tf.lite.TargetSpec

Specification of target device used to optimize the model.

tf.lite.TargetSpec(
    supported_ops=None, supported_types=None, experimental_select_user_tf_ops=None
)

supported_ops Experimental flag, subject to change. Set of tf.lite.OpsSet options, where each option represents a set of operators supported by the target device. (default {tf.lite.OpsSet.TFLITE_BUILTINS}))
supported_types Set of tf.dtypes.DType data types supported on the target device. If initialized, optimization might be driven by the smallest type in this set. (default set())
experimental_select_user_tf_ops Experimental flag, subject to change. Set of user's TensorFlow operators' names that are required in the TensorFlow Lite runtime. These ops will be exported as select TensorFlow ops in the model (in conjunction with the tf.lite.OpsSet.SELECT_TF_OPS flag). This is an advanced feature that should only be used if the client is using TF ops that may not be linked in by default with the TF ops that are provided when using the SELECT_TF_OPS path. The client is responsible for linking these ops into the target runtime.
_experimental_custom_op_registerers Experimental flag, subject to change. List of str (symbol names) or functions that take a pointer to a MutableOpResolver and register TensorFlow Lite custom ops. When passing functions, use a pybind function that takes a uintptr_t that can be recast as a pointer to a MutableOpResolver. The TensorFlow Lite custom ops in the registerers will be used when the representative data is given and the post training quantization is enabled at the same time.