onnx_diagnostic.helpers.mini_onnx_builder

class onnx_diagnostic.helpers.mini_onnx_builder.MiniOnnxBuilder(target_opset: int = 18, ir_version: int = 10, sep: str = '___')

Simplified builder to build very simple model.

Parameters:

• target_opset – opset to specify

• ir_verison – IR version to use

• sep – separator to build output names

append_output_dict(name: str, tensors: Dict[str, ndarray | Tensor])

Adds two outputs, a string tensors for the keys and a sequence of tensors for the values.

The output name is name___keys and name___values.

append_output_initializer(name: str, tensor: ndarray | Tensor, randomize: bool = False)

Adds an initializer as an output. The initializer name is prefixed by t_. The output name is name. If randomize is True, the tensor is not stored but replaced by a random generator.

append_output_sequence(name: str, tensors: List[ndarray | Tensor])

Adds a sequence of initializers as an output. The initializers names are prefixed by seq_. The output name is name.

to_onnx() → ModelProto

Conversion to onnx. :return: the proto

onnx_diagnostic.helpers.mini_onnx_builder.create_input_tensors_from_onnx_model(proto: str | ModelProto, device: str = 'cpu', engine: str = 'ExtendedReferenceEvaluator', sep: str = '___') → Any

Deserializes tensors stored with function create_onnx_model_from_input_tensors(). It relies on ExtendedReferenceEvaluator to restore the tensors.

Parameters:

• proto – ModelProto or the file itself

• device – moves the tensor to this device

• engine – runtime to use, onnx, the default value, onnxruntime

• sep – separator

Returns:

restored data

onnx_diagnostic.helpers.mini_onnx_builder.create_onnx_model_from_input_tensors(inputs: Any, switch_low_high: bool | None = None, randomize: bool = False, sep: str = '___') → ModelProto

Creates a model proto including all the value as initializers. They can be restored by executing the model. We assume these inputs are not bigger than 2Gb, the limit of protobuf.

Parameters:

• inputs – anything

• switch_low_high – if None, it is equal to switch_low_high=sys.byteorder != "big"

• randomize – if True, float tensors are not stored but randomized to save space

• sep – separator

Returns:

ModelProto

The function raises an error if not supported.

onnx_diagnostic.helpers.mini_onnx_builder.proto_from_array(arr: Tensor, name: str | None = None, verbose: int = 0) → TensorProto

Converts a torch Tensor into a TensorProto.

Parameters:

• arr – tensor

• verbose – display the type and shape

Returns:

a TensorProto