Implementation of "Adversarial Discriminative Domain Adaptation" in PyTorch
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import argparse
import experiment
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# 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)
parser.add_argument('--trained', type=str, default='')
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parser.add_argument('--slope', type=float, default=0.1)
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# train
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parser.add_argument('--lr', type=float, default=2e-4)
parser.add_argument('--weight_decay', type=float, default=2.5e-5)
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parser.add_argument('--epochs', type=int, default=512)
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parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--betas', type=float, nargs='+', default=(.5, .999))
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# misc
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--n_workers', type=int, default=0)
parser.add_argument('--logdir', type=str, default='outputs/garbage')
parser.add_argument('--message', '-m', type=str, default='')
args, unknown = parser.parse_known_args()
experiment.run(args)