# 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.
# ==============================================================================
"""Common utility functions for evaluation."""
import collections
import os
import time

import numpy as np
import tensorflow as tf

from research.object_detection.core import box_list
from research.object_detection.core import box_list_ops
from research.object_detection.core import keypoint_ops
from research.object_detection.core import standard_fields as fields
from research.object_detection.metrics import coco_evaluation
from research.object_detection.utils import label_map_util
from research.object_detection.utils import ops
from research.object_detection.utils import visualization_utils as vis_utils

slim = tf.contrib.slim

# A dictionary of metric names to classes that implement the metric. The classes
# in the dictionary must implement
# utils.object_detection_evaluation.DetectionEvaluator interface.
EVAL_METRICS_CLASS_DICT = {
    'coco_detection_metrics':
        coco_evaluation.CocoDetectionEvaluator,
    'coco_mask_metrics':
        coco_evaluation.CocoMaskEvaluator,
}

EVAL_DEFAULT_METRIC = 'coco_detection_metrics'


def write_metrics(metrics, global_step, summary_dir):
    """Write metrics to a summary directory.

  Args:
    metrics: A dictionary containing metric names and values.
    global_step: Global step at which the metrics are computed.
    summary_dir: Directory to write tensorflow summaries to.
  """
    tf.logging.info('Writing metrics to tf summary.')
    summary_writer = tf.summary.FileWriterCache.get(summary_dir)
    for key in sorted(metrics):
        summary = tf.Summary(value=[
            tf.Summary.Value(tag=key, simple_value=metrics[key]),
        ])
        summary_writer.add_summary(summary, global_step)
        tf.logging.info('%s: %f', key, metrics[key])
    tf.logging.info('Metrics written to tf summary.')


# TODO(rathodv): Add tests.
def visualize_detection_results(result_dict,
                                tag,
                                global_step,
                                categories,
                                summary_dir='',
                                export_dir='',
                                agnostic_mode=False,
                                show_groundtruth=False,
                                groundtruth_box_visualization_color='black',
                                min_score_thresh=.5,
                                max_num_predictions=20,
                                skip_scores=False,
                                skip_labels=False,
                                keep_image_id_for_visualization_export=False):
    """Visualizes detection results and writes visualizations to image summaries.

  This function visualizes an image with its detected bounding boxes and writes
  to image summaries which can be viewed on tensorboard.  It optionally also
  writes images to a directory. In the case of missing entry in the label map,
  unknown class name in the visualization is shown as "N/A".

  Args:
    result_dict: a dictionary holding groundtruth and detection
      data corresponding to each image being evaluated.  The following keys
      are required:
        'original_image': a numpy array representing the image with shape
          [1, height, width, 3] or [1, height, width, 1]
        'detection_boxes': a numpy array of shape [N, 4]
        'detection_scores': a numpy array of shape [N]
        'detection_classes': a numpy array of shape [N]
      The following keys are optional:
        'groundtruth_boxes': a numpy array of shape [N, 4]
        'groundtruth_keypoints': a numpy array of shape [N, num_keypoints, 2]
      Detections are assumed to be provided in decreasing order of score and for
      display, and we assume that scores are probabilities between 0 and 1.
    tag: tensorboard tag (string) to associate with image.
    global_step: global step at which the visualization are generated.
    categories: a list of dictionaries representing all possible categories.
      Each dict in this list has the following keys:
          'id': (required) an integer id uniquely identifying this category
          'name': (required) string representing category name
            e.g., 'cat', 'dog', 'pizza'
          'supercategory': (optional) string representing the supercategory
            e.g., 'animal', 'vehicle', 'food', etc
    summary_dir: the output directory to which the image summaries are written.
    export_dir: the output directory to which images are written.  If this is
      empty (default), then images are not exported.
    agnostic_mode: boolean (default: False) controlling whether to evaluate in
      class-agnostic mode or not.
    show_groundtruth: boolean (default: False) controlling whether to show
      groundtruth boxes in addition to detected boxes
    groundtruth_box_visualization_color: box color for visualizing groundtruth
      boxes
    min_score_thresh: minimum score threshold for a box to be visualized
    max_num_predictions: maximum number of detections to visualize
    skip_scores: whether to skip score when drawing a single detection
    skip_labels: whether to skip label when drawing a single detection
    keep_image_id_for_visualization_export: whether to keep image identifier in
      filename when exported to export_dir
  Raises:
    ValueError: if result_dict does not contain the expected keys (i.e.,
      'original_image', 'detection_boxes', 'detection_scores',
      'detection_classes')
  """
    detection_fields = fields.DetectionResultFields
    input_fields = fields.InputDataFields
    if not set([
        input_fields.original_image,
        detection_fields.detection_boxes,
        detection_fields.detection_scores,
        detection_fields.detection_classes,
    ]).issubset(set(result_dict.keys())):
        raise ValueError('result_dict does not contain all expected keys.')
    if show_groundtruth and input_fields.groundtruth_boxes not in result_dict:
        raise ValueError('If show_groundtruth is enabled, result_dict must contain '
                         'groundtruth_boxes.')
    tf.logging.info('Creating detection visualizations.')
    category_index = label_map_util.create_category_index(categories)

    image = np.squeeze(result_dict[input_fields.original_image], axis=0)
    if image.shape[2] == 1:  # If one channel image, repeat in RGB.
        image = np.tile(image, [1, 1, 3])
    detection_boxes = result_dict[detection_fields.detection_boxes]
    detection_scores = result_dict[detection_fields.detection_scores]
    detection_classes = np.int32((result_dict[
        detection_fields.detection_classes]))
    detection_keypoints = result_dict.get(detection_fields.detection_keypoints)
    detection_masks = result_dict.get(detection_fields.detection_masks)
    detection_boundaries = result_dict.get(detection_fields.detection_boundaries)

    # Plot groundtruth underneath detections
    if show_groundtruth:
        groundtruth_boxes = result_dict[input_fields.groundtruth_boxes]
        groundtruth_keypoints = result_dict.get(input_fields.groundtruth_keypoints)
        vis_utils.visualize_boxes_and_labels_on_image_array(
            image=image,
            boxes=groundtruth_boxes,
            classes=None,
            scores=None,
            category_index=category_index,
            keypoints=groundtruth_keypoints,
            use_normalized_coordinates=False,
            max_boxes_to_draw=None,
            groundtruth_box_visualization_color=groundtruth_box_visualization_color)
    vis_utils.visualize_boxes_and_labels_on_image_array(
        image,
        detection_boxes,
        detection_classes,
        detection_scores,
        category_index,
        instance_masks=detection_masks,
        instance_boundaries=detection_boundaries,
        keypoints=detection_keypoints,
        use_normalized_coordinates=False,
        max_boxes_to_draw=max_num_predictions,
        min_score_thresh=min_score_thresh,
        agnostic_mode=agnostic_mode,
        skip_scores=skip_scores,
        skip_labels=skip_labels)

    if export_dir:
        if keep_image_id_for_visualization_export and result_dict[fields.
                InputDataFields()
                .key]:
            export_path = os.path.join(export_dir, 'export-{}-{}.png'.format(
                tag, result_dict[fields.InputDataFields().key]))
        else:
            export_path = os.path.join(export_dir, 'export-{}.png'.format(tag))
        vis_utils.save_image_array_as_png(image, export_path)

    summary = tf.Summary(value=[
        tf.Summary.Value(
            tag=tag,
            image=tf.Summary.Image(
                encoded_image_string=vis_utils.encode_image_array_as_png_str(
                    image)))
    ])
    summary_writer = tf.summary.FileWriterCache.get(summary_dir)
    summary_writer.add_summary(summary, global_step)

    tf.logging.info('Detection visualizations written to summary with tag %s.',
                    tag)


def _run_checkpoint_once(tensor_dict,
                         evaluators=None,
                         batch_processor=None,
                         checkpoint_dirs=None,
                         variables_to_restore=None,
                         restore_fn=None,
                         num_batches=1,
                         master='',
                         save_graph=False,
                         save_graph_dir='',
                         losses_dict=None,
                         eval_export_path=None):
    """Evaluates metrics defined in evaluators and returns summaries.

  This function loads the latest checkpoint in checkpoint_dirs and evaluates
  all metrics defined in evaluators. The metrics are processed in batch by the
  batch_processor.

  Args:
    tensor_dict: a dictionary holding tensors representing a batch of detections
      and corresponding groundtruth annotations.
    evaluators: a list of object of type DetectionEvaluator to be used for
      evaluation. Note that the metric names produced by different evaluators
      must be unique.
    batch_processor: a function taking four arguments:
      1. tensor_dict: the same tensor_dict that is passed in as the first
        argument to this function.
      2. sess: a tensorflow session
      3. batch_index: an integer representing the index of the batch amongst
        all batches
      By default, batch_processor is None, which defaults to running:
        return sess.run(tensor_dict)
      To skip an image, it suffices to return an empty dictionary in place of
      result_dict.
    checkpoint_dirs: list of directories to load into an EnsembleModel. If it
      has only one directory, EnsembleModel will not be used --
        a DetectionModel
      will be instantiated directly. Not used if restore_fn is set.
    variables_to_restore: None, or a dictionary mapping variable names found in
      a checkpoint to model variables. The dictionary would normally be
      generated by creating a tf.train.ExponentialMovingAverage object and
      calling its variables_to_restore() method. Not used if restore_fn is set.
    restore_fn: None, or a function that takes a tf.Session object and correctly
      restores all necessary variables from the correct checkpoint file. If
      None, attempts to restore from the first directory in checkpoint_dirs.
    num_batches: the number of batches to use for evaluation.
    master: the location of the Tensorflow session.
    save_graph: whether or not the Tensorflow graph is stored as a pbtxt file.
    save_graph_dir: where to store the Tensorflow graph on disk. If save_graph
      is True this must be non-empty.
    losses_dict: optional dictionary of scalar detection losses.
    eval_export_path: Path for saving a json file that contains the detection
      results in json format.

  Returns:
    global_step: the count of global steps.
    all_evaluator_metrics: A dictionary containing metric names and values.

  Raises:
    ValueError: if restore_fn is None and checkpoint_dirs doesn't have at least
      one element.
    ValueError: if save_graph is True and save_graph_dir is not defined.
  """
    if save_graph and not save_graph_dir:
        raise ValueError('`save_graph_dir` must be defined.')
    sess = tf.Session(master, graph=tf.get_default_graph())
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())
    sess.run(tf.tables_initializer())
    if restore_fn:
        restore_fn(sess)
    else:
        if not checkpoint_dirs:
            raise ValueError('`checkpoint_dirs` must have at least one entry.')
        checkpoint_file = tf.train.latest_checkpoint(checkpoint_dirs[0])
        saver = tf.train.Saver(variables_to_restore)
        saver.restore(sess, checkpoint_file)

    if save_graph:
        tf.train.write_graph(sess.graph_def, save_graph_dir, 'eval.pbtxt')

    counters = {'skipped': 0, 'success': 0}
    aggregate_result_losses_dict = collections.defaultdict(list)
    with tf.contrib.slim.queues.QueueRunners(sess):
        try:
            for batch in range(int(num_batches)):
                if (batch + 1) % 100 == 0:
                    tf.logging.info('Running eval ops batch %d/%d', batch + 1,
                                    num_batches)
                if not batch_processor:
                    try:
                        if not losses_dict:
                            losses_dict = {}
                        result_dict, result_losses_dict = sess.run([tensor_dict,
                                                                    losses_dict])
                        counters['success'] += 1
                    except tf.errors.InvalidArgumentError:
                        tf.logging.info('Skipping image')
                        counters['skipped'] += 1
                        result_dict = {}
                else:
                    result_dict, result_losses_dict = batch_processor(
                        tensor_dict, sess, batch, counters, losses_dict=losses_dict)
                if not result_dict:
                    continue
                for key, value in iter(result_losses_dict.items()):
                    aggregate_result_losses_dict[key].append(value)
                for evaluator in evaluators:
                    # TODO(b/65130867): Use image_id tensor once we fix the input data
                    # decoders to return correct image_id.
                    # TODO(akuznetsa): result_dict contains batches of images, while
                    # add_single_ground_truth_image_info expects a single image. Fix
                    if (isinstance(result_dict, dict) and
                            result_dict[fields.InputDataFields.key]):
                        image_id = result_dict[fields.InputDataFields.key]
                    else:
                        image_id = batch
                    evaluator.add_single_ground_truth_image_info(
                        image_id=image_id, groundtruth_dict=result_dict)
                    evaluator.add_single_detected_image_info(
                        image_id=image_id, detections_dict=result_dict)
            tf.logging.info('Running eval batches done.')
        except tf.errors.OutOfRangeError:
            tf.logging.info('Done evaluating -- epoch limit reached')
        finally:
            # When done, ask the threads to stop.
            tf.logging.info('# success: %d', counters['success'])
            tf.logging.info('# skipped: %d', counters['skipped'])
            all_evaluator_metrics = {}
            if eval_export_path and eval_export_path is not None:
                for evaluator in evaluators:
                    if (isinstance(evaluator, coco_evaluation.CocoDetectionEvaluator) or
                            isinstance(evaluator, coco_evaluation.CocoMaskEvaluator)):
                        tf.logging.info('Started dumping to json file.')
                        evaluator.dump_detections_to_json_file(
                            json_output_path=eval_export_path)
                        tf.logging.info('Finished dumping to json file.')
            for evaluator in evaluators:
                metrics = evaluator.evaluate()
                evaluator.clear()
                if any(key in all_evaluator_metrics for key in metrics):
                    raise ValueError('Metric names between evaluators must not collide.')
                all_evaluator_metrics.update(metrics)
            global_step = tf.train.global_step(sess, tf.train.get_global_step())

            for key, value in iter(aggregate_result_losses_dict.items()):
                all_evaluator_metrics['Losses/' + key] = np.mean(value)
    sess.close()
    return (global_step, all_evaluator_metrics)


# TODO(rathodv): Add tests.
def repeated_checkpoint_run(tensor_dict,
                            summary_dir,
                            evaluators,
                            batch_processor=None,
                            checkpoint_dirs=None,
                            variables_to_restore=None,
                            restore_fn=None,
                            num_batches=1,
                            eval_interval_secs=120,
                            max_number_of_evaluations=None,
                            master='',
                            save_graph=False,
                            save_graph_dir='',
                            losses_dict=None,
                            eval_export_path=None):
    """Periodically evaluates desired tensors using checkpoint_dirs or restore_fn.

  This function repeatedly loads a checkpoint and evaluates a desired
  set of tensors (provided by tensor_dict) and hands the resulting numpy
  arrays to a function result_processor which can be used to further
  process/save/visualize the results.

  Args:
    tensor_dict: a dictionary holding tensors representing a batch of detections
      and corresponding groundtruth annotations.
    summary_dir: a directory to write metrics summaries.
    evaluators: a list of object of type DetectionEvaluator to be used for
      evaluation. Note that the metric names produced by different evaluators
      must be unique.
    batch_processor: a function taking three arguments:
      1. tensor_dict: the same tensor_dict that is passed in as the first
        argument to this function.
      2. sess: a tensorflow session
      3. batch_index: an integer representing the index of the batch amongst
        all batches
      By default, batch_processor is None, which defaults to running:
        return sess.run(tensor_dict)
    checkpoint_dirs: list of directories to load into a DetectionModel or an
      EnsembleModel if restore_fn isn't set. Also used to determine when to run
      next evaluation. Must have at least one element.
    variables_to_restore: None, or a dictionary mapping variable names found in
      a checkpoint to model variables. The dictionary would normally be
      generated by creating a tf.train.ExponentialMovingAverage object and
      calling its variables_to_restore() method. Not used if restore_fn is set.
    restore_fn: a function that takes a tf.Session object and correctly restores
      all necessary variables from the correct checkpoint file.
    num_batches: the number of batches to use for evaluation.
    eval_interval_secs: the number of seconds between each evaluation run.
    max_number_of_evaluations: the max number of iterations of the evaluation.
      If the value is left as None the evaluation continues indefinitely.
    master: the location of the Tensorflow session.
    save_graph: whether or not the Tensorflow graph is saved as a pbtxt file.
    save_graph_dir: where to save on disk the Tensorflow graph. If store_graph
      is True this must be non-empty.
    losses_dict: optional dictionary of scalar detection losses.
    eval_export_path: Path for saving a json file that contains the detection
      results in json format.

  Returns:
    metrics: A dictionary containing metric names and values in the latest
      evaluation.

  Raises:
    ValueError: if max_num_of_evaluations is not None or a positive number.
    ValueError: if checkpoint_dirs doesn't have at least one element.
  """
    if max_number_of_evaluations and max_number_of_evaluations <= 0:
        raise ValueError(
            '`number_of_steps` must be either None or a positive number.')

    if not checkpoint_dirs:
        raise ValueError('`checkpoint_dirs` must have at least one entry.')

    last_evaluated_model_path = None
    number_of_evaluations = 0
    metrics = None
    while True:
        start = time.time()
        tf.logging.info('Starting evaluation at ' + time.strftime(
            '%Y-%m-%d-%H:%M:%S', time.gmtime()))
        model_path = tf.train.latest_checkpoint(checkpoint_dirs[0])
        if not model_path:
            tf.logging.info('No model found in %s. Will try again in %d seconds',
                            checkpoint_dirs[0], eval_interval_secs)
        elif model_path == last_evaluated_model_path:
            tf.logging.info('Found already evaluated checkpoint. Will try again in '
                            '%d seconds', eval_interval_secs)
        else:
            last_evaluated_model_path = model_path
            global_step, metrics = _run_checkpoint_once(
                tensor_dict,
                evaluators,
                batch_processor,
                checkpoint_dirs,
                variables_to_restore,
                restore_fn,
                num_batches,
                master,
                save_graph,
                save_graph_dir,
                losses_dict=losses_dict,
                eval_export_path=eval_export_path)
            write_metrics(metrics, global_step, summary_dir)
        number_of_evaluations += 1

        if (max_number_of_evaluations and
                number_of_evaluations >= max_number_of_evaluations):
            tf.logging.info('Finished evaluation!')
            break
        time_to_next_eval = start + eval_interval_secs - time.time()
        if time_to_next_eval > 0:
            time.sleep(time_to_next_eval)

    return metrics


def result_dict_for_single_example(image,
                                   key,
                                   detections,
                                   groundtruth=None,
                                   class_agnostic=False,
                                   scale_to_absolute=False):
    """Merges all detection and groundtruth information for a single example.

  Note that evaluation tools require classes that are 1-indexed, and so this
  function performs the offset. If `class_agnostic` is True, all output classes
  have label 1.

  Args:
    image: A single 4D uint8 image tensor of shape [1, H, W, C].
    key: A single string tensor identifying the image.
    detections: A dictionary of detections, returned from
      DetectionModel.postprocess().
    groundtruth: (Optional) Dictionary of groundtruth items, with fields:
      'groundtruth_boxes': [num_boxes, 4] float32 tensor of boxes, in
        normalized coordinates.
      'groundtruth_classes': [num_boxes] int64 tensor of 1-indexed classes.
      'groundtruth_area': [num_boxes] float32 tensor of bbox area. (Optional)
      'groundtruth_is_crowd': [num_boxes] int64 tensor. (Optional)
      'groundtruth_difficult': [num_boxes] int64 tensor. (Optional)
      'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
      'groundtruth_instance_masks': 3D int64 tensor of instance masks
        (Optional).
    class_agnostic: Boolean indicating whether the detections are class-agnostic
      (i.e. binary). Default False.
    scale_to_absolute: Boolean indicating whether boxes and keypoints should be
      scaled to absolute coordinates. Note that for IoU based evaluations, it
      does not matter whether boxes are expressed in absolute or relative
      coordinates. Default False.

  Returns:
    A dictionary with:
    'original_image': A [1, H, W, C] uint8 image tensor.
    'key': A string tensor with image identifier.
    'detection_boxes': [max_detections, 4] float32 tensor of boxes, in
      normalized or absolute coordinates, depending on the value of
      `scale_to_absolute`.
    'detection_scores': [max_detections] float32 tensor of scores.
    'detection_classes': [max_detections] int64 tensor of 1-indexed classes.
    'detection_masks': [max_detections, H, W] float32 tensor of binarized
      masks, reframed to full image masks.
    'groundtruth_boxes': [num_boxes, 4] float32 tensor of boxes, in
      normalized or absolute coordinates, depending on the value of
      `scale_to_absolute`. (Optional)
    'groundtruth_classes': [num_boxes] int64 tensor of 1-indexed classes.
      (Optional)
    'groundtruth_area': [num_boxes] float32 tensor of bbox area. (Optional)
    'groundtruth_is_crowd': [num_boxes] int64 tensor. (Optional)
    'groundtruth_difficult': [num_boxes] int64 tensor. (Optional)
    'groundtruth_group_of': [num_boxes] int64 tensor. (Optional)
    'groundtruth_instance_masks': 3D int64 tensor of instance masks
      (Optional).

  """
    label_id_offset = 1  # Applying label id offset (b/63711816)

    input_data_fields = fields.InputDataFields
    output_dict = {
        input_data_fields.original_image: image,
        input_data_fields.key: key,
    }

    detection_fields = fields.DetectionResultFields
    detection_boxes = detections[detection_fields.detection_boxes][0]
    image_shape = tf.shape(image)
    detection_scores = detections[detection_fields.detection_scores][0]

    if class_agnostic:
        detection_classes = tf.ones_like(detection_scores, dtype=tf.int64)
    else:
        detection_classes = (
                tf.to_int64(detections[detection_fields.detection_classes][0]) +
                label_id_offset)

    num_detections = tf.to_int32(detections[detection_fields.num_detections][0])
    detection_boxes = tf.slice(
        detection_boxes, begin=[0, 0], size=[num_detections, -1])
    detection_classes = tf.slice(
        detection_classes, begin=[0], size=[num_detections])
    detection_scores = tf.slice(
        detection_scores, begin=[0], size=[num_detections])

    if scale_to_absolute:
        absolute_detection_boxlist = box_list_ops.to_absolute_coordinates(
            box_list.BoxList(detection_boxes), image_shape[1], image_shape[2])
        output_dict[detection_fields.detection_boxes] = (
            absolute_detection_boxlist.get())
    else:
        output_dict[detection_fields.detection_boxes] = detection_boxes
    output_dict[detection_fields.detection_classes] = detection_classes
    output_dict[detection_fields.detection_scores] = detection_scores

    if detection_fields.detection_masks in detections:
        detection_masks = detections[detection_fields.detection_masks][0]
        # TODO(rathodv): This should be done in model's postprocess
        # function ideally.
        detection_masks = tf.slice(
            detection_masks, begin=[0, 0, 0], size=[num_detections, -1, -1])
        detection_masks_reframed = ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image_shape[1], image_shape[2])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        output_dict[detection_fields.detection_masks] = detection_masks_reframed
    if detection_fields.detection_keypoints in detections:
        detection_keypoints = detections[detection_fields.detection_keypoints][0]
        output_dict[detection_fields.detection_keypoints] = detection_keypoints
        if scale_to_absolute:
            absolute_detection_keypoints = keypoint_ops.scale(
                detection_keypoints, image_shape[1], image_shape[2])
            output_dict[detection_fields.detection_keypoints] = (
                absolute_detection_keypoints)

    if groundtruth:
        if input_data_fields.groundtruth_instance_masks in groundtruth:
            masks = groundtruth[input_data_fields.groundtruth_instance_masks]
            masks = tf.expand_dims(masks, 3)
            masks = tf.image.resize_images(
                masks,
                image_shape[1:3],
                method=tf.image.ResizeMethod.NEAREST_NEIGHBOR,
                align_corners=True)
            masks = tf.squeeze(masks, 3)
            groundtruth[input_data_fields.groundtruth_instance_masks] = tf.cast(
                masks, tf.uint8)
        output_dict.update(groundtruth)
        if scale_to_absolute:
            groundtruth_boxes = groundtruth[input_data_fields.groundtruth_boxes]
            absolute_gt_boxlist = box_list_ops.to_absolute_coordinates(
                box_list.BoxList(groundtruth_boxes), image_shape[1], image_shape[2])
            output_dict[input_data_fields.groundtruth_boxes] = (
                absolute_gt_boxlist.get())
        # For class-agnostic models, groundtruth classes all become 1.
        if class_agnostic:
            groundtruth_classes = groundtruth[input_data_fields.groundtruth_classes]
            groundtruth_classes = tf.ones_like(groundtruth_classes, dtype=tf.int64)
            output_dict[input_data_fields.groundtruth_classes] = groundtruth_classes

    return output_dict


def get_evaluators(eval_config, categories, evaluator_options=None):
    """Returns the evaluator class according to eval_config, valid for categories.

  Args:
    eval_config: An `eval_pb2.EvalConfig`.
    categories: A list of dicts, each of which has the following keys -
        'id': (required) an integer id uniquely identifying this category.
        'name': (required) string representing category name e.g., 'cat', 'dog'.
    evaluator_options: A dictionary of metric names (see
      EVAL_METRICS_CLASS_DICT) to `DetectionEvaluator` initialization
      keyword arguments. For example:
      evalator_options = {
        'coco_detection_metrics': {'include_metrics_per_category': True}
      }

  Returns:
    An list of instances of DetectionEvaluator.

  Raises:
    ValueError: if metric is not in the metric class dictionary.
  """
    evaluator_options = evaluator_options or {}
    eval_metric_fn_keys = eval_config.metrics_set
    if not eval_metric_fn_keys:
        eval_metric_fn_keys = [EVAL_DEFAULT_METRIC]
    evaluators_list = []
    for eval_metric_fn_key in eval_metric_fn_keys:
        if eval_metric_fn_key not in EVAL_METRICS_CLASS_DICT:
            raise ValueError('Metric not found: {}'.format(eval_metric_fn_key))
        kwargs_dict = (evaluator_options[eval_metric_fn_key] if eval_metric_fn_key
                                                                in evaluator_options else {})
        evaluators_list.append(EVAL_METRICS_CLASS_DICT[eval_metric_fn_key](
            categories,
            **kwargs_dict))
    return evaluators_list


def get_eval_metric_ops_for_evaluators(eval_config,
                                       categories,
                                       eval_dict):
    """Returns eval metrics ops to use with `tf.estimator.EstimatorSpec`.

  Args:
    eval_config: An `eval_pb2.EvalConfig`.
    categories: A list of dicts, each of which has the following keys -
        'id': (required) an integer id uniquely identifying this category.
        'name': (required) string representing category name e.g., 'cat', 'dog'.
    eval_dict: An evaluation dictionary, returned from
      result_dict_for_single_example().

  Returns:
    A dictionary of metric names to tuple of value_op and update_op that can be
    used as eval metric ops in tf.EstimatorSpec.
  """
    eval_metric_ops = {}
    evaluator_options = evaluator_options_from_eval_config(eval_config)
    evaluators_list = get_evaluators(eval_config, categories, evaluator_options)
    for evaluator in evaluators_list:
        eval_metric_ops.update(evaluator.get_estimator_eval_metric_ops(
            eval_dict))
    return eval_metric_ops


def evaluator_options_from_eval_config(eval_config):
    """Produces a dictionary of evaluation options for each eval metric.

  Args:
    eval_config: An `eval_pb2.EvalConfig`.

  Returns:
    evaluator_options: A dictionary of metric names (see
      EVAL_METRICS_CLASS_DICT) to `DetectionEvaluator` initialization
      keyword arguments. For example:
      evalator_options = {
        'coco_detection_metrics': {'include_metrics_per_category': True}
      }
  """
    eval_metric_fn_keys = eval_config.metrics_set
    evaluator_options = {}
    for eval_metric_fn_key in eval_metric_fn_keys:
        if eval_metric_fn_key in ('coco_detection_metrics', 'coco_mask_metrics'):
            evaluator_options[eval_metric_fn_key] = {
                'include_metrics_per_category': (
                    eval_config.include_metrics_per_category)
            }
    return evaluator_options
