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)
