| | 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 |
