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							- 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):
 
-     if opt.model == 'pix2pixHD':
 
-         #from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
 
-         from .fs_model import fsModel
 
-         model = fsModel()
 
-     else:
 
-         from .ui_model import UIModel
 
-         model = UIModel()
 
-     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  # cos(theta + 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
 
 
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