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)
line_list = line.strip().split(' ')
fine_coarse_map.append([int(line_list[i]) for i in range(7)])
line = file_map.readline()
# network construction
feature_len = 2048
# fine-grained feature extractor + fine-grained label predictor
my_fine_net = fine_net(feature_len)
my_fine_net = my_fine_net.to(device)
my_fine_net.train(True)
# domain discriminator
my_discriminator = discriminator(256)
my_discriminator = my_discriminator.to(device)
my_discriminator.train(True)
# gradient reversal layer
my_grl = ad.AdversarialLayer(max_iter=args.max_iteration)
# coarse-grained feature extractor + coarse-grained label predictor
my_coarse_extractor = coarse_extractor()
my_coarse_extractor = my_coarse_extractor.to(device)
my_coarse_extractor.train(True)
my_coarse_predictor = []
for i in range(num_coarse_cate_sel):
my_coarse_predictor.append(predictor(256, cate_all[i + 1]))
my_coarse_predictor[i].to(device)
my_coarse_predictor[i].train(True)
# criterion and optimizer
criterion = {
"classifier": nn.CrossEntropyLoss(),
"kl_loss": nn.KLDivLoss(size_average=False),
"adversarial": nn.BCELoss()
