text: # Copyright 2019 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 google3.third_party.tensorflow_models.object_detection.core.batch_multiclass_nms."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy as np
  from six.moves import range
  import tensorflow as tf
  from object_detection.core import post_processing
  from object_detection.utils import test_case
  
  
  class BatchMulticlassNonMaxSuppressionTest(test_case.TestCase,
                                             parameterized.TestCase):
  
    @parameterized.named_parameters(('', False), ('_use_static_shapes', True))
    def test_batch_multiclass_nms_with_batch_size_1(self, use_static_shapes):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]],
                            [[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0],
                             [.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = [[[0, 10, 1, 11],
                          [0, 0, 1, 1],
                          [0, 999, 2, 1004],
                          [0, 100, 1, 101]]]
      exp_nms_scores = [[.95, .9, .85, .3]]
      exp_nms_classes = [[0, 0, 1, 0]]
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields,
       num_detections) = post_processing.batch_multiclass_non_max_suppression(
           boxes,
           scores,
           score_thresh,
           iou_thresh,
           max_size_per_class=max_output_size,
           max_total_size=max_output_size,
           use_static_shapes=use_static_shapes)
  
      self.assertIsNone(nmsed_masks)
      self.assertIsNone(nmsed_additional_fields)
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     num_detections])
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertEqual(num_detections, [4])
  
    def test_batch_iou_with_negative_data(self):
      boxes = tf.constant([[[0, -0.01, 0.1, 1.1], [0, 0.2, 0.2, 5.0],
                            [0, -0.01, 0.1, 1.], [-1, -1, -1, -1]]], tf.float32)
      iou = post_processing.batch_iou(boxes, boxes)
      expected_iou = [[[0.99999994, 0.0917431, 0.9099099, -1.],
                       [0.0917431, 1., 0.08154944, -1.],
                       [0.9099099, 0.08154944, 1., -1.], [-1., -1., -1., -1.]]]
      with self.test_session() as sess:
        iou = sess.run(iou)
        self.assertAllClose(iou, expected_iou)
  
    def test_batch_multiclass_nms_with_batch_size_2(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 10, 1, 11],
                                   [0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 999, 2, 1004],
                                   [0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.95, .9, 0, 0],
                                 [.85, .5, .3, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [1, 0, 0, 0]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size)
  
      self.assertIsNone(nmsed_masks)
      self.assertIsNone(nmsed_additional_fields)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(),
                          exp_nms_corners.shape)
      self.assertAllEqual(nmsed_scores.shape.as_list(),
                          exp_nms_scores.shape)
      self.assertAllEqual(nmsed_classes.shape.as_list(),
                          exp_nms_classes.shape)
      self.assertEqual(num_detections.shape.as_list(), [2])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     num_detections])
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [2, 3])
  
    def test_batch_multiclass_nms_with_per_batch_clip_window(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      clip_window = tf.constant([0., 0., 200., 200.])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 10, 1, 11],
                                   [0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.95, .9, 0, 0],
                                 [.5, .3, 0, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [0, 0, 0, 0]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          clip_window=clip_window)
  
      self.assertIsNone(nmsed_masks)
      self.assertIsNone(nmsed_additional_fields)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(),
                          exp_nms_corners.shape)
      self.assertAllEqual(nmsed_scores.shape.as_list(),
                          exp_nms_scores.shape)
      self.assertAllEqual(nmsed_classes.shape.as_list(),
                          exp_nms_classes.shape)
      self.assertEqual(num_detections.shape.as_list(), [2])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     num_detections])
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [2, 2])
  
    def test_batch_multiclass_nms_with_per_image_clip_window(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      clip_window = tf.constant([[0., 0., 5., 5.],
                                 [0., 0., 200., 200.]])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.9, 0., 0., 0.],
                                 [.5, .3, 0, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [0, 0, 0, 0]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          clip_window=clip_window)
  
      self.assertIsNone(nmsed_masks)
      self.assertIsNone(nmsed_additional_fields)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(),
                          exp_nms_corners.shape)
      self.assertAllEqual(nmsed_scores.shape.as_list(),
                          exp_nms_scores.shape)
      self.assertAllEqual(nmsed_classes.shape.as_list(),
                          exp_nms_classes.shape)
      self.assertEqual(num_detections.shape.as_list(), [2])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     num_detections])
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [1, 2])
  
    def test_batch_multiclass_nms_with_masks(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      masks = tf.constant([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
                            [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
                            [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
                            [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
                           [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
                            [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
                            [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
                            [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]],
                          tf.float32)
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 10, 1, 11],
                                   [0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 999, 2, 1004],
                                   [0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.95, .9, 0, 0],
                                 [.85, .5, .3, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [1, 0, 0, 0]])
      exp_nms_masks = np.array([[[[6, 7], [8, 9]],
                                 [[0, 1], [2, 3]],
                                 [[0, 0], [0, 0]],
                                 [[0, 0], [0, 0]]],
                                [[[13, 14], [15, 16]],
                                 [[8, 9], [10, 11]],
                                 [[10, 11], [12, 13]],
                                 [[0, 0], [0, 0]]]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          masks=masks)
  
      self.assertIsNone(nmsed_additional_fields)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(), exp_nms_corners.shape)
      self.assertAllEqual(nmsed_scores.shape.as_list(), exp_nms_scores.shape)
      self.assertAllEqual(nmsed_classes.shape.as_list(), exp_nms_classes.shape)
      self.assertAllEqual(nmsed_masks.shape.as_list(), exp_nms_masks.shape)
      self.assertEqual(num_detections.shape.as_list(), [2])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     nmsed_masks, num_detections])
  
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [2, 3])
        self.assertAllClose(nmsed_masks, exp_nms_masks)
  
    def test_batch_multiclass_nms_with_additional_fields(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      additional_fields = {
          'keypoints': tf.constant(
              [[[[6, 7], [8, 9]],
                [[0, 1], [2, 3]],
                [[0, 0], [0, 0]],
                [[0, 0], [0, 0]]],
               [[[13, 14], [15, 16]],
                [[8, 9], [10, 11]],
                [[10, 11], [12, 13]],
                [[0, 0], [0, 0]]]],
              tf.float32)
      }
      additional_fields['size'] = tf.constant(
          [[[[6], [8]], [[0], [2]], [[0], [0]], [[0], [0]]],
           [[[13], [15]], [[8], [10]], [[10], [12]], [[0], [0]]]], tf.float32)
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 10, 1, 11],
                                   [0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 999, 2, 1004],
                                   [0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.95, .9, 0, 0],
                                 [.85, .5, .3, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [1, 0, 0, 0]])
      exp_nms_additional_fields = {
          'keypoints': np.array([[[[0, 0], [0, 0]],
                                  [[6, 7], [8, 9]],
                                  [[0, 0], [0, 0]],
                                  [[0, 0], [0, 0]]],
                                 [[[10, 11], [12, 13]],
                                  [[13, 14], [15, 16]],
                                  [[8, 9], [10, 11]],
                                  [[0, 0], [0, 0]]]])
      }
      exp_nms_additional_fields['size'] = np.array([[[[0], [0]], [[6], [8]],
                                                     [[0], [0]], [[0], [0]]],
                                                    [[[10], [12]], [[13], [15]],
                                                     [[8], [10]], [[0], [0]]]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          additional_fields=additional_fields)
  
      self.assertIsNone(nmsed_masks)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(), exp_nms_corners.shape)
      self.assertAllEqual(nmsed_scores.shape.as_list(), exp_nms_scores.shape)
      self.assertAllEqual(nmsed_classes.shape.as_list(), exp_nms_classes.shape)
      self.assertEqual(len(nmsed_additional_fields),
                       len(exp_nms_additional_fields))
      for key in exp_nms_additional_fields:
        self.assertAllEqual(nmsed_additional_fields[key].shape.as_list(),
                            exp_nms_additional_fields[key].shape)
      self.assertEqual(num_detections.shape.as_list(), [2])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_additional_fields,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     nmsed_additional_fields, num_detections])
  
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        for key in exp_nms_additional_fields:
          self.assertAllClose(nmsed_additional_fields[key],
                              exp_nms_additional_fields[key])
        self.assertAllClose(num_detections, [2, 3])
  
    def test_batch_multiclass_nms_with_dynamic_batch_size(self):
      boxes_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 4))
      scores_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2))
      masks_placeholder = tf.placeholder(tf.float32, shape=(None, None, 2, 2, 2))
  
      boxes = np.array([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                         [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                         [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                         [[0, 10, 1, 11], [0, 10, 1, 11]]],
                        [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                         [[0, 100, 1, 101], [0, 100, 1, 101]],
                         [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                         [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]])
      scores = np.array([[[.9, 0.01], [.75, 0.05],
                          [.6, 0.01], [.95, 0]],
                         [[.5, 0.01], [.3, 0.01],
                          [.01, .85], [.01, .5]]])
      masks = np.array([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
                         [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
                         [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
                         [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
                        [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
                         [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
                         [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
                         [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = np.array([[[0, 10, 1, 11],
                                   [0, 0, 1, 1],
                                   [0, 0, 0, 0],
                                   [0, 0, 0, 0]],
                                  [[0, 999, 2, 1004],
                                   [0, 10.1, 1, 11.1],
                                   [0, 100, 1, 101],
                                   [0, 0, 0, 0]]])
      exp_nms_scores = np.array([[.95, .9, 0, 0],
                                 [.85, .5, .3, 0]])
      exp_nms_classes = np.array([[0, 0, 0, 0],
                                  [1, 0, 0, 0]])
      exp_nms_masks = np.array([[[[6, 7], [8, 9]],
                                 [[0, 1], [2, 3]],
                                 [[0, 0], [0, 0]],
                                 [[0, 0], [0, 0]]],
                                [[[13, 14], [15, 16]],
                                 [[8, 9], [10, 11]],
                                 [[10, 11], [12, 13]],
                                 [[0, 0], [0, 0]]]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes_placeholder, scores_placeholder, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          masks=masks_placeholder)
  
      self.assertIsNone(nmsed_additional_fields)
      # Check static shapes
      self.assertAllEqual(nmsed_boxes.shape.as_list(), [None, 4, 4])
      self.assertAllEqual(nmsed_scores.shape.as_list(), [None, 4])
      self.assertAllEqual(nmsed_classes.shape.as_list(), [None, 4])
      self.assertAllEqual(nmsed_masks.shape.as_list(), [None, 4, 2, 2])
      self.assertEqual(num_detections.shape.as_list(), [None])
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     nmsed_masks, num_detections],
                                    feed_dict={boxes_placeholder: boxes,
                                               scores_placeholder: scores,
                                               masks_placeholder: masks})
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [2, 3])
        self.assertAllClose(nmsed_masks, exp_nms_masks)
  
    def test_batch_multiclass_nms_with_masks_and_num_valid_boxes(self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      masks = tf.constant([[[[[0, 1], [2, 3]], [[1, 2], [3, 4]]],
                            [[[2, 3], [4, 5]], [[3, 4], [5, 6]]],
                            [[[4, 5], [6, 7]], [[5, 6], [7, 8]]],
                            [[[6, 7], [8, 9]], [[7, 8], [9, 10]]]],
                           [[[[8, 9], [10, 11]], [[9, 10], [11, 12]]],
                            [[[10, 11], [12, 13]], [[11, 12], [13, 14]]],
                            [[[12, 13], [14, 15]], [[13, 14], [15, 16]]],
                            [[[14, 15], [16, 17]], [[15, 16], [17, 18]]]]],
                          tf.float32)
      num_valid_boxes = tf.constant([1, 1], tf.int32)
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = [[[0, 0, 1, 1],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0]],
                         [[0, 10.1, 1, 11.1],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0]]]
      exp_nms_scores = [[.9, 0, 0, 0],
                        [.5, 0, 0, 0]]
      exp_nms_classes = [[0, 0, 0, 0],
                         [0, 0, 0, 0]]
      exp_nms_masks = [[[[0, 1], [2, 3]],
                        [[0, 0], [0, 0]],
                        [[0, 0], [0, 0]],
                        [[0, 0], [0, 0]]],
                       [[[8, 9], [10, 11]],
                        [[0, 0], [0, 0]],
                        [[0, 0], [0, 0]],
                        [[0, 0], [0, 0]]]]
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          num_valid_boxes=num_valid_boxes, masks=masks)
  
      self.assertIsNone(nmsed_additional_fields)
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     nmsed_masks, num_detections])
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        self.assertAllClose(num_detections, [1, 1])
        self.assertAllClose(nmsed_masks, exp_nms_masks)
  
    def test_batch_multiclass_nms_with_additional_fields_and_num_valid_boxes(
        self):
      boxes = tf.constant([[[[0, 0, 1, 1], [0, 0, 4, 5]],
                            [[0, 0.1, 1, 1.1], [0, 0.1, 2, 1.1]],
                            [[0, -0.1, 1, 0.9], [0, -0.1, 1, 0.9]],
                            [[0, 10, 1, 11], [0, 10, 1, 11]]],
                           [[[0, 10.1, 1, 11.1], [0, 10.1, 1, 11.1]],
                            [[0, 100, 1, 101], [0, 100, 1, 101]],
                            [[0, 1000, 1, 1002], [0, 999, 2, 1004]],
                            [[0, 1000, 1, 1002.1], [0, 999, 2, 1002.7]]]],
                          tf.float32)
      scores = tf.constant([[[.9, 0.01], [.75, 0.05],
                             [.6, 0.01], [.95, 0]],
                            [[.5, 0.01], [.3, 0.01],
                             [.01, .85], [.01, .5]]])
      additional_fields = {
          'keypoints': tf.constant(
              [[[[6, 7], [8, 9]],
                [[0, 1], [2, 3]],
                [[0, 0], [0, 0]],
                [[0, 0], [0, 0]]],
               [[[13, 14], [15, 16]],
                [[8, 9], [10, 11]],
                [[10, 11], [12, 13]],
                [[0, 0], [0, 0]]]],
              tf.float32)
      }
  
      additional_fields['size'] = tf.constant(
          [[[[7], [9]], [[1], [3]], [[0], [0]], [[0], [0]]],
           [[[14], [16]], [[9], [11]], [[11], [13]], [[0], [0]]]], tf.float32)
  
      num_valid_boxes = tf.constant([1, 1], tf.int32)
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = [[[0, 0, 1, 1],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0]],
                         [[0, 10.1, 1, 11.1],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0],
                          [0, 0, 0, 0]]]
      exp_nms_scores = [[.9, 0, 0, 0],
                        [.5, 0, 0, 0]]
      exp_nms_classes = [[0, 0, 0, 0],
                         [0, 0, 0, 0]]
      exp_nms_additional_fields = {
          'keypoints': np.array([[[[6, 7], [8, 9]],
                                  [[0, 0], [0, 0]],
                                  [[0, 0], [0, 0]],
                                  [[0, 0], [0, 0]]],
                                 [[[13, 14], [15, 16]],
                                  [[0, 0], [0, 0]],
                                  [[0, 0], [0, 0]],
                                  [[0, 0], [0, 0]]]])
      }
  
      exp_nms_additional_fields['size'] = np.array([[[[7], [9]], [[0], [0]],
                                                     [[0], [0]], [[0], [0]]],
                                                    [[[14], [16]], [[0], [0]],
                                                     [[0], [0]], [[0], [0]]]])
  
      (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks,
       nmsed_additional_fields, num_detections
      ) = post_processing.batch_multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh,
          max_size_per_class=max_output_size, max_total_size=max_output_size,
          num_valid_boxes=num_valid_boxes,
          additional_fields=additional_fields)
  
      self.assertIsNone(nmsed_masks)
  
      with self.test_session() as sess:
        (nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_additional_fields,
         num_detections) = sess.run([nmsed_boxes, nmsed_scores, nmsed_classes,
                                     nmsed_additional_fields, num_detections])
  
        self.assertAllClose(nmsed_boxes, exp_nms_corners)
        self.assertAllClose(nmsed_scores, exp_nms_scores)
        self.assertAllClose(nmsed_classes, exp_nms_classes)
        for key in exp_nms_additional_fields:
          self.assertAllClose(nmsed_additional_fields[key],
                              exp_nms_additional_fields[key])
        self.assertAllClose(num_detections, [1, 1])
  
    # TODO(bhattad): Remove conditional after CMLE moves to TF 1.9
  
  if __name__ == '__main__':
    tf.test.main()
  