forked from mindspore-Ecosystem/mindspore
fix voc/coco docstring problem
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xiefangqi
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@ -4036,8 +4036,8 @@ class VOCDataset(MappableDataset):
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A source dataset for reading and parsing VOC dataset.
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The generated dataset has two columns :
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task='Detection' : ['image', 'annotation'].
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task='Segmentation' : ['image', 'target']
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task='Detection' : ['image', 'annotation'];
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task='Segmentation' : ['image', 'target'].
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The shape of both column 'image' and 'target' is [image_size] if decode flag is False, or [H, W, C]
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otherwise.
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The type of both tensor 'image' and 'target' is uint8.
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@ -4072,20 +4072,20 @@ class VOCDataset(MappableDataset):
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- False
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- not allowed
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Citation of VOC dataset.
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Citation of VOC dataset.
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.. code-block::
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@article{Everingham10,
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author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
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title = {The Pascal Visual Object Classes (VOC) Challenge},
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journal = {International Journal of Computer Vision},
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volume = {88},
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year = {2010},
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number = {2},
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month = {jun},
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pages = {303--338},
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biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
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author = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
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title = {The Pascal Visual Object Classes (VOC) Challenge},
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journal = {International Journal of Computer Vision},
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volume = {88},
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year = {2010},
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number = {2},
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month = {jun},
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pages = {303--338},
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biburl = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
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howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc{year}/index.html},
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description = {The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual
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object category recognition and detection, providing the vision and machine
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@ -4096,8 +4096,8 @@ class VOCDataset(MappableDataset):
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Args:
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dataset_dir (str): Path to the root directory that contains the dataset.
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task (str): Set the task type of reading voc data, now only support "Segmentation" or "Detection"
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(default="Segmentation")
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mode(str): Set the data list txt file to be readed (default="train")
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(default="Segmentation").
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mode (str): Set the data list txt file to be readed (default="train").
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class_indexing (dict, optional): A str-to-int mapping from label name to index
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(default=None, the folder names will be sorted alphabetically and each
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class will be given a unique index starting from 0).
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@ -4116,9 +4116,9 @@ class VOCDataset(MappableDataset):
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argument should be specified only when num_shards is also specified.
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Raises:
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RuntimeError: If xml of Annotations is a invalid format
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RuntimeError: If xml of Annotations loss attribution of "object"
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RuntimeError: If xml of Annotations loss attribution of "bndbox"
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RuntimeError: If xml of Annotations is a invalid format.
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RuntimeError: If xml of Annotations loss attribution of "object".
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RuntimeError: If xml of Annotations loss attribution of "bndbox".
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RuntimeError: If sampler and shuffle are specified at the same time.
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RuntimeError: If sampler and sharding are specified at the same time.
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RuntimeError: If num_shards is specified but shard_id is None.
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@ -4232,10 +4232,10 @@ class CocoDataset(MappableDataset):
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"""
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A source dataset for reading and parsing COCO dataset.
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CocoDataset support four kinds of task:
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2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic.
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CocoDataset support four kinds of task: 2017 Train/Val/Test Detection, Keypoints, Stuff, Panoptic.
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The generated dataset has multi-columns :
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- task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['category_id', dtype=uint32],
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['iscrowd', dtype=uint32]].
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- task='Stuff', column: [['image', dtype=uint8], ['segmentation',dtype=float32], ['iscrowd',dtype=uint32]].
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@ -4273,35 +4273,35 @@ class CocoDataset(MappableDataset):
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- False
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- not allowed
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Citation of Coco dataset.
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Citation of Coco dataset.
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.. code-block::
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@article{DBLP:journals/corr/LinMBHPRDZ14,
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author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
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Lubomir D. Bourdev and Ross B. Girshick and James Hays and
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Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick},
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title = {Microsoft {COCO:} Common Objects in Context},
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journal = {CoRR},
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volume = {abs/1405.0312},
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year = {2014},
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url = {http://arxiv.org/abs/1405.0312},
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author = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and
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Lubomir D. Bourdev and Ross B. Girshick and James Hays and
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Pietro Perona and Deva Ramanan and Piotr Doll{\'{a}}r and C. Lawrence Zitnick},
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title = {Microsoft {COCO:} Common Objects in Context},
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journal = {CoRR},
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volume = {abs/1405.0312},
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year = {2014},
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url = {http://arxiv.org/abs/1405.0312},
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archivePrefix = {arXiv},
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eprint = {1405.0312},
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timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
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biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org},
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description = {COCO is a large-scale object detection, segmentation, and captioning dataset.
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It contains 91 common object categories with 82 of them having more than 5,000
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labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
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categories but more instances per category.}
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eprint = {1405.0312},
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timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
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biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org},
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description = {COCO is a large-scale object detection, segmentation, and captioning dataset.
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It contains 91 common object categories with 82 of them having more than 5,000
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labeled instances. In contrast to the popular ImageNet dataset, COCO has fewer
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categories but more instances per category.}
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}
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Args:
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dataset_dir (str): Path to the root directory that contains the dataset.
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annotation_file (str): Path to the annotation json.
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task (str): Set the task type of reading coco data, now support 'Detection'/'Stuff'/'Panoptic'/'Keypoint'
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(default='Detection')
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(default='Detection').
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num_samples (int, optional): The number of images to be included in the dataset
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(default=None, all images).
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num_parallel_workers (int, optional): Number of workers to read the data
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