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109 lines
3.2 KiB
109 lines
3.2 KiB
from torch import nn
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import torch.nn.functional as F
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from torchvision import models
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class ResNet50Mod(nn.Module):
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def __init__(self):
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super(ResNet50Mod, self).__init__()
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model_resnet50 = models.resnet50(pretrained=True)
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self.freezed_rn50 = nn.Sequential(
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model_resnet50.conv1,
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model_resnet50.bn1,
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model_resnet50.relu,
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model_resnet50.maxpool,
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model_resnet50.layer1,
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model_resnet50.layer2,
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model_resnet50.layer3,
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)
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self.layer4 = model_resnet50.layer4
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self.avgpool = model_resnet50.avgpool
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self.__in_features = model_resnet50.fc.in_features
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def forward(self, x):
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x = self.freezed_rn50(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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return x
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class Encoder(nn.Module):
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def __init__(self, in_channels=3, h=256, dropout=0.5, srcTrain=False):
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super(Encoder, self).__init__()
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# resnetModel = models.resnet50(pretrained=True)
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# feature_map = list(resnetModel.children())
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# feature_map.pop()
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# self.feature_extractor = nn.Sequential(*feature_map)
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rnMod = ResNet50Mod()
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self.feature_extractor = rnMod.freezed_rn50
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self.layer4 = rnMod.layer4
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self.avgpool = rnMod.avgpool
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if srcTrain:
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for param in self.feature_extractor.parameters():
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param.requires_grad = False
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def forward(self, x):
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x = x.expand(x.data.shape[0], 3, 224, 224)
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x = self.feature_extractor(x)
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###
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x = self.layer4(x)
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x = self.avgpool(x)
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###
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x = x.view(x.size(0), -1)
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return x
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class Classifier(nn.Module):
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def __init__(self, n_classes, dropout=0.5):
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super(Classifier, self).__init__()
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self.l1 = nn.Linear(2048, n_classes)
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# for m in self.modules():
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# if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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# nn.init.kaiming_normal_(m.weight)
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def forward(self, x):
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x = self.l1(x)
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return x
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class CNN(nn.Module):
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def __init__(self, in_channels=3, n_classes=31, target=False, srcTrain=False):
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super(CNN, self).__init__()
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self.encoder = Encoder(in_channels=in_channels, srcTrain=srcTrain)
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self.classifier = Classifier(n_classes)
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if target:
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for param in self.classifier.parameters():
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param.requires_grad = False
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def forward(self, x):
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x = self.encoder(x)
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x = self.classifier(x)
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return x
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class Discriminator(nn.Module):
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def __init__(self, h=500, args=None):
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super(Discriminator, self).__init__()
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self.l1 = nn.Linear(2048, h)
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self.l2 = nn.Linear(h, h)
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self.l3 = nn.Linear(h, 2)
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###
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self.l4 = nn.LogSoftmax(dim=1)
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###
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self.slope = args.slope
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# for m in self.modules():
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# if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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# nn.init.kaiming_normal_(m.weight)
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def forward(self, x):
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x = F.leaky_relu(self.l1(x), self.slope)
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x = F.leaky_relu(self.l2(x), self.slope)
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x = self.l3(x)
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###
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x = self.l4(x)
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###
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return x
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