convert_tools¶
convert_helper¶
inline_model_proto¶
- experimental_experiment.convert.convert_helper.inline_model_proto(model_proto: ModelProto) ModelProto [source]¶
Inlines a model.
- Parameters:
model_proto – ModelProto
- Returns:
inlined model
optimize_model_proto¶
- experimental_experiment.convert.convert_helper.optimize_model_proto(model_proto: ModelProto, verbose: int = 0, onnx_shape_inference: bool = False, inplace: bool = True) ModelProto [source]¶
Optimizes a model proto to optimize onnxruntime.
- Parameters:
model_proto – ModelProto
verbose – verbosity
onnx_shape_inference – enable shape inference
inplace – the function modifies the proto inplace as well
- Returns:
optimized model
You should run that before calling this function
onnx_model = exported.to_model_proto( opset_version=self._resolved_onnx_exporter_options.onnx_registry.opset_version ) from experimental_experiment.convert.convert_helper import optimize_model_proto onnx_model = optimize_model_proto(onnx_model)
ort_optimize¶
- experimental_experiment.convert.convert_helper.ort_optimize(onnx_model: str | ModelProto, output: str, providers: str | List[str] = 'cpu', disable_aot: bool = False)[source]¶
Optimizes the model with onnxruntime.
- Parameters:
onnx_model – ModelProto or file path
output – path for the output
providers – providers, cpu, cuda or a list of providers
disable_aot – disable AOT
onnx_tools¶
onnx_lighten¶
- experimental_experiment.onnx_tools.onnx_lighten(onx: str | ModelProto, verbose: int = 0) Tuple[ModelProto, Dict[str, Dict[str, float]]] [source]¶
Creates a model without big initializers but stores statistics into dictionaries. The function can be reversed with
experimental_experiment.onnx_tools.onnx_unlighten()
. The model is modified inplace.- Parameters:
onx – model
verbose – verbosity
- Returns:
new model, statistics
onnx_unlighten¶
- experimental_experiment.onnx_tools.onnx_unlighten(onx: str | ModelProto, stats: Dict[str, Dict[str, float]] | None = None, verbose: int = 0) ModelProto [source]¶
Function fixing the model produced by function
experimental_experiment.onnx_tools.onnx_lighten()
. The model is modified inplace.- Parameters:
onx – model
stats – statics, can be None if onx is a file, then it loads the file
<filename>.stats
, it assumes it is json formatverbose – verbosity
- Returns:
new model, statistics