# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import os

import os.path as osp

import shutil

import time

import warnings

import mmcv

import torch

from mmcv.parallel import MMDataParallel, MMDistributedDataParallel

from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,

                         wrap_fp16_model)

from mmcv.utils import DictAction

from mmseg.apis import multi_gpu_test, single_gpu_test

from mmseg.datasets import build_dataloader, build_dataset

from mmseg.models import build_segmentor

def parse_args():

    parser = argparse.ArgumentParser(

        description='mmseg test (and eval) a model')

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

    parser.add_argument('checkpoint', help='checkpoint file')

    parser.add_argument(

        '--work-dir',

        help=('if specified, the evaluation metric results will be dumped'

              'into the directory as json'))

    parser.add_argument(

        '--aug-test', action='store_true', help='Use Flip and Multi scale aug')

    parser.add_argument('--out', help='output result file in pickle format')

    parser.add_argument(

        '--format-only',

        action='store_true',

        help='Format the output results without perform evaluation. It is'

        'useful when you want to format the result to a specific format and '

        'submit it to the test server')

    parser.add_argument(

        '--eval',

        type=str,

        nargs='+',

        help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'

        ' for generic datasets, and "cityscapes" for Cityscapes')

    parser.add_argument('--show', action='store_true', help='show results')

    parser.add_argument(

        '--show-dir', help='directory where painted images will be saved')

    parser.add_argument(

        '--gpu-collect',

        action='store_true',

        help='whether to use gpu to collect results.')

    parser.add_argument(

        '--tmpdir',

        help='tmp directory used for collecting results from multiple '

        'workers, available when gpu_collect is not specified')

    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(

        '--eval-options',

        nargs='+',

        action=DictAction,

        help='custom options for evaluation')

    parser.add_argument(

        '--launcher',

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

        default='none',

        help='job launcher')

    parser.add_argument(

        '--opacity',

        type=float,

        default=0.5,

        help='Opacity of painted segmentation map. In (0, 1] range.')

    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()

    assert args.out or args.eval or args.format_only or args.show \

        or args.show_dir, \

        ('Please specify at least one operation (save/eval/format/show the '

         'results / save the results) with the argument "--out", "--eval"'

         ', "--format-only", "--show" or "--show-dir"')

    if args.eval and args.format_only:

        raise ValueError('--eval and --format_only cannot be both specified')

    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):

        raise ValueError('The output file must be a pkl file.')

    cfg = mmcv.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

    if args.aug_test:

        # hard code index

        cfg.data.test.pipeline[1].img_ratios = [

            0.5, 0.75, 1.0, 1.25, 1.5, 1.75

        ]

        cfg.data.test.pipeline[1].flip = True

    cfg.model.pretrained = None

    cfg.data.test.test_mode = True

    # 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)

    rank, _ = get_dist_info()

    # allows not to create

    if args.work_dir is not None and rank == 0:

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

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

        json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')

    # build the dataloader

    # TODO: support multiple images per gpu (only minor changes are needed)

    dataset = build_dataset(cfg.data.test)

    data_loader = build_dataloader(

        dataset,

        samples_per_gpu=1,

        workers_per_gpu=cfg.data.workers_per_gpu,

        dist=distributed,

        shuffle=False)

    # build the model and load checkpoint

    cfg.model.train_cfg = None

    model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))

    fp16_cfg = cfg.get('fp16', None)

    if fp16_cfg is not None:

        wrap_fp16_model(model)

    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')

    if 'CLASSES' in checkpoint.get('meta', {}):

        model.CLASSES = checkpoint['meta']['CLASSES']

    else:

        print('"CLASSES" not found in meta, use dataset.CLASSES instead')

        model.CLASSES = dataset.CLASSES

    if 'PALETTE' in checkpoint.get('meta', {}):

        model.PALETTE = checkpoint['meta']['PALETTE']

    else:

        print('"PALETTE" not found in meta, use dataset.PALETTE instead')

        model.PALETTE = dataset.PALETTE

    # clean gpu memory when starting a new evaluation.

    torch.cuda.empty_cache()

    eval_kwargs = {} if args.eval_options is None else args.eval_options

    # Deprecated

    efficient_test = eval_kwargs.get('efficient_test', False)

    if efficient_test:

        warnings.warn(

            '``efficient_test=True`` does not have effect in tools/test.py, '

            'the evaluation and format results are CPU memory efficient by '

            'default')

    eval_on_format_results = (

        args.eval is not None and 'cityscapes' in args.eval)

    if eval_on_format_results:

        assert len(args.eval) == 1, 'eval on format results is not ' \

                                    'applicable for metrics other than ' \

                                    'cityscapes'

    if args.format_only or eval_on_format_results:

        if 'imgfile_prefix' in eval_kwargs:

            tmpdir = eval_kwargs['imgfile_prefix']

        else:

            tmpdir = '.format_cityscapes'

            eval_kwargs.setdefault('imgfile_prefix', tmpdir)

        mmcv.mkdir_or_exist(tmpdir)

    else:

        tmpdir = None

    if not distributed:

        model = MMDataParallel(model, device_ids=[0])

        results = single_gpu_test(

            model,

            data_loader,

            args.show,

            args.show_dir,

            False,

            args.opacity,

            pre_eval=args.eval is not None and not eval_on_format_results,

            format_only=args.format_only or eval_on_format_results,

            format_args=eval_kwargs)

    else:

        model = MMDistributedDataParallel(

            model.cuda(),

            device_ids=[torch.cuda.current_device()],

            broadcast_buffers=False)

        results = multi_gpu_test(

            model,

            data_loader,

            args.tmpdir,

            args.gpu_collect,

            False,

            pre_eval=args.eval is not None and not eval_on_format_results,

            format_only=args.format_only or eval_on_format_results,

            format_args=eval_kwargs)

    rank, _ = get_dist_info()

    if rank == 0:

        if args.out:

            warnings.warn(

                'The behavior of ``args.out`` has been changed since MMSeg '

                'v0.16, the pickled outputs could be seg map as type of '

                'np.array, pre-eval results or file paths for '

                '``dataset.format_results()``.')

            print(f'\nwriting results to {args.out}')

            mmcv.dump(results, args.out)

        if args.eval:

            eval_kwargs.update(metric=args.eval)

            metric = dataset.evaluate(results, **eval_kwargs)

            metric_dict = dict(config=args.config, metric=metric)

            if args.work_dir is not None and rank == 0:

                mmcv.dump(metric_dict, json_file, indent=4)

            if tmpdir is not None and eval_on_format_results:

                # remove tmp dir when cityscapes evaluation

                shutil.rmtree(tmpdir)

if __name__ == '__main__':

    main()