TensorFlow定义文件:Python TF-Lite解释器

本节提供了tf.lite.Interpreter函数的帮助文件:tensorflow/lite/python/interpreter.py
# 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 sys
import numpy as np
from tensorflow.python.util.lazy_loader import LazyLoader
from tensorflow.python.util.tf_export import tf_export as _tf_export

# Lazy load since some of the performance benchmark skylark rules
# break dependencies. Must use double quotes to match code internal rewrite
# rule.
# pylint: disable=g-inconsistent-quotes
_interpreter_wrapper = LazyLoader(
    "_interpreter_wrapper", globals(),
    "tensorflow.lite.python.interpreter_wrapper."
    "tensorflow_wrap_interpreter_wrapper")
# pylint: enable=g-inconsistent-quotes

del LazyLoader


@_tf_export('lite.Interpreter')
class Interpreter(object):
  """Interpreter inferace for TF-Lite Models."""

  def __init__(self, model_path=None, model_content=None):
    """Constructor.
    Args:
      model_path: Path to TF-Lite Flatbuffer file.
      model_content: Content of model.
    Raises:
      ValueError: If the interpreter was unable to create.
    """
    if model_path and not model_content:
      self._interpreter = (
          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromFile(
              model_path))
      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.InterpreterWrapper_CreateWrapperCPPFromBuffer(
              model_content))
    elif not model_path 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`')

  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.""")

  def _get_tensor_details(self, tensor_index):
    """Gets tensor details.
    Args:
      tensor_index: Tensor index of tensor to query.
    Returns:
      a dictionary containing the name, index, shape and type of the tensor.
    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_type = self._interpreter.TensorType(tensor_index)
    tensor_quantization = self._interpreter.TensorQuantization(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,
        'dtype': tensor_type,
        'quantization': tensor_quantization,
    }

    return details

  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):
    """Resizes an input tensor.
    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.
    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)

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

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


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