def test_mini_batch(self):
with self.cached_session():
# create simple FilterDetections layer
layer = layers.FilterDetections()
# create input with batch_size=2
boxes = np.array(
[
[
[0, 0, 10, 10], # this will be suppressed
[0, 0, 10, 10],
],
[
[100, 100, 150, 150],
[100, 100, 150, 150], # this will be suppressed
],
],
dtype=K.floatx())
boxes = K.constant(boxes)
classification = np.array(
[
[
[0, 0.9], # this will be suppressed
[0, 1],
],
[
[1, 0],
[0.9, 0], # this will be suppressed
],
],
dtype=K.floatx())
classification = K.constant(classification)
# compute output
actual_boxes, actual_scores, actual_labels = layer.call(
[boxes, classification])
actual_boxes = K.get_value(actual_boxes)
actual_scores = K.get_value(actual_scores)
actual_labels = K.get_value(actual_labels)
# define expected output
expected_boxes = -1 * np.ones((2, 300, 4), dtype=K.floatx())
expected_boxes[0, 0, :] = [0, 0, 10, 10]
expected_boxes[1, 0, :] = [100, 100, 150, 150]
expected_scores = -1 * np.ones((2, 300), dtype=K.floatx())
expected_scores[0, 0] = 1
expected_scores[1, 0] = 1
expected_labels = -1 * np.ones((2, 300), dtype=K.floatx())
expected_labels[0, 0] = 1
expected_labels[1, 0] = 0
# assert actual and expected are equal
self.assertAllEqual(actual_boxes, expected_boxes)
self.assertAllEqual(actual_scores, expected_scores)
self.assertAllEqual(actual_labels, expected_labels)
def test_simple_3d(self):
with self.test_session():
# create simple FilterDetections layer
layer = layers.FilterDetections()
# create simple input
boxes = np.array(
[[
[0, 0, 10, 10],
[0, 0, 10, 10], # this will be suppressed
]],
dtype=K.floatx())
boxes = np.expand_dims(boxes, 0)
boxes = K.constant(boxes)
classification = np.array(
[[
[0, 0.9], # this will be suppressed
[0, 1],
]],
dtype=K.floatx())
classification = np.expand_dims(classification, 0)
classification = K.constant(classification)
# compute output
actual_boxes, actual_scores, actual_labels = layer.call(
[boxes, classification])
actual_boxes = K.get_value(actual_boxes)
actual_scores = K.get_value(actual_scores)
actual_labels = K.get_value(actual_labels)
# define expected output
expected_boxes = -1 * np.ones((1, 1, 300, 4), dtype=K.floatx())
expected_boxes[0, 0, 0, :] = [0, 0, 10, 10]
expected_scores = -1 * np.ones((1, 1, 300), dtype=K.floatx())
expected_scores[0, 0, 0] = 1
expected_labels = -1 * np.ones((1, 1, 300), dtype=K.floatx())
expected_labels[0, 0, 0] = 1
# assert actual and expected are equal
self.assertAllEqual(actual_boxes, expected_boxes)
self.assertAllEqual(actual_scores, expected_scores)
self.assertAllEqual(actual_labels, expected_labels)
def test_simple_with_other(self):
with self.test_session():
# create simple FilterDetections layer
layer = layers.FilterDetections()
# create simple input
boxes = np.array(
[[
[0, 0, 10, 10],
[0, 0, 10, 10], # this will be suppressed
]],
dtype=K.floatx())
boxes = K.constant(boxes)
classification = np.array(
[[
[0, 0.9], # this will be suppressed
[0, 1],
]],
dtype=K.floatx())
classification = K.constant(classification)
other = []
other.append(
np.array(
[[
[0, 1234], # this will be suppressed
[0, 5678],
]],
dtype=K.floatx()))
other.append(
np.array(
[[
5678, # this will be suppressed
1234,
]],
dtype=K.floatx()))
other = [K.constant(o) for o in other]
# compute output
actual = layer.call([boxes, classification] + other)
actual_boxes = K.get_value(actual[0])
actual_scores = K.get_value(actual[1])
actual_labels = K.get_value(actual[2])
actual_other = [K.get_value(a) for a in actual[3:]]
# define expected output
expected_boxes = -1 * np.ones((1, 300, 4), dtype=K.floatx())
expected_boxes[0, 0, :] = [0, 0, 10, 10]
expected_scores = -1 * np.ones((1, 300), dtype=K.floatx())
expected_scores[0, 0] = 1
expected_labels = -1 * np.ones((1, 300), dtype=K.floatx())
expected_labels[0, 0] = 1
expected_other = []
expected_other.append(-1 * np.ones((1, 300, 2), dtype=K.floatx()))
expected_other[-1][0, 0, :] = [0, 5678]
expected_other.append(-1 * np.ones((1, 300), dtype=K.floatx()))
expected_other[-1][0, 0] = 1234
# assert actual and expected are equal
self.assertAllEqual(actual_boxes, expected_boxes)
self.assertAllEqual(actual_scores, expected_scores)
self.assertAllEqual(actual_labels, expected_labels)
for a, e in zip(actual_other, expected_other):
self.assertAllEqual(a, e)
