main_cls.py [34:60]:
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default_model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

customized_models_names = sorted(name for name in customized_models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(customized_models.__dict__[name]))

for name in customized_models.__dict__:
    if name.islower() and not name.startswith("__") and callable(customized_models.__dict__[name]):
        models.__dict__[name] = customized_models.__dict__[name]

model_names = default_model_names + customized_models_names

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--trainindex', default=None, type=str, metavar='PATH',
                    help='path to train annotation (default: None)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')
parser.add_argument('--cls', default=1000, type=int, metavar='N',
                    help='number of classes')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
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main_lincls.py [33:59]:
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default_model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

customized_models_names = sorted(name for name in customized_models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(customized_models.__dict__[name]))

for name in customized_models.__dict__:
    if name.islower() and not name.startswith("__") and callable(customized_models.__dict__[name]):
        models.__dict__[name] = customized_models.__dict__[name]

model_names = default_model_names + customized_models_names

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--trainindex', default=None, type=str, metavar='PATH',
                    help='path to train annotation (default: None)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet50)')
parser.add_argument('--cls', default=1000, type=int, metavar='N',
                    help='number of classes')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
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