| """ |
| Code Reference: |
| https://github.com/G-U-N/PyCIL/blob/master/convs/resnet_cbam.py |
| """ |
|
|
| import torch |
| import torch.nn as nn |
| import math |
| import torch.utils.model_zoo as model_zoo |
| import torch.nn.functional as F |
|
|
| __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 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) |
|
|
| 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) |
|
|
|
|
| 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) |
| 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=100, args=None): |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| assert args is not None, "you should pass args to resnet" |
| if 'cifar' in args["dataset"]: |
| self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
| nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) |
| elif 'imagenet' in args["dataset"]: |
| if args["init_cls"] == args["increment"]: |
| self.conv1 = nn.Sequential( |
| nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), |
| nn.BatchNorm2d(self.inplanes), |
| nn.ReLU(inplace=True), |
| nn.MaxPool2d(kernel_size=3, stride=2, padding=1), |
| ) |
| else: |
| self.conv1 = nn.Sequential( |
| nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
| nn.BatchNorm2d(self.inplanes), |
| nn.ReLU(inplace=True), |
| 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.feature = nn.AvgPool2d(4, stride=1) |
| |
| self.out_dim = 512 * block.expansion |
|
|
| 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.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| dim = x.size()[-1] |
| pool = nn.AvgPool2d(dim, stride=1) |
| x = pool(x) |
| x = x.view(x.size(0), -1) |
| 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 |
