CBAM非官方代码及官方代码_cbam模型代码

非官方代码

两大模块:

class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        # 共享权重的MLP
        self.fc1   = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2   = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        return self.sigmoid(out)


class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()
        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        padding = 3 if kernel_size == 7 else 1
        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)
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应用于resnet中:

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

__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam',
           'resnet152_cbam']

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.ca = ChannelAttention(planes)
        self.sa = SpatialAttention()

        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.ca(out) * out
        out = self.sa(out) * 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.ca = ChannelAttention(planes * 4)
        self.sa = SpatialAttention()

        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)

        out = self.ca(out) * out
        out = self.sa(out) * 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)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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)

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

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18_cbam(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:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
        now_state_dict        = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model


def resnet34_cbam(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:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet34'])
        now_state_dict        = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model


def resnet50_cbam(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:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
        now_state_dict        = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model


def resnet101_cbam(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:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet101'])
        now_state_dict        = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model


def resnet152_cbam(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:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet152'])
        now_state_dict        = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model
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官方代码

import torch
import math
import torch.nn as nn
import torch.nn.functional as F

# 基础卷积套餐：conv+bn+relu
class BasicConv(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
        super(BasicConv, self).__init__()
        self.out_channels = out_planes
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None
        self.relu = nn.ReLU() if relu else None

    def forward(self, x):
        x = self.conv(x)
        if self.bn is not None:
            x = self.bn(x)
        if self.relu is not None:
            x = self.relu(x)
        return x

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

class ChannelGate(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
        super(ChannelGate, self).__init__()
        self.gate_channels = gate_channels
        # shared MLP 多层感知机
        self.mlp = nn.Sequential(
            Flatten(),
            nn.Linear(gate_channels, gate_channels // reduction_ratio),
            nn.ReLU(),
            nn.Linear(gate_channels // reduction_ratio, gate_channels)
            )
        self.pool_types = pool_types
    def forward(self, x):
        channel_att_sum = None
        for pool_type in self.pool_types:
            if pool_type=='avg':
                avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))		
                # avg_pool size: [bs, gate_channels, 1, 1]
                channel_att_raw = self.mlp( avg_pool )
            elif pool_type=='max':
                max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
                channel_att_raw = self.mlp( max_pool )
            elif pool_type=='lp':
                lp_pool = F.lp_pool2d( x, 2, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
                channel_att_raw = self.mlp( lp_pool )
            elif pool_type=='lse':
                # LSE pool only
                lse_pool = logsumexp_2d(x)
                channel_att_raw = self.mlp( lse_pool )

            if channel_att_sum is None:
                channel_att_sum = channel_att_raw
            else:
            	# avg_pool + max_pool
                channel_att_sum = channel_att_sum + channel_att_raw

        scale = F.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)
        return x * scale

def logsumexp_2d(tensor):
    tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)
    # [bs, channel, w, h] to [bs, channel, w*h]
    s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)
    outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()
    return outputs

class ChannelPool(nn.Module):
    def forward(self, x):
    	# 沿着原feature的channel分别做max_pool和avg_pool，然后将两者concat
        return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )

class SpatialGate(nn.Module):
    def __init__(self):
        super(SpatialGate, self).__init__()
        kernel_size = 7
        self.compress = ChannelPool()
        self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)
    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = F.sigmoid(x_out) # broadcasting
        return x * scale

class CBAM(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
        super(CBAM, self).__init__()
        self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)
        self.no_spatial=no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()
    def forward(self, x):
        x_out = self.ChannelGate(x)
        if not self.no_spatial:
            x_out = self.SpatialGate(x_out)
        return x_out
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