def testNMS128From1024(self):
with compat.forward_compatibility_horizon(2018, 8, 8):
num_boxes = 1024
boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4")
scores_np = np.random.normal(0.5, 0.1, (num_boxes,)).astype("f4")
max_output_size = 128
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.0, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
score_threshold: score_threshold_np,
iou_threshold: iou_threshold_np
}
(indices_tf, _) = sess.run(selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
def testNMS128From1024(self):
num_boxes = 1024
boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4")
scores_np = np.random.normal(0.5, 0.1, (num_boxes, )).astype("f4")
max_output_size = 128
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.0, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
score_threshold: score_threshold_np,
iou_threshold: iou_threshold_np
}
(indices_tf, _) = sess.run(selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
def testNMS3Then2WithScoreThresh(self):
# Three boxes are selected based on IOU.
# One is filtered out by score threshold.
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
with compat.forward_compatibility_horizon(2018, 8, 8):
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.4, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype,
shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(
score_threshold_np.dtype, score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(selected_indices,
feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 2)
self.assertAllClose(indices_tf[:num_valid], [3, 0])
def testNMS3Then2WithScoreThresh(self):
# Three boxes are selected based on IOU.
# One is filtered out by score threshold.
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
with compat.forward_compatibility_horizon(2018, 8, 8):
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.4, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(
selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 2)
self.assertAllClose(indices_tf[:num_valid], [3, 0])
def testNMS3Then1WithScoreMaxThresh(self):
# Three boxes are selected based on IOU.
# One is filtered out by score threshold.
# One is filtered out by max_output_size.
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 1
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.4, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(selected_indices,
feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 1)
self.assertAllClose(indices_tf[:num_valid], [3])
def testNMS3From6Boxes(self):
with compat.forward_compatibility_horizon(2018, 8, 8):
# Three boxes are selected based on IOU.
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.0, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype,
shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(
score_threshold_np.dtype, score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
score_threshold: score_threshold_np,
iou_threshold: iou_threshold_np
}
(indices_tf, num_valid) = sess.run(selected_indices,
feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 3)
self.assertAllClose(indices_tf[:num_valid], [3, 0, 5])
def testNMS3Then1WithScoreMaxThresh(self):
# Three boxes are selected based on IOU.
# One is filtered out by score threshold.
# One is filtered out by max_output_size.
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 1
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.4, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(
selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 1)
self.assertAllClose(indices_tf[:num_valid], [3])
def testSelectFromContinuousOverLap(self):
# Tests that a suppressed box does not itself suppress other boxes.
boxes_data = [[0, 0, 1, 1], [0, 0.2, 1, 1.2], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 3]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.5, 0.4, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.1, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(selected_indices,
feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 3)
self.assertAllClose(indices_tf[:num_valid], [0, 2, 4])
def testNMS3From6Boxes(self):
with compat.forward_compatibility_horizon(2018, 8, 8):
# Three boxes are selected based on IOU.
boxes_data = [[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]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.0, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
score_threshold: score_threshold_np,
iou_threshold: iou_threshold_np
}
(indices_tf, num_valid) = sess.run(
selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 3)
self.assertAllClose(indices_tf[:num_valid], [3, 0, 5])
def testNMS128From1024(self):
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
with compat.forward_compatibility_horizon(2018, 8, 8):
num_boxes = 1024
boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4")
scores_np = np.random.normal(0.5, 0.1, (num_boxes, )).astype("f4")
max_output_size = 128
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.0, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype,
shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype,
shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(
score_threshold_np.dtype, score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
score_threshold: score_threshold_np,
iou_threshold: iou_threshold_np
}
(indices_tf, _) = sess.run(selected_indices,
feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
def testSelectFromContinuousOverLap(self):
# Tests that a suppressed box does not itself suppress other boxes.
boxes_data = [[0, 0, 1, 1], [0, 0.2, 1, 1.2], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 3]]
boxes_np = np.array(boxes_data, dtype=np.float32)
scores_data = [0.9, 0.75, 0.6, 0.5, 0.4, 0.3]
scores_np = np.array(scores_data, dtype=np.float32)
max_output_size = 3
iou_threshold_np = np.array(0.5, dtype=np.float32)
score_threshold_np = np.array(0.1, dtype=np.float32)
with self.cached_session() as sess:
boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape)
scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape)
iou_threshold = array_ops.placeholder(iou_threshold_np.dtype,
iou_threshold_np.shape)
score_threshold = array_ops.placeholder(score_threshold_np.dtype,
score_threshold_np.shape)
with self.test_scope():
selected_indices = image_ops.non_max_suppression_padded(
boxes=boxes,
scores=scores,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
pad_to_max_output_size=True)
inputs_feed = {
boxes: boxes_np,
scores: scores_np,
iou_threshold: iou_threshold_np,
score_threshold: score_threshold_np
}
(indices_tf, num_valid) = sess.run(
selected_indices, feed_dict=inputs_feed)
self.assertEqual(indices_tf.size, max_output_size)
self.assertEqual(num_valid, 3)
self.assertAllClose(indices_tf[:num_valid], [0, 2, 4])
