yobx.litert.to_onnx#

yobx.litert.to_onnx(model: str | ~os.PathLike[str] | bytes, args: ~typing.Tuple[~typing.Any, ...], input_names: ~typing.Sequence[str] | None = None, dynamic_shapes: ~typing.Tuple[~typing.Dict[int, str], ...] | None = None, target_opset: int | ~typing.Dict[str, int] = 21, builder_cls: type | ~typing.Callable = <class 'yobx.xbuilder.graph_builder.GraphBuilder'>, verbose: int = 0, extra_converters: ~typing.Dict[int, ~typing.Callable] | None = None, large_model: bool = False, external_threshold: int = 1024, subgraph_index: int = 0) → ExportArtifact[source]#

Convert a TFLite/LiteRT model to ONNX.

The function parses the binary FlatBuffer that every .tflite file uses and walks every operator in the requested subgraph. Each operator is converted to its ONNX equivalent by a registered converter (see yobx.litert.register).

Parameters:

  • model – path to a .tflite file or raw model bytes

  • args – dummy inputs (numpy arrays or onnx.ValueInfoProto descriptors); used to determine dtypes and shapes when the model does not carry that information.

  • input_names – optional list of names for the ONNX input tensors; defaults to the tensor names stored inside the TFLite model.

  • dynamic_shapes – optional per-input axis-to-dim-name mappings. When None, axis 0 is treated as a dynamic batch dimension.

  • target_opset – opset version; either an integer for the default domain ("") or a Dict[str, int] mapping domain names to versions.

  • builder_cls – by default the graph builder is a yobx.xbuilder.GraphBuilder but any builder can be used as long it implements the Shape and type tracking.

  • verbose – verbosity level (0 = silent).

  • extra_converters – optional mapping from BuiltinOperator int (or custom-op name string) to converter function with signature (g, sts, outputs, op). Entries here take priority over the built-in op converters.

  • large_model – if True the returned ExportArtifact has its container attribute set to an ExtendedModelContainer.

  • external_threshold – if large_model is True, every tensor whose element count exceeds this threshold is stored as external data.

  • subgraph_index – index of the subgraph to convert (default: 0).

Returns:

ExportArtifact wrapping the exported ONNX proto together with an ExportReport.

Example:

import numpy as np
from yobx.litert import to_onnx

X = np.random.rand(1, 4).astype(np.float32)
artifact = to_onnx("model.tflite", (X,))
proto = artifact.proto
artifact.save("model.onnx")
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