| import torch.nn as nn
|
| from basicsr.utils.registry import ARCH_REGISTRY
|
|
|
|
|
| def conv3x3(inplanes, outplanes, stride=1):
|
| """A simple wrapper for 3x3 convolution with padding.
|
|
|
| Args:
|
| inplanes (int): Channel number of inputs.
|
| outplanes (int): Channel number of outputs.
|
| stride (int): Stride in convolution. Default: 1.
|
| """
|
| return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
|
|
|
|
|
| class BasicBlock(nn.Module):
|
| """Basic residual block used in the ResNetArcFace architecture.
|
|
|
| Args:
|
| inplanes (int): Channel number of inputs.
|
| planes (int): Channel number of outputs.
|
| stride (int): Stride in convolution. Default: 1.
|
| downsample (nn.Module): The downsample module. Default: None.
|
| """
|
| 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 IRBlock(nn.Module):
|
| """Improved residual block (IR Block) used in the ResNetArcFace architecture.
|
|
|
| Args:
|
| inplanes (int): Channel number of inputs.
|
| planes (int): Channel number of outputs.
|
| stride (int): Stride in convolution. Default: 1.
|
| downsample (nn.Module): The downsample module. Default: None.
|
| use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
|
| """
|
| 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 Bottleneck(nn.Module):
|
| """Bottleneck block used in the ResNetArcFace architecture.
|
|
|
| Args:
|
| inplanes (int): Channel number of inputs.
|
| planes (int): Channel number of outputs.
|
| stride (int): Stride in convolution. Default: 1.
|
| downsample (nn.Module): The downsample module. Default: None.
|
| """
|
| 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):
|
| """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
|
|
|
| Args:
|
| channel (int): Channel number of inputs.
|
| reduction (int): Channel reduction ration. Default: 16.
|
| """
|
|
|
| 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
|
|
|
|
|
| @ARCH_REGISTRY.register()
|
| class ResNetArcFace(nn.Module):
|
| """ArcFace with ResNet architectures.
|
|
|
| Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
|
|
|
| Args:
|
| block (str): Block used in the ArcFace architecture.
|
| layers (tuple(int)): Block numbers in each layer.
|
| use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
|
| """
|
|
|
| def __init__(self, block, layers, use_se=True):
|
| if block == 'IRBlock':
|
| block = IRBlock
|
| self.inplanes = 64
|
| self.use_se = use_se
|
| super(ResNetArcFace, self).__init__()
|
|
|
| self.conv1 = nn.Conv2d(1, 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, num_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 _ in range(1, num_blocks):
|
| layers.append(block(self.inplanes, planes, use_se=self.use_se))
|
|
|
| return nn.Sequential(*layers)
|
|
|
| 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 |
