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80 lines
2.9 KiB
80 lines
2.9 KiB
import os
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import torch
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from torch import nn, optim
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from torch.utils.data import DataLoader
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from torchvision.datasets import SVHN, MNIST
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from torchvision import transforms
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from models import CNN, Discriminator
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from trainer import train_source_cnn, train_target_cnn
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def run(args):
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if not os.path.exists(args.logdir):
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os.makedirs(args.logdir)
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# data
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source_transform = transforms.Compose([
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# transforms.Grayscale(),
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transforms.ToTensor()]
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)
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target_transform = transforms.Compose([
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transforms.Resize(32),
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transforms.ToTensor(),
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transforms.Lambda(lambda x: x.repeat(3, 1, 1))
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])
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source_dataset_train = SVHN(
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'./input', 'train', transform=source_transform, download=True)
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source_dataset_test = SVHN(
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'./input', 'test', transform=source_transform, download=True)
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target_dataset_train = MNIST(
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'./input', 'train', transform=target_transform, download=True)
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target_dataset_test = MNIST(
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'./input', 'test', transform=target_transform, download=True)
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source_train_loader = DataLoader(
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source_dataset_train, args.batch_size, shuffle=True,
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drop_last=True,
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num_workers=args.n_workers)
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source_test_loader = DataLoader(
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source_dataset_test, args.batch_size, shuffle=False,
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num_workers=args.n_workers)
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target_train_loader = DataLoader(
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target_dataset_train, args.batch_size, shuffle=True,
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drop_last=True,
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num_workers=args.n_workers)
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target_test_loader = DataLoader(
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target_dataset_test, args.batch_size, shuffle=False,
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num_workers=args.n_workers)
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# train source CNN
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source_cnn = CNN(in_channels=args.in_channels).to(args.device)
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if os.path.isfile(args.trained):
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c = torch.load(args.trained)
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source_cnn.load_state_dict(c['model'])
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print('Loaded `{}`'.format(args.trained))
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else:
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(
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source_cnn.parameters(),
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lr=args.lr, weight_decay=args.weight_decay)
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source_cnn = train_source_cnn(
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source_cnn, source_train_loader, source_test_loader,
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criterion, optimizer, args=args)
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# train target CNN
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target_cnn = CNN(in_channels=args.in_channels, target=True).to(args.device)
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target_cnn.load_state_dict(source_cnn.state_dict())
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discriminator = Discriminator(args=args).to(args.device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.RMSprop( # optim.Adam(
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target_cnn.encoder.parameters(),
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lr=args.lr, weight_decay=args.weight_decay)
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d_optimizer = optim.RMSprop( # optim.Adam(
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discriminator.parameters(),
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lr=args.lr, weight_decay=args.weight_decay)
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train_target_cnn(
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source_cnn, target_cnn, discriminator,
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criterion, optimizer, d_optimizer,
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source_train_loader, target_train_loader, target_test_loader,
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args=args)
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