def test_invalid_input(self):
bad_mean = 'invalid_data_type'
bad_std = 'invalid_data_type'
with self.assertRaises(ValueError):
layers.RegressBoxes(mean=bad_mean, std=None)
with self.assertRaises(ValueError):
layers.RegressBoxes(mean=None, std=bad_std)
def test_simple(self):
# create simple RegressBoxes layer
layer = layers.RegressBoxes()
# create input
anchors = np.array([[
[0, 0, 10, 10],
[50, 50, 100, 100],
[20, 20, 40, 40],
]], dtype=K.floatx())
anchors = K.variable(anchors)
regression = np.array([[
[0, 0, 0, 0],
[0.1, 0.1, 0, 0],
[0, 0, 0.1, 0.1],
]], dtype=K.floatx())
regression = K.variable(regression)
# compute output
computed_shape = layer.compute_output_shape(
[anchors.shape, regression.shape])
actual = layer.call([anchors, regression])
actual = K.get_value(actual)
self.assertEqual(actual.shape, computed_shape)
# compute expected output
expected = np.array([[
[0, 0, 10, 10],
[51, 51, 100, 100],
[20, 20, 40.4, 40.4],
]], dtype=K.floatx())
self.assertAllClose(actual, expected)
def test_mini_batch(self):
mean = [0, 0, 0, 0]
std = [0.2, 0.2, 0.2, 0.2]
# create simple RegressBoxes layer
layer = layers.RegressBoxes(mean=mean, std=std)
# create input
anchors = np.array([
[
[0, 0, 10, 10], # 1
[50, 50, 100, 100], # 2
[20, 20, 40, 40], # 3
],
[
[20, 20, 40, 40], # 3
[0, 0, 10, 10], # 1
[50, 50, 100, 100], # 2
],
], dtype=K.floatx())
anchors = K.variable(anchors)
regression = np.array([
[
[0, 0, 0, 0], # 1
[0.1, 0.1, 0, 0], # 2
[0, 0, 0.1, 0.1], # 3
],
[
[0, 0, 0.1, 0.1], # 3
[0, 0, 0, 0], # 1
[0.1, 0.1, 0, 0], # 2
],
], dtype=K.floatx())
regression = K.variable(regression)
# compute output
actual = layer.call([anchors, regression])
actual = K.get_value(actual)
# compute expected output
expected = np.array([
[
[0, 0, 10, 10], # 1
[51, 51, 100, 100], # 2
[20, 20, 40.4, 40.4], # 3
],
[
[20, 20, 40.4, 40.4], # 3
[0, 0, 10, 10], # 1
[51, 51, 100, 100], # 2
],
], dtype=K.floatx())
self.assertAllClose(actual, expected)