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')
