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import math |
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import torch |
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import torch.nn.functional as F |
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from torch import nn |
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from torch.nn import Parameter |
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from .config import device, num_classes |
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class SEBlock(nn.Module): |
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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(1) |
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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 IRBlock(nn.Module): |
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expansion = 1 |
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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 ResNet(nn.Module): |
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def __init__(self, block, layers, use_se=True): |
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self.inplanes = 64 |
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self.use_se = use_se |
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super(ResNet, self).__init__() |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=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.bn2 = nn.BatchNorm2d(512) |
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self.dropout = nn.Dropout() |
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self.fc = nn.Linear(512 * 7 * 7, 512) |
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self.bn3 = nn.BatchNorm1d(512) |
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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, 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(self.inplanes, planes * block.expansion, |
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kernel_size=1, stride=stride, bias=False), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se)) |
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self.inplanes = planes |
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for i in range(1, 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.bn2(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.fc(x) |
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x = self.bn3(x) |
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return x |
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class ArcMarginModel(nn.Module): |
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def __init__(self, args): |
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super(ArcMarginModel, self).__init__() |
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self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size)) |
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nn.init.xavier_uniform_(self.weight) |
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self.easy_margin = args.easy_margin |
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self.m = args.margin_m |
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self.s = args.margin_s |
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self.cos_m = math.cos(self.m) |
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self.sin_m = math.sin(self.m) |
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self.th = math.cos(math.pi - self.m) |
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self.mm = math.sin(math.pi - self.m) * self.m |
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def forward(self, input, label): |
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x = F.normalize(input) |
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W = F.normalize(self.weight) |
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cosine = F.linear(x, W) |
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sine = torch.sqrt(1.0 - torch.pow(cosine, 2)) |
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phi = cosine * self.cos_m - sine * self.sin_m |
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if self.easy_margin: |
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phi = torch.where(cosine > 0, phi, cosine) |
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else: |
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phi = torch.where(cosine > self.th, phi, cosine - self.mm) |
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one_hot = torch.zeros(cosine.size(), device=device) |
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one_hot.scatter_(1, label.view(-1, 1).long(), 1) |
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output = (one_hot * phi) + ((1.0 - one_hot) * cosine) |
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output *= self.s |
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return output |
