def test_nonzero(self):
for p in TEST_NDARRAYS:
image = p(np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]])) # std = 1
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, nonzero=True)
result = std_shifter(image)
expected = p(np.asarray([[4 + factor, 0, 2 + factor], [0, 2 + factor, 4 + factor]], dtype=np.float32))
torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)
def test_dtype(self):
trans_dtype = np.float32
for dtype in [int, np.float32, np.float64]:
image = np.random.rand(2, 2, 2).astype(dtype)
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, dtype=trans_dtype)
result = std_shifter(image)
np.testing.assert_equal(result.dtype, trans_dtype)
def test_nonzero(self):
image = np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]]) # std = 1
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, nonzero=True)
result = std_shifter(image)
expected = np.asarray([[4 + factor, 0, 2 + factor],
[0, 2 + factor, 4 + factor]])
np.testing.assert_equal(result, expected)
def test_value(self):
factor = np.random.rand()
offset = np.std(self.imt) * factor
shifter = ShiftIntensity(offset=offset)
expected = shifter(self.imt)
std_shifter = StdShiftIntensity(factor=factor)
result = std_shifter(self.imt)
np.testing.assert_allclose(result, expected, rtol=1e-5)
def test_zerostd(self):
for p in TEST_NDARRAYS:
image = p(np.ones([2, 3, 3], dtype=np.float32))
for nonzero in [True, False]:
for channel_wise in [True, False]:
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, nonzero=nonzero, channel_wise=channel_wise)
result = std_shifter(image)
torch.testing.assert_allclose(result, image, atol=0, rtol=1e-5)
def test_value(self):
for p in TEST_NDARRAYS:
imt = p(self.imt)
factor = np.random.rand()
offset = np.std(self.imt) * factor
shifter = ShiftIntensity(offset=offset)
expected = shifter(imt)
std_shifter = StdShiftIntensity(factor=factor)
result = std_shifter(imt)
torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)
def test_channel_wise(self):
for p in TEST_NDARRAYS:
image = p(np.stack((np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0])))) # std: 0.5, 0
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, channel_wise=True)
result = std_shifter(image)
expected = p(
np.stack((np.asarray([1 + 0.5 * factor, 2 + 0.5 * factor]), np.asarray([1, 1]))).astype(np.float32)
)
torch.testing.assert_allclose(result, expected, atol=0, rtol=1e-5)
def test_channel_wise(self):
image = np.stack(
(np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0]))) # std: 0.5, 0
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor, channel_wise=True)
result = std_shifter(image)
expected = np.stack((np.asarray([1 + 0.5 * factor,
2 + 0.5 * factor]), np.asarray([1,
1])))
np.testing.assert_equal(result, expected)
def test_zerostd(self):
image = np.ones([2, 3, 3])
for nonzero in [True, False]:
for channel_wise in [True, False]:
factor = np.random.rand()
std_shifter = StdShiftIntensity(factor=factor,
nonzero=nonzero,
channel_wise=channel_wise)
result = std_shifter(image)
np.testing.assert_equal(result, image)
