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import torch |
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import torch.nn as nn |
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import math |
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import torch.utils.model_zoo as model_zoo |
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import torch.nn.functional as F |
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__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam', |
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'resnet152_cbam'] |
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model_urls = { |
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
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} |
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def conv3x3(in_planes, out_planes, stride=1): |
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"3x3 convolution with padding" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
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padding=1, bias=False) |
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class ChannelAttention(nn.Module): |
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def __init__(self, in_planes, ratio=16): |
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super(ChannelAttention, self).__init__() |
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self.avg_pool = nn.AdaptiveAvgPool2d(1) |
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self.max_pool = nn.AdaptiveMaxPool2d(1) |
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self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False) |
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self.relu1 = nn.ReLU() |
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self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False) |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x): |
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avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) |
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max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) |
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out = avg_out + max_out |
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return self.sigmoid(out) |
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class SpatialAttention(nn.Module): |
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def __init__(self, kernel_size=7): |
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super(SpatialAttention, self).__init__() |
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assert kernel_size in (3, 7), 'kernel size must be 3 or 7' |
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padding = 3 if kernel_size == 7 else 1 |
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self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x): |
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avg_out = torch.mean(x, dim=1, keepdim=True) |
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max_out, _ = torch.max(x, dim=1, keepdim=True) |
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x = torch.cat([avg_out, max_out], dim=1) |
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x = self.conv1(x) |
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return self.sigmoid(x) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.ca = ChannelAttention(planes) |
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self.sa = SpatialAttention() |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(Bottleneck, self).__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, |
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padding=1, bias=False) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * 4) |
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self.relu = nn.ReLU(inplace=True) |
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self.ca = ChannelAttention(planes * 4) |
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self.sa = SpatialAttention() |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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out = self.ca(out) * out |
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out = self.sa(out) * out |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__(self, block, layers, num_classes=100, args=None): |
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self.inplanes = 64 |
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super(ResNet, self).__init__() |
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assert args is not None, "you should pass args to resnet" |
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if 'cifar' in args["dataset"]: |
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self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) |
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elif 'imagenet' in args["dataset"]: |
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if args["init_cls"] == args["increment"]: |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), |
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nn.BatchNorm2d(self.inplanes), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1), |
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) |
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else: |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.BatchNorm2d(self.inplanes), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1), |
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) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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self.feature = nn.AvgPool2d(4, stride=1) |
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self.out_dim = 512 * block.expansion |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2. / n)) |
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elif isinstance(m, nn.BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, stride=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d(self.inplanes, planes * block.expansion, |
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kernel_size=1, stride=stride, bias=False), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample)) |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.layer4(x) |
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dim = x.size()[-1] |
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pool = nn.AvgPool2d(dim, stride=1) |
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x = pool(x) |
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x = x.view(x.size(0), -1) |
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return {"features": x} |
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def resnet18_cbam(pretrained=False, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
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if pretrained: |
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pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
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def resnet34_cbam(pretrained=False, **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
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def resnet50_cbam(pretrained=False, **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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pretrained_state_dict = model_zoo.load_url(model_urls['resnet50']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
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def resnet101_cbam(pretrained=False, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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pretrained_state_dict = model_zoo.load_url(model_urls['resnet101']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
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def resnet152_cbam(pretrained=False, **kwargs): |
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"""Constructs a ResNet-152 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
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if pretrained: |
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pretrained_state_dict = model_zoo.load_url(model_urls['resnet152']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
