature = output_c // 2

        # 当stride为1时,input_channel应该是branch_features的两倍
        # python中 '<<' 是位运算,可理解为计算×2的快速方法
        # 当 stride = 2时就往下走,如果等于2 就判断 input_c == branch_features << 1
        assert (self.stride!=1) or(input_c == branch_feature << 1)

        #左边的分支
        if self.stride ==2 :
            self.branch1 = nn.Sequential(
                self.depthwise_conv(input_c, input_c, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(input_c),
                nn.Conv2d(input_c, branch_feature, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_feature),
                nn.ReLU(inplace=True)
            )

        else:
            self.branch1 = nn.Sequential()


        self.branch2 = nn.Sequential(
            nn.Conv2d(input_c if self.stride>1 else branch_feature, branch_feature, kernel_size=1,
                      stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_feature),
            nn.ReLU(inplace=True),
            self.depthwise_conv(branch_feature, branch_feature, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_feature),
            nn.Conv2d(branch_feature, branch_feature,kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_feature),
            nn.ReLU(inplace=True)
        )


    @staticmethod
    def depthwise_conv(input_c, output_c, kernel_size, stride=1, padding=0, bias=False)->nn.Conv2d:
        return nn.Conv2d(in_channels=input_c,
                         out_channels=output_c,
                         kernel_size=kernel_size,
                         stride=stride,
                         padding=padding,
                         bias=bias,
                         groups=input_c)

    def forward(self, x: Tensor) -> Tensor:

        if self.stride ==1 :
            #dim=1 在channel维度上
            x1, x2 = x.chunk(2, dim=1)
            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,
                 stages_repeats: List[int],
                 stages_out_channels: List[int],
                 num_classes: int = 1000,
                 inverted_residual: Callable[..., nn.Module] = InvertedResidual):
        super(ShuffleNetV2,self).__init__()


        if len(stages_repeats) != 3:
            raise ValueError("expected stages_repeats as list of 3 positive ints")
        if len(stages_out_channels) != 5:
            raise ValueError("expected stages_out_channels as list of 5 positive ints")

        self._stage_out_channels = stages_out_channels
        #RGB
        input_channels = 3
        output_channels = self._stage_out_channels[0]

        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=2, padding=1,bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True)
        )

        input_channels = output_channels
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # Static annotations for mypy
        self.stage2: nn.Sequential
        self.stage3: nn.Sequential
        self.stage4: nn.Sequential

        stage_names =["stage{}".format(i) for i in [2,3,4]]

        for name,repeats,output_channels in zip(stage_names, stages_repeats, self._stage_out_channels[1:]):
            seq = [inverted_residual(input_channels, output_channels, 2)]
            for i in range(repeats-1):
                seq.append(inverted_residual(output_channels, output_channels, 1))
            #给self.stage_ 设置值
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels

        output_channels = self._stage_out_channels[-1]

        self.conv5 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1,padding=0, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True)
        )

        self.fc = nn.Linear(output_channels, num_classes)

    def _forward_impl(self,x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        x = x.mean([2,3]) #avgpool  2,3 是h和w维度
        x = self.fc(x)
        return x

    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)



def shufflenet_v2_x0_5(num_classes = 1000):
    #weight: https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth
    model = ShuffleNetV2(stages_repeats=[4, 8, 4],
                         stages_out_channels = [24, 48, 96, 192, 1024],
                         num_classes = num_classes
                         )
    return model


def shufflenet_v2_x1_0(num_classes = 1000):
    #weight: https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth
    model = ShuffleNetV2(stages_repeats=[4, 8, 4],
                         stages_out_channels = [24, 116, 232, 464, 1024],
                         num_classes = num_classes
                         )
    return model

def shufflenet_v2_x1_5(num_classes = 1000):
    #weight: https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth
    model = ShuffleNetV2(stages_repeats=[4, 8, 4],
                         stages_out_channels = [24, 176, 352, 704, 1024],
                         num_classes = num_classes
                         )
    return model

def shufflenet_v2_x2_0(num_classes = 1000):
    #weight: https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth
    model = ShuffleNetV2(stages_repeats=[4, 8, 4],
                         stages_out_channels = [24, 244, 488, 976, 2048],
                         num_classes = num_classes
                         )
    return model



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