def main():
parser = argparse.ArgumentParser(description='Baseline')
parser.add_argument('--conf_path',
type=str,
metavar='conf_path',
help='input the path of config file')
parser.add_argument('--id',
type=int,
metavar='experiment_id',
help='Experiment ID')
args = parser.parse_args()
option = Option(args.conf_path)
option.manualSeed = args.id + 1
option.experimentID = option.experimentID + "{:0>2d}_repeat".format(
args.id)
if option.dataset in ["cifar100"]:
generator = Generator(option)
elif option.dataset in ["imagenet"]:
generator = Generator_imagenet(option)
else:
assert False, "invalid data set"
experiment = ExperimentDesign(generator, option)
experiment.run()
def main():
parser = argparse.ArgumentParser(description='Baseline')
parser.add_argument('conf_path', type=str, metavar='conf_path',
help='input batch size for training (default: 64)')
args = parser.parse_args()
option = Option(args.conf_path)
save_path = option.save_path
experimentID = option.experimentID
for i in range(0,1):
option.experimentID = experimentID + "{:0>2d}_repeat".format(i + 1)
option.save_path = save_path
option.manualSeed = i + 1
experiment = ExperimentDesign(option)
best_top1, best_top5 = experiment.run()
experiment.logger.handlers = []
def main():
parser = argparse.ArgumentParser(description='Baseline')
parser.add_argument('conf_path',
type=str,
metavar='conf_path',
help='input batch size for training (default: 64)')
parser.add_argument('id',
type=int,
metavar='experiment_id',
help='Experiment ID')
args = parser.parse_args()
option = Option(args.conf_path)
option.manualSeed = args.id + 1
option.experimentID = option.experimentID + "{:0>2d}_repeat".format(
args.id + 1)
experiment = ExperimentDesign(option)
experiment.run()