mindspore.dataset.Dataset.batch

Dataset.batch(batch_size, drop_remainder=False, num_parallel_workers=None, **kwargs)[source]

Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.

For any column, all the elements within that column must have the same shape.

Refer to the following figure for the execution process:

../../../../_images/batch_en.png

Note

The order of using repeat and batch reflects the number of batches and per_batch_map. It is recommended that the repeat operation applied after the batch operation finished.

Parameters

  • batch_size (Union[int, Callable]) – The number of rows each batch is created with. An int or callable object which takes exactly 1 parameter, BatchInfo.

  • drop_remainder (bool, optional) – Determines whether or not to drop the last block whose data row number is less than batch size. Default: False . If True , and if there are less than batch_size rows available to make the last batch, then those rows will be dropped and not propagated to the child node.

  • num_parallel_workers (int, optional) – Number of workers(threads) to process the dataset in parallel. Default: None .

  • **kwargs –

    • per_batch_map (Callable[[List[numpy.ndarray], …, List[numpy.ndarray], BatchInfo], (List[numpy.ndarray], …, List[numpy.ndarray])], optional): Per batch map callable. Default: None. A callable which takes (List[numpy.ndarray], …, List[numpy.ndarray], BatchInfo) as input parameters. Each list[numpy.ndarray] represents a batch of numpy.ndarray on a given column. The number of lists should match with the number of entries in input_columns. The last parameter of the callable should always be a BatchInfo object. Per_batch_map should return (list[numpy.ndarray], list[numpy.ndarray], …). The length of each list in output should be the same as the input. output_columns is required if the number of output lists is different from input.

    • input_columns (Union[str, list[str]], optional): List of names of the input columns. The size of the list should match with signature of per_batch_map callable. Default: None .

    • output_columns (Union[str, list[str]], optional): List of names assigned to the columns outputted by the last operation. This parameter is mandatory if len(input_columns) != len(output_columns). The size of this list must match the number of output columns of the last operation. Default: None , output columns will have the same name as the input columns, i.e., the columns will be replaced.

    • python_multiprocessing (bool, optional): Parallelize Python function per_batch_map with multi-processing or multi-threading mode, True means multi-processing, False means multi-threading If per_batch_map is a I/O bound task, use multi-threading mode. If per_batch_map is a CPU bound task, it is recommended to use multi-processing mode. Default: False , use python multi-threading mode.

    • max_rowsize(int, optional): Maximum size of row in MB that is used for shared memory allocation to copy data between processes. This is only used if python_multiprocessing is set to True. Default: 16 .

Returns

BatchDataset, dataset batched.

Examples

>>> # 1) Create a dataset where every 5 rows are combined into a batch
>>> # and drops the last incomplete batch if there is one.
>>> import mindspore.dataset as ds
>>> from PIL import Image
>>>
>>> cifar10_dataset_dir = "/path/to/cifar10_dataset_directory"
>>> dataset = ds.Cifar10Dataset(dataset_dir=cifar10_dataset_dir, num_samples=10)
>>> dataset = dataset.batch(5, True)
>>>
>>> # 2) resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
>>> def np_resize(col, BatchInfo):
...     output = col.copy()
...     s = (BatchInfo.get_batch_num() + 1) ** 2
...     index = 0
...     for c in col:
...         img = Image.fromarray(c.astype('uint8')).convert('RGB')
...         img = img.resize((s, s))
...         output[index] = np.array(img)
...         index += 1
...     return (output,)
>>> dataset = dataset.batch(batch_size=8, input_columns=["image"], per_batch_map=np_resize)
>>>
>>> # 3) Create a dataset where its batch size is dynamic
>>> # Define a callable batch size function and let batch size increase 1 each time.
>>> def add_one(BatchInfo):
...     return BatchInfo.get_batch_num() + 1
>>> dataset = dataset.batch(batch_size=add_one, drop_remainder=True)