torch_to_nnef.op.custom_extractors.base

Classes:

Name	Description
`ModuleInfoExtractor`	Class to take manual control of NNEF expansion of a nn.Module.

ModuleInfoExtractor

ModuleInfoExtractor()

Class to take manual control of NNEF expansion of a nn.Module.

You need to subclass it, and set MODULE_CLASS according to your targeted module.

Then write .convert_to_nnef according to your need.

Methods:

Name	Description
`convert_to_nnef`	Control NNEF content to be written for each MODULE_CLASS.
`generate_in_torch_graph`	Internal method called by torch_to_nnef ir_graph.
`get_by_kind`	Get ModuleInfoExtractor by kind in torch_to_nnef internal IR.
`get_by_module`	Search if the module is one of the MODULE_CLASS registered.
`ordered_args`	Odered args for the module call.

convert_to_nnef(g, node, name_to_tensor, null_ref, torch_graph, inference_target, **kwargs)

Control NNEF content to be written for each MODULE_CLASS.

This happen at macro level when converting from internal IR to NNEF IR stage.

This is the Core method to overwrite in subclass.

It is no different than any op implemented in torch_to_nnef in the module

generate_in_torch_graph(torch_graph, *args, **kwargs)

Internal method called by torch_to_nnef ir_graph.

get_by_kind(kind: str)

Get ModuleInfoExtractor by kind in torch_to_nnef internal IR.

get_by_module(module: Module)

Search if the module is one of the MODULE_CLASS registered.

return appropriate ModuleInfoExtractor subclass if found

ordered_args(torch_graph)

Odered args for the module call.

Sometimes torch jit may reorder inputs. compared to targeted python ops in such case ordering need to be re-addressed