MyCity/traffic_analyzer/object_detection/metrics/coco_evaluation_test.py

# 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.
# ==============================================================================
"""Tests for tensorflow_models.object_detection.metrics.coco_evaluation."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.metrics import coco_evaluation


def _get_categories_list():
  return [{
      'id': 1,
      'name': 'person'
  }, {
      'id': 2,
      'name': 'dog'
  }, {
      'id': 3,
      'name': 'cat'
  }]


class CocoDetectionEvaluationTest(tf.test.TestCase):

  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
    """Tests that mAP is calculated correctly on GT and Detections."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image2',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[50., 50., 100., 100.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image2',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[50., 50., 100., 100.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image3',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[25., 25., 50., 50.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image3',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[25., 25., 50., 50.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsSkipCrowd(self):
    """Tests computing mAP with is_crowd GT boxes skipped."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.], [99., 99., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1, 2]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([0, 1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsEmptyCrowd(self):
    """Tests computing mAP with empty is_crowd array passed in."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)

  def testRejectionOnDuplicateGroundtruth(self):
    """Tests that groundtruth cannot be added more than once for an image."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    #  Add groundtruth
    image_key1 = 'img1'
    groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
                                  dtype=float)
    groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
    coco_evaluator.add_single_ground_truth_image_info(image_key1, {
        standard_fields.InputDataFields.groundtruth_boxes:
            groundtruth_boxes1,
        standard_fields.InputDataFields.groundtruth_classes:
            groundtruth_class_labels1
    })
    groundtruth_lists_len = len(coco_evaluator._groundtruth_list)

    # Add groundtruth with the same image id.
    coco_evaluator.add_single_ground_truth_image_info(image_key1, {
        standard_fields.InputDataFields.groundtruth_boxes:
            groundtruth_boxes1,
        standard_fields.InputDataFields.groundtruth_classes:
            groundtruth_class_labels1
    })
    self.assertEqual(groundtruth_lists_len,
                     len(coco_evaluator._groundtruth_list))

  def testRejectionOnDuplicateDetections(self):
    """Tests that detections cannot be added more than once for an image."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    #  Add groundtruth
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[99., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    detections_lists_len = len(coco_evaluator._detection_boxes_list)
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',  # Note that this image id was previously added.
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    self.assertEqual(detections_lists_len,
                     len(coco_evaluator._detection_boxes_list))

  def testExceptionRaisedWithMissingGroundtruth(self):
    """Tests that exception is raised for detection with missing groundtruth."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    with self.assertRaises(ValueError):
      coco_evaluator.add_single_detected_image_info(
          image_id='image1',
          detections_dict={
              standard_fields.DetectionResultFields.detection_boxes:
                  np.array([[100., 100., 200., 200.]]),
              standard_fields.DetectionResultFields.detection_scores:
                  np.array([.8]),
              standard_fields.DetectionResultFields.detection_classes:
                  np.array([1])
          })


class CocoEvaluationPyFuncTest(tf.test.TestCase):

  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    image_id = tf.placeholder(tf.string, shape=())
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    detection_scores = tf.placeholder(tf.float32, shape=(None))
    detection_classes = tf.placeholder(tf.float32, shape=(None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      sess.run(update_op,
               feed_dict={
                   image_id: 'image1',
                   groundtruth_boxes: np.array([[100., 100., 200., 200.]]),
                   groundtruth_classes: np.array([1]),
                   detection_boxes: np.array([[100., 100., 200., 200.]]),
                   detection_scores: np.array([.8]),
                   detection_classes: np.array([1])
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image2',
                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
                   groundtruth_classes: np.array([3]),
                   detection_boxes: np.array([[50., 50., 100., 100.]]),
                   detection_scores: np.array([.7]),
                   detection_classes: np.array([3])
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image3',
                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
                   groundtruth_classes: np.array([2]),
                   detection_boxes: np.array([[25., 25., 50., 50.]]),
                   detection_scores: np.array([.9]),
                   detection_classes: np.array([2])
               })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_boxes_list)
    self.assertFalse(coco_evaluator._image_ids)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsIsAnnotated(self):
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    image_id = tf.placeholder(tf.string, shape=())
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
    is_annotated = tf.placeholder(tf.bool, shape=())
    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    detection_scores = tf.placeholder(tf.float32, shape=(None))
    detection_classes = tf.placeholder(tf.float32, shape=(None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        'is_annotated': is_annotated,
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      sess.run(update_op,
               feed_dict={
                   image_id: 'image1',
                   groundtruth_boxes: np.array([[100., 100., 200., 200.]]),
                   groundtruth_classes: np.array([1]),
                   is_annotated: True,
                   detection_boxes: np.array([[100., 100., 200., 200.]]),
                   detection_scores: np.array([.8]),
                   detection_classes: np.array([1])
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image2',
                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
                   groundtruth_classes: np.array([3]),
                   is_annotated: True,
                   detection_boxes: np.array([[50., 50., 100., 100.]]),
                   detection_scores: np.array([.7]),
                   detection_classes: np.array([3])
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image3',
                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
                   groundtruth_classes: np.array([2]),
                   is_annotated: True,
                   detection_boxes: np.array([[25., 25., 50., 50.]]),
                   detection_scores: np.array([.9]),
                   detection_classes: np.array([2])
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image4',
                   groundtruth_boxes: np.zeros((0, 4)),
                   groundtruth_classes: np.zeros((0)),
                   is_annotated: False,  # Note that this image isn't annotated.
                   detection_boxes: np.array([[25., 25., 50., 50.],
                                              [25., 25., 70., 50.],
                                              [25., 25., 80., 50.],
                                              [25., 25., 90., 50.]]),
                   detection_scores: np.array([0.6, 0.7, 0.8, 0.9]),
                   detection_classes: np.array([1, 2, 2, 3])
               })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_boxes_list)
    self.assertFalse(coco_evaluator._image_ids)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsPadded(self):
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    image_id = tf.placeholder(tf.string, shape=())
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
    detection_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    detection_scores = tf.placeholder(tf.float32, shape=(None))
    detection_classes = tf.placeholder(tf.float32, shape=(None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      sess.run(
          update_op,
          feed_dict={
              image_id:
                  'image1',
              groundtruth_boxes:
                  np.array([[100., 100., 200., 200.], [-1, -1, -1, -1]]),
              groundtruth_classes:
                  np.array([1, -1]),
              detection_boxes:
                  np.array([[100., 100., 200., 200.], [0., 0., 0., 0.]]),
              detection_scores:
                  np.array([.8, 0.]),
              detection_classes:
                  np.array([1, -1])
          })
      sess.run(
          update_op,
          feed_dict={
              image_id:
                  'image2',
              groundtruth_boxes:
                  np.array([[50., 50., 100., 100.], [-1, -1, -1, -1]]),
              groundtruth_classes:
                  np.array([3, -1]),
              detection_boxes:
                  np.array([[50., 50., 100., 100.], [0., 0., 0., 0.]]),
              detection_scores:
                  np.array([.7, 0.]),
              detection_classes:
                  np.array([3, -1])
          })
      sess.run(
          update_op,
          feed_dict={
              image_id:
                  'image3',
              groundtruth_boxes:
                  np.array([[25., 25., 50., 50.], [10., 10., 15., 15.]]),
              groundtruth_classes:
                  np.array([2, 2]),
              detection_boxes:
                  np.array([[25., 25., 50., 50.], [10., 10., 15., 15.]]),
              detection_scores:
                  np.array([.95, .9]),
              detection_classes:
                  np.array([2, 2])
          })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 0.83333331)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_boxes_list)
    self.assertFalse(coco_evaluator._image_ids)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsBatched(self):
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    batch_size = 3
    image_id = tf.placeholder(tf.string, shape=(batch_size))
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
    detection_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      sess.run(update_op,
               feed_dict={
                   image_id: ['image1', 'image2', 'image3'],
                   groundtruth_boxes: np.array([[[100., 100., 200., 200.]],
                                                [[50., 50., 100., 100.]],
                                                [[25., 25., 50., 50.]]]),
                   groundtruth_classes: np.array([[1], [3], [2]]),
                   detection_boxes: np.array([[[100., 100., 200., 200.]],
                                              [[50., 50., 100., 100.]],
                                              [[25., 25., 50., 50.]]]),
                   detection_scores: np.array([[.8], [.7], [.9]]),
                   detection_classes: np.array([[1], [3], [2]])
               })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_boxes_list)
    self.assertFalse(coco_evaluator._image_ids)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsPaddedBatches(self):
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    batch_size = 3
    image_id = tf.placeholder(tf.string, shape=(batch_size))
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
    num_gt_boxes_per_image = tf.placeholder(tf.int32, shape=(None))
    detection_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
    num_det_boxes_per_image = tf.placeholder(tf.int32, shape=(None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        'num_groundtruth_boxes_per_image': num_gt_boxes_per_image,
        'num_det_boxes_per_image': num_det_boxes_per_image
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionBoxes_Precision/mAP']

    with self.test_session() as sess:
      sess.run(
          update_op,
          feed_dict={
              image_id: ['image1', 'image2', 'image3'],
              groundtruth_boxes:
                  np.array([[[100., 100., 200., 200.], [-1, -1, -1, -1]],
                            [[50., 50., 100., 100.], [-1, -1, -1, -1]],
                            [[25., 25., 50., 50.], [10., 10., 15., 15.]]]),
              groundtruth_classes:
                  np.array([[1, -1], [3, -1], [2, 2]]),
              num_gt_boxes_per_image:
                  np.array([1, 1, 2]),
              detection_boxes:
                  np.array([[[100., 100., 200., 200.],
                             [0., 0., 0., 0.],
                             [0., 0., 0., 0.]],
                            [[50., 50., 100., 100.],
                             [0., 0., 0., 0.],
                             [0., 0., 0., 0.]],
                            [[25., 25., 50., 50.],
                             [10., 10., 15., 15.],
                             [10., 10., 15., 15.]]]),
              detection_scores:
                  np.array([[.8, 0., 0.], [.7, 0., 0.], [.95, .9, 0.9]]),
              detection_classes:
                  np.array([[1, -1, -1], [3, -1, -1], [2, 2, 2]]),
              num_det_boxes_per_image:
                  np.array([1, 1, 3]),
          })

    # Check the number of bounding boxes added.
    self.assertEqual(len(coco_evaluator._groundtruth_list), 4)
    self.assertEqual(len(coco_evaluator._detection_boxes_list), 5)

    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@1'], 0.83333331)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionBoxes_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_boxes_list)
    self.assertFalse(coco_evaluator._image_ids)


class CocoMaskEvaluationTest(tf.test.TestCase):

  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
            standard_fields.InputDataFields.groundtruth_instance_masks:
            np.pad(np.ones([1, 100, 100], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1]),
            standard_fields.DetectionResultFields.detection_masks:
            np.pad(np.ones([1, 100, 100], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image2',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[50., 50., 100., 100.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
            standard_fields.InputDataFields.groundtruth_instance_masks:
            np.pad(np.ones([1, 50, 50], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image2',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[50., 50., 100., 100.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1]),
            standard_fields.DetectionResultFields.detection_masks:
            np.pad(np.ones([1, 50, 50], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image3',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[25., 25., 50., 50.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1]),
            standard_fields.InputDataFields.groundtruth_instance_masks:
            np.pad(np.ones([1, 25, 25], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image3',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[25., 25., 50., 50.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1]),
            standard_fields.DetectionResultFields.detection_masks:
            np.pad(np.ones([1, 25, 25], dtype=np.uint8),
                   ((0, 0), (10, 10), (10, 10)), mode='constant')
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
    coco_evaluator.clear()
    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
    self.assertFalse(coco_evaluator._image_ids_with_detections)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._detection_masks_list)


class CocoMaskEvaluationPyFuncTest(tf.test.TestCase):

  def testGetOneMAPWithMatchingGroundtruthAndDetections(self):
    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
    image_id = tf.placeholder(tf.string, shape=())
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(None))
    groundtruth_masks = tf.placeholder(tf.uint8, shape=(None, None, None))
    detection_scores = tf.placeholder(tf.float32, shape=(None))
    detection_classes = tf.placeholder(tf.float32, shape=(None))
    detection_masks = tf.placeholder(tf.uint8, shape=(None, None, None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_masks,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      sess.run(
          update_op,
          feed_dict={
              image_id:
                  'image1',
              groundtruth_boxes:
                  np.array([[100., 100., 200., 200.], [50., 50., 100., 100.]]),
              groundtruth_classes:
                  np.array([1, 2]),
              groundtruth_masks:
                  np.stack([
                      np.pad(
                          np.ones([100, 100], dtype=np.uint8), ((10, 10),
                                                                (10, 10)),
                          mode='constant'),
                      np.pad(
                          np.ones([50, 50], dtype=np.uint8), ((0, 70), (0, 70)),
                          mode='constant')
                  ]),
              detection_scores:
                  np.array([.9, .8]),
              detection_classes:
                  np.array([2, 1]),
              detection_masks:
                  np.stack([
                      np.pad(
                          np.ones([50, 50], dtype=np.uint8), ((0, 70), (0, 70)),
                          mode='constant'),
                      np.pad(
                          np.ones([100, 100], dtype=np.uint8), ((10, 10),
                                                                (10, 10)),
                          mode='constant'),
                  ])
          })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image2',
                   groundtruth_boxes: np.array([[50., 50., 100., 100.]]),
                   groundtruth_classes: np.array([1]),
                   groundtruth_masks: np.pad(np.ones([1, 50, 50],
                                                     dtype=np.uint8),
                                             ((0, 0), (10, 10), (10, 10)),
                                             mode='constant'),
                   detection_scores: np.array([.8]),
                   detection_classes: np.array([1]),
                   detection_masks: np.pad(np.ones([1, 50, 50], dtype=np.uint8),
                                           ((0, 0), (10, 10), (10, 10)),
                                           mode='constant')
               })
      sess.run(update_op,
               feed_dict={
                   image_id: 'image3',
                   groundtruth_boxes: np.array([[25., 25., 50., 50.]]),
                   groundtruth_classes: np.array([1]),
                   groundtruth_masks: np.pad(np.ones([1, 25, 25],
                                                     dtype=np.uint8),
                                             ((0, 0), (10, 10), (10, 10)),
                                             mode='constant'),
                   detection_scores: np.array([.8]),
                   detection_classes: np.array([1]),
                   detection_masks: np.pad(np.ones([1, 25, 25],
                                                   dtype=np.uint8),
                                           ((0, 0), (10, 10), (10, 10)),
                                           mode='constant')
               })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@1'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._image_ids_with_detections)
    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
    self.assertFalse(coco_evaluator._detection_masks_list)

  def testGetOneMAPWithMatchingGroundtruthAndDetectionsBatched(self):
    coco_evaluator = coco_evaluation.CocoMaskEvaluator(_get_categories_list())
    batch_size = 3
    image_id = tf.placeholder(tf.string, shape=(batch_size))
    groundtruth_boxes = tf.placeholder(tf.float32, shape=(batch_size, None, 4))
    groundtruth_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
    groundtruth_masks = tf.placeholder(
        tf.uint8, shape=(batch_size, None, None, None))
    detection_scores = tf.placeholder(tf.float32, shape=(batch_size, None))
    detection_classes = tf.placeholder(tf.float32, shape=(batch_size, None))
    detection_masks = tf.placeholder(
        tf.uint8, shape=(batch_size, None, None, None))

    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    eval_dict = {
        input_data_fields.key: image_id,
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_masks,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
    }

    eval_metric_ops = coco_evaluator.get_estimator_eval_metric_ops(eval_dict)

    _, update_op = eval_metric_ops['DetectionMasks_Precision/mAP']

    with self.test_session() as sess:
      sess.run(
          update_op,
          feed_dict={
              image_id: ['image1', 'image2', 'image3'],
              groundtruth_boxes:
                  np.array([[[100., 100., 200., 200.]],
                            [[50., 50., 100., 100.]],
                            [[25., 25., 50., 50.]]]),
              groundtruth_classes:
                  np.array([[1], [1], [1]]),
              groundtruth_masks:
                  np.stack([
                      np.pad(
                          np.ones([1, 100, 100], dtype=np.uint8),
                          ((0, 0), (0, 0), (0, 0)),
                          mode='constant'),
                      np.pad(
                          np.ones([1, 50, 50], dtype=np.uint8),
                          ((0, 0), (25, 25), (25, 25)),
                          mode='constant'),
                      np.pad(
                          np.ones([1, 25, 25], dtype=np.uint8),
                          ((0, 0), (37, 38), (37, 38)),
                          mode='constant')
                  ],
                           axis=0),
              detection_scores:
                  np.array([[.8], [.8], [.8]]),
              detection_classes:
                  np.array([[1], [1], [1]]),
              detection_masks:
                  np.stack([
                      np.pad(
                          np.ones([1, 100, 100], dtype=np.uint8),
                          ((0, 0), (0, 0), (0, 0)),
                          mode='constant'),
                      np.pad(
                          np.ones([1, 50, 50], dtype=np.uint8),
                          ((0, 0), (25, 25), (25, 25)),
                          mode='constant'),
                      np.pad(
                          np.ones([1, 25, 25], dtype=np.uint8),
                          ((0, 0), (37, 38), (37, 38)),
                          mode='constant')
                  ],
                           axis=0)
          })
    metrics = {}
    for key, (value_op, _) in eval_metric_ops.iteritems():
      metrics[key] = value_op
    metrics = sess.run(metrics)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.50IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP@.75IOU'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Precision/mAP (small)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@1'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@10'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (large)'], 1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (medium)'],
                           1.0)
    self.assertAlmostEqual(metrics['DetectionMasks_Recall/AR@100 (small)'], 1.0)
    self.assertFalse(coco_evaluator._groundtruth_list)
    self.assertFalse(coco_evaluator._image_ids_with_detections)
    self.assertFalse(coco_evaluator._image_id_to_mask_shape_map)
    self.assertFalse(coco_evaluator._detection_masks_list)


if __name__ == '__main__':
  tf.test.main()