def setUp(self):
super(RCNNTargetTest, self).setUp()
# We don't care about the class labels or the batch number in most of
# these tests.
self._num_classes = 5
self._placeholder_label = 3.0
self._config = EasyDict({
"foreground_threshold": 0.5,
"background_threshold_high": 0.5,
"background_threshold_low": 0.1,
"foreground_fraction": 0.5,
"minibatch_size": 2,
})
# We check for a difference smaller than this numbers in our tests
# instead of checking for exact equality.
self._equality_delta = 1e-03
self._shared_model = RCNNTarget(self._num_classes,
self._config,
seed=0)
tf.reset_default_graph()
def testOddMinibatchSize(self):
"""Tests we're getting the right results when there's an odd minibatch
size.
"""
config = EasyDict({
'foreground_threshold': 0.5,
'background_threshold_high': 0.5,
'background_threshold_low': 0.1,
'foreground_fraction': 0.5,
'minibatch_size': 5,
})
model = RCNNTarget(self._num_classes, config, seed=0)
gt_boxes = tf.constant(
[(200, 300, 250, 390, self._placeholder_label)],
dtype=tf.float32
)
proposed_boxes = tf.constant(
[
(12, 70, 350, 540), # noise
(190, 310, 240, 370), # IoU: 0.4763
(197, 300, 252, 389), # IoU: 0.9015
(196, 300, 252, 389), # IoU: 0.8859
(197, 303, 252, 394), # IoU: 0.8459
(180, 310, 235, 370), # IoU: 0.3747
(0, 0, 400, 400), # noise
(197, 302, 252, 389), # IoU: 0.8832
(180, 310, 235, 370), # IoU: 0.3747
(180, 310, 235, 370), # IoU: 0.3747
(0, 0, 400, 400), # noise
],
dtype=tf.float32
)
(proposals_label, bbox_targets) = self._run_rcnn_target(
model,
gt_boxes,
proposed_boxes
)
foreground_number = proposals_label[proposals_label >= 1].shape[0]
background_number = proposals_label[proposals_label == 0].shape[0]
foreground_fraction = config.foreground_fraction
minibatch_size = config.minibatch_size
self.assertLessEqual(
foreground_number,
np.floor(foreground_fraction * minibatch_size)
)
self.assertGreater(foreground_number, 0)
self.assertLessEqual(
background_number,
minibatch_size - foreground_number
)
self.assertEqual(
proposals_label[proposals_label >= 0].shape[0],
config.minibatch_size
)