o }oi@@s`ddlmZddlZddlmZddlmZmZmZddl m Z dgZ GdddeeeeZ dS))contextmanagerN)Module)FileIO SerializationTyping)logging NeuralModulec@s`eZdZdZeddZejjddZ dddZ dd d Z dd e d dfddZ eddZdS)rzV Abstract class offering interface shared between all PyTorch Neural Modules. cCs|S)z_ Utility property that returns the total number of parameters of NeuralModule. ) _num_weights)selfr L/home/ubuntu/.local/lib/python3.10/site-packages/nemo/core/classes/module.py num_weightsszNeuralModule.num_weightscCs(d}|D] }|jr||7}q|S)Nr) parameters requires_gradnumel)r numpr r r r &s   zNeuralModule._num_weightsNcCsdS)z Override this method if random inputs won't work Returns: A tuple sample of valid input data. Nr )r max_batchmax_dimr r r input_example.szNeuralModule.input_examplereturncCsPi}|D] \}}|j||<d|_qt|ds||_n|j||dS)aJ Freeze all params for inference. This method sets `requires_grad` to False for all parameters of the module. It also stores the original `requires_grad` state of each parameter in a dictionary, so that `unfreeze()` can restore the original state if `partial=True` is set in `unfreeze()`. F_frozen_grad_mapN)named_parametersrhasattrrupdateeval)r grad_mappnameparamr r r freeze7s    zNeuralModule.freezeFpartialcCs|r t|ds td|D]"\}}|sd|_q||jvr%|j||_qtd|dd|_qt|drs