pytorch_pfn_extras.handler.ClousureLogic#

class pytorch_pfn_extras.handler.ClousureLogic(model_name='main', options=None)#

Bases: Logic

A set of methods that defines the training logic.

Parameters:

  • model_name (str) – Name of the model. Default is 'main'.

  • options (dict, optional) –

    The configuration options.

    • 'backward_outputs' (list of str):

      A list of names of outputs that require compution of the gradient.

    • 'autocast' (bool or dict):

      If True, torch.autocast is enabled, using {"enabled": True, "device_type": "cuda"} as autocast options. The default is False which corresponds to the following options {"enabled": False, "device_type": "cuda"}. If dict, options are passed to torch.autocast.

    • 'grad_scaler' (torch.cuda.amp.GradScaler):

      A gradient scaler that outputs are applied to.

Methods

__init__([model_name, options])

A set of methods that defines the training logic.

consume_options(options)

A method to update options of Logic.

eval_step(models, batch_idx, batch)

A method for an evaluation step.

train_epoch_begin(models, epoch, loader)

A method called when starting a new epoch of training.

train_epoch_end(models, epoch)

A method called when completing an epoch of training.

train_step(models, optimizers, batch_idx, batch)

A method invokes the model forward and backward passes and performs an optimization step.

train_step_optimizers(models, optimizers, ...)

In clousure mode, the stepping of the optimizer cannot be changed.

train_validation_begin(models)

A method called when starting a validation.

train_validation_end(models)

A method called when the validation completes.
consume_options(options)#

A method to update options of Logic.

Note that the given dict will be modified.

Parameters:

options (dict) – Option key-values to be set.

Return type:

None

train_step(models, optimizers, batch_idx, batch)#

A method invokes the model forward and backward passes and performs an optimization step.

Parameters:

  • models (dict of torch.nn.Module) – The models.

  • optimizers (dict of torch.optim.Optimizer) – The optimizers.

  • batch_idx (int) – Number of training steps already finished.

  • batch (torch.Tensor, list of torch.Tensor, dict of torch.Tensor) – Input tensors feeded to the model of the current step.

Return type:

Any

train_step_optimizers(models, optimizers, batch_idx)#

In clousure mode, the stepping of the optimizer cannot be changed.

If you want to change the stepping of the optimizer, please use the normal Logic class.

Parameters:

  • optimizers (dict of torch.optim.Optimizer) – The optimizers.

  • batch_idx (int) – Number of steps already finished.

  • models (Mapping[str, Module]) –

Return type:

None