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Unverified Commit 96ad7f0c authored by Ishan Kumar's avatar Ishan Kumar Committed by GitHub
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Port grayscale tests in test_tranforms to pytest (#3962)

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test/test_transforms.py
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......@@ -1367,149 +1367,6 @@ class Tester(unittest.TestCase):
t = transforms.RandomAffine(10, interpolation=2)
self.assertEqual(t.interpolation, transforms.InterpolationMode.BILINEAR)
def test_to_grayscale(self):
"""Unit tests for grayscale transform"""
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
# Test Set: Grayscale an image with desired number of output channels
# Case 1: RGB -> 1 channel grayscale
trans1 = transforms.Grayscale(num_output_channels=1)
gray_pil_1 = trans1(x_pil)
gray_np_1 = np.array(gray_pil_1)
self.assertEqual(gray_pil_1.mode, 'L', 'mode should be L')
self.assertEqual(gray_np_1.shape, tuple(x_shape[0:2]), 'should be 1 channel')
assert_equal(gray_np, gray_np_1)
# Case 2: RGB -> 3 channel grayscale
trans2 = transforms.Grayscale(num_output_channels=3)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
self.assertEqual(gray_pil_2.mode, 'RGB', 'mode should be RGB')
self.assertEqual(gray_np_2.shape, tuple(x_shape), 'should be 3 channel')
assert_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1])
assert_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2])
assert_equal(gray_np, gray_np_2[:, :, 0], check_stride=False)
# Case 3: 1 channel grayscale -> 1 channel grayscale
trans3 = transforms.Grayscale(num_output_channels=1)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
self.assertEqual(gray_pil_3.mode, 'L', 'mode should be L')
self.assertEqual(gray_np_3.shape, tuple(x_shape[0:2]), 'should be 1 channel')
assert_equal(gray_np, gray_np_3)
# Case 4: 1 channel grayscale -> 3 channel grayscale
trans4 = transforms.Grayscale(num_output_channels=3)
gray_pil_4 = trans4(x_pil_2)
gray_np_4 = np.array(gray_pil_4)
self.assertEqual(gray_pil_4.mode, 'RGB', 'mode should be RGB')
self.assertEqual(gray_np_4.shape, tuple(x_shape), 'should be 3 channel')
assert_equal(gray_np_4[:, :, 0], gray_np_4[:, :, 1])
assert_equal(gray_np_4[:, :, 1], gray_np_4[:, :, 2])
assert_equal(gray_np, gray_np_4[:, :, 0], check_stride=False)
# Checking if Grayscale can be printed as string
trans4.__repr__()
@unittest.skipIf(stats is None, 'scipy.stats not available')
def test_random_grayscale(self):
"""Unit tests for random grayscale transform"""
# Test Set 1: RGB -> 3 channel grayscale
random_state = random.getstate()
random.seed(42)
x_shape = [2, 2, 3]
x_np = np.random.randint(0, 256, x_shape, np.uint8)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
num_samples = 250
num_gray = 0
for _ in range(num_samples):
gray_pil_2 = transforms.RandomGrayscale(p=0.5)(x_pil)
gray_np_2 = np.array(gray_pil_2)
if np.array_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1]) and \
np.array_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2]) and \
np.array_equal(gray_np, gray_np_2[:, :, 0]):
num_gray = num_gray + 1
p_value = stats.binom_test(num_gray, num_samples, p=0.5)
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
# Test Set 2: grayscale -> 1 channel grayscale
random_state = random.getstate()
random.seed(42)
x_shape = [2, 2, 3]
x_np = np.random.randint(0, 256, x_shape, np.uint8)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
num_samples = 250
num_gray = 0
for _ in range(num_samples):
gray_pil_3 = transforms.RandomGrayscale(p=0.5)(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
if np.array_equal(gray_np, gray_np_3):
num_gray = num_gray + 1
p_value = stats.binom_test(num_gray, num_samples, p=1.0) # Note: grayscale is always unchanged
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
# Test set 3: Explicit tests
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
# Case 3a: RGB -> 3 channel grayscale (grayscaled)
trans2 = transforms.RandomGrayscale(p=1.0)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
self.assertEqual(gray_pil_2.mode, 'RGB', 'mode should be RGB')
self.assertEqual(gray_np_2.shape, tuple(x_shape), 'should be 3 channel')
assert_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1])
assert_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2])
assert_equal(gray_np, gray_np_2[:, :, 0], check_stride=False)
# Case 3b: RGB -> 3 channel grayscale (unchanged)
trans2 = transforms.RandomGrayscale(p=0.0)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
self.assertEqual(gray_pil_2.mode, 'RGB', 'mode should be RGB')
self.assertEqual(gray_np_2.shape, tuple(x_shape), 'should be 3 channel')
assert_equal(x_np, gray_np_2)
# Case 3c: 1 channel grayscale -> 1 channel grayscale (grayscaled)
trans3 = transforms.RandomGrayscale(p=1.0)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
self.assertEqual(gray_pil_3.mode, 'L', 'mode should be L')
self.assertEqual(gray_np_3.shape, tuple(x_shape[0:2]), 'should be 1 channel')
assert_equal(gray_np, gray_np_3)
# Case 3d: 1 channel grayscale -> 1 channel grayscale (unchanged)
trans3 = transforms.RandomGrayscale(p=0.0)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
self.assertEqual(gray_pil_3.mode, 'L', 'mode should be L')
self.assertEqual(gray_np_3.shape, tuple(x_shape[0:2]), 'should be 1 channel')
assert_equal(gray_np, gray_np_3)
# Checking if RandomGrayscale can be printed as string
trans3.__repr__()
def test_autoaugment(self):
for policy in transforms.AutoAugmentPolicy:
for fill in [None, 85, (128, 128, 128)]:
......@@ -1992,5 +1849,150 @@ def test_lambda():
trans.__repr__()
def test_to_grayscale():
"""Unit tests for grayscale transform"""
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
# Test Set: Grayscale an image with desired number of output channels
# Case 1: RGB -> 1 channel grayscale
trans1 = transforms.Grayscale(num_output_channels=1)
gray_pil_1 = trans1(x_pil)
gray_np_1 = np.array(gray_pil_1)
assert gray_pil_1.mode == 'L', 'mode should be L'
assert gray_np_1.shape == tuple(x_shape[0:2]), 'should be 1 channel'
assert_equal(gray_np, gray_np_1)
# Case 2: RGB -> 3 channel grayscale
trans2 = transforms.Grayscale(num_output_channels=3)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
assert gray_pil_2.mode == 'RGB', 'mode should be RGB'
assert gray_np_2.shape == tuple(x_shape), 'should be 3 channel'
assert_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1])
assert_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2])
assert_equal(gray_np, gray_np_2[:, :, 0], check_stride=False)
# Case 3: 1 channel grayscale -> 1 channel grayscale
trans3 = transforms.Grayscale(num_output_channels=1)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
assert gray_pil_3.mode == 'L', 'mode should be L'
assert gray_np_3.shape == tuple(x_shape[0:2]), 'should be 1 channel'
assert_equal(gray_np, gray_np_3)
# Case 4: 1 channel grayscale -> 3 channel grayscale
trans4 = transforms.Grayscale(num_output_channels=3)
gray_pil_4 = trans4(x_pil_2)
gray_np_4 = np.array(gray_pil_4)
assert gray_pil_4.mode == 'RGB', 'mode should be RGB'
assert gray_np_4.shape == tuple(x_shape), 'should be 3 channel'
assert_equal(gray_np_4[:, :, 0], gray_np_4[:, :, 1])
assert_equal(gray_np_4[:, :, 1], gray_np_4[:, :, 2])
assert_equal(gray_np, gray_np_4[:, :, 0], check_stride=False)
# Checking if Grayscale can be printed as string
trans4.__repr__()
@pytest.mark.skipif(stats is None, reason="scipy.stats not available")
def test_random_grayscale():
"""Unit tests for random grayscale transform"""
# Test Set 1: RGB -> 3 channel grayscale
random_state = random.getstate()
random.seed(42)
x_shape = [2, 2, 3]
x_np = np.random.randint(0, 256, x_shape, np.uint8)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
num_samples = 250
num_gray = 0
for _ in range(num_samples):
gray_pil_2 = transforms.RandomGrayscale(p=0.5)(x_pil)
gray_np_2 = np.array(gray_pil_2)
if np.array_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1]) and \
np.array_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2]) and \
np.array_equal(gray_np, gray_np_2[:, :, 0]):
num_gray = num_gray + 1
p_value = stats.binom_test(num_gray, num_samples, p=0.5)
random.setstate(random_state)
assert p_value > 0.0001
# Test Set 2: grayscale -> 1 channel grayscale
random_state = random.getstate()
random.seed(42)
x_shape = [2, 2, 3]
x_np = np.random.randint(0, 256, x_shape, np.uint8)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
num_samples = 250
num_gray = 0
for _ in range(num_samples):
gray_pil_3 = transforms.RandomGrayscale(p=0.5)(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
if np.array_equal(gray_np, gray_np_3):
num_gray = num_gray + 1
p_value = stats.binom_test(num_gray, num_samples, p=1.0) # Note: grayscale is always unchanged
random.setstate(random_state)
assert p_value > 0.0001
# Test set 3: Explicit tests
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
x_pil = Image.fromarray(x_np, mode='RGB')
x_pil_2 = x_pil.convert('L')
gray_np = np.array(x_pil_2)
# Case 3a: RGB -> 3 channel grayscale (grayscaled)
trans2 = transforms.RandomGrayscale(p=1.0)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
assert gray_pil_2.mode == 'RGB', 'mode should be RGB'
assert gray_np_2.shape == tuple(x_shape), 'should be 3 channel'
assert_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1])
assert_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2])
assert_equal(gray_np, gray_np_2[:, :, 0], check_stride=False)
# Case 3b: RGB -> 3 channel grayscale (unchanged)
trans2 = transforms.RandomGrayscale(p=0.0)
gray_pil_2 = trans2(x_pil)
gray_np_2 = np.array(gray_pil_2)
assert gray_pil_2.mode == 'RGB', 'mode should be RGB'
assert gray_np_2.shape == tuple(x_shape), 'should be 3 channel'
assert_equal(x_np, gray_np_2)
# Case 3c: 1 channel grayscale -> 1 channel grayscale (grayscaled)
trans3 = transforms.RandomGrayscale(p=1.0)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
assert gray_pil_3.mode == 'L', 'mode should be L'
assert gray_np_3.shape == tuple(x_shape[0:2]), 'should be 1 channel'
assert_equal(gray_np, gray_np_3)
# Case 3d: 1 channel grayscale -> 1 channel grayscale (unchanged)
trans3 = transforms.RandomGrayscale(p=0.0)
gray_pil_3 = trans3(x_pil_2)
gray_np_3 = np.array(gray_pil_3)
assert gray_pil_3.mode == 'L', 'mode should be L'
assert gray_np_3.shape == tuple(x_shape[0:2]), 'should be 1 channel'
assert_equal(gray_np, gray_np_3)
# Checking if RandomGrayscale can be printed as string
trans3.__repr__()
if __name__ == '__main__':
unittest.main()
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