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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 os
- import contextlib
- import copy
- import numpy as np
- import torch
-
- from pycocotools.cocoeval import COCOeval
- from pycocotools.coco import COCO
- import pycocotools.mask as mask_util
-
- from util.misc import all_gather
-
-
- 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_gt
-
- self.iou_types = iou_types
- self.coco_eval = {}
- for iou_type in iou_types:
- self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)
-
- self.img_ids = []
- self.eval_imgs = {k: [] for k in 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)
-
- # 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)
- img_ids, eval_imgs = evaluate(coco_eval)
-
- self.eval_imgs[iou_type].append(eval_imgs)
-
- def synchronize_between_processes(self):
- 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):
- 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 = all_gather(img_ids)
- all_eval_imgs = 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.append(p)
-
- merged_img_ids = np.array(merged_img_ids)
- merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
-
- # keep only unique (and in sorted order) images
- merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
- merged_eval_imgs = merged_eval_imgs[..., idx]
-
- return merged_img_ids, merged_eval_imgs
-
-
- def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
- img_ids, eval_imgs = merge(img_ids, eval_imgs)
- img_ids = list(img_ids)
- eval_imgs = list(eval_imgs.flatten())
-
- coco_eval.evalImgs = eval_imgs
- coco_eval.params.imgIds = img_ids
- coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
-
-
- #################################################################
- # From pycocotools, just removed the prints and fixed
- # a Python3 bug about unicode not defined
- #################################################################
-
-
- def evaluate(self):
- '''
- Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
- :return: None
- '''
- # tic = time.time()
- # print('Running per image evaluation...')
- p = self.params
- # add backward compatibility if useSegm is specified in params
- if p.useSegm is not None:
- p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
- print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))
- # print('Evaluate annotation type *{}*'.format(p.iouType))
- p.imgIds = list(np.unique(p.imgIds))
- if p.useCats:
- p.catIds = list(np.unique(p.catIds))
- p.maxDets = sorted(p.maxDets)
- self.params = p
-
- self._prepare()
- # loop through images, area range, max detection number
- catIds = p.catIds if p.useCats else [-1]
-
- if p.iouType == 'segm' or p.iouType == 'bbox':
- computeIoU = self.computeIoU
- elif p.iouType == 'keypoints':
- computeIoU = self.computeOks
- self.ious = {
- (imgId, catId): computeIoU(imgId, catId)
- for imgId in p.imgIds
- for catId in catIds}
-
- evaluateImg = self.evaluateImg
- maxDet = p.maxDets[-1]
- evalImgs = [
- evaluateImg(imgId, catId, areaRng, maxDet)
- for catId in catIds
- for areaRng in p.areaRng
- for imgId in p.imgIds
- ]
- # this is NOT in the pycocotools code, but could be done outside
- evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))
- self._paramsEval = copy.deepcopy(self.params)
- # toc = time.time()
- # print('DONE (t={:0.2f}s).'.format(toc-tic))
- return p.imgIds, evalImgs
-
- #################################################################
- # end of straight copy from pycocotools, just removing the prints
- #################################################################
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