mindspore.dataset.Dataset.sync_update

Dataset.sync_update(condition_name, num_batch=None, data=None)

Release a blocking condition and trigger callback with given data.

Parameters

• condition_name (str) – The condition name that is used to toggle sending next row.

• num_batch (Union[int, None]) – The number of batches (rows) that are released. When num_batch is None, it will default to the number specified by the sync_wait operation. Default: None.

• data (Any) – The data passed to the callback, user defined. Default: None.

Examples

>>> import numpy as np
>>>
>>>
>>> def gen():
...     for i in range(100):
...         yield (np.array(i),)
>>>
>>>
>>> class Augment:
...     def __init__(self, loss):
...         self.loss = loss
...
...     def preprocess(self, input_):
...         return input_
...
...     def update(self, data):
...         self.loss = data["loss"]
>>>
>>>
>>> batch_size = 10
>>> dataset = ds.GeneratorDataset(gen, column_names=["input"])
>>> aug = Augment(0)
>>> dataset = dataset.sync_wait(condition_name='', num_batch=1)
>>> dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
>>> dataset = dataset.batch(batch_size)
>>>
>>> count = 0
>>> for data in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     count += 1
...     data = {"loss": count}
...     dataset.sync_update(condition_name="", data=data)