# -*- coding: utf-8 -*- """ 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 # __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', } 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 # self.l1 = nn.Linear(num_classes, in_channel*4, bias=True) #bias is good :) def c_norm(self, x, bs, ch, eps=1e-7): # assert isinstance(x, torch.cuda.FloatTensor) 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 is content, c is style 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.dropout = nn.Dropout() self.fc5 = nn.Linear(512 * 8 * 8, 512) #self.bn5 = nn.BatchNorm1d(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 = self.dropout(x) x = x.view(x.size(0), -1) x = self.fc5(x) #x = self.bn5(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(3, 64, kernel_size=7, stride=2, padding=3, # bias=False) 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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) 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.avgpool = nn.AvgPool2d(8, stride=1) # self.fc = nn.Linear(512 * block.expansion, num_classes) 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.maxpool(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.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # x = nn.AvgPool2d(kernel_size=x.size()[2:])(x) # x = self.avgpool(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())