run_qdq_debug.py

importargparse
importonnx
fromonnxruntime.quantization.qdq_loss_debugimport (
collect_activations, compute_activation_error, compute_weight_error,
create_activation_matching, create_weight_matching,
modify_model_output_intermediate_tensors)

importresnet50_data_reader


defget_args():
parser=argparse.ArgumentParser()
parser.add_argument(
"--float_model", required=True, help="Path to original floating point model"
 )
parser.add_argument("--qdq_model", required=True, help="Path to qdq model")
parser.add_argument(
"--calibrate_dataset", default="./test_images", help="calibration data set"
 )
args=parser.parse_args()
returnargs


def_generate_aug_model_path(model_path: str) ->str:
aug_model_path= (
model_path[: -len(".onnx")] ifmodel_path.endswith(".onnx") elsemodel_path
 )
returnaug_model_path+".save_tensors.onnx"


defmain():
# Process input parameters and setup model input data reader
args=get_args()
float_model_path=args.float_model
qdq_model_path=args.qdq_model
calibration_dataset_path=args.calibrate_dataset

print("------------------------------------------------\n")
print("Comparing weights of float model vs qdq model.....")

matched_weights=create_weight_matching(float_model_path, qdq_model_path)
weights_error=compute_weight_error(matched_weights)
forweight_name, errinweights_error.items():
print(f"Cross model error of '{weight_name}': {err}\n")

print("------------------------------------------------\n")
print("Augmenting models to save intermediate activations......")

aug_float_model_path=_generate_aug_model_path(float_model_path)
modify_model_output_intermediate_tensors(float_model_path, aug_float_model_path)

aug_qdq_model_path=_generate_aug_model_path(qdq_model_path)
modify_model_output_intermediate_tensors(qdq_model_path, aug_qdq_model_path)

print("------------------------------------------------\n")
print("Running the augmented floating point model to collect activations......")
input_data_reader=resnet50_data_reader.ResNet50DataReader(
calibration_dataset_path, float_model_path
 )
float_activations=collect_activations(aug_float_model_path, input_data_reader)

print("------------------------------------------------\n")
print("Running the augmented qdq model to collect activations......")
input_data_reader.rewind()
qdq_activations=collect_activations(aug_qdq_model_path, input_data_reader)

print("------------------------------------------------\n")
print("Comparing activations of float model vs qdq model......")

act_matching=create_activation_matching(qdq_activations, float_activations)
act_error=compute_activation_error(act_matching)
foract_name, errinact_error.items():
print(f"Cross model error of '{act_name}': {err['xmodel_err']}\n")
print(f"QDQ error of '{act_name}': {err['qdq_err']}\n")


if__name__=="__main__":
main()