update method name

nncf/onnx/graph/model_transformer.py

@@ -23,8 +23,8 @@
 from nncf.onnx.graph.onnx_helper import get_children_node_mapping
 from nncf.onnx.graph.onnx_helper import get_edge_dtype
 from nncf.onnx.graph.onnx_helper import get_edge_info_mapping
+from nncf.onnx.graph.onnx_helper import get_name_to_node_map
 from nncf.onnx.graph.onnx_helper import get_node_index
-from nncf.onnx.graph.onnx_helper import get_node_mapping
 from nncf.onnx.graph.onnx_helper import get_tensor
 from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand
 from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand
@@ -131,7 +131,7 @@ def _apply_output_insertion_transformations(
         :return: New model with inserted outputs.
         """
         model_outputs = set(output.name for output in self._model.graph.output)
-        node_mapping = get_node_mapping(self._model)
+        node_mapping = get_name_to_node_map(self._model)
         for transformation in transformations:
             port_id = transformation.target_point.port_id
             node_name = transformation.target_point.target_node_name
@@ -313,7 +313,7 @@ def _insert_quantizer_dequantizer(
         :param children_node_mapping: Mapping from edge name to nodes which consume this edge as an input.
         :return: Updated model with inserted QuantizeLinear-DequantizeLinear pair.
         """
-        node_mapping = get_node_mapping(model)
+        node_mapping = get_name_to_node_map(model)
         target_edge_name = self._get_quantizer_dequantizer_edge_name(transformation, node_mapping)
         quantizer, dequantizer = self._get_quantize_dequantize_nodes(transformation, target_edge_name)
         onnx_scale_tensor, onnx_zero_point_tensor = ONNXModelTransformer._get_scale_zero_point_tensors(
@@ -363,7 +363,7 @@ def _apply_bias_correction_transformations(
         :param transformations: Bias correction transformations.
         :return: Copy of original model with updated biases.
         """
-        node_mapping = get_node_mapping(model)
+        node_mapping = get_name_to_node_map(model)
         for transformation in transformations:
             bias_tensor_position = transformation.target_point.port_id
             node_name = transformation.target_point.target_node_name
@@ -383,7 +383,7 @@ def _apply_model_extraction_transformation(self, transformation: ONNXModelExtrac
         :return: Extracted sub-model.
         """
         input_tensor_names = []
-        node_mapping = get_node_mapping(self._model)
+        node_mapping = get_name_to_node_map(self._model)
         for input_node_name in transformation.inputs:
             input_onnx_node = node_mapping[input_node_name]
             input_tensor_names.append(input_onnx_node.input[0])
@@ -409,7 +409,7 @@ def _apply_qdq_node_removing_transformations(
         :return: Model with removed nodes.
         """
         for transformation in transformations:
-            node_mapping = get_node_mapping(model)
+            node_mapping = get_name_to_node_map(model)
             children_node_mapping = get_children_node_mapping(model)
             node = node_mapping[transformation.target_point.target_node_name]
 

nncf/onnx/graph/onnx_helper.py

@@ -16,9 +16,9 @@
 from onnx import numpy_helper
 
 
-def get_node_mapping(model: onnx.ModelProto) -> Dict[str, onnx.NodeProto]:
+def get_name_to_node_map(model: onnx.ModelProto) -> Dict[str, onnx.NodeProto]:
     """
-    Retuns mapping from node name to the node.
+    Returns mapping from node name to the node.
 
     :param model: Model from mapping is built.
     :return: Mapping.

nncf/onnx/statistics/aggregator.py

@@ -22,15 +22,15 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.onnx.graph.node_utils import get_input_edge
 from nncf.onnx.graph.node_utils import get_input_edges_mapping
-from nncf.onnx.graph.onnx_helper import get_node_mapping
+from nncf.onnx.graph.onnx_helper import get_name_to_node_map
 from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand
 from nncf.onnx.tensor import ONNXNNCFTensor
 
 
 class ONNXStatisticsAggregator(StatisticsAggregator):
     def collect_statistics(self, model: onnx.ModelProto, graph: NNCFGraph) -> None:
         self.input_edges_mapping = get_input_edges_mapping(graph)
-        self.node_mapping = get_node_mapping(model)
+        self.node_mapping = get_name_to_node_map(model)
         self._registered_weights = set()
         super().collect_statistics(model, graph)
 

nncf/quantization/algorithms/bias_correction/onnx_backend.py

@@ -22,7 +22,7 @@
 from nncf.onnx.graph.node_utils import get_bias_value
 from nncf.onnx.graph.node_utils import is_any_weight_quantized
 from nncf.onnx.graph.node_utils import is_node_with_bias
-from nncf.onnx.graph.onnx_helper import get_node_mapping
+from nncf.onnx.graph.onnx_helper import get_name_to_node_map
 from nncf.onnx.graph.transformations.command_creation import create_bias_correction_command
 from nncf.onnx.graph.transformations.commands import ONNXBiasCorrectionCommand
 from nncf.onnx.graph.transformations.commands import ONNXModelExtractionCommand
@@ -101,12 +101,12 @@ def get_bias_value(node: NNCFNode, model: onnx.ModelProto, nncf_graph: NNCFGraph
 
     @staticmethod
     def get_input_name(model: onnx.ModelProto, node_name: str) -> str:
-        node_mapping = get_node_mapping(model)
+        node_mapping = get_name_to_node_map(model)
         return node_mapping[node_name].input[0]
 
     @staticmethod
     def get_output_name(model: onnx.ModelProto, node_name: str, output_id: int) -> List[str]:
-        node_mapping = get_node_mapping(model)
+        node_mapping = get_name_to_node_map(model)
         return node_mapping[node_name].output[output_id]
 
     @staticmethod