def test_correct_results(self, min_zoom, max_zoom, mode, align_corners,
keep_size):
key = "img"
random_zoom = RandZoomd(
key,
prob=1.0,
min_zoom=min_zoom,
max_zoom=max_zoom,
mode=mode,
align_corners=align_corners,
keep_size=keep_size,
)
random_zoom.set_random_state(1234)
zoomed = random_zoom({key: self.imt[0]})
expected = []
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
zoom=random_zoom._zoom,
mode="nearest",
order=0,
prefilter=False))
expected = np.stack(expected).astype(np.float32)
np.testing.assert_allclose(expected, zoomed[key], atol=1.0)
def test_gpu_zoom(self, min_zoom, max_zoom, order, mode, cval, prefilter):
key = "img"
if importlib.util.find_spec("cupy"):
random_zoom = RandZoomd(
key,
prob=1.0,
min_zoom=min_zoom,
max_zoom=max_zoom,
interp_order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
use_gpu=True,
keep_size=False,
)
random_zoom.set_random_state(234)
zoomed = random_zoom({key: self.imt[0]})
expected = list()
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
zoom=random_zoom._zoom,
mode=mode,
order=order,
cval=cval,
prefilter=prefilter))
expected = np.stack(expected).astype(np.float32)
self.assertTrue(np.allclose(expected, zoomed[key]))
def test_correct_results(self, min_zoom, max_zoom, order, mode, cval,
prefilter, use_gpu, keep_size):
key = "img"
random_zoom = RandZoomd(
key,
prob=1.0,
min_zoom=min_zoom,
max_zoom=max_zoom,
interp_order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
use_gpu=use_gpu,
keep_size=keep_size,
)
random_zoom.set_random_state(234)
zoomed = random_zoom({key: self.imt[0]})
expected = list()
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
zoom=random_zoom._zoom,
mode=mode,
order=order,
cval=cval,
prefilter=prefilter))
expected = np.stack(expected).astype(np.float32)
self.assertTrue(np.allclose(expected, zoomed[key]))
def test_correct_results(self, min_zoom, max_zoom, mode, align_corners,
keep_size):
key = "img"
random_zoom = RandZoomd(
key,
prob=1.0,
min_zoom=min_zoom,
max_zoom=max_zoom,
mode=mode,
align_corners=align_corners,
keep_size=keep_size,
)
for p in TEST_NDARRAYS:
random_zoom.set_random_state(1234)
zoomed = random_zoom({key: p(self.imt[0])})
expected = [
zoom_scipy(channel,
zoom=random_zoom.rand_zoom._zoom,
mode="nearest",
order=0,
prefilter=False) for channel in self.imt[0]
]
expected = np.stack(expected).astype(np.float32)
assert_allclose(zoomed[key], p(expected), atol=1.0)
def test_auto_expand_3d(self):
random_zoom = RandZoomd(
keys="img", prob=1.0, min_zoom=[0.8, 0.7], max_zoom=[1.2, 1.3], mode="nearest", keep_size=False
)
for p in TEST_NDARRAYS:
random_zoom.set_random_state(1234)
test_data = {"img": p(np.random.randint(0, 2, size=[2, 2, 3, 4]))}
zoomed = random_zoom(test_data)
assert_allclose(random_zoom.rand_zoom._zoom, (1.048844, 1.048844, 0.962637), atol=1e-2)
assert_allclose(zoomed["img"].shape, (2, 2, 3, 3))