# Copyright 2017 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.
# ==============================================================================

"""Functions to export object detection inference graph."""
import os
import tempfile
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.tools import freeze_graph  # pylint: disable=g-direct-tensorflow-import
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.core import standard_fields as fields
from object_detection.data_decoders import tf_example_decoder
from object_detection.utils import config_util
from object_detection.utils import shape_utils

# pylint: disable=g-import-not-at-top
try:
  from tensorflow.contrib import tfprof as contrib_tfprof
  from tensorflow.contrib.quantize.python import graph_matcher
except ImportError:
  # TF 2.0 doesn't ship with contrib.
  pass
# pylint: enable=g-import-not-at-top

freeze_graph_with_def_protos = freeze_graph.freeze_graph_with_def_protos


def parse_side_inputs(side_input_shapes_string, side_input_names_string,
                      side_input_types_string):
  """Parses side input flags.

  Args:
    side_input_shapes_string: The shape of the side input tensors, provided as a
      comma-separated list of integers. A value of -1 is used for unknown
      dimensions. A `/` denotes a break, starting the shape of the next side
      input tensor.
    side_input_names_string: The names of the side input tensors, provided as a
      comma-separated list of strings.
    side_input_types_string: The type of the side input tensors, provided as a
      comma-separated list of types, each of `string`, `integer`, or `float`.

  Returns:
    side_input_shapes: A list of shapes.
    side_input_names: A list of strings.
    side_input_types: A list of tensorflow dtypes.

  """
  if side_input_shapes_string:
    side_input_shapes = []
    for side_input_shape_list in side_input_shapes_string.split('/'):
      side_input_shape = [
          int(dim) if dim != '-1' else None
          for dim in side_input_shape_list.split(',')
      ]
      side_input_shapes.append(side_input_shape)
  else:
    raise ValueError('When using side_inputs, side_input_shapes must be '
                     'specified in the input flags.')
  if side_input_names_string:
    side_input_names = list(side_input_names_string.split(','))
  else:
    raise ValueError('When using side_inputs, side_input_names must be '
                     'specified in the input flags.')
  if side_input_types_string:
    typelookup = {'float': tf.float32, 'int': tf.int32, 'string': tf.string}
    side_input_types = [
        typelookup[side_input_type]
        for side_input_type in side_input_types_string.split(',')
    ]
  else:
    raise ValueError('When using side_inputs, side_input_types must be '
                     'specified in the input flags.')
  return side_input_shapes, side_input_names, side_input_types


def rewrite_nn_resize_op(is_quantized=False):
  """Replaces a custom nearest-neighbor resize op with the Tensorflow version.

  Some graphs use this custom version for TPU-compatibility.

  Args:
    is_quantized: True if the default graph is quantized.
  """
  def remove_nn():
    """Remove nearest neighbor upsampling structures and replace with TF op."""
    input_pattern = graph_matcher.OpTypePattern(
        'FakeQuantWithMinMaxVars' if is_quantized else '*')
    stack_1_pattern = graph_matcher.OpTypePattern(
        'Pack', inputs=[input_pattern, input_pattern], ordered_inputs=False)
    stack_2_pattern = graph_matcher.OpTypePattern(
        'Pack', inputs=[stack_1_pattern, stack_1_pattern], ordered_inputs=False)
    reshape_pattern = graph_matcher.OpTypePattern(
        'Reshape', inputs=[stack_2_pattern, 'Const'], ordered_inputs=False)
    consumer_pattern1 = graph_matcher.OpTypePattern(
        'Add|AddV2|Max|Mul', inputs=[reshape_pattern, '*'],
        ordered_inputs=False)
    consumer_pattern2 = graph_matcher.OpTypePattern(
        'StridedSlice', inputs=[reshape_pattern, '*', '*', '*'],
        ordered_inputs=False)

    def replace_matches(consumer_pattern):
      """Search for nearest neighbor pattern and replace with TF op."""
      match_counter = 0
      matcher = graph_matcher.GraphMatcher(consumer_pattern)
      for match in matcher.match_graph(tf.get_default_graph()):
        match_counter += 1
        projection_op = match.get_op(input_pattern)
        reshape_op = match.get_op(reshape_pattern)
        consumer_op = match.get_op(consumer_pattern)
        nn_resize = tf.image.resize_nearest_neighbor(
            projection_op.outputs[0],
            reshape_op.outputs[0].shape.dims[1:3],
            align_corners=False,
            name=os.path.split(reshape_op.name)[0] + '/resize_nearest_neighbor')

        for index, op_input in enumerate(consumer_op.inputs):
          if op_input == reshape_op.outputs[0]:
            consumer_op._update_input(index, nn_resize)  # pylint: disable=protected-access
            break

      return match_counter

    match_counter = replace_matches(consumer_pattern1)
    match_counter += replace_matches(consumer_pattern2)

    tf.logging.info('Found and fixed {} matches'.format(match_counter))
    return match_counter

  # Applying twice because both inputs to Add could be NN pattern
  total_removals = 0
  while remove_nn():
    total_removals += 1
    # This number is chosen based on the nas-fpn architecture.
    if total_removals > 4:
      raise ValueError('Graph removal encountered a infinite loop.')


def replace_variable_values_with_moving_averages(graph,
                                                 current_checkpoint_file,
                                                 new_checkpoint_file,
                                                 no_ema_collection=None):
  """Replaces variable values in the checkpoint with their moving averages.

  If the current checkpoint has shadow variables maintaining moving averages of
  the variables defined in the graph, this function generates a new checkpoint
  where the variables contain the values of their moving averages.

  Args:
    graph: a tf.Graph object.
    current_checkpoint_file: a checkpoint containing both original variables and
      their moving averages.
    new_checkpoint_file: file path to write a new checkpoint.
    no_ema_collection: A list of namescope substrings to match the variables
      to eliminate EMA.
  """
  with graph.as_default():
    variable_averages = tf.train.ExponentialMovingAverage(0.0)
    ema_variables_to_restore = variable_averages.variables_to_restore()
    ema_variables_to_restore = config_util.remove_unecessary_ema(
        ema_variables_to_restore, no_ema_collection)
    with tf.Session() as sess:
      read_saver = tf.train.Saver(ema_variables_to_restore)
      read_saver.restore(sess, current_checkpoint_file)
      write_saver = tf.train.Saver()
      write_saver.save(sess, new_checkpoint_file)


def _image_tensor_input_placeholder(input_shape=None):
  """Returns input placeholder and a 4-D uint8 image tensor."""
  if input_shape is None:
    input_shape = (None, None, None, 3)
  input_tensor = tf.placeholder(
      dtype=tf.uint8, shape=input_shape, name='image_tensor')
  return input_tensor, input_tensor


def _side_input_tensor_placeholder(side_input_shape, side_input_name,
                                   side_input_type):
  """Returns side input placeholder and side input tensor."""
  side_input_tensor = tf.placeholder(
      dtype=side_input_type, shape=side_input_shape, name=side_input_name)
  return side_input_tensor, side_input_tensor


def _tf_example_input_placeholder(input_shape=None):
  """Returns input that accepts a batch of strings with tf examples.

  Args:
    input_shape: the shape to resize the output decoded images to (optional).

  Returns:
    a tuple of input placeholder and the output decoded images.
  """
  batch_tf_example_placeholder = tf.placeholder(
      tf.string, shape=[None], name='tf_example')
  def decode(tf_example_string_tensor):
    tensor_dict = tf_example_decoder.TfExampleDecoder().decode(
        tf_example_string_tensor)
    image_tensor = tensor_dict[fields.InputDataFields.image]
    if input_shape is not None:
      image_tensor = tf.image.resize(image_tensor, input_shape[1:3])
    return image_tensor
  return (batch_tf_example_placeholder,
          shape_utils.static_or_dynamic_map_fn(
              decode,
              elems=batch_tf_example_placeholder,
              dtype=tf.uint8,
              parallel_iterations=32,
              back_prop=False))


def _encoded_image_string_tensor_input_placeholder(input_shape=None):
  """Returns input that accepts a batch of PNG or JPEG strings.

  Args:
    input_shape: the shape to resize the output decoded images to (optional).

  Returns:
    a tuple of input placeholder and the output decoded images.
  """
  batch_image_str_placeholder = tf.placeholder(
      dtype=tf.string,
      shape=[None],
      name='encoded_image_string_tensor')
  def decode(encoded_image_string_tensor):
    image_tensor = tf.image.decode_image(encoded_image_string_tensor,
                                         channels=3)
    image_tensor.set_shape((None, None, 3))
    if input_shape is not None:
      image_tensor = tf.image.resize(image_tensor, input_shape[1:3])
    return image_tensor
  return (batch_image_str_placeholder,
          tf.map_fn(
              decode,
              elems=batch_image_str_placeholder,
              dtype=tf.uint8,
              parallel_iterations=32,
              back_prop=False))


input_placeholder_fn_map = {
    'image_tensor': _image_tensor_input_placeholder,
    'encoded_image_string_tensor':
    _encoded_image_string_tensor_input_placeholder,
    'tf_example': _tf_example_input_placeholder
}


def add_output_tensor_nodes(postprocessed_tensors,
                            output_collection_name='inference_op'):
  """Adds output nodes for detection boxes and scores.

  Adds the following nodes for output tensors -
    * num_detections: float32 tensor of shape [batch_size].
    * detection_boxes: float32 tensor of shape [batch_size, num_boxes, 4]
      containing detected boxes.
    * detection_scores: float32 tensor of shape [batch_size, num_boxes]
      containing scores for the detected boxes.
    * detection_multiclass_scores: (Optional) float32 tensor of shape
      [batch_size, num_boxes, num_classes_with_background] for containing class
      score distribution for detected boxes including background if any.
    * detection_features: (Optional) float32 tensor of shape
      [batch, num_boxes, roi_height, roi_width, depth]
      containing classifier features
      for each detected box
    * detection_classes: float32 tensor of shape [batch_size, num_boxes]
      containing class predictions for the detected boxes.
    * detection_keypoints: (Optional) float32 tensor of shape
      [batch_size, num_boxes, num_keypoints, 2] containing keypoints for each
      detection box.
    * detection_masks: (Optional) float32 tensor of shape
      [batch_size, num_boxes, mask_height, mask_width] containing masks for each
      detection box.

  Args:
    postprocessed_tensors: a dictionary containing the following fields
      'detection_boxes': [batch, max_detections, 4]
      'detection_scores': [batch, max_detections]
      'detection_multiclass_scores': [batch, max_detections,
        num_classes_with_background]
      'detection_features': [batch, num_boxes, roi_height, roi_width, depth]
      'detection_classes': [batch, max_detections]
      'detection_masks': [batch, max_detections, mask_height, mask_width]
        (optional).
      'detection_keypoints': [batch, max_detections, num_keypoints, 2]
        (optional).
      'num_detections': [batch]
    output_collection_name: Name of collection to add output tensors to.

  Returns:
    A tensor dict containing the added output tensor nodes.
  """
  detection_fields = fields.DetectionResultFields
  label_id_offset = 1
  boxes = postprocessed_tensors.get(detection_fields.detection_boxes)
  scores = postprocessed_tensors.get(detection_fields.detection_scores)
  multiclass_scores = postprocessed_tensors.get(
      detection_fields.detection_multiclass_scores)
  box_classifier_features = postprocessed_tensors.get(
      detection_fields.detection_features)
  raw_boxes = postprocessed_tensors.get(detection_fields.raw_detection_boxes)
  raw_scores = postprocessed_tensors.get(detection_fields.raw_detection_scores)
  raw_embedding = postprocessed_tensors.get(detection_fields.raw_embedding)
  nms_embedding = postprocessed_tensors.get(detection_fields.embedding)
  classes = postprocessed_tensors.get(
      detection_fields.detection_classes) + label_id_offset
  keypoints = postprocessed_tensors.get(detection_fields.detection_keypoints)
  masks = postprocessed_tensors.get(detection_fields.detection_masks)
  num_detections = postprocessed_tensors.get(detection_fields.num_detections)
  outputs = {}
  outputs[detection_fields.detection_boxes] = tf.identity(
      boxes, name=detection_fields.detection_boxes)
  outputs[detection_fields.detection_scores] = tf.identity(
      scores, name=detection_fields.detection_scores)
  if multiclass_scores is not None:
    outputs[detection_fields.detection_multiclass_scores] = tf.identity(
        multiclass_scores, name=detection_fields.detection_multiclass_scores)
  if box_classifier_features is not None:
    outputs[detection_fields.detection_features] = tf.identity(
        box_classifier_features,
        name=detection_fields.detection_features)
  outputs[detection_fields.detection_classes] = tf.identity(
      classes, name=detection_fields.detection_classes)
  outputs[detection_fields.num_detections] = tf.identity(
      num_detections, name=detection_fields.num_detections)
  if raw_boxes is not None:
    outputs[detection_fields.raw_detection_boxes] = tf.identity(
        raw_boxes, name=detection_fields.raw_detection_boxes)
  if raw_scores is not None:
    outputs[detection_fields.raw_detection_scores] = tf.identity(
        raw_scores, name=detection_fields.raw_detection_scores)
  if raw_embedding is not None:
    outputs[detection_fields.raw_embedding] = tf.identity(
        raw_scores, name=detection_fields.raw_embedding)
  if nms_embedding is not None:
    outputs[detection_fields.embedding] = tf.identity(
        nms_embedding, name=detection_fields.embedding)
  if keypoints is not None:
    outputs[detection_fields.detection_keypoints] = tf.identity(
        keypoints, name=detection_fields.detection_keypoints)
  if masks is not None:
    outputs[detection_fields.detection_masks] = tf.identity(
        masks, name=detection_fields.detection_masks)
  for output_key in outputs:
    tf.add_to_collection(output_collection_name, outputs[output_key])

  return outputs


def write_saved_model(saved_model_path,
                      frozen_graph_def,
                      inputs,
                      outputs):
  """Writes SavedModel to disk.

  If checkpoint_path is not None bakes the weights into the graph thereby
  eliminating the need of checkpoint files during inference. If the model
  was trained with moving averages, setting use_moving_averages to true
  restores the moving averages, otherwise the original set of variables
  is restored.

  Args:
    saved_model_path: Path to write SavedModel.
    frozen_graph_def: tf.GraphDef holding frozen graph.
    inputs: A tensor dictionary containing the inputs to a DetectionModel.
    outputs: A tensor dictionary containing the outputs of a DetectionModel.
  """
  with tf.Graph().as_default():
    with tf.Session() as sess:

      tf.import_graph_def(frozen_graph_def, name='')

      builder = tf.saved_model.builder.SavedModelBuilder(saved_model_path)

      tensor_info_inputs = {}
      if isinstance(inputs, dict):
        for k, v in inputs.items():
          tensor_info_inputs[k] = tf.saved_model.utils.build_tensor_info(v)
      else:
        tensor_info_inputs['inputs'] = tf.saved_model.utils.build_tensor_info(
            inputs)
      tensor_info_outputs = {}
      for k, v in outputs.items():
        tensor_info_outputs[k] = tf.saved_model.utils.build_tensor_info(v)

      detection_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs=tensor_info_inputs,
              outputs=tensor_info_outputs,
              method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
          ))

      builder.add_meta_graph_and_variables(
          sess,
          [tf.saved_model.tag_constants.SERVING],
          signature_def_map={
              tf.saved_model.signature_constants
              .DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  detection_signature,
          },
      )
      builder.save()


def write_graph_and_checkpoint(inference_graph_def,
                               model_path,
                               input_saver_def,
                               trained_checkpoint_prefix):
  """Writes the graph and the checkpoint into disk."""
  for node in inference_graph_def.node:
    node.device = ''
  with tf.Graph().as_default():
    tf.import_graph_def(inference_graph_def, name='')
    with tf.Session() as sess:
      saver = tf.train.Saver(
          saver_def=input_saver_def, save_relative_paths=True)
      saver.restore(sess, trained_checkpoint_prefix)
      saver.save(sess, model_path)


def _get_outputs_from_inputs(input_tensors, detection_model,
                             output_collection_name, **side_inputs):
  inputs = tf.cast(input_tensors, dtype=tf.float32)
  preprocessed_inputs, true_image_shapes = detection_model.preprocess(inputs)
  output_tensors = detection_model.predict(
      preprocessed_inputs, true_image_shapes, **side_inputs)
  postprocessed_tensors = detection_model.postprocess(
      output_tensors, true_image_shapes)
  return add_output_tensor_nodes(postprocessed_tensors,
                                 output_collection_name)


def build_detection_graph(input_type, detection_model, input_shape,
                          output_collection_name, graph_hook_fn,
                          use_side_inputs=False, side_input_shapes=None,
                          side_input_names=None, side_input_types=None):
  """Build the detection graph."""
  if input_type not in input_placeholder_fn_map:
    raise ValueError('Unknown input type: {}'.format(input_type))
  placeholder_args = {}
  side_inputs = {}
  if input_shape is not None:
    if (input_type != 'image_tensor' and
        input_type != 'encoded_image_string_tensor' and
        input_type != 'tf_example' and
        input_type != 'tf_sequence_example'):
      raise ValueError('Can only specify input shape for `image_tensor`, '
                       '`encoded_image_string_tensor`, `tf_example`, '
                       ' or `tf_sequence_example` inputs.')
    placeholder_args['input_shape'] = input_shape
  placeholder_tensor, input_tensors = input_placeholder_fn_map[input_type](
      **placeholder_args)
  placeholder_tensors = {'inputs': placeholder_tensor}
  if use_side_inputs:
    for idx, side_input_name in enumerate(side_input_names):
      side_input_placeholder, side_input = _side_input_tensor_placeholder(
          side_input_shapes[idx], side_input_name, side_input_types[idx])
      print(side_input)
      side_inputs[side_input_name] = side_input
      placeholder_tensors[side_input_name] = side_input_placeholder
  outputs = _get_outputs_from_inputs(
      input_tensors=input_tensors,
      detection_model=detection_model,
      output_collection_name=output_collection_name,
      **side_inputs)

  # Add global step to the graph.
  slim.get_or_create_global_step()

  if graph_hook_fn: graph_hook_fn()

  return outputs, placeholder_tensors


def _export_inference_graph(input_type,
                            detection_model,
                            use_moving_averages,
                            trained_checkpoint_prefix,
                            output_directory,
                            additional_output_tensor_names=None,
                            input_shape=None,
                            output_collection_name='inference_op',
                            graph_hook_fn=None,
                            write_inference_graph=False,
                            temp_checkpoint_prefix='',
                            use_side_inputs=False,
                            side_input_shapes=None,
                            side_input_names=None,
                            side_input_types=None):
  """Export helper."""
  tf.gfile.MakeDirs(output_directory)
  frozen_graph_path = os.path.join(output_directory,
                                   'frozen_inference_graph.pb')
  saved_model_path = os.path.join(output_directory, 'saved_model')
  model_path = os.path.join(output_directory, 'model.ckpt')

  outputs, placeholder_tensor_dict = build_detection_graph(
      input_type=input_type,
      detection_model=detection_model,
      input_shape=input_shape,
      output_collection_name=output_collection_name,
      graph_hook_fn=graph_hook_fn,
      use_side_inputs=use_side_inputs,
      side_input_shapes=side_input_shapes,
      side_input_names=side_input_names,
      side_input_types=side_input_types)

  profile_inference_graph(tf.get_default_graph())
  saver_kwargs = {}
  if use_moving_averages:
    if not temp_checkpoint_prefix:
      # This check is to be compatible with both version of SaverDef.
      if os.path.isfile(trained_checkpoint_prefix):
        saver_kwargs['write_version'] = saver_pb2.SaverDef.V1
        temp_checkpoint_prefix = tempfile.NamedTemporaryFile().name
      else:
        temp_checkpoint_prefix = tempfile.mkdtemp()
    replace_variable_values_with_moving_averages(
        tf.get_default_graph(), trained_checkpoint_prefix,
        temp_checkpoint_prefix)
    checkpoint_to_use = temp_checkpoint_prefix
  else:
    checkpoint_to_use = trained_checkpoint_prefix

  saver = tf.train.Saver(**saver_kwargs)
  input_saver_def = saver.as_saver_def()

  write_graph_and_checkpoint(
      inference_graph_def=tf.get_default_graph().as_graph_def(),
      model_path=model_path,
      input_saver_def=input_saver_def,
      trained_checkpoint_prefix=checkpoint_to_use)
  if write_inference_graph:
    inference_graph_def = tf.get_default_graph().as_graph_def()
    inference_graph_path = os.path.join(output_directory,
                                        'inference_graph.pbtxt')
    for node in inference_graph_def.node:
      node.device = ''
    with tf.gfile.GFile(inference_graph_path, 'wb') as f:
      f.write(str(inference_graph_def))

  if additional_output_tensor_names is not None:
    output_node_names = ','.join(list(outputs.keys())+(
        additional_output_tensor_names))
  else:
    output_node_names = ','.join(outputs.keys())

  frozen_graph_def = freeze_graph.freeze_graph_with_def_protos(
      input_graph_def=tf.get_default_graph().as_graph_def(),
      input_saver_def=input_saver_def,
      input_checkpoint=checkpoint_to_use,
      output_node_names=output_node_names,
      restore_op_name='save/restore_all',
      filename_tensor_name='save/Const:0',
      output_graph=frozen_graph_path,
      clear_devices=True,
      initializer_nodes='')

  write_saved_model(saved_model_path, frozen_graph_def,
                    placeholder_tensor_dict, outputs)


def export_inference_graph(input_type,
                           pipeline_config,
                           trained_checkpoint_prefix,
                           output_directory,
                           input_shape=None,
                           output_collection_name='inference_op',
                           additional_output_tensor_names=None,
                           write_inference_graph=False,
                           use_side_inputs=False,
                           side_input_shapes=None,
                           side_input_names=None,
                           side_input_types=None):
  """Exports inference graph for the model specified in the pipeline config.

  Args:
    input_type: Type of input for the graph. Can be one of ['image_tensor',
      'encoded_image_string_tensor', 'tf_example'].
    pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
    trained_checkpoint_prefix: Path to the trained checkpoint file.
    output_directory: Path to write outputs.
    input_shape: Sets a fixed shape for an `image_tensor` input. If not
      specified, will default to [None, None, None, 3].
    output_collection_name: Name of collection to add output tensors to.
      If None, does not add output tensors to a collection.
    additional_output_tensor_names: list of additional output
      tensors to include in the frozen graph.
    write_inference_graph: If true, writes inference graph to disk.
    use_side_inputs: If True, the model requires side_inputs.
    side_input_shapes: List of shapes of the side input tensors,
      required if use_side_inputs is True.
    side_input_names: List of names of the side input tensors,
      required if use_side_inputs is True.
    side_input_types: List of types of the side input tensors,
      required if use_side_inputs is True.
  """
  detection_model = model_builder.build(pipeline_config.model,
                                        is_training=False)
  graph_rewriter_fn = None
  if pipeline_config.HasField('graph_rewriter'):
    graph_rewriter_config = pipeline_config.graph_rewriter
    graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
                                                     is_training=False)
  _export_inference_graph(
      input_type,
      detection_model,
      pipeline_config.eval_config.use_moving_averages,
      trained_checkpoint_prefix,
      output_directory,
      additional_output_tensor_names,
      input_shape,
      output_collection_name,
      graph_hook_fn=graph_rewriter_fn,
      write_inference_graph=write_inference_graph,
      use_side_inputs=use_side_inputs,
      side_input_shapes=side_input_shapes,
      side_input_names=side_input_names,
      side_input_types=side_input_types)
  pipeline_config.eval_config.use_moving_averages = False
  config_util.save_pipeline_config(pipeline_config, output_directory)


def profile_inference_graph(graph):
  """Profiles the inference graph.

  Prints model parameters and computation FLOPs given an inference graph.
  BatchNorms are excluded from the parameter count due to the fact that
  BatchNorms are usually folded. BatchNorm, Initializer, Regularizer
  and BiasAdd are not considered in FLOP count.

  Args:
    graph: the inference graph.
  """
  tfprof_vars_option = (
      contrib_tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
  tfprof_flops_option = contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS

  # Batchnorm is usually folded during inference.
  tfprof_vars_option['trim_name_regexes'] = ['.*BatchNorm.*']
  # Initializer and Regularizer are only used in training.
  tfprof_flops_option['trim_name_regexes'] = [
      '.*BatchNorm.*', '.*Initializer.*', '.*Regularizer.*', '.*BiasAdd.*'
  ]

  contrib_tfprof.model_analyzer.print_model_analysis(
      graph, tfprof_options=tfprof_vars_option)

  contrib_tfprof.model_analyzer.print_model_analysis(
      graph, tfprof_options=tfprof_flops_option)

# Copyright 2017 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.
# ==============================================================================

"""Functions to export object detection inference graph."""
import os
import tempfile
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.tools import freeze_graph  # pylint: disable=g-direct-tensorflow-import
from object_detection.builders import graph_rewriter_builder
from object_detection.builders import model_builder
from object_detection.core import standard_fields as fields
from object_detection.data_decoders import tf_example_decoder
from object_detection.utils import config_util
from object_detection.utils import shape_utils

# pylint: disable=g-import-not-at-top
try:
  from tensorflow.contrib import tfprof as contrib_tfprof
  from tensorflow.contrib.quantize.python import graph_matcher
except ImportError:
  # TF 2.0 doesn't ship with contrib.
  pass
# pylint: enable=g-import-not-at-top

freeze_graph_with_def_protos = freeze_graph.freeze_graph_with_def_protos


def parse_side_inputs(side_input_shapes_string, side_input_names_string,
                      side_input_types_string):
  """Parses side input flags.

  Args:
    side_input_shapes_string: The shape of the side input tensors, provided as a
      comma-separated list of integers. A value of -1 is used for unknown
      dimensions. A `/` denotes a break, starting the shape of the next side
      input tensor.
    side_input_names_string: The names of the side input tensors, provided as a
      comma-separated list of strings.
    side_input_types_string: The type of the side input tensors, provided as a
      comma-separated list of types, each of `string`, `integer`, or `float`.

  Returns:
    side_input_shapes: A list of shapes.
    side_input_names: A list of strings.
    side_input_types: A list of tensorflow dtypes.

  """
  if side_input_shapes_string:
    side_input_shapes = []
    for side_input_shape_list in side_input_shapes_string.split('/'):
      side_input_shape = [
          int(dim) if dim != '-1' else None
          for dim in side_input_shape_list.split(',')
      ]
      side_input_shapes.append(side_input_shape)
  else:
    raise ValueError('When using side_inputs, side_input_shapes must be '
                     'specified in the input flags.')
  if side_input_names_string:
    side_input_names = list(side_input_names_string.split(','))
  else:
    raise ValueError('When using side_inputs, side_input_names must be '
                     'specified in the input flags.')
  if side_input_types_string:
    typelookup = {'float': tf.float32, 'int': tf.int32, 'string': tf.string}
    side_input_types = [
        typelookup[side_input_type]
        for side_input_type in side_input_types_string.split(',')
    ]
  else:
    raise ValueError('When using side_inputs, side_input_types must be '
                     'specified in the input flags.')
  return side_input_shapes, side_input_names, side_input_types


def rewrite_nn_resize_op(is_quantized=False):
  """Replaces a custom nearest-neighbor resize op with the Tensorflow version.

  Some graphs use this custom version for TPU-compatibility.

  Args:
    is_quantized: True if the default graph is quantized.
  """
  def remove_nn():
    """Remove nearest neighbor upsampling structures and replace with TF op."""
    input_pattern = graph_matcher.OpTypePattern(
        'FakeQuantWithMinMaxVars' if is_quantized else '*')
    stack_1_pattern = graph_matcher.OpTypePattern(
        'Pack', inputs=[input_pattern, input_pattern], ordered_inputs=False)
    stack_2_pattern = graph_matcher.OpTypePattern(
        'Pack', inputs=[stack_1_pattern, stack_1_pattern], ordered_inputs=False)
    reshape_pattern = graph_matcher.OpTypePattern(
        'Reshape', inputs=[stack_2_pattern, 'Const'], ordered_inputs=False)
    consumer_pattern1 = graph_matcher.OpTypePattern(
        'Add|AddV2|Max|Mul', inputs=[reshape_pattern, '*'],
        ordered_inputs=False)
    consumer_pattern2 = graph_matcher.OpTypePattern(
        'StridedSlice', inputs=[reshape_pattern, '*', '*', '*'],
        ordered_inputs=False)

    def replace_matches(consumer_pattern):
      """Search for nearest neighbor pattern and replace with TF op."""
      match_counter = 0
      matcher = graph_matcher.GraphMatcher(consumer_pattern)
      for match in matcher.match_graph(tf.get_default_graph()):
        match_counter += 1
        projection_op = match.get_op(input_pattern)
        reshape_op = match.get_op(reshape_pattern)
        consumer_op = match.get_op(consumer_pattern)
        nn_resize = tf.image.resize_nearest_neighbor(
            projection_op.outputs[0],
            reshape_op.outputs[0].shape.dims[1:3],
            align_corners=False,
            name=os.path.split(reshape_op.name)[0] + '/resize_nearest_neighbor')

        for index, op_input in enumerate(consumer_op.inputs):
          if op_input == reshape_op.outputs[0]:
            consumer_op._update_input(index, nn_resize)  # pylint: disable=protected-access
            break

      return match_counter

    match_counter = replace_matches(consumer_pattern1)
    match_counter += replace_matches(consumer_pattern2)

    tf.logging.info('Found and fixed {} matches'.format(match_counter))
    return match_counter

  # Applying twice because both inputs to Add could be NN pattern
  total_removals = 0
  while remove_nn():
    total_removals += 1
    # This number is chosen based on the nas-fpn architecture.
    if total_removals > 4:
      raise ValueError('Graph removal encountered a infinite loop.')


def replace_variable_values_with_moving_averages(graph,
                                                 current_checkpoint_file,
                                                 new_checkpoint_file,
                                                 no_ema_collection=None):
  """Replaces variable values in the checkpoint with their moving averages.

  If the current checkpoint has shadow variables maintaining moving averages of
  the variables defined in the graph, this function generates a new checkpoint
  where the variables contain the values of their moving averages.

  Args:
    graph: a tf.Graph object.
    current_checkpoint_file: a checkpoint containing both original variables and
      their moving averages.
    new_checkpoint_file: file path to write a new checkpoint.
    no_ema_collection: A list of namescope substrings to match the variables
      to eliminate EMA.
  """
  with graph.as_default():
    variable_averages = tf.train.ExponentialMovingAverage(0.0)
    ema_variables_to_restore = variable_averages.variables_to_restore()
    ema_variables_to_restore = config_util.remove_unecessary_ema(
        ema_variables_to_restore, no_ema_collection)
    with tf.Session() as sess:
      read_saver = tf.train.Saver(ema_variables_to_restore)
      read_saver.restore(sess, current_checkpoint_file)
      write_saver = tf.train.Saver()
      write_saver.save(sess, new_checkpoint_file)


def _image_tensor_input_placeholder(input_shape=None):
  """Returns input placeholder and a 4-D uint8 image tensor."""
  if input_shape is None:
    input_shape = (None, None, None, 3)
  input_tensor = tf.placeholder(
      dtype=tf.uint8, shape=input_shape, name='image_tensor')
  return input_tensor, input_tensor


def _side_input_tensor_placeholder(side_input_shape, side_input_name,
                                   side_input_type):
  """Returns side input placeholder and side input tensor."""
  side_input_tensor = tf.placeholder(
      dtype=side_input_type, shape=side_input_shape, name=side_input_name)
  return side_input_tensor, side_input_tensor


def _tf_example_input_placeholder(input_shape=None):
  """Returns input that accepts a batch of strings with tf examples.

  Args:
    input_shape: the shape to resize the output decoded images to (optional).

  Returns:
    a tuple of input placeholder and the output decoded images.
  """
  batch_tf_example_placeholder = tf.placeholder(
      tf.string, shape=[None], name='tf_example')
  def decode(tf_example_string_tensor):
    tensor_dict = tf_example_decoder.TfExampleDecoder().decode(
        tf_example_string_tensor)
    image_tensor = tensor_dict[fields.InputDataFields.image]
    if input_shape is not None:
      image_tensor = tf.image.resize(image_tensor, input_shape[1:3])
    return image_tensor
  return (batch_tf_example_placeholder,
          shape_utils.static_or_dynamic_map_fn(
              decode,
              elems=batch_tf_example_placeholder,
              dtype=tf.uint8,
              parallel_iterations=32,
              back_prop=False))


def _encoded_image_string_tensor_input_placeholder(input_shape=None):
  """Returns input that accepts a batch of PNG or JPEG strings.

  Args:
    input_shape: the shape to resize the output decoded images to (optional).

  Returns:
    a tuple of input placeholder and the output decoded images.
  """
  batch_image_str_placeholder = tf.placeholder(
      dtype=tf.string,
      shape=[None],
      name='encoded_image_string_tensor')
  def decode(encoded_image_string_tensor):
    image_tensor = tf.image.decode_image(encoded_image_string_tensor,
                                         channels=3)
    image_tensor.set_shape((None, None, 3))
    if input_shape is not None:
      image_tensor = tf.image.resize(image_tensor, input_shape[1:3])
    return image_tensor
  return (batch_image_str_placeholder,
          tf.map_fn(
              decode,
              elems=batch_image_str_placeholder,
              dtype=tf.uint8,
              parallel_iterations=32,
              back_prop=False))


input_placeholder_fn_map = {
    'image_tensor': _image_tensor_input_placeholder,
    'encoded_image_string_tensor':
    _encoded_image_string_tensor_input_placeholder,
    'tf_example': _tf_example_input_placeholder
}


def add_output_tensor_nodes(postprocessed_tensors,
                            output_collection_name='inference_op'):
  """Adds output nodes for detection boxes and scores.

  Adds the following nodes for output tensors -
    * num_detections: float32 tensor of shape [batch_size].
    * detection_boxes: float32 tensor of shape [batch_size, num_boxes, 4]
      containing detected boxes.
    * detection_scores: float32 tensor of shape [batch_size, num_boxes]
      containing scores for the detected boxes.
    * detection_multiclass_scores: (Optional) float32 tensor of shape
      [batch_size, num_boxes, num_classes_with_background] for containing class
      score distribution for detected boxes including background if any.
    * detection_features: (Optional) float32 tensor of shape
      [batch, num_boxes, roi_height, roi_width, depth]
      containing classifier features
      for each detected box
    * detection_classes: float32 tensor of shape [batch_size, num_boxes]
      containing class predictions for the detected boxes.
    * detection_keypoints: (Optional) float32 tensor of shape
      [batch_size, num_boxes, num_keypoints, 2] containing keypoints for each
      detection box.
    * detection_masks: (Optional) float32 tensor of shape
      [batch_size, num_boxes, mask_height, mask_width] containing masks for each
      detection box.

  Args:
    postprocessed_tensors: a dictionary containing the following fields
      'detection_boxes': [batch, max_detections, 4]
      'detection_scores': [batch, max_detections]
      'detection_multiclass_scores': [batch, max_detections,
        num_classes_with_background]
      'detection_features': [batch, num_boxes, roi_height, roi_width, depth]
      'detection_classes': [batch, max_detections]
      'detection_masks': [batch, max_detections, mask_height, mask_width]
        (optional).
      'detection_keypoints': [batch, max_detections, num_keypoints, 2]
        (optional).
      'num_detections': [batch]
    output_collection_name: Name of collection to add output tensors to.

  Returns:
    A tensor dict containing the added output tensor nodes.
  """
  detection_fields = fields.DetectionResultFields
  label_id_offset = 1
  boxes = postprocessed_tensors.get(detection_fields.detection_boxes)
  scores = postprocessed_tensors.get(detection_fields.detection_scores)
  multiclass_scores = postprocessed_tensors.get(
      detection_fields.detection_multiclass_scores)
  box_classifier_features = postprocessed_tensors.get(
      detection_fields.detection_features)
  raw_boxes = postprocessed_tensors.get(detection_fields.raw_detection_boxes)
  raw_scores = postprocessed_tensors.get(detection_fields.raw_detection_scores)
  raw_embedding = postprocessed_tensors.get(detection_fields.raw_embedding)
  nms_embedding = postprocessed_tensors.get(detection_fields.embedding)
  classes = postprocessed_tensors.get(
      detection_fields.detection_classes) + label_id_offset
  keypoints = postprocessed_tensors.get(detection_fields.detection_keypoints)
  masks = postprocessed_tensors.get(detection_fields.detection_masks)
  num_detections = postprocessed_tensors.get(detection_fields.num_detections)
  outputs = {}
  outputs[detection_fields.detection_boxes] = tf.identity(
      boxes, name=detection_fields.detection_boxes)
  outputs[detection_fields.detection_scores] = tf.identity(
      scores, name=detection_fields.detection_scores)
  if multiclass_scores is not None:
    outputs[detection_fields.detection_multiclass_scores] = tf.identity(
        multiclass_scores, name=detection_fields.detection_multiclass_scores)
  if box_classifier_features is not None:
    outputs[detection_fields.detection_features] = tf.identity(
        box_classifier_features,
        name=detection_fields.detection_features)
  outputs[detection_fields.detection_classes] = tf.identity(
      classes, name=detection_fields.detection_classes)
  outputs[detection_fields.num_detections] = tf.identity(
      num_detections, name=detection_fields.num_detections)
  if raw_boxes is not None:
    outputs[detection_fields.raw_detection_boxes] = tf.identity(
        raw_boxes, name=detection_fields.raw_detection_boxes)
  if raw_scores is not None:
    outputs[detection_fields.raw_detection_scores] = tf.identity(
        raw_scores, name=detection_fields.raw_detection_scores)
  if raw_embedding is not None:
    outputs[detection_fields.raw_embedding] = tf.identity(
        raw_scores, name=detection_fields.raw_embedding)
  if nms_embedding is not None:
    outputs[detection_fields.embedding] = tf.identity(
        nms_embedding, name=detection_fields.embedding)
  if keypoints is not None:
    outputs[detection_fields.detection_keypoints] = tf.identity(
        keypoints, name=detection_fields.detection_keypoints)
  if masks is not None:
    outputs[detection_fields.detection_masks] = tf.identity(
        masks, name=detection_fields.detection_masks)
  for output_key in outputs:
    tf.add_to_collection(output_collection_name, outputs[output_key])

  return outputs


def write_saved_model(saved_model_path,
                      frozen_graph_def,
                      inputs,
                      outputs):
  """Writes SavedModel to disk.

  If checkpoint_path is not None bakes the weights into the graph thereby
  eliminating the need of checkpoint files during inference. If the model
  was trained with moving averages, setting use_moving_averages to true
  restores the moving averages, otherwise the original set of variables
  is restored.

  Args:
    saved_model_path: Path to write SavedModel.
    frozen_graph_def: tf.GraphDef holding frozen graph.
    inputs: A tensor dictionary containing the inputs to a DetectionModel.
    outputs: A tensor dictionary containing the outputs of a DetectionModel.
  """
  with tf.Graph().as_default():
    with tf.Session() as sess:

      tf.import_graph_def(frozen_graph_def, name='')

      builder = tf.saved_model.builder.SavedModelBuilder(saved_model_path)

      tensor_info_inputs = {}
      if isinstance(inputs, dict):
        for k, v in inputs.items():
          tensor_info_inputs[k] = tf.saved_model.utils.build_tensor_info(v)
      else:
        tensor_info_inputs['inputs'] = tf.saved_model.utils.build_tensor_info(
            inputs)
      tensor_info_outputs = {}
      for k, v in outputs.items():
        tensor_info_outputs[k] = tf.saved_model.utils.build_tensor_info(v)

      detection_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs=tensor_info_inputs,
              outputs=tensor_info_outputs,
              method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
          ))

      builder.add_meta_graph_and_variables(
          sess,
          [tf.saved_model.tag_constants.SERVING],
          signature_def_map={
              tf.saved_model.signature_constants
              .DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  detection_signature,
          },
      )
      builder.save()


def write_graph_and_checkpoint(inference_graph_def,
                               model_path,
                               input_saver_def,
                               trained_checkpoint_prefix):
  """Writes the graph and the checkpoint into disk."""
  for node in inference_graph_def.node:
    node.device = ''
  with tf.Graph().as_default():
    tf.import_graph_def(inference_graph_def, name='')
    with tf.Session() as sess:
      saver = tf.train.Saver(
          saver_def=input_saver_def, save_relative_paths=True)
      saver.restore(sess, trained_checkpoint_prefix)
      saver.save(sess, model_path)


def _get_outputs_from_inputs(input_tensors, detection_model,
                             output_collection_name, **side_inputs):
  inputs = tf.cast(input_tensors, dtype=tf.float32)
  preprocessed_inputs, true_image_shapes = detection_model.preprocess(inputs)
  output_tensors = detection_model.predict(
      preprocessed_inputs, true_image_shapes, **side_inputs)
  postprocessed_tensors = detection_model.postprocess(
      output_tensors, true_image_shapes)
  return add_output_tensor_nodes(postprocessed_tensors,
                                 output_collection_name)


def build_detection_graph(input_type, detection_model, input_shape,
                          output_collection_name, graph_hook_fn,
                          use_side_inputs=False, side_input_shapes=None,
                          side_input_names=None, side_input_types=None):
  """Build the detection graph."""
  if input_type not in input_placeholder_fn_map:
    raise ValueError('Unknown input type: {}'.format(input_type))
  placeholder_args = {}
  side_inputs = {}
  if input_shape is not None:
    if (input_type != 'image_tensor' and
        input_type != 'encoded_image_string_tensor' and
        input_type != 'tf_example' and
        input_type != 'tf_sequence_example'):
      raise ValueError('Can only specify input shape for `image_tensor`, '
                       '`encoded_image_string_tensor`, `tf_example`, '
                       ' or `tf_sequence_example` inputs.')
    placeholder_args['input_shape'] = input_shape
  placeholder_tensor, input_tensors = input_placeholder_fn_map[input_type](
      **placeholder_args)
  placeholder_tensors = {'inputs': placeholder_tensor}
  if use_side_inputs:
    for idx, side_input_name in enumerate(side_input_names):
      side_input_placeholder, side_input = _side_input_tensor_placeholder(
          side_input_shapes[idx], side_input_name, side_input_types[idx])
      print(side_input)
      side_inputs[side_input_name] = side_input
      placeholder_tensors[side_input_name] = side_input_placeholder
  outputs = _get_outputs_from_inputs(
      input_tensors=input_tensors,
      detection_model=detection_model,
      output_collection_name=output_collection_name,
      **side_inputs)

  # Add global step to the graph.
  slim.get_or_create_global_step()

  if graph_hook_fn: graph_hook_fn()

  return outputs, placeholder_tensors


def _export_inference_graph(input_type,
                            detection_model,
                            use_moving_averages,
                            trained_checkpoint_prefix,
                            output_directory,
                            additional_output_tensor_names=None,
                            input_shape=None,
                            output_collection_name='inference_op',
                            graph_hook_fn=None,
                            write_inference_graph=False,
                            temp_checkpoint_prefix='',
                            use_side_inputs=False,
                            side_input_shapes=None,
                            side_input_names=None,
                            side_input_types=None):
  """Export helper."""
  tf.gfile.MakeDirs(output_directory)
  frozen_graph_path = os.path.join(output_directory,
                                   'frozen_inference_graph.pb')
  saved_model_path = os.path.join(output_directory, 'saved_model')
  model_path = os.path.join(output_directory, 'model.ckpt')

  outputs, placeholder_tensor_dict = build_detection_graph(
      input_type=input_type,
      detection_model=detection_model,
      input_shape=input_shape,
      output_collection_name=output_collection_name,
      graph_hook_fn=graph_hook_fn,
      use_side_inputs=use_side_inputs,
      side_input_shapes=side_input_shapes,
      side_input_names=side_input_names,
      side_input_types=side_input_types)

  profile_inference_graph(tf.get_default_graph())
  saver_kwargs = {}
  if use_moving_averages:
    if not temp_checkpoint_prefix:
      # This check is to be compatible with both version of SaverDef.
      if os.path.isfile(trained_checkpoint_prefix):
        saver_kwargs['write_version'] = saver_pb2.SaverDef.V1
        temp_checkpoint_prefix = tempfile.NamedTemporaryFile().name
      else:
        temp_checkpoint_prefix = tempfile.mkdtemp()
    replace_variable_values_with_moving_averages(
        tf.get_default_graph(), trained_checkpoint_prefix,
        temp_checkpoint_prefix)
    checkpoint_to_use = temp_checkpoint_prefix
  else:
    checkpoint_to_use = trained_checkpoint_prefix

  saver = tf.train.Saver(**saver_kwargs)
  input_saver_def = saver.as_saver_def()

  write_graph_and_checkpoint(
      inference_graph_def=tf.get_default_graph().as_graph_def(),
      model_path=model_path,
      input_saver_def=input_saver_def,
      trained_checkpoint_prefix=checkpoint_to_use)
  if write_inference_graph:
    inference_graph_def = tf.get_default_graph().as_graph_def()
    inference_graph_path = os.path.join(output_directory,
                                        'inference_graph.pbtxt')
    for node in inference_graph_def.node:
      node.device = ''
    with tf.gfile.GFile(inference_graph_path, 'wb') as f:
      f.write(str(inference_graph_def))

  if additional_output_tensor_names is not None:
    output_node_names = ','.join(list(outputs.keys())+(
        additional_output_tensor_names))
  else:
    output_node_names = ','.join(outputs.keys())

  frozen_graph_def = freeze_graph.freeze_graph_with_def_protos(
      input_graph_def=tf.get_default_graph().as_graph_def(),
      input_saver_def=input_saver_def,
      input_checkpoint=checkpoint_to_use,
      output_node_names=output_node_names,
      restore_op_name='save/restore_all',
      filename_tensor_name='save/Const:0',
      output_graph=frozen_graph_path,
      clear_devices=True,
      initializer_nodes='')

  write_saved_model(saved_model_path, frozen_graph_def,
                    placeholder_tensor_dict, outputs)


def export_inference_graph(input_type,
                           pipeline_config,
                           trained_checkpoint_prefix,
                           output_directory,
                           input_shape=None,
                           output_collection_name='inference_op',
                           additional_output_tensor_names=None,
                           write_inference_graph=False,
                           use_side_inputs=False,
                           side_input_shapes=None,
                           side_input_names=None,
                           side_input_types=None):
  """Exports inference graph for the model specified in the pipeline config.

  Args:
    input_type: Type of input for the graph. Can be one of ['image_tensor',
      'encoded_image_string_tensor', 'tf_example'].
    pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
    trained_checkpoint_prefix: Path to the trained checkpoint file.
    output_directory: Path to write outputs.
    input_shape: Sets a fixed shape for an `image_tensor` input. If not
      specified, will default to [None, None, None, 3].
    output_collection_name: Name of collection to add output tensors to.
      If None, does not add output tensors to a collection.
    additional_output_tensor_names: list of additional output
      tensors to include in the frozen graph.
    write_inference_graph: If true, writes inference graph to disk.
    use_side_inputs: If True, the model requires side_inputs.
    side_input_shapes: List of shapes of the side input tensors,
      required if use_side_inputs is True.
    side_input_names: List of names of the side input tensors,
      required if use_side_inputs is True.
    side_input_types: List of types of the side input tensors,
      required if use_side_inputs is True.
  """
  detection_model = model_builder.build(pipeline_config.model,
                                        is_training=False)
  graph_rewriter_fn = None
  if pipeline_config.HasField('graph_rewriter'):
    graph_rewriter_config = pipeline_config.graph_rewriter
    graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
                                                     is_training=False)
  _export_inference_graph(
      input_type,
      detection_model,
      pipeline_config.eval_config.use_moving_averages,
      trained_checkpoint_prefix,
      output_directory,
      additional_output_tensor_names,
      input_shape,
      output_collection_name,
      graph_hook_fn=graph_rewriter_fn,
      write_inference_graph=write_inference_graph,
      use_side_inputs=use_side_inputs,
      side_input_shapes=side_input_shapes,
      side_input_names=side_input_names,
      side_input_types=side_input_types)
  pipeline_config.eval_config.use_moving_averages = False
  config_util.save_pipeline_config(pipeline_config, output_directory)


def profile_inference_graph(graph):
  """Profiles the inference graph.

  Prints model parameters and computation FLOPs given an inference graph.
  BatchNorms are excluded from the parameter count due to the fact that
  BatchNorms are usually folded. BatchNorm, Initializer, Regularizer
  and BiasAdd are not considered in FLOP count.

  Args:
    graph: the inference graph.
  """
  tfprof_vars_option = (
      contrib_tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
  tfprof_flops_option = contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS

  # Batchnorm is usually folded during inference.
  tfprof_vars_option['trim_name_regexes'] = ['.*BatchNorm.*']
  # Initializer and Regularizer are only used in training.
  tfprof_flops_option['trim_name_regexes'] = [
      '.*BatchNorm.*', '.*Initializer.*', '.*Regularizer.*', '.*BiasAdd.*'
  ]

  contrib_tfprof.model_analyzer.print_model_analysis(
      graph, tfprof_options=tfprof_vars_option)

  contrib_tfprof.model_analyzer.print_model_analysis(
      graph, tfprof_options=tfprof_flops_option)

# Copyright 2017 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. # ============================================================================== Functions to export object detection inference graph. # pylint: disable=g-direct-tensorflow-import # pylint: disable=g-import-not-at-top # TF 2.0 doesn't ship with contrib. # pylint: enable=g-import-not-at-top Parses side input flags.    Args:     side_input_shapes_string: The shape of the side input tensors, provided as a       comma-separated list of integers. A value of -1 is used for unknown       dimensions. A `/` denotes a break, starting the shape of the next side       input tensor.     side_input_names_string: The names of the side input tensors, provided as a       comma-separated list of strings.     side_input_types_string: The type of the side input tensors, provided as a       comma-separated list of types, each of `string`, `integer`, or `float`.    Returns:     side_input_shapes: A list of shapes.     side_input_names: A list of strings.     side_input_types: A list of tensorflow dtypes. Replaces a custom nearest-neighbor resize op with the Tensorflow version.    Some graphs use this custom version for TPU-compatibility.    Args:     is_quantized: True if the default graph is quantized. Remove nearest neighbor upsampling structures and replace with TF op. Search for nearest neighbor pattern and replace with TF op. # pylint: disable=protected-access # Applying twice because both inputs to Add could be NN pattern # This number is chosen based on the nas-fpn architecture. Replaces variable values in the checkpoint with their moving averages.    If the current checkpoint has shadow variables maintaining moving averages of   the variables defined in the graph, this function generates a new checkpoint   where the variables contain the values of their moving averages.    Args:     graph: a tf.Graph object.     current_checkpoint_file: a checkpoint containing both original variables and       their moving averages.     new_checkpoint_file: file path to write a new checkpoint.     no_ema_collection: A list of namescope substrings to match the variables       to eliminate EMA. Returns input placeholder and a 4-D uint8 image tensor. Returns side input placeholder and side input tensor. Returns input that accepts a batch of strings with tf examples.    Args:     input_shape: the shape to resize the output decoded images to (optional).    Returns:     a tuple of input placeholder and the output decoded images. Returns input that accepts a batch of PNG or JPEG strings.    Args:     input_shape: the shape to resize the output decoded images to (optional).    Returns:     a tuple of input placeholder and the output decoded images. Adds output nodes for detection boxes and scores.    Adds the following nodes for output tensors -     * num_detections: float32 tensor of shape [batch_size].     * detection_boxes: float32 tensor of shape [batch_size, num_boxes, 4]       containing detected boxes.     * detection_scores: float32 tensor of shape [batch_size, num_boxes]       containing scores for the detected boxes.     * detection_multiclass_scores: (Optional) float32 tensor of shape       [batch_size, num_boxes, num_classes_with_background] for containing class       score distribution for detected boxes including background if any.     * detection_features: (Optional) float32 tensor of shape       [batch, num_boxes, roi_height, roi_width, depth]       containing classifier features       for each detected box     * detection_classes: float32 tensor of shape [batch_size, num_boxes]       containing class predictions for the detected boxes.     * detection_keypoints: (Optional) float32 tensor of shape       [batch_size, num_boxes, num_keypoints, 2] containing keypoints for each       detection box.     * detection_masks: (Optional) float32 tensor of shape       [batch_size, num_boxes, mask_height, mask_width] containing masks for each       detection box.    Args:     postprocessed_tensors: a dictionary containing the following fields       'detection_boxes': [batch, max_detections, 4]       'detection_scores': [batch, max_detections]       'detection_multiclass_scores': [batch, max_detections,         num_classes_with_background]       'detection_features': [batch, num_boxes, roi_height, roi_width, depth]       'detection_classes': [batch, max_detections]       'detection_masks': [batch, max_detections, mask_height, mask_width]         (optional).       'detection_keypoints': [batch, max_detections, num_keypoints, 2]         (optional).       'num_detections': [batch]     output_collection_name: Name of collection to add output tensors to.    Returns:     A tensor dict containing the added output tensor nodes. Writes SavedModel to disk.    If checkpoint_path is not None bakes the weights into the graph thereby   eliminating the need of checkpoint files during inference. If the model   was trained with moving averages, setting use_moving_averages to true   restores the moving averages, otherwise the original set of variables   is restored.    Args:     saved_model_path: Path to write SavedModel.     frozen_graph_def: tf.GraphDef holding frozen graph.     inputs: A tensor dictionary containing the inputs to a DetectionModel.     outputs: A tensor dictionary containing the outputs of a DetectionModel. Writes the graph and the checkpoint into disk. Build the detection graph. # Add global step to the graph. Export helper. # This check is to be compatible with both version of SaverDef. Exports inference graph for the model specified in the pipeline config.    Args:     input_type: Type of input for the graph. Can be one of ['image_tensor',       'encoded_image_string_tensor', 'tf_example'].     pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.     trained_checkpoint_prefix: Path to the trained checkpoint file.     output_directory: Path to write outputs.     input_shape: Sets a fixed shape for an `image_tensor` input. If not       specified, will default to [None, None, None, 3].     output_collection_name: Name of collection to add output tensors to.       If None, does not add output tensors to a collection.     additional_output_tensor_names: list of additional output       tensors to include in the frozen graph.     write_inference_graph: If true, writes inference graph to disk.     use_side_inputs: If True, the model requires side_inputs.     side_input_shapes: List of shapes of the side input tensors,       required if use_side_inputs is True.     side_input_names: List of names of the side input tensors,       required if use_side_inputs is True.     side_input_types: List of types of the side input tensors,       required if use_side_inputs is True. Profiles the inference graph.    Prints model parameters and computation FLOPs given an inference graph.   BatchNorms are excluded from the parameter count due to the fact that   BatchNorms are usually folded. BatchNorm, Initializer, Regularizer   and BiasAdd are not considered in FLOP count.    Args:     graph: the inference graph. # Batchnorm is usually folded during inference. # Initializer and Regularizer are only used in training.