今日，辗转反侧，该💩的代码就是跑不成功，来看看 COCODataSet 到底是怎么写的，本文只参考当前版本的代码，当前版本 PaddleDetection2.5 COCODataSet 源码见附录

COCODataSet 类内部就三个函数：

__init__
parse_dataset  
_sample_empty     # 该函数供 parse_dataset 调用

来看一下 COCODataSet 的基类实现函数，咱挨个看

__init__
__len__
__call__
__getitem__
check_or_download_dataset
set_kwargs
set_transform
set_epoch
parse_dataset
get_anno

1. 基类parse_dataset

def parse_dataset(self, ):raise NotImplementedError("Need to implement parse_dataset method of Dataset")

该类必须要被继承之后实现该方法，继承该类中必须解析数据集，并将数据集中的内容传给变量 self.roidbs，具体内容之后看， self.roidbs 变量是一个列表，每一项都是一张照片的内容

parse_dataset 唯一要做的一件事就是解析数据并传给变量 self.roidbs

self.roidbs 中一个 item 是：

{'gt_bbox': array([[133.51,  24.77, 366.11, 562.92]], dtype=float32),'gt_class': array([[14]], dtype=int32),'h': 640.0,'im_file': 'dataset/coco/COCO/val2017/000000270705.jpg','im_id': array([270705]),'is_crowd': array([[0]], dtype=int32),'w': 475.0}

2. 基类__len__

def __len__(self, ):return len(self.roidbs) * self.repeat

len(self.roidbs) 就是原始数据的内容，self.repeat 是重复次数，所以在__getitem__ 有这么一句：

if self.repeat > 1:idx %= n

用来进行重复操作

3. 基类__call__

def __call__(self, *args, **kwargs):return self

做这个操作其实没啥说的了，实例化之后call一下还是返回自己

4. 基类其他不重要函数

• 设置部分，用来设置自身的属性，基本没被调用

def set_kwargs(self, **kwargs):self.mixup_epoch = kwargs.get('mixup_epoch', -1)self.cutmix_epoch = kwargs.get('cutmix_epoch', -1)self.mosaic_epoch = kwargs.get('mosaic_epoch', -1)def set_transform(self, transform):self.transform = transformdef set_epoch(self, epoch_id):self._epoch = epoch_id

• 获取部分：

def get_anno(self):if self.anno_path is None:returnreturn os.path.join(self.dataset_dir, self.anno_path)

获取标注 ann.json 的路径

• 检查数据路径函数，也没被调用，不重要跳过

def check_or_download_dataset(self):self.dataset_dir = get_dataset_path(self.dataset_dir, self.anno_path,self.image_dir)

以上函数供 read 在 dataset类 外部调用(之后会讲到)

所以 self.mixup_epoch , self.cutmix_epoch 和 self.mosaic_epoch 默认值都是 -1

5. 基类 __getitem__ 函数

def __getitem__(self, idx):# ------- 用来进行重复操作的部分 -------n = len(self.roidbs)if self.repeat > 1:idx %= n# ------- 深拷贝当前的数据项 -------roidb = copy.deepcopy(self.roidbs[idx])# 以下仨 if 和数据增强有关if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch:idx = np.random.randint(n)roidb = [roidb, copy.deepcopy(self.roidbs[idx])]elif self.cutmix_epoch == 0 or self._epoch < self.cutmix_epoch:idx = np.random.randint(n)roidb = [roidb, copy.deepcopy(self.roidbs[idx])]elif self.mosaic_epoch == 0 or self._epoch < self.mosaic_epoch:roidb = [roidb, ] + [copy.deepcopy(self.roidbs[np.random.randint(n)])for _ in range(4)]# ------- 设置 curr_iter -------if isinstance(roidb, Sequence):for r in roidb:r['curr_iter'] = self._curr_iterelse:roidb['curr_iter'] = self._curr_iterself._curr_iter += 1# ------- 对当前数据项进行之前的 transform ------- return self.transform(roidb)

6. 基类 __init__ 函数

self.dataset_dir = dataset_dir if dataset_dir is not None else ''
self.anno_path = anno_path
self.image_dir = image_dir if image_dir is not None else ''
self.data_fields = data_fields           

看上边这4个参数，是和 yaml 文件中的内容是对应的：

基本都在 parse_dataset 调用

self.sample_num = sample_num                # parse_dataset 中调用
self.use_default_label = use_default_label  # 这个变量可能是 COCO 每个id对应的类名? 暂时没发现使用处
self.repeat = repeat
self._epoch = 0
self._curr_iter = 0

5. 子类 parse_dataset 函数

明天再说，睡了

附录

顺便备份一下当前版本PaddleDetection2.5的 COCODataSet 代码

class COCODataSet(DetDataset):"""Load dataset with COCO format.Args:dataset_dir (str): root directory for dataset.image_dir (str): directory for images.anno_path (str): coco annotation file path.data_fields (list): key name of data dictionary, at least have 'image'.sample_num (int): number of samples to load, -1 means all.load_crowd (bool): whether to load crowded ground-truth. False as defaultallow_empty (bool): whether to load empty entry. False as defaultempty_ratio (float): the ratio of empty record number to total record's, if empty_ratio is out of [0. ,1.), do not sample the records and use all the empty entries. 1. as defaultrepeat (int): repeat times for dataset, use in benchmark."""def __init__(self,dataset_dir=None,image_dir=None,anno_path=None,data_fields=['image'],sample_num=-1,load_crowd=False,allow_empty=False,empty_ratio=1.,repeat=1):super(COCODataSet, self).__init__(dataset_dir,image_dir,anno_path,data_fields,sample_num,repeat=repeat)self.load_image_only = Falseself.load_semantic = Falseself.load_crowd = load_crowdself.allow_empty = allow_emptyself.empty_ratio = empty_ratiodef _sample_empty(self, records, num):# if empty_ratio is out of [0. ,1.), do not sample the recordsif self.empty_ratio < 0. or self.empty_ratio >= 1.:return recordsimport randomsample_num = min(int(num * self.empty_ratio / (1 - self.empty_ratio)), len(records))records = random.sample(records, sample_num)return recordsdef parse_dataset(self):anno_path = os.path.join(self.dataset_dir, self.anno_path)image_dir = os.path.join(self.dataset_dir, self.image_dir)assert anno_path.endswith('.json'), \'invalid coco annotation file: ' + anno_pathfrom pycocotools.coco import COCOcoco = COCO(anno_path)img_ids = coco.getImgIds()img_ids.sort()cat_ids = coco.getCatIds()records = []empty_records = []ct = 0self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})self.cname2cid = dict({coco.loadCats(catid)[0]['name']: clsidfor catid, clsid in self.catid2clsid.items()})if 'annotations' not in coco.dataset:self.load_image_only = Truelogger.warning('Annotation file: {} does not contains ground truth ''and load image information only.'.format(anno_path))for img_id in img_ids:img_anno = coco.loadImgs([img_id])[0]im_fname = img_anno['file_name']im_w = float(img_anno['width'])im_h = float(img_anno['height'])im_path = os.path.join(image_dir,im_fname) if image_dir else im_fnameis_empty = Falseif not os.path.exists(im_path):logger.warning('Illegal image file: {}, and it will be ''ignored'.format(im_path))continueif im_w < 0 or im_h < 0:logger.warning('Illegal width: {} or height: {} in annotation, ''and im_id: {} will be ignored'.format(im_w, im_h, img_id))continuecoco_rec = {'im_file': im_path,'im_id': np.array([img_id]),'h': im_h,'w': im_w,} if 'image' in self.data_fields else {}if not self.load_image_only:ins_anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=None if self.load_crowd else False)instances = coco.loadAnns(ins_anno_ids)bboxes = []is_rbox_anno = Falsefor inst in instances:# check gt bboxif inst.get('ignore', False):continueif 'bbox' not in inst.keys():continueelse:if not any(np.array(inst['bbox'])):continuex1, y1, box_w, box_h = inst['bbox']x2 = x1 + box_wy2 = y1 + box_heps = 1e-5if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps:inst['clean_bbox'] = [round(float(x), 3) for x in [x1, y1, x2, y2]]bboxes.append(inst)else:logger.warning('Found an invalid bbox in annotations: im_id: {}, ''area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format(img_id, float(inst['area']), x1, y1, x2, y2))num_bbox = len(bboxes)if num_bbox <= 0 and not self.allow_empty:continueelif num_bbox <= 0:is_empty = Truegt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)gt_class = np.zeros((num_bbox, 1), dtype=np.int32)is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)gt_poly = [None] * num_bboxhas_segmentation = Falsefor i, box in enumerate(bboxes):catid = box['category_id']gt_class[i][0] = self.catid2clsid[catid]gt_bbox[i, :] = box['clean_bbox']is_crowd[i][0] = box['iscrowd']# check RLE format if 'segmentation' in box and box['iscrowd'] == 1:gt_poly[i] = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]elif 'segmentation' in box and box['segmentation']:if not np.array(box['segmentation']).size > 0 and not self.allow_empty:bboxes.pop(i)gt_poly.pop(i)np.delete(is_crowd, i)np.delete(gt_class, i)np.delete(gt_bbox, i)else:gt_poly[i] = box['segmentation']has_segmentation = Trueif has_segmentation and not any(gt_poly) and not self.allow_empty:continuegt_rec = {'is_crowd': is_crowd,'gt_class': gt_class,'gt_bbox': gt_bbox,'gt_poly': gt_poly,}for k, v in gt_rec.items():if k in self.data_fields:coco_rec[k] = v# TODO: remove load_semanticif self.load_semantic and 'semantic' in self.data_fields:seg_path = os.path.join(self.dataset_dir, 'stuffthingmaps','train2017', im_fname[:-3] + 'png')coco_rec.update({'semantic': seg_path})logger.debug('Load file: {}, im_id: {}, h: {}, w: {}.'.format(im_path, img_id, im_h, im_w))if is_empty:empty_records.append(coco_rec)else:records.append(coco_rec)ct += 1if self.sample_num > 0 and ct >= self.sample_num:breakassert ct > 0, 'not found any coco record in %s' % (anno_path)logger.debug('{} samples in file {}'.format(ct, anno_path))if self.allow_empty and len(empty_records) > 0:empty_records = self._sample_empty(empty_records, len(records))records += empty_recordsself.roidbs = records

其基类 DetDataset :

from paddle.io import Datasetclass DetDataset(Dataset):"""Load detection dataset.Args:dataset_dir (str): root directory for dataset.image_dir (str): directory for images.anno_path (str): annotation file path.data_fields (list): key name of data dictionary, at least have 'image'.sample_num (int): number of samples to load, -1 means all.use_default_label (bool): whether to load default label list.repeat (int): repeat times for dataset, use in benchmark."""def __init__(self,dataset_dir=None,image_dir=None,anno_path=None,data_fields=['image'],sample_num=-1,use_default_label=None,repeat=1,**kwargs):super(DetDataset, self).__init__()self.dataset_dir = dataset_dir if dataset_dir is not None else ''self.anno_path = anno_pathself.image_dir = image_dir if image_dir is not None else ''self.data_fields = data_fieldsself.sample_num = sample_numself.use_default_label = use_default_labelself.repeat = repeatself._epoch = 0self._curr_iter = 0def __len__(self, ):return len(self.roidbs) * self.repeatdef __call__(self, *args, **kwargs):return selfdef __getitem__(self, idx):n = len(self.roidbs)if self.repeat > 1:idx %= n# data batchroidb = copy.deepcopy(self.roidbs[idx])if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch:idx = np.random.randint(n)roidb = [roidb, copy.deepcopy(self.roidbs[idx])]elif self.cutmix_epoch == 0 or self._epoch < self.cutmix_epoch:idx = np.random.randint(n)roidb = [roidb, copy.deepcopy(self.roidbs[idx])]elif self.mosaic_epoch == 0 or self._epoch < self.mosaic_epoch:roidb = [roidb, ] + [copy.deepcopy(self.roidbs[np.random.randint(n)])for _ in range(4)]if isinstance(roidb, Sequence):for r in roidb:r['curr_iter'] = self._curr_iterelse:roidb['curr_iter'] = self._curr_iterself._curr_iter += 1# roidb['num_classes'] = len(self.catid2clsid) # COCODataset 80 clsreturn self.transform(roidb)def check_or_download_dataset(self):self.dataset_dir = get_dataset_path(self.dataset_dir, self.anno_path,self.image_dir)def set_kwargs(self, **kwargs):self.mixup_epoch = kwargs.get('mixup_epoch', -1)self.cutmix_epoch = kwargs.get('cutmix_epoch', -1)self.mosaic_epoch = kwargs.get('mosaic_epoch', -1)def set_transform(self, transform):self.transform = transformdef set_epoch(self, epoch_id):self._epoch = epoch_iddef parse_dataset(self, ):raise NotImplementedError("Need to implement parse_dataset method of Dataset")def get_anno(self):if self.anno_path is None:returnreturn os.path.join(self.dataset_dir, self.anno_path)