Add ONNXModifier for optimising the ONNX model before converting for RVC4 execution

.github/workflows/unittests.yaml

@@ -33,5 +33,6 @@ jobs:
         AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
         AWS_S3_ENDPOINT_URL: ${{ secrets.AWS_S3_ENDPOINT_URL }}
         GOOGLE_APPLICATION_CREDENTIALS: ${{ secrets.GCP_CREDENTIALS }}
+        HUB_AI_API_KEY: ${{ secrets.HUB_AI_API_KEY }}
       run: python -m pytest tests/test_utils
 

modelconverter/cli/utils.py

@@ -376,6 +376,7 @@ def get_target_specific_options(
     json_cfg = cfg.model_dump(mode="json")
     options = {
         "disable_onnx_simplification": cfg.disable_onnx_simplification,
+        "disable_onnx_optimisation": cfg.disable_onnx_optimisation,
         "inputs": json_cfg["inputs"],
     }
     if target == "rvc4":

modelconverter/packages/base_exporter.py

@@ -39,6 +39,7 @@ def __init__(
         self.outputs = {out.name: out for out in config.outputs}
         self.keep_intermediate_outputs = config.keep_intermediate_outputs
         self.disable_onnx_simplification = config.disable_onnx_simplification
+        self.disable_onnx_optimisation = config.disable_onnx_optimisation
 
         self.model_name = self.input_model.stem
 

modelconverter/packages/rvc4/exporter.py

@@ -1,3 +1,4 @@
+import os
 import shutil
 import subprocess
 import time
@@ -6,6 +7,7 @@
 from typing import Any, Dict, List, NamedTuple, Optional, cast
 
 from modelconverter.utils import (
+    ONNXModifier,
     exit_with,
     onnx_attach_normalization_to_inputs,
     read_image,
@@ -57,6 +59,24 @@ def __init__(self, config: SingleStageConfig, output_dir: Path):
                 self._attach_suffix(self.input_model, "modified.onnx"),
                 self.inputs,
             )
+
+            if not config.disable_onnx_optimisation:
+                onnx_modifier = ONNXModifier(
+                    model_path=self.input_model,
+                    output_path=self._attach_suffix(
+                        self.input_model, "modified_optimised.onnx"
+                    ),
+                )
+
+                if (
+                    onnx_modifier.modify_onnx()
+                    and onnx_modifier.compare_outputs()
+                ):
+                    logger.info("ONNX model has been optimised for RVC4.")
+                    shutil.move(onnx_modifier.output_path, self.input_model)
+                else:
+                    if os.path.exists(onnx_modifier.output_path):
+                        os.remove(onnx_modifier.output_path)
         else:
             logger.warning(
                 "Input file type is not ONNX. Skipping pre-processing."

modelconverter/utils/__init__.py

@@ -28,7 +28,7 @@
     modelconverter_config_to_nn,
     process_nn_archive,
 )
-from .onnx_tools import onnx_attach_normalization_to_inputs
+from .onnx_tools import ONNXModifier, onnx_attach_normalization_to_inputs
 from .subprocess import subprocess_run
 
 __all__ = [
@@ -37,6 +37,7 @@
     "S3Exception",
     "SubprocessException",
     "exit_with",
+    "ONNXModifier",
     "onnx_attach_normalization_to_inputs",
     "read_calib_dir",
     "read_image",

modelconverter/utils/config.py

@@ -284,6 +284,7 @@ class SingleStageConfig(CustomBaseModel):
 
     keep_intermediate_outputs: bool = True
     disable_onnx_simplification: bool = False
+    disable_onnx_optimisation: bool = False
     output_remote_url: Optional[str] = None
     put_file_plugin: Optional[str] = None