mindspore.dataset.ManifestDataset

class mindspore.dataset.ManifestDataset(dataset_file, usage='train', num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, class_indexing=None, decode=False, num_shards=None, shard_id=None, cache=None)[source]

A source dataset for reading images from a Manifest file.

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 uint64 type.

Parameters
  • dataset_file (str) – File to be read.

  • usage (str, optional) – Acceptable usages include ‘train’, ‘eval’ and ‘inference’. Default: ‘train’.

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

  • num_parallel_workers (int, optional) – Number of workers to read the data. Default: None, will use value set in the config.

  • shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default: None, expected order behavior shown in the table below.

  • sampler (Sampler, optional) – Object used to choose samples from the dataset. Default: None, expected order behavior shown in the table below.

  • 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.

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

  • 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 number of samples 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_files are not valid or do not exist.

  • ValueError – If num_parallel_workers exceeds the max thread numbers.

  • 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 class_indexing is not a dictionary.

  • ValueError – If shard_id is invalid (< 0 or >= num_shards).

Note

  • The shape of the image column is [image_size] if decode flag is False, or [H,W,C] otherwise.

  • 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

>>> manifest_dataset_dir = "/path/to/manifest_dataset_file"
>>>
>>> # 1) Read all samples specified in manifest_dataset_dir dataset with 8 threads for training
>>> dataset = ds.ManifestDataset(dataset_file=manifest_dataset_dir, usage="train", num_parallel_workers=8)
>>>
>>> # 2) Read samples (specified in manifest_file.manifest) for shard 0 in a 2-way distributed training setup
>>> dataset = ds.ManifestDataset(dataset_file=manifest_dataset_dir, num_shards=2, shard_id=0)

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.