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"""
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
COCO evaluator that works in distributed mode.
Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
The difference is that there is less copy-pasting from pycocotools
in the end of the file, as python3 can suppress prints with contextlib
"""
import contextlib
import copy
import os
import faster_coco_eval.core.mask as mask_util
import numpy as np
import torch
from faster_coco_eval import COCO, COCOeval_faster
from ...core import register
from ...misc import dist_utils
__all__ = [
"CocoEvaluator",
]
@register()
class CocoEvaluator(object):
def __init__(self, coco_gt, iou_types):
assert isinstance(iou_types, (list, tuple))
coco_gt = copy.deepcopy(coco_gt)
self.coco_gt: COCO = coco_gt
self.iou_types = iou_types
self.coco_eval = {}
for iou_type in iou_types:
self.coco_eval[iou_type] = COCOeval_faster(
coco_gt, iouType=iou_type, print_function=print, separate_eval=True
)
self.img_ids = []
self.eval_imgs = {k: [] for k in iou_types}
def cleanup(self):
self.coco_eval = {}
for iou_type in self.iou_types:
self.coco_eval[iou_type] = COCOeval_faster(
self.coco_gt, iouType=iou_type, print_function=print, separate_eval=True
)
self.img_ids = []
self.eval_imgs = {k: [] for k in self.iou_types}
def update(self, predictions):
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)
coco_eval = self.coco_eval[iou_type]
# suppress pycocotools prints
with open(os.devnull, "w") as devnull:
with contextlib.redirect_stdout(devnull):
coco_dt = self.coco_gt.loadRes(results) if results else COCO()
coco_eval.cocoDt = coco_dt
coco_eval.params.imgIds = list(img_ids)
coco_eval.evaluate()
self.eval_imgs[iou_type].append(
np.array(coco_eval._evalImgs_cpp).reshape(
len(coco_eval.params.catIds),
len(coco_eval.params.areaRng),
len(coco_eval.params.imgIds),
)
)
def synchronize_between_processes(self):
for iou_type in self.iou_types:
img_ids, eval_imgs = merge(self.img_ids, self.eval_imgs[iou_type])
coco_eval = self.coco_eval[iou_type]
coco_eval.params.imgIds = img_ids
coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
coco_eval._evalImgs_cpp = eval_imgs
def accumulate(self):
for coco_eval in self.coco_eval.values():
coco_eval.accumulate()
def summarize(self):
for iou_type, coco_eval in self.coco_eval.items():
print("IoU metric: {}".format(iou_type))
coco_eval.summarize()
def prepare(self, predictions, iou_type):
if iou_type == "bbox":
return self.prepare_for_coco_detection(predictions)
elif iou_type == "segm":
return self.prepare_for_coco_segmentation(predictions)
elif iou_type == "keypoints":
return self.prepare_for_coco_keypoint(predictions)
else:
raise ValueError("Unknown iou type {}".format(iou_type))
def prepare_for_coco_detection(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
scores = prediction["scores"].tolist()
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
def prepare_for_coco_segmentation(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
scores = prediction["scores"]
labels = prediction["labels"]
masks = prediction["masks"]
masks = masks > 0.5
scores = prediction["scores"].tolist()
labels = prediction["labels"].tolist()
rles = [
mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
for mask in masks
]
for rle in rles:
rle["counts"] = rle["counts"].decode("utf-8")
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
"segmentation": rle,
"score": scores[k],
}
for k, rle in enumerate(rles)
]
)
return coco_results
def prepare_for_coco_keypoint(self, predictions):
coco_results = []
for original_id, prediction in predictions.items():
if len(prediction) == 0:
continue
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
scores = prediction["scores"].tolist()
labels = prediction["labels"].tolist()
keypoints = prediction["keypoints"]
keypoints = keypoints.flatten(start_dim=1).tolist()
coco_results.extend(
[
{
"image_id": original_id,
"category_id": labels[k],
"keypoints": keypoint,
"score": scores[k],
}
for k, keypoint in enumerate(keypoints)
]
)
return coco_results
def convert_to_xywh(boxes):
xmin, ymin, xmax, ymax = boxes.unbind(1)
return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
def merge(img_ids, eval_imgs):
all_img_ids = dist_utils.all_gather(img_ids)
all_eval_imgs = dist_utils.all_gather(eval_imgs)
merged_img_ids = []
for p in all_img_ids:
merged_img_ids.extend(p)
merged_eval_imgs = []
for p in all_eval_imgs:
merged_eval_imgs.extend(p)
merged_img_ids = np.array(merged_img_ids)
merged_eval_imgs = np.concatenate(merged_eval_imgs, axis=2).ravel()
# merged_eval_imgs = np.array(merged_eval_imgs).T.ravel()
# keep only unique (and in sorted order) images
merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
return merged_img_ids.tolist(), merged_eval_imgs.tolist()
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