towhee.trainer.callback.Callback

class towhee.trainer.callback.Callback

Bases: object

Callback defines a set of functions which will be called in the training process. Customized Callback could inherent the base Callback and overwrite its methods to control the training process or handle the training information.

Methods

on_batch_begin	Hook function invoked before every batch calculation.
on_batch_end	Hook function invoked after every batch calculation.
on_epoch_begin	Hook function invoked before each epoch.
on_epoch_end	Hook function invoked after each epoch.
on_eval_batch_begin	Hook function invoked before every batch calculation in evaluate stage.
on_eval_batch_end	Hook function invoked after every batch calculation in evaluate stage.
on_eval_begin	Hook function invoked before evaluate stage.
on_eval_end	Hook function invoked after evaluate stage.
on_train_batch_begin	Hook function invoked before train stage.
on_train_batch_end	Hook function invoked before every batch calculation in train stage.
on_train_begin	Hook function invoked before train stage.
on_train_end	Hook function invoked after train stage.
set_model	Set the model to callback.
set_optimizer	Set the optimizer to callback.
set_trainercontrol	Set the trainercontrol to callback.

__init__()

on_batch_begin(batch: Tuple, logs: Dict) → None

Hook function invoked before every batch calculation.

Parameters:

• batch (Tuple) – The data batch to calculate.

• logs (Dict) – Kv store to save and load info.

on_batch_end(batch: Tuple, logs: Dict) → None

Hook function invoked after every batch calculation.

Parameters:

• batch (Tuple) – The data batch to calculate.

• logs (Dict) – Kv store to save and load info.

on_epoch_begin(epochs: int, logs: Dict) → None

Hook function invoked before each epoch.

Parameters:

• epochs (int) – Epoch index.

• logs (Dict) – Kv store to save and load info.

on_epoch_end(epochs: int, logs: Dict) → None

Hook function invoked after each epoch.

Parameters:

• epochs (int) – Epoch index.

• logs (Dict) – Kv store to save and load info.

on_eval_batch_begin(batch: Tuple, logs: Dict) → None

Hook function invoked before every batch calculation in evaluate stage.

Parameters:

• batch (Tuple) – The data batch to calculate.

• logs (Dict) – Kv store to save and load info.

on_eval_batch_end(batch: Tuple, logs: Dict) → None

Hook function invoked after every batch calculation in evaluate stage.

Parameters:

• batch (Tuple) – The data batch to calculate.

• logs (Dict) – Kv store to save and load info.

on_eval_begin(logs: Dict) → None

Hook function invoked before evaluate stage.

Parameters:

logs (Dict) – Kv store to save and load info.

on_eval_end(logs: Dict) → None

Hook function invoked after evaluate stage.

Parameters:

logs (Dict) – Kv store to save and load info.

on_train_batch_begin(batch: Tuple, logs: Dict) → None

Hook function invoked before train stage.

Parameters:

logs (Dict) – Kv store to save and load info.

on_train_batch_end(batch: Tuple, logs: Dict) → None

Hook function invoked before every batch calculation in train stage.

Parameters:

• batch (Tuple) – The data batch to calculate.

• logs (Dict) – Kv store to save and load info.

on_train_begin(logs: Dict) → None

Hook function invoked before train stage.

Parameters:

logs (Dict) – Kv store to save and load info.

on_train_end(logs: Dict) → None

Hook function invoked after train stage.

Parameters:

logs (Dict) – Kv store to save and load info.

set_model(model: Module) → None

Set the model to callback.

Parameters:

model (torch.nn.Module) – The model which callback can operate.

set_optimizer(optimizer: Optimizer) → None

Set the optimizer to callback.

Parameters:

optimizer (torch.optim.Optimizer) – The optimizer which callback can operate.

set_trainercontrol(trainercontrol: TrainerControl) → None

Set the trainercontrol to callback.

Parameters:

trainercontrol (towhee.trainer.callback.TrainerControl) – The trainercontrol which callback can operate.