Python add_data_aug_argsの例

コード例 #1

ファイル: train_imagenet.py プロジェクト: sandeep-krishnamurthy/incubator-mxnet

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,
        lr_step_epochs   = '30,60',

コード例 #2

ファイル: train_imagenet.py プロジェクト: jplapp/learning_by_association_mxnet

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',