def test_keep_size(self):
zoom_fn = Zoom(zoom=[0.6, 0.6], keep_size=True, align_corners=True)
zoomed = zoom_fn(self.imt[0], interp_order="bilinear")
np.testing.assert_allclose(zoomed.shape, self.imt.shape[1:])
zoom_fn = Zoom(zoom=[1.3, 1.3], keep_size=True)
zoomed = zoom_fn(self.imt[0])
np.testing.assert_allclose(zoomed.shape, self.imt.shape[1:])
def test_keep_size(self):
zoom_fn = Zoom(zoom=0.6, keep_size=True)
zoomed = zoom_fn(self.imt[0])
self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:]))
zoom_fn = Zoom(zoom=1.3, keep_size=True)
zoomed = zoom_fn(self.imt[0])
self.assertTrue(np.array_equal(zoomed.shape, self.imt.shape[1:]))
def test_keep_size(self):
for p in TEST_NDARRAYS:
zoom_fn = Zoom(zoom=[0.6, 0.6], keep_size=True, align_corners=True)
zoomed = zoom_fn(p(self.imt[0]), mode="bilinear")
assert_allclose(zoomed.shape, self.imt.shape[1:])
zoom_fn = Zoom(zoom=[1.3, 1.3], keep_size=True)
zoomed = zoom_fn(p(self.imt[0]))
assert_allclose(zoomed.shape, self.imt.shape[1:])
def test_keep_size(self):
for p in TEST_NDARRAYS_ALL:
zoom_fn = Zoom(zoom=[0.6, 0.6], keep_size=True, align_corners=True)
im = p(self.imt[0])
zoomed = zoom_fn(im, mode="bilinear")
assert_allclose(zoomed.shape, self.imt.shape[1:], type_test=False)
test_local_inversion(zoom_fn, zoomed, im)
zoom_fn = Zoom(zoom=[1.3, 1.3], keep_size=True)
im = p(self.imt[0])
zoomed = zoom_fn(im)
assert_allclose(zoomed.shape, self.imt.shape[1:], type_test=False)
test_local_inversion(zoom_fn, zoomed, p(self.imt[0]))
set_track_meta(False)
rotated = zoom_fn(im)
self.assertNotIsInstance(rotated, MetaTensor)
np.testing.assert_allclose(zoomed.shape, self.imt.shape[1:])
set_track_meta(True)
def test_correct_results(self, zoom, order, mode, cval, prefilter, use_gpu, keep_size):
zoom_fn = Zoom(
zoom=zoom, order=order, mode=mode, cval=cval, prefilter=prefilter, use_gpu=use_gpu, keep_size=keep_size
)
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))
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_padding_mode(self):
zoom_fn = Zoom(zoom=0.5,
mode="nearest",
padding_mode="constant",
keep_size=True)
test_data = np.array([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]])
zoomed = zoom_fn(test_data)
expected = np.array([[[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])
np.testing.assert_allclose(zoomed, expected)
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))
def test_padding_mode(self):
for p in TEST_NDARRAYS:
zoom_fn = Zoom(zoom=0.5,
mode="nearest",
padding_mode="constant",
keep_size=True)
test_data = p([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]])
zoomed = zoom_fn(test_data)
expected = p([[[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]])
torch.testing.assert_allclose(zoomed, expected)
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_invalid_inputs(self, zoom, order, raises):
with self.assertRaises(raises):
zoom_fn = Zoom(zoom=zoom, interp_order=order)
zoom_fn(self.imt[0])
def test_invalid_inputs(self, zoom, mode, raises):
for p in TEST_NDARRAYS:
with self.assertRaises(raises):
zoom_fn = Zoom(zoom=zoom, mode=mode)
zoom_fn(p(self.imt[0]))
def test_invalid_inputs(self, zoom, mode, raises):
with self.assertRaises(raises):
zoom_fn = Zoom(zoom=zoom, mode=mode)
zoom_fn(self.imt[0])
