| 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 |
|
|
|
|
| def create_model(opt): |
| |
| from .fs_model import fsModel |
| model = fsModel() |
|
|
| model.initialize(opt) |
| if opt.verbose: |
| print("model [%s] was created" % (model.name())) |
|
|
| if opt.isTrain and len(opt.gpu_ids) and not opt.fp16: |
| model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids) |
|
|
| return model |
|
|
|
|
|
|
| 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 |
|
|
