def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
num_classes = 4
num_valid_boxes = 3
num_boxes = 10
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32, shape=(num_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = (feed_dict[dense_location_placeholder]
[:num_valid_boxes])
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)
def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
num_classes = 4
num_valid_boxes = 3
num_boxes = 10
code_size = 4
dense_location_placeholder = tf.placeholder(tf.float32, shape=(num_boxes,
code_size))
dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
box_locations, box_classes = ops.dense_to_sparse_boxes(
dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
feed_dict = {dense_location_placeholder: np.random.uniform(
size=[num_boxes, code_size]),
dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
dtype=np.int32)}
expected_box_locations = (feed_dict[dense_location_placeholder]
[:num_valid_boxes])
expected_box_classses = np.array([0, 3, 3])
with self.test_session() as sess:
box_locations, box_classes = sess.run([box_locations, box_classes],
feed_dict=feed_dict)
self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
atol=1e-6)
self.assertAllEqual(box_classes, expected_box_classses)