def __init__(self,
model_path=None,
model_content=None,
experimental_delegates=None,
num_threads=None):
"""Constructor.
Args:
model_path: Path to TF-Lite Flatbuffer file.
model_content: Content of model.
experimental_delegates: Experimental. Subject to change. List of
[TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates)
objects returned by lite.load_delegate().
num_threads: Sets the number of threads used by the interpreter and
available to CPU kernels. If not set, the interpreter will use an
implementation-dependent default number of threads. Currently,
only a subset of kernels, such as conv, support multi-threading.
Raises:
ValueError: If the interpreter was unable to create.
"""
if not hasattr(self, '_custom_op_registerers'):
self._custom_op_registerers = []
if model_path and not model_content:
self._interpreter = (_interpreter_wrapper.CreateWrapperFromFile(
model_path, self._custom_op_registerers))
if not self._interpreter:
raise ValueError('Failed to open {}'.format(model_path))
elif model_content and not model_path:
# Take a reference, so the pointer remains valid.
# Since python strings are immutable then PyString_XX functions
# will always return the same pointer.
self._model_content = model_content
self._interpreter = (_interpreter_wrapper.CreateWrapperFromBuffer(
model_content, self._custom_op_registerers))
elif not model_content and not model_path:
raise ValueError(
'`model_path` or `model_content` must be specified.')
else:
raise ValueError(
'Can\'t both provide `model_path` and `model_content`')
if num_threads is not None:
if not isinstance(num_threads, int):
raise ValueError('type of num_threads should be int')
if num_threads < 1:
raise ValueError('num_threads should >= 1')
self._interpreter.SetNumThreads(num_threads)
# Each delegate is a wrapper that owns the delegates that have been loaded
# as plugins. The interpreter wrapper will be using them, but we need to
# hold them in a list so that the lifetime is preserved at least as long as
# the interpreter wrapper.
self._delegates = []
if experimental_delegates:
self._delegates = experimental_delegates
for delegate in self._delegates:
self._interpreter.ModifyGraphWithDelegate(
delegate._get_native_delegate_pointer()) # pylint: disable=protected-access
def __init__(self,
model_path=None,
model_content=None,
experimental_delegates=None):
"""Constructor.
Args:
model_path: Path to TF-Lite Flatbuffer file.
model_content: Content of model.
experimental_delegates: Experimental. Subject to change. List of
[TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates)
objects returned by lite.load_delegate().
Raises:
ValueError: If the interpreter was unable to create.
"""
if not hasattr(self, '_custom_op_registerers'):
self._custom_op_registerers = []
if model_path and not model_content:
self._interpreter = (_interpreter_wrapper.CreateWrapperFromFile(
model_path, self._custom_op_registerers))
if not self._interpreter:
raise ValueError('Failed to open {}'.format(model_path))
elif model_content and not model_path:
# Take a reference, so the pointer remains valid.
# Since python strings are immutable then PyString_XX functions
# will always return the same pointer.
self._model_content = model_content
self._interpreter = (_interpreter_wrapper.CreateWrapperFromBuffer(
model_content, self._custom_op_registerers))
elif not model_content and not model_path:
raise ValueError(
'`model_path` or `model_content` must be specified.')
else:
raise ValueError(
'Can\'t both provide `model_path` and `model_content`')
# Each delegate is a wrapper that owns the delegates that have been loaded
# as plugins. The interpreter wrapper will be using them, but we need to
# hold them in a list so that the lifetime is preserved at least as long as
# the interpreter wrapper.
self._delegates = []
if experimental_delegates:
self._delegates = experimental_delegates
for delegate in self._delegates:
self._interpreter.ModifyGraphWithDelegate(
delegate._get_native_delegate_pointer()) # pylint: disable=protected-access
def __init__(self,
model_path=None,
model_content=None,
experimental_delegates=None,
num_threads=None,
experimental_op_resolver_type=OpResolverType.AUTO,
experimental_preserve_all_tensors=False):
"""Constructor.
Args:
model_path: Path to TF-Lite Flatbuffer file.
model_content: Content of model.
experimental_delegates: Experimental. Subject to change. List of
[TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates)
objects returned by lite.load_delegate().
num_threads: Sets the number of threads used by the interpreter and
available to CPU kernels. If not set, the interpreter will use an
implementation-dependent default number of threads. Currently, only a
subset of kernels, such as conv, support multi-threading. num_threads
should be >= -1. Setting num_threads to 0 has the effect to disable
multithreading, which is equivalent to setting num_threads to 1. If set
to the value -1, the number of threads used will be
implementation-defined and platform-dependent.
experimental_op_resolver_type: The op resolver used by the interpreter. It
must be an instance of OpResolverType. By default, we use the built-in
op resolver which corresponds to tflite::ops::builtin::BuiltinOpResolver
in C++.
experimental_preserve_all_tensors: If true, then intermediate tensors
used during computation are preserved for inspection. Otherwise, reading
intermediate tensors provides undefined values.
Raises:
ValueError: If the interpreter was unable to create.
"""
if not hasattr(self, '_custom_op_registerers'):
self._custom_op_registerers = []
op_resolver_id = _get_op_resolver_id(experimental_op_resolver_type)
if op_resolver_id is None:
raise ValueError('Unrecognized passed in op resolver type: {}'.format(
experimental_op_resolver_type))
if model_path and not model_content:
custom_op_registerers_by_name = [
x for x in self._custom_op_registerers if isinstance(x, str)
]
custom_op_registerers_by_func = [
x for x in self._custom_op_registerers if not isinstance(x, str)
]
self._interpreter = (
_interpreter_wrapper.CreateWrapperFromFile(
model_path, op_resolver_id, custom_op_registerers_by_name,
custom_op_registerers_by_func,
experimental_preserve_all_tensors))
if not self._interpreter:
raise ValueError('Failed to open {}'.format(model_path))
elif model_content and not model_path:
custom_op_registerers_by_name = [
x for x in self._custom_op_registerers if isinstance(x, str)
]
custom_op_registerers_by_func = [
x for x in self._custom_op_registerers if not isinstance(x, str)
]
# Take a reference, so the pointer remains valid.
# Since python strings are immutable then PyString_XX functions
# will always return the same pointer.
self._model_content = model_content
self._interpreter = (
_interpreter_wrapper.CreateWrapperFromBuffer(
model_content, op_resolver_id, custom_op_registerers_by_name,
custom_op_registerers_by_func,
experimental_preserve_all_tensors))
elif not model_content and not model_path:
raise ValueError('`model_path` or `model_content` must be specified.')
else:
raise ValueError('Can\'t both provide `model_path` and `model_content`')
if num_threads is not None:
if not isinstance(num_threads, int):
raise ValueError('type of num_threads should be int')
if num_threads < 1:
raise ValueError('num_threads should >= 1')
self._interpreter.SetNumThreads(num_threads)
# Each delegate is a wrapper that owns the delegates that have been loaded
# as plugins. The interpreter wrapper will be using them, but we need to
# hold them in a list so that the lifetime is preserved at least as long as
# the interpreter wrapper.
self._delegates = []
if experimental_delegates:
self._delegates = experimental_delegates
for delegate in self._delegates:
self._interpreter.ModifyGraphWithDelegate(
delegate._get_native_delegate_pointer()) # pylint: disable=protected-access
self._signature_defs = self.get_signature_list()
self._metrics = metrics.TFLiteMetrics()
self._metrics.increase_counter_interpreter_creation()
