SEResNet_IR.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File   ：ResNet -> SEResNet-IR
@IDE    ：PyCharm
@Author ：DIPTE
@Date   ：2020/7/29 4:24
@Desc   ：
=================================================='''
#!/usr/bin/env python
# encoding: utf-8

import torch
import torch.nn as nn
from torchsummary import summary
def ResNet18():
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    return model

def ResNet34():
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    return model

__all__ = ['ResNet', 'ResNet18', 'ResNet34']



def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

class SEModule(nn.Module):
    def __init__(self, channels, reduction=16):
        super(SEModule, self).__init__()
        # squeeze
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        # excitation
        self.fc = nn.Sequential(
                nn.Linear(channels, channels // reduction),
                nn.ReLU(inplace=True),
                nn.Linear(channels // reduction, channels),
                nn.Sigmoid()
        )
    def forward(self, x):
            B, C, _, _ = x.size()
            y = self.avg_pool(x).view(B, C)
            y = self.fc(y)
            y = y.view(B, C, 1, 1)
            return x*y
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.bn0 = nn.BatchNorm2d(inplanes)
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.PReLU(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.semodule=SEModule(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.bn0(x)
        out = self.conv1(out)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.semodule(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Flatten(nn.Module):
    def forward(self, input):
        return input.view(input.size(0), -1)


class ResNet(nn.Module):

    def __init__(self, block, layers, feature_dim=512, drop_ratio=0.4, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.output_layer = nn.Sequential(nn.BatchNorm2d(512 * block.expansion),
                                          nn.Dropout(drop_ratio),
                                          Flatten(),
                                          nn.Linear(512 * block.expansion * 7 * 7, feature_dim),
                                          nn.BatchNorm1d(feature_dim))

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.output_layer(x)

        return x


if __name__ == "__main__":
    input = torch.Tensor(2, 3, 112, 112)
    net = ResNet18()#ResNet34()
    print(net)

    x = net(input)
    print(x.shape)


    summary(net.cuda(), (3, 112, 112))