File size: 27,109 Bytes
159f437
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
# Part of the code is from https://github.com/tensorflow/models/blob/master/research/object_detection/metrics/oid_challenge_evaluation.py
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
# The original code is under Apache License, Version 2.0 (the "License");
# Part of the code is from https://github.com/lvis-dataset/lvis-api/blob/master/lvis/eval.py
# Copyright (c) 2019, Agrim Gupta and Ross Girshick
# Modified by Xingyi Zhou
# This script re-implement OpenImages evaluation in detectron2
# The code is from https://github.com/xingyizhou/UniDet/blob/master/projects/UniDet/unidet/evaluation/oideval.py
# The original code is under Apache-2.0 License
# Copyright (c) Facebook, Inc. and its affiliates.
import os 
import datetime
import logging
import itertools
from collections import OrderedDict
from collections import defaultdict
import copy
import json
import numpy as np
import torch
from tabulate import tabulate

from lvis.lvis import LVIS
from lvis.results import LVISResults

import pycocotools.mask as mask_utils

from fvcore.common.file_io import PathManager
import detectron2.utils.comm as comm
from detectron2.data import MetadataCatalog
from detectron2.evaluation.coco_evaluation import instances_to_coco_json
from detectron2.utils.logger import create_small_table
from detectron2.evaluation import DatasetEvaluator

def compute_average_precision(precision, recall):
  """Compute Average Precision according to the definition in VOCdevkit.
  Precision is modified to ensure that it does not decrease as recall
  decrease.
  Args:
    precision: A float [N, 1] numpy array of precisions
    recall: A float [N, 1] numpy array of recalls
  Raises:
    ValueError: if the input is not of the correct format
  Returns:
    average_precison: The area under the precision recall curve. NaN if
      precision and recall are None.
  """
  if precision is None:
    if recall is not None:
      raise ValueError("If precision is None, recall must also be None")
    return np.NAN

  if not isinstance(precision, np.ndarray) or not isinstance(
      recall, np.ndarray):
    raise ValueError("precision and recall must be numpy array")
  if precision.dtype != np.float or recall.dtype != np.float:
    raise ValueError("input must be float numpy array.")
  if len(precision) != len(recall):
    raise ValueError("precision and recall must be of the same size.")
  if not precision.size:
    return 0.0
  if np.amin(precision) < 0 or np.amax(precision) > 1:
    raise ValueError("Precision must be in the range of [0, 1].")
  if np.amin(recall) < 0 or np.amax(recall) > 1:
    raise ValueError("recall must be in the range of [0, 1].")
  if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
    raise ValueError("recall must be a non-decreasing array")

  recall = np.concatenate([[0], recall, [1]])
  precision = np.concatenate([[0], precision, [0]])

  for i in range(len(precision) - 2, -1, -1):
    precision[i] = np.maximum(precision[i], precision[i + 1])
  indices = np.where(recall[1:] != recall[:-1])[0] + 1
  average_precision = np.sum(
      (recall[indices] - recall[indices - 1]) * precision[indices])
  return average_precision

class OIDEval:
    def __init__(
        self, lvis_gt, lvis_dt, iou_type="bbox", expand_pred_label=False, 
        oid_hierarchy_path='./datasets/oid/annotations/challenge-2019-label500-hierarchy.json'):
        """Constructor for OIDEval.
        Args:
            lvis_gt (LVIS class instance, or str containing path of annotation file)
            lvis_dt (LVISResult class instance, or str containing path of result file,
            or list of dict)
            iou_type (str): segm or bbox evaluation
        """
        self.logger = logging.getLogger(__name__)

        if iou_type not in ["bbox", "segm"]:
            raise ValueError("iou_type: {} is not supported.".format(iou_type))

        if isinstance(lvis_gt, LVIS):
            self.lvis_gt = lvis_gt
        elif isinstance(lvis_gt, str):
            self.lvis_gt = LVIS(lvis_gt)
        else:
            raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt))

        if isinstance(lvis_dt, LVISResults):
            self.lvis_dt = lvis_dt
        elif isinstance(lvis_dt, (str, list)):
            # self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt, max_dets=-1)
            self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt)
        else:
            raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt))

        if expand_pred_label:
            oid_hierarchy = json.load(open(oid_hierarchy_path, 'r'))
            cat_info = self.lvis_gt.dataset['categories']
            freebase2id = {x['freebase_id']: x['id'] for x in cat_info}
            id2freebase = {x['id']: x['freebase_id'] for x in cat_info}
            id2name = {x['id']: x['name'] for x in cat_info}
            
            fas = defaultdict(set)
            def dfs(hierarchy, cur_id):
                all_childs = set()
                all_keyed_child = {}
                if 'Subcategory' in hierarchy:
                    for x in hierarchy['Subcategory']:
                        childs = dfs(x, freebase2id[x['LabelName']])
                        all_childs.update(childs)
                if cur_id != -1:
                    for c in all_childs:
                        fas[c].add(cur_id)
                all_childs.add(cur_id)
                return all_childs
            dfs(oid_hierarchy, -1)
            
            expanded_pred = []
            id_count = 0
            for d in self.lvis_dt.dataset['annotations']:
                cur_id = d['category_id']
                ids = [cur_id] + [x for x in fas[cur_id]]
                for cat_id in ids:
                    new_box = copy.deepcopy(d)
                    id_count = id_count + 1
                    new_box['id'] = id_count
                    new_box['category_id'] = cat_id
                    expanded_pred.append(new_box)

            print('Expanding original {} preds to {} preds'.format(
                len(self.lvis_dt.dataset['annotations']),
                len(expanded_pred)
                ))
            self.lvis_dt.dataset['annotations'] = expanded_pred
            self.lvis_dt._create_index()
        
        # per-image per-category evaluation results
        self.eval_imgs = defaultdict(list)
        self.eval = {}  # accumulated evaluation results
        self._gts = defaultdict(list)  # gt for evaluation
        self._dts = defaultdict(list)  # dt for evaluation
        self.params = Params(iou_type=iou_type)  # parameters
        self.results = OrderedDict()
        self.ious = {}  # ious between all gts and dts

        self.params.img_ids = sorted(self.lvis_gt.get_img_ids())
        self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids())

    def _to_mask(self, anns, lvis):
        for ann in anns:
            rle = lvis.ann_to_rle(ann)
            ann["segmentation"] = rle

    def _prepare(self):
        """Prepare self._gts and self._dts for evaluation based on params."""

        cat_ids = self.params.cat_ids if self.params.cat_ids else None

        gts = self.lvis_gt.load_anns(
            self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)
        )
        dts = self.lvis_dt.load_anns(
            self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)
        )
        # convert ground truth to mask if iou_type == 'segm'
        if self.params.iou_type == "segm":
            self._to_mask(gts, self.lvis_gt)
            self._to_mask(dts, self.lvis_dt)

        for gt in gts:
            self._gts[gt["image_id"], gt["category_id"]].append(gt)

        # For federated dataset evaluation we will filter out all dt for an
        # image which belong to categories not present in gt and not present in
        # the negative list for an image. In other words detector is not penalized
        # for categories about which we don't have gt information about their
        # presence or absence in an image.
        img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids)
        # per image map of categories not present in image
        img_nl = {d["id"]: d["neg_category_ids"] for d in img_data}
        # per image list of categories present in image
        img_pl = {d["id"]: d["pos_category_ids"] for d in img_data}
        # img_pl = defaultdict(set)
        for ann in gts:
            # img_pl[ann["image_id"]].add(ann["category_id"])
            assert ann["category_id"] in img_pl[ann["image_id"]]
        # print('check pos ids OK.')
        
        for dt in dts:
            img_id, cat_id = dt["image_id"], dt["category_id"]
            if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]:
                continue
            self._dts[img_id, cat_id].append(dt)

    def evaluate(self):
        """
        Run per image evaluation on given images and store results
        (a list of dict) in self.eval_imgs.
        """
        self.logger.info("Running per image evaluation.")
        self.logger.info("Evaluate annotation type *{}*".format(self.params.iou_type))

        self.params.img_ids = list(np.unique(self.params.img_ids))

        if self.params.use_cats:
            cat_ids = self.params.cat_ids
        else:
            cat_ids = [-1]

        self._prepare()

        self.ious = {
            (img_id, cat_id): self.compute_iou(img_id, cat_id)
            for img_id in self.params.img_ids
            for cat_id in cat_ids
        }

        # loop through images, area range, max detection number
        print('Evaluating ...')
        self.eval_imgs = [
            self.evaluate_img_google(img_id, cat_id, area_rng)
            for cat_id in cat_ids
            for area_rng in self.params.area_rng
            for img_id in self.params.img_ids
        ]

    def _get_gt_dt(self, img_id, cat_id):
        """Create gt, dt which are list of anns/dets. If use_cats is true
        only anns/dets corresponding to tuple (img_id, cat_id) will be
        used. Else, all anns/dets in image are used and cat_id is not used.
        """
        if self.params.use_cats:
            gt = self._gts[img_id, cat_id]
            dt = self._dts[img_id, cat_id]
        else:
            gt = [
                _ann
                for _cat_id in self.params.cat_ids
                for _ann in self._gts[img_id, cat_id]
            ]
            dt = [
                _ann
                for _cat_id in self.params.cat_ids
                for _ann in self._dts[img_id, cat_id]
            ]
        return gt, dt

    def compute_iou(self, img_id, cat_id):
        gt, dt = self._get_gt_dt(img_id, cat_id)

        if len(gt) == 0 and len(dt) == 0:
            return []

        # Sort detections in decreasing order of score.
        idx = np.argsort([-d["score"] for d in dt], kind="mergesort")
        dt = [dt[i] for i in idx]

        # iscrowd = [int(False)] * len(gt)
        iscrowd = [int('iscrowd' in g and g['iscrowd'] > 0) for g in gt]

        if self.params.iou_type == "segm":
            ann_type = "segmentation"
        elif self.params.iou_type == "bbox":
            ann_type = "bbox"
        else:
            raise ValueError("Unknown iou_type for iou computation.")
        gt = [g[ann_type] for g in gt]
        dt = [d[ann_type] for d in dt]

        # compute iou between each dt and gt region
        # will return array of shape len(dt), len(gt)
        ious = mask_utils.iou(dt, gt, iscrowd)
        return ious

    def evaluate_img_google(self, img_id, cat_id, area_rng):
        gt, dt = self._get_gt_dt(img_id, cat_id)
        if len(gt) == 0 and len(dt) == 0:
            return None
        
        if len(dt) == 0:
            return {
                "image_id": img_id,
                "category_id": cat_id,
                "area_rng": area_rng,
                "dt_ids": [],
                "dt_matches": np.array([], dtype=np.int32).reshape(1, -1),
                "dt_scores": [],
                "dt_ignore": np.array([], dtype=np.int32).reshape(1, -1),
                'num_gt': len(gt)
            }

        no_crowd_inds = [i for i, g in enumerate(gt) \
            if ('iscrowd' not in g) or g['iscrowd'] == 0]
        crowd_inds = [i for i, g in enumerate(gt) \
            if 'iscrowd' in g and g['iscrowd'] == 1]
        dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort")

        if len(self.ious[img_id, cat_id]) > 0:
            ious = self.ious[img_id, cat_id]
            iou = ious[:, no_crowd_inds]
            iou = iou[dt_idx]
            ioa = ious[:, crowd_inds]
            ioa = ioa[dt_idx]
        else:
            iou = np.zeros((len(dt_idx), 0))
            ioa = np.zeros((len(dt_idx), 0))
        scores = np.array([dt[i]['score'] for i in dt_idx])

        num_detected_boxes = len(dt)
        tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool)
        is_matched_to_group_of = np.zeros(num_detected_boxes, dtype=bool)

        def compute_match_iou(iou):
            max_overlap_gt_ids = np.argmax(iou, axis=1)
            is_gt_detected = np.zeros(iou.shape[1], dtype=bool)
            for i in range(num_detected_boxes):
                gt_id = max_overlap_gt_ids[i]
                is_evaluatable = (not tp_fp_labels[i] and
                                iou[i, gt_id] >= 0.5 and
                                not is_matched_to_group_of[i])
                if is_evaluatable:
                    if not is_gt_detected[gt_id]:
                        tp_fp_labels[i] = True
                        is_gt_detected[gt_id] = True

        def compute_match_ioa(ioa):
            scores_group_of = np.zeros(ioa.shape[1], dtype=float)
            tp_fp_labels_group_of = np.ones(
                ioa.shape[1], dtype=float)
            max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1)
            for i in range(num_detected_boxes):
                gt_id = max_overlap_group_of_gt_ids[i]
                is_evaluatable = (not tp_fp_labels[i] and
                                ioa[i, gt_id] >= 0.5 and
                                not is_matched_to_group_of[i])
                if is_evaluatable:
                    is_matched_to_group_of[i] = True
                    scores_group_of[gt_id] = max(scores_group_of[gt_id], scores[i])
            selector = np.where((scores_group_of > 0) & (tp_fp_labels_group_of > 0))
            scores_group_of = scores_group_of[selector]
            tp_fp_labels_group_of = tp_fp_labels_group_of[selector]

            return scores_group_of, tp_fp_labels_group_of

        if iou.shape[1] > 0:
            compute_match_iou(iou)

        scores_box_group_of = np.ndarray([0], dtype=float)
        tp_fp_labels_box_group_of = np.ndarray([0], dtype=float)

        if ioa.shape[1] > 0:
            scores_box_group_of, tp_fp_labels_box_group_of = compute_match_ioa(ioa)

        valid_entries = (~is_matched_to_group_of)

        scores =  np.concatenate(
            (scores[valid_entries], scores_box_group_of))
        tp_fps = np.concatenate(
            (tp_fp_labels[valid_entries].astype(float),
             tp_fp_labels_box_group_of))
    
        return {
            "image_id": img_id,
            "category_id": cat_id,
            "area_rng": area_rng,
            "dt_matches": np.array([1 if x > 0 else 0 for x in tp_fps], dtype=np.int32).reshape(1, -1),
            "dt_scores": [x for x in scores],
            "dt_ignore":  np.array([0 for x in scores], dtype=np.int32).reshape(1, -1),
            'num_gt': len(gt)
        }

    def accumulate(self):
        """Accumulate per image evaluation results and store the result in
        self.eval.
        """
        self.logger.info("Accumulating evaluation results.")

        if not self.eval_imgs:
            self.logger.warn("Please run evaluate first.")

        if self.params.use_cats:
            cat_ids = self.params.cat_ids
        else:
            cat_ids = [-1]

        num_thrs = 1
        num_recalls = 1

        num_cats = len(cat_ids)
        num_area_rngs = 1
        num_imgs = len(self.params.img_ids)

        # -1 for absent categories
        precision = -np.ones(
            (num_thrs, num_recalls, num_cats, num_area_rngs)
        )
        recall = -np.ones((num_thrs, num_cats, num_area_rngs))

        # Initialize dt_pointers
        dt_pointers = {}
        for cat_idx in range(num_cats):
            dt_pointers[cat_idx] = {}
            for area_idx in range(num_area_rngs):
                dt_pointers[cat_idx][area_idx] = {}

        # Per category evaluation
        for cat_idx in range(num_cats):
            Nk = cat_idx * num_area_rngs * num_imgs
            for area_idx in range(num_area_rngs):
                Na = area_idx * num_imgs
                E = [
                    self.eval_imgs[Nk + Na + img_idx]
                    for img_idx in range(num_imgs)
                ]
                # Remove elements which are None
                E = [e for e in E if not e is None]
                if len(E) == 0:
                    continue

                dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0)
                dt_idx = np.argsort(-dt_scores, kind="mergesort")
                dt_scores = dt_scores[dt_idx]
                dt_m = np.concatenate([e["dt_matches"] for e in E], axis=1)[:, dt_idx]
                dt_ig = np.concatenate([e["dt_ignore"] for e in E], axis=1)[:, dt_idx]

                num_gt = sum([e['num_gt'] for e in E])
                if num_gt == 0:
                    continue

                tps = np.logical_and(dt_m, np.logical_not(dt_ig))
                fps = np.logical_and(np.logical_not(dt_m), np.logical_not(dt_ig))
                tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
                fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)

                dt_pointers[cat_idx][area_idx] = {
                    "tps": tps,
                    "fps": fps,
                }

                for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                    tp = np.array(tp)
                    fp = np.array(fp)
                    num_tp = len(tp)
                    rc = tp / num_gt
                    
                    if num_tp:
                        recall[iou_thr_idx, cat_idx, area_idx] = rc[
                            -1
                        ]
                    else:
                        recall[iou_thr_idx, cat_idx, area_idx] = 0

                    # np.spacing(1) ~= eps
                    pr = tp / (fp + tp + np.spacing(1))
                    pr = pr.tolist()

                    for i in range(num_tp - 1, 0, -1):
                        if pr[i] > pr[i - 1]:
                            pr[i - 1] = pr[i]

                    mAP = compute_average_precision(
                        np.array(pr, np.float).reshape(-1), 
                        np.array(rc, np.float).reshape(-1))
                    precision[iou_thr_idx, :, cat_idx, area_idx] = mAP

        self.eval = {
            "params": self.params,
            "counts": [num_thrs, num_recalls, num_cats, num_area_rngs],
            "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "precision": precision,
            "recall": recall,
            "dt_pointers": dt_pointers,
        }

    def _summarize(self, summary_type):
        s = self.eval["precision"]
        if len(s[s > -1]) == 0:
            mean_s = -1
        else:
            mean_s = np.mean(s[s > -1])
            # print(s.reshape(1, 1, -1, 1))
        return mean_s

    def summarize(self):
        """Compute and display summary metrics for evaluation results."""
        if not self.eval:
            raise RuntimeError("Please run accumulate() first.")

        max_dets = self.params.max_dets
        self.results["AP50"] = self._summarize('ap')

    def run(self):
        """Wrapper function which calculates the results."""
        self.evaluate()
        self.accumulate()
        self.summarize()

    def print_results(self):
        template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}"

        for key, value in self.results.items():
            max_dets = self.params.max_dets
            if "AP" in key:
                title = "Average Precision"
                _type = "(AP)"
            else:
                title = "Average Recall"
                _type = "(AR)"

            if len(key) > 2 and key[2].isdigit():
                iou_thr = (float(key[2:]) / 100)
                iou = "{:0.2f}".format(iou_thr)
            else:
                iou = "{:0.2f}:{:0.2f}".format(
                    self.params.iou_thrs[0], self.params.iou_thrs[-1]
                )

            cat_group_name = "all"
            area_rng = "all"

            print(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value))

    def get_results(self):
        if not self.results:
            self.logger.warn("results is empty. Call run().")
        return self.results


class Params:
    def __init__(self, iou_type):
        self.img_ids = []
        self.cat_ids = []
        # np.arange causes trouble.  the data point on arange is slightly
        # larger than the true value
        self.iou_thrs = np.linspace(
            0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
        )
        self.google_style = True
        # print('Using google style PR curve')
        self.iou_thrs = self.iou_thrs[:1]
        self.max_dets = 1000

        self.area_rng = [
            [0 ** 2, 1e5 ** 2],
        ]
        self.area_rng_lbl = ["all"]
        self.use_cats = 1
        self.iou_type = iou_type


class OIDEvaluator(DatasetEvaluator):
    def __init__(self, dataset_name, cfg, distributed, output_dir=None):
        self._distributed = distributed
        self._output_dir = output_dir

        self._cpu_device = torch.device("cpu")
        self._logger = logging.getLogger(__name__)

        self._metadata = MetadataCatalog.get(dataset_name)
        json_file = PathManager.get_local_path(self._metadata.json_file)
        self._oid_api = LVIS(json_file)
        # Test set json files do not contain annotations (evaluation must be
        # performed using the LVIS evaluation server).
        self._do_evaluation = len(self._oid_api.get_ann_ids()) > 0
        self._mask_on = cfg.MODEL.MASK_ON

    def reset(self):
        self._predictions = []
        self._oid_results = []

    def process(self, inputs, outputs):
        for input, output in zip(inputs, outputs):
            prediction = {"image_id": input["image_id"]}
            instances = output["instances"].to(self._cpu_device)
            prediction["instances"] = instances_to_coco_json(
                instances, input["image_id"])
            self._predictions.append(prediction)

    def evaluate(self):
        if self._distributed:
            comm.synchronize()
            self._predictions = comm.gather(self._predictions, dst=0)
            self._predictions = list(itertools.chain(*self._predictions))

            if not comm.is_main_process():
                return

        if len(self._predictions) == 0:
            self._logger.warning("[LVISEvaluator] Did not receive valid predictions.")
            return {}

        self._logger.info("Preparing results in the OID format ...")
        self._oid_results = list(
            itertools.chain(*[x["instances"] for x in self._predictions]))

        # unmap the category ids for LVIS (from 0-indexed to 1-indexed)
        for result in self._oid_results:
            result["category_id"] += 1

        PathManager.mkdirs(self._output_dir)
        file_path = os.path.join(
            self._output_dir, "oid_instances_results.json")
        self._logger.info("Saving results to {}".format(file_path))
        with PathManager.open(file_path, "w") as f:
            f.write(json.dumps(self._oid_results))
            f.flush()

        if not self._do_evaluation:
            self._logger.info("Annotations are not available for evaluation.")
            return

        self._logger.info("Evaluating predictions ...")
        self._results = OrderedDict()
        res, mAP = _evaluate_predictions_on_oid(
            self._oid_api,
            file_path,
            eval_seg=self._mask_on,
            class_names=self._metadata.get("thing_classes"),
        )
        self._results['bbox'] = res
        mAP_out_path = os.path.join(self._output_dir, "oid_mAP.npy")
        self._logger.info('Saving mAP to' + mAP_out_path)
        np.save(mAP_out_path, mAP)
        return copy.deepcopy(self._results)

def _evaluate_predictions_on_oid(
    oid_gt, oid_results_path, eval_seg=False,
    class_names=None):
    logger = logging.getLogger(__name__)
    metrics = ["AP50", "AP50_expand"]

    results = {}
    oid_eval = OIDEval(oid_gt, oid_results_path, 'bbox', expand_pred_label=False)
    oid_eval.run()
    oid_eval.print_results()
    results["AP50"] = oid_eval.get_results()["AP50"]

    if eval_seg:
        oid_eval = OIDEval(oid_gt, oid_results_path, 'segm', expand_pred_label=False)
        oid_eval.run()
        oid_eval.print_results()
        results["AP50_segm"] = oid_eval.get_results()["AP50"]
    else:
        oid_eval = OIDEval(oid_gt, oid_results_path, 'bbox', expand_pred_label=True)
        oid_eval.run()
        oid_eval.print_results()
        results["AP50_expand"] = oid_eval.get_results()["AP50"]

    mAP = np.zeros(len(class_names)) - 1
    precisions = oid_eval.eval['precision']
    assert len(class_names) == precisions.shape[2]
    results_per_category = []
    id2apiid = sorted(oid_gt.get_cat_ids())
    inst_aware_ap, inst_count = 0, 0
    for idx, name in enumerate(class_names):
        precision = precisions[:, :, idx, 0]
        precision = precision[precision > -1]
        ap = np.mean(precision) if precision.size else float("nan")
        inst_num = len(oid_gt.get_ann_ids(cat_ids=[id2apiid[idx]]))
        if inst_num > 0:
            results_per_category.append(("{} {}".format(
                name.replace(' ', '_'), 
                inst_num if inst_num < 1000 else '{:.1f}k'.format(inst_num / 1000)), 
                float(ap * 100)))
            inst_aware_ap += inst_num * ap
            inst_count += inst_num
            mAP[idx] = ap
            # logger.info("{} {} {:.2f}".format(name, inst_num, ap * 100))
    inst_aware_ap = inst_aware_ap * 100 / inst_count
    N_COLS = min(6, len(results_per_category) * 2)
    results_flatten = list(itertools.chain(*results_per_category))
    results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
    table = tabulate(
        results_2d,
        tablefmt="pipe",
        floatfmt=".3f",
        headers=["category", "AP"] * (N_COLS // 2),
        numalign="left",
    )
    logger.info("Per-category {} AP: \n".format('bbox') + table)
    logger.info("Instance-aware {} AP: {:.4f}".format('bbox', inst_aware_ap))

    logger.info("Evaluation results for bbox: \n" + \
        create_small_table(results))
    return results, mAP