mindspore.dataset.GeneratorDataset

class mindspore.dataset.GeneratorDataset(source, column_names=None, column_types=None, schema=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None, python_multiprocessing=True, max_rowsize=6)

A source dataset that generates data from Python by invoking Python data source each epoch.

The column names and column types of generated dataset depend on Python data defined by users.

Parameters

• source (Union[Callable, Iterable, Random Accessible]) – A generator callable object, an iterable Python object or a random accessible Python object. Callable source is required to return a tuple of NumPy arrays as a row of the dataset on source().next(). Iterable source is required to return a tuple of NumPy arrays as a row of the dataset on iter(source).next(). Random accessible source is required to return a tuple of NumPy arrays as a row of the dataset on source[idx].

• column_names (Union[str, list[str]], optional) – List of column names of the dataset. Default: None . Users are required to provide either column_names or schema.

• column_types (list[mindspore.dtype], optional) – List of column data types of the dataset. Default: None . If provided, sanity check will be performed on generator output.

• schema (Union[str, Schema], optional) – Data format policy, which specifies the data types and shapes of the data column to be read. Both JSON file path and objects constructed by mindspore.dataset.Schema are acceptable. Default: None .

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

• num_parallel_workers (int, optional) – Number of worker threads/subprocesses used to fetch the dataset in parallel. Default: 1.

• shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Random accessible input is required. Default: None , expected order behavior shown in the table below.

• sampler (Union[Sampler, Iterable], optional) – Object used to choose samples from the dataset. Random accessible input is required. 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 . Random accessible input is required. 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 must be specified only when num_shards is also specified. Random accessible input is required.

• python_multiprocessing (bool, optional) – Parallelize Python operations with multiple worker process. This option could be beneficial if the Python operation is computational heavy. Default: True.

• max_rowsize (int, optional) – Maximum size of row in MB that is used for shared memory allocation to copy data between processes, the total occupied shared memory will increase as num_parallel_workers and mindspore.dataset.config.set_prefetch_size() increase. If set to -1, shared memory will be dynamically allocated with the actual size of data. This is only used if python_multiprocessing is set to True. Default: 16.

Raises

• RuntimeError – If source raises an exception during execution.

• RuntimeError – If len of column_names does not match output len of source.

• ValueError – If num_parallel_workers exceeds the max thread numbers.

• ValueError – If sampler and shuffle are specified at the same time.

• ValueError – If sampler and sharding are specified at the same time.

• ValueError – If num_shards is specified but shard_id is None.

• ValueError – If shard_id is specified but num_shards is None.

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

Tutorial Examples:

• Load & Process Data With Dataset Pipeline

Note

• If you configure python_multiprocessing=True (Default: True ) and num_parallel_workers>1 (default: 1 ) indicates that the multi-process mode is started for data load acceleration. At this time, as the datasetiterates, the memory consumption of the subprocess will gradually increase, mainly because the subprocess of the user-defined dataset obtains the member variables from the main process in the Copy On Write way. Example: If you define a dataset with __ init__ function which contains a large number of member variable data (for example, a very large file name list is loaded during the dataset construction) and uses the multi-process mode, which may cause the problem of OOM (the estimated total memory usage is: (num_parallel_workers+1) * size of the parent process ). The simplest solution is to replace Python objects (such as list/dict/int/float/string) with non referenced data types (such as Pandas, Numpy or PyArrow objects) for member variables, or load less meta data in member variables, or configure python_multiprocessing=False to use multi-threading mode.

  There are several classes/functions that can help you reduce the size of member variables, and you can choose to use them:

  1. mindspore.dataset.utils.LineReader: Use this class to initialize your text file object in the __init__ function. Then read the file content based on the line number of the object with the __getitem__ function.

• Input source accepts user-defined Python functions (PyFuncs), Do not add network computing operators from mindspore.nn and mindspore.ops or others into this source .

• 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
>>> import numpy as np
>>>
>>> # 1) Multidimensional generator function as callable input.
>>> def generator_multidimensional():
...     for i in range(64):
...         yield (np.array([[i, i + 1], [i + 2, i + 3]]),)
>>>
>>> dataset = ds.GeneratorDataset(source=generator_multidimensional, column_names=["multi_dimensional_data"])
>>>
>>> # 2) Multi-column generator function as callable input.
>>> def generator_multi_column():
...     for i in range(64):
...         yield np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]])
>>>
>>> dataset = ds.GeneratorDataset(source=generator_multi_column, column_names=["col1", "col2"])
>>>
>>> # 3) Iterable dataset as iterable input.
>>> class MyIterable:
...     def __init__(self):
...         self._index = 0
...         self._data = np.random.sample((5, 2))
...         self._label = np.random.sample((5, 1))
...
...     def __next__(self):
...         if self._index >= len(self._data):
...             raise StopIteration
...         else:
...             item = (self._data[self._index], self._label[self._index])
...             self._index += 1
...             return item
...
...     def __iter__(self):
...         self._index = 0
...         return self
...
...     def __len__(self):
...         return len(self._data)
>>>
>>> dataset = ds.GeneratorDataset(source=MyIterable(), column_names=["data", "label"])
>>>
>>> # 4) Random accessible dataset as random accessible input.
>>> class MyAccessible:
...     def __init__(self):
...         self._data = np.random.sample((5, 2))
...         self._label = np.random.sample((5, 1))
...
...     def __getitem__(self, index):
...         return self._data[index], self._label[index]
...
...     def __len__(self):
...         return len(self._data)
>>>
>>> dataset = ds.GeneratorDataset(source=MyAccessible(), column_names=["data", "label"])
>>>
>>> # list, dict, tuple of Python is also random accessible
>>> dataset = ds.GeneratorDataset(source=[(np.array(0),), (np.array(1),), (np.array(2),)], column_names=["col"])

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.