mindspore.dataset.CelebADataset

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class mindspore.dataset.CelebADataset(dataset_dir, num_parallel_workers=None, shuffle=None, usage='all', sampler=None, decode=False, extensions=None, num_samples=None, num_shards=None, shard_id=None, cache=None, decrypt=None)[source]

CelebA(CelebFaces Attributes) dataset.

Only support to read list_attr_celeba.txt currently, which is the attribute annotations of the dataset. The generated dataset has two columns: [image, attr] . The tensor of column image is of the uint8 type. The tensor of column attr is of the uint32 type and one hot encoded.

Parameters
  • dataset_dir (str) – Path to the root directory that contains the dataset.

  • 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 .

  • usage (str, optional) – Specify the 'train', 'valid', 'test' part or 'all' parts of dataset. Default: 'all', will read all samples.

  • sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None .

  • decode (bool, optional) – Whether to decode the images after reading. Default: False.

  • extensions (list[str], optional) – List of file extensions to be included in the dataset. Default: None .

  • num_samples (int, optional) – The number of images to be included in the dataset. Default: None , will include all images.

  • 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.

  • 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.

  • 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.

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.

  • ValueError – If shard_id is not in range of [0, num_shards ).

  • ValueError – If num_parallel_workers exceeds the max thread numbers.

  • ValueError – If usage is not 'train', 'valid', 'test' or 'all'.

Tutorial Examples:

Note

  • 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
>>> celeba_dataset_dir = "/path/to/celeba_dataset_directory"
>>>
>>> # Read 5 samples from CelebA dataset
>>> dataset = ds.CelebADataset(dataset_dir=celeba_dataset_dir, usage='train', num_samples=5)
>>>
>>> # Note: In celeba dataset, each data dictionary owns keys "image" and "attr"

About CelebA dataset:

CelebFaces Attributes Dataset (CelebA) is a large-scale dataset with more than 200K celebrity images, each with 40 attribute annotations.

The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including

  • 10,177 number of identities,

  • 202,599 number of images,

  • 5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: attribute recognition, detection, landmark (or facial part) and localization.

Original CelebA dataset structure:

.
└── CelebA
     ├── README.md
     ├── Img
     │    ├── img_celeba.7z
     │    ├── img_align_celeba_png.7z
     │    └── img_align_celeba.zip
     ├── Eval
     │    └── list_eval_partition.txt
     └── Anno
          ├── list_landmarks_celeba.txt
          ├── list_landmarks_align_celeba.txt
          ├── list_bbox_celeba.txt
          ├── list_attr_celeba.txt
          └── identity_CelebA.txt

You can unzip the dataset files into the following structure and read by MindSpore's API.

.
└── celeba_dataset_directory
    ├── list_attr_celeba.txt
    ├── 000001.jpg
    ├── 000002.jpg
    ├── 000003.jpg
    ├── ...

Citation:

@article{DBLP:journals/corr/LiuLWT14,
author        = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
title         = {Deep Learning Attributes in the Wild},
journal       = {CoRR},
volume        = {abs/1411.7766},
year          = {2014},
url           = {http://arxiv.org/abs/1411.7766},
archivePrefix = {arXiv},
eprint        = {1411.7766},
timestamp     = {Tue, 10 Dec 2019 15:37:26 +0100},
biburl        = {https://dblp.org/rec/journals/corr/LiuLWT14.bib},
bibsource     = {dblp computer science bibliography, https://dblp.org},
howpublished  = {http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html}
}

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.