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266 lines
8.1 KiB
266 lines
8.1 KiB
2 years ago
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import torch.nn as nn
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def conv3x3(inplanes, outplanes, stride=1):
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"""A simple wrapper for 3x3 convolution with padding.
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Args:
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inplanes (int): Channel number of inputs.
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outplanes (int): Channel number of outputs.
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stride (int): Stride in convolution. Default: 1.
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"""
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return nn.Conv2d(
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inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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class BasicBlock(nn.Module):
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"""Basic residual block used in the ResNetArcFace architecture.
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Args:
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inplanes (int): Channel number of inputs.
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planes (int): Channel number of outputs.
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stride (int): Stride in convolution. Default: 1.
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downsample (nn.Module): The downsample module. Default: None.
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"""
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expansion = 1 # output channel expansion ratio
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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.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 IRBlock(nn.Module):
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"""Improved residual block (IR Block) used in the ResNetArcFace architecture.
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Args:
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inplanes (int): Channel number of inputs.
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planes (int): Channel number of outputs.
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stride (int): Stride in convolution. Default: 1.
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downsample (nn.Module): The downsample module. Default: None.
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use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
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"""
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expansion = 1 # output channel expansion ratio
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def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
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super(IRBlock, self).__init__()
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self.bn0 = nn.BatchNorm2d(inplanes)
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self.conv1 = conv3x3(inplanes, inplanes)
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self.bn1 = nn.BatchNorm2d(inplanes)
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self.prelu = nn.PReLU()
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self.conv2 = conv3x3(inplanes, planes, stride)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.stride = stride
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self.use_se = use_se
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if self.use_se:
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self.se = SEBlock(planes)
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def forward(self, x):
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residual = x
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out = self.bn0(x)
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out = self.conv1(out)
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out = self.bn1(out)
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out = self.prelu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.use_se:
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out = self.se(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.prelu(out)
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return out
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class Bottleneck(nn.Module):
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"""Bottleneck block used in the ResNetArcFace architecture.
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Args:
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inplanes (int): Channel number of inputs.
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planes (int): Channel number of outputs.
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stride (int): Stride in convolution. Default: 1.
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downsample (nn.Module): The downsample module. Default: None.
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"""
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expansion = 4 # output channel expansion ratio
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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(
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(
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planes, planes * self.expansion, kernel_size=1, bias=False
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)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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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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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 SEBlock(nn.Module):
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"""The squeeze-and-excitation block (SEBlock) used in the IRBlock.
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Args:
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channel (int): Channel number of inputs.
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reduction (int): Channel reduction ration. Default: 16.
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"""
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def __init__(self, channel, reduction=16):
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super(SEBlock, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(
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1
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) # pool to 1x1 without spatial information
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction),
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nn.PReLU(),
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nn.Linear(channel // reduction, channel),
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nn.Sigmoid(),
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y
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class ResNetArcFace(nn.Module):
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"""ArcFace with ResNet architectures.
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Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
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Args:
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block (str): Block used in the ArcFace architecture.
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layers (tuple(int)): Block numbers in each layer.
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use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
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"""
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def __init__(self, block, layers, use_se=True):
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if block == "IRBlock":
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block = IRBlock
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self.inplanes = 64
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self.use_se = use_se
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super(ResNetArcFace, self).__init__()
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self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.prelu = nn.PReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
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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.bn4 = nn.BatchNorm2d(512)
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self.dropout = nn.Dropout()
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self.fc5 = nn.Linear(512 * 8 * 8, 512)
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self.bn5 = nn.BatchNorm1d(512)
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# initialization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.xavier_normal_(m.weight)
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elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.xavier_normal_(m.weight)
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, block, planes, num_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(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(
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block(self.inplanes, planes, stride, downsample, use_se=self.use_se)
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)
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self.inplanes = planes
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for _ in range(1, num_blocks):
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layers.append(block(self.inplanes, planes, use_se=self.use_se))
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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.bn1(x)
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x = self.prelu(x)
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x = self.maxpool(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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x = self.bn4(x)
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x = self.dropout(x)
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x = x.view(x.size(0), -1)
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x = self.fc5(x)
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x = self.bn5(x)
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return x
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