returnn.torch.engine#

Main engine for PyTorch

returnn.torch.engine.random() → x in the interval [0, 1).[source]#
class returnn.torch.engine.Engine(config: Config)[source]#

PyTorch engine

Parameters:

config –

init_network_from_config(config: Config | None = None)[source]#

init model

init_train_from_config(config: Config | None = None, train_data: Dataset | None = None, dev_data: Dataset | None = None, eval_data: Dataset | None = None)[source]#
Parameters:

  • config –

  • train_data –

  • dev_data –

  • eval_data –

train()[source]#

Main training loop.

init_train_epoch()[source]#

init train (sub)epoch. LR etc

train_epoch()[source]#

train one (sub)epoch

eval_model(*, skip_already_evaluated: bool = False)[source]#

Runs model on all eval datasets and calculates the loss.

get_pt_model() → Module | None[source]#
Returns:

PyTorch Module. in case this is using RF, it will return the wrapped module

get_pt_optimizer() → Optimizer | None[source]#
Returns:

PyTorch optimizer

forward_with_callback(*, dataset: Dataset, callback: ForwardCallbackIface)[source]#

forward

static delete_model(filename)[source]#
Parameters:

filename (str) –

Returns:

accumulated file-size in bytes of deleted files

Return type:

int

returnn.torch.engine.get_device_from_config_opt(device: str | None) → ResultWithReason[str][source]#
Parameters:

device – as in config

Returns:

resolved device