mindspore.data_sink

mindspore.data_sink(fn, dataset, sink_size=1, jit_config=None, input_signature=None)[source]

A wrapper function to generate a function for the input function.

Note

When using data sinking, the dataset will automatically cycle. At this time, only the total number of training steps (total_step) and the number of steps for each sinking (sink_size) need to be considered. When switching from training by rounds (epochs) to data sinking, the formula is as follows:

total_step = epochs * dataset_size

train_sink_step = total_step / sink_size

After transforming from mindspore.data_sink, you need to execute train_sink_step step for training.

Parameters

  • fn (Function) – The Python function that will be run with dataset.

  • dataset (Dataset) – The dataset iterator. The dataset can be generated by dataset generator API in mindspore.dataset, such as mindspore.dataset.ImageFolderDataset.

  • sink_size (int) – Control the amount of data in each sink. sink_size must be positive integer. Default: 1.

  • jit_config (JitConfig) – Controls the execution mode(Graph mode/PyNative mode) of the generated function, and Jit config for compile. Default: None, means running in PyNative mode.

  • input_signature (Union[Tensor, List or Tuple of Tensors]) – The Tensor which describes the input arguments. The shape and dtype of the Tensor will be supplied to this function. If input_signature is specified, each input to fn must be a Tensor. And the input parameters of fn cannot accept **kwargs. The shape and dtype of actual inputs should keep the same as input_signature. Otherwise, TypeError will be raised. Default: None.

Returns

Function, the generated function will be executed in data sinking mode.

Raises

ValueError – If sink_size is not positive integer.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import dataset as ds
>>>
>>> data = {"x": np.ones((1,), dtype=np.int32), "y": np.ones((1,), dtype=np.int32)}
>>> dataset = ds.NumpySlicesDataset(data=data)
>>>
>>> def func_net(x, y):
...     out = x + y
...     return out
>>>
>>> sink_process = ms.train.data_sink(func_net, dataset, sink_size=1)
>>> for _ in range(2):
...     out = sink_process()
...     print(out)
2
2