告别项目中“依index生成路径”的方法（咋可能把所有数据集转化成按照index排列？不解决其他任务了？），直接使用我们在生成.json标签时就已经写入的图片路径来获取图片。
需要做的，用以下代码替换SNIPER/lib/dataset/coco.py
# --------------------------------------------------------------- # SNIPER: Efficient Multi-scale Training # Licensed under The Apache-2.0 License [see LICENSE for details] # Modified from https://github.com/msracver/Deformable-ConvNets # Modified by Mahyar Najibi
# Second Modified by aimhabo # --------------------------------------------------------------- import cPickle import os import json import numpy as np from imdb import IMDB # coco api from .pycocotools.coco import COCO from .pycocotools.cocoeval import COCOeval from mask.mask_voc2coco import mask_voc2coco from bbox.bbox_transform import clip_boxes, bbox_overlaps_py import multiprocessing as mp def coco_results_one_category_kernel(data_pack): cat_id = data_pack['cat_id'] ann_type = data_pack['ann_type'] binary_thresh = data_pack['binary_thresh'] all_im_info = data_pack['all_im_info'] boxes = data_pack['boxes'] if ann_type == 'bbox': masks = [] elif ann_type == 'segm': masks = data_pack['masks'] else: print 'unimplemented ann_type: ' + ann_type cat_results = [] for im_ind, im_info in enumerate(all_im_info): index = im_info['index'] dets = boxes[im_ind].astype(np.float) if len(dets) == 0: continue scores = dets[:, -1] if ann_type == 'bbox': xs = dets[:, 0] ys = dets[:, 1] ws = dets[:, 2] - xs + 1 hs = dets[:, 3] - ys + 1 result = [{'image_id': index, 'category_id': cat_id, 'bbox': [round(xs[k], 1), round(ys[k], 1), round(ws[k], 1), round(hs[k], 1)], 'score': round(scores[k], 8)} for k in xrange(dets.shape[0])] elif ann_type == 'segm': width = im_info['width'] height = im_info['height'] dets[:, :4] = clip_boxes(dets[:, :4], [height, width]) mask_encode = mask_voc2coco(masks[im_ind], dets[:, :4], height, width, binary_thresh) result = [{'image_id': index, 'category_id': cat_id, 'segmentation': mask_encode[k], 'score': scores[k]} for k in xrange(len(mask_encode))] cat_results.extend(result) return cat_results class coco(IMDB): def __init__(self, image_set, root_path, data_path, result_path=None, mask_size=-1, binary_thresh=None, load_mask=False): """ fill basic information to initialize imdb :param image_set: train2014, val2014, test2015 :param root_path: 'data', will write 'rpn_data', 'cache' :param data_path: 'data/coco' """ super(coco, self).__init__('COCO', image_set, root_path, data_path, result_path) self.root_path = root_path self.data_path = data_path self.coco = COCO(self._get_ann_file()) # deal with class names cats = [cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds())] self.classes = ['__background__'] + cats self.num_classes = len(self.classes) self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes))) self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds())) self._coco_ind_to_class_ind = dict([(self._class_to_coco_ind[cls], self._class_to_ind[cls]) for cls in self.classes[1:]]) # load image file names self.image_set_index = self._load_image_set_index() self.num_images = len(self.image_set_index) print 'num_images', self.num_images self.mask_size = mask_size self.binary_thresh = binary_thresh self.load_mask = load_mask # deal with data name view_map = {'minival2014': 'val2014', 'sminival2014': 'val2014', 'valminusminival2014': 'val2014', 'test-dev2015': 'test2015'} self.data_name = view_map[image_set] if image_set in view_map else image_set self.image_id = self.coco.getImgIds() #print 'image_id = ', self.image_id self.file_name = ['None'] for id in self.image_id: self.file_name.append(self.coco.imgs[id]['file_name']) #print 'file_name = ', self.file_name self.image_id = [] def _get_ann_file(self): """ self.data_path / annotations / instances_train2014.json """ prefix = 'instances' if 'test' not in self.image_set else 'image_info' return os.path.join(self.data_path, 'annotations', prefix + '_' + self.image_set + '.json') def _load_image_set_index(self): """ image id: int """ image_ids = self.coco.getImgIds() return image_ids def image_path_from_index(self, index): """???example: images / train2014 / COCO_train2014_000000119993.jpg???""" #filename = 'COCO_%s_%012d.jpg' % (self.data_name, index) #image_path = os.path.join(self.data_path, 'images', self.data_name, filename) image_path = self.file_name[index] assert os.path.exists(image_path), 'Path does not exist: {}'.format(image_path) return image_path def gt_roidb(self): cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl') index_file = os.path.join(self.cache_path, self.name + '_index_roidb.pkl') sindex_file = os.path.join(self.cache_path, self.name + '_sindex_roidb.pkl') if os.path.exists(cache_file) and os.path.exists(index_file): with open(cache_file, 'rb') as fid: roidb = cPickle.load(fid) with open(index_file, 'rb') as fid: self.image_set_index = cPickle.load(fid) print '{} gt roidb loaded from {}'.format(self.name, cache_file) return roidb gt_roidb = [] valid_id = [] vids = [] ct = 0 for index in self.image_set_index: roientry,flag = self._load_coco_annotation(index) if flag: gt_roidb.append(roientry) valid_id.append(index) vids.append(ct) ct = ct + 1 self.image_set_index = valid_id with open(cache_file, 'wb') as fid: cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL) with open(index_file, 'wb') as fid: cPickle.dump(valid_id, fid, cPickle.HIGHEST_PROTOCOL) with open(sindex_file, 'wb') as fid: cPickle.dump(vids, fid, cPickle.HIGHEST_PROTOCOL) print 'wrote gt roidb to {}'.format(cache_file) return gt_roidb def _load_coco_annotation(self, index): def _polys2boxes(polys): boxes_from_polys = np.zeros((len(polys), 4), dtype=np.float32) for i in range(len(polys)): poly = polys[i] x0 = min(min(p[::2]) for p in poly) x1 = max(max(p[::2]) for p in poly) y0 = min(min(p[1::2]) for p in poly) y1 = max(max(p[1::2]) for p in poly) boxes_from_polys[i, :] = [x0, y0, x1, y1] return boxes_from_polys """ coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id'] iscrowd: crowd instances are handled by marking their overlaps with all categories to -1 and later excluded in training bbox: [x1, y1, w, h] :param index: coco image id :return: roidb entry """ im_ann = self.coco.loadImgs(index)[0] width = im_ann['width'] height = im_ann['height'] annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False) objs = self.coco.loadAnns(annIds) annIds = self.coco.getAnnIds(imgIds=index, iscrowd=True) objsc = self.coco.loadAnns(annIds) # sanitize bboxes valid_objs = [] for obj in objs: x, y, w, h = obj['bbox'] x1 = np.max((0, x)) y1 = np.max((0, y)) x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) if obj['area'] > 0 and x2 >= x1 and y2 >= y1: obj['clean_bbox'] = [x1, y1, x2, y2] valid_objs.append(obj) valid_objsc = [] for obj in objsc: x, y, w, h = obj['bbox'] x1 = np.max((0, x)) y1 = np.max((0, y)) x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) if obj['area'] > 0 and x2 >= x1 and y2 >= y1: obj['clean_bbox'] = [x1, y1, x2, y2] valid_objsc.append(obj) objs = valid_objs objc = valid_objsc num_objs = len(objs) num_objsc = len(objsc) boxes = np.zeros((num_objs, 4), dtype=np.uint16) boxesc = np.zeros((num_objsc, 4), dtype=np.uint16) gt_classes = np.zeros((num_objs), dtype=np.int32) overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32) #for ix, obj in enumerate(objsc): # boxesc[ix, :] = obj['clean_bbox'] for ix, obj in enumerate(objs): cls = self._coco_ind_to_class_ind[obj['category_id']] boxes[ix, :] = obj['clean_bbox'] gt_classes[ix] = cls if obj['iscrowd']: overlaps[ix, :] = -1.0 else: overlaps[ix, cls] = 1.0 ws = boxes[:, 2] - boxes[:, 0] hs = boxes[:, 3] - boxes[:, 1] flag = True roi_rec = { 'image':self.image_path_from_index(index), 'height': height, 'width': width, 'boxes': boxes, 'boxesc': boxesc, 'gt_classes': gt_classes, 'gt_overlaps': overlaps, 'max_classes': overlaps.argmax(axis=1), 'max_overlaps': overlaps.max(axis=1), 'flipped': False} if self.load_mask: # we only care about valid polygons segs = [] for obj in objs: if not isinstance(obj['segmentation'], list): # This is a crowd box segs.append([]) else: segs.append([np.array(p) for p in obj['segmentation'] if len(p)>=6]) roi_rec['gt_masks'] = segs # Uncomment if you need to compute gts based on segmentation masks # seg_boxes = _polys2boxes(segs) # roi_rec['mask_boxes'] = seg_boxes return roi_rec, flag def evaluate_detections(self, detections, ann_type='bbox', all_masks=None, extra_path=''): """ detections_val2014_results.json """ res_folder = os.path.join(self.result_path + extra_path, 'results') if not os.path.exists(res_folder): os.makedirs(res_folder) res_file = os.path.join(res_folder, 'detections_%s_results.json' % self.image_set) self._write_coco_results(detections, res_file, ann_type, all_masks) if 'test' not in self.image_set: info_str = self._do_python_eval(res_file, res_folder, ann_type) return info_str def evaluate_sds(self, all_boxes, all_masks): info_str = self.evaluate_detections(all_boxes, 'segm', all_masks) return info_str def _write_coco_results(self, all_boxes, res_file, ann_type, all_masks): """ example results [{"image_id": 42, "category_id": 18, "bbox": [258.15,41.29,348.26,243.78], "score": 0.236}, ...] """ all_im_info = [{'index': index, 'height': self.coco.loadImgs(index)[0]['height'], 'width': self.coco.loadImgs(index)[0]['width']} for index in self.image_set_index] if ann_type == 'bbox': data_pack = [{'cat_id': self._class_to_coco_ind[cls], 'cls_ind': cls_ind, 'cls': cls, 'ann_type': ann_type, 'binary_thresh': self.binary_thresh, 'all_im_info': all_im_info, 'boxes': all_boxes[cls_ind]} for cls_ind, cls in enumerate(self.classes) if not cls == '__background__'] elif ann_type == 'segm': data_pack = [{'cat_id': self._class_to_coco_ind[cls], 'cls_ind': cls_ind, 'cls': cls, 'ann_type': ann_type, 'binary_thresh': self.binary_thresh, 'all_im_info': all_im_info, 'boxes': all_boxes[cls_ind], 'masks': all_masks[cls_ind]} for cls_ind, cls in enumerate(self.classes) if not cls == '__background__'] else: print 'unimplemented ann_type: '+ann_type # results = coco_results_one_category_kernel(data_pack[1]) # print results[0] pool = mp.Pool(mp.cpu_count()) results = pool.map(coco_results_one_category_kernel, data_pack) pool.close() pool.join() results = sum(results, []) print 'Writing results json to %s' % res_file with open(res_file, 'w') as f: json.dump(results, f, sort_keys=True, indent=4) def _do_python_eval(self, res_file, res_folder, ann_type): coco_dt = self.coco.loadRes(res_file) coco_eval = COCOeval(self.coco, coco_dt) coco_eval.params.useSegm = (ann_type == 'segm') coco_eval.evaluate() coco_eval.accumulate() info_str = self._print_detection_metrics(coco_eval) eval_file = os.path.join(res_folder, 'detections_%s_results.pkl' % self.image_set) with open(eval_file, 'w') as f: cPickle.dump(coco_eval, f, cPickle.HIGHEST_PROTOCOL) print 'coco eval results saved to %s' % eval_file info_str += 'coco eval results saved to %s\n' % eval_file return info_str def _print_detection_metrics(self, coco_eval): info_str = '' IoU_lo_thresh = 0.5 IoU_hi_thresh = 0.95 def _get_thr_ind(coco_eval, thr): ind = np.where((coco_eval.params.iouThrs > thr - 1e-5) & (coco_eval.params.iouThrs < thr + 1e-5))[0][0] iou_thr = coco_eval.params.iouThrs[ind] assert np.isclose(iou_thr, thr) return ind ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh) ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh) # precision has dims (iou, recall, cls, area range, max dets) # area range index 0: all area ranges # max dets index 2: 100 per image precision = \ coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2] ap_default = np.mean(precision[precision > -1]) print '~~~~ Mean and per-category AP @ IoU=%.2f,%.2f] ~~~~' % (IoU_lo_thresh, IoU_hi_thresh) info_str += '~~~~ Mean and per-category AP @ IoU=%.2f,%.2f] ~~~~\n' % (IoU_lo_thresh, IoU_hi_thresh) print '%-15s %5.1f' % ('all', 100 * ap_default) info_str += '%-15s %5.1f\n' % ('all', 100 * ap_default) for cls_ind, cls in enumerate(self.classes): if cls == '__background__': continue # minus 1 because of __background__ precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2] ap = np.mean(precision[precision > -1]) print '%-15s %5.1f' % (cls, 100 * ap) info_str += '%-15s %5.1f\n' % (cls, 100 * ap) print '~~~~ Summary metrics ~~~~' coco_eval.summarize() return info_str