mindspore.dataset.LSUNDataset

class mindspore.dataset.LSUNDataset(dataset_dir, usage=None, classes=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None)

LSUN(Large-scale Scene UNderstarding) dataset.

The generated dataset has two columns: [image, label] . The tensor of column image is of the uint8 type. The tensor of column label is of a scalar of uint32 type.

Parameters

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

• usage (str, optional) – Usage of this dataset, can be train , test , valid or all Default: None, will be set to all .

• classes (Union[str, list[str]], optional) – Choose the specific classes to load. Default: None, means loading all classes in root directory.

• 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 or not 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 max 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.

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 sharding 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 invalid (< 0 or >= num_shards ).

• ValueError – If usage or classes is invalid (not in specific types).

Note

• This dataset can take in a sampler . sampler and shuffle are mutually exclusive. The table below shows what input arguments are allowed and their expected behavior.

Expected Order Behavior of Using sampler and shuffle

Parameter ‘sampler’	Parameter ‘shuffle’	Expected Order Behavior
None	None	random order
None	True	random order
None	False	sequential order
Sampler object	None	order defined by sampler
Sampler object	True	not allowed
Sampler object	False	not allowed

Examples

>>> lsun_dataset_dir = "/path/to/lsun_dataset_directory"
>>>
>>> # 1) Read all samples (image files) in lsun_dataset_dir with 8 threads
>>> dataset = ds.LSUNDataset(dataset_dir=lsun_dataset_dir,
...                          num_parallel_workers=8)
>>>
>>> # 2) Read all train samples (image files) from folder "bedroom" and "classroom"
>>> dataset = ds.LSUNDataset(dataset_dir=lsun_dataset_dir, usage="train",
...                          classes=["bedroom", "classroom"])

About LSUN dataset:

The LSUN (Large-Scale Scene Understanding) is a large-scale dataset used for indoors scene understanding. It was originally launched by Stanford University in 2015 with the aim of providing a challenging and diverse dataset for research in computer vision and machine learning. The main application of this dataset for research is indoor scene analysis.

This dataset contains ten different categories of scenes, including bedrooms, living rooms, restaurants, lounges, studies, kitchens, bathrooms, corridors, children’s room, and outdoors. Each category contains tens of thousands of images from different perspectives, and these images are high-quality, high-resolusion real-world images.

You can unzip the dataset files into this directory structure and read by MindSpore’s API.

.
└── lsun_dataset_directory
    ├── test
    │    ├── ...
    ├── bedroom_train
    │    ├── 1_1.jpg
    │    ├── 1_2.jpg
    ├── bedroom_val
    │    ├── ...
    ├── classroom_train
    │    ├── ...
    ├── classroom_val
    │    ├── ...

Citation:

article{yu15lsun,
    title={LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop},
    author={Yu, Fisher and Zhang, Yinda and Song, Shuran and Seff, Ari and Xiao, Jianxiong},
    journal={arXiv preprint arXiv:1506.03365},
    year={2015}
}

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	Takes at most given numbers of elements 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.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Return the class index.
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.device_que	Return a transferred Dataset that transfers data through a device.
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.