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| # Adapted from Detectron.pytorch/lib/datasets/voc_eval.py for | ||
| # this project by Ji Zhang, 2019 | ||
| #----------------------------------------------------------------------------- | ||
| # Copyright (c) 2017-present, Facebook, Inc. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| ############################################################################## | ||
| # | ||
| # Based on: | ||
| # -------------------------------------------------------- | ||
| # Fast/er R-CNN | ||
| # Licensed under The MIT License [see LICENSE for details] | ||
| # Written by Bharath Hariharan | ||
| # -------------------------------------------------------- | ||
|
|
||
| """relationship AP evaluation code.""" | ||
|
|
||
| from six.moves import cPickle as pickle | ||
| import logging | ||
| import numpy as np | ||
| import os | ||
| from tqdm import tqdm | ||
| from lib.fpn.box_intersections_cpu.bbox import bbox_overlaps | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
| def boxes_union(boxes1, boxes2): | ||
| assert boxes1.shape == boxes2.shape | ||
| xmin = np.minimum(boxes1[:, 0], boxes2[:, 0]) | ||
| ymin = np.minimum(boxes1[:, 1], boxes2[:, 1]) | ||
| xmax = np.maximum(boxes1[:, 2], boxes2[:, 2]) | ||
| ymax = np.maximum(boxes1[:, 3], boxes2[:, 3]) | ||
| return np.vstack((xmin, ymin, xmax, ymax)).transpose() | ||
|
|
||
| def prepare_mAP_dets(topk_dets, cls_num): | ||
| cls_image_ids = [[] for _ in range(cls_num)] | ||
| cls_dets = [{'confidence': np.empty(0), | ||
| 'BB_s': np.empty((0, 4)), | ||
| 'BB_o': np.empty((0, 4)), | ||
| 'BB_r': np.empty((0, 4)), | ||
| 'LBL_s': np.empty(0), | ||
| 'LBL_o': np.empty(0)} for _ in range(cls_num)] | ||
| cls_gts = [{} for _ in range(cls_num)] | ||
| npos = [0 for _ in range(cls_num)] | ||
| for dets in tqdm(topk_dets): | ||
| image_id = dets['image'].split('/')[-1].split('.')[0] | ||
| sbj_boxes = dets['det_boxes_s_top'] | ||
| obj_boxes = dets['det_boxes_o_top'] | ||
| rel_boxes = boxes_union(sbj_boxes, obj_boxes) | ||
| sbj_labels = dets['det_labels_s_top'] | ||
| obj_labels = dets['det_labels_o_top'] | ||
| prd_labels = dets['det_labels_p_top'] | ||
| det_scores = dets['det_scores_top'] | ||
| gt_boxes_sbj = dets['gt_boxes_sbj'] | ||
| gt_boxes_obj = dets['gt_boxes_obj'] | ||
| gt_boxes_rel = boxes_union(gt_boxes_sbj, gt_boxes_obj) | ||
| gt_labels_sbj = dets['gt_labels_sbj'] | ||
| gt_labels_prd = dets['gt_labels_prd'] | ||
| gt_labels_obj = dets['gt_labels_obj'] | ||
| for c in range(cls_num): | ||
| cls_inds = np.where(prd_labels == c)[0] | ||
| # logger.info(cls_inds) | ||
| if len(cls_inds): | ||
| cls_sbj_boxes = sbj_boxes[cls_inds] | ||
| cls_obj_boxes = obj_boxes[cls_inds] | ||
| cls_rel_boxes = rel_boxes[cls_inds] | ||
| cls_sbj_labels = sbj_labels[cls_inds] | ||
| cls_obj_labels = obj_labels[cls_inds] | ||
| cls_det_scores = det_scores[cls_inds] | ||
| cls_dets[c]['confidence'] = np.concatenate((cls_dets[c]['confidence'], cls_det_scores)) | ||
| cls_dets[c]['BB_s'] = np.concatenate((cls_dets[c]['BB_s'], cls_sbj_boxes), 0) | ||
| cls_dets[c]['BB_o'] = np.concatenate((cls_dets[c]['BB_o'], cls_obj_boxes), 0) | ||
| cls_dets[c]['BB_r'] = np.concatenate((cls_dets[c]['BB_r'], cls_rel_boxes), 0) | ||
| cls_dets[c]['LBL_s'] = np.concatenate((cls_dets[c]['LBL_s'], cls_sbj_labels)) | ||
| cls_dets[c]['LBL_o'] = np.concatenate((cls_dets[c]['LBL_o'], cls_obj_labels)) | ||
| cls_image_ids[c] += [image_id] * len(cls_inds) | ||
| cls_gt_inds = np.where(gt_labels_prd == c)[0] | ||
| cls_gt_boxes_sbj = gt_boxes_sbj[cls_gt_inds] | ||
| cls_gt_boxes_obj = gt_boxes_obj[cls_gt_inds] | ||
| cls_gt_boxes_rel = gt_boxes_rel[cls_gt_inds] | ||
| cls_gt_labels_sbj = gt_labels_sbj[cls_gt_inds] | ||
| cls_gt_labels_obj = gt_labels_obj[cls_gt_inds] | ||
| cls_gt_num = len(cls_gt_inds) | ||
| det = [False] * cls_gt_num | ||
| npos[c] = npos[c] + cls_gt_num | ||
| cls_gts[c][image_id] = {'gt_boxes_sbj': cls_gt_boxes_sbj, | ||
| 'gt_boxes_obj': cls_gt_boxes_obj, | ||
| 'gt_boxes_rel': cls_gt_boxes_rel, | ||
| 'gt_labels_sbj': cls_gt_labels_sbj, | ||
| 'gt_labels_obj': cls_gt_labels_obj, | ||
| 'gt_num': cls_gt_num, | ||
| 'det': det} | ||
| return cls_image_ids, cls_dets, cls_gts, npos | ||
|
|
||
|
|
||
| def get_ap(rec, prec): | ||
| """Compute AP given precision and recall. | ||
| """ | ||
| # correct AP calculation | ||
| # first append sentinel values at the end | ||
| mrec = np.concatenate(([0.], rec, [1.])) | ||
| mpre = np.concatenate(([0.], prec, [0.])) | ||
|
|
||
| # compute the precision envelope | ||
| for i in range(mpre.size - 1, 0, -1): | ||
| mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) | ||
|
|
||
| # to calculate area under PR curve, look for points | ||
| # where X axis (recall) changes value | ||
| i = np.where(mrec[1:] != mrec[:-1])[0] | ||
|
|
||
| # and sum (\Delta recall) * prec | ||
| ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) | ||
| return ap | ||
|
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||
|
|
||
| def ap_eval(image_ids, | ||
| dets, | ||
| gts, | ||
| npos, | ||
| rel_or_phr=True, | ||
| ovthresh=0.5): | ||
| """ | ||
| Top level function that does the relationship AP evaluation. | ||
| detpath: Path to detections | ||
| detpath.format(classname) should produce the detection results file. | ||
| classname: Category name (duh) | ||
| [ovthresh]: Overlap threshold (default = 0.5) | ||
| """ | ||
|
|
||
| confidence = dets['confidence'] | ||
| BB_s = dets['BB_s'] | ||
| BB_o = dets['BB_o'] | ||
| BB_r = dets['BB_r'] | ||
| LBL_s = dets['LBL_s'] | ||
| LBL_o = dets['LBL_o'] | ||
|
|
||
| # sort by confidence | ||
| sorted_ind = np.argsort(-confidence) | ||
| BB_s = BB_s[sorted_ind, :] | ||
| BB_o = BB_o[sorted_ind, :] | ||
| BB_r = BB_r[sorted_ind, :] | ||
| LBL_s = LBL_s[sorted_ind] | ||
| LBL_o = LBL_o[sorted_ind] | ||
| image_ids = [image_ids[x] for x in sorted_ind] | ||
|
|
||
| # go down dets and mark TPs and FPs | ||
| nd = len(image_ids) | ||
| tp = np.zeros(nd) | ||
| fp = np.zeros(nd) | ||
| gts_visited = {k: [False] * v['gt_num'] for k, v in gts.items()} | ||
| for d in range(nd): | ||
| R = gts[image_ids[d]] | ||
| visited = gts_visited[image_ids[d]] | ||
| bb_s = BB_s[d, :].astype(float) | ||
| bb_o = BB_o[d, :].astype(float) | ||
| bb_r = BB_r[d, :].astype(float) | ||
| lbl_s = LBL_s[d] | ||
| lbl_o = LBL_o[d] | ||
| ovmax = -np.inf | ||
| BBGT_s = R['gt_boxes_sbj'].astype(float) | ||
| BBGT_o = R['gt_boxes_obj'].astype(float) | ||
| BBGT_r = R['gt_boxes_rel'].astype(float) | ||
| LBLGT_s = R['gt_labels_sbj'] | ||
| LBLGT_o = R['gt_labels_obj'] | ||
| if BBGT_s.size > 0: | ||
| valid_mask = np.logical_and(LBLGT_s == lbl_s, LBLGT_o == lbl_o) | ||
| if valid_mask.any(): | ||
| if rel_or_phr: # means it is evaluating relationships | ||
| overlaps_s = bbox_overlaps( | ||
| bb_s[None, :].astype(dtype=np.float32, copy=False), | ||
| BBGT_s.astype(dtype=np.float32, copy=False))[0] | ||
| overlaps_o = bbox_overlaps( | ||
| bb_o[None, :].astype(dtype=np.float32, copy=False), | ||
| BBGT_o.astype(dtype=np.float32, copy=False))[0] | ||
| overlaps = np.minimum(overlaps_s, overlaps_o) | ||
| else: | ||
| overlaps = bbox_overlaps( | ||
| bb_r[None, :].astype(dtype=np.float32, copy=False), | ||
| BBGT_r.astype(dtype=np.float32, copy=False))[0] | ||
| overlaps *= valid_mask | ||
| ovmax = np.max(overlaps) | ||
| jmax = np.argmax(overlaps) | ||
| else: | ||
| ovmax = 0. | ||
| jmax = -1 | ||
|
|
||
| if ovmax > ovthresh: | ||
| if not visited[jmax]: | ||
| tp[d] = 1. | ||
| visited[jmax] = 1 | ||
| else: | ||
| fp[d] = 1. | ||
| else: | ||
| fp[d] = 1. | ||
|
|
||
| # compute precision recall | ||
| fp = np.cumsum(fp) | ||
| tp = np.cumsum(tp) | ||
| rec = tp / (float(npos) + 1e-12) | ||
| # ground truth | ||
| prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) | ||
| ap = get_ap(rec, prec) | ||
|
|
||
| return rec, prec, ap |
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