text: # 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.core.post_processing."""
  import numpy as np
  import tensorflow as tf
  from object_detection.core import post_processing
  from object_detection.core import standard_fields as fields
  
  
  class MulticlassNonMaxSuppressionTest(tf.test.TestCase):
  
    def test_with_invalid_scores_size(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]]], tf.float32)
      scores = tf.constant([[.9], [.75], [.6], [.95], [.5]])
      iou_thresh = .5
      score_thresh = 0.6
      max_output_size = 3
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size)
      with self.test_session() as sess:
        with self.assertRaisesWithPredicateMatch(
            tf.errors.InvalidArgumentError, 'Incorrect scores field length'):
          sess.run(nms.get())
  
    def test_multiclass_nms_select_with_shared_boxes(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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, 1000, 1, 1002],
                         [0, 100, 1, 101]]
      exp_nms_scores = [.95, .9, .85, .3]
      exp_nms_classes = [0, 0, 1, 0]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_multiclass_nms_select_with_shared_boxes_given_keypoints(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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]])
      num_keypoints = 6
      keypoints = tf.tile(
          tf.reshape(tf.range(8), [8, 1, 1]),
          [1, num_keypoints, 2])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = [[0, 10, 1, 11],
                         [0, 0, 1, 1],
                         [0, 1000, 1, 1002],
                         [0, 100, 1, 101]]
      exp_nms_scores = [.95, .9, .85, .3]
      exp_nms_classes = [0, 0, 1, 0]
      exp_nms_keypoints_tensor = tf.tile(
          tf.reshape(tf.constant([3, 0, 6, 5], dtype=tf.float32), [4, 1, 1]),
          [1, num_keypoints, 2])
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size,
          additional_fields={
              fields.BoxListFields.keypoints: keypoints})
  
      with self.test_session() as sess:
        (nms_corners_output,
         nms_scores_output,
         nms_classes_output,
         nms_keypoints,
         exp_nms_keypoints) = sess.run([
             nms.get(),
             nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes),
             nms.get_field(fields.BoxListFields.keypoints),
             exp_nms_keypoints_tensor
         ])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
        self.assertAllEqual(nms_keypoints, exp_nms_keypoints)
  
    def test_multiclass_nms_with_shared_boxes_given_keypoint_heatmaps(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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]])
  
      num_boxes = tf.shape(boxes)[0]
      heatmap_height = 5
      heatmap_width = 5
      num_keypoints = 17
      keypoint_heatmaps = tf.ones(
          [num_boxes, heatmap_height, heatmap_width, num_keypoints],
          dtype=tf.float32)
  
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
      exp_nms_corners = [[0, 10, 1, 11],
                         [0, 0, 1, 1],
                         [0, 1000, 1, 1002],
                         [0, 100, 1, 101]]
  
      exp_nms_scores = [.95, .9, .85, .3]
      exp_nms_classes = [0, 0, 1, 0]
      exp_nms_keypoint_heatmaps = np.ones(
          (4, heatmap_height, heatmap_width, num_keypoints), dtype=np.float32)
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size,
          additional_fields={
              fields.BoxListFields.keypoint_heatmaps: keypoint_heatmaps})
  
      with self.test_session() as sess:
        (nms_corners_output,
         nms_scores_output,
         nms_classes_output,
         nms_keypoint_heatmaps) = sess.run(
             [nms.get(),
              nms.get_field(fields.BoxListFields.scores),
              nms.get_field(fields.BoxListFields.classes),
              nms.get_field(fields.BoxListFields.keypoint_heatmaps)])
  
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
        self.assertAllEqual(nms_keypoint_heatmaps, exp_nms_keypoint_heatmaps)
  
    def test_multiclass_nms_with_additional_fields(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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]])
  
      coarse_boxes_key = 'coarse_boxes'
      coarse_boxes = tf.constant([[0.1, 0.1, 1.1, 1.1],
                                  [0.1, 0.2, 1.1, 1.2],
                                  [0.1, -0.2, 1.1, 1.0],
                                  [0.1, 10.1, 1.1, 11.1],
                                  [0.1, 10.2, 1.1, 11.2],
                                  [0.1, 100.1, 1.1, 101.1],
                                  [0.1, 1000.1, 1.1, 1002.1],
                                  [0.1, 1000.1, 1.1, 1002.2]], 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, 1000, 1, 1002],
                                  [0, 100, 1, 101]], dtype=np.float32)
  
      exp_nms_coarse_corners = np.array([[0.1, 10.1, 1.1, 11.1],
                                         [0.1, 0.1, 1.1, 1.1],
                                         [0.1, 1000.1, 1.1, 1002.1],
                                         [0.1, 100.1, 1.1, 101.1]],
                                        dtype=np.float32)
  
      exp_nms_scores = [.95, .9, .85, .3]
      exp_nms_classes = [0, 0, 1, 0]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size,
          additional_fields={coarse_boxes_key: coarse_boxes})
  
      with self.test_session() as sess:
        (nms_corners_output,
         nms_scores_output,
         nms_classes_output,
         nms_coarse_corners) = sess.run(
             [nms.get(),
              nms.get_field(fields.BoxListFields.scores),
              nms.get_field(fields.BoxListFields.classes),
              nms.get_field(coarse_boxes_key)])
  
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
        self.assertAllEqual(nms_coarse_corners, exp_nms_coarse_corners)
  
    def test_multiclass_nms_select_with_shared_boxes_given_masks(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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]])
      num_classes = 2
      mask_height = 3
      mask_width = 3
      masks = tf.tile(
          tf.reshape(tf.range(8), [8, 1, 1, 1]),
          [1, num_classes, mask_height, mask_width])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 4
  
      exp_nms_corners = [[0, 10, 1, 11],
                         [0, 0, 1, 1],
                         [0, 1000, 1, 1002],
                         [0, 100, 1, 101]]
      exp_nms_scores = [.95, .9, .85, .3]
      exp_nms_classes = [0, 0, 1, 0]
      exp_nms_masks_tensor = tf.tile(
          tf.reshape(tf.constant([3, 0, 6, 5], dtype=tf.float32), [4, 1, 1]),
          [1, mask_height, mask_width])
  
      nms = post_processing.multiclass_non_max_suppression(boxes, scores,
                                                           score_thresh,
                                                           iou_thresh,
                                                           max_output_size,
                                                           masks=masks)
      with self.test_session() as sess:
        (nms_corners_output,
         nms_scores_output,
         nms_classes_output,
         nms_masks,
         exp_nms_masks) = sess.run([nms.get(),
                                    nms.get_field(fields.BoxListFields.scores),
                                    nms.get_field(fields.BoxListFields.classes),
                                    nms.get_field(fields.BoxListFields.masks),
                                    exp_nms_masks_tensor])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
        self.assertAllEqual(nms_masks, exp_nms_masks)
  
    def test_multiclass_nms_select_with_clip_window(self):
      boxes = tf.constant([[[0, 0, 10, 10]],
                           [[1, 1, 11, 11]]], tf.float32)
      scores = tf.constant([[.9], [.75]])
      clip_window = tf.constant([5, 4, 8, 7], tf.float32)
      score_thresh = 0.0
      iou_thresh = 0.5
      max_output_size = 100
  
      exp_nms_corners = [[5, 4, 8, 7]]
      exp_nms_scores = [.9]
      exp_nms_classes = [0]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size,
          clip_window=clip_window)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_multiclass_nms_select_with_clip_window_change_coordinate_frame(self):
      boxes = tf.constant([[[0, 0, 10, 10]],
                           [[1, 1, 11, 11]]], tf.float32)
      scores = tf.constant([[.9], [.75]])
      clip_window = tf.constant([5, 4, 8, 7], tf.float32)
      score_thresh = 0.0
      iou_thresh = 0.5
      max_output_size = 100
  
      exp_nms_corners = [[0, 0, 1, 1]]
      exp_nms_scores = [.9]
      exp_nms_classes = [0]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size,
          clip_window=clip_window, change_coordinate_frame=True)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_multiclass_nms_select_with_per_class_cap(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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_size_per_class = 2
  
      exp_nms_corners = [[0, 10, 1, 11],
                         [0, 0, 1, 1],
                         [0, 1000, 1, 1002]]
      exp_nms_scores = [.95, .9, .85]
      exp_nms_classes = [0, 0, 1]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_size_per_class)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_multiclass_nms_select_with_total_cap(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], 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_size_per_class = 4
      max_total_size = 2
  
      exp_nms_corners = [[0, 10, 1, 11],
                         [0, 0, 1, 1]]
      exp_nms_scores = [.95, .9]
      exp_nms_classes = [0, 0]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_size_per_class,
          max_total_size)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_multiclass_nms_threshold_then_select_with_shared_boxes(self):
      boxes = tf.constant([[[0, 0, 1, 1]],
                           [[0, 0.1, 1, 1.1]],
                           [[0, -0.1, 1, 0.9]],
                           [[0, 10, 1, 11]],
                           [[0, 10.1, 1, 11.1]],
                           [[0, 100, 1, 101]],
                           [[0, 1000, 1, 1002]],
                           [[0, 1000, 1, 1002.1]]], tf.float32)
      scores = tf.constant([[.9], [.75], [.6], [.95], [.5], [.3], [.01], [.01]])
      score_thresh = 0.1
      iou_thresh = .5
      max_output_size = 3
  
      exp_nms = [[0, 10, 1, 11],
                 [0, 0, 1, 1],
                 [0, 100, 1, 101]]
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size)
      with self.test_session() as sess:
        nms_output = sess.run(nms.get())
        self.assertAllClose(nms_output, exp_nms)
  
    def test_multiclass_nms_select_with_separate_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]])
      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]
  
      nms = post_processing.multiclass_non_max_suppression(
          boxes, scores, score_thresh, iou_thresh, max_output_size)
      with self.test_session() as sess:
        nms_corners_output, nms_scores_output, nms_classes_output = sess.run(
            [nms.get(), nms.get_field(fields.BoxListFields.scores),
             nms.get_field(fields.BoxListFields.classes)])
        self.assertAllClose(nms_corners_output, exp_nms_corners)
        self.assertAllClose(nms_scores_output, exp_nms_scores)
        self.assertAllClose(nms_classes_output, exp_nms_classes)
  
    def test_batch_multiclass_nms_with_batch_size_1(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 = [[[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]]
  
      nms_dict = 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)
      with self.test_session() as sess:
        nms_output = sess.run(nms_dict)
        self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
        self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
        self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
        self.assertEqual(nms_output['num_detections'], [4])
  
    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 = [[[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 = [[.95, .9, 0, 0],
                        [.85, .5, .3, 0]]
      exp_nms_classes = [[0, 0, 0, 0],
                         [1, 0, 0, 0]]
  
      nms_dict = 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)
      with self.test_session() as sess:
        nms_output = sess.run(nms_dict)
        self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
        self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
        self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
        self.assertAllClose(nms_output['num_detections'], [2, 3])
  
    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 = [[[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 = [[.95, .9, 0, 0],
                        [.85, .5, .3, 0]]
      exp_nms_classes = [[0, 0, 0, 0],
                         [1, 0, 0, 0]]
      exp_nms_masks = [[[[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]]]]
  
      nms_dict = 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)
      with self.test_session() as sess:
        nms_output = sess.run(nms_dict)
        self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
        self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
        self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
        self.assertAllClose(nms_output['num_detections'], [2, 3])
        self.assertAllClose(nms_output['detection_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]]]]
  
      nms_dict = 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)
      with self.test_session() as sess:
        nms_output = sess.run(nms_dict)
        self.assertAllClose(nms_output['detection_boxes'], exp_nms_corners)
        self.assertAllClose(nms_output['detection_scores'], exp_nms_scores)
        self.assertAllClose(nms_output['detection_classes'], exp_nms_classes)
        self.assertAllClose(nms_output['num_detections'], [1, 1])
        self.assertAllClose(nms_output['detection_masks'], exp_nms_masks)
  
  
  if __name__ == '__main__':
    tf.test.main()
  