Coverage for mlprodict/onnx_tools/optim/onnx_optimisation.py: 100%

1""" 

2@file 

3@brief Optimisations of :epkg:`ONNX` graphs. 

4""" 

5from ._onnx_optimisation_common import _apply_optimisation_on_graph 

6from .onnx_optimisation_identity import onnx_remove_node_identity 

7from .onnx_optimisation_redundant import onnx_remove_node_redundant 

8from .onnx_optimisation_unused import onnx_remove_node_unused 

9 

10 

11def onnx_remove_node(onnx_model, recursive=True, debug_info=None, **options): 

12 """ 

13 Removes as many nodes as possible without changing 

14 the outcome. It applies @see fn onnx_remove_node_unused, 

15 @see fn onnx_remove_node_identity, 

16 and @see fn onnx_remove_node_redundant. 

17 

18 @param onnx_model onnx model 

19 @param recursive looks into subgraphs 

20 @param debug_info debug information (private) 

21 @param options additional options 

22 @return new onnx model 

23 """ 

24 if debug_info is None: 

25 debug_info = [str(type(onnx_model)).rsplit( 

26 '.', maxsplit=1)[-1].strip("'>")] 

27 else: 

28 debug_info = (debug_info + 

29 [str(type(onnx_model)).rsplit('.', maxsplit=1)[-1].strip("'>")]) 

30 

31 if hasattr(onnx_model, 'graph'): 

32 return _apply_optimisation_on_graph( 

33 onnx_remove_node, onnx_model, 

34 recursive=recursive, debug_info=debug_info, 

35 **options) 

36 

37 graph = onnx_model 

38 graph = onnx_remove_node_unused( 

39 graph, recursive=recursive, debug_info=debug_info, **options) 

40 graph = onnx_remove_node_identity( 

41 graph, recursive=recursive, debug_info=debug_info, **options) 

42 graph = onnx_remove_node_redundant( 

43 graph, recursive=recursive, debug_info=debug_info, **options) 

44 return graph