[AZ-180] Refactor inference and engine factory for improved model handling

- Updated the autopilot state to reflect the current task status as in progress.
- Refactored the inference module to streamline model downloading and conversion processes, replacing the download_model method with a more efficient load_source method.
- Introduced asynchronous model building in the inference module to enhance performance during model conversion.
- Enhanced the engine factory to include a new method for building and caching models, improving error handling and logging during the upload process.
- Added calibration cache handling in the Jetson TensorRT engine for better resource management.

Made-with: Cursor

src/engines/engine_factory.pyx

@@ -1,5 +1,3 @@
-import os
-import tempfile
 from loader_http_client cimport LoaderHttpClient, LoadResult
 
 
@@ -29,9 +27,28 @@ class EngineFactory:
     def get_source_filename(self):
         return None
 
+    def load_source(self, LoaderHttpClient loader_client, str models_dir):
+        cdef LoadResult res
+        filename = self.get_source_filename()
+        if filename is None:
+            return None
+        res = loader_client.load_big_small_resource(filename, models_dir)
+        if res.err is not None:
+            raise Exception(res.err)
+        return res.data
+
     def build_from_source(self, onnx_bytes, loader_client, models_dir):
         raise NotImplementedError(f"{type(self).__name__} does not support building from source")
 
+    def build_and_cache(self, bytes source_bytes, LoaderHttpClient loader_client, str models_dir):
+        cdef LoadResult res
+        import constants_inf
+        engine_bytes, engine_filename = self.build_from_source(source_bytes, loader_client, models_dir)
+        res = loader_client.upload_big_small_resource(engine_bytes, engine_filename, models_dir)
+        if res.err is not None:
+            constants_inf.log(f"Failed to upload converted model: {res.err}")
+        return engine_bytes
+
 
 class OnnxEngineFactory(EngineFactory):
     def create(self, model_bytes: bytes):
@@ -83,34 +100,7 @@ class JetsonTensorRTEngineFactory(TensorRTEngineFactory):
         return TensorRTEngine.get_engine_filename("int8")
 
     def build_from_source(self, onnx_bytes, LoaderHttpClient loader_client, str models_dir):
-        cdef str calib_cache_path
+        from engines.jetson_tensorrt_engine import JetsonTensorRTEngine
         from engines.tensorrt_engine import TensorRTEngine
-        calib_cache_path = self._download_calib_cache(loader_client, models_dir)
-        try:
-            engine_bytes = TensorRTEngine.convert_from_source(onnx_bytes, calib_cache_path)
-            return engine_bytes, TensorRTEngine.get_engine_filename("int8")
-        finally:
-            if calib_cache_path is not None:
-                try:
-                    os.unlink(calib_cache_path)
-                except Exception:
-                    pass
-
-    def _download_calib_cache(self, LoaderHttpClient loader_client, str models_dir):
-        cdef LoadResult res
-        import constants_inf
-        try:
-            res = loader_client.load_big_small_resource(
-                constants_inf.INT8_CALIB_CACHE_FILE, models_dir
-            )
-            if res.err is not None:
-                constants_inf.log(f"INT8 calibration cache not available: {res.err}")
-                return None
-            fd, path = tempfile.mkstemp(suffix=".cache")
-            with os.fdopen(fd, "wb") as f:
-                f.write(res.data)
-            constants_inf.log("INT8 calibration cache downloaded")
-            return path
-        except Exception as e:
-            constants_inf.log(f"INT8 calibration cache download failed: {str(e)}")
-            return None
+        engine_bytes = JetsonTensorRTEngine.convert_from_source(onnx_bytes, loader_client, models_dir)
+        return engine_bytes, TensorRTEngine.get_engine_filename("int8")