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mixin 🔗

Tensorflow Lite operations.

TFLiteMixin 🔗

Mixin providing Tensorflow Lite operations for a Session.

This mixin is intended to be used in combination with BaseSession and should not be instantiated on its own.

Intended usage

class Session(BaseSession, TensorflowLiteMixin): ...

tflite_model_delete 🔗

tflite_model_delete(resource: Resource) -> None

Performs the Tensorflow Lite model deletion operation.

Wraps the vaccel_tflite_session_delete() C operation.

Parameters:

Name Type Description Default

resource 🔗

 Resource

A resource with the model to unload.

 required

Returns:

Type Description
None

The status of the operation execution.

Raises:

Type Description
FFIError

If the C operation fails.
Source code in vaccel/ops/tf/lite/mixin.py 
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def tflite_model_delete(self, resource: Resource) -> None:
    """Performs the Tensorflow Lite model deletion operation.

    Wraps the `vaccel_tflite_session_delete()` C operation.

    Args:
        resource: A resource with the model to unload.

    Returns:
        The status of the operation execution.

    Raises:
        FFIError: If the C operation fails.
    """
    ret = lib.vaccel_tflite_session_delete(
        self._c_ptr_or_raise, resource._c_ptr
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model delete failed")

tflite_model_load 🔗

tflite_model_load(resource: Resource) -> None

Performs the Tensorflow Lite model loading operation.

Wraps the vaccel_tflite_session_load() C operation.

Parameters:

Name Type Description Default

resource 🔗

 Resource

A resource with the model to load.

 required

Raises:

Type Description
FFIError

If the C operation fails.
Source code in vaccel/ops/tf/lite/mixin.py 
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def tflite_model_load(self, resource: Resource) -> None:
    """Performs the Tensorflow Lite model loading operation.

    Wraps the `vaccel_tflite_session_load()` C operation.

    Args:
        resource: A resource with the model to load.

    Raises:
        FFIError: If the C operation fails.
    """
    ret = lib.vaccel_tflite_session_load(
        self._c_ptr_or_raise, resource._c_ptr
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model loading failed")

tflite_model_run 🔗

tflite_model_run(
    resource: Resource, in_tensors: list[Tensor], nr_out_tensors: int = 1
) -> (list[Tensor], int)

Performs the Tensorflow Lite model run operation.

Wraps the vaccel_tflite_session_run() C operation.

Parameters:

Name Type Description Default

resource 🔗

 Resource

A resource with the model to run.

 required

in_tensors 🔗

 list[Tensor]

The input tensors for the inference.

 required

nr_out_tensors 🔗

 int

The number of output tensors. Defaults to 1.

 1

Returns:

Type Description
(list[Tensor], int)

A tuple containing: - The output tensors - The status of the operation execution.

Raises:

Type Description
FFIError

If the C operation fails.
Source code in vaccel/ops/tf/lite/mixin.py 
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def tflite_model_run(
    self,
    resource: Resource,
    in_tensors: list[Tensor],
    nr_out_tensors: int = 1,
) -> (list[Tensor], int):
    """Performs the Tensorflow Lite model run operation.

    Wraps the `vaccel_tflite_session_run()` C operation.

    Args:
        resource: A resource with the model to run.
        in_tensors: The input tensors for the inference.
        nr_out_tensors: The number of output tensors. Defaults to 1.

    Returns:
        A tuple containing:
            - The output tensors
            - The status of the operation execution.

    Raises:
        FFIError: If the C operation fails.
    """
    c_in_tensors = CList.from_ptrs(in_tensors)
    c_out_tensors = CList.from_ptrs([Tensor.empty()] * nr_out_tensors)
    status = CInt(0, "uint8_t")

    ret = lib.vaccel_tflite_session_run(
        self._c_ptr_or_raise,
        resource._c_ptr,
        c_in_tensors._c_ptr,
        len(c_in_tensors),
        c_out_tensors._c_ptr,
        len(c_out_tensors),
        status._c_ptr,
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model run failed")

    out_tensors = [Tensor.from_c_obj(t) for t in c_out_tensors.value]
    return (out_tensors, status.value)