def test_prob_k_spatial_axes(self):
rotate = Rotate90(k=2, spatial_axes=(0, 1))
rotated = rotate(self.imt[0])
expected = []
for channel in self.imt[0]:
expected.append(np.rot90(channel, 2, (0, 1)))
expected = np.stack(expected)
self.assertTrue(np.allclose(rotated, expected))
def test_rotate90_default(self):
rotate = Rotate90()
rotated = rotate(self.imt[0])
expected = []
for channel in self.imt[0]:
expected.append(np.rot90(channel, 1, (0, 1)))
expected = np.stack(expected)
self.assertTrue(np.allclose(rotated, expected))
def test_k(self):
rotate = Rotate90(k=2)
rotated = rotate(self.imt[0])
expected = list()
for channel in self.imt[0]:
expected.append(np.rot90(channel, 2, (0, 1)))
expected = np.stack(expected)
self.assertTrue(np.allclose(rotated, expected))
def test_prob_k_spatial_axes(self):
rotate = Rotate90(k=2, spatial_axes=(0, 1))
for p in TEST_NDARRAYS:
rotated = rotate(p(self.imt[0]))
expected = [
np.rot90(channel, 2, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
def test_rotate90_default(self):
rotate = Rotate90()
for p in TEST_NDARRAYS:
rotated = rotate(p(self.imt[0]))
expected = [
np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated, p(expected), rtol=1.0e-5, atol=1.0e-8)
def test_rotate90_default(self):
rotate = Rotate90()
for p in TEST_NDARRAYS_ALL:
im = p(self.imt[0])
rotated = rotate(im)
test_local_inversion(rotate, rotated, im)
expected = [
np.rot90(channel, 1, (0, 1)) for channel in self.imt[0]
]
expected = np.stack(expected)
assert_allclose(rotated,
p(expected),
rtol=1.0e-5,
atol=1.0e-8,
type_test="tensor")
def test_tranform_randomized(self, input):
# Compose deterministic and randomized transforms
transforms = Compose([
Range("flip")(Flip()),
Rotate90(),
Range()(RandAdjustContrast(prob=0.0)),
Range("random flip")(RandFlip(prob=1.0)),
ToTensor(),
])
# Apply transforms
output = transforms(input)
# Decorate with NVTX Range
transforms1 = Range()(transforms)
transforms2 = Range("Transforms2")(transforms)
transforms3 = Range(name="Transforms3", methods="__call__")(transforms)
# Apply transforms with Range
output1 = transforms1(input)
output2 = transforms2(input)
output3 = transforms3(input)
# Check if the outputs are equal
self.assertIsInstance(output, torch.Tensor)
self.assertIsInstance(output1, torch.Tensor)
self.assertIsInstance(output2, torch.Tensor)
self.assertIsInstance(output3, torch.Tensor)
np.testing.assert_equal(output.numpy(), output1.numpy())
np.testing.assert_equal(output.numpy(), output2.numpy())
np.testing.assert_equal(output.numpy(), output3.numpy())
# Check if the first randomized is RandAdjustContrast
for tran in transforms.transforms:
if isinstance(tran, Randomizable):
self.assertIsInstance(tran, RandAdjustContrast)
break
