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import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
import data, dali, fit
import mxnet as mx
import numpy as np

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 resnet on imagenet",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    fit.add_fit_args(parser)
    data.add_data_args(parser)
    dali.add_dali_args(parser)
    data.add_data_aug_args(parser)
    
    # Instead, to get standard resnet augmentation on a per-use basis, invoke as in:
    # train_imagenet.py --set-resnet-aug ...
    # Finally, to get the legacy MXNet v1.2 training settings on a per-use basis, invoke as in:
    # train_imagenet.py --set-data-aug-level 3
    parser.set_defaults(
        # network
        num_layers       = 50,

        # data
        resize           = 256,
        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       = 90,
        lr_step_epochs   = '30,60,80',
        dtype            = 'float32'
    )
    args = parser.parse_args()

    if not args.use_dali:
        data.set_data_aug_level(parser, 0)

    # load network
    import resnet as net
    sym = net.get_symbol(**vars(args))

    # train
    fit.fit(args, sym, dali.get_rec_iter)
