Function from_array

onnx/numpy_helper.py:321–376

Converts an array into a TensorProto including Args: array: a numpy array. name: (optional) the name of the tensor. Returns: TensorProto: the converted tensor def.

(array: np.ndarray, /, name: str | None = None)

def from_array(array: np.ndarray, /, name: str | None = None) -> onnx.TensorProto:
    """Converts an array into a TensorProto including

    Args:
        array: a numpy array.
        name: (optional) the name of the tensor.

    Returns:
        TensorProto: the converted tensor def.
    """
    tensor = onnx.TensorProto()
    tensor.dims.extend(array.shape)
    if name:
        tensor.name = name
    if array.dtype == object or np.issubdtype(array.dtype, np.str_):
        # Special care for strings.
        tensor.data_type = onnx.TensorProto.STRING
        # TODO: Introduce full string support.
        # We flatten the array in case there are n-D arrays are specified
        # If you want more complex shapes then follow the below instructions.
        # Unlike other types where the shape is automatically inferred from
        # nested arrays of values, the only reliable way now to feed strings
        # is to put them into a flat array then specify type astype(object)
        # (otherwise all strings may have different types depending on their length)
        # and then specify shape .reshape([x, y, z])
        flat_array = array.flatten()
        for e in flat_array:
            if isinstance(e, str):
                tensor.string_data.append(e.encode("utf-8"))
            elif isinstance(e, bytes):
                tensor.string_data.append(e)
            else:
                raise NotImplementedError(
                    f"Unrecognized object in the object array, expect a string, or array of bytes: {type(e)}"
                )
        return tensor

    dtype = helper.np_dtype_to_tensor_dtype(array.dtype)
    if dtype in {
        onnx.TensorProto.INT4,
        onnx.TensorProto.UINT4,
        onnx.TensorProto.FLOAT4E2M1,
    }:
        # Pack the array into int4
        array = _pack_4bitx2(array)

    if dtype in {
        onnx.TensorProto.UINT2,
        onnx.TensorProto.INT2,
    }:
        # Pack the array into int2
        array = _pack_2bitx4(array)

    tensor.raw_data = tobytes_little_endian(array)
    tensor.data_type = dtype  # type: ignore[assignment]
    return tensor