178 lines
5.5 KiB
Python
178 lines
5.5 KiB
Python
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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def create_model(opt):
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#from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
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from .fs_model import fsModel
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model = fsModel()
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model.initialize(opt)
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if opt.verbose:
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print("model [%s] was created" % (model.name()))
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if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
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model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
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return model
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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 # cos(theta + 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
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