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Custom export model name #2458

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Custom export model name #2458

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caffff0
custom model name
alexriedel1 Dec 6, 2024
6636931
revert vis changes
alexriedel1 Dec 6, 2024
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2 changes: 1 addition & 1 deletion src/anomalib/data/utils/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -440,7 +440,7 @@ def save_image(filename: Path | str, image: np.ndarray | Figure, root: Path | No
# if file_path is absolute, then root is ignored
# so we remove the top level directory from the path
if file_path.is_absolute() and root:
file_path = Path(*file_path.parts[2:]) # OS-AGNOSTIC
file_path = Path(*file_path.parts[-2:]) # OS-AGNOSTIC
if root:
file_path = root / file_path

Expand Down
6 changes: 6 additions & 0 deletions src/anomalib/engine/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -876,6 +876,7 @@ def export(
model: AnomalyModule,
export_type: ExportType | str,
export_root: str | Path | None = None,
model_file_name: str = "model",
input_size: tuple[int, int] | None = None,
transform: Transform | None = None,
compression_type: CompressionType | None = None,
Expand All @@ -891,6 +892,8 @@ def export(
export_type (ExportType): Export type.
export_root (str | Path | None, optional): Path to the output directory. If it is not set, the model is
exported to trainer.default_root_dir. Defaults to None.
model_file_name (str = "model"): Name of the exported model file. If it is not set, the model is
is called "model". Defaults to "model".
input_size (tuple[int, int] | None, optional): A statis input shape for the model, which is exported to ONNX
and OpenVINO format. Defaults to None.
transform (Transform | None, optional): Input transform to include in the exported model. If not provided,
Expand Down Expand Up @@ -951,19 +954,22 @@ def export(
if export_type == ExportType.TORCH:
exported_model_path = model.to_torch(
export_root=export_root,
model_file_name=model_file_name,
transform=transform,
task=self.task,
)
elif export_type == ExportType.ONNX:
exported_model_path = model.to_onnx(
export_root=export_root,
model_file_name=model_file_name,
input_size=input_size,
transform=transform,
task=self.task,
)
elif export_type == ExportType.OPENVINO:
exported_model_path = model.to_openvino(
export_root=export_root,
model_file_name=model_file_name,
input_size=input_size,
transform=transform,
task=self.task,
Expand Down
14 changes: 10 additions & 4 deletions src/anomalib/models/components/base/export_mixin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,13 +44,15 @@ class ExportMixin:
def to_torch(
self,
export_root: Path | str,
model_file_name: str,
transform: Transform | None = None,
task: TaskType | None = None,
) -> Path:
"""Export AnomalibModel to torch.

Args:
export_root (Path): Path to the output folder.
model_file_name (str): Name of the exported model
transform (Transform, optional): Input transforms used for the model. If not provided, the transform is
taken from the model.
Defaults to ``None``.
Expand Down Expand Up @@ -85,7 +87,7 @@ def to_torch(
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"
pt_model_path = export_root / (model_file_name + ".pt")
torch.save(
obj={"model": inference_model, "metadata": metadata},
f=pt_model_path,
Expand All @@ -95,6 +97,7 @@ def to_torch(
def to_onnx(
self,
export_root: Path | str,
model_file_name: str,
input_size: tuple[int, int] | None = None,
transform: Transform | None = None,
task: TaskType | None = None,
Expand All @@ -103,6 +106,7 @@ def to_onnx(

Args:
export_root (Path): Path to the root folder of the exported model.
model_file_name (str): Name 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
Expand Down Expand Up @@ -147,7 +151,7 @@ def to_onnx(
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"
onnx_path = export_root / (model_file_name + ".onnx")
torch.onnx.export(
inference_model,
input_shape.to(self.device),
Expand All @@ -163,6 +167,7 @@ def to_onnx(
def to_openvino(
self,
export_root: Path | str,
model_file_name: str,
input_size: tuple[int, int] | None = None,
transform: Transform | None = None,
compression_type: CompressionType | None = None,
Expand All @@ -175,6 +180,7 @@ def to_openvino(

Args:
export_root (Path): Path to the export folder.
model_file_name (str): Name of the exported model
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
Expand Down Expand Up @@ -252,9 +258,9 @@ def to_openvino(
import openvino as ov

with TemporaryDirectory() as onnx_directory:
model_path = self.to_onnx(onnx_directory, input_size, transform, task)
model_path = self.to_onnx(onnx_directory, model_file_name, input_size, transform, task)
export_root = _create_export_root(export_root, ExportType.OPENVINO)
ov_model_path = export_root / "model.xml"
ov_model_path = export_root / (model_file_name + ".xml")
ov_args = {} if ov_args is None else ov_args

model = ov.convert_model(model_path, **ov_args)
Expand Down
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