def __init__(self, num_features):
super(GRL, self).__init__()
self.model_fc = model.Resnet50Fc()
# for param in self.model_fc.parameters():
# param.requires_grad=False
self.bottleneck_layer1 = nn.Linear(num_features, 256)
self.bottleneck_layer1.weight.data.normal_(0, 0.005)
self.bottleneck_layer1.bias.data.fill_(0.1)
self.bottleneck_layer = nn.Sequential(self.bottleneck_layer1,
nn.ReLU(), nn.Dropout(0.5))
self.classifier_layer = nn.Linear(256, len(dset_classes))
self.classifier_layer.weight.data.normal_(0, 0.01)
self.classifier_layer.bias.data.fill_(0.0)
self.predict_layer = nn.Sequential(self.model_fc,
self.bottleneck_layer,
self.classifier_layer)
self.ad_layer1 = nn.Linear(256 * len(dset_classes), 1024)
self.ad_layer2 = nn.Linear(1024, 1024)
self.ad_layer3 = nn.Linear(1024, 1)
self.ad_layer1.weight.data.normal_(0, 0.01)
self.ad_layer2.weight.data.normal_(0, 0.01)
self.ad_layer3.weight.data.normal_(0, 0.3)
self.ad_layer1.bias.data.fill_(0.0)
self.ad_layer2.bias.data.fill_(0.0)
self.ad_layer3.bias.data.fill_(0.0)
self.ad_net = nn.Sequential(self.ad_layer1, nn.ReLU(),
nn.Dropout(0.5), self.ad_layer2,
nn.ReLU(), nn.Dropout(0.5),
self.ad_layer3, nn.Sigmoid())
self.grl = ad.AdversarialLayer(high=1.0)
def __init__(self, num_features):
super(Net, self).__init__()
self.model_fc = model_no.Resnet50Fc()
self.classifier_layer = nn.Linear(num_features, len(dset_classes))
self.classifier_layer.weight.data.normal_(0, 0.01)
self.classifier_layer.bias.data.fill_(0.0)
self.predict_layer = nn.Sequential(self.model_fc,
self.classifier_layer)
def __init__(self, num_features):
super(BSP_CDAN, self).__init__()
if visda == True:
self.model_fc = model.Resnet101Fc()
else:
self.model_fc = model.Resnet50Fc()
self.bottleneck_layer1 = nn.Linear(num_features, 256)
self.bottleneck_layer1.apply(init_weights)
self.bottleneck_layer = nn.Sequential(self.bottleneck_layer1, nn.ReLU(), nn.Dropout(0.5))
self.classifier_layer = nn.Linear(256, len(dset_classes))
self.classifier_layer.apply(init_weights)
self.predict_layer = nn.Sequential(self.model_fc, self.bottleneck_layer, self.classifier_layer)
