# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import copy

import os

import os.path as osp

import time

import warnings

import mmcv

import torch

from mmcv.cnn.utils import revert_sync_batchnorm

from mmcv.runner import get_dist_info, init_dist

from mmcv.utils import Config, DictAction, get_git_hash

from mmseg import __version__

from mmseg.apis import init_random_seed, set_random_seed, train_segmentor

from mmseg.datasets import build_dataset

from mmseg.models import build_segmentor

from mmseg.utils import collect_env, get_root_logger

def parse_args():

    parser = argparse.ArgumentParser(description='Train a segmentor')

    parser.add_argument('config', help='train config file path')

    parser.add_argument('--work-dir', help='the dir to save logs and models')

    parser.add_argument(

        '--load-from', help='the checkpoint file to load weights from')

    parser.add_argument(

        '--resume-from', help='the checkpoint file to resume from')

    parser.add_argument(

        '--no-validate',

        action='store_true',

        help='whether not to evaluate the checkpoint during training')

    group_gpus = parser.add_mutually_exclusive_group()

    group_gpus.add_argument(

        '--gpus',

        type=int,

        help='number of gpus to use '

        '(only applicable to non-distributed training)')

    group_gpus.add_argument(

        '--gpu-ids',

        type=int,

        nargs='+',

        help='ids of gpus to use '

        '(only applicable to non-distributed training)')

    parser.add_argument('--seed', type=int, default=None, help='random seed')

    parser.add_argument(

        '--deterministic',

        action='store_true',

        help='whether to set deterministic options for CUDNN backend.')

    parser.add_argument(

        '--options',

        nargs='+',

        action=DictAction,

        help="--options is deprecated in favor of --cfg_options' and it will "

        'not be supported in version v0.22.0. Override some settings in the '

        'used config, the key-value pair in xxx=yyy format will be merged '

        'into config file. If the value to be overwritten is a list, it '

        'should be like key="[a,b]" or key=a,b It also allows nested '

        'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '

        'marks are necessary and that no white space is allowed.')

    parser.add_argument(

        '--cfg-options',

        nargs='+',

        action=DictAction,

        help='override some settings in the used config, the key-value pair '

        'in xxx=yyy format will be merged into config file. If the value to '

        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '

        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '

        'Note that the quotation marks are necessary and that no white space '

        'is allowed.')

    parser.add_argument(

        '--launcher',

        choices=['none', 'pytorch', 'slurm', 'mpi'],

        default='none',

        help='job launcher')

    parser.add_argument('--local_rank', type=int, default=0)

    args = parser.parse_args()

    if 'LOCAL_RANK' not in os.environ:

        os.environ['LOCAL_RANK'] = str(args.local_rank)

    if args.options and args.cfg_options:

        raise ValueError(

            '--options and --cfg-options cannot be both '

            'specified, --options is deprecated in favor of --cfg-options. '

            '--options will not be supported in version v0.22.0.')

    if args.options:

        warnings.warn('--options is deprecated in favor of --cfg-options. '

                      '--options will not be supported in version v0.22.0.')

        args.cfg_options = args.options

    return args

def main():

    args = parse_args()

    cfg = Config.fromfile(args.config)

    if args.cfg_options is not None:

        cfg.merge_from_dict(args.cfg_options)

    # set cudnn_benchmark

    if cfg.get('cudnn_benchmark', False):

        torch.backends.cudnn.benchmark = True

    # work_dir is determined in this priority: CLI > segment in file > filename

    if args.work_dir is not None:

        # update configs according to CLI args if args.work_dir is not None

        cfg.work_dir = args.work_dir

    elif cfg.get('work_dir', None) is None:

        # use config filename as default work_dir if cfg.work_dir is None

        cfg.work_dir = osp.join('./work_dirs',

                                osp.splitext(osp.basename(args.config))[0])

    if args.load_from is not None:

        cfg.load_from = args.load_from

    if args.resume_from is not None:

        cfg.resume_from = args.resume_from

    if args.gpu_ids is not None:

        cfg.gpu_ids = args.gpu_ids

    else:

        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)

    # 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)

        # gpu_ids is used to calculate iter when resuming checkpoint

        _, world_size = get_dist_info()

        cfg.gpu_ids = range(world_size)

    # create work_dir

    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))

    # dump config

    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))

    # init the logger before other steps

    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())

    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')

    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)

    # init the meta dict to record some important information such as

    # environment info and seed, which will be logged

    meta = dict()

    # log env info

    env_info_dict = collect_env()

    env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])

    dash_line = '-' * 60 + '\n'

    logger.info('Environment info:\n' + dash_line + env_info + '\n' +

                dash_line)

    meta['env_info'] = env_info

    # log some basic info

    logger.info(f'Distributed training: {distributed}')

    logger.info(f'Config:\n{cfg.pretty_text}')

    # set random seeds

    seed = init_random_seed(args.seed)

    logger.info(f'Set random seed to {seed}, '

                f'deterministic: {args.deterministic}')

    set_random_seed(seed, deterministic=args.deterministic)

    cfg.seed = seed

    meta['seed'] = seed

    meta['exp_name'] = osp.basename(args.config)

    model = build_segmentor(

        cfg.model,

        train_cfg=cfg.get('train_cfg'),

        test_cfg=cfg.get('test_cfg'))

    model.init_weights()

    # SyncBN is not support for DP

    if not distributed:

        warnings.warn(

            'SyncBN is only supported with DDP. To be compatible with DP, '

            'we convert SyncBN to BN. Please use dist_train.sh which can '

            'avoid this error.')

        model = revert_sync_batchnorm(model)

    logger.info(model)

    datasets = [build_dataset(cfg.data.train)]

    if len(cfg.workflow) == 2:

        val_dataset = copy.deepcopy(cfg.data.val)

        val_dataset.pipeline = cfg.data.train.pipeline

        datasets.append(build_dataset(val_dataset))

    if cfg.checkpoint_config is not None:

        # save mmseg version, config file content and class names in

        # checkpoints as meta data

        cfg.checkpoint_config.meta = dict(

            mmseg_version=f'{__version__}+{get_git_hash()[:7]}',

            config=cfg.pretty_text,

            CLASSES=datasets[0].CLASSES,

            PALETTE=datasets[0].PALETTE)

    # add an attribute for visualization convenience

    model.CLASSES = datasets[0].CLASSES

    # passing checkpoint meta for saving best checkpoint

    meta.update(cfg.checkpoint_config.meta)

    train_segmentor(

        model,

        datasets,

        cfg,

        distributed=distributed,

        validate=(not args.no_validate),

        timestamp=timestamp,

        meta=meta)

if __name__ == '__main__':

    main()