| |
| """ |
| Created on 18-5-21 下午5:26 |
| |
| @author: ronghuaiyang |
| """ |
| import torch |
| import torch.nn as nn |
| import math |
| import torch.utils.model_zoo as model_zoo |
| import torch.nn.utils.weight_norm as weight_norm |
| import torch.nn.functional as F |
|
|
|
|
| |
| |
|
|
|
|
| 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 AdaIN(nn.Module): |
| def __init__(self, eps=1e-5): |
| super().__init__() |
| self.eps = eps |
| |
|
|
| def c_norm(self, x, bs, ch, eps=1e-7): |
| |
| x_var = x.var(dim=-1) + eps |
| x_std = x_var.sqrt().view(bs, ch, 1, 1) |
| x_mean = x.mean(dim=-1).view(bs, ch, 1, 1) |
| return x_std, x_mean |
|
|
| def forward(self, x, y): |
| assert x.size(0)==y.size(0) |
| size = x.size() |
| bs, ch = size[:2] |
| x_ = x.view(bs, ch, -1) |
| y_ = y.reshape(bs, ch, -1) |
| x_std, x_mean = self.c_norm(x_, bs, ch, eps=self.eps) |
| y_std, y_mean = self.c_norm(y_, bs, ch, eps=self.eps) |
| out = ((x - x_mean.expand(size)) / x_std.expand(size)) \ |
| * y_std.expand(size) + y_mean.expand(size) |
| return out |
|
|
|
|
| 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 BasicBlock_adain(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None): |
| super(BasicBlock_adain, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.adain1 = AdaIN() |
| self.relu = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| self.adain2 = AdaIN() |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, feat): |
| x, c = feat |
| residual = x |
|
|
| x = self.conv1(x) |
| out = self.adain1(x, c) |
| out = self.relu(out) |
|
|
| out = self.conv2(out) |
| out = self.adain2(out, c) |
|
|
| if self.downsample is not None: |
| residual = self.downsample(residual) |
|
|
| out += residual |
| out = self.relu(out) |
|
|
| return (out, c) |
|
|
|
|
| class IRBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): |
| super(IRBlock, self).__init__() |
| self.bn0 = nn.BatchNorm2d(inplanes) |
| self.conv1 = conv3x3(inplanes, inplanes) |
| self.bn1 = nn.BatchNorm2d(inplanes) |
| self.prelu = nn.PReLU() |
| self.conv2 = conv3x3(inplanes, planes, stride) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.downsample = downsample |
| self.stride = stride |
| self.use_se = use_se |
| if self.use_se: |
| self.se = SEBlock(planes) |
|
|
| def forward(self, x): |
| residual = x |
| out = self.bn0(x) |
| out = self.conv1(out) |
| out = self.bn1(out) |
| out = self.prelu(out) |
|
|
| out = self.conv2(out) |
| out = self.bn2(out) |
| if self.use_se: |
| out = self.se(out) |
|
|
| if self.downsample is not None: |
| residual = self.downsample(x) |
|
|
| out += residual |
| out = self.prelu(out) |
|
|
| return out |
|
|
|
|
| class IRBlock_3conv(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): |
| super(IRBlock_3conv, self).__init__() |
| self.bn0 = nn.BatchNorm2d(inplanes) |
| self.conv1 = conv3x3(inplanes, inplanes) |
| self.bn1 = nn.BatchNorm2d(inplanes) |
| self.prelu1 = nn.PReLU() |
| self.conv2 = conv3x3(inplanes, planes, stride) |
| self.bn2 = nn.BatchNorm2d(planes) |
| self.prelu2 = nn.PReLU() |
| self.conv3 = conv3x3(planes, planes) |
| self.bn3 = nn.BatchNorm2d(planes) |
| self.downsample = downsample |
| self.stride = stride |
| self.use_se = use_se |
| if self.use_se: |
| self.se = SEBlock(planes) |
| self.prelu = nn.PReLU() |
|
|
| def forward(self, x): |
| residual = x |
| out = self.bn0(x) |
| out = self.conv1(out) |
| out = self.bn1(out) |
| out = self.prelu1(out) |
|
|
| out = self.conv2(out) |
| out = self.bn2(out) |
| out = self.prelu2(out) |
|
|
| out = self.conv3(out) |
| out = self.bn3(out) |
| if self.use_se: |
| out = self.se(out) |
|
|
| if self.downsample is not None: |
| residual = self.downsample(x) |
|
|
| out += residual |
| out = self.prelu(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 * self.expansion, kernel_size=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(planes * self.expansion) |
| 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 SEBlock(nn.Module): |
| def __init__(self, channel, reduction=16): |
| super(SEBlock, self).__init__() |
| self.avg_pool = nn.AdaptiveAvgPool2d(1) |
| self.fc = nn.Sequential( |
| nn.Linear(channel, channel // reduction), |
| nn.PReLU(), |
| nn.Linear(channel // reduction, channel), |
| nn.Sigmoid() |
| ) |
|
|
| def forward(self, x): |
| b, c, _, _ = x.size() |
| y = self.avg_pool(x).view(b, c) |
| y = self.fc(y).view(b, c, 1, 1) |
| return x * y |
|
|
|
|
| class ResNetFace(nn.Module): |
| def __init__(self, block, layers, use_se=True, inc=3): |
| self.inplanes = 64 |
| self.use_se = use_se |
| super(ResNetFace, self).__init__() |
| self.conv1 = nn.Conv2d(inc, 64, kernel_size=3, padding=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(64) |
| self.prelu = nn.PReLU() |
| self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) |
| 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.bn4 = nn.BatchNorm2d(512) |
| |
| self.fc5 = nn.Linear(512 * 8 * 8, 512) |
| |
|
|
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.xavier_normal_(m.weight) |
| elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
| elif isinstance(m, nn.Linear): |
| nn.init.xavier_normal_(m.weight) |
| nn.init.constant_(m.bias, 0) |
|
|
| 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, use_se=self.use_se)) |
| self.inplanes = planes |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes, use_se=self.use_se)) |
|
|
| return nn.Sequential(*layers) |
|
|
| def features(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.prelu(x) |
| x = self.maxpool(x) |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| x = self.bn4(x) |
|
|
| return x |
| |
| def classifier(self, x): |
| x = x.view(x.size(0), -1) |
| x = self.fc5(x) |
|
|
| return x |
|
|
| def forward(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.prelu(x) |
| x = self.maxpool(x) |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| x = self.bn4(x) |
| |
| x = x.view(x.size(0), -1) |
| x = self.fc5(x) |
| |
|
|
| return x |
|
|
|
|
| class ResNet(nn.Module): |
|
|
| def __init__(self, block, layers, basedim=32, inc=1): |
| self.inplanes = basedim |
| super(ResNet, self).__init__() |
| |
| |
| self.conv1 = nn.Conv2d(inc, self.inplanes, kernel_size=3, stride=1, padding=1, |
| bias=False) |
| self.bn1 = nn.BatchNorm2d(self.inplanes) |
| self.relu = nn.ReLU(inplace=True) |
| |
| self.layer1 = self._make_layer(block, basedim, layers[0], stride=2) |
| self.layer2 = self._make_layer(block, 2*basedim, layers[1], stride=2) |
| self.layer3 = self._make_layer(block, 4*basedim, layers[2], stride=2) |
| self.layer4 = self._make_layer(block, 8*basedim, layers[3], stride=2) |
| |
| |
| self.fc5 = nn.Linear(512 * 8 * 8, 512) |
|
|
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_( |
| m.weight, mode='fan_out', nonlinearity='relu') |
| elif isinstance(m, nn.BatchNorm2d): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
|
|
| 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 features(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
|
|
| return x |
| |
| def classifier(self, x): |
| x = x.view(x.size(0), -1) |
| x = self.fc5(x) |
|
|
| return x |
|
|
| def forward(self, x): |
| x = self.conv1(x) |
| x = self.bn1(x) |
| x = self.relu(x) |
| |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| x = self.layer3(x) |
| x = self.layer4(x) |
| |
| |
| x = x.view(x.size(0), -1) |
| x = self.fc5(x) |
|
|
| return x |
|
|
|
|
| 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: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) |
| 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: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) |
| 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: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) |
| 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: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) |
| 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: |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) |
| return model |
|
|
|
|
| def resnet_face18(use_se=True, **kwargs): |
| model = ResNetFace(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs) |
| return model |
|
|
|
|
| def resnet_face62(use_se=True, **kwargs): |
| model = ResNetFace(IRBlock_3conv, [3, 4, 10, 3], use_se=use_se, **kwargs) |
| return model |
|
|
| if __name__ == "__main__": |
| net = HR_resnet() |
| dummy = torch.rand(10,3,256,256) |
| x = net(dummy) |
| print('output:', x.size()) |
|
|
