# under the License. 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 imagenet-1k", formatter_class=argparse.ArgumentDefaultsHelpFormatter) fit.add_fit_args(parser) data.add_data_args(parser) data.add_data_aug_args(parser) # uncomment to set standard augmentation for resnet training # data.set_resnet_aug(parser) 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,
import mxnet as mx def set_imagenet_aug(aug): # standard data augmentation setting for imagenet training aug.set_defaults(rgb_mean='123.68,116.779,103.939', rgb_std='58.393,57.12,57.375') aug.set_defaults(random_crop=0, random_resized_crop=1, random_mirror=1) aug.set_defaults(min_random_area=0.08) aug.set_defaults(max_random_aspect_ratio=4./3., min_random_aspect_ratio=3./4.) aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, pca_noise=0.1) if __name__ == '__main__': # parse args parser = argparse.ArgumentParser(description="train imagenet-1k", formatter_class=argparse.ArgumentDefaultsHelpFormatter) fit.add_fit_args(parser) data.add_data_args(parser) data.add_data_aug_args(parser) # uncomment to set standard augmentations for imagenet training # set_imagenet_aug(parser) 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,