def get_results(self):
"""
Reruns the evaluation using the accumulated detections, returns COCO results with AP metrics
:return: dict with COCO AP metrics
"""
if self.cached_results:
return self.results
self.coco_evaluator = CocoEvaluator(self.coco, self.iou_types)
self.coco_evaluator.update(self.detections)
self.coco_evaluator.evaluate()
self.coco_evaluator.accumulate()
self.coco_evaluator.summarize()
self.results = get_coco_metrics(self.coco_evaluator)
self.speed_mem_metrics[
'Max Memory Allocated (Total)'] = get_max_memory_allocated()
return self.results
def add(self, detections: list):
"""
Update the evaluator with new detections
:param annotations (list): List of detections, that will be used by the COCO.loadRes method in the
pycocotools API. Each detection can take a dictionary format like the following:
{'image_id': 397133, 'bbox': [386.1628112792969, 69.48855590820312, 110.14895629882812, 278.2847595214844],
'score': 0.999152421951294, 'category_id': 1}
I.e is a list of dictionaries.
:return: void - updates self.detection with the new IDSs and prediction
Examples:
Update the evaluator with two results:
.. code-block:: python
my_evaluator.add([{'image_id': 397133, 'bbox': [386.1628112792969, 69.48855590820312,
110.14895629882812, 278.2847595214844], 'score': 0.999152421951294, 'category_id': 1}])
"""
self.detections.extend(detections)
self.coco_evaluator.update(detections)
if not self.first_batch_processed:
self.coco_evaluator.evaluate()
self.coco_evaluator.accumulate()
if any(
[detection["bbox"] for detection in detections]
): # we can only hash if we have predictions
self.batch_hash = calculate_batch_hash(
self.cache_values(
annotations=detections,
metrics=get_coco_metrics(self.coco_evaluator),
)
)
self.first_batch_processed = True