miniosl.inference

inference modules

class miniosl.inference.InferenceModel(device)

interface for inference using trained models

eval(input: ndarray, *, take_softmax: bool = False) → tuple

return (move, value, aux) tuple, after softmax

abstractmethod infer(inputs: Tensor) → tuple[ndarray, ndarray, ndarray]

return inference results for batch

infer_int8(inputs: ndarray | Tensor) → tuple[ndarray, ndarray, ndarray]

an optimized path

infer_one(input: ndarray) → list

return inference results for a single instance

class miniosl.inference.OnnxInfer(path: str, device: str)

infer(inputs: Tensor)

return inference results for batch

infer_iobinding(inputs: Tensor)

work with torch 2.5.1, onnxruntime-gpu 1.20.1

infer_naive(inputs: Tensor)

inefficient in gpu-cpu transfer if inputs are on already gpu

class miniosl.inference.TorchInfer(model, device: str)

infer(inputs: Tensor)

return inference results for batch

class miniosl.inference.TorchScriptInfer(path: str, device: str)

infer(inputs: Tensor)

return inference results for batch

class miniosl.inference.TorchTRTInfer(path: str, device: str)

infer(inputs: Tensor)

return inference results for batch

miniosl.inference.export_executorch(model: PVNetwork, device: str, filename: str, quiet: bool) → None

export for executorch

miniosl.inference.load(path: str, device: str = '', torch_cfg: Mapping = mappingproxy({}), *, compiled: bool = False, strict: bool = True, remove_aux_head: bool = False) → InferenceModel

factory method to load a model from file

Parameters:

• path – filepath,

• device – torch device such as ‘cuda’, ‘cpu’,

• torch_cfg – network specification needed for TorchInfer.