from __future__ import division
import argparse
import os
import torch
from mmcv import Config
from mmdet import __version__
from mmdet.apis import (get_root_logger, init_dist, set_random_seed)
from mmdet.apis.train import *
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
import pdb
from parsing import parse_args


def train(args):
    cfg = Config.fromfile(args.config)
    cfg.data.workers_per_gpu = args.workers_per_gpu
    cfg.data.imgs_per_gpu = args.imgs_per_gpu
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    else:
        args.work_dir = cfg.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    if args.autoscale_lr:
        # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
        cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    datasets = [build_dataset(cfg.data.train)]
    if len(cfg.workflow) == 2:
        datasets.append(build_dataset(cfg.data.val))
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(
        model,
        datasets,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger)
    torch.save(model, args.work_dir + '/' + args.model_name + '.pth')
    return


if __name__ == '__main__':
    args = parse_args()
    train(args)


