mmdet/evaluation/metrics/lvis_metric.py

# Copyright (c) OpenMMLab. All rights reserved.
import itertools
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
from typing import Dict, List, Optional, Sequence, Union

import numpy as np
from mmengine.fileio import get_local_path
from mmengine.logging import MMLogger
from terminaltables import AsciiTable

from mmdet.registry import METRICS
from mmdet.structures.mask import encode_mask_results
from ..functional import eval_recalls
from .coco_metric import CocoMetric

try:
    import lvis
    if getattr(lvis, '__version__', '0') >= '10.5.3':
        warnings.warn(
            'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"',  # noqa: E501
            UserWarning)
    from lvis import LVIS, LVISEval, LVISResults
except ImportError:
    lvis = None
    LVISEval = None
    LVISResults = None


@METRICS.register_module()
class LVISMetric(CocoMetric):
    """LVIS evaluation metric.

    Args:
        ann_file (str, optional): Path to the coco format annotation file.
            If not specified, ground truth annotations from the dataset will
            be converted to coco format. Defaults to None.
        metric (str | List[str]): Metrics to be evaluated. Valid metrics
            include 'bbox', 'segm', 'proposal', and 'proposal_fast'.
            Defaults to 'bbox'.
        classwise (bool): Whether to evaluate the metric class-wise.
            Defaults to False.
        proposal_nums (Sequence[int]): Numbers of proposals to be evaluated.
            Defaults to (100, 300, 1000).
        iou_thrs (float | List[float], optional): IoU threshold to compute AP
            and AR. If not specified, IoUs from 0.5 to 0.95 will be used.
            Defaults to None.
        metric_items (List[str], optional): Metric result names to be
            recorded in the evaluation result. 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.
        outfile_prefix (str, optional): The prefix of json files. It includes
            the file path and the prefix of filename, e.g., "a/b/prefix".
            If not specified, a temp file will be created. Defaults to None.
        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.
        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] = 'lvis'

    def __init__(self,
                 ann_file: Optional[str] = None,
                 metric: Union[str, List[str]] = 'bbox',
                 classwise: bool = False,
                 proposal_nums: Sequence[int] = (100, 300, 1000),
                 iou_thrs: Optional[Union[float, Sequence[float]]] = None,
                 metric_items: Optional[Sequence[str]] = None,
                 format_only: bool = False,
                 outfile_prefix: Optional[str] = None,
                 collect_device: str = 'cpu',
                 prefix: Optional[str] = None,
                 file_client_args: dict = None,
                 backend_args: dict = None) -> None:
        if lvis is None:
            raise RuntimeError(
                'Package lvis is not installed. Please run "pip install '
                'git+https://github.com/lvis-dataset/lvis-api.git".')
        super().__init__(collect_device=collect_device, prefix=prefix)
        # coco evaluation metrics
        self.metrics = metric if isinstance(metric, list) else [metric]
        allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
        for metric in self.metrics:
            if metric not in allowed_metrics:
                raise KeyError(
                    "metric should be one of 'bbox', 'segm', 'proposal', "
                    f"'proposal_fast', but got {metric}.")

        # do class wise evaluation, default False
        self.classwise = classwise

        # proposal_nums used to compute recall or precision.
        self.proposal_nums = list(proposal_nums)

        # iou_thrs used to compute recall or precision.
        if iou_thrs is None:
            iou_thrs = np.linspace(
                .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
        self.iou_thrs = iou_thrs
        self.metric_items = metric_items
        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.'

        self.outfile_prefix = outfile_prefix
        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
            )

        # if ann_file is not specified,
        # initialize lvis api with the converted dataset
        if ann_file is not None:
            with get_local_path(
                    ann_file, backend_args=self.backend_args) as local_path:
                self._lvis_api = LVIS(local_path)
        else:
            self._lvis_api = None

        # handle dataset lazy init
        self.cat_ids = None
        self.img_ids = None

    def fast_eval_recall(self,
                         results: List[dict],
                         proposal_nums: Sequence[int],
                         iou_thrs: Sequence[float],
                         logger: Optional[MMLogger] = None) -> np.ndarray:
        """Evaluate proposal recall with LVIS's fast_eval_recall.

        Args:
            results (List[dict]): Results of the dataset.
            proposal_nums (Sequence[int]): Proposal numbers used for
                evaluation.
            iou_thrs (Sequence[float]): IoU thresholds used for evaluation.
            logger (MMLogger, optional): Logger used for logging the recall
                summary.
        Returns:
            np.ndarray: Averaged recall results.
        """
        gt_bboxes = []
        pred_bboxes = [result['bboxes'] for result in results]
        for i in range(len(self.img_ids)):
            ann_ids = self._lvis_api.get_ann_ids(img_ids=[self.img_ids[i]])
            ann_info = self._lvis_api.load_anns(ann_ids)
            if len(ann_info) == 0:
                gt_bboxes.append(np.zeros((0, 4)))
                continue
            bboxes = []
            for ann in ann_info:
                x1, y1, w, h = ann['bbox']
                bboxes.append([x1, y1, x1 + w, y1 + h])
            bboxes = np.array(bboxes, dtype=np.float32)
            if bboxes.shape[0] == 0:
                bboxes = np.zeros((0, 4))
            gt_bboxes.append(bboxes)

        recalls = eval_recalls(
            gt_bboxes, pred_bboxes, proposal_nums, iou_thrs, logger=logger)
        ar = recalls.mean(axis=1)
        return ar

    # 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:
            result = dict()
            pred = data_sample['pred_instances']
            result['img_id'] = data_sample['img_id']
            result['bboxes'] = pred['bboxes'].cpu().numpy()
            result['scores'] = pred['scores'].cpu().numpy()
            result['labels'] = pred['labels'].cpu().numpy()
            # encode mask to RLE
            if 'masks' in pred:
                result['masks'] = encode_mask_results(
                    pred['masks'].detach().cpu().numpy())
            # some detectors use different scores for bbox and mask
            if 'mask_scores' in pred:
                result['mask_scores'] = pred['mask_scores'].cpu().numpy()

            # parse gt
            gt = dict()
            gt['width'] = data_sample['ori_shape'][1]
            gt['height'] = data_sample['ori_shape'][0]
            gt['img_id'] = data_sample['img_id']
            if self._lvis_api is None:
                # TODO: Need to refactor to support LoadAnnotations
                assert 'instances' in data_sample, \
                    'ground truth is required for evaluation when ' \
                    '`ann_file` is not provided'
                gt['anns'] = data_sample['instances']
            # add converted result to the results list
            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()

        # split gt and prediction list
        gts, preds = zip(*results)

        tmp_dir = None
        if self.outfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            outfile_prefix = osp.join(tmp_dir.name, 'results')
        else:
            outfile_prefix = self.outfile_prefix

        if self._lvis_api is None:
            # use converted gt json file to initialize coco api
            logger.info('Converting ground truth to coco format...')
            coco_json_path = self.gt_to_coco_json(
                gt_dicts=gts, outfile_prefix=outfile_prefix)
            self._lvis_api = LVIS(coco_json_path)

        # handle lazy init
        if self.cat_ids is None:
            self.cat_ids = self._lvis_api.get_cat_ids()
        if self.img_ids is None:
            self.img_ids = self._lvis_api.get_img_ids()

        # convert predictions to coco format and dump to json file
        result_files = self.results2json(preds, outfile_prefix)

        eval_results = OrderedDict()
        if self.format_only:
            logger.info('results are saved in '
                        f'{osp.dirname(outfile_prefix)}')
            return eval_results

        lvis_gt = self._lvis_api

        for metric in self.metrics:
            logger.info(f'Evaluating {metric}...')

            # TODO: May refactor fast_eval_recall to an independent metric?
            # fast eval recall
            if metric == 'proposal_fast':
                ar = self.fast_eval_recall(
                    preds, self.proposal_nums, self.iou_thrs, logger=logger)
                log_msg = []
                for i, num in enumerate(self.proposal_nums):
                    eval_results[f'AR@{num}'] = ar[i]
                    log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
                log_msg = ''.join(log_msg)
                logger.info(log_msg)
                continue

            try:
                lvis_dt = LVISResults(lvis_gt, result_files[metric])
            except IndexError:
                logger.info(
                    'The testing results of the whole dataset is empty.')
                break

            iou_type = 'bbox' if metric == 'proposal' else metric
            lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type)
            lvis_eval.params.imgIds = self.img_ids
            metric_items = self.metric_items
            if metric == 'proposal':
                lvis_eval.params.useCats = 0
                lvis_eval.params.maxDets = list(self.proposal_nums)
                lvis_eval.evaluate()
                lvis_eval.accumulate()
                lvis_eval.summarize()
                if metric_items is None:
                    metric_items = ['AR@300', 'ARs@300', 'ARm@300', 'ARl@300']
                for k, v in lvis_eval.get_results().items():
                    if k in metric_items:
                        val = float('{:.3f}'.format(float(v)))
                        eval_results[k] = val

            else:
                lvis_eval.evaluate()
                lvis_eval.accumulate()
                lvis_eval.summarize()
                lvis_results = lvis_eval.get_results()
                if self.classwise:  # Compute per-category AP
                    # Compute per-category AP
                    # from https://github.com/facebookresearch/detectron2/
                    precisions = lvis_eval.eval['precision']
                    # precision: (iou, recall, cls, area range, max dets)
                    assert len(self.cat_ids) == precisions.shape[2]

                    results_per_category = []
                    for idx, catId in enumerate(self.cat_ids):
                        # area range index 0: all area ranges
                        # max dets index -1: typically 100 per image
                        # the dimensions of precisions are
                        # [num_thrs, num_recalls, num_cats, num_area_rngs]
                        nm = self._lvis_api.load_cats([catId])[0]
                        precision = precisions[:, :, idx, 0]
                        precision = precision[precision > -1]
                        if precision.size:
                            ap = np.mean(precision)
                        else:
                            ap = float('nan')
                        results_per_category.append(
                            (f'{nm["name"]}', f'{float(ap):0.3f}'))
                        eval_results[f'{nm["name"]}_precision'] = round(ap, 3)

                    num_columns = min(6, len(results_per_category) * 2)
                    results_flatten = list(
                        itertools.chain(*results_per_category))
                    headers = ['category', 'AP'] * (num_columns // 2)
                    results_2d = itertools.zip_longest(*[
                        results_flatten[i::num_columns]
                        for i in range(num_columns)
                    ])
                    table_data = [headers]
                    table_data += [result for result in results_2d]
                    table = AsciiTable(table_data)
                    logger.info('\n' + table.table)

                if metric_items is None:
                    metric_items = [
                        'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'APr',
                        'APc', 'APf'
                    ]

                for k, v in lvis_results.items():
                    if k in metric_items:
                        key = '{}_{}'.format(metric, k)
                        val = float('{:.3f}'.format(float(v)))
                        eval_results[key] = val

            lvis_eval.print_results()
        if tmp_dir is not None:
            tmp_dir.cleanup()
        return eval_results