resnet50特征图分辨率和通道数的变化

resnet50构建的核心代码如下,注释内容说明了特征图分辨率和通道数的变化情况

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False) #特征图分辨率降低为1/2,通道数从3升为64
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)#特征图分辨率降低为1/4,通道数仍然为64
        self.layer1 = self._make_layer(block, 64, layers[0]) #stride为1,不改变分辨率,依然为1/4,通道数从64升为64*4=256
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)#stride为2,特征图分辨率降低为1/8,通道数从256升为128*4=512
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)#stride为2,特征图分辨率降低为1/16,通道数从512升为256*4=1024
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)#stride为2,特征图分辨率降低为1/32,通道数从512升为256*4=2048

一般使用resnet50输出的4个特征图。4个特征图的分辨率依次为原始图片的1/4, 1/8, 1/16, 1/32,通道数依次为256,512,1024,2048。

    def forward(self, x):
        x = self.conv1(x) #特征图分辨率降低为1/2,通道数变为64
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)#特征图分辨率降低为1/4,通道数仍然为64

        x1 = self.layer1(x)#特征图分辨率仍然为1/4,通道数变为为256
        x2 = self.layer2(x1)#特征图分辨率变为1/8,通道数变为为512
        x3 = self.layer3(x2)#特征图分辨率变为1/16,通道数变为为1024
        x4 = self.layer4(x3)#特征图分辨率变为1/32,通道数变为为2048

        return [x4, x3, x2, x1]

完整代码如下

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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        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):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        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):

    def __init__(self, block, layers, num_classes=1000):
        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=2)

        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_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        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))
        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)

        x1 = self.layer1(x)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)

        return [x4, x3, x2, x1]


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:
        from collections import OrderedDict
        state_dict = model.state_dict()
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
        for k, v in pretrained_state_dict.items():
            if k not in state_dict:
                continue
            state_dict[k] = v
        model.load_state_dict(state_dict)
    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:
        from collections import OrderedDict
        state_dict = model.state_dict()
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet34'])
        for k, v in pretrained_state_dict.items():
            if k not in state_dict:
                continue
            state_dict[k] = v
        model.load_state_dict(state_dict)
    return model


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:
        print('Initialize with pre-trained ResNet')
        from collections import OrderedDict
        state_dict = model.state_dict()
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
        for k, v in pretrained_state_dict.items():
            if k not in state_dict:
                continue
            state_dict[k] = v
        print('successfully load '+str(len(state_dict.keys()))+' keys')
        model.load_state_dict(state_dict)
    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:
        print('Initialize with pre-trained ResNet')
        from collections import OrderedDict
        state_dict = model.state_dict()
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet101'])
        for k, v in pretrained_state_dict.items():
            if k not in state_dict:
                continue
            state_dict[k] = v
        print('successfully load '+str(len(state_dict.keys()))+' keys')
        model.load_state_dict(state_dict)
    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:
        from collections import OrderedDict
        state_dict = model.state_dict()
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet152'])
        for k, v in pretrained_state_dict.items():
            if k not in state_dict:
                continue
            state_dict[k] = v
        model.load_state_dict(state_dict)
    return model

if __name__=='__main__':
    import torch
    model = resnet50()
    input = torch.rand([4,3,256,192])
    print(input.shape)
    output = model(input)
    print(type(output))
    print(len(output))
    print(output[0].shape)
    print(output[1].shape)
    print(output[2].shape)
    print(output[3].shape)

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