tensorflow/lite/python/interpreter.py - platform/external/tensorflow - Git at Google

 # Lint as: python2, python3
 # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
 """Python TF-Lite interpreter."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import ctypes
 import platform
 import sys

 import numpy as np

 # pylint: disable=g-import-not-at-top
 if not __file__.endswith('tflite_runtime/interpreter.py'):
   # This file is part of tensorflow package.
   from tensorflow.lite.python.interpreter_wrapper import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper
   from tensorflow.python.util.tf_export import tf_export as _tf_export
 else:
   # This file is part of tflite_runtime package.
   from tflite_runtime import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper

   def _tf_export(*x, **kwargs):
     del x, kwargs
     return lambda x: x


 class Delegate(object):
   """Python wrapper class to manage TfLiteDelegate objects.

   The shared library is expected to have two functions:
     TfLiteDelegate* tflite_plugin_create_delegate(
         char**, char**, size_t, void (*report_error)(const char *))
     void tflite_plugin_destroy_delegate(TfLiteDelegate*)

   The first one creates a delegate object. It may return NULL to indicate an
   error (with a suitable error message reported by calling report_error()).
   The second one destroys delegate object and must be called for every
   created delegate object. Passing NULL as argument value is allowed, i.e.

     tflite_plugin_destroy_delegate(tflite_plugin_create_delegate(...))

   always works.
   """

   def __init__(self, library, options=None):
     """Loads delegate from the shared library.

     Args:
       library: Shared library name.
       options: Dictionary of options that are required to load the delegate. All
         keys and values in the dictionary should be serializable. Consult the
         documentation of the specific delegate for required and legal options.
         (default None)

     Raises:
       RuntimeError: This is raised if the Python implementation is not CPython.
     """

     # TODO(b/136468453): Remove need for __del__ ordering needs of CPython
     # by using explicit closes(). See implementation of Interpreter __del__.
     if platform.python_implementation() != 'CPython':
       raise RuntimeError('Delegates are currently only supported into CPython'
                          'due to missing immediate reference counting.')

     self._library = ctypes.pydll.LoadLibrary(library)
     self._library.tflite_plugin_create_delegate.argtypes = [
         ctypes.POINTER(ctypes.c_char_p),
         ctypes.POINTER(ctypes.c_char_p), ctypes.c_int,
         ctypes.CFUNCTYPE(None, ctypes.c_char_p)
     ]
     self._library.tflite_plugin_create_delegate.restype = ctypes.c_void_p

     # Convert the options from a dictionary to lists of char pointers.
     options = options or {}
     options_keys = (ctypes.c_char_p * len(options))()
     options_values = (ctypes.c_char_p * len(options))()
     for idx, (key, value) in enumerate(options.items()):
       options_keys[idx] = str(key).encode('utf-8')
       options_values[idx] = str(value).encode('utf-8')

     class ErrorMessageCapture(object):

       def __init__(self):
         self.message = ''

       def report(self, x):
         self.message += x if isinstance(x, str) else x.decode('utf-8')

     capture = ErrorMessageCapture()
     error_capturer_cb = ctypes.CFUNCTYPE(None, ctypes.c_char_p)(capture.report)
     # Do not make a copy of _delegate_ptr. It is freed by Delegate's finalizer.
     self._delegate_ptr = self._library.tflite_plugin_create_delegate(
         options_keys, options_values, len(options), error_capturer_cb)
     if self._delegate_ptr is None:
       raise ValueError(capture.message)

   def __del__(self):
     # __del__ can not be called multiple times, so if the delegate is destroyed.
     # don't try to destroy it twice.
     if self._library is not None:
       self._library.tflite_plugin_destroy_delegate.argtypes = [ctypes.c_void_p]
       self._library.tflite_plugin_destroy_delegate(self._delegate_ptr)
       self._library = None

   def _get_native_delegate_pointer(self):
     """Returns the native TfLiteDelegate pointer.

     It is not safe to copy this pointer because it needs to be freed.

     Returns:
       TfLiteDelegate *
     """
     return self._delegate_ptr


 @_tf_export('lite.experimental.load_delegate')
 def load_delegate(library, options=None):
   """Returns loaded Delegate object.

   Args:
     library: Name of shared library containing the
       [TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates).
     options: Dictionary of options that are required to load the delegate. All
       keys and values in the dictionary should be convertible to str. Consult
       the documentation of the specific delegate for required and legal options.
       (default None)

   Returns:
     Delegate object.

   Raises:
     ValueError: Delegate failed to load.
     RuntimeError: If delegate loading is used on unsupported platform.
   """
   try:
     delegate = Delegate(library, options)
   except ValueError as e:
     raise ValueError('Failed to load delegate from {}\n{}'.format(
         library, str(e)))
   return delegate


 @_tf_export('lite.Interpreter')
 class Interpreter(object):
   """Interpreter interface for TensorFlow Lite Models.

   This makes the TensorFlow Lite interpreter accessible in Python.
   It is possible to use this interpreter in a multithreaded Python environment,
   but you must be sure to call functions of a particular instance from only
   one thread at a time. So if you want to have 4 threads running different
   inferences simultaneously, create  an interpreter for each one as thread-local
   data. Similarly, if you are calling invoke() in one thread on a single
   interpreter but you want to use tensor() on another thread once it is done,
   you must use a synchronization primitive between the threads to ensure invoke
   has returned before calling tensor().
   """

   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: Set the number of threads used by TFLite kernels.
         If not set, kernels are running single-threaded. Note that currently,
         only some kernels, such as conv, are multithreaded.

     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:
       if not isinstance(num_threads, int):
         raise ValueError('type of num_threads should be int')
       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 __del__(self):
     # Must make sure the interpreter is destroyed before things that
     # are used by it like the delegates. NOTE this only works on CPython
     # probably.
     # TODO(b/136468453): Remove need for __del__ ordering needs of CPython
     # by using explicit closes(). See implementation of Interpreter __del__.
     self._interpreter = None
     self._delegates = None

   def allocate_tensors(self):
     self._ensure_safe()
     return self._interpreter.AllocateTensors()

   def _safe_to_run(self):
     """Returns true if there exist no numpy array buffers.

     This means it is safe to run tflite calls that may destroy internally
     allocated memory. This works, because in the wrapper.cc we have made
     the numpy base be the self._interpreter.
     """
     # NOTE, our tensor() call in cpp will use _interpreter as a base pointer.
     # If this environment is the only _interpreter, then the ref count should be
     # 2 (1 in self and 1 in temporary of sys.getrefcount).
     return sys.getrefcount(self._interpreter) == 2

   def _ensure_safe(self):
     """Makes sure no numpy arrays pointing to internal buffers are active.

     This should be called from any function that will call a function on
     _interpreter that may reallocate memory e.g. invoke(), ...

     Raises:
       RuntimeError: If there exist numpy objects pointing to internal memory
         then we throw.
     """
     if not self._safe_to_run():
       raise RuntimeError("""There is at least 1 reference to internal data
       in the interpreter in the form of a numpy array or slice. Be sure to
       only hold the function returned from tensor() if you are using raw
       data access.""")

   # Experimental and subject to change
   def _get_op_details(self, op_index):
     """Gets a dictionary with arrays of ids for tensors involved with an op.

     Args:
       op_index: Operation/node index of node to query.

     Returns:
       a dictionary containing the index, op name, and arrays with lists of the
       indices for the inputs and outputs of the op/node.
     """
     op_index = int(op_index)
     op_name = self._interpreter.NodeName(op_index)
     op_inputs = self._interpreter.NodeInputs(op_index)
     op_outputs = self._interpreter.NodeOutputs(op_index)

     details = {
         'index': op_index,
         'op_name': op_name,
         'inputs': op_inputs,
         'outputs': op_outputs,
     }

     return details

   def _get_tensor_details(self, tensor_index):
     """Gets tensor details.

     Args:
       tensor_index: Tensor index of tensor to query.

     Returns:
       A dictionary containing the following fields of the tensor:
         'name': The tensor name.
         'index': The tensor index in the interpreter.
         'shape': The shape of the tensor.
         'quantization': Deprecated, use 'quantization_parameters'. This field
             only works for per-tensor quantization, whereas
             'quantization_parameters' works in all cases.
         'quantization_parameters': The parameters used to quantize the tensor:
           'scales': List of scales (one if per-tensor quantization)
           'zero_points': List of zero_points (one if per-tensor quantization)
           'quantized_dimension': Specifies the dimension of per-axis
               quantization, in the case of multiple scales/zero_points.

     Raises:
       ValueError: If tensor_index is invalid.
     """
     tensor_index = int(tensor_index)
     tensor_name = self._interpreter.TensorName(tensor_index)
     tensor_size = self._interpreter.TensorSize(tensor_index)
     tensor_size_signature = self._interpreter.TensorSizeSignature(tensor_index)
     tensor_type = self._interpreter.TensorType(tensor_index)
     tensor_quantization = self._interpreter.TensorQuantization(tensor_index)
     tensor_quantization_params = self._interpreter.TensorQuantizationParameters(
         tensor_index)
     tensor_sparsity_params = self._interpreter.TensorSparsityParameters(
         tensor_index)

     if not tensor_name or not tensor_type:
       raise ValueError('Could not get tensor details')

     details = {
         'name': tensor_name,
         'index': tensor_index,
         'shape': tensor_size,
         'shape_signature': tensor_size_signature,
         'dtype': tensor_type,
         'quantization': tensor_quantization,
         'quantization_parameters': {
             'scales': tensor_quantization_params[0],
             'zero_points': tensor_quantization_params[1],
             'quantized_dimension': tensor_quantization_params[2],
         },
         'sparsity_parameters': tensor_sparsity_params
     }

     return details

   # Experimental and subject to change
   def _get_ops_details(self):
     """Gets op details for every node.

     Returns:
       A list of dictionaries containing arrays with lists of tensor ids for
       tensors involved in the op.
     """
     return [
         self._get_op_details(idx) for idx in range(self._interpreter.NumNodes())
     ]

   def get_tensor_details(self):
     """Gets tensor details for every tensor with valid tensor details.

     Tensors where required information about the tensor is not found are not
     added to the list. This includes temporary tensors without a name.

     Returns:
       A list of dictionaries containing tensor information.
     """
     tensor_details = []
     for idx in range(self._interpreter.NumTensors()):
       try:
         tensor_details.append(self._get_tensor_details(idx))
       except ValueError:
         pass
     return tensor_details

   def get_input_details(self):
     """Gets model input details.

     Returns:
       A list of input details.
     """
     return [
         self._get_tensor_details(i) for i in self._interpreter.InputIndices()
     ]

   def set_tensor(self, tensor_index, value):
     """Sets the value of the input tensor.

     Note this copies data in `value`.

     If you want to avoid copying, you can use the `tensor()` function to get a
     numpy buffer pointing to the input buffer in the tflite interpreter.

     Args:
       tensor_index: Tensor index of tensor to set. This value can be gotten from
         the 'index' field in get_input_details.
       value: Value of tensor to set.

     Raises:
       ValueError: If the interpreter could not set the tensor.
     """
     self._interpreter.SetTensor(tensor_index, value)

   def resize_tensor_input(self, input_index, tensor_size, strict=False):
     """Resizes an input tensor.

     ```
     interpreter = Interpreter(model_content=tflite_model)
     interpreter.resize_tensor_input(0, [1, 224, 224, 3], strict=True)
     interpreter.allocate_tensors()
     interpreter.invoke()
     ```

     Args:
       input_index: Tensor index of input to set. This value can be gotten from
         the 'index' field in get_input_details.
       tensor_size: The tensor_shape to resize the input to.
       strict: Only unknown dimensions can be resized when `strict` is True.
         Unknown dimensions are indicated as `-1` in the `shape_signature`
         attribute of a given tensor. (default False)

     Raises:
       ValueError: If the interpreter could not resize the input tensor.
     """
     self._ensure_safe()
     # `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size
     # parameter.
     tensor_size = np.array(tensor_size, dtype=np.int32)
     self._interpreter.ResizeInputTensor(input_index, tensor_size, strict)

   def get_output_details(self):
     """Gets model output details.

     Returns:
       A list of output details.
     """
     return [
         self._get_tensor_details(i) for i in self._interpreter.OutputIndices()
     ]

   def get_tensor(self, tensor_index):
     """Gets the value of the input tensor (get a copy).

     If you wish to avoid the copy, use `tensor()`. This function cannot be used
     to read intermediate results.

     Args:
       tensor_index: Tensor index of tensor to get. This value can be gotten from
         the 'index' field in get_output_details.

     Returns:
       a numpy array.
     """
     return self._interpreter.GetTensor(tensor_index)

   def tensor(self, tensor_index):
     """Returns function that gives a numpy view of the current tensor buffer.

     This allows reading and writing to this tensors w/o copies. This more
     closely mirrors the C++ Interpreter class interface's tensor() member, hence
     the name. Be careful to not hold these output references through calls
     to `allocate_tensors()` and `invoke()`. This function cannot be used to read
     intermediate results.

     Usage:

     ```
     interpreter.allocate_tensors()
     input = interpreter.tensor(interpreter.get_input_details()[0]["index"])
     output = interpreter.tensor(interpreter.get_output_details()[0]["index"])
     for i in range(10):
       input().fill(3.)
       interpreter.invoke()
       print("inference %s" % output())
     ```

     Notice how this function avoids making a numpy array directly. This is
     because it is important to not hold actual numpy views to the data longer
     than necessary. If you do, then the interpreter can no longer be invoked,
     because it is possible the interpreter would resize and invalidate the
     referenced tensors. The NumPy API doesn't allow any mutability of the
     the underlying buffers.

     WRONG:

     ```
     input = interpreter.tensor(interpreter.get_input_details()[0]["index"])()
     output = interpreter.tensor(interpreter.get_output_details()[0]["index"])()
     interpreter.allocate_tensors()  # This will throw RuntimeError
     for i in range(10):
       input.fill(3.)
       interpreter.invoke()  # this will throw RuntimeError since input,output
     ```

     Args:
       tensor_index: Tensor index of tensor to get. This value can be gotten from
         the 'index' field in get_output_details.

     Returns:
       A function that can return a new numpy array pointing to the internal
       TFLite tensor state at any point. It is safe to hold the function forever,
       but it is not safe to hold the numpy array forever.
     """
     return lambda: self._interpreter.tensor(self._interpreter, tensor_index)

   def invoke(self):
     """Invoke the interpreter.

     Be sure to set the input sizes, allocate tensors and fill values before
     calling this. Also, note that this function releases the GIL so heavy
     computation can be done in the background while the Python interpreter
     continues. No other function on this object should be called while the
     invoke() call has not finished.

     Raises:
       ValueError: When the underlying interpreter fails raise ValueError.
     """
     self._ensure_safe()
     self._interpreter.Invoke()

   def reset_all_variables(self):
     return self._interpreter.ResetVariableTensors()


 class InterpreterWithCustomOps(Interpreter):
   """Interpreter interface for TensorFlow Lite Models that accepts custom ops.

   The interface provided by this class is experimental and therefore not exposed
   as part of the public API.

   Wraps the tf.lite.Interpreter class and adds the ability to load custom ops
   by providing the names of functions that take a pointer to a BuiltinOpResolver
   and add a custom op.
   """

   def __init__(self,
                model_path=None,
                model_content=None,
                experimental_delegates=None,
                custom_op_registerers=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().
       custom_op_registerers: List of str, symbol names of functions that take a
         pointer to a MutableOpResolver and register a custom op.

     Raises:
       ValueError: If the interpreter was unable to create.
     """
     self._custom_op_registerers = custom_op_registerers
     super(InterpreterWithCustomOps, self).__init__(
         model_path=model_path,
         model_content=model_content,
         experimental_delegates=experimental_delegates)
	# Lint as: python2, python3
	# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# ==============================================================================
	"""Python TF-Lite interpreter."""
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import ctypes
	import platform
	import sys

	import numpy as np

	# pylint: disable=g-import-not-at-top
	if not __file__.endswith('tflite_runtime/interpreter.py'):
	# This file is part of tensorflow package.
	from tensorflow.lite.python.interpreter_wrapper import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper
	from tensorflow.python.util.tf_export import tf_export as _tf_export
	else:
	# This file is part of tflite_runtime package.
	from tflite_runtime import _pywrap_tensorflow_interpreter_wrapper as _interpreter_wrapper

	def _tf_export(x, *kwargs):
	del x, kwargs
	return lambda x: x


	class Delegate(object):
	"""Python wrapper class to manage TfLiteDelegate objects.

	The shared library is expected to have two functions:
	TfLiteDelegate* tflite_plugin_create_delegate(
	char, char, size_t, void (report_error)(const char ))
	void tflite_plugin_destroy_delegate(TfLiteDelegate*)

	The first one creates a delegate object. It may return NULL to indicate an
	error (with a suitable error message reported by calling report_error()).
	The second one destroys delegate object and must be called for every
	created delegate object. Passing NULL as argument value is allowed, i.e.

	tflite_plugin_destroy_delegate(tflite_plugin_create_delegate(...))

	always works.
	"""

	def __init__(self, library, options=None):
	"""Loads delegate from the shared library.

	Args:
	library: Shared library name.
	options: Dictionary of options that are required to load the delegate. All
	keys and values in the dictionary should be serializable. Consult the
	documentation of the specific delegate for required and legal options.
	(default None)

	Raises:
	RuntimeError: This is raised if the Python implementation is not CPython.
	"""

	# TODO(b/136468453): Remove need for __del__ ordering needs of CPython
	# by using explicit closes(). See implementation of Interpreter __del__.
	if platform.python_implementation() != 'CPython':
	raise RuntimeError('Delegates are currently only supported into CPython'
	'due to missing immediate reference counting.')

	self._library = ctypes.pydll.LoadLibrary(library)
	self._library.tflite_plugin_create_delegate.argtypes = [
	ctypes.POINTER(ctypes.c_char_p),
	ctypes.POINTER(ctypes.c_char_p), ctypes.c_int,
	ctypes.CFUNCTYPE(None, ctypes.c_char_p)
	]
	self._library.tflite_plugin_create_delegate.restype = ctypes.c_void_p

	# Convert the options from a dictionary to lists of char pointers.
	options = options or {}
	options_keys = (ctypes.c_char_p * len(options))()
	options_values = (ctypes.c_char_p * len(options))()
	for idx, (key, value) in enumerate(options.items()):
	options_keys[idx] = str(key).encode('utf-8')
	options_values[idx] = str(value).encode('utf-8')

	class ErrorMessageCapture(object):

	def __init__(self):
	self.message = ''

	def report(self, x):
	self.message += x if isinstance(x, str) else x.decode('utf-8')

	capture = ErrorMessageCapture()
	error_capturer_cb = ctypes.CFUNCTYPE(None, ctypes.c_char_p)(capture.report)
	# Do not make a copy of _delegate_ptr. It is freed by Delegate's finalizer.
	self._delegate_ptr = self._library.tflite_plugin_create_delegate(
	options_keys, options_values, len(options), error_capturer_cb)
	if self._delegate_ptr is None:
	raise ValueError(capture.message)

	def __del__(self):
	# __del__ can not be called multiple times, so if the delegate is destroyed.
	# don't try to destroy it twice.
	if self._library is not None:
	self._library.tflite_plugin_destroy_delegate.argtypes = [ctypes.c_void_p]
	self._library.tflite_plugin_destroy_delegate(self._delegate_ptr)
	self._library = None

	def _get_native_delegate_pointer(self):
	"""Returns the native TfLiteDelegate pointer.

	It is not safe to copy this pointer because it needs to be freed.

	Returns:
	TfLiteDelegate *
	"""
	return self._delegate_ptr


	@_tf_export('lite.experimental.load_delegate')
	def load_delegate(library, options=None):
	"""Returns loaded Delegate object.

	Args:
	library: Name of shared library containing the
	[TfLiteDelegate](https://www.tensorflow.org/lite/performance/delegates).
	options: Dictionary of options that are required to load the delegate. All
	keys and values in the dictionary should be convertible to str. Consult
	the documentation of the specific delegate for required and legal options.
	(default None)

	Returns:
	Delegate object.

	Raises:
	ValueError: Delegate failed to load.
	RuntimeError: If delegate loading is used on unsupported platform.
	"""
	try:
	delegate = Delegate(library, options)
	except ValueError as e:
	raise ValueError('Failed to load delegate from {}\n{}'.format(
	library, str(e)))
	return delegate


	@_tf_export('lite.Interpreter')
	class Interpreter(object):
	"""Interpreter interface for TensorFlow Lite Models.

	This makes the TensorFlow Lite interpreter accessible in Python.
	It is possible to use this interpreter in a multithreaded Python environment,
	but you must be sure to call functions of a particular instance from only
	one thread at a time. So if you want to have 4 threads running different
	inferences simultaneously, create an interpreter for each one as thread-local
	data. Similarly, if you are calling invoke() in one thread on a single
	interpreter but you want to use tensor() on another thread once it is done,
	you must use a synchronization primitive between the threads to ensure invoke
	has returned before calling tensor().
	"""

	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: Set the number of threads used by TFLite kernels.
	If not set, kernels are running single-threaded. Note that currently,
	only some kernels, such as conv, are multithreaded.

	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:
	if not isinstance(num_threads, int):
	raise ValueError('type of num_threads should be int')
	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 __del__(self):
	# Must make sure the interpreter is destroyed before things that
	# are used by it like the delegates. NOTE this only works on CPython
	# probably.
	# TODO(b/136468453): Remove need for __del__ ordering needs of CPython
	# by using explicit closes(). See implementation of Interpreter __del__.
	self._interpreter = None
	self._delegates = None

	def allocate_tensors(self):
	self._ensure_safe()
	return self._interpreter.AllocateTensors()

	def _safe_to_run(self):
	"""Returns true if there exist no numpy array buffers.

	This means it is safe to run tflite calls that may destroy internally
	allocated memory. This works, because in the wrapper.cc we have made
	the numpy base be the self._interpreter.
	"""
	# NOTE, our tensor() call in cpp will use _interpreter as a base pointer.
	# If this environment is the only _interpreter, then the ref count should be
	# 2 (1 in self and 1 in temporary of sys.getrefcount).
	return sys.getrefcount(self._interpreter) == 2

	def _ensure_safe(self):
	"""Makes sure no numpy arrays pointing to internal buffers are active.

	This should be called from any function that will call a function on
	_interpreter that may reallocate memory e.g. invoke(), ...

	Raises:
	RuntimeError: If there exist numpy objects pointing to internal memory
	then we throw.
	"""
	if not self._safe_to_run():
	raise RuntimeError("""There is at least 1 reference to internal data
	in the interpreter in the form of a numpy array or slice. Be sure to
	only hold the function returned from tensor() if you are using raw
	data access.""")

	# Experimental and subject to change
	def _get_op_details(self, op_index):
	"""Gets a dictionary with arrays of ids for tensors involved with an op.

	Args:
	op_index: Operation/node index of node to query.

	Returns:
	a dictionary containing the index, op name, and arrays with lists of the
	indices for the inputs and outputs of the op/node.
	"""
	op_index = int(op_index)
	op_name = self._interpreter.NodeName(op_index)
	op_inputs = self._interpreter.NodeInputs(op_index)
	op_outputs = self._interpreter.NodeOutputs(op_index)

	details = {
	'index': op_index,
	'op_name': op_name,
	'inputs': op_inputs,
	'outputs': op_outputs,
	}

	return details

	def _get_tensor_details(self, tensor_index):
	"""Gets tensor details.

	Args:
	tensor_index: Tensor index of tensor to query.

	Returns:
	A dictionary containing the following fields of the tensor:
	'name': The tensor name.
	'index': The tensor index in the interpreter.
	'shape': The shape of the tensor.
	'quantization': Deprecated, use 'quantization_parameters'. This field
	only works for per-tensor quantization, whereas
	'quantization_parameters' works in all cases.
	'quantization_parameters': The parameters used to quantize the tensor:
	'scales': List of scales (one if per-tensor quantization)
	'zero_points': List of zero_points (one if per-tensor quantization)
	'quantized_dimension': Specifies the dimension of per-axis
	quantization, in the case of multiple scales/zero_points.

	Raises:
	ValueError: If tensor_index is invalid.
	"""
	tensor_index = int(tensor_index)
	tensor_name = self._interpreter.TensorName(tensor_index)
	tensor_size = self._interpreter.TensorSize(tensor_index)
	tensor_size_signature = self._interpreter.TensorSizeSignature(tensor_index)
	tensor_type = self._interpreter.TensorType(tensor_index)
	tensor_quantization = self._interpreter.TensorQuantization(tensor_index)
	tensor_quantization_params = self._interpreter.TensorQuantizationParameters(
	tensor_index)
	tensor_sparsity_params = self._interpreter.TensorSparsityParameters(
	tensor_index)

	if not tensor_name or not tensor_type:
	raise ValueError('Could not get tensor details')

	details = {
	'name': tensor_name,
	'index': tensor_index,
	'shape': tensor_size,
	'shape_signature': tensor_size_signature,
	'dtype': tensor_type,
	'quantization': tensor_quantization,
	'quantization_parameters': {
	'scales': tensor_quantization_params[0],
	'zero_points': tensor_quantization_params[1],
	'quantized_dimension': tensor_quantization_params[2],
	},
	'sparsity_parameters': tensor_sparsity_params
	}

	return details

	# Experimental and subject to change
	def _get_ops_details(self):
	"""Gets op details for every node.

	Returns:
	A list of dictionaries containing arrays with lists of tensor ids for
	tensors involved in the op.
	"""
	return [
	self._get_op_details(idx) for idx in range(self._interpreter.NumNodes())
	]

	def get_tensor_details(self):
	"""Gets tensor details for every tensor with valid tensor details.

	Tensors where required information about the tensor is not found are not
	added to the list. This includes temporary tensors without a name.

	Returns:
	A list of dictionaries containing tensor information.
	"""
	tensor_details = []
	for idx in range(self._interpreter.NumTensors()):
	try:
	tensor_details.append(self._get_tensor_details(idx))
	except ValueError:
	pass
	return tensor_details

	def get_input_details(self):
	"""Gets model input details.

	Returns:
	A list of input details.
	"""
	return [
	self._get_tensor_details(i) for i in self._interpreter.InputIndices()
	]

	def set_tensor(self, tensor_index, value):
	"""Sets the value of the input tensor.

	Note this copies data in `value`.

	If you want to avoid copying, you can use the `tensor()` function to get a
	numpy buffer pointing to the input buffer in the tflite interpreter.

	Args:
	tensor_index: Tensor index of tensor to set. This value can be gotten from
	the 'index' field in get_input_details.
	value: Value of tensor to set.

	Raises:
	ValueError: If the interpreter could not set the tensor.
	"""
	self._interpreter.SetTensor(tensor_index, value)

	def resize_tensor_input(self, input_index, tensor_size, strict=False):
	"""Resizes an input tensor.

	```
	interpreter = Interpreter(model_content=tflite_model)
	interpreter.resize_tensor_input(0, [1, 224, 224, 3], strict=True)
	interpreter.allocate_tensors()
	interpreter.invoke()
	```

	Args:
	input_index: Tensor index of input to set. This value can be gotten from
	the 'index' field in get_input_details.
	tensor_size: The tensor_shape to resize the input to.
	strict: Only unknown dimensions can be resized when `strict` is True.
	Unknown dimensions are indicated as `-1` in the `shape_signature`
	attribute of a given tensor. (default False)

	Raises:
	ValueError: If the interpreter could not resize the input tensor.
	"""
	self._ensure_safe()
	# `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size
	# parameter.
	tensor_size = np.array(tensor_size, dtype=np.int32)
	self._interpreter.ResizeInputTensor(input_index, tensor_size, strict)

	def get_output_details(self):
	"""Gets model output details.

	Returns:
	A list of output details.
	"""
	return [
	self._get_tensor_details(i) for i in self._interpreter.OutputIndices()
	]

	def get_tensor(self, tensor_index):
	"""Gets the value of the input tensor (get a copy).

	If you wish to avoid the copy, use `tensor()`. This function cannot be used
	to read intermediate results.

	Args:
	tensor_index: Tensor index of tensor to get. This value can be gotten from
	the 'index' field in get_output_details.

	Returns:
	a numpy array.
	"""
	return self._interpreter.GetTensor(tensor_index)

	def tensor(self, tensor_index):
	"""Returns function that gives a numpy view of the current tensor buffer.

	This allows reading and writing to this tensors w/o copies. This more
	closely mirrors the C++ Interpreter class interface's tensor() member, hence
	the name. Be careful to not hold these output references through calls
	to `allocate_tensors()` and `invoke()`. This function cannot be used to read
	intermediate results.

	Usage:

	```
	interpreter.allocate_tensors()
	input = interpreter.tensor(interpreter.get_input_details()[0]["index"])
	output = interpreter.tensor(interpreter.get_output_details()[0]["index"])
	for i in range(10):
	input().fill(3.)
	interpreter.invoke()
	print("inference %s" % output())
	```

	Notice how this function avoids making a numpy array directly. This is
	because it is important to not hold actual numpy views to the data longer
	than necessary. If you do, then the interpreter can no longer be invoked,
	because it is possible the interpreter would resize and invalidate the
	referenced tensors. The NumPy API doesn't allow any mutability of the
	the underlying buffers.

	WRONG:

	```
	input = interpreter.tensor(interpreter.get_input_details()[0]["index"])()
	output = interpreter.tensor(interpreter.get_output_details()[0]["index"])()
	interpreter.allocate_tensors() # This will throw RuntimeError
	for i in range(10):
	input.fill(3.)
	interpreter.invoke() # this will throw RuntimeError since input,output
	```

	Args:
	tensor_index: Tensor index of tensor to get. This value can be gotten from
	the 'index' field in get_output_details.

	Returns:
	A function that can return a new numpy array pointing to the internal
	TFLite tensor state at any point. It is safe to hold the function forever,
	but it is not safe to hold the numpy array forever.
	"""
	return lambda: self._interpreter.tensor(self._interpreter, tensor_index)

	def invoke(self):
	"""Invoke the interpreter.

	Be sure to set the input sizes, allocate tensors and fill values before
	calling this. Also, note that this function releases the GIL so heavy
	computation can be done in the background while the Python interpreter
	continues. No other function on this object should be called while the
	invoke() call has not finished.

	Raises:
	ValueError: When the underlying interpreter fails raise ValueError.
	"""
	self._ensure_safe()
	self._interpreter.Invoke()

	def reset_all_variables(self):
	return self._interpreter.ResetVariableTensors()


	class InterpreterWithCustomOps(Interpreter):
	"""Interpreter interface for TensorFlow Lite Models that accepts custom ops.

	The interface provided by this class is experimental and therefore not exposed
	as part of the public API.

	Wraps the tf.lite.Interpreter class and adds the ability to load custom ops
	by providing the names of functions that take a pointer to a BuiltinOpResolver
	and add a custom op.
	"""

	def __init__(self,
	model_path=None,
	model_content=None,
	experimental_delegates=None,
	custom_op_registerers=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().
	custom_op_registerers: List of str, symbol names of functions that take a
	pointer to a MutableOpResolver and register a custom op.

	Raises:
	ValueError: If the interpreter was unable to create.
	"""
	self._custom_op_registerers = custom_op_registerers
	super(InterpreterWithCustomOps, self).__init__(
	model_path=model_path,
	model_content=model_content,
	experimental_delegates=experimental_delegates)