mindspore.dataset.CocoDataset

class mindspore.dataset.CocoDataset(dataset_dir, annotation_file, task='Detection', num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None, extra_metadata=False, decrypt=None)[source]

COCO(Common Objects in Context) dataset.

CocoDataset supports five kinds of tasks, which are Object Detection, Keypoint Detection, Stuff Segmentation, Panoptic Segmentation and Captioning of 2017 Train/Val/Test dataset.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
annotation_file (str) – Path to the annotation JSON file.
task (str, optional) – Set the task type for reading COCO data. Supported task types: 'Detection', 'Stuff', 'Panoptic', 'Keypoint' and 'Captioning'. 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 worker threads to read the data. Default: None , will use global default workers(8), it can be set by mindspore.dataset.config.set_num_parallel_workers() .
shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.
decode (bool, optional) – Decode the images after reading. Default: False.
sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None , expected order behavior shown in the table below.
num_shards (int, optional) – Number of shards that the dataset will be divided into. Default: None . When this argument is specified, num_samples reflects the maximum sample number of per shard. Used in data parallel training .
shard_id (int, optional) – The shard ID within num_shards . Default: None . This argument can only be specified when num_shards is also specified.
cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default: None , which means no cache is used.
extra_metadata (bool, optional) – Flag to add extra meta-data to row. If True, an additional column will be output at the end [_meta-filename, dtype=string] . Default: False.
decrypt (callable, optional) – Image decryption function, which accepts the path of the encrypted image file and returns the decrypted bytes data. Default: None , no decryption.

The generated dataset with different task setting has different output columns:

task	Output column
Detection	[image, dtype=uint8] [bbox, dtype=float32] [category_id, dtype=uint32] [iscrowd, dtype=uint32]
Stuff	[image, dtype=uint8] [segmentation, dtype=float32] [iscrowd, dtype=uint32]
Keypoint	[image, dtype=uint8] [keypoints, dtype=float32] [num_keypoints, dtype=uint32]
Panoptic	[image, dtype=uint8] [bbox, dtype=float32] [category_id, dtype=uint32] [iscrowd, dtype=uint32] [area, dtype=uint32]
Captioning	[image, dtype=uint8] [captions, dtype=string]

Raises

RuntimeError – If dataset_dir does not contain data files.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and num_shards/shard_id are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
RuntimeError – If parse JSON file failed.
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If task is not 'Detection', 'Stuff', 'Panoptic', 'Keypoint' or 'Captioning'.
ValueError – If annotation_file is not exist.
ValueError – If dataset_dir is not exist.
ValueError – If shard_id is not in range of [0, num_shards ).

Tutorial Examples:

Load & Process Data With Dataset Pipeline

Note

Column '[_meta-filename, dtype=string]' won't be output unless an explicit rename dataset op is added to remove the prefix('_meta-').
Not support mindspore.dataset.PKSampler for sampler parameter yet.
The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.

Sampler obtained by different combinations of parameters sampler and num_samples , shuffle , num_shards , shard_id
Parameter sampler	Parameter num_shards / shard_id	Parameter shuffle	Parameter num_samples	Sampler Used
mindspore.dataset.Sampler type	None	None	None	sampler
numpy.ndarray,list,tuple,int type	/	/	num_samples	SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type	/	/	num_samples	IterSampler(sampler = sampler , num_samples = num_samples )
None	num_shards / shard_id	None / True	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None	num_shards / shard_id	False	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None	None	None / True	None	RandomSampler(num_samples = num_samples )
None	None	None / True	num_samples	RandomSampler(replacement = True , num_samples = num_samples )
None	None	False	num_samples	SequentialSampler(num_samples = num_samples )

Examples

>>> import mindspore.dataset as ds
>>> coco_dataset_dir = "/path/to/coco_dataset_directory/images"
>>> coco_annotation_file = "/path/to/coco_dataset_directory/annotation_file"
>>>
>>> # 1) Read COCO data for Detection task
>>> dataset = ds.CocoDataset(dataset_dir=coco_dataset_dir,
...                          annotation_file=coco_annotation_file,
...                          task='Detection')
>>>
>>> # 2) Read COCO data for Stuff task
>>> dataset = ds.CocoDataset(dataset_dir=coco_dataset_dir,
...                          annotation_file=coco_annotation_file,
...                          task='Stuff')
>>>
>>> # 3) Read COCO data for Panoptic task
>>> dataset = ds.CocoDataset(dataset_dir=coco_dataset_dir,
...                          annotation_file=coco_annotation_file,
...                          task='Panoptic')
>>>
>>> # 4) Read COCO data for Keypoint task
>>> dataset = ds.CocoDataset(dataset_dir=coco_dataset_dir,
...                          annotation_file=coco_annotation_file,
...                          task='Keypoint')
>>>
>>> # 5) Read COCO data for Captioning task
>>> dataset = ds.CocoDataset(dataset_dir=coco_dataset_dir,
...                          annotation_file=coco_annotation_file,
...                          task='Captioning')
>>>
>>> # In COCO dataset, each dictionary has keys "image" and "annotation"

About COCO dataset:

COCO(Microsoft Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset with several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints. In contrast to the popular ImageNet dataset, COCO has fewer categories but more instances in per category.

You can unzip the original COCO-2017 dataset files into this directory structure and read by MindSpore's API.

.
└── coco_dataset_directory
     ├── train2017
     │    ├── 000000000009.jpg
     │    ├── 000000000025.jpg
     │    ├── ...
     ├── test2017
     │    ├── 000000000001.jpg
     │    ├── 000000058136.jpg
     │    ├── ...
     ├── val2017
     │    ├── 000000000139.jpg
     │    ├── 000000057027.jpg
     │    ├── ...
     └── annotations
          ├── captions_train2017.json
          ├── captions_val2017.json
          ├── instances_train2017.json
          ├── instances_val2017.json
          ├── person_keypoints_train2017.json
          └── person_keypoints_val2017.json

Citation:

@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},
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}
}

Pre-processing Operation

`mindspore.dataset.Dataset.apply`	Apply a function in this dataset.
`mindspore.dataset.Dataset.concat`	Concatenate the dataset objects in the input list.
`mindspore.dataset.Dataset.filter`	Filter dataset by prediction.
`mindspore.dataset.Dataset.flat_map`	Map func to each row in dataset and flatten the result.
`mindspore.dataset.Dataset.map`	Apply each operation in operations to this dataset.
`mindspore.dataset.Dataset.project`	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
`mindspore.dataset.Dataset.rename`	Rename the columns in input datasets.
`mindspore.dataset.Dataset.repeat`	Repeat this dataset count times.
`mindspore.dataset.Dataset.reset`	Reset the dataset for next epoch.
`mindspore.dataset.Dataset.save`	Save the dynamic data processed by the dataset pipeline in common dataset format.
`mindspore.dataset.Dataset.shuffle`	Shuffle the dataset by creating a cache with the size of buffer_size .
`mindspore.dataset.Dataset.skip`	Skip the first N elements of this dataset.
`mindspore.dataset.Dataset.split`	Split the dataset into smaller, non-overlapping datasets.
`mindspore.dataset.Dataset.take`	Take the first specified number of samples from the dataset.
`mindspore.dataset.Dataset.zip`	Zip the datasets in the sense of input tuple of datasets.

Batch

`mindspore.dataset.Dataset.batch`	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
`mindspore.dataset.Dataset.bucket_batch_by_length`	Bucket elements according to their lengths.
`mindspore.dataset.Dataset.padded_batch`	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

`mindspore.dataset.Dataset.create_dict_iterator`	Create an iterator over the dataset that yields samples of type dict, while the key is the column name and the value is the data.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Get the mapping dictionary from category names to category indexes.
`mindspore.dataset.Dataset.get_col_names`	Return the names of the columns in dataset.
`mindspore.dataset.Dataset.get_dataset_size`	Return the number of batches in an epoch.
`mindspore.dataset.Dataset.get_repeat_count`	Get the replication times in RepeatDataset.
`mindspore.dataset.Dataset.input_indexs`	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
`mindspore.dataset.Dataset.num_classes`	Get the number of classes in a dataset.
`mindspore.dataset.Dataset.output_shapes`	Get the shapes of output data.
`mindspore.dataset.Dataset.output_types`	Get the types of output data.

Apply Sampler

`mindspore.dataset.MappableDataset.add_sampler`	Add a child sampler for the current dataset.
`mindspore.dataset.MappableDataset.use_sampler`	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

`mindspore.dataset.Dataset.sync_update`	Release a blocking condition and trigger callback with given data.
`mindspore.dataset.Dataset.sync_wait`	Add a blocking condition to the input Dataset and a synchronize action will be applied.
`mindspore.dataset.Dataset.to_json`	Serialize a pipeline into JSON string and dump into file if filename is provided.