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ort

Classes:

  • ORT –

    Base ONNX Runtime backend configuration.

  • ORT_COREML –

    ONNX Runtime Core ML execution provider.

  • ORT_CPU –

    ONNX Runtime CPU execution provider.

  • ORT_CUDA –

    ONNX Runtime CUDA execution provider for Nvidia GPUs.

  • ORT_DML –

    ONNX Runtime DirectML execution provider for D3D12 devices.

Attributes:

logger module-attribute 

logger = getLogger(__name__)

ORT dataclass 

ORT(
    *,
    num_streams: int = 1,
    verbosity: int | None = None,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: OutputFormat | None = None,
)

Bases: BackendAutoConvertFloat

Base ONNX Runtime backend configuration.

Initialize the backend.

Parameters:

  • num_streams

    (int, default: 1 ) –

    Number of parallel inference streams.

  • verbosity

    (int | None, default: None ) –

    ONNX Runtime logging verbosity.

  • fp16

    (bool, default: True ) –

    Convert model execution to FP16 where supported.

  • fp16_blacklist_ops

    (Collection[str] | None, default: None ) –

    ONNX node or op names to keep in FP32 during FP16 conversion.

  • output_format

    (OutputFormat | None, default: None ) –

    Requested output precision. Defaults to FP16 when fp16 is enabled, otherwise FP32.

Classes:

Methods:

  • autoselect –

    Try to select the best backend for the current system.

  • get_args –

    Return backend plugin arguments derived from this configuration.

  • inference –

    Run inference with this backend.

Attributes:

Source code in vsscale/mlrt/backend/ort.py 
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def __init__(
    self,
    *,
    num_streams: int = 1,
    verbosity: int | None = None,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: Backend.OutputFormat | None = None,
) -> None:
    """
    Initialize the backend.

    Args:
        num_streams: Number of parallel inference streams.
        verbosity: ONNX Runtime logging verbosity.
        fp16: Convert model execution to FP16 where supported.
        fp16_blacklist_ops: ONNX node or op names to keep in FP32 during FP16 conversion.
        output_format: Requested output precision. Defaults to FP16 when `fp16` is enabled, otherwise FP32.
    """
    object.__setattr__(self, "fp16", fp16)
    object.__setattr__(self, "fp16_blacklist_ops", fp16_blacklist_ops)
    object.__setattr__(self, "num_streams", num_streams)
    object.__setattr__(
        self,
        "verbosity",
        ORT.Verbosity.from_logging(logger.getEffectiveLevel()) if verbosity is None else verbosity,
    )
    object.__setattr__(self, "output_format", output_format)
    super().__init__()

MIGX class-attribute instance-attribute 

MIGX = MIGX

NCNN class-attribute instance-attribute 

NCNN = NCNN

NCNN_VK class-attribute instance-attribute 

NCNN_VK = NCNN

ORT class-attribute instance-attribute 

ORT = ORT

ORT_COREML class-attribute instance-attribute 

ORT_COREML = ORT_COREML

ORT_CPU class-attribute instance-attribute 

ORT_CPU = ORT_CPU

ORT_CUDA class-attribute instance-attribute 

ORT_CUDA = ORT_CUDA

ORT_DML class-attribute instance-attribute 

ORT_DML = ORT_DML

OV class-attribute instance-attribute 

OV = OV

OV_CPU class-attribute instance-attribute 

OV_CPU = OV_CPU

OV_GPU class-attribute instance-attribute 

OV_GPU = OV_GPU

OV_NPU class-attribute instance-attribute 

OV_NPU = OV_NPU

TRT class-attribute instance-attribute 

TRT = TRT

TRT_RTX class-attribute instance-attribute 

TRT_RTX = TRT_RTX

flexible_output_prop class-attribute 

flexible_output_prop: str = 'MlrtFlexible'

fp16 instance-attribute 

fp16: bool | None

fp16_blacklist_ops instance-attribute 

fp16_blacklist_ops: Collection[str] | None

num_streams instance-attribute 

num_streams: int

output_format instance-attribute 

output_format: OutputFormat | None

plugin class-attribute instance-attribute 

plugin = ort

provider class-attribute 

provider: str

verbosity instance-attribute 

verbosity: int

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

Verbosity

Bases: IntEnum

Methods:

Attributes:

ERROR class-attribute instance-attribute 

ERROR = 3

FATAL class-attribute instance-attribute 

FATAL = 4

INFO class-attribute instance-attribute 

INFO = 1

VERBOSE class-attribute instance-attribute 

VERBOSE = 0

WARNING class-attribute instance-attribute 

WARNING = 2

from_logging classmethod 

from_logging(level: int) -> Verbosity
Source code in vsscale/mlrt/backend/ort.py 
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@classmethod
def from_logging(cls, level: int) -> ORT.Verbosity:
    mapping = {
        DEBUG: cls.VERBOSE,
        INFO: cls.INFO,
        WARNING: cls.WARNING,
        ERROR: cls.ERROR,
        CRITICAL: cls.FATAL,
    }
    return mapping.get(level, cls.WARNING)

autoselect classmethod 

autoselect(device_id: int = 0, **kwargs: Any) -> Backend

Try to select the best backend for the current system.

Parameters:

  • device_id

    (int, default: 0 ) –

    The GPU device id.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the backend.

Returns:

  • Backend –

    The selected backend.

Source code in vsscale/mlrt/backend/base.py 
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@classmethod
def autoselect(cls, device_id: int = 0, **kwargs: Any) -> Backend:
    """
    Try to select the best backend for the current system.

    Args:
        device_id: The GPU device id.
        **kwargs: Additional arguments to pass to the backend.

    Returns:
        The selected backend.
    """

    gpu = get_gpu(device_id)
    vendor = (
        cast(str | None, gpu.vendor)
        if gpu
        else "apple"
        # macOS x86_64 is unsupported
        if platform.system().lower() == "darwin" and platform.machine() == "x86_64"
        else None
    )

    match vendor:
        # Windows & Linux
        case "nvidia":
            if hasattr(core, "trt"):
                backend = Backend.TRT
            elif hasattr(core, "trt_rtx"):
                backend = Backend.TRT_RTX
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ort"):
                backend = Backend.ORT_CUDA
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "amd":
            if platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "migx"):
                backend = Backend.MIGX
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "intel":
            # device-smi can't detect Intel NPUs in 0.5.6
            # https://github.com/ModelCloud/Device-SMI#roadmap
            if hasattr(core, "ov"):
                backend = Backend.OV_GPU
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # macOS ARM64 & x86_64
        case "apple":
            if hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            elif hasattr(core, "ort"):
                backend = Backend.ORT_COREML
            else:
                backend = Backend.OV_CPU
        case _:
            backend = Backend.OV_CPU

    del gpu

    return backend(**kwargs)

get_args 

get_args(clips: VideoNode | Sequence[VideoNode]) -> dict[str, Any]

Return backend plugin arguments derived from this configuration.

Source code in vsscale/mlrt/backend/ort.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    return {
        "fp16": self.fp16,
        "output_format": int(fallback(self.output_format, self.fp16)),
        "provider": self.provider,
        "num_streams": self.num_streams,
        "verbosity": self.verbosity,
        "fp16_blacklist_ops": self.fp16_blacklist_ops,
    }

inference 

inference(
    clips: VideoNode | Sequence[VideoNode],
    network_path: str | PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> VideoNode | list[VideoNode]

Run inference with this backend.

Parameters:

  • clips

    (VideoNode | Sequence[VideoNode]) –

    Input clip or clips passed to the backend model.

  • network_path

    (str | PathLike[str]) –

    Path to the model file or backend artifact.

  • overlap

    (tuple[int, int]) –

    Horizontal and vertical tile overlap in pixels.

  • tilesize

    (tuple[int, int]) –

    Horizontal and vertical tile size in pixels.

  • flexible

    (bool, default: False ) –

    Return each flexible output plane as a separate clip.

  • **kwargs

    (Any, default: {} ) –

    Additional backend plugin arguments forwarded unchanged.

Returns:

  • VideoNode | list[VideoNode] –

    A single output clip, or a list of output clips when flexible is enabled.

Source code in vsscale/mlrt/backend/base.py 
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@copy_signature(Backend.inference)
def inference(
    self,
    clips: vs.VideoNode | Sequence[vs.VideoNode],
    network_path: str | os.PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> vs.VideoNode | list[vs.VideoNode]:
    clips = [
        depth(c, 16 if self.fp16 else 32, sample_type=vs.FLOAT) if c.format.sample_type != vs.FLOAT else c
        for c in to_arr(clips)
    ]
    return super().inference(clips, network_path, overlap, tilesize, flexible=flexible, **kwargs)

ORT_COREML dataclass 

ORT_COREML(
    *,
    ml_program: int = NEURAL_NETWORK,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: OutputFormat | None = None,
    num_streams: int = 1,
    verbosity: int | None = None,
)

Bases: ORT

ONNX Runtime Core ML execution provider.

Initialize the backend.

Parameters:

  • num_streams

    (int, default: 1 ) –

    Number of parallel inference streams.

  • verbosity

    (int | None, default: None ) –

    ONNX Runtime logging verbosity.

  • fp16

    (bool, default: True ) –

    Convert model execution to FP16 where supported.

  • fp16_blacklist_ops

    (Collection[str] | None, default: None ) –

    ONNX node or op names to keep in FP32 during FP16 conversion.

  • output_format

    (OutputFormat | None, default: None ) –

    Requested output precision. Defaults to FP16 when fp16 is enabled, otherwise FP32.

Classes:

Methods:

  • autoselect –

    Try to select the best backend for the current system.

  • get_args –

    Return backend plugin arguments derived from this configuration.

  • inference –

    Run inference with this backend.

Attributes:

Source code in vsscale/mlrt/backend/ort.py 
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def __init__(
    self,
    *,
    ml_program: int = Provider.NEURAL_NETWORK,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: Backend.OutputFormat | None = None,
    num_streams: int = 1,
    verbosity: int | None = None,
) -> None:
    object.__setattr__(self, "ml_program", ORT_COREML.Provider(ml_program))
    super().__init__(
        num_streams=num_streams,
        verbosity=verbosity,
        fp16=fp16,
        fp16_blacklist_ops=fp16_blacklist_ops,
        output_format=output_format,
    )

MIGX class-attribute instance-attribute 

MIGX = MIGX

NCNN class-attribute instance-attribute 

NCNN = NCNN

NCNN_VK class-attribute instance-attribute 

NCNN_VK = NCNN

ORT class-attribute instance-attribute 

ORT = ORT

ORT_COREML class-attribute instance-attribute 

ORT_COREML = ORT_COREML

ORT_CPU class-attribute instance-attribute 

ORT_CPU = ORT_CPU

ORT_CUDA class-attribute instance-attribute 

ORT_CUDA = ORT_CUDA

ORT_DML class-attribute instance-attribute 

ORT_DML = ORT_DML

OV class-attribute instance-attribute 

OV = OV

OV_CPU class-attribute instance-attribute 

OV_CPU = OV_CPU

OV_GPU class-attribute instance-attribute 

OV_GPU = OV_GPU

OV_NPU class-attribute instance-attribute 

OV_NPU = OV_NPU

TRT class-attribute instance-attribute 

TRT = TRT

TRT_RTX class-attribute instance-attribute 

TRT_RTX = TRT_RTX

flexible_output_prop class-attribute 

flexible_output_prop: str = 'MlrtFlexible'

fp16 instance-attribute 

fp16: bool | None

fp16_blacklist_ops instance-attribute 

fp16_blacklist_ops: Collection[str] | None

ml_program instance-attribute 

ml_program: Provider

Core ML provider mode.

num_streams instance-attribute 

num_streams: int

output_format instance-attribute 

output_format: OutputFormat | None

plugin class-attribute instance-attribute 

plugin = ort

provider class-attribute instance-attribute 

provider = 'COREML'

verbosity instance-attribute 

verbosity: int

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

Provider

Bases: IntEnum

Attributes:

ML_PROGRAM class-attribute instance-attribute 

ML_PROGRAM = 1

NEURAL_NETWORK class-attribute instance-attribute 

NEURAL_NETWORK = 0

Verbosity

Bases: IntEnum

Methods:

Attributes:

ERROR class-attribute instance-attribute 

ERROR = 3

FATAL class-attribute instance-attribute 

FATAL = 4

INFO class-attribute instance-attribute 

INFO = 1

VERBOSE class-attribute instance-attribute 

VERBOSE = 0

WARNING class-attribute instance-attribute 

WARNING = 2

from_logging classmethod 

from_logging(level: int) -> Verbosity
Source code in vsscale/mlrt/backend/ort.py 
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@classmethod
def from_logging(cls, level: int) -> ORT.Verbosity:
    mapping = {
        DEBUG: cls.VERBOSE,
        INFO: cls.INFO,
        WARNING: cls.WARNING,
        ERROR: cls.ERROR,
        CRITICAL: cls.FATAL,
    }
    return mapping.get(level, cls.WARNING)

autoselect classmethod 

autoselect(device_id: int = 0, **kwargs: Any) -> Backend

Try to select the best backend for the current system.

Parameters:

  • device_id

    (int, default: 0 ) –

    The GPU device id.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the backend.

Returns:

  • Backend –

    The selected backend.

Source code in vsscale/mlrt/backend/base.py 
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@classmethod
def autoselect(cls, device_id: int = 0, **kwargs: Any) -> Backend:
    """
    Try to select the best backend for the current system.

    Args:
        device_id: The GPU device id.
        **kwargs: Additional arguments to pass to the backend.

    Returns:
        The selected backend.
    """

    gpu = get_gpu(device_id)
    vendor = (
        cast(str | None, gpu.vendor)
        if gpu
        else "apple"
        # macOS x86_64 is unsupported
        if platform.system().lower() == "darwin" and platform.machine() == "x86_64"
        else None
    )

    match vendor:
        # Windows & Linux
        case "nvidia":
            if hasattr(core, "trt"):
                backend = Backend.TRT
            elif hasattr(core, "trt_rtx"):
                backend = Backend.TRT_RTX
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ort"):
                backend = Backend.ORT_CUDA
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "amd":
            if platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "migx"):
                backend = Backend.MIGX
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "intel":
            # device-smi can't detect Intel NPUs in 0.5.6
            # https://github.com/ModelCloud/Device-SMI#roadmap
            if hasattr(core, "ov"):
                backend = Backend.OV_GPU
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # macOS ARM64 & x86_64
        case "apple":
            if hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            elif hasattr(core, "ort"):
                backend = Backend.ORT_COREML
            else:
                backend = Backend.OV_CPU
        case _:
            backend = Backend.OV_CPU

    del gpu

    return backend(**kwargs)

get_args 

get_args(clips: VideoNode | Sequence[VideoNode]) -> dict[str, Any]

Return backend plugin arguments derived from this configuration.

Source code in vsscale/mlrt/backend/ort.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    return super().get_args(clips) | {"ml_program": self.ml_program}

inference 

inference(
    clips: VideoNode | Sequence[VideoNode],
    network_path: str | PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> VideoNode | list[VideoNode]

Run inference with this backend.

Parameters:

  • clips

    (VideoNode | Sequence[VideoNode]) –

    Input clip or clips passed to the backend model.

  • network_path

    (str | PathLike[str]) –

    Path to the model file or backend artifact.

  • overlap

    (tuple[int, int]) –

    Horizontal and vertical tile overlap in pixels.

  • tilesize

    (tuple[int, int]) –

    Horizontal and vertical tile size in pixels.

  • flexible

    (bool, default: False ) –

    Return each flexible output plane as a separate clip.

  • **kwargs

    (Any, default: {} ) –

    Additional backend plugin arguments forwarded unchanged.

Returns:

  • VideoNode | list[VideoNode] –

    A single output clip, or a list of output clips when flexible is enabled.

Source code in vsscale/mlrt/backend/base.py 
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@copy_signature(Backend.inference)
def inference(
    self,
    clips: vs.VideoNode | Sequence[vs.VideoNode],
    network_path: str | os.PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> vs.VideoNode | list[vs.VideoNode]:
    clips = [
        depth(c, 16 if self.fp16 else 32, sample_type=vs.FLOAT) if c.format.sample_type != vs.FLOAT else c
        for c in to_arr(clips)
    ]
    return super().inference(clips, network_path, overlap, tilesize, flexible=flexible, **kwargs)

ORT_CPU dataclass 

ORT_CPU()

Bases: ORT

ONNX Runtime CPU execution provider.

Classes:

Methods:

  • autoselect –

    Try to select the best backend for the current system.

  • get_args –

    Return backend plugin arguments derived from this configuration.

  • inference –

    Run inference with this backend.

Attributes:

MIGX class-attribute instance-attribute 

MIGX = MIGX

NCNN class-attribute instance-attribute 

NCNN = NCNN

NCNN_VK class-attribute instance-attribute 

NCNN_VK = NCNN

ORT class-attribute instance-attribute 

ORT = ORT

ORT_COREML class-attribute instance-attribute 

ORT_COREML = ORT_COREML

ORT_CPU class-attribute instance-attribute 

ORT_CPU = ORT_CPU

ORT_CUDA class-attribute instance-attribute 

ORT_CUDA = ORT_CUDA

ORT_DML class-attribute instance-attribute 

ORT_DML = ORT_DML

OV class-attribute instance-attribute 

OV = OV

OV_CPU class-attribute instance-attribute 

OV_CPU = OV_CPU

OV_GPU class-attribute instance-attribute 

OV_GPU = OV_GPU

OV_NPU class-attribute instance-attribute 

OV_NPU = OV_NPU

TRT class-attribute instance-attribute 

TRT = TRT

TRT_RTX class-attribute instance-attribute 

TRT_RTX = TRT_RTX

flexible_output_prop class-attribute 

flexible_output_prop: str = 'MlrtFlexible'

fp16 instance-attribute 

fp16: bool | None

fp16_blacklist_ops instance-attribute 

fp16_blacklist_ops: Collection[str] | None

num_streams instance-attribute 

num_streams: int

output_format instance-attribute 

output_format: OutputFormat | None

plugin class-attribute instance-attribute 

plugin = ort

provider class-attribute instance-attribute 

provider = 'CPU'

verbosity instance-attribute 

verbosity: int

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

Verbosity

Bases: IntEnum

Methods:

Attributes:

ERROR class-attribute instance-attribute 

ERROR = 3

FATAL class-attribute instance-attribute 

FATAL = 4

INFO class-attribute instance-attribute 

INFO = 1

VERBOSE class-attribute instance-attribute 

VERBOSE = 0

WARNING class-attribute instance-attribute 

WARNING = 2

from_logging classmethod 

from_logging(level: int) -> Verbosity
Source code in vsscale/mlrt/backend/ort.py 
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@classmethod
def from_logging(cls, level: int) -> ORT.Verbosity:
    mapping = {
        DEBUG: cls.VERBOSE,
        INFO: cls.INFO,
        WARNING: cls.WARNING,
        ERROR: cls.ERROR,
        CRITICAL: cls.FATAL,
    }
    return mapping.get(level, cls.WARNING)

autoselect classmethod 

autoselect(device_id: int = 0, **kwargs: Any) -> Backend

Try to select the best backend for the current system.

Parameters:

  • device_id

    (int, default: 0 ) –

    The GPU device id.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the backend.

Returns:

  • Backend –

    The selected backend.

Source code in vsscale/mlrt/backend/base.py 
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@classmethod
def autoselect(cls, device_id: int = 0, **kwargs: Any) -> Backend:
    """
    Try to select the best backend for the current system.

    Args:
        device_id: The GPU device id.
        **kwargs: Additional arguments to pass to the backend.

    Returns:
        The selected backend.
    """

    gpu = get_gpu(device_id)
    vendor = (
        cast(str | None, gpu.vendor)
        if gpu
        else "apple"
        # macOS x86_64 is unsupported
        if platform.system().lower() == "darwin" and platform.machine() == "x86_64"
        else None
    )

    match vendor:
        # Windows & Linux
        case "nvidia":
            if hasattr(core, "trt"):
                backend = Backend.TRT
            elif hasattr(core, "trt_rtx"):
                backend = Backend.TRT_RTX
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ort"):
                backend = Backend.ORT_CUDA
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "amd":
            if platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "migx"):
                backend = Backend.MIGX
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "intel":
            # device-smi can't detect Intel NPUs in 0.5.6
            # https://github.com/ModelCloud/Device-SMI#roadmap
            if hasattr(core, "ov"):
                backend = Backend.OV_GPU
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # macOS ARM64 & x86_64
        case "apple":
            if hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            elif hasattr(core, "ort"):
                backend = Backend.ORT_COREML
            else:
                backend = Backend.OV_CPU
        case _:
            backend = Backend.OV_CPU

    del gpu

    return backend(**kwargs)

get_args 

get_args(clips: VideoNode | Sequence[VideoNode]) -> dict[str, Any]

Return backend plugin arguments derived from this configuration.

Source code in vsscale/mlrt/backend/ort.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    return {
        "fp16": self.fp16,
        "output_format": int(fallback(self.output_format, self.fp16)),
        "provider": self.provider,
        "num_streams": self.num_streams,
        "verbosity": self.verbosity,
        "fp16_blacklist_ops": self.fp16_blacklist_ops,
    }

inference 

inference(
    clips: VideoNode | Sequence[VideoNode],
    network_path: str | PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> VideoNode | list[VideoNode]

Run inference with this backend.

Parameters:

  • clips

    (VideoNode | Sequence[VideoNode]) –

    Input clip or clips passed to the backend model.

  • network_path

    (str | PathLike[str]) –

    Path to the model file or backend artifact.

  • overlap

    (tuple[int, int]) –

    Horizontal and vertical tile overlap in pixels.

  • tilesize

    (tuple[int, int]) –

    Horizontal and vertical tile size in pixels.

  • flexible

    (bool, default: False ) –

    Return each flexible output plane as a separate clip.

  • **kwargs

    (Any, default: {} ) –

    Additional backend plugin arguments forwarded unchanged.

Returns:

  • VideoNode | list[VideoNode] –

    A single output clip, or a list of output clips when flexible is enabled.

Source code in vsscale/mlrt/backend/base.py 
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@copy_signature(Backend.inference)
def inference(
    self,
    clips: vs.VideoNode | Sequence[vs.VideoNode],
    network_path: str | os.PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> vs.VideoNode | list[vs.VideoNode]:
    clips = [
        depth(c, 16 if self.fp16 else 32, sample_type=vs.FLOAT) if c.format.sample_type != vs.FLOAT else c
        for c in to_arr(clips)
    ]
    return super().inference(clips, network_path, overlap, tilesize, flexible=flexible, **kwargs)

ORT_CUDA dataclass 

ORT_CUDA(
    *,
    num_streams: int = 1,
    verbosity: int | None = None,
    device_id: int = 0,
    cudnn_benchmark: bool = True,
    use_cuda_graph: bool = False,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: OutputFormat | None = None,
    tf32: bool = False,
    prefer_nhwc: bool = False,
)

Bases: ORT

ONNX Runtime CUDA execution provider for Nvidia GPUs.

Initialize the backend.

Parameters:

  • num_streams

    (int, default: 1 ) –

    Number of parallel inference streams.

  • verbosity

    (int | None, default: None ) –

    ONNX Runtime logging verbosity.

  • device_id

    (int, default: 0 ) –

    CUDA device index.

  • cudnn_benchmark

    (bool, default: True ) –

    Let cuDNN search for faster convolution algorithms.

  • use_cuda_graph

    (bool, default: False ) –

    Enable CUDA graph capture to improve performance and reduce CPU overhead for compatible models.

  • fp16

    (bool, default: True ) –

    Convert model execution to FP16 where supported.

  • fp16_blacklist_ops

    (Collection[str] | None, default: None ) –

    ONNX node or op names to keep in FP32 during FP16 conversion.

  • output_format

    (OutputFormat | None, default: None ) –

    Requested output precision. Defaults to FP16 when fp16 is enabled, otherwise FP32.

  • tf32

    (bool, default: False ) –

    Allow TensorFloat-32 math on supported Nvidia GPUs.

  • prefer_nhwc

    (bool, default: False ) –

    Prefer NHWC layout where ONNX Runtime supports it.

Classes:

Methods:

  • autoselect –

    Try to select the best backend for the current system.

  • get_args –

    Return backend plugin arguments derived from this configuration.

  • inference –

    Run inference with this backend.

Attributes:

Source code in vsscale/mlrt/backend/ort.py 
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def __init__(
    self,
    *,
    num_streams: int = 1,
    verbosity: int | None = None,
    device_id: int = 0,
    cudnn_benchmark: bool = True,
    use_cuda_graph: bool = False,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: Backend.OutputFormat | None = None,
    tf32: bool = False,
    prefer_nhwc: bool = False,
) -> None:
    """
    Initialize the backend.

    Args:
        num_streams: Number of parallel inference streams.
        verbosity: ONNX Runtime logging verbosity.
        device_id: CUDA device index.
        cudnn_benchmark: Let cuDNN search for faster convolution algorithms.
        use_cuda_graph: Enable CUDA graph capture to improve performance and reduce CPU overhead
            for compatible models.
        fp16: Convert model execution to FP16 where supported.
        fp16_blacklist_ops: ONNX node or op names to keep in FP32 during FP16 conversion.
        output_format: Requested output precision. Defaults to FP16 when `fp16` is enabled, otherwise FP32.
        tf32: Allow TensorFloat-32 math on supported Nvidia GPUs.
        prefer_nhwc: Prefer NHWC layout where ONNX Runtime supports it.
    """
    object.__setattr__(self, "device_id", device_id)
    object.__setattr__(self, "cudnn_benchmark", cudnn_benchmark)
    object.__setattr__(self, "use_cuda_graph", use_cuda_graph)
    object.__setattr__(self, "prefer_nhwc", prefer_nhwc)
    object.__setattr__(self, "tf32", tf32)
    super().__init__(
        num_streams=num_streams,
        verbosity=verbosity,
        fp16=fp16,
        fp16_blacklist_ops=fp16_blacklist_ops,
        output_format=output_format,
    )

MIGX class-attribute instance-attribute 

MIGX = MIGX

NCNN class-attribute instance-attribute 

NCNN = NCNN

NCNN_VK class-attribute instance-attribute 

NCNN_VK = NCNN

ORT class-attribute instance-attribute 

ORT = ORT

ORT_COREML class-attribute instance-attribute 

ORT_COREML = ORT_COREML

ORT_CPU class-attribute instance-attribute 

ORT_CPU = ORT_CPU

ORT_CUDA class-attribute instance-attribute 

ORT_CUDA = ORT_CUDA

ORT_DML class-attribute instance-attribute 

ORT_DML = ORT_DML

OV class-attribute instance-attribute 

OV = OV

OV_CPU class-attribute instance-attribute 

OV_CPU = OV_CPU

OV_GPU class-attribute instance-attribute 

OV_GPU = OV_GPU

OV_NPU class-attribute instance-attribute 

OV_NPU = OV_NPU

TRT class-attribute instance-attribute 

TRT = TRT

TRT_RTX class-attribute instance-attribute 

TRT_RTX = TRT_RTX

cudnn_benchmark instance-attribute 

cudnn_benchmark: bool

device_id instance-attribute 

device_id: int

flexible_output_prop class-attribute 

flexible_output_prop: str = 'MlrtFlexible'

fp16 instance-attribute 

fp16: bool | None

fp16_blacklist_ops instance-attribute 

fp16_blacklist_ops: Collection[str] | None

num_streams instance-attribute 

num_streams: int

output_format instance-attribute 

output_format: OutputFormat | None

plugin class-attribute instance-attribute 

plugin = ort

prefer_nhwc instance-attribute 

prefer_nhwc: bool

provider class-attribute instance-attribute 

provider = 'CUDA'

tf32 instance-attribute 

tf32: bool

use_cuda_graph instance-attribute 

use_cuda_graph: bool

verbosity instance-attribute 

verbosity: int

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

Verbosity

Bases: IntEnum

Methods:

Attributes:

ERROR class-attribute instance-attribute 

ERROR = 3

FATAL class-attribute instance-attribute 

FATAL = 4

INFO class-attribute instance-attribute 

INFO = 1

VERBOSE class-attribute instance-attribute 

VERBOSE = 0

WARNING class-attribute instance-attribute 

WARNING = 2

from_logging classmethod 

from_logging(level: int) -> Verbosity
Source code in vsscale/mlrt/backend/ort.py 
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@classmethod
def from_logging(cls, level: int) -> ORT.Verbosity:
    mapping = {
        DEBUG: cls.VERBOSE,
        INFO: cls.INFO,
        WARNING: cls.WARNING,
        ERROR: cls.ERROR,
        CRITICAL: cls.FATAL,
    }
    return mapping.get(level, cls.WARNING)

autoselect classmethod 

autoselect(device_id: int = 0, **kwargs: Any) -> Backend

Try to select the best backend for the current system.

Parameters:

  • device_id

    (int, default: 0 ) –

    The GPU device id.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the backend.

Returns:

  • Backend –

    The selected backend.

Source code in vsscale/mlrt/backend/base.py 
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@classmethod
def autoselect(cls, device_id: int = 0, **kwargs: Any) -> Backend:
    """
    Try to select the best backend for the current system.

    Args:
        device_id: The GPU device id.
        **kwargs: Additional arguments to pass to the backend.

    Returns:
        The selected backend.
    """

    gpu = get_gpu(device_id)
    vendor = (
        cast(str | None, gpu.vendor)
        if gpu
        else "apple"
        # macOS x86_64 is unsupported
        if platform.system().lower() == "darwin" and platform.machine() == "x86_64"
        else None
    )

    match vendor:
        # Windows & Linux
        case "nvidia":
            if hasattr(core, "trt"):
                backend = Backend.TRT
            elif hasattr(core, "trt_rtx"):
                backend = Backend.TRT_RTX
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ort"):
                backend = Backend.ORT_CUDA
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "amd":
            if platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "migx"):
                backend = Backend.MIGX
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "intel":
            # device-smi can't detect Intel NPUs in 0.5.6
            # https://github.com/ModelCloud/Device-SMI#roadmap
            if hasattr(core, "ov"):
                backend = Backend.OV_GPU
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # macOS ARM64 & x86_64
        case "apple":
            if hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            elif hasattr(core, "ort"):
                backend = Backend.ORT_COREML
            else:
                backend = Backend.OV_CPU
        case _:
            backend = Backend.OV_CPU

    del gpu

    return backend(**kwargs)

get_args 

get_args(clips: VideoNode | Sequence[VideoNode]) -> dict[str, Any]

Return backend plugin arguments derived from this configuration.

Source code in vsscale/mlrt/backend/ort.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    return super().get_args(clips) | {
        "device_id": self.device_id,
        "cudnn_benchmark": self.cudnn_benchmark,
        "use_cuda_graph": self.use_cuda_graph,
        "prefer_nhwc": self.prefer_nhwc,
        "tf32": self.tf32,
    }

inference 

inference(
    clips: VideoNode | Sequence[VideoNode],
    network_path: str | PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> VideoNode | list[VideoNode]

Run inference with this backend.

Parameters:

  • clips

    (VideoNode | Sequence[VideoNode]) –

    Input clip or clips passed to the backend model.

  • network_path

    (str | PathLike[str]) –

    Path to the model file or backend artifact.

  • overlap

    (tuple[int, int]) –

    Horizontal and vertical tile overlap in pixels.

  • tilesize

    (tuple[int, int]) –

    Horizontal and vertical tile size in pixels.

  • flexible

    (bool, default: False ) –

    Return each flexible output plane as a separate clip.

  • **kwargs

    (Any, default: {} ) –

    Additional backend plugin arguments forwarded unchanged.

Returns:

  • VideoNode | list[VideoNode] –

    A single output clip, or a list of output clips when flexible is enabled.

Source code in vsscale/mlrt/backend/base.py 
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@copy_signature(Backend.inference)
def inference(
    self,
    clips: vs.VideoNode | Sequence[vs.VideoNode],
    network_path: str | os.PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> vs.VideoNode | list[vs.VideoNode]:
    clips = [
        depth(c, 16 if self.fp16 else 32, sample_type=vs.FLOAT) if c.format.sample_type != vs.FLOAT else c
        for c in to_arr(clips)
    ]
    return super().inference(clips, network_path, overlap, tilesize, flexible=flexible, **kwargs)

ORT_DML dataclass 

ORT_DML(
    *,
    device_id: int = 0,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: OutputFormat | None = None,
    num_streams: int = 1,
    verbosity: int | None = None,
)

Bases: ORT

ONNX Runtime DirectML execution provider for D3D12 devices.

Initialize the backend.

Parameters:

  • device_id

    (int, default: 0 ) –

    DirectML adapter index.

  • num_streams

    (int, default: 1 ) –

    Number of parallel inference streams.

  • verbosity

    (int | None, default: None ) –

    ONNX Runtime logging verbosity.

  • fp16

    (bool, default: True ) –

    Convert model execution to FP16 where supported.

  • fp16_blacklist_ops

    (Collection[str] | None, default: None ) –

    ONNX node or op names to keep in FP32 during FP16 conversion.

  • output_format

    (OutputFormat | None, default: None ) –

    Requested output precision. Defaults to FP16 when fp16 is enabled, otherwise FP32.

Classes:

Methods:

  • autoselect –

    Try to select the best backend for the current system.

  • get_args –

    Return backend plugin arguments derived from this configuration.

  • inference –

    Run inference with this backend.

Attributes:

Source code in vsscale/mlrt/backend/ort.py 
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def __init__(
    self,
    *,
    device_id: int = 0,
    fp16: bool = True,
    fp16_blacklist_ops: Collection[str] | None = None,
    output_format: Backend.OutputFormat | None = None,
    num_streams: int = 1,
    verbosity: int | None = None,
) -> None:
    """
    Initialize the backend.

    Args:
        device_id: DirectML adapter index.
        num_streams: Number of parallel inference streams.
        verbosity: ONNX Runtime logging verbosity.
        fp16: Convert model execution to FP16 where supported.
        fp16_blacklist_ops: ONNX node or op names to keep in FP32 during FP16 conversion.
        output_format: Requested output precision. Defaults to FP16 when `fp16` is enabled, otherwise FP32.
    """
    object.__setattr__(self, "device_id", device_id)
    super().__init__(
        num_streams=num_streams,
        verbosity=verbosity,
        fp16=fp16,
        fp16_blacklist_ops=fp16_blacklist_ops,
        output_format=output_format,
    )

MIGX class-attribute instance-attribute 

MIGX = MIGX

NCNN class-attribute instance-attribute 

NCNN = NCNN

NCNN_VK class-attribute instance-attribute 

NCNN_VK = NCNN

ORT class-attribute instance-attribute 

ORT = ORT

ORT_COREML class-attribute instance-attribute 

ORT_COREML = ORT_COREML

ORT_CPU class-attribute instance-attribute 

ORT_CPU = ORT_CPU

ORT_CUDA class-attribute instance-attribute 

ORT_CUDA = ORT_CUDA

ORT_DML class-attribute instance-attribute 

ORT_DML = ORT_DML

OV class-attribute instance-attribute 

OV = OV

OV_CPU class-attribute instance-attribute 

OV_CPU = OV_CPU

OV_GPU class-attribute instance-attribute 

OV_GPU = OV_GPU

OV_NPU class-attribute instance-attribute 

OV_NPU = OV_NPU

TRT class-attribute instance-attribute 

TRT = TRT

TRT_RTX class-attribute instance-attribute 

TRT_RTX = TRT_RTX

device_id instance-attribute 

device_id: int

flexible_output_prop class-attribute 

flexible_output_prop: str = 'MlrtFlexible'

fp16 instance-attribute 

fp16: bool | None

fp16_blacklist_ops instance-attribute 

fp16_blacklist_ops: Collection[str] | None

num_streams instance-attribute 

num_streams: int

output_format instance-attribute 

output_format: OutputFormat | None

plugin class-attribute instance-attribute 

plugin = ort

provider class-attribute instance-attribute 

provider = 'DML'

verbosity instance-attribute 

verbosity: int

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

Verbosity

Bases: IntEnum

Methods:

Attributes:

ERROR class-attribute instance-attribute 

ERROR = 3

FATAL class-attribute instance-attribute 

FATAL = 4

INFO class-attribute instance-attribute 

INFO = 1

VERBOSE class-attribute instance-attribute 

VERBOSE = 0

WARNING class-attribute instance-attribute 

WARNING = 2

from_logging classmethod 

from_logging(level: int) -> Verbosity
Source code in vsscale/mlrt/backend/ort.py 
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@classmethod
def from_logging(cls, level: int) -> ORT.Verbosity:
    mapping = {
        DEBUG: cls.VERBOSE,
        INFO: cls.INFO,
        WARNING: cls.WARNING,
        ERROR: cls.ERROR,
        CRITICAL: cls.FATAL,
    }
    return mapping.get(level, cls.WARNING)

autoselect classmethod 

autoselect(device_id: int = 0, **kwargs: Any) -> Backend

Try to select the best backend for the current system.

Parameters:

  • device_id

    (int, default: 0 ) –

    The GPU device id.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the backend.

Returns:

  • Backend –

    The selected backend.

Source code in vsscale/mlrt/backend/base.py 
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@classmethod
def autoselect(cls, device_id: int = 0, **kwargs: Any) -> Backend:
    """
    Try to select the best backend for the current system.

    Args:
        device_id: The GPU device id.
        **kwargs: Additional arguments to pass to the backend.

    Returns:
        The selected backend.
    """

    gpu = get_gpu(device_id)
    vendor = (
        cast(str | None, gpu.vendor)
        if gpu
        else "apple"
        # macOS x86_64 is unsupported
        if platform.system().lower() == "darwin" and platform.machine() == "x86_64"
        else None
    )

    match vendor:
        # Windows & Linux
        case "nvidia":
            if hasattr(core, "trt"):
                backend = Backend.TRT
            elif hasattr(core, "trt_rtx"):
                backend = Backend.TRT_RTX
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ort"):
                backend = Backend.ORT_CUDA
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "amd":
            if platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "migx"):
                backend = Backend.MIGX
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # Windows & Linux
        case "intel":
            # device-smi can't detect Intel NPUs in 0.5.6
            # https://github.com/ModelCloud/Device-SMI#roadmap
            if hasattr(core, "ov"):
                backend = Backend.OV_GPU
            elif platform.system().lower() == "windows" and hasattr(core, "ort"):
                backend = Backend.ORT_DML
            elif hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            else:
                backend = Backend.OV_CPU
        # macOS ARM64 & x86_64
        case "apple":
            if hasattr(core, "ncnn"):
                backend = Backend.NCNN_VK
            elif hasattr(core, "ort"):
                backend = Backend.ORT_COREML
            else:
                backend = Backend.OV_CPU
        case _:
            backend = Backend.OV_CPU

    del gpu

    return backend(**kwargs)

get_args 

get_args(clips: VideoNode | Sequence[VideoNode]) -> dict[str, Any]

Return backend plugin arguments derived from this configuration.

Source code in vsscale/mlrt/backend/ort.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    return super().get_args(clips) | {"device_id": self.device_id}

inference 

inference(
    clips: VideoNode | Sequence[VideoNode],
    network_path: str | PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> VideoNode | list[VideoNode]

Run inference with this backend.

Parameters:

  • clips

    (VideoNode | Sequence[VideoNode]) –

    Input clip or clips passed to the backend model.

  • network_path

    (str | PathLike[str]) –

    Path to the model file or backend artifact.

  • overlap

    (tuple[int, int]) –

    Horizontal and vertical tile overlap in pixels.

  • tilesize

    (tuple[int, int]) –

    Horizontal and vertical tile size in pixels.

  • flexible

    (bool, default: False ) –

    Return each flexible output plane as a separate clip.

  • **kwargs

    (Any, default: {} ) –

    Additional backend plugin arguments forwarded unchanged.

Returns:

  • VideoNode | list[VideoNode] –

    A single output clip, or a list of output clips when flexible is enabled.

Source code in vsscale/mlrt/backend/base.py 
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@copy_signature(Backend.inference)
def inference(
    self,
    clips: vs.VideoNode | Sequence[vs.VideoNode],
    network_path: str | os.PathLike[str],
    /,
    overlap: tuple[int, int],
    tilesize: tuple[int, int],
    *,
    flexible: bool = False,
    **kwargs: Any,
) -> vs.VideoNode | list[vs.VideoNode]:
    clips = [
        depth(c, 16 if self.fp16 else 32, sample_type=vs.FLOAT) if c.format.sample_type != vs.FLOAT else c
        for c in to_arr(clips)
    ]
    return super().inference(clips, network_path, overlap, tilesize, flexible=flexible, **kwargs)