end = time.time() if i % args.print_freq == 0: progress.display(i) if __name__ == '__main__': parser = argparse.ArgumentParser( description='ADDA for Unsupervised Domain Adaptation') # dataset parameters parser.add_argument('root', metavar='DIR', help='root path of dataset') parser.add_argument('-d', '--data', metavar='DATA', default='Office31', choices=utils.get_dataset_names(), help='dataset: ' + ' | '.join(utils.get_dataset_names()) + ' (default: Office31)') parser.add_argument('-s', '--source', help='source domain(s)', nargs='+') parser.add_argument('-t', '--target', help='target domain(s)', nargs='+') parser.add_argument('--train-resizing', type=str, default='default') parser.add_argument('--val-resizing', type=str, default='default') parser.add_argument('--resize-size', type=int, default=224, help='the image size after resizing') parser.add_argument('--no-hflip', action='store_true', help='no random horizontal flipping during training') parser.add_argument('--norm-mean',
if i % args.print_freq == 0: progress.display(i) if __name__ == '__main__': parser = argparse.ArgumentParser( description='VREx for Domain Generalization') # dataset parameters parser.add_argument('root', metavar='DIR', help='root path of dataset') parser.add_argument('-d', '--data', metavar='DATA', default='PACS', help='dataset: ' + ' | '.join(utils.get_dataset_names()) + ' (default: PACS)') parser.add_argument('-s', '--sources', nargs='+', default=None, help='source domain(s)') parser.add_argument('-t', '--targets', nargs='+', default=None, help='target domain(s)') parser.add_argument('--train-resizing', type=str, default='default') parser.add_argument('--val-resizing', type=str, default='default') # model parameters parser.add_argument('-a',
unknown = accs[-1].item() * 100 h_score = 2 * known * unknown / (known + unknown) if args.per_class_eval: print(confmat.format(classes)) print(' * All {all:.3f} Known {known:.3f} Unknown {unknown:.3f} H-score {h_score:.3f}' .format(all=all_acc, known=known, unknown=unknown, h_score=h_score)) return h_score if __name__ == '__main__': parser = argparse.ArgumentParser(description='DANN for Openset Domain Adaptation') # dataset parameters parser.add_argument('root', metavar='DIR', help='root path of dataset') parser.add_argument('-d', '--data', metavar='DATA', default='Office31', choices=utils.get_dataset_names(), help='dataset: ' + ' | '.join(utils.get_dataset_names()) + ' (default: Office31)') parser.add_argument('-s', '--source', help='source domain') parser.add_argument('-t', '--target', help='target domain') parser.add_argument('--train-resizing', type=str, default='default') parser.add_argument('--val-resizing', type=str, default='default') # model parameters parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=utils.get_model_names(), help='backbone architecture: ' + ' | '.join(utils.get_model_names()) + ' (default: resnet18)') parser.add_argument('--no-pool', action='store_true', help='no pool layer after the feature extractor.') parser.add_argument('--bottleneck-dim', default=256, type=int,
# measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i) if __name__ == '__main__': parser = argparse.ArgumentParser(description='CORAL for Domain Generalization') # dataset parameters parser.add_argument('root', metavar='DIR', help='root path of dataset') parser.add_argument('-d', '--data', metavar='DATA', default='PACS', help='dataset: ' + ' | '.join(utils.get_dataset_names()) + ' (default: PACS)') parser.add_argument('-s', '--sources', nargs='+', default=None, help='source domain(s)') parser.add_argument('-t', '--targets', nargs='+', default=None, help='target domain(s)') parser.add_argument('--train-resizing', type=str, default='default') parser.add_argument('--val-resizing', type=str, default='default') # model parameters parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50', choices=utils.get_model_names(), help='backbone architecture: ' + ' | '.join(utils.get_model_names()) + ' (default: resnet50)') parser.add_argument('--no-pool', action='store_true', help='no pool layer after the feature extractor.') parser.add_argument('--finetune', action='store_true', help='whether use 10x smaller lr for backbone')