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62 lines
2.2 KiB
62 lines
2.2 KiB
import argparse
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import os
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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
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from torchvision import transforms
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from models import CNN
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from trainer import train_source_cnn
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def main(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.ToTensor()]
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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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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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# train source CNN
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source_cnn = CNN(in_channels=args.in_channels).to(args.device)
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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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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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# NN
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parser.add_argument('--in_channels', type=int, default=3)
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parser.add_argument('--n_classes', type=int, default=10)
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parser.add_argument('--trained', type=str, default='')
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parser.add_argument('--slope', type=float, default=0.2)
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# train
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parser.add_argument('--lr', type=float, default=1e-3)
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parser.add_argument('--weight_decay', type=float, default=2.5e-5)
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parser.add_argument('--epochs', type=int, default=50)
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parser.add_argument('--batch_size', type=int, default=128)
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# misc
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parser.add_argument('--device', type=str, default='cuda:0')
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parser.add_argument('--n_workers', type=int, default=0)
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parser.add_argument('--logdir', type=str, default='outputs/garbage')
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parser.add_argument('--message', '-m', type=str, default='')
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args, unknown = parser.parse_known_args()
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main(args)
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