def test_multiclass_nms_select_with_separate_boxes_handle_as_single_class(self):
rboxes = tf.constant([[[0.5, 0.5, 1, 1, 0.0], [2, 2.5, 4, 5, 0]],
[[0.5, 0.6, 1, 1, 0.0], [1, 0.6, 1, 1, 0]],
[[0.5, 0.4, 1, 1, 0.0], [0.5, 0.4, 1, 1, 0]],
[[0.5, 10.5, 1, 1, 0.0], [0.5, 10.5, 1, 1, 0]],
[[0.5, 10.6, 1, 1, 0.0], [0.5, 10.6, 1, 1, 0]],
[[0.5, 100.5, 1, 1, 0.0], [0.5, 101.5, 1, 1, 0]],
[[0.5, 1001, 1, 2, 0.0], [1, 1001.5, 2, 5, 0]],
[[0.5, 1001.05, 1, 2.1, 0.0], [1, 1000.85, 2, 3.7, 0]]], tf.float32)
scores = tf.constant([[.9, 0.01], [.75, 0.05],
[.6, .01], [.95, 0.94],
[.5, 0.01], [.3, 0.31],
[.01, .85], [.01, .5]])
score_thresh = 0.1
iou_thresh = .5
max_output_size = 4
exp_nms_corners = [[0.5, 10.5, 1, 1, 0.0],
[0.5, 0.5, 1, 1, 0.0],
[1, 1001.5, 2, 5, 0],
[0.5, 101.5, 1, 1, 0]]
exp_nms_scores = [.95, .9, .85, .31]
exp_nms_classes = [0., 0., 1., 1.]
nms = post_processing_rbox.multiclass_non_max_suppression_rbox(
rboxes, scores, score_thresh, iou_thresh, max_output_size, handle_as_single_class=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_total_cap(self):
rboxes = tf.constant([[[0.5, 0.5, 1, 1, 0.0]],
[[0.5, 0.6, 1, 1, 0.0]],
[[0.5, 0.4, 1, 1, 0.0]],
[[0.5, 10.5, 1, 1, 0.0]],
[[0.5, 10.6, 1, 1, 0.0]],
[[0.5, 100.5, 1, 1, 0.0]],
[[0.5, 1001, 1, 2, 0.0]],
[[0.5, 1001.05, 1, 2.1, 0.0]]], 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 = [[0.5, 10.5, 1, 1, 0.0],
[0.5, 0.5, 1, 1, 0.0]]
exp_nms_scores = [.95, .9]
exp_nms_classes = [0, 0]
nms = post_processing_rbox.multiclass_non_max_suppression_rbox(
rboxes, 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_with_invalid_scores_size(self):
rboxes = tf.constant([[[0.5, 0.5, 1, 1, 0.0]],
[[0.5, 0.6, 1, 1, 0.0]],
[[0.5, 0.4, 1, 1, 0.0]],
[[0.5, 10.5, 1, 1, 0.0]],
[[0.5, 10.6, 1, 1, 0.0]],
[[0.5, 100.5, 1, 1, 0.0]]], tf.float32)
scores = tf.constant([[.9], [.75], [.6], [.95], [.5]])
iou_thresh = .5
score_thresh = 0.6
max_output_size = 3
nms = post_processing_rbox.multiclass_non_max_suppression_rbox(rboxes,
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_threshold_then_select_with_shared_boxes(self):
rboxes = tf.constant([[[0.5, 0.5, 1, 1, 0.0]],
[[0.5, 0.6, 1, 1, 0.0]],
[[0.5, 0.4, 1, 1, 0.0]],
[[0.5, 10.5, 1, 1, 0.0]],
[[0.5, 10.6, 1, 1, 0.0]],
[[0.5, 100.5, 1, 1, 0.0]],
[[0.5, 1001, 1, 2, 0.0]],
[[0.5, 1001.05, 1, 2.1, 0.0]]], 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 = [[0.5, 10.5, 1, 1, 0.0],
[0.5, 0.5, 1, 1, 0.0],
[0.5, 100.5, 1, 1, 0.0]]
nms = post_processing_rbox.multiclass_non_max_suppression_rbox(
rboxes, 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_clip_window_change_coordinate_frame(self):
boxes = tf.constant([[[5, 5, 10, 10, 0]],
[[6, 6, 10, 10, 0]]], 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 - 5)/3, (5 - 4)/3, 10/3, 10/3, 0]]
exp_nms_scores = [.9]
exp_nms_classes = [0]
nms = post_processing_rbox.multiclass_non_max_suppression_rbox(
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_with_additional_fields(self):
rboxes = tf.constant([[[0.5, 0.5, 1, 1, 0.0]],
[[0.5, 0.6, 1, 1, 0.0]],
[[0.5, 0.4, 1, 1, 0.0]],
[[0.5, 10.5, 1, 1, 0.0]],
[[0.5, 10.6, 1, 1, 0.0]],
[[0.5, 100.5, 1, 1, 0.0]],
[[0.5, 1001, 1, 2, 0.0]],
[[0.5, 1001.05, 1, 2.1, 0.0]]], 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([[0.5, 10.5, 1, 1, 0.0],
[0.5, 0.5, 1, 1, 0.0],
[0.5, 1001, 1, 2, 0.0],
[0.5, 100.5, 1, 1, 0.0]], 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_rbox.multiclass_non_max_suppression_rbox(
rboxes, 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)
