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"""Mixin for exporting models to disk."""
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import json
import logging
from collections.abc import Callable
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import TYPE_CHECKING, Any
import numpy as np
import torch
from torch import nn
from torchvision.transforms.v2 import Transform
from anomalib import TaskType
from anomalib.deploy.export import ExportType, InferenceModel
from anomalib.utils.exceptions import try_import
if TYPE_CHECKING:
from torch.types import Number
logger = logging.getLogger(__name__)
class ExportMixin:
"""This mixin allows exporting models to torch and ONNX/OpenVINO."""
model: nn.Module
transform: Transform
configure_transforms: Callable
device: torch.device
def to_torch(
self,
export_root: Path | str,
transform: Transform | None = None,
task: TaskType | None = None,
) -> Path:
"""Export AnomalibModel to torch.
Args:
export_root (Path): Path to the output folder.
transform (Transform, optional): Input transforms used for the model. If not provided, the transform is
taken from the model.
Defaults to ``None``.
task (TaskType | None): Task type.
Defaults to ``None``.
Returns:
Path: Path to the exported pytorch model.
Examples:
Assume that we have a model to train and we want to export it to torch format.
>>> from anomalib.data import Visa
>>> from anomalib.models import Patchcore
>>> from anomalib.engine import Engine
...
>>> datamodule = Visa()
>>> model = Patchcore()
>>> engine = Engine()
...
>>> engine.fit(model, datamodule)
Now that we have a model trained, we can export it to torch format.
>>> model.to_torch(
... export_root="path/to/export",
... transform=datamodule.test_data.transform,
... task=datamodule.test_data.task,
... )
"""
transform = transform or self.transform or self.configure_transforms()
inference_model = InferenceModel(model=self.model, transform=transform)
export_root = _create_export_root(export_root, ExportType.TORCH)
metadata = self._get_metadata(task=task)
pt_model_path = export_root / "model.pt"
torch.save(
obj={"model": inference_model, "metadata": metadata},
f=pt_model_path,
)
return pt_model_path
def to_onnx(
self,
export_root: Path | str,
input_size: tuple[int, int] | None = None,
transform: Transform | None = None,
task: TaskType | None = None,
) -> Path:
"""Export model to onnx.
Args:
export_root (Path): Path to the root folder of the exported model.
input_size (tuple[int, int] | None, optional): Image size used as the input for onnx converter.
Defaults to None.
transform (Transform, optional): Input transforms used for the model. If not provided, the transform is
taken from the model.
Defaults to ``None``.
task (TaskType | None): Task type.
Defaults to ``None``.
Returns:
Path: Path to the exported onnx model.
Examples:
Export the Lightning Model to ONNX:
>>> from anomalib.models import Patchcore
>>> from anomalib.data import Visa
...
>>> datamodule = Visa()
>>> model = Patchcore()
...
>>> model.to_onnx(
... export_root="path/to/export",
... transform=datamodule.test_data.transform,
... task=datamodule.test_data.task
... )
Using Custom Transforms:
This example shows how to use a custom ``Compose`` object for the ``transform`` argument.
>>> model.to_onnx(
... export_root="path/to/export",
... task="segmentation",
... )
"""
transform = transform or self.transform or self.configure_transforms()
inference_model = InferenceModel(model=self.model, transform=transform, disable_antialias=True)
export_root = _create_export_root(export_root, ExportType.ONNX)
input_shape = torch.zeros((1, 3, *input_size)) if input_size else torch.zeros((1, 3, 1, 1))
dynamic_axes = (
None if input_size else {"input": {0: "batch_size", 2: "height", 3: "weight"}, "output": {0: "batch_size"}}
)
_write_metadata_to_json(self._get_metadata(task), export_root)
onnx_path = export_root / "model.onnx"
torch.onnx.export(
inference_model,
input_shape.to(self.device),
str(onnx_path),
opset_version=14,
dynamic_axes=dynamic_axes,
input_names=["input"],
output_names=["output"],
)
return onnx_path
def to_openvino(
self,
export_root: Path | str,
input_size: tuple[int, int] | None = None,
transform: Transform | None = None,
ov_args: dict[str, Any] | None = None,
task: TaskType | None = None,
) -> Path:
"""Convert onnx model to OpenVINO IR.
Args:
export_root (Path): Path to the export folder.
input_size (tuple[int, int] | None, optional): Input size of the model. Used for adding metadata to the IR.
Defaults to None.
transform (Transform, optional): Input transforms used for the model. If not provided, the transform is
taken from the model.
Defaults to ``None``.
ov_args: Model optimizer arguments for OpenVINO model conversion.
Defaults to ``None``.
task (TaskType | None): Task type.
Defaults to ``None``.
Returns:
Path: Path to the exported onnx model.
Raises:
ModuleNotFoundError: If OpenVINO is not installed.
Returns:
Path: Path to the exported OpenVINO IR.
Examples:
Export the Lightning Model to OpenVINO IR:
This example demonstrates how to export the Lightning Model to OpenVINO IR.
>>> from anomalib.models import Patchcore
>>> from anomalib.data import Visa
...
>>> datamodule = Visa()
>>> model = Patchcore()
...
>>> model.to_openvino(
... export_root="path/to/export",
... transform=datamodule.test_data.transform,
... task=datamodule.test_data.task
... )
Using Custom Transforms:
This example shows how to use a custom ``Transform`` object for the ``transform`` argument.
>>> from torchvision.transforms.v2 import Resize
>>> transform = Resize(224, 224)
...
>>> model.to_openvino(
... export_root="path/to/export",
... transform=transform,
... task="segmentation",
... )
"""
if not try_import("openvino"):
logger.exception("Could not find OpenVINO. Please check OpenVINO installation.")
raise ModuleNotFoundError
import openvino as ov
with TemporaryDirectory() as onnx_directory:
model_path = self.to_onnx(onnx_directory, input_size, transform, task)
export_root = _create_export_root(export_root, ExportType.OPENVINO)
ov_model_path = export_root / "model.xml"
ov_args = {} if ov_args is None else ov_args
# fp16 compression is enabled by default
compress_to_fp16 = ov_args.get("compress_to_fp16", True)
model = ov.convert_model(model_path, **ov_args)
ov.save_model(model, ov_model_path, compress_to_fp16=compress_to_fp16)
_write_metadata_to_json(self._get_metadata(task), export_root)
return ov_model_path
def _get_metadata(
self,
task: TaskType | None = None,
) -> dict[str, Any]:
"""Get metadata for the exported model.
Args:
task (TaskType | None): Task type.
Defaults to None.
Returns:
dict[str, Any]: Metadata for the exported model.
"""
model_metadata = {}
cached_metadata: dict[str, Number | torch.Tensor] = {}
for threshold_name in ("image_threshold", "pixel_threshold"):
if hasattr(self, threshold_name):
cached_metadata[threshold_name] = getattr(self, threshold_name).cpu().value.item()
if hasattr(self, "normalization_metrics") and self.normalization_metrics.state_dict() is not None:
for key, value in self.normalization_metrics.state_dict().items():
cached_metadata[key] = value.cpu()
# Remove undefined values by copying in a new dict
for key, val in cached_metadata.items():
if not np.isinf(val).all():
model_metadata[key] = val
del cached_metadata
metadata = {"task": task, **model_metadata}
# Convert torch tensors to python lists or values for json serialization.
for key, value in metadata.items():
if isinstance(value, torch.Tensor):
metadata[key] = value.numpy().tolist()
return metadata
def _write_metadata_to_json(metadata: dict[str, Any], export_root: Path) -> None:
"""Write metadata to json file.
Args:
metadata (dict[str, Any]): Metadata to export.
export_root (Path): Path to the exported model.
"""
with (export_root / "metadata.json").open("w", encoding="utf-8") as metadata_file:
json.dump(metadata, metadata_file, ensure_ascii=False, indent=4)
def _create_export_root(export_root: str | Path, export_type: ExportType) -> Path:
"""Create export directory.
Args:
export_root (str | Path): Path to the root folder of the exported model.
export_type (ExportType): Mode to export the model. Torch, ONNX or OpenVINO.
Returns:
Path: Path to the export directory.
"""
export_root = Path(export_root) / "weights" / export_type.value
export_root.mkdir(parents=True, exist_ok=True)
return export_root
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