# --------------------------------------------------------
# Group R-CNN
# Copyright (c) OpenMMLab. All rights reserved.
# Written by <NAME>
# --------------------------------------------------------
import argparse
import copy
import os
import os.path as osp
import time
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
                         build_runner, get_dist_info, init_dist)
from mmcv.utils import build_from_cfg, get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed
from mmdet.datasets import (build_dataloader, build_dataset,
                            replace_ImageToTensor)
from mmdet.models import build_detector
from mmdet.utils import collect_env, get_root_logger

from projects.api.test import PointdetDistEvalHook, PointdetEvalHook
from projects.datasets import *  # noqa
from projects.datasets.builder import build_point_dataloader
from projects.models import *  # noqa


def train_detector(model,
                   dataset,
                   cfg,
                   distributed=False,
                   validate=False,
                   timestamp=None,
                   meta=None):
    logger = get_root_logger(log_level=cfg.log_level)
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    if 'imgs_per_gpu' in cfg.data:
        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
                       'Please use "samples_per_gpu" instead')
        if 'samples_per_gpu' in cfg.data:
            logger.warning(
                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
                f'={cfg.data.imgs_per_gpu} is used in this experiments')
        else:
            logger.warning(
                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
                f'{cfg.data.imgs_per_gpu} in this experiments')
        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu

    data_loaders = [
        build_point_dataloader(
            ds,
            cfg.data.samples_per_gpu,
            cfg.data.workers_per_gpu,
            # cfg.gpus will be ignored if distributed
            len(cfg.gpu_ids),
            dist=distributed,
            seed=cfg.seed) for ds in dataset
    ]

    # put model on gpus
    if distributed:
        find_unused_parameters = cfg.get('find_unused_parameters', False)
        # Sets the `find_unused_parameters` parameter in
        # torch.nn.parallel.DistributedDataParallel
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False,
            find_unused_parameters=find_unused_parameters)
    else:
        model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
                               device_ids=cfg.gpu_ids)

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)

    if 'runner' not in cfg:
        cfg.runner = {
            'type': 'EpochBasedRunner',
            'max_epochs': cfg.total_epochs
        }
        warnings.warn(
            'config is now expected to have a `runner` section, '
            'please set `runner` in your config.', UserWarning)
    else:
        if 'total_epochs' in cfg:
            assert cfg.total_epochs == cfg.runner.max_epochs

    runner = build_runner(cfg.runner,
                          default_args=dict(model=model,
                                            optimizer=optimizer,
                                            work_dir=cfg.work_dir,
                                            logger=logger,
                                            meta=meta))

    # an ugly workaround to make .log and .log.json filenames the same
    runner.timestamp = timestamp

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg,
                                             distributed=distributed)
    elif distributed and 'type' not in cfg.optimizer_config:
        optimizer_config = OptimizerHook(**cfg.optimizer_config)
    else:
        optimizer_config = cfg.optimizer_config

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config,
                                   cfg.get('momentum_config', None))
    if distributed:
        if isinstance(runner, EpochBasedRunner):
            runner.register_hook(DistSamplerSeedHook())

    # register eval hooks
    if validate:
        # Support batch_size > 1 in validation
        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
        if val_samples_per_gpu > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.val.pipeline = replace_ImageToTensor(
                cfg.data.val.pipeline)
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        val_dataloader = build_dataloader(
            val_dataset,
            samples_per_gpu=val_samples_per_gpu,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=distributed,
            shuffle=False)
        eval_cfg = cfg.get('evaluation', {})
        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
        eval_hook = PointdetDistEvalHook if distributed else PointdetEvalHook
        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
        runner.register_hook(eval_hook(val_dataloader, **eval_cfg),
                             priority='LOW')

    # user-defined hooks
    if cfg.get('custom_hooks', None):
        custom_hooks = cfg.custom_hooks
        assert isinstance(custom_hooks, list), \
            f'custom_hooks expect list type, but got {type(custom_hooks)}'
        for hook_cfg in cfg.custom_hooks:
            assert isinstance(hook_cfg, dict), \
                'Each item in custom_hooks expects dict type, but got ' \
                f'{type(hook_cfg)}'
            hook_cfg = hook_cfg.copy()
            priority = hook_cfg.pop('priority', 'NORMAL')
            hook = build_from_cfg(hook_cfg, HOOKS)
            runner.register_hook(hook, priority=priority)

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow)


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    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('--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('--port', type=int, default=20001, 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='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file (deprecate), '
        'change to --cfg-options instead.')

    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')
    if args.options:
        warnings.warn('--options is deprecated in favor of --cfg-options')
        args.cfg_options = args.options

    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        for k, v in args.cfg_options.items():
            args.cfg_options[k] = eval(v)
        cfg.merge_from_dict(args.cfg_options)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    # 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.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)
        # re-set gpu_ids with distributed training mode
        _, 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
    meta['config'] = cfg.pretty_text
    # log some basic info
    logger.info(f'Distributed training: {distributed}')
    logger.info(f'Config:\n{cfg.pretty_text}')

    # set random seeds
    if args.seed is not None:
        logger.info(f'Set random seed to {args.seed}, '
                    f'deterministic: {args.deterministic}')
        set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['exp_name'] = osp.basename(args.config)

    model = build_detector(cfg.model,
                           train_cfg=cfg.get('train_cfg'),
                           test_cfg=cfg.get('test_cfg'))
    model.init_weights()

    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 mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(mmdet_version=__version__ +
                                          get_git_hash()[:7],
                                          CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(model,
                   datasets,
                   cfg,
                   distributed=distributed,
                   validate=(not args.no_validate),
                   timestamp=timestamp,
                   meta=meta)


if __name__ == '__main__':
    main()

# --------------------------------------------------------
# Group R-CNN
# Copyright (c) OpenMMLab. All rights reserved.
# Written by <NAME>
# --------------------------------------------------------
import argparse
import copy
import os
import os.path as osp
import time
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
                         build_runner, get_dist_info, init_dist)
from mmcv.utils import build_from_cfg, get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed
from mmdet.datasets import (build_dataloader, build_dataset,
                            replace_ImageToTensor)
from mmdet.models import build_detector
from mmdet.utils import collect_env, get_root_logger

from projects.api.test import PointdetDistEvalHook, PointdetEvalHook
from projects.datasets import *  # noqa
from projects.datasets.builder import build_point_dataloader
from projects.models import *  # noqa


def train_detector(model,
                   dataset,
                   cfg,
                   distributed=False,
                   validate=False,
                   timestamp=None,
                   meta=None):
    logger = get_root_logger(log_level=cfg.log_level)
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    if 'imgs_per_gpu' in cfg.data:
        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
                       'Please use "samples_per_gpu" instead')
        if 'samples_per_gpu' in cfg.data:
            logger.warning(
                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
                f'={cfg.data.imgs_per_gpu} is used in this experiments')
        else:
            logger.warning(
                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
                f'{cfg.data.imgs_per_gpu} in this experiments')
        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu

    data_loaders = [
        build_point_dataloader(
            ds,
            cfg.data.samples_per_gpu,
            cfg.data.workers_per_gpu,
            # cfg.gpus will be ignored if distributed
            len(cfg.gpu_ids),
            dist=distributed,
            seed=cfg.seed) for ds in dataset
    ]

    # put model on gpus
    if distributed:
        find_unused_parameters = cfg.get('find_unused_parameters', False)
        # Sets the `find_unused_parameters` parameter in
        # torch.nn.parallel.DistributedDataParallel
        model = MMDistributedDataParallel(
            model.cuda(),
            device_ids=[torch.cuda.current_device()],
            broadcast_buffers=False,
            find_unused_parameters=find_unused_parameters)
    else:
        model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
                               device_ids=cfg.gpu_ids)

    # build runner
    optimizer = build_optimizer(model, cfg.optimizer)

    if 'runner' not in cfg:
        cfg.runner = {
            'type': 'EpochBasedRunner',
            'max_epochs': cfg.total_epochs
        }
        warnings.warn(
            'config is now expected to have a `runner` section, '
            'please set `runner` in your config.', UserWarning)
    else:
        if 'total_epochs' in cfg:
            assert cfg.total_epochs == cfg.runner.max_epochs

    runner = build_runner(cfg.runner,
                          default_args=dict(model=model,
                                            optimizer=optimizer,
                                            work_dir=cfg.work_dir,
                                            logger=logger,
                                            meta=meta))

    # an ugly workaround to make .log and .log.json filenames the same
    runner.timestamp = timestamp

    # fp16 setting
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
                                             **fp16_cfg,
                                             distributed=distributed)
    elif distributed and 'type' not in cfg.optimizer_config:
        optimizer_config = OptimizerHook(**cfg.optimizer_config)
    else:
        optimizer_config = cfg.optimizer_config

    # register hooks
    runner.register_training_hooks(cfg.lr_config, optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config,
                                   cfg.get('momentum_config', None))
    if distributed:
        if isinstance(runner, EpochBasedRunner):
            runner.register_hook(DistSamplerSeedHook())

    # register eval hooks
    if validate:
        # Support batch_size > 1 in validation
        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
        if val_samples_per_gpu > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.val.pipeline = replace_ImageToTensor(
                cfg.data.val.pipeline)
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        val_dataloader = build_dataloader(
            val_dataset,
            samples_per_gpu=val_samples_per_gpu,
            workers_per_gpu=cfg.data.workers_per_gpu,
            dist=distributed,
            shuffle=False)
        eval_cfg = cfg.get('evaluation', {})
        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
        eval_hook = PointdetDistEvalHook if distributed else PointdetEvalHook
        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
        runner.register_hook(eval_hook(val_dataloader, **eval_cfg),
                             priority='LOW')

    # user-defined hooks
    if cfg.get('custom_hooks', None):
        custom_hooks = cfg.custom_hooks
        assert isinstance(custom_hooks, list), \
            f'custom_hooks expect list type, but got {type(custom_hooks)}'
        for hook_cfg in cfg.custom_hooks:
            assert isinstance(hook_cfg, dict), \
                'Each item in custom_hooks expects dict type, but got ' \
                f'{type(hook_cfg)}'
            hook_cfg = hook_cfg.copy()
            priority = hook_cfg.pop('priority', 'NORMAL')
            hook = build_from_cfg(hook_cfg, HOOKS)
            runner.register_hook(hook, priority=priority)

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow)


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    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('--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('--port', type=int, default=20001, 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='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file (deprecate), '
        'change to --cfg-options instead.')

    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')
    if args.options:
        warnings.warn('--options is deprecated in favor of --cfg-options')
        args.cfg_options = args.options

    return args


def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        for k, v in args.cfg_options.items():
            args.cfg_options[k] = eval(v)
        cfg.merge_from_dict(args.cfg_options)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    # 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.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)
        # re-set gpu_ids with distributed training mode
        _, 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
    meta['config'] = cfg.pretty_text
    # log some basic info
    logger.info(f'Distributed training: {distributed}')
    logger.info(f'Config:\n{cfg.pretty_text}')

    # set random seeds
    if args.seed is not None:
        logger.info(f'Set random seed to {args.seed}, '
                    f'deterministic: {args.deterministic}')
        set_random_seed(args.seed, deterministic=args.deterministic)
    cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['exp_name'] = osp.basename(args.config)

    model = build_detector(cfg.model,
                           train_cfg=cfg.get('train_cfg'),
                           test_cfg=cfg.get('test_cfg'))
    model.init_weights()

    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 mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(mmdet_version=__version__ +
                                          get_git_hash()[:7],
                                          CLASSES=datasets[0].CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = datasets[0].CLASSES
    train_detector(model,
                   datasets,
                   cfg,
                   distributed=distributed,
                   validate=(not args.no_validate),
                   timestamp=timestamp,
                   meta=meta)


if __name__ == '__main__':
    main()

# -------------------------------------------------------- # Group R-CNN # Copyright (c) OpenMMLab. All rights reserved. # Written by <NAME> # -------------------------------------------------------- # noqa # noqa # prepare data loaders # cfg.gpus will be ignored if distributed # put model on gpus # Sets the `find_unused_parameters` parameter in # torch.nn.parallel.DistributedDataParallel # build runner # an ugly workaround to make .log and .log.json filenames the same # fp16 setting # register hooks # register eval hooks # Support batch_size > 1 in validation # Replace 'ImageToTensor' to 'DefaultFormatBundle' # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'. # user-defined hooks # import modules from string list. # set cudnn_benchmark # work_dir is determined in this priority: CLI > segment in file > filename # update configs according to CLI args if args.work_dir is not None # use config filename as default work_dir if cfg.work_dir is None # init distributed env first, since logger depends on the dist info. # re-set gpu_ids with distributed training mode # create work_dir # dump config # init the logger before other steps # init the meta dict to record some important information such as # environment info and seed, which will be logged # log env info # log some basic info # set random seeds # save mmdet version, config file content and class names in # checkpoints as meta data # add an attribute for visualization convenience