| import os |
| import argparse |
| import logging |
| logging.basicConfig(level=logging.DEBUG) |
| from common import find_mxnet, data, fit |
| from common.util import download_file |
| import mxnet as mx |
| |
| if __name__ == '__main__': |
| # parse args |
| parser = argparse.ArgumentParser(description="train cifar10", |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
| fit.add_fit_args(parser) |
| data.add_data_args(parser) |
| data.add_data_aug_args(parser) |
| # use a large aug level |
| data.set_data_aug_level(parser, 3) |
| parser.set_defaults( |
| # network |
| network = 'resnet', |
| num_layers = 50, |
| # data |
| num_classes = 1000, |
| num_examples = 1281167, |
| image_shape = '3,224,224', |
| min_random_scale = 1, # if input image has min size k, suggest to use |
| # 256.0/x, e.g. 0.533 for 480 |
| # train |
| num_epochs = 80, |
| lr_step_epochs = '30,60', |
| ) |
| args = parser.parse_args() |
| |
| # load network |
| from importlib import import_module |
| net = import_module('symbols.'+args.network) |
| sym = net.get_symbol(**vars(args)) |
| |
| # train |
| fit.fit(args, sym, data.get_rec_iter) |