| | import math |
| | import torch |
| | import torch.nn.functional as F |
| | from torch import nn |
| | from torch.nn import Parameter |
| | from .config import device, num_classes |
| |
|
| |
|
| |
|
| | 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 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 ResNet(nn.Module): |
| |
|
| | def __init__(self, block, layers, use_se=True): |
| | self.inplanes = 64 |
| | self.use_se = use_se |
| | super(ResNet, self).__init__() |
| | self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=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.bn2 = nn.BatchNorm2d(512) |
| | self.dropout = nn.Dropout() |
| | self.fc = nn.Linear(512 * 7 * 7, 512) |
| | self.bn3 = 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 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.bn2(x) |
| | x = self.dropout(x) |
| | |
| | x = x.view(x.size(0), -1) |
| | x = self.fc(x) |
| | x = self.bn3(x) |
| |
|
| | return x |
| |
|
| |
|
| | class ArcMarginModel(nn.Module): |
| | def __init__(self, args): |
| | super(ArcMarginModel, self).__init__() |
| |
|
| | self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size)) |
| | nn.init.xavier_uniform_(self.weight) |
| |
|
| | self.easy_margin = args.easy_margin |
| | self.m = args.margin_m |
| | self.s = args.margin_s |
| |
|
| | self.cos_m = math.cos(self.m) |
| | self.sin_m = math.sin(self.m) |
| | self.th = math.cos(math.pi - self.m) |
| | self.mm = math.sin(math.pi - self.m) * self.m |
| |
|
| | def forward(self, input, label): |
| | x = F.normalize(input) |
| | W = F.normalize(self.weight) |
| | cosine = F.linear(x, W) |
| | sine = torch.sqrt(1.0 - torch.pow(cosine, 2)) |
| | phi = cosine * self.cos_m - sine * self.sin_m |
| | if self.easy_margin: |
| | phi = torch.where(cosine > 0, phi, cosine) |
| | else: |
| | phi = torch.where(cosine > self.th, phi, cosine - self.mm) |
| | one_hot = torch.zeros(cosine.size(), device=device) |
| | one_hot.scatter_(1, label.view(-1, 1).long(), 1) |
| | output = (one_hot * phi) + ((1.0 - one_hot) * cosine) |
| | output *= self.s |
| | return output |
