| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181 | import mathimport torchimport torch.nn.functional as Ffrom torch import nnfrom torch.nn import Parameterfrom .config import device, num_classesdef 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 modelclass 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 * yclass 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 outclass 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 xclass 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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