mindspore.dataset.BatchInfo

class mindspore.dataset.BatchInfo

This class helps to get dataset information dynamically when the input of batch_size or per_batch_map in batch operation is a callable object.

get_batch_num()

Return the batch number being processed in current epoch, start from 0.

Examples

>>> # Create a dataset where its batch size is dynamic
>>> # Define a callable batch size function and let batch size increase 1 each time.
>>> import mindspore.dataset as ds
>>> from mindspore.dataset import BatchInfo
>>>
>>> dataset = ds.GeneratorDataset([i for i in range(3)], "column1", shuffle=False)
>>> def add_one(BatchInfo):
...     return BatchInfo.get_batch_num() + 1
>>> dataset = dataset.batch(batch_size=add_one)
>>> print(list(dataset))
[[Tensor(shape=[1], dtype=Int64, value= [0])], [Tensor(shape=[2], dtype=Int64, value= [1, 2])]]

get_epoch_num()

Return the epoch number, start from 0.

Examples

>>> # Create a dataset where its batch size is dynamic
>>> # Define a callable batch size function and let batch size increase 1 each epoch.
>>> import mindspore.dataset as ds
>>> from mindspore.dataset import BatchInfo
>>>
>>> dataset = ds.GeneratorDataset([i for i in range(4)], "column1", shuffle=False)
>>> def add_one_by_epoch(BatchInfo):
...     return BatchInfo.get_epoch_num() + 1
>>> dataset = dataset.batch(batch_size=add_one_by_epoch)
>>>
>>> result = []
>>> epoch = 2
>>> iterator = dataset.create_tuple_iterator(num_epochs=epoch)
>>> for i in range(epoch):
...    result.extend(list(iterator))
>>> # result:
>>> # [[Tensor(shape=[1], dtype=Int64, value= [0])], [Tensor(shape=[1], dtype=Int64, value= [1])],
>>> #  [Tensor(shape=[1], dtype=Int64, value= [2])], [Tensor(shape=[1], dtype=Int64, value= [3])],
>>> #  [Tensor(shape=[2], dtype=Int64, value= [0, 1])], [Tensor(shape=[2], dtype=Int64, value= [2, 3])]]