def run_evaluation(categories, groundtruth, detections, verbose=True):
"""AVA evaluation main logic."""
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
boxes, labels, _ = groundtruth
gt_keys = []
pred_keys = []
for image_key in boxes:
pascal_evaluator.add_single_ground_truth_image_info(
image_key,
{
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool),
},
)
gt_keys.append(image_key)
boxes, labels, scores = detections
for image_key in boxes:
pascal_evaluator.add_single_detected_image_info(
image_key,
{
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float),
},
)
pred_keys.append(image_key)
metrics = pascal_evaluator.evaluate()
if du.is_master_proc():
pprint.pprint(metrics, indent=2)
return metrics
def run_evaluation(categories,
groundtruth,
detections,
excluded_keys,
verbose=True):
"""AVA evaluation main logic."""
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
categories)
boxes, labels, _ = groundtruth
gt_keys = []
pred_keys = []
for image_key in boxes:
if image_key in excluded_keys:
logging.info(
("Found excluded timestamp in ground truth: %s. "
"It will be ignored."),
image_key,
)
continue
pascal_evaluator.add_single_ground_truth_image_info(
image_key,
{
standard_fields.InputDataFields.groundtruth_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.InputDataFields.groundtruth_classes:
np.array(labels[image_key], dtype=int),
standard_fields.InputDataFields.groundtruth_difficult:
np.zeros(len(boxes[image_key]), dtype=bool),
},
)
gt_keys.append(image_key)
boxes, labels, scores = detections
for image_key in boxes:
if image_key in excluded_keys:
logging.info(
("Found excluded timestamp in detections: %s. "
"It will be ignored."),
image_key,
)
continue
pascal_evaluator.add_single_detected_image_info(
image_key,
{
standard_fields.DetectionResultFields.detection_boxes:
np.array(boxes[image_key], dtype=float),
standard_fields.DetectionResultFields.detection_classes:
np.array(labels[image_key], dtype=int),
standard_fields.DetectionResultFields.detection_scores:
np.array(scores[image_key], dtype=float),
},
)
pred_keys.append(image_key)
metrics = pascal_evaluator.evaluate()
pprint.pprint(metrics, indent=2)
return metrics