Delete SimSwap/models/models.py

SimSwap/models/models.py

@@ -1,177 +0,0 @@
-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 .pix2pixHD_model import Pix2PixHDModel, InferenceModel
-    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  # 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