onnxoptimizer_load_model_apply_optimization_passes.py

1import onnx
2import onnxoptimizer
3
4# Load the ONNX model
5model_path = 'model.onnx'
6onnx_model = onnx.load(model_path)
7
8# List available optimization passes (optional)
9# passes = onnxoptimizer.get_available_passes()
10# print(f"Available passes: {passes}")
11
12# Define the passes you want to apply
13# If no list is provided, it applies a default set of passes
14passes = ['fuse_add_bias_into_conv', 'fuse_bn_into_conv']
15
16# Optimize the model
17optimized_model = onnxoptimizer.optimize(onnx_model, passes)
18
19# Save the optimized model
20onnx.save(optimized_model, 'optimized_model.onnx')