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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from collections import OrderedDict
from typing import List, Optional, Sequence, Union
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger, print_log
from mmdet.registry import METRICS
from ..functional import eval_map
@METRICS.register_module()
class OpenImagesMetric(BaseMetric):
"""OpenImages evaluation metric.
Evaluate detection mAP for OpenImages. Please refer to
https://storage.googleapis.com/openimages/web/evaluation.html for more
details.
Args:
iou_thrs (float or List[float]): IoU threshold. Defaults to 0.5.
ioa_thrs (float or List[float]): IoA threshold. Defaults to 0.5.
scale_ranges (List[tuple], optional): Scale ranges for evaluating
mAP. If not specified, all bounding boxes would be included in
evaluation. Defaults to None
use_group_of (bool): Whether consider group of groud truth bboxes
during evaluating. Defaults to True.
get_supercategory (bool): Whether to get parent class of the
current class. Default: True.
filter_labels (bool): Whether filter unannotated classes.
Default: True.
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.
"""
default_prefix: Optional[str] = 'openimages'
def __init__(self,
iou_thrs: Union[float, List[float]] = 0.5,
ioa_thrs: Union[float, List[float]] = 0.5,
scale_ranges: Optional[List[tuple]] = None,
use_group_of: bool = True,
get_supercategory: bool = True,
filter_labels: bool = True,
collect_device: str = 'cpu',
prefix: Optional[str] = None) -> None:
super().__init__(collect_device=collect_device, prefix=prefix)
self.iou_thrs = [iou_thrs] if isinstance(iou_thrs, float) else iou_thrs
self.ioa_thrs = [ioa_thrs] if (isinstance(ioa_thrs, float)
or ioa_thrs is None) else ioa_thrs
assert isinstance(self.iou_thrs, list) and isinstance(
self.ioa_thrs, list)
assert len(self.iou_thrs) == len(self.ioa_thrs)
self.scale_ranges = scale_ranges
self.use_group_of = use_group_of
self.get_supercategory = get_supercategory
self.filter_labels = filter_labels
def _get_supercategory_ann(self, instances: List[dict]) -> List[dict]:
"""Get parent classes's annotation of the corresponding class.
Args:
instances (List[dict]): A list of annotations of the instances.
Returns:
List[dict]: Annotations extended with super-category.
"""
supercat_instances = []
relation_matrix = self.dataset_meta['RELATION_MATRIX']
for instance in instances:
labels = np.where(relation_matrix[instance['bbox_label']])[0]
for label in labels:
if label == instance['bbox_label']:
continue
new_instance = copy.deepcopy(instance)
new_instance['bbox_label'] = label
supercat_instances.append(new_instance)
return supercat_instances
def _process_predictions(self, pred_bboxes: np.ndarray,
pred_scores: np.ndarray, pred_labels: np.ndarray,
gt_instances: list,
image_level_labels: np.ndarray) -> tuple:
"""Process results of the corresponding class of the detection bboxes.
Note: It will choose to do the following two processing according to
the parameters:
1. Whether to add parent classes of the corresponding class of the
detection bboxes.
2. Whether to ignore the classes that unannotated on that image.
Args:
pred_bboxes (np.ndarray): bboxes predicted by the model
pred_scores (np.ndarray): scores predicted by the model
pred_labels (np.ndarray): labels predicted by the model
gt_instances (list): ground truth annotations
image_level_labels (np.ndarray): human-verified image level labels
Returns:
tuple: Processed bboxes, scores, and labels.
"""
processed_bboxes = copy.deepcopy(pred_bboxes)
processed_scores = copy.deepcopy(pred_scores)
processed_labels = copy.deepcopy(pred_labels)
gt_labels = np.array([ins['bbox_label'] for ins in gt_instances],
dtype=np.int64)
if image_level_labels is not None:
allowed_classes = np.unique(
np.append(gt_labels, image_level_labels))
else:
allowed_classes = np.unique(gt_labels)
relation_matrix = self.dataset_meta['RELATION_MATRIX']
pred_classes = np.unique(pred_labels)
for pred_class in pred_classes:
classes = np.where(relation_matrix[pred_class])[0]
for cls in classes:
if (cls in allowed_classes and cls != pred_class
and self.get_supercategory):
# add super-supercategory preds
index = np.where(pred_labels == pred_class)[0]
processed_scores = np.concatenate(
[processed_scores, pred_scores[index]])
processed_bboxes = np.concatenate(
[processed_bboxes, pred_bboxes[index]])
extend_labels = np.full(index.shape, cls, dtype=np.int64)
processed_labels = np.concatenate(
[processed_labels, extend_labels])
elif cls not in allowed_classes and self.filter_labels:
# remove unannotated preds
index = np.where(processed_labels != cls)[0]
processed_scores = processed_scores[index]
processed_bboxes = processed_bboxes[index]
processed_labels = processed_labels[index]
return processed_bboxes, processed_scores, processed_labels
# 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:
gt = copy.deepcopy(data_sample)
# add super-category instances
# TODO: Need to refactor to support LoadAnnotations
instances = gt['instances']
if self.get_supercategory:
supercat_instances = self._get_supercategory_ann(instances)
instances.extend(supercat_instances)
gt_labels = []
gt_bboxes = []
is_group_ofs = []
for ins in instances:
gt_labels.append(ins['bbox_label'])
gt_bboxes.append(ins['bbox'])
is_group_ofs.append(ins['is_group_of'])
ann = dict(
labels=np.array(gt_labels, dtype=np.int64),
bboxes=np.array(gt_bboxes, dtype=np.float32).reshape((-1, 4)),
gt_is_group_ofs=np.array(is_group_ofs, dtype=bool))
image_level_labels = gt.get('image_level_labels', None)
pred = data_sample['pred_instances']
pred_bboxes = pred['bboxes'].cpu().numpy()
pred_scores = pred['scores'].cpu().numpy()
pred_labels = pred['labels'].cpu().numpy()
pred_bboxes, pred_scores, pred_labels = self._process_predictions(
pred_bboxes, pred_scores, pred_labels, instances,
image_level_labels)
dets = []
for label in range(len(self.dataset_meta['classes'])):
index = np.where(pred_labels == label)[0]
pred_bbox_scores = np.hstack(
[pred_bboxes[index], pred_scores[index].reshape((-1, 1))])
dets.append(pred_bbox_scores)
self.results.append((ann, dets))
def compute_metrics(self, results: list) -> dict:
"""Compute the metrics from processed results.
Args:
results (list): The processed results of each batch.
Returns:
dict: The computed metrics. The keys are the names of the metrics,
and the values are corresponding results.
"""
logger = MMLogger.get_current_instance()
gts, preds = zip(*results)
eval_results = OrderedDict()
# get dataset type
dataset_type = self.dataset_meta.get('dataset_type')
if dataset_type not in ['oid_challenge', 'oid_v6']:
dataset_type = 'oid_v6'
print_log(
'Cannot infer dataset type from the length of the'
' classes. Set `oid_v6` as dataset type.',
logger='current')
mean_aps = []
for i, (iou_thr,
ioa_thr) in enumerate(zip(self.iou_thrs, self.ioa_thrs)):
if self.use_group_of:
assert ioa_thr is not None, 'ioa_thr must have value when' \
' using group_of in evaluation.'
print_log(f'\n{"-" * 15}iou_thr, ioa_thr: {iou_thr}, {ioa_thr}'
f'{"-" * 15}')
mean_ap, _ = eval_map(
preds,
gts,
scale_ranges=self.scale_ranges,
iou_thr=iou_thr,
ioa_thr=ioa_thr,
dataset=dataset_type,
logger=logger,
use_group_of=self.use_group_of)
mean_aps.append(mean_ap)
eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
return eval_results
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