diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index 87701ffa26..d8fda008e9 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -4034,8 +4034,8 @@ class VOCDataset(MappableDataset): A source dataset for reading and parsing VOC dataset. The generated dataset has two columns : - task='Detection' : ['image', 'annotation']. - task='Segmentation' : ['image', 'target'] + task='Detection' : ['image', 'annotation']; + task='Segmentation' : ['image', 'target']. The shape of both column 'image' and 'target' is [image_size] if decode flag is False, or [H, W, C] otherwise. The type of both tensor 'image' and 'target' is uint8. @@ -4070,20 +4070,20 @@ class VOCDataset(MappableDataset): - False - not allowed - Citation of VOC dataset. + Citation of VOC dataset. .. code-block:: @article{Everingham10, - author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.}, - title = {The Pascal Visual Object Classes (VOC) Challenge}, - journal = {International Journal of Computer Vision}, - volume = {88}, - year = {2010}, - number = {2}, - month = {jun}, - pages = {303--338}, - biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex}, + author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.}, + title = {The Pascal Visual Object Classes (VOC) Challenge}, + journal = {International Journal of Computer Vision}, + volume = {88}, + year = {2010}, + number = {2}, + month = {jun}, + pages = {303--338}, + biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex}, howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html}, description = {The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine @@ -4094,8 +4094,8 @@ class VOCDataset(MappableDataset): Args: dataset_dir (str): Path to the root directory that contains the dataset. task (str): Set the task type of reading voc data, now only support "Segmentation" or "Detection" - (default="Segmentation") - mode(str): Set the data list txt file to be readed (default="train") + (default="Segmentation"). + mode (str): Set the data list txt file to be readed (default="train"). class_indexing (dict, optional): A str-to-int mapping from label name to index (default=None, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0). @@ -4114,9 +4114,9 @@ class VOCDataset(MappableDataset): argument should be specified only when num_shards is also specified. Raises: - RuntimeError: If xml of Annotations is a invalid format - RuntimeError: If xml of Annotations loss attribution of "object" - RuntimeError: If xml of Annotations loss attribution of "bndbox" + RuntimeError: If xml of Annotations is a invalid format. + RuntimeError: If xml of Annotations loss attribution of "object". + RuntimeError: If xml of Annotations loss attribution of "bndbox". RuntimeError: If sampler and shuffle are specified at the same time. RuntimeError: If sampler and sharding are specified at the same time. RuntimeError: If num_shards is specified but shard_id is None. @@ -4230,10 +4230,10 @@ class CocoDataset(MappableDataset): """ A source dataset for reading and parsing COCO dataset. - CocoDataset support four kinds of task: - 2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic. + CocoDataset support four kinds of task: 2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic. The generated dataset has multi-columns : + - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32], ['iscrowd', dtype=uint32]]. - task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd',dtype=uint32]]. @@ -4271,35 +4271,35 @@ class CocoDataset(MappableDataset): - False - not allowed - Citation of Coco dataset. + Citation of Coco dataset. .. code-block:: @article{DBLP:journals/corr/LinMBHPRDZ14, - author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and - Lubomir D. Bourdev and Ross B. Girshick and James Hays and - Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick}, - title = {Microsoft {COCO:} Common Objects in Context}, - journal = {CoRR}, - volume = {abs/1405.0312}, - year = {2014}, - url = {http://arxiv.org/abs/1405.0312}, + author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and + Lubomir D. Bourdev and Ross B. Girshick and James Hays and + Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick}, + title = {Microsoft {COCO:} Common Objects in Context}, + journal = {CoRR}, + volume = {abs/1405.0312}, + year = {2014}, + url = {http://arxiv.org/abs/1405.0312}, archivePrefix = {arXiv}, - eprint = {1405.0312}, - timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, - biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib}, - bibsource = {dblp computer science bibliography, https://dblp.org}, - description = {COCO is a large-scale object detection, segmentation, and captioning dataset. - It contains 91 common object categories with 82 of them having more than 5,000 - labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer - categories but more instances per category.} + eprint = {1405.0312}, + timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, + biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org}, + description = {COCO is a large-scale object detection, segmentation, and captioning dataset. + It contains 91 common object categories with 82 of them having more than 5,000 + labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer + categories but more instances per category.} } Args: dataset_dir (str): Path to the root directory that contains the dataset. annotation_file (str): Path to the annotation json. task (str): Set the task type of reading coco data, now support 'Detection'/'Stuff'/'Panoptic'/'Keypoint' - (default='Detection') + (default='Detection'). num_samples (int, optional): The number of images to be included in the dataset (default=None, all images). num_parallel_workers (int, optional): Number of workers to read the data