onnxslim

import onnx from onnxslim.slim import slim import os # Create a minimal dummy ONNX model for demonstration # In a real-world scenario, you would load your existing model: # input_model_path = "path/to/your/model.onnx" # If you don't have one, this creates a simple Add operation model: from onnx.helper import make_model, make_node, make_graph, make_tensor_value_info from onnx import TensorProto input_name = 'input' output_name = 'output' input_tensor = make_tensor_value_info(input_name, TensorProto.FLOAT, [1, 2, 3]) output_tensor = make_tensor_value_info(output_name, TensorProto.FLOAT, [1, 2, 3]) node = make_node('Add', [input_name, input_name], [output_name]) # example: output = input + input graph = make_graph([node], 'simple_add_graph', [input_tensor], [output_tensor]) dummy_model = make_model(graph, opset_imports=[onnx.helper.make_opsetid("", 13)]) # Opset 13 is common input_model_path = "dummy_model_to_slim.onnx" output_model_path = "dummy_model_slimmed.onnx" onnx.save(dummy_model, input_model_path) print(f"Dummy ONNX model created at {input_model_path}") try: # Perform the slimming slimmed_model_proto = slim(input_model_path) # The 'slim' function returns an ONNX model proto object. # To save it, use onnx.save: onnx.save(slimmed_model_proto, output_model_path) print(f"Model successfully slimmed and saved to {output_model_path}") # Optional: Load and verify the slimmed model # slimmed_model_loaded = onnx.load(output_model_path) # print(f"Loaded slimmed model with graph name: {slimmed_model_loaded.graph.name}") except Exception as e: print(f"An error occurred during slimming: {e}") finally: # Clean up dummy files if os.path.exists(input_model_path): os.remove(input_model_path) if os.path.exists(output_model_path): os.remove(output_model_path) print("Cleaned up dummy ONNX files.")