# Copyright (c) OpenMMLab. All rights reserved. import os import os.path as osp import shutil import tempfile from collections import OrderedDict from typing import Dict, Optional, Sequence import mmcv import numpy as np from mmengine.dist import is_main_process from mmengine.evaluator import BaseMetric from mmengine.logging import MMLogger from mmdet.registry import METRICS try: import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval # noqa: E501 import cityscapesscripts.helpers.labels as CSLabels from mmdet.evaluation.functional import evaluateImgLists HAS_CITYSCAPESAPI = True except ImportError: HAS_CITYSCAPESAPI = False @METRICS.register_module() class CityScapesMetric(BaseMetric): """CityScapes metric for instance segmentation. Args: outfile_prefix (str): The prefix of txt and png files. The txt and png file will be save in a directory whose path is "outfile_prefix.results/". seg_prefix (str, optional): Path to the directory which contains the cityscapes instance segmentation masks. It's necessary when training and validation. It could be None when infer on test dataset. Defaults to None. format_only (bool): 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. Defaults to False. collect_device (str): Device name used for collecting results from different ranks during distributed training. Must be 'cpu' or 'gpu'. Defaults to 'cpu'. prefix (str, optional): The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None. dump_matches (bool): Whether dump matches.json file during evaluating. Defaults to False. file_client_args (dict, optional): Arguments to instantiate the corresponding backend in mmdet <= 3.0.0rc6. Defaults to None. backend_args (dict, optional): Arguments to instantiate the corresponding backend. Defaults to None. """ default_prefix: Optional[str] = 'cityscapes' def __init__(self, outfile_prefix: str, seg_prefix: Optional[str] = None, format_only: bool = False, collect_device: str = 'cpu', prefix: Optional[str] = None, dump_matches: bool = False, file_client_args: dict = None, backend_args: dict = None) -> None: if not HAS_CITYSCAPESAPI: raise RuntimeError('Failed to import `cityscapesscripts`.' 'Please try to install official ' 'cityscapesscripts by ' '"pip install cityscapesscripts"') super().__init__(collect_device=collect_device, prefix=prefix) self.tmp_dir = None self.format_only = format_only if self.format_only: assert outfile_prefix is not None, 'outfile_prefix must be not' 'None when format_only is True, otherwise the result files will' 'be saved to a temp directory which will be cleaned up at the end.' else: assert seg_prefix is not None, '`seg_prefix` is necessary when ' 'computing the CityScapes metrics' if outfile_prefix is None: self.tmp_dir = tempfile.TemporaryDirectory() self.outfile_prefix = osp.join(self.tmp_dir.name, 'results') else: # the directory to save predicted panoptic segmentation mask self.outfile_prefix = osp.join(outfile_prefix, 'results') # type: ignore # yapf: disable # noqa: E501 dir_name = osp.expanduser(self.outfile_prefix) if osp.exists(dir_name) and is_main_process(): logger: MMLogger = MMLogger.get_current_instance() logger.info('remove previous results.') shutil.rmtree(dir_name) os.makedirs(dir_name, exist_ok=True) self.backend_args = backend_args if file_client_args is not None: raise RuntimeError( 'The `file_client_args` is deprecated, ' 'please use `backend_args` instead, please refer to' 'https://github.com/open-mmlab/mmdetection/blob/main/configs/_base_/datasets/coco_detection.py' # noqa: E501 ) self.seg_prefix = seg_prefix self.dump_matches = dump_matches def __del__(self) -> None: """Clean up the results if necessary.""" if self.tmp_dir is not None: self.tmp_dir.cleanup() # TODO: data_batch is no longer needed, consider adjusting the # parameter position def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: """Process one batch of data samples and predictions. The processed results should be stored in ``self.results``, which will be used to compute the metrics when all batches have been processed. Args: data_batch (dict): A batch of data from the dataloader. data_samples (Sequence[dict]): A batch of data samples that contain annotations and predictions. """ for data_sample in data_samples: # parse pred result = dict() pred = data_sample['pred_instances'] filename = data_sample['img_path'] basename = osp.splitext(osp.basename(filename))[0] pred_txt = osp.join(self.outfile_prefix, basename + '_pred.txt') result['pred_txt'] = pred_txt labels = pred['labels'].cpu().numpy() masks = pred['masks'].cpu().numpy().astype(np.uint8) if 'mask_scores' in pred: # some detectors use different scores for bbox and mask mask_scores = pred['mask_scores'].cpu().numpy() else: mask_scores = pred['scores'].cpu().numpy() with open(pred_txt, 'w') as f: for i, (label, mask, mask_score) in enumerate( zip(labels, masks, mask_scores)): class_name = self.dataset_meta['classes'][label] class_id = CSLabels.name2label[class_name].id png_filename = osp.join( self.outfile_prefix, basename + f'_{i}_{class_name}.png') mmcv.imwrite(mask, png_filename) f.write(f'{osp.basename(png_filename)} ' f'{class_id} {mask_score}\n') # parse gt gt = dict() img_path = filename.replace('leftImg8bit.png', 'gtFine_instanceIds.png') gt['file_name'] = img_path.replace('leftImg8bit', 'gtFine') self.results.append((gt, result)) def compute_metrics(self, results: list) -> Dict[str, float]: """Compute the metrics from processed results. Args: results (list): The processed results of each batch. Returns: Dict[str, float]: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. """ logger: MMLogger = MMLogger.get_current_instance() if self.format_only: logger.info( f'results are saved to {osp.dirname(self.outfile_prefix)}') return OrderedDict() logger.info('starts to compute metric') gts, preds = zip(*results) # set global states in cityscapes evaluation API gt_instances_file = osp.join(self.outfile_prefix, 'gtInstances.json') # type: ignore # yapf: disable # noqa: E501 # split gt and prediction list gts, preds = zip(*results) CSEval.args.JSONOutput = False CSEval.args.colorized = False CSEval.args.gtInstancesFile = gt_instances_file groundTruthImgList = [gt['file_name'] for gt in gts] predictionImgList = [pred['pred_txt'] for pred in preds] CSEval_results = evaluateImgLists( predictionImgList, groundTruthImgList, CSEval.args, self.backend_args, dump_matches=self.dump_matches)['averages'] eval_results = OrderedDict() eval_results['mAP'] = CSEval_results['allAp'] eval_results['AP@50'] = CSEval_results['allAp50%'] return eval_results