format code

speechx/examples/ds2_ol/onnx/local/onnx_convert_opset.py

 #!/usr/bin/env python3
 import argparse
-import onnx
-from onnx import version_converter, helper
 
+import onnx
+from onnx import version_converter
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(prog=__doc__)
-    parser.add_argument("--model-file", type=str, required=True, help='path/to/the/model.onnx.')
-    parser.add_argument("--save-model", type=str, required=True, help='path/to/saved/model.onnx.')
+    parser.add_argument(
+        "--model-file", type=str, required=True, help='path/to/the/model.onnx.')
+    parser.add_argument(
+        "--save-model",
+        type=str,
+        required=True,
+        help='path/to/saved/model.onnx.')
     # Models must be opset10 or higher to be quantized.
-    parser.add_argument("--target-opset", type=int, default=11, help='path/to/the/model.onnx.')
+    parser.add_argument(
+        "--target-opset", type=int, default=11, help='path/to/the/model.onnx.')
 
     args = parser.parse_args()
 
@@ -24,7 +30,8 @@ if __name__ == '__main__':
     # A full list of supported adapters can be found here:
     # https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21
     # Apply the version conversion on the original model
-    converted_model = version_converter.convert_version(original_model, args.target_opset)
+    converted_model = version_converter.convert_version(original_model,
+                                                        args.target_opset)
 
     # print('The model after conversion:\n{}'.format(converted_model))
     onnx.save(converted_model, args.save_model)

speechx/examples/ds2_ol/onnx/local/onnx_infer_shape.py

@@ -494,6 +494,8 @@ class SymbolicShapeInference:
             # contrib ops
 
 
+
+
             'Attention', 'BiasGelu', \
             'EmbedLayerNormalization', \
             'FastGelu', 'Gelu', 'LayerNormalization', \

speechx/examples/ds2_ol/onnx/local/ort_dyanmic_quant.py

 #!/usr/bin/env python3
 import argparse
-import onnx
-from onnxruntime.quantization import quantize_dynamic, QuantType
 
-def quantize_onnx_model(onnx_model_path, quantized_model_path, nodes_to_exclude=[]):
+from onnxruntime.quantization import quantize_dynamic
+from onnxruntime.quantization import QuantType
+
+
+def quantize_onnx_model(onnx_model_path,
+                        quantized_model_path,
+                        nodes_to_exclude=[]):
     print("Starting quantization...")
-    from onnxruntime.quantization import QuantType, quantize_dynamic
 
-    quantize_dynamic(onnx_model_path, quantized_model_path, weight_type=QuantType.QInt8, nodes_to_exclude=nodes_to_exclude)
+    quantize_dynamic(
+        onnx_model_path,
+        quantized_model_path,
+        weight_type=QuantType.QInt8,
+        nodes_to_exclude=nodes_to_exclude)
 
     print(f"Quantized model saved to: {quantized_model_path}")
 
@@ -18,26 +25,24 @@ def main():
         "--model-in",
         type=str,
         required=True,
-        help="ONNX model",
-    )
+        help="ONNX model", )
     parser.add_argument(
         "--model-out",
         type=str,
         required=True,
         default='model.quant.onnx',
-        help="ONNX model",
-    )
+        help="ONNX model", )
     parser.add_argument(
         "--nodes-to-exclude",
         type=str,
         required=True,
-        help="nodes to exclude. e.g. conv,linear.",
-    )
+        help="nodes to exclude. e.g. conv,linear.", )
 
     args = parser.parse_args()
 
     nodes_to_exclude = args.nodes_to_exclude.split(',')
     quantize_onnx_model(args.model_in, args.model_out, nodes_to_exclude)
 
+
 if __name__ == "__main__":
     main()