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base

Type Aliases:

Classes:

Attributes:

logger module-attribute 

logger = getLogger(__name__)

Shape 

Shape = tuple[int, int]

Backend dataclass 

Backend()

Base wrapper for a MLRT VapourSynth backend plugin.

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'

plugin class-attribute 

plugin: Plugin

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

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/base.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    """Return backend plugin arguments derived from this configuration."""
    return {}

inference 

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

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

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

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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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]:
    """
    Run inference with this backend.

    Args:
        clips: Input clip or clips passed to the backend model.
        network_path: Path to the model file or backend artifact.
        overlap: Horizontal and vertical tile overlap in pixels.
        tilesize: Horizontal and vertical tile size in pixels.
        flexible: Return each flexible output plane as a separate clip.
        **kwargs: Additional backend plugin arguments forwarded unchanged.

    Returns:
        A single output clip, or a list of output clips when `flexible` is enabled.
    """
    args = self.get_args(clips)

    if flexible:
        args = args.copy()
        args["flexible_output_prop"] = self.flexible_output_prop

    logger.info("Calling %s.Model", self.plugin.namespace)
    logger.info("Clips: %r", clips)
    logger.info("Network Path: %s", network_path)
    logger.info("overlap=%s, tilesize=%s, %s", overlap, tilesize, args | kwargs)
    output = self.plugin.Model(clips, network_path, overlap, tilesize, **args | kwargs)

    if flexible:
        clip = output["clip"]
        num_planes = output["num_planes"]

        output = [clip.std.PropToClip(prop=f"{self.flexible_output_prop}{i}") for i in range(num_planes)]

    return output

BackendAutoConvertFloat dataclass 

BackendAutoConvertFloat()

Bases: Backend

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

plugin class-attribute 

plugin: Plugin

OutputFormat

Bases: IntEnum

Output format for the backend plugin.

Attributes:

FP16 class-attribute instance-attribute 

FP16 = 1

FP32 class-attribute instance-attribute 

FP32 = 0

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/base.py 
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def get_args(self, clips: vs.VideoNode | Sequence[vs.VideoNode]) -> dict[str, Any]:
    """Return backend plugin arguments derived from this configuration."""
    return {}

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)