sng4onnx

import onnx from onnx import helper, TensorProto import numpy as np from sng4onnx import generate import os # 1. Create a dummy ONNX model for demonstration def create_dummy_onnx(path): # Define graph inputs X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 2, 3]) Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 2, 3]) # Define graph outputs Z = helper.make_tensor_value_info('Z', TensorProto.FLOAT, [1, 2, 3]) # Create a node (Mul operator) node_def = helper.make_node( 'Add', ['X', 'Y'], ['Z'], name='MyAddOperation' # Can be empty in an 'old format' model ) # Create the graph graph_def = helper.make_graph( [node_def], 'simple_graph', [X, Y], [Z] ) # Create the model model_def = helper.make_model(graph_def, producer_name='dummy-model') # Save the model onnx.save(model_def, path) input_onnx_path = 'input_model.onnx' output_onnx_path = 'output_model_renamed.onnx' create_dummy_onnx(input_onnx_path) # 2. Use sng4onnx to generate/assign OP names print(f"Processing {input_onnx_path}...") renamed_model = generate( input_onnx_file_path=input_onnx_path, output_onnx_file_path=output_onnx_path, non_verbose=False ) # 3. Verify the output (optional) if os.path.exists(output_onnx_path): print(f"Successfully generated {output_onnx_path}") loaded_model = onnx.load(output_onnx_path) print(f"Nodes in renamed model: {[node.name for node in loaded_model.graph.node]}") else: print("Error: Output model not found.") # Clean up dummy files os.remove(input_onnx_path) os.remove(output_onnx_path)