code stringlengths 3 6.57k |
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layers.append(nn.InstanceNorm2d(out_features) |
block(256, 512, normalization=False) |
block(512, 1024) |
block(1024, 2048) |
nn.Linear(2048*input_size**2, opt.n_classes) |
nn.Softmax() |
forward(self, img) |
self.model(img) |
feature_repr.view(feature_repr.size(0) |
self.output_layer(feature_repr) |
Classifier(nn.Module) |
__init__(self) |
super(Classifier, self) |
__init__() |
block(in_features, out_features, normalization=True) |
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1) |
nn.LeakyReLU(0.2, inplace=True) |
layers.append(nn.InstanceNorm2d(out_features) |
block(opt.channels, 64, normalization=False) |
block(64, 128) |
block(128, 256) |
block(256, 512) |
nn.Linear(512*input_size**2, opt.n_classes) |
nn.Softmax() |
forward(self, img) |
self.model(img) |
feature_repr.view(feature_repr.size(0) |
self.output_layer(feature_repr) |
torch.nn.MSELoss() |
torch.nn.MSELoss() |
torch.nn.CrossEntropyLoss() |
target_encode_Generator() |
target_decode_Generator() |
source_encode_Generator() |
source_decode_Generator() |
encode_Discriminator() |
Discriminator() |
Classifier() |
target_encode_generator.cuda() |
target_decode_generator.cuda() |
source_encode_generator.cuda() |
source_decode_generator.cuda() |
encode_discriminator.cuda() |
discriminator.cuda() |
classifier.cuda() |
adversarial_loss.cuda() |
encode_adversarial_loss.cuda() |
task_loss.cuda() |
target_encode_generator.apply(weights_init_normal) |
target_decode_generator.apply(weights_init_normal) |
source_encode_generator.apply(weights_init_normal) |
source_decode_generator.apply(weights_init_normal) |
encode_discriminator.apply(weights_init_normal) |
discriminator.apply(weights_init_normal) |
classifier.apply(weights_init_normal) |
os.makedirs('../../data/mnist', exist_ok=True) |
transforms.Resize(opt.img_size) |
transforms.ToTensor() |
transforms.Normalize((0.5, 0.5, 0.5) |
os.makedirs('../../data/mnistm', exist_ok=True) |
transforms.Resize(opt.img_size) |
transforms.ToTensor() |
transforms.Normalize((0.5, 0.5, 0.5) |
torch.optim.Adam( itertools.chain(target_encode_generator.parameters() |
source_encode_generator.parameters() |
target_decode_generator.parameters() |
source_decode_generator.parameters() |
classifier.parameters() |
torch.optim.Adam(itertools.chain(encode_discriminator.parameters() |
discriminator.parameters() |
range(opt.n_epochs) |
enumerate(zip(dataloader_A, dataloader_B) |
imgs_A.size(0) |
Variable(FloatTensor(batch_size, *patch) |
fill_(1.0) |
Variable(FloatTensor(batch_size, *patch) |
fill_(0.0) |
Variable(imgs_A.type(FloatTensor) |
expand(batch_size, 3, opt.img_size, opt.img_size) |
Variable(labels_A.type(LongTensor) |
Variable(imgs_B.type(FloatTensor) |
optimizer_G.zero_grad() |
Variable(FloatTensor(np.random.uniform(-1, 1, (batch_size, opt.latent_dim) |
source_encode_generator(imgs_A, z) |
source_decode_generator(imgs_A_x, encode_fake_B) |
classifier(decode_fake_B) |
task_loss(label_pred, labels_A) |
task_loss(classifier(imgs_A) |
adversarial_loss(discriminator(decode_fake_B) |
encode_adversarial_loss(encode_discriminator(encode_fake_B) |
g_loss.backward() |
optimizer_G.step() |
optimizer_D.zero_grad() |
target_encode_generator(imgs_B, z) |
target_decode_generator(imgs_B_x, encode_real_B) |
adversarial_loss(encode_discriminator(encode_real_B) |
adversarial_loss(encode_discriminator(encode_fake_B.detach() |
adversarial_loss(discriminator(decode_real_B) |
adversarial_loss(discriminator(decode_fake_B.detach() |
d_loss.backward() |
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