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265 lines
8.1 KiB
265 lines
8.1 KiB
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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