---恢复内容开始---
MAP是衡量object dectection算法的重要criteria，然而一直没有仔细阅读相关代码，今天就好好看一下：
1. 测试test过程是由FRCN/tools/test_net.py中调用的test_net()完成 #from model.test import test_net
test_net()定义在FRCN/lib/model/test.py （193-194行）：调用了imdb.evaluate_detections
print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir)
imdb是从FRCN/lib/model/test.py（84行）传入的：
imdb = get_imdb(args.imdb_name)
from datasets.factory import get_imdb，为了了解如何定义一个imdb，我们去FRCN/lib/datasets/factory.py
1 """Factory method for easily getting imdbs by name.""" 2 from __future__ import absolute_import 3 from __future__ import division 4 from __future__ import print_function 5 6 __sets = {} 7 from datasets.pascal_voc import pascal_voc 8 9 import numpy as np 10 11 # Set up voc_<year>_<split> 12 for year in ['2007', '2012']: 13 for split in ['train', 'val', 'trainval', 'test']: 14 name = 'voc_{}_{}'.format(year, split) 15 __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) 16 17 for year in ['2007', '2012']: 18 for split in ['train', 'val', 'trainval', 'test']: 19 name = 'voc_{}_{}_diff'.format(year, split) 20 __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, use_diff=True)) 21 22 def get_imdb(name): 23 """Get an imdb (image database) by name.""" 24 if name not in __sets: 25 raise KeyError('Unknown dataset: {}'.format(name)) 26 return __sets[name]() 27 28 def list_imdbs(): 29 """List all registered imdbs.""" 30 return list(__sets.keys())
coco数据集的定义同pascal_voc. 可以看到，get_imdb(args.imdb_name)将会返回的就是pascal_voc(split, year)这样一个对象。
2. 来到pascal_voc.py ：
1 # -------------------------------------------------------- 2 # Fast R-CNN 3 # Copyright (c) 2015 Microsoft 4 # Licensed under The MIT License [see LICENSE for details] 5 # Written by Ross Girshick and Xinlei Chen 6 # -------------------------------------------------------- 7 from __future__ import absolute_import 8 from __future__ import division 9 from __future__ import print_function 10 11 import os 12 from datasets.imdb import imdb 13 import datasets.ds_utils as ds_utils 14 import xml.etree.ElementTree as ET 15 import numpy as np 16 import scipy.sparse 17 import scipy.io as sio 18 import pickle 19 import subprocess 20 import uuid 21 from .voc_eval import voc_eval 22 from model.config import cfg 23 24 25 class pascal_voc(imdb): 26 def __init__(self, image_set, year, use_diff=False): 27 name = 'voc_' + year + '_' + image_set 28 if use_diff: 29 name += '_diff' 30 imdb.__init__(self, name) 31 self._year = year 32 self._image_set = image_set 33 self._devkit_path = self._get_default_path() 34 self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) 35 self._classes = ('__background__', # always index 0 36 'title', 'xlabel', 'ylabel') 37 #### 'text', 'ylabel') 38 # 'aeroplane', 'bicycle', 'bird', 'boat', 39 # 'bottle', 'bus', 'car', 'cat', 'chair', 40 # 'cow', 'diningtable', 'dog', 'horse', 41 # 'motorbike', 'person', 'pottedplant', 42 # 'sheep', 'sofa', 'train', 'tvmonitor') 43 self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) 44 self._image_ext = '.jpg' 45 self._image_index = self._load_image_set_index() 46 # Default to roidb handler 47 self._roidb_handler = self.gt_roidb 48 self._salt = str(uuid.uuid4()) 49 self._comp_id = 'comp4' 50 51 # PASCAL specific config options 52 self.config = {'cleanup': True, 53 'use_salt': True, 54 'use_diff': use_diff, 55 'matlab_eval': False, 56 'rpn_file': None} 57 58 assert os.path.exists(self._devkit_path), \ 59 'VOCdevkit path does not exist: {}'.format(self._devkit_path) 60 assert os.path.exists(self._data_path), \ 61 'Path does not exist: {}'.format(self._data_path) 62 63 def image_path_at(self, i): 64 """ 65 Return the absolute path to image i in the image sequence. 66 """ 67 return self.image_path_from_index(self._image_index[i]) 68 69 def image_path_from_index(self, index): 70 """ 71 Construct an image path from the image's "index" identifier. 72 """ 73 image_path = os.path.join(self._data_path, 'JPEGImages', 74 index + self._image_ext) 75 assert os.path.exists(image_path), \ 76 'Path does not exist: {}'.format(image_path) 77 return image_path 78 79 def _load_image_set_index(self): 80 """ 81 Load the indexes listed in this dataset's image set file. 82 """ 83 # Example path to image set file: 84 # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt 85 image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main', 86 self._image_set + '.txt') 87 assert os.path.exists(image_set_file), \ 88 'Path does not exist: {}'.format(image_set_file) 89 with open(image_set_file) as f: 90 image_index = [x.strip() for x in f.readlines()] 91 return image_index 92 93 def _get_default_path(self): 94 """ 95 Return the default path where PASCAL VOC is expected to be installed. 96 """ 97 return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year) 98 99 def gt_roidb(self): 100 """ 101 Return the database of ground-truth regions of interest. 102 103 This function loads/saves from/to a cache file to speed up future calls. 104 """ 105 cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') 106 if os.path.exists(cache_file): 107 with open(cache_file, 'rb') as fid: 108 try: 109 roidb = pickle.load(fid) 110 except: 111 roidb = pickle.load(fid, encoding='bytes') 112 print('{} gt roidb loaded from {}'.format(self.name, cache_file)) 113 return roidb 114 115 gt_roidb = [self._load_pascal_annotation(index) 116 for index in self.image_index] 117 with open(cache_file, 'wb') as fid: 118 pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL) 119 print('wrote gt roidb to {}'.format(cache_file)) 120 121 return gt_roidb 122 123 def rpn_roidb(self): 124 if int(self._year) == 2007 or self._image_set != 'test': 125 gt_roidb = self.gt_roidb() 126 rpn_roidb = self._load_rpn_roidb(gt_roidb) 127 roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb) 128 else: 129 roidb = self._load_rpn_roidb(None) 130 131 return roidb 132 133 def _load_rpn_roidb(self, gt_roidb): 134 filename = self.config['rpn_file'] 135 print('loading {}'.format(filename)) 136 assert os.path.exists(filename), \ 137 'rpn data not found at: {}'.format(filename) 138 with open(filename, 'rb') as f: 139 box_list = pickle.load(f) 140 return self.create_roidb_from_box_list(box_list, gt_roidb) 141 142 def _load_pascal_annotation(self, index): 143 """ 144 Load image and bounding boxes info from XML file in the PASCAL VOC 145 format. 146 """ 147 filename = os.path.join(self._data_path, 'Annotations', index + '.xml') 148 tree = ET.parse(filename) 149 objs = tree.findall('object') 150 if not self.config['use_diff']: 151 # Exclude the samples labeled as difficult 152 non_diff_objs = [ 153 obj for obj in objs if int(obj.find('difficult').text) == 0] 154 # if len(non_diff_objs) != len(objs): 155 # print 'Removed {} difficult objects'.format( 156 # len(objs) - len(non_diff_objs)) 157 objs = non_diff_objs 158 num_objs = len(objs) 159 160 boxes = np.zeros((num_objs, 4), dtype=np.uint16) 161 gt_classes = np.zeros((num_objs), dtype=np.int32) 162 overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) 163 # "Seg" area for pascal is just the box area 164 seg_areas = np.zeros((num_objs), dtype=np.float32) 165 166 # Load object bounding boxes into a data frame. 167 for ix, obj in enumerate(objs): 168 bbox = obj.find('bndbox') 169 # Make pixel indexes 0-based 170 x1 = float(bbox.find('xmin').text) - 1 171 y1 = float(bbox.find('ymin').text) - 1 172 x2 = float(bbox.find('xmax').text) - 1 173 y2 = float(bbox.find('ymax').text) - 1 174 cls = self._class_to_ind[obj.find('name').text.lower().strip()] 175 boxes[ix, :] = [x1, y1, x2, y2] 176 gt_classes[ix] = cls 177 overlaps[ix, cls] = 1.0 178 seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1) 179 180 overlaps = scipy.sparse.csr_matrix(overlaps) 181 182 return {'boxes': boxes, 183 'gt_classes': gt_classes, 184 'gt_overlaps': overlaps, 185 'flipped': False, 186 'seg_areas': seg_areas} 187 188 def _get_comp_id(self): 189 comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt'] 190 else self._comp_id) 191 return comp_id 192 193 def _get_voc_results_file_template(self): 194 # VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt 195 filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt' 196 path = os.path.join( 197 self._devkit_path, 198 'results', 199 'VOC' + self._year, 200 'Main', 201 filename) 202 return path 203 204 def _write_voc_results_file(self, all_boxes): 205 for cls_ind, cls in enumerate(self.classes): 206 if cls == '__background__': 207 continue 208 print('Writing {} VOC results file'.format(cls)) 209 filename = self._get_voc_results_file_template().format(cls) 210 with open(filename, 'wt') as f: 211 for im_ind, index in enumerate(self.image_index): 212 dets = all_boxes[cls_ind][im_ind] 213 if dets == []: 214 continue 215 # the VOCdevkit expects 1-based indices 216 for k in range(dets.shape[0]): 217 f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. 218 format(index, dets[k, -1], 219 dets[k, 0] + 1, dets[k, 1] + 1, 220 dets[k, 2] + 1, dets[k, 3] + 1)) 221 222 def _do_python_eval(self, output_dir='output'): 223 annopath = os.path.join( 224 self._devkit_path, 225 'VOC' + self._year, 226 'Annotations', 227 '{:s}.xml') 228 imagesetfile = os.path.join( 229 self._devkit_path, 230 'VOC' + self._year, 231 'ImageSets', 232 'Main', 233 self._image_set + '.txt') 234 cachedir = os.path.join(self._devkit_path, 'annotations_cache') 235 aps = [] 236 # The PASCAL VOC metric changed in 2010 237 use_07_metric = True if int(self._year) < 2010 else False 238 print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No')) 239 if not os.path.isdir(output_dir): 240 os.mkdir(output_dir) 241 for i, cls in enumerate(self._classes): 242 if cls == '__background__': 243 continue 244 filename = self._get_voc_results_file_template().format(cls) 245 rec, prec, ap = voc_eval( 246 filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, 247 use_07_metric=use_07_metric, use_diff=self.config['use_diff']) 248 aps += [ap] 249 print(('AP for {} = {:.4f}'.format(cls, ap))) 250 with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f: 251 pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f) 252 print(('Mean AP = {:.4f}'.format(np.mean(aps)))) 253 print('~~~~~~~~') 254 print('Results:') 255 for ap in aps: 256 print(('{:.3f}'.format(ap))) 257 print(('{:.3f}'.format(np.mean(aps)))) 258 print('~~~~~~~~') 259 print('') 260 print('--------------------------------------------------------------') 261 print('Results computed with the **unofficial** Python eval code.') 262 print('Results should be very close to the official MATLAB eval code.') 263 print('Recompute with `./tools/reval.py --matlab ...` for your paper.') 264 print('-- Thanks, The Management') 265 print('--------------------------------------------------------------') 266 267 def _do_matlab_eval(self, output_dir='output'): 268 print('-----------------------------------------------------') 269 print('Computing results with the official MATLAB eval code.') 270 print('-----------------------------------------------------') 271 path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets', 272 'VOCdevkit-matlab-wrapper') 273 cmd = 'cd {} && '.format(path) 274 cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB) 275 cmd += '-r "dbstop if error; ' 276 cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \ 277 .format(self._devkit_path, self._get_comp_id(), 278 self._image_set, output_dir) 279 print(('Running:\n{}'.format(cmd))) 280 status = subprocess.call(cmd, shell=True) 281 282 def evaluate_detections(self, all_boxes, output_dir): 283 self._write_voc_results_file(all_boxes) 284 self._do_python_eval(output_dir) 285 if self.config['matlab_eval']: 286 self._do_matlab_eval(output_dir) 287 if self.config['cleanup']: 288 for cls in self._classes: 289 if cls == '__background__': 290 continue 291 filename = self._get_voc_results_file_template().format(cls) 292 os.remove(filename) 293 294 def competition_mode(self, on): 295 if on: 296 self.config['use_salt'] = False 297 self.config['cleanup'] = False 298 else: 299 self.config['use_salt'] = True 300 self.config['cleanup'] = True 301 302 303 if __name__ == '__main__': 304 from datasets.pascal_voc import pascal_voc 305 306 d = pascal_voc('trainval', '2007') 307 res = d.roidb 308 from IPython import embed; 309 310 embed()
我们先看涉及到MAP的方法，其他方法暂时放下。
这里通过evaluate_detections方法调用了_do_python_eval方法，后者通过调用voc_eval函数进行了AP和MAP的计算（245-247行）。
1 # -------------------------------------------------------- 2 # Fast/er R-CNN 3 # Licensed under The MIT License [see LICENSE for details] 4 # Written by Bharath Hariharan 5 # -------------------------------------------------------- 6 from __future__ import absolute_import 7 from __future__ import division 8 from __future__ import print_function 9 10 import xml.etree.ElementTree as ET 11 import os 12 import pickle 13 import numpy as np 14 15 def parse_rec(filename): 16 """ Parse a PASCAL VOC xml file """ 17 tree = ET.parse(filename) 18 objects = [] 19 for obj in tree.findall('object'): 20 obj_struct = {} 21 obj_struct['name'] = obj.find('name').text 22 obj_struct['pose'] = obj.find('pose').text 23 obj_struct['truncated'] = int(obj.find('truncated').text) 24 obj_struct['difficult'] = int(obj.find('difficult').text) 25 bbox = obj.find('bndbox') 26 obj_struct['bbox'] = [int(float(bbox.find('xmin').text)), 27 int(float(bbox.find('ymin').text)), 28 int(float(bbox.find('xmax').text)), 29 int(float(bbox.find('ymax').text))] 30 objects.append(obj_struct) 31 32 return objects 33 34 35 def voc_ap(rec, prec, use_07_metric=False): 36 """ ap = voc_ap(rec, prec, [use_07_metric]) 37 Compute VOC AP given precision and recall. 38 If use_07_metric is true, uses the 39 VOC 07 11 point method (default:False). 40 """ 41 if use_07_metric: 42 # 11 point metric 43 ap = 0. 44 for t in np.arange(0., 1.1, 0.1): 45 if np.sum(rec >= t) == 0: 46 p = 0 47 else: 48 p = np.max(prec[rec >= t]) 49 ap = ap + p / 11. 50 else: 51 # correct AP calculation 52 # first append sentinel values at the end 53 mrec = np.concatenate(([0.], rec, [1.])) 54 mpre = np.concatenate(([0.], prec, [0.])) 55 56 # compute the precision envelope 57 for i in range(mpre.size - 1, 0, -1): 58 mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) 59 60 # to calculate area under PR curve, look for points 61 # where X axis (recall) changes value 62 i = np.where(mrec[1:] != mrec[:-1])[0] 63 64 # and sum (\Delta recall) * prec 65 ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) 66 return ap 67 68 69 def voc_eval(detpath, 70 annopath, 71 imagesetfile, 72 classname, 73 cachedir, 74 ovthresh=0.5, 75 use_07_metric=False, 76 use_diff=False): 77 """rec, prec, ap = voc_eval(detpath, 78 annopath, 79 imagesetfile, 80 classname, 81 [ovthresh], 82 [use_07_metric]) 83 84 Top level function that does the PASCAL VOC evaluation. 85 86 detpath: Path to detections 87 detpath.format(classname) should produce the detection results file. 88 annopath: Path to annotations 89 annopath.format(imagename) should be the xml annotations file. 90 imagesetfile: Text file containing the list of images, one image per line. 91 classname: Category name (duh) 92 cachedir: Directory for caching the annotations 93 [ovthresh]: Overlap threshold (default = 0.5) 94 [use_07_metric]: Whether to use VOC07's 11 point AP computation 95 (default False) 96 """ 97 # assumes detections are in detpath.format(classname) 98 # assumes annotations are in annopath.format(imagename) 99 # assumes imagesetfile is a text file with each line an image name 100 # cachedir caches the annotations in a pickle file 101 102 # first load gt 103 if not os.path.isdir(cachedir): 104 os.mkdir(cachedir) 105 cachefile = os.path.join(cachedir, '%s_annots.pkl' % imagesetfile) 106 # read list of images 107 with open(imagesetfile, 'r') as f: 108 lines = f.readlines() 109 imagenames = [x.strip() for x in lines] #test.txt中的所有标号 110 111 # load annotations 112 if not os.path.isfile(cachefile): 113 recs = {} 114 for i, imagename in enumerate(imagenames): 115 recs[imagename] = parse_rec(annopath.format(imagename)) 116 if i % 100 == 0: 117 print('Reading annotation for {:d}/{:d}'.format( 118 i + 1, len(imagenames))) 119 # save 120 print('Saving cached annotations to {:s}'.format(cachefile)) 121 with open(cachefile, 'wb') as f: 122 pickle.dump(recs, f) 123 else: 124 # load 125 with open(cachefile, 'rb') as f: 126 try: 127 recs = pickle.load(f) 128 except: 129 recs = pickle.load(f, encoding='bytes') 130 131 # extract gt objects for this class 132 class_recs = {} 133 npos = 0 134 for imagename in imagenames: 135 R = [obj for obj in recs[imagename] if obj['name'] == classname] 136 bbox = np.array([x['bbox'] for x in R]) 137 if use_diff: 138 difficult = np.array([False for x in R]).astype(np.bool) 139 else: 140 difficult = np.array([x['difficult'] for x in R]).astype(np.bool) 141 det = [False] * len(R) 142 npos = npos + sum(~difficult) 143 class_recs[imagename] = {'bbox': bbox, 144 'difficult': difficult, 145 'det': det} 146 147 # read dets 148 detfile = detpath.format(classname) 149 with open(detfile, 'r') as f: 150 lines = f.readlines() 151 152 splitlines = [x.strip().split(' ') for x in lines] 153 image_ids = [x[0] for x in splitlines] 154 confidence = np.array([float(x[1]) for x in splitlines]) 155 BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) 156 157 nd = len(image_ids) 158 tp = np.zeros(nd) 159 fp = np.zeros(nd) 160 161 if BB.shape[0] > 0: 162 # sort by confidence 163 sorted_ind = np.argsort(-confidence) 164 sorted_scores = np.sort(-confidence) 165 BB = BB[sorted_ind, :] 166 image_ids = [image_ids[x] for x in sorted_ind] 167 168 # go down dets and mark TPs and FPs 169 for d in range(nd): 170 R = class_recs[image_ids[d]] 171 bb = BB[d, :].astype(float) 172 ovmax = -np.inf 173 BBGT = R['bbox'].astype(float) 174 175 if BBGT.size > 0: 176 # compute overlaps 177 # intersection 178 ixmin = np.maximum(BBGT[:, 0], bb[0]) 179 iymin = np.maximum(BBGT[:, 1], bb[1]) 180 ixmax = np.minimum(BBGT[:, 2], bb[2]) 181 iymax = np.minimum(BBGT[:, 3], bb[3]) 182 iw = np.maximum(ixmax - ixmin + 1., 0.) 183 ih = np.maximum(iymax - iymin + 1., 0.) 184 inters = iw * ih 185 186 # union 187 uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + 188 (BBGT[:, 2] - BBGT[:, 0] + 1.) * 189 (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) 190 191 overlaps = inters / uni 192 ovmax = np.max(overlaps) 193 jmax = np.argmax(overlaps) 194 195 if ovmax > ovthresh: 196 if not R['difficult'][jmax]: 197 if not R['det'][jmax]: 198 tp[d] = 1. 199 R['det'][jmax] = 1 200 else: 201 fp[d] = 1. 202 else: 203 fp[d] = 1. 204 205 # compute precision recall 206 fp = np.cumsum(fp) 207 tp = np.cumsum(tp) 208 rec = tp / float(npos) 209 # avoid divide by zero in case the first detection matches a difficult 210 # ground truth 211 prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) 212 ap = voc_ap(rec, prec, use_07_metric) 213 214 return rec, prec, ap
voc_eval(filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5, use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
def voc_eval(detpath, annopath, imagesetfile, classname, cachedir, ovthresh=0.5, use_07_metric=False, use_diff=False):
filename: detpath: Path to detections 存储detection结果的pkl文件地址
annopath: 存储Annotations的地址
imagesetfile: 图片集的txt文档
classname: 当前的class
cachedir: 存储Annotations的pkl所在目录（可能不存在）
ovthresh=0.5: IoU的threshold，默认为0.5
use_07_metric=Flase: 是否使用2007PASCAL_VOC的MAP计算规则
use_diff=False: 是否考虑difficult的检测样本
经过一番数据处理，得到了：
BB： 当前class的所有proposal bbox （predicted）
image_ids: 当前imageset的所有image序号
class_recs: image所包含的当前class的bbox （GT）
1 if BB.shape[0] > 0: 2 # sort by confidence 3 #''' 4 sorted_ind = np.argsort(-confidence) 5 sorted_scores = np.sort(-confidence) 6 BB = BB[sorted_ind, :] # 现在的BB是按照conf降序排列的所有predicted bbox 7 image_ids = [image_ids[x] for x in sorted_ind] # image_id 是BB每组bbox所属于的image的序号 8 9 #''' 10 11 # go down dets and mark TPs and FPs 12 for d in range(nd): #对所有proposal bbox 遍历 13 R = class_recs[image_ids[d]] # 找到当前bbox对应的image 14 bb = BB[d, :].astype(float) # bb 为当前proposal bbox的坐标 15 ovmax = -np.inf # 设置np极小值 16 BBGT = R['bbox'].astype(float) 17 18 if BBGT.size > 0: 19 # compute overlaps 20 # intersection 21 ixmin = np.maximum(BBGT[:, 0], bb[0]) 22 iymin = np.maximum(BBGT[:, 1], bb[1]) 23 ixmax = np.minimum(BBGT[:, 2], bb[2]) 24 iymax = np.minimum(BBGT[:, 3], bb[3]) 25 iw = np.maximum(ixmax - ixmin + 1., 0.) 26 ih = np.maximum(iymax - iymin + 1., 0.) 27 inters = iw * ih 28 29 # union 30 uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + 31 (BBGT[:, 2] - BBGT[:, 0] + 1.) * 32 (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) 33 34 overlaps = inters / uni 35 ovmax = np.max(overlaps) 36 jmax = np.argmax(overlaps) 37 print(overlaps) 38 39 if ovmax > ovthresh: 40 if not R['difficult'][jmax]: 41 if not R['det'][jmax]: #是否已经被检测过 42 tp[d] = 1. 43 R['det'][jmax] = 1 44 else: 45 fp[d] = 1. 46 else: 47 fp[d] = 1.
疑惑：
这里的Recall计算（voc_eval.py 208行）使用了：
rec = tp / float(npos)，npos实际上是所有bbox-GT的数量，并不应该等于tp+fn吧？当且仅当：fn（包含但未被检测出bbox的image数量）==npos-tp（未被检测出的bbox数量）
ref: 1. https://datascience.stackexchange.com/questions/25119/how-to-calculate-map-for-detection-task-for-the-pascal-voc-challenge
2. http://mp.weixin.qq.com/s/FaNC9RppIhPf6T_qAz3Slg
3. https://ils.unc.edu/courses/2013_spring/inls509_001/lectures/10-EvaluationMetrics.pdf
4. https://stats.stackexchange.com/questions/260430/average-precision-in-object-detection/263758#263758
本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：Pytorch-Faster-RCNN 中的 MAP 实现 （解析imdb.py 和 pascal_voc.py） - Python技术站
微信扫一扫 
支付宝扫一扫 
