def __init__(self,
annotation_file,
include_mask,
need_rescale_bboxes=True,
per_category_metrics=False):
"""Constructs COCO evaluation class.
The class provides the interface to COCO metrics_fn. The
_update_op() takes detections from each image and push them to
self.detections. The _evaluate() loads a JSON file in COCO annotation format
as the groundtruths and runs COCO evaluation.
Args:
annotation_file: a JSON file that stores annotations of the eval dataset.
If `annotation_file` is None, groundtruth annotations will be loaded
from the dataloader.
include_mask: a boolean to indicate whether or not to include the mask
eval.
need_rescale_bboxes: If true bboxes in `predictions` will be rescaled back
to absolute values (`image_info` is needed in this case).
per_category_metrics: Whether to return per category metrics.
"""
if annotation_file:
if annotation_file.startswith('gs://'):
_, local_val_json = tempfile.mkstemp(suffix='.json')
tf.io.gfile.remove(local_val_json)
tf.io.gfile.copy(annotation_file, local_val_json)
atexit.register(tf.io.gfile.remove, local_val_json)
else:
local_val_json = annotation_file
self._coco_gt = coco_utils.COCOWrapper(
eval_type=('mask' if include_mask else 'box'),
annotation_file=local_val_json)
self._annotation_file = annotation_file
self._include_mask = include_mask
self._per_category_metrics = per_category_metrics
self._metric_names = [
'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'ARmax1', 'ARmax10',
'ARmax100', 'ARs', 'ARm', 'ARl'
]
self._required_prediction_fields = [
'source_id', 'num_detections', 'detection_classes',
'detection_scores', 'detection_boxes'
]
self._need_rescale_bboxes = need_rescale_bboxes
if self._need_rescale_bboxes:
self._required_prediction_fields.append('image_info')
self._required_groundtruth_fields = [
'source_id', 'height', 'width', 'classes', 'boxes'
]
if self._include_mask:
mask_metric_names = ['mask_' + x for x in self._metric_names]
self._metric_names.extend(mask_metric_names)
self._required_prediction_fields.extend(['detection_masks'])
self._required_groundtruth_fields.extend(['masks'])
self.reset_states()
def evaluate(self):
"""Evaluates with detections from all images with COCO API.
Returns:
coco_metric: float numpy array with shape [24] representing the
coco-style evaluation metrics (box and mask).
"""
if not self._annotation_file:
logging.info('There is no annotation_file in COCOEvaluator.')
gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
self._groundtruths)
coco_gt = coco_utils.COCOWrapper(
eval_type=('mask' if self._include_mask else 'box'),
gt_dataset=gt_dataset)
else:
logging.info('Using annotation file: %s', self._annotation_file)
coco_gt = self._coco_gt
coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
self._predictions)
coco_dt = coco_gt.loadRes(predictions=coco_predictions)
image_ids = [ann['image_id'] for ann in coco_predictions]
coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_metrics = coco_eval.stats
if self._include_mask:
mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
mcoco_eval.params.imgIds = image_ids
mcoco_eval.evaluate()
mcoco_eval.accumulate()
mcoco_eval.summarize()
mask_coco_metrics = mcoco_eval.stats
if self._include_mask:
metrics = np.hstack((coco_metrics, mask_coco_metrics))
else:
metrics = coco_metrics
metrics_dict = {}
for i, name in enumerate(self._metric_names):
metrics_dict[name] = metrics[i].astype(np.float32)
# Adds metrics per category.
if self._per_category_metrics:
metrics_dict.update(self._retrieve_per_category_metrics(coco_eval))
if self._include_mask:
metrics_dict.update(
self._retrieve_per_category_metrics(mcoco_eval,
prefix='mask'))
return metrics_dict
def evaluate(self):
"""Evaluates with detections from all images with COCO API.
Returns:
coco_metric: float numpy array with shape [24] representing the
coco-style evaluation metrics (box and mask).
"""
if not self._annotation_file:
logging.info('There is no annotation_file in COCOEvaluator.')
gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
self._groundtruths)
coco_gt = coco_utils.COCOWrapper(
eval_type=('mask' if self._include_mask else 'box'),
gt_dataset=gt_dataset)
else:
logging.info('Using annotation file: %s', self._annotation_file)
coco_gt = self._coco_gt
coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
self._predictions)
coco_dt = coco_gt.loadRes(predictions=coco_predictions)
image_ids = [ann['image_id'] for ann in coco_predictions]
coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
coco_metrics = coco_eval.stats
if self._include_mask:
mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
mcoco_eval.params.imgIds = image_ids
mcoco_eval.evaluate()
mcoco_eval.accumulate()
mcoco_eval.summarize()
mask_coco_metrics = mcoco_eval.stats
if self._include_mask:
metrics = np.hstack((coco_metrics, mask_coco_metrics))
else:
metrics = coco_metrics
metrics_dict = {}
for i, name in enumerate(self._metric_names):
metrics_dict[name] = metrics[i].astype(np.float32)
# Adds metrics per category.
if self._per_category_metrics and hasattr(coco_eval, 'category_stats'):
for category_index, category_id in enumerate(
coco_eval.params.catIds):
metrics_dict['Precision mAP ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[0][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory@50IoU/{}'.format(
category_id
)] = coco_eval.category_stats[1][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory@75IoU/{}'.format(
category_id
)] = coco_eval.category_stats[2][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (small) /{}'.format(
category_id
)] = coco_eval.category_stats[3][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (medium) /{}'.format(
category_id
)] = coco_eval.category_stats[4][category_index].astype(
np.float32)
metrics_dict['Precision mAP ByCategory (large) /{}'.format(
category_id
)] = coco_eval.category_stats[5][category_index].astype(
np.float32)
metrics_dict['Recall AR@1 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[6][category_index].astype(
np.float32)
metrics_dict['Recall AR@10 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[7][category_index].astype(
np.float32)
metrics_dict['Recall AR@100 ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[8][category_index].astype(
np.float32)
metrics_dict['Recall AR (small) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[9][category_index].astype(
np.float32)
metrics_dict['Recall AR (medium) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[10][category_index].astype(
np.float32)
metrics_dict['Recall AR (large) ByCategory/{}'.format(
category_id
)] = coco_eval.category_stats[11][category_index].astype(
np.float32)
return metrics_dict