resnet.py

import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride, dilation)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

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

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

        return out


class Bottleneck(nn.Module):
    '''BottleNeck 为了减少计算量'''
    # 定义一个类变量
    expansion = 4

    # downsample = None
    def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        # 卷积层之后总会添加BatchNorm2d进行数据的归一化处理，这使得数据在进行Relu之前不会因为数据过大而导致网络性能的不稳定。
        self.bn1 = nn.BatchNorm2d(planes)
        # original padding is 1; original dilation is 1
        self.conv2 = nn.Conv2d(planes, planes,
                               kernel_size=3, stride=stride,
                               padding=dilation, bias=False, dilation=dilation)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out


class ResNet(nn.Module):
    '''
    使用Bottleneck 构造 Resnet

    :parameter block: Bottleneck
    :parameter layers: 整数list。legnth为层数， 每个元素是每层中包含的Bottleneck的个数
    '''
    # model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    # **kwargs: last_conv_stride=1, last_conv_dilation=1
    # block = Bottleneck, layers = [3, 4, 6, 3]
    def __init__(self, block, layers, last_conv_stride=2, last_conv_dilation=1):

        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, 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=last_conv_stride,
                                       dilation=last_conv_dilation)
        # 遍历网络中的每个 module，进行初始化
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    # layers = [3, 4, 6, 3]
    #   _make_layer(      block, 64,     layers[0]=3)
    #   _make_layer(      block, 128,    layers[1]=4, stride=2)
    #   _make_layer(      block, 256,    layers[2]=6, stride=2)
    #   _make_layer(      block, 512,    layers[3]=3, stride=last_conv_stride,
    #                                              dilation=last_conv_dilation)
    #
    # blokc = Bottleneck


    '''使用多个 Bottleneck 来构造一个新的层'''
    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        '''
        使用多个 Bottleneck 来构造一个新的层

         :parameter block: Bottleneck
         :parameter planes: 输出维度
         :parameter blocks: 当前层包含几个Bottlenec
         '''
        downsample = None
        #                self.inplanes = 64    Bottleneck.expansion = 4
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, dilation))
        self.inplanes = planes * block.expansion
        for i 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)

        return x


def remove_fc(state_dict):
    """Remove the fc layer parameters from state_dict."""
    # for key, value in state_dict.items():
    for key, value in list(state_dict.items()):
        if key.startswith('fc.'):
            del state_dict[key]
    return state_dict


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet18'])))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet34'])))
    return model


# model_v = resnet50(pretrained=True, last_conv_stride=1, last_conv_dilation=1)
def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet50'])))
        model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet50'])))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(
            remove_fc(model_zoo.load_url(model_urls['resnet101'])))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(
            remove_fc(model_zoo.load_url(model_urls['resnet152'])))
    return model