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_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]] (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) 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_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) } 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]]]]) } (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])
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_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)