标签：__ layer nn self Attention stride pytorch Coordinate out

# CA (coordinate attention)

import torch
import torch.nn as nn
import math
import torch.nn.functional as F
from torchsummary import summary
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',
}
import torch
import torch.nn as nn


class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6


class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)

    def forward(self, x):
        return x * self.sigmoid(x)


class CoordAttention(nn.Module):

    def __init__(self, in_channels, out_channels, reduction=32):
        super(CoordAttention, self).__init__()
        self.pool_w, self.pool_h = nn.AdaptiveAvgPool2d((1, None)), nn.AdaptiveAvgPool2d((None, 1))
        temp_c = max(8, in_channels // reduction)
        self.conv1 = nn.Conv2d(in_channels, temp_c, kernel_size=1, stride=1, padding=0)

        self.bn1 = nn.BatchNorm2d(temp_c)
        self.act1 = h_swish()

        self.conv2 = nn.Conv2d(temp_c, out_channels, kernel_size=1, stride=1, padding=0)
        self.conv3 = nn.Conv2d(temp_c, out_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        short = x
        n, c, H, W = x.shape
        x_h, x_w = self.pool_h(x), self.pool_w(x).permute(0, 1, 3, 2)
        x_cat = torch.cat([x_h, x_w], dim=2)
        out = self.act1(self.bn1(self.conv1(x_cat)))
        x_h, x_w = torch.split(out, [H, W], dim=2)
        x_w = x_w.permute(0, 1, 3, 2)
        out_h = torch.sigmoid(self.conv2(x_h))
        out_w = torch.sigmoid(self.conv3(x_w))
        return short * out_w * out_h


# 搭建CA_ResNet34
class BottleneckBlock(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1,
                 norm_layer=None):
        super(BottleneckBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        self.conv1 = nn.Conv2d(inplanes, width, 1, bias=False)
        self.bn1 = norm_layer(width)
        self.conv2 = nn.Conv2d(width, width, 3, padding=dilation, stride=stride, groups=groups, dilation=dilation,
                               bias=False)
        self.bn2 = norm_layer(width)
        self.conv3 = nn.Conv2d(width, planes * self.expansion, 1, bias=False)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU()
        self.downsample = downsample
        self.stride = stride
        self.ca = CoordAttention(in_channels=planes * self.expansion, out_channels=planes * self.expansion)

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

        out = self.ca(out)  # add CA
        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, depth, n_class=1000, with_pool=True):
        super(ResNet, self).__init__()
        layer_cfg = {
            18: [2, 2, 2, 2],
            34: [3, 4, 6, 3],
            50: [3, 4, 6, 3],
            101: [3, 4, 23, 3],
            152: [3, 8, 36, 3]
        }
        layers = layer_cfg[depth]
        self.num_classes = n_class
        self.with_pool = with_pool
        self._norm_layer = nn.BatchNorm2d

        self.inplanes = 64
        self.dilation = 1

        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = self._norm_layer(self.inplanes)
        self.relu = nn.ReLU()
        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)
        if with_pool:
            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        if n_class > 0:
            self.fc = nn.Linear(512 * block.expansion, n_class)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, 1, stride=stride, bias=False),
                norm_layer(planes * block.expansion), )

        layers = []
        layers.append(
            block(self.inplanes, planes, stride, downsample, 1, 64, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, norm_layer=norm_layer))

        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)

        if self.with_pool:
            x = self.avgpool(x)

        if self.num_classes > 0:
            x = torch.flatten(x, 1)
            x = self.fc(x)

        return x


def ca_resnet34(**kwargs):
    return ResNet(BottleneckBlock, 34, **kwargs)


def resnet_CA_instance(n_class, pretrained=False, **kwargs):  # resnet34的模型
    model = ResNet(BottleneckBlock, 34, n_class, **kwargs)
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls['resnet34'])
        model_dict = model.state_dict()
        # 筛除不加载的层结构
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 更新当前网络的结构字典
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)

    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, n_class)  # 15 output classes
    stdv = 1.0 / math.sqrt(1000)
    for p in model.fc.parameters():
        p.data.uniform_(-stdv, stdv)

    return model


# 利用高阶 API 查看模型
ca_res34 = ca_resnet34(n_class=15)
print(ca_res34)
x = torch.rand(1, 3, 224, 224)
i = ca_res34(x)
print(i.shape)
summary(ca_res34, (3, 224, 224))

引用请附属作者名：叫我小张就行了

标签：__,layer,nn,self,Attention,stride,pytorch,Coordinate,out
来源： https://blog.csdn.net/qq_37278761/article/details/117249138

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。