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Minor changes #26

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872c065
Changes to accomodate newer pytorch version
ArkAung Jul 26, 2020
c5b5ab6
Fix shape issue in labels for discriminator
ArkAung Jul 26, 2020
8993aa7
Add learning rates as arguments
ArkAung Jul 26, 2020
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3 changes: 3 additions & 0 deletions arguments.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,9 @@ def get_args():
parser.add_argument('--dataset', type=str, default='cifar10', help='Name of the dataset used.')
parser.add_argument('--batch_size', type=int, default=128, help='Batch size used for training and testing')
parser.add_argument('--train_epochs', type=int, default=100, help='Number of training epochs')
parser.add_argument('--lr_vae', type=float, default=5e-4, help='Learning rate for VAE')
parser.add_argument('--lr_dis', type=float, default=5e-4, help='Learning rate for Discriminator')
parser.add_argument('--lr_task', type=float, default=5e-4, help='Learning rate for Task Module')
parser.add_argument('--latent_dim', type=int, default=32, help='The dimensionality of the VAE latent dimension')
parser.add_argument('--data_path', type=str, default='./data', help='Path to where the data is')
parser.add_argument('--beta', type=float, default=1, help='Hyperparameter for training. The parameter for VAE')
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2 changes: 1 addition & 1 deletion model.py
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Expand Up @@ -110,7 +110,7 @@ def forward(self, z):

def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
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14 changes: 7 additions & 7 deletions solver.py
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Expand Up @@ -42,9 +42,9 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_data = self.read_data(querry_dataloader)
unlabeled_data = self.read_data(unlabeled_dataloader, labels=False)

optim_vae = optim.Adam(vae.parameters(), lr=5e-4)
optim_task_model = optim.SGD(task_model.parameters(), lr=0.01, weight_decay=5e-4, momentum=0.9)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=5e-4)
optim_vae = optim.Adam(vae.parameters(), lr=self.args.lr_vae)
optim_task_model = optim.SGD(task_model.parameters(), lr=self.args.lr_task, weight_decay=5e-4, momentum=0.9)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=self.args.lr_dis)


vae.train()
Expand Down Expand Up @@ -87,8 +87,8 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)

lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_real_preds = torch.ones(unlabeled_imgs.size(0))
lab_real_preds = torch.ones(labeled_imgs.size(0), 1)
unlab_real_preds = torch.ones(unlabeled_imgs.size(0), 1)

if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
Expand Down Expand Up @@ -120,8 +120,8 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)

lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_fake_preds = torch.zeros(unlabeled_imgs.size(0))
lab_real_preds = torch.ones(labeled_imgs.size(0), 1)
unlab_fake_preds = torch.zeros(unlabeled_imgs.size(0), 1)

if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
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