mindspore.dataset.WaitedDSCallback
- class mindspore.dataset.WaitedDSCallback(step_size=1)[source]
Abstract base class used to build a dataset callback class that is synchronized with the training callback.
This class can be used to execute a user defined logic right after the previous step or epoch. For example, one augmentation needs the loss from the previous trained epoch to update some of its parameters.
- Parameters
step_size (int, optional) – The number of rows in each step. Usually the step size will be equal to the batch size (Default=1).
Examples
>>> from mindspore.dataset import WaitedDSCallback >>> >>> my_cb = WaitedDSCallback(32) >>> # dataset is an instance of Dataset object >>> dataset = dataset.map(operations=AugOp(), callbacks=my_cb) >>> dataset = dataset.batch(32) >>> # define the model >>> model.train(epochs, data, callbacks=[my_cb])
- begin(run_context)
Called once before the network executing.
- Parameters
run_context (RunContext) – Include some information of the model.
- create_runtime_obj()[source]
Creates a runtime (C++) object from the callback methods defined by the user. This method is internal.
- Returns
_c_dataengine.PyDSCallback.
- ds_begin(ds_run_context)
Called before the data pipeline is started.
- Parameters
ds_run_context (RunContext) – Include some information of the pipeline.
- ds_epoch_begin(ds_run_context)[source]
Internal method, do not call/override. Defines ds_epoch_begin of DSCallback to wait for MS epoch_end callback.
- Parameters
ds_run_context – Include some information of the pipeline.
- ds_epoch_end(ds_run_context)
Called after an epoch is finished.
- Parameters
ds_run_context (RunContext) – Include some information of the pipeline.
- ds_step_begin(ds_run_context)[source]
Internal method, do not call/override. Defines ds_step_begin of DSCallback to wait for MS step_end callback.
- Parameters
ds_run_context – Include some information of the pipeline.
- ds_step_end(ds_run_context)
Called after each step finished.
- Parameters
ds_run_context (RunContext) – Include some information of the pipeline.
- end(run_context)[source]
Internal method, release the wait if training is ended.
- Parameters
run_context – Include some information of the model.
- epoch_begin(run_context)
Called before each epoch beginning.
- Parameters
run_context (RunContext) – Include some information of the model.
- epoch_end(run_context)[source]
Internal method, do not call/override. Defines epoch_end of Callback to release the wait in ds_epoch_begin.
- Parameters
run_context – Include some information of the model.
- step_begin(run_context)
Called before each step beginning.
- Parameters
run_context (RunContext) – Include some information of the model.
- step_end(run_context)[source]
Internal method, do not call/override. Defines step_end of Callback to release the wait in ds_step_begin.
- Parameters
run_context – Include some information of the model.
- sync_epoch_begin(train_run_context, ds_run_context)[source]
Called before a new dataset epoch is started and after the previous training epoch is ended.
- Parameters
train_run_context – Include some information of the model with feedback from the previous epoch.
ds_run_context – Include some information of the dataset pipeline.
- sync_step_begin(train_run_context, ds_run_context)[source]
Called before a new dataset step is started and after the previous training step is ended.
- Parameters
train_run_context – Include some information of the model with feedback from the previous step.
ds_run_context – Include some information of the dataset pipeline.