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import contextlib
import copy
import os
from typing import Dict, List, Union
import numpy as np
import torch
from .coco import COCO
from .cocoeval import COCOeval
from .utils import (
_TYPING_BOX,
_TYPING_PREDICTIONS,
convert_to_xywh,
create_common_coco_eval,
)
_SUPPORTED_TYPES = ["bbox"]
class COCOEvaluator(object):
"""
Class to perform evaluation for the COCO dataset.
"""
def __init__(self, coco_gt: COCO, iou_types: List[str] = ["bbox"]):
"""
Initializes COCOEvaluator with the ground truth COCO dataset and IoU types.
Args:
coco_gt: The ground truth COCO dataset.
iou_types: Intersection over Union (IoU) types for evaluation (Supported: "bbox").
"""
self.coco_gt = copy.deepcopy(coco_gt)
self.coco_eval = {}
for iou_type in iou_types:
assert iou_type in _SUPPORTED_TYPES, ValueError(
f"IoU type not supported {iou_type}"
)
self.coco_eval[iou_type] = COCOeval(self.coco_gt, iouType=iou_type)
self.iou_types = iou_types
self.img_ids = []
self.eval_imgs = {k: [] for k in iou_types}
def update(self, predictions: _TYPING_PREDICTIONS) -> None:
"""
Update the evaluator with new predictions.
Args:
predictions: The predictions to update.
"""
img_ids = list(np.unique(list(predictions.keys())))
self.img_ids.extend(img_ids)
for iou_type in self.iou_types:
results = self.prepare(predictions, iou_type)
# suppress pycocotools prints
with open(os.devnull, "w") as devnull:
with contextlib.redirect_stdout(devnull):
coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO()
coco_eval = self.coco_eval[iou_type]
coco_eval.cocoDt = coco_dt
coco_eval.params.imgIds = list(img_ids)
eval_imgs = coco_eval.evaluate()
self.eval_imgs[iou_type].append(eval_imgs)
def synchronize_between_processes(self) -> None:
"""
Synchronizes evaluation images between processes.
"""
for iou_type in self.iou_types:
self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
create_common_coco_eval(
self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type]
)
def accumulate(self) -> None:
"""
Accumulates the evaluation results.
"""
for coco_eval in self.coco_eval.values():
coco_eval.accumulate()
def summarize(self) -> None:
"""
Prints the IoU metric and summarizes the evaluation results.
"""
for iou_type, coco_eval in self.coco_eval.items():
print("IoU metric: {}".format(iou_type))
coco_eval.summarize()
def prepare(
self, predictions: _TYPING_PREDICTIONS, iou_type: str
) -> List[Dict[str, Union[int, _TYPING_BOX, float]]]:
"""
Prepares the predictions for COCO detection.
Args:
predictions: The predictions to prepare.
iou_type: The Intersection over Union (IoU) type for evaluation.
Returns:
A dictionary with the prepared predictions.
"""
if iou_type == "bbox":
return self.prepare_for_coco_detection(predictions)
else:
raise ValueError(f"IoU type not supported {iou_type}")
def _post_process_stats(
self, stats, coco_eval_object, iou_type="bbox"
) -> Dict[str, float]:
"""
Prepares the predictions for COCO detection.
Args:
predictions: The predictions to prepare.
iou_type: The Intersection over Union (IoU) type for evaluation.
Returns:
A dictionary with the prepared predictions.
"""
if iou_type not in _SUPPORTED_TYPES:
raise ValueError(f"iou_type '{iou_type}' not supported")
current_max_dets = coco_eval_object.params.maxDets
index_to_title = {
"bbox": {
0: f"AP-IoU=0.50:0.95-area=all-maxDets={current_max_dets[2]}",
1: f"AP-IoU=0.50-area=all-maxDets={current_max_dets[2]}",
2: f"AP-IoU=0.75-area=all-maxDets={current_max_dets[2]}",
3: f"AP-IoU=0.50:0.95-area=small-maxDets={current_max_dets[2]}",
4: f"AP-IoU=0.50:0.95-area=medium-maxDets={current_max_dets[2]}",
5: f"AP-IoU=0.50:0.95-area=large-maxDets={current_max_dets[2]}",
6: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[0]}",
7: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[1]}",
8: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[2]}",
9: f"AR-IoU=0.50:0.95-area=small-maxDets={current_max_dets[2]}",
10: f"AR-IoU=0.50:0.95-area=medium-maxDets={current_max_dets[2]}",
11: f"AR-IoU=0.50:0.95-area=large-maxDets={current_max_dets[2]}",
},
"keypoints": {
0: "AP-IoU=0.50:0.95-area=all-maxDets=20",
1: "AP-IoU=0.50-area=all-maxDets=20",
2: "AP-IoU=0.75-area=all-maxDets=20",
3: "AP-IoU=0.50:0.95-area=medium-maxDets=20",
4: "AP-IoU=0.50:0.95-area=large-maxDets=20",
5: "AR-IoU=0.50:0.95-area=all-maxDets=20",
6: "AR-IoU=0.50-area=all-maxDets=20",
7: "AR-IoU=0.75-area=all-maxDets=20",
8: "AR-IoU=0.50:0.95-area=medium-maxDets=20",
9: "AR-IoU=0.50:0.95-area=large-maxDets=20",
},
}
output_dict: Dict[str, float] = {}
for index, stat in enumerate(stats):
output_dict[index_to_title[iou_type][index]] = stat
return output_dict
def get_results(self) -> Dict[str, Dict[str, float]]:
"""
Gets the results of the COCO evaluation.
Returns:
A dictionary with the results of the COCO evaluation.
"""
output_dict = {}
for iou_type, coco_eval in self.coco_eval.items():
if iou_type == "segm":
iou_type = "bbox"
output_dict[f"iou_{iou_type}"] = self._post_process_stats(
coco_eval.stats, coco_eval, iou_type
)
return output_dict
def prepare_for_coco_detection(
self, predictions: _TYPING_PREDICTIONS
) -> List[Dict[str, Union[int, _TYPING_BOX, float]]]:
"""
Prepares the predictions for COCO detection.
Args:
predictions: The predictions to prepare.
Returns:
A list of dictionaries with the prepared predictions.
"""
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
boxes = prediction["boxes"]
if len(boxes) == 0:
continue
if not isinstance(boxes, torch.Tensor):
boxes = torch.as_tensor(boxes)
boxes = boxes.tolist()
scores = prediction["scores"]
if not isinstance(scores, list):
scores = scores.tolist()
labels = prediction["labels"]
if not isinstance(labels, list):
labels = prediction["labels"].tolist()
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
"bbox": box,
"score": scores[k],
}
for k, box in enumerate(boxes)
]
)
return coco_results
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