def test_estimate_gradient(self):
num_space = 5
encoder = ThermometerEncoding(num_space=num_space)
x = np.random.rand(5, 28, 28, 1)
grad = np.ones((5, 28, 28, num_space))
estimated_grads = encoder.estimate_gradient(grad=grad, x=x)
self.assertTrue(np.isin(estimated_grads, [0, 1]).all())
def test_all(self):
# Test data
x = np.array([[[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]]],
[[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]]]])
# Create an instance of ThermometerEncoding
thencoder = ThermometerEncoding(num_space=4)
# Preprocess
pp_x = thencoder(x)
# Test
self.assertTrue(pp_x.shape == (2, 2, 3, 12))
true_value = np.array([[[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]],
[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]]],
[[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]],
[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]]]])
self.assertTrue((pp_x == true_value).all())
def test_channel_last(self):
# Test data
x = np.array([[[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]]],
[[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
[[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]]]])
# Create an instance of ThermometerEncoding
th_encoder = ThermometerEncoding(clip_values=(0, 1), num_space=4)
# Preprocess
x_preproc, _ = th_encoder(x)
# Test
self.assertEqual(x_preproc.shape, (2, 2, 3, 12))
true_value = np.array([[[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]], [[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]]],
[[[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]], [[1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1]]]])
self.assertTrue((x_preproc == true_value).all())
def test_channel_first(self):
x = np.random.rand(5, 2, 28, 28)
x_copy = x.copy()
num_space = 5
encoder = ThermometerEncoding(num_space=num_space, channel_index=1)
encoded_x, _ = encoder(x)
self.assertTrue((x == x_copy).all())
self.assertTrue(encoded_x.shape == (5, 10, 28, 28))
def test_channel_first(self):
x = np.random.rand(5, 2, 28, 28)
x_copy = x.copy()
num_space = 5
encoder = ThermometerEncoding(clip_values=(0, 1), num_space=num_space, channel_index=1)
x_encoded, _ = encoder(x)
self.assertTrue((x == x_copy).all())
self.assertEqual(x_encoded.shape, (5, 10, 28, 28))
def test_feature_vectors(self):
x = np.random.rand(10, 4)
num_space = 5
encoder = ThermometerEncoding(num_space=num_space, channel_index=1)
encoded_x, _ = encoder(x)
self.assertTrue(encoded_x.shape == (10, 20))
def test_feature_vectors(self):
x = np.random.rand(10, 4)
num_space = 5
encoder = ThermometerEncoding(clip_values=(0, 1), num_space=num_space, channel_index=1)
x_encoded, _ = encoder(x)
self.assertEqual(x_encoded.shape, (10, 20))