refactor(ml): model sessions (#10559)

machine-learning/app/models/ann.py

@@ -1,68 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any, NamedTuple
-
-import numpy as np
-from numpy.typing import NDArray
-
-from ann.ann import Ann
-
-from ..config import log, settings
-
-
-class AnnSession:
-    """
-    Wrapper for ANN to be drop-in replacement for ONNX session.
-    """
-
-    def __init__(self, model_path: Path):
-        tuning_file = Path(settings.cache_folder) / "gpu-tuning.ann"
-        with tuning_file.open(mode="a"):
-            # make sure tuning file exists (without clearing contents)
-            # once filled, the tuning file reduces the cost/time of the first
-            # inference after model load by 10s of seconds
-            pass
-        self.ann = Ann(tuning_level=3, tuning_file=tuning_file.as_posix())
-        log.info("Loading ANN model %s ...", model_path)
-        cache_file = model_path.with_suffix(".anncache")
-        save = False
-        if not cache_file.is_file():
-            save = True
-            with cache_file.open(mode="a"):
-                # create empty model cache file
-                pass
-
-        self.model = self.ann.load(
-            model_path.as_posix(),
-            save_cached_network=save,
-            cached_network_path=cache_file.as_posix(),
-        )
-        log.info("Loaded ANN model with ID %d", self.model)
-
-    def __del__(self) -> None:
-        self.ann.unload(self.model)
-        log.info("Unloaded ANN model %d", self.model)
-        self.ann.destroy()
-
-    def get_inputs(self) -> list[AnnNode]:
-        shapes = self.ann.input_shapes[self.model]
-        return [AnnNode(None, s) for s in shapes]
-
-    def get_outputs(self) -> list[AnnNode]:
-        shapes = self.ann.output_shapes[self.model]
-        return [AnnNode(None, s) for s in shapes]
-
-    def run(
-        self,
-        output_names: list[str] | None,
-        input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
-        run_options: Any = None,
-    ) -> list[NDArray[np.float32]]:
-        inputs: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
-        return self.ann.execute(self.model, inputs)
-
-
-class AnnNode(NamedTuple):
-    name: str | None
-    shape: tuple[int, ...]