# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
"""Class definition for native model exporter used in OTX."""
from __future__ import annotations
import logging as log
import tempfile
from pathlib import Path
from typing import TYPE_CHECKING, Any, Literal
import onnx
import openvino
import torch
from otx.core.exporter.base import OTXModelExporter
from otx.core.types.export import TaskLevelExportParameters
from otx.core.types.precision import OTXPrecisionType
if TYPE_CHECKING:
from otx.core.model.base import OTXModel
[docs]
class OTXNativeModelExporter(OTXModelExporter):
"""Exporter that uses native torch and OpenVINO conversion tools."""
def __init__(
self,
task_level_export_parameters: TaskLevelExportParameters,
input_size: tuple[int, ...],
mean: tuple[float, float, float] = (0.0, 0.0, 0.0),
std: tuple[float, float, float] = (1.0, 1.0, 1.0),
resize_mode: Literal["crop", "standard", "fit_to_window", "fit_to_window_letterbox"] = "standard",
pad_value: int = 0,
swap_rgb: bool = False,
via_onnx: bool = False,
onnx_export_configuration: dict[str, Any] | None = None,
output_names: list[str] | None = None,
) -> None:
super().__init__(
task_level_export_parameters=task_level_export_parameters,
input_size=input_size,
mean=mean,
std=std,
resize_mode=resize_mode,
pad_value=pad_value,
swap_rgb=swap_rgb,
output_names=output_names,
)
self.via_onnx = via_onnx
self.onnx_export_configuration = onnx_export_configuration if onnx_export_configuration is not None else {}
if output_names is not None:
self.onnx_export_configuration.update({"output_names": output_names})
[docs]
def to_openvino(
self,
model: OTXModel,
output_dir: Path,
base_model_name: str = "exported_model",
precision: OTXPrecisionType = OTXPrecisionType.FP32,
) -> Path:
"""Export to OpenVINO Intermediate Representation format.
In this implementation the export is done only via standard OV/ONNX tools.
"""
dummy_tensor = torch.rand(self.input_size).to(next(model.parameters()).device)
if self.via_onnx:
with tempfile.TemporaryDirectory() as tmpdirname:
tmp_dir = Path(tmpdirname)
self.to_onnx(
model,
tmp_dir,
base_model_name,
OTXPrecisionType.FP32,
False,
)
exported_model = openvino.convert_model(
tmp_dir / (base_model_name + ".onnx"),
input=(openvino.runtime.PartialShape(self.input_size),),
)
else:
exported_model = openvino.convert_model(
model,
example_input=dummy_tensor,
input=(openvino.runtime.PartialShape(self.input_size),),
)
exported_model = self._postprocess_openvino_model(exported_model)
save_path = output_dir / (base_model_name + ".xml")
openvino.save_model(exported_model, save_path, compress_to_fp16=(precision == OTXPrecisionType.FP16))
log.info("Converting to OpenVINO is done.")
return Path(save_path)
[docs]
def to_onnx(
self,
model: OTXModel,
output_dir: Path,
base_model_name: str = "exported_model",
precision: OTXPrecisionType = OTXPrecisionType.FP32,
embed_metadata: bool = True,
) -> Path:
"""Export the given PyTorch model to ONNX format and save it to the specified output directory.
Args:
model (OTXModel): The PyTorch model to be exported.
output_dir (Path): The directory where the ONNX model will be saved.
base_model_name (str, optional): The base name for the exported model. Defaults to "exported_model".
precision (OTXPrecisionType, optional): The precision type for the exported model.
Defaults to OTXPrecisionType.FP32.
embed_metadata (bool, optional): Whether to embed metadata in the ONNX model. Defaults to True.
Returns:
Path: The path to the saved ONNX model.
"""
dummy_tensor = torch.rand(self.input_size).to(next(model.parameters()).device)
save_path = str(output_dir / (base_model_name + ".onnx"))
torch.onnx.export(model, dummy_tensor, save_path, **self.onnx_export_configuration)
onnx_model = onnx.load(save_path)
onnx_model = self._postprocess_onnx_model(onnx_model, embed_metadata, precision)
onnx.save(onnx_model, save_path)
log.info("Converting to ONNX is done.")
return Path(save_path)
