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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# 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
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,
lr_step_epochs = '30,60',
dtype = 'float32'
)
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)