export

export_model(config, model, export_folder, half_precision, input_shapes=None, idx_to_class=None, pytorch_model_type='model')

Generate deployment models for the task.

Parameters:

• config (DictConfig) –

  Experiment config

• model (Any) –

  Model to be exported

• export_folder (str) –

  Path to save the exported model

• half_precision (bool) –

  Whether to use half precision for the exported model

• input_shapes (Optional[List[Any]], default: None ) –

  Input shapes for the exported model

• idx_to_class (Optional[Dict[int, str]], default: None ) –

  Mapping from class index to class name

• pytorch_model_type (Literal['backbone', 'model'], default: 'model' ) –

  Type of the pytorch model config to be exported, if it's backbone on disk we will save the config.backbone config, otherwise we will save the config.model

Returns:

• Dict[str, Any] –

  If the model is exported successfully, return a dictionary containing information about the exported model and

• Dict[str, str] –

  a second dictionary containing the paths to the exported models. Otherwise, return two empty dictionaries.

Source code in quadra/utils/export.py

def export_model(
    config: DictConfig,
    model: Any,
    export_folder: str,
    half_precision: bool,
    input_shapes: Optional[List[Any]] = None,
    idx_to_class: Optional[Dict[int, str]] = None,
    pytorch_model_type: Literal["backbone", "model"] = "model",
) -> Tuple[Dict[str, Any], Dict[str, str]]:
    """Generate deployment models for the task.

    Args:
        config: Experiment config
        model: Model to be exported
        export_folder: Path to save the exported model
        half_precision: Whether to use half precision for the exported model
        input_shapes: Input shapes for the exported model
        idx_to_class: Mapping from class index to class name
        pytorch_model_type: Type of the pytorch model config to be exported, if it's backbone on disk we will save the
            config.backbone config, otherwise we will save the config.model

    Returns:
        If the model is exported successfully, return a dictionary containing information about the exported model and
        a second dictionary containing the paths to the exported models. Otherwise, return two empty dictionaries.
    """
    if config.export is None or len(config.export.types) == 0:
        log.info("No export type specified skipping export")
        return {}, {}

    os.makedirs(export_folder, exist_ok=True)

    if input_shapes is None:
        # Try to get input shapes from config
        # If this is also None we will try to retrieve it from the ModelSignatureWrapper, if it fails we can't export
        input_shapes = config.export.input_shapes

    export_paths = {}

    for export_type in config.export.types:
        if export_type == "torchscript":
            out = export_torchscript_model(
                model=model,
                input_shapes=input_shapes,
                output_path=export_folder,
                half_precision=half_precision,
            )

            if out is None:
                log.warning("Torchscript export failed, enable debug logging for more details")
                continue

            export_path, input_shapes = out
            export_paths[export_type] = export_path
        elif export_type == "pytorch":
            export_path = export_pytorch_model(
                model=model,
                output_path=export_folder,
            )
            export_paths[export_type] = export_path
            with open(os.path.join(export_folder, "model_config.yaml"), "w") as f:
                OmegaConf.save(getattr(config, pytorch_model_type), f, resolve=True)
        elif export_type == "onnx":
            if not hasattr(config.export, "onnx"):
                log.warning("No onnx configuration found, skipping onnx export")
                continue

            out = export_onnx_model(
                model=model,
                output_path=export_folder,
                onnx_config=config.export.onnx,
                input_shapes=input_shapes,
                half_precision=half_precision,
            )

            if out is None:
                log.warning("ONNX export failed, enable debug logging for more details")
                continue

            export_path, input_shapes = out
            export_paths[export_type] = export_path
        else:
            log.warning("Export type: %s not implemented", export_type)

    if len(export_paths) == 0:
        log.warning("No export type was successful, no model will be available for deployment")
        return {}, export_paths

    model_json = {
        "input_size": input_shapes,
        "classes": idx_to_class,
        "mean": list(config.transforms.mean),
        "std": list(config.transforms.std),
    }

    return model_json, export_paths

export_onnx_model(model, output_path, onnx_config, input_shapes=None, half_precision=False, model_name='model.onnx')

Export a PyTorch model with ONNX.

Parameters:

• model (Module) –

  PyTorch model to be exported

• output_path (str) –

  Path to save the model

• input_shapes (Optional[List[Any]], default: None ) –

  Input shapes for tracing

• onnx_config (DictConfig) –

  ONNX export configuration

• half_precision (bool, default: False ) –

  If True, the model will be exported with half precision

• model_name (str, default: 'model.onnx' ) –

  Name of the exported model

Source code in quadra/utils/export.py

@torch.inference_mode()
def export_onnx_model(
    model: nn.Module,
    output_path: str,
    onnx_config: DictConfig,
    input_shapes: Optional[List[Any]] = None,
    half_precision: bool = False,
    model_name: str = "model.onnx",
) -> Optional[Tuple[str, Any]]:
    """Export a PyTorch model with ONNX.

    Args:
        model: PyTorch model to be exported
        output_path: Path to save the model
        input_shapes: Input shapes for tracing
        onnx_config: ONNX export configuration
        half_precision: If True, the model will be exported with half precision
        model_name: Name of the exported model
    """
    if not ONNX_AVAILABLE:
        log.warning("ONNX is not installed, can not export model in this format.")
        log.warning("Please install ONNX capabilities for quadra with: poetry install -E onnx")
        return None

    model.eval()
    if half_precision:
        model.to("cuda:0")
        model = model.half()
    else:
        model.cpu()

    if hasattr(onnx_config, "fixed_batch_size") and onnx_config.fixed_batch_size is not None:
        batch_size = onnx_config.fixed_batch_size
    else:
        batch_size = 1

    model_inputs = extract_torch_model_inputs(
        model=model, input_shapes=input_shapes, half_precision=half_precision, batch_size=batch_size
    )
    if model_inputs is None:
        return None

    if isinstance(model, ModelSignatureWrapper):
        model = model.instance

    inp, input_shapes = model_inputs

    os.makedirs(output_path, exist_ok=True)

    model_path = os.path.join(output_path, model_name)

    input_names = onnx_config.input_names if hasattr(onnx_config, "input_names") else None

    if input_names is None:
        input_names = []
        for i, _ in enumerate(inp):
            input_names.append(f"input_{i}")

    output = [model(*inp)]
    output_names = onnx_config.output_names if hasattr(onnx_config, "output_names") else None

    if output_names is None:
        output_names = []
        for i, _ in enumerate(output):
            output_names.append(f"output_{i}")

    dynamic_axes = onnx_config.dynamic_axes if hasattr(onnx_config, "dynamic_axes") else None

    if hasattr(onnx_config, "fixed_batch_size") and onnx_config.fixed_batch_size is not None:
        dynamic_axes = None
    else:
        if dynamic_axes is None:
            dynamic_axes = {}
            for i, _ in enumerate(input_names):
                dynamic_axes[input_names[i]] = {0: "batch_size"}

            for i, _ in enumerate(output_names):
                dynamic_axes[output_names[i]] = {0: "batch_size"}

    onnx_config = cast(Dict[str, Any], OmegaConf.to_container(onnx_config, resolve=True))

    onnx_config["input_names"] = input_names
    onnx_config["output_names"] = output_names
    onnx_config["dynamic_axes"] = dynamic_axes

    simplify = onnx_config.pop("simplify", False)
    _ = onnx_config.pop("fixed_batch_size", None)

    if len(inp) == 1:
        inp = inp[0]

    if isinstance(inp, list):
        inp = tuple(inp)  # onnx doesn't like lists representing tuples of inputs

    if isinstance(inp, dict):
        raise ValueError("ONNX export does not support model with dict inputs")

    try:
        torch.onnx.export(model=model, args=inp, f=model_path, **onnx_config)

        onnx_model = onnx.load(model_path)
        # Check if ONNX model is valid
        onnx.checker.check_model(onnx_model)
    except Exception as e:
        log.debug("ONNX export failed with error: %s", e)
        return None

    log.info("ONNX model saved to %s", os.path.join(os.getcwd(), model_path))

    if simplify:
        log.info("Attempting to simplify ONNX model")
        onnx_model = onnx.load(model_path)

        try:
            simplified_model, check = onnx_simplify(onnx_model)
        except Exception as e:
            log.debug("ONNX simplification failed with error: %s", e)
            check = False

        if not check:
            log.warning("Something failed during model simplification, only original ONNX model will be exported")
        else:
            model_filename, model_extension = os.path.splitext(model_name)
            model_name = f"{model_filename}_simplified{model_extension}"
            model_path = os.path.join(output_path, model_name)
            onnx.save(simplified_model, model_path)
            log.info("Simplified ONNX model saved to %s", os.path.join(os.getcwd(), model_path))

    return os.path.join(os.getcwd(), model_path), input_shapes

export_pytorch_model(model, output_path, model_name='model.pth')

Export pytorch model's parameter dictionary using a deserialized state_dict.

Parameters:

• model (Module) –

  PyTorch model to be exported

• output_path (str) –

  Path to save the model

• model_name (str, default: 'model.pth' ) –

  Name of the exported model

Returns:

• str –

  If the model is exported successfully, the path to the model is returned.

Source code in quadra/utils/export.py

def export_pytorch_model(model: nn.Module, output_path: str, model_name: str = "model.pth") -> str:
    """Export pytorch model's parameter dictionary using a deserialized state_dict.

    Args:
        model: PyTorch model to be exported
        output_path: Path to save the model
        model_name: Name of the exported model

    Returns:
        If the model is exported successfully, the path to the model is returned.

    """
    if isinstance(model, ModelSignatureWrapper):
        model = model.instance

    os.makedirs(output_path, exist_ok=True)
    model.eval()
    model.cpu()
    model_path = os.path.join(output_path, model_name)
    torch.save(model.state_dict(), model_path)
    log.info("Pytorch model saved to %s", os.path.join(output_path, model_name))

    return os.path.join(os.getcwd(), model_path)

export_torchscript_model(model, output_path, input_shapes=None, half_precision=False, model_name='model.pt')

Export a PyTorch model with TorchScript.

Parameters:

• model (Module) –

  PyTorch model to be exported

• input_shapes (Optional[List[Any]], default: None ) –

  Inputs shape for tracing

• output_path (str) –

  Path to save the model

• half_precision (bool, default: False ) –

  If True, the model will be exported with half precision

• model_name (str, default: 'model.pt' ) –

  Name of the exported model

Returns:

• Optional[Tuple[str, Any]] –

  If the model is exported successfully, the path to the model and the input shape are returned.

Source code in quadra/utils/export.py

@torch.inference_mode()
def export_torchscript_model(
    model: nn.Module,
    output_path: str,
    input_shapes: Optional[List[Any]] = None,
    half_precision: bool = False,
    model_name: str = "model.pt",
) -> Optional[Tuple[str, Any]]:
    """Export a PyTorch model with TorchScript.

    Args:
        model: PyTorch model to be exported
        input_shapes: Inputs shape for tracing
        output_path: Path to save the model
        half_precision: If True, the model will be exported with half precision
        model_name: Name of the exported model

    Returns:
        If the model is exported successfully, the path to the model and the input shape are returned.

    """
    if isinstance(model, CflowLightning):
        log.warning("Exporting cflow model with torchscript is not supported yet.")
        return None

    model.eval()
    if half_precision:
        model.to("cuda:0")
        model = model.half()
    else:
        model.cpu()

    model_inputs = extract_torch_model_inputs(model, input_shapes, half_precision)

    if model_inputs is None:
        return None

    if isinstance(model, ModelSignatureWrapper):
        model = model.instance

    inp, input_shapes = model_inputs

    try:
        try:
            model_jit = torch.jit.trace(model, inp)
        except RuntimeError as e:
            log.warning("Standard tracing failed with exception %s, attempting tracing with strict=False", e)
            model_jit = torch.jit.trace(model, inp, strict=False)

        os.makedirs(output_path, exist_ok=True)

        model_path = os.path.join(output_path, model_name)
        model_jit.save(model_path)

        log.info("Torchscript model saved to %s", os.path.join(os.getcwd(), model_path))

        return os.path.join(os.getcwd(), model_path), input_shapes
    except Exception as e:
        log.debug("Failed to export torchscript model with exception: %s", e)
        return None

extract_torch_model_inputs(model, input_shapes=None, half_precision=False, batch_size=1)

Extract the input shapes for the given model and generate a list of torch tensors with the given device and dtype.

Parameters:

• model (Union[Module, ModelSignatureWrapper]) –

  Module or ModelSignatureWrapper

• input_shapes (Optional[List[Any]], default: None ) –

  Inputs shapes

• half_precision (bool, default: False ) –

  If True, the model will be exported with half precision

• batch_size (int, default: 1 ) –

  Batch size for the input shapes

Source code in quadra/utils/export.py

def extract_torch_model_inputs(
    model: Union[nn.Module, ModelSignatureWrapper],
    input_shapes: Optional[List[Any]] = None,
    half_precision: bool = False,
    batch_size: int = 1,
) -> Optional[Tuple[Union[List[Any], Tuple[Any, ...], torch.Tensor], List[Any]]]:
    """Extract the input shapes for the given model and generate a list of torch tensors with the
    given device and dtype.

    Args:
        model: Module or ModelSignatureWrapper
        input_shapes: Inputs shapes
        half_precision: If True, the model will be exported with half precision
        batch_size: Batch size for the input shapes
    """
    if isinstance(model, ModelSignatureWrapper):
        if input_shapes is None:
            input_shapes = model.input_shapes

    if input_shapes is None:
        log.warning(
            "Input shape is None, can not trace model! Please provide input_shapes in the task export configuration."
        )
        return None

    if half_precision:
        # TODO: This doesn't support bfloat16!!
        inp = generate_torch_inputs(
            input_shapes=input_shapes, device="cuda:0", half_precision=True, dtype=torch.float16, batch_size=batch_size
        )
    else:
        inp = generate_torch_inputs(
            input_shapes=input_shapes, device="cpu", half_precision=False, dtype=torch.float32, batch_size=batch_size
        )

    return inp, input_shapes

generate_torch_inputs(input_shapes, device, half_precision=False, dtype=torch.float32, batch_size=1)

Given a list of input shapes that can contain either lists, tuples or dicts, with tuples being the input shapes of the model, generate a list of torch tensors with the given device and dtype.

Source code in quadra/utils/export.py

def generate_torch_inputs(
    input_shapes: List[Any],
    device: Union[str, torch.device],
    half_precision: bool = False,
    dtype: torch.dtype = torch.float32,
    batch_size: int = 1,
) -> Union[List[Any], Tuple[Any, ...], torch.Tensor]:
    """Given a list of input shapes that can contain either lists, tuples or dicts, with tuples being the input shapes
    of the model, generate a list of torch tensors with the given device and dtype.
    """
    inp = None

    if isinstance(input_shapes, (ListConfig, DictConfig)):
        input_shapes = OmegaConf.to_container(input_shapes)

    if isinstance(input_shapes, list):
        if any(isinstance(inp, (Sequence, dict)) for inp in input_shapes):
            return [generate_torch_inputs(inp, device, half_precision, dtype) for inp in input_shapes]

        # Base case
        inp = torch.randn((batch_size, *input_shapes), dtype=dtype, device=device)

    if isinstance(input_shapes, dict):
        return {k: generate_torch_inputs(v, device, half_precision, dtype) for k, v in input_shapes.items()}

    if isinstance(input_shapes, tuple):
        if any(isinstance(inp, (Sequence, dict)) for inp in input_shapes):
            # The tuple contains a list, tuple or dict
            return tuple(generate_torch_inputs(inp, device, half_precision, dtype) for inp in input_shapes)

        # Base case
        inp = torch.randn((batch_size, *input_shapes), dtype=dtype, device=device)

    if inp is None:
        raise RuntimeError("Something went wrong during model export, unable to parse input shapes")

    if half_precision:
        inp = inp.half()

    return inp

get_export_extension(export_type)

Get the extension of the exported model.

Parameters:

• export_type (str) –

  The type of the exported model.

Returns:

• str –

  The extension of the exported model.

Source code in quadra/utils/export.py

def get_export_extension(export_type: str) -> str:
    """Get the extension of the exported model.

    Args:
        export_type: The type of the exported model.

    Returns:
        The extension of the exported model.
    """
    if export_type == "onnx":
        extension = "onnx"
    elif export_type == "torchscript":
        extension = "pt"
    elif export_type == "pytorch":
        extension = "pth"
    else:
        raise ValueError(f"Unsupported export type {export_type}")

    return extension

import_deployment_model(model_path, inference_config, device, model_architecture=None)

Try to import a model for deployment, currently only supports torchscript .pt files and state dictionaries .pth files.

Parameters:

• model_path (str) –

  Path to the model

• inference_config (DictConfig) –

  Inference configuration, should contain keys for the different deployment models

• device (str) –

  Device to load the model on

• model_architecture (Optional[Module], default: None ) –

  Optional model architecture to use for loading a plain pytorch model

Returns:

• BaseEvaluationModel –

  A tuple containing the model and the model type

Source code in quadra/utils/export.py

def import_deployment_model(
    model_path: str,
    inference_config: DictConfig,
    device: str,
    model_architecture: Optional[nn.Module] = None,
) -> BaseEvaluationModel:
    """Try to import a model for deployment, currently only supports torchscript .pt files and
    state dictionaries .pth files.

    Args:
        model_path: Path to the model
        inference_config: Inference configuration, should contain keys for the different deployment models
        device: Device to load the model on
        model_architecture: Optional model architecture to use for loading a plain pytorch model

    Returns:
        A tuple containing the model and the model type
    """
    log.info("Importing trained model")

    file_extension = os.path.splitext(os.path.basename(model_path))[1]
    deployment_model: Optional[BaseEvaluationModel] = None

    if file_extension == ".pt":
        deployment_model = TorchscriptEvaluationModel(config=inference_config.torchscript)
    elif file_extension == ".pth":
        if model_architecture is None:
            raise ValueError("model_architecture must be specified when loading a .pth file")

        deployment_model = TorchEvaluationModel(config=inference_config.pytorch, model_architecture=model_architecture)
    elif file_extension == ".onnx":
        deployment_model = ONNXEvaluationModel(config=inference_config.onnx)

    if deployment_model is not None:
        deployment_model.load_from_disk(model_path=model_path, device=device)

        log.info("Imported %s model", deployment_model.__class__.__name__)

        return deployment_model

    raise ValueError(f"Unable to load model with extension {file_extension}, valid extensions are: ['.pt', 'pth']")