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,
)
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, 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_gpu_zoom(self, _, zoom, order, mode, cval, prefilter):
key = "img"
if importlib.util.find_spec("cupy"):
zoom_fn = Zoomd(
key,
zoom=zoom,
interp_order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
use_gpu=True,
keep_size=False,
)
zoomed = zoom_fn({key: self.imt[0]})
expected = list()
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
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, zoom, order, mode, cval, prefilter, use_gpu,
keep_size):
key = "img"
zoom_fn = Zoomd(
key,
zoom=zoom,
interp_order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
use_gpu=use_gpu,
keep_size=keep_size,
)
zoomed = zoom_fn({key: self.imt[0]})
expected = list()
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
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, keep_size):
random_zoom = RandZoom(prob=1.0, min_zoom=min_zoom, max_zoom=max_zoom, interp_order=order, keep_size=keep_size,)
random_zoom.set_random_state(1234)
zoomed = random_zoom(self.imt[0])
expected = list()
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(zoomed, expected, atol=1.0)
def test_correct_results(self, zoom, interp_order):
zoom_fn = Zoom(zoom=zoom, interp_order=interp_order, keep_size=False)
zoomed = zoom_fn(self.imt[0])
_order = 0
if interp_order.endswith("linear"):
_order = 1
expected = list()
for channel in self.imt[0]:
expected.append(zoom_scipy(channel, zoom=zoom, mode="nearest", order=_order, prefilter=False))
expected = np.stack(expected).astype(np.float32)
np.testing.assert_allclose(zoomed, expected, atol=1.0)
def test_correct_results(self, zoom, mode, keep_size):
key = "img"
zoom_fn = Zoomd(key, zoom=zoom, mode=mode, keep_size=keep_size,)
zoomed = zoom_fn({key: self.imt[0]})
_order = 0
if mode.endswith("linear"):
_order = 1
expected = list()
for channel in self.imt[0]:
expected.append(zoom_scipy(channel, zoom=zoom, mode="nearest", order=_order, prefilter=False))
expected = np.stack(expected).astype(np.float32)
np.testing.assert_allclose(expected, zoomed[key], atol=1.0)
def test_correct_results(self, zoom, mode):
for p in TEST_NDARRAYS:
zoom_fn = Zoom(zoom=zoom, mode=mode, keep_size=False)
zoomed = zoom_fn(p(self.imt[0]))
_order = 0
if mode.endswith("linear"):
_order = 1
expected = []
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
zoom=zoom,
mode="nearest",
order=_order,
prefilter=False))
expected = np.stack(expected).astype(np.float32)
assert_allclose(zoomed, p(expected), atol=1.0)
def test_correct_results(self, zoom, mode, keep_size):
key = "img"
zoom_fn = Zoomd(key, zoom=zoom, mode=mode, keep_size=keep_size)
for p in TEST_NDARRAYS:
zoomed = zoom_fn({key: p(self.imt[0])})
_order = 0
if mode.endswith("linear"):
_order = 1
expected = [
zoom_scipy(channel,
zoom=zoom,
mode="nearest",
order=_order,
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_correct_results(self, min_zoom, max_zoom, mode, keep_size):
for p in TEST_NDARRAYS:
random_zoom = RandZoom(prob=1.0,
min_zoom=min_zoom,
max_zoom=max_zoom,
mode=mode,
keep_size=keep_size)
random_zoom.set_random_state(1234)
zoomed = random_zoom(p(self.imt[0]))
expected = [
zoom_scipy(channel,
zoom=random_zoom._zoom,
mode="nearest",
order=0,
prefilter=False) for channel in self.imt[0]
]
expected = np.stack(expected).astype(np.float32)
assert_allclose(zoomed, p(expected), atol=1.0)
def test_gpu_zoom(self, _, zoom, order, mode, cval, prefilter):
if importlib.util.find_spec('cupy'):
zoom_fn = Zoom(zoom=zoom,
order=order,
mode=mode,
cval=cval,
prefilter=prefilter,
use_gpu=True,
keep_size=False)
zoomed = zoom_fn(self.imt[0])
expected = list()
for channel in self.imt[0]:
expected.append(
zoom_scipy(channel,
zoom=zoom,
mode=mode,
order=order,
cval=cval,
prefilter=prefilter))
expected = np.stack(expected).astype(np.float32)
self.assertTrue(np.allclose(expected, zoomed))
