def test_sliding_window_default(self, image_shape, roi_shape,
sw_batch_size, overlap, mode, device):
n_total = np.prod(image_shape)
if mode == "constant":
inputs = torch.arange(n_total,
dtype=torch.float).reshape(*image_shape)
else:
inputs = torch.ones(*image_shape, dtype=torch.float)
if device.type == "cuda" and not torch.cuda.is_available():
device = torch.device("cpu:0")
def compute(data):
return data + 1
if mode == "constant":
expected_val = np.arange(
n_total, dtype=np.float32).reshape(*image_shape) + 1.0
else:
expected_val = np.ones(image_shape, dtype=np.float32) + 1.0
result = sliding_window_inference(inputs.to(device),
roi_shape,
sw_batch_size,
compute,
overlap,
mode=mode)
np.testing.assert_string_equal(device.type, result.device.type)
np.testing.assert_allclose(result.cpu().numpy(), expected_val)
result = SlidingWindowInferer(roi_shape, sw_batch_size, overlap,
mode)(inputs.to(device), compute)
np.testing.assert_string_equal(device.type, result.device.type)
np.testing.assert_allclose(result.cpu().numpy(), expected_val)
def test_cval(self):
device = "cuda" if torch.cuda.is_available() else "cpu:0"
inputs = torch.ones((1, 1, 3, 3)).to(device=device)
roi_shape = (5, 5)
sw_batch_size = 10
def compute(data):
return data + data.sum()
result = sliding_window_inference(
inputs,
roi_shape,
sw_batch_size,
compute,
overlap=0.5,
padding_mode="constant",
cval=-1,
mode="constant",
sigma_scale=1.0,
)
expected = np.ones((1, 1, 3, 3)) * -6.0
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
result = SlidingWindowInferer(roi_shape,
sw_batch_size,
overlap=0.5,
mode="constant",
cval=-1)(inputs, compute)
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
def test_args_kwargs(self):
device = "cuda" if torch.cuda.is_available() else "cpu:0"
inputs = torch.ones((1, 1, 3, 3)).to(device=device)
t1 = torch.ones(1).to(device=device)
t2 = torch.ones(1).to(device=device)
roi_shape = (5, 5)
sw_batch_size = 10
def compute(data, test1, test2):
return data + test1 + test2
result = sliding_window_inference(
inputs,
roi_shape,
sw_batch_size,
compute,
0.5,
"constant",
1.0,
"constant",
0.0,
device,
device,
t1,
test2=t2,
)
expected = np.ones((1, 1, 3, 3)) + 2.0
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
result = SlidingWindowInferer(roi_shape,
sw_batch_size,
overlap=0.5,
mode="constant",
cval=-1)(inputs, compute, t1, test2=t2)
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
def test_sigma(self):
device = "cuda" if torch.cuda.is_available() else "cpu:0"
inputs = torch.ones((1, 1, 7, 7)).to(device=device)
roi_shape = (3, 3)
sw_batch_size = 10
class _Pred:
add = 1
def compute(self, data):
self.add += 1
return data + self.add
result = sliding_window_inference(
inputs,
roi_shape,
sw_batch_size,
_Pred().compute,
overlap=0.5,
padding_mode="constant",
cval=-1,
mode="constant",
sigma_scale=1.0,
)
expected = np.array([[[
[3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
[3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000, 3.0000],
[3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333, 3.3333],
[3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667, 3.6667],
[4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333, 4.3333],
[4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000, 4.5000],
[5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000, 5.0000],
]]])
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
result = sliding_window_inference(
inputs,
roi_shape,
sw_batch_size,
_Pred().compute,
overlap=0.5,
padding_mode="constant",
cval=-1,
mode="gaussian",
sigma_scale=1.0,
)
expected = np.array([[[
[3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
[3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0],
[
3.3271625, 3.3271623, 3.3271623, 3.3271623, 3.3271623,
3.3271623, 3.3271625
],
[
3.6728377, 3.6728377, 3.6728377, 3.6728377, 3.6728377,
3.6728377, 3.6728377
],
[
4.3271623, 4.3271623, 4.3271627, 4.3271627, 4.3271627,
4.3271623, 4.3271623
],
[
4.513757, 4.513757, 4.513757, 4.513757, 4.513757, 4.513757,
4.513757
],
[4.9999995, 5.0, 5.0, 5.0, 5.0, 5.0, 4.9999995],
]]])
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
result = SlidingWindowInferer(roi_shape,
sw_batch_size,
overlap=0.5,
mode="gaussian",
sigma_scale=1.0)(inputs, _Pred().compute)
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
result = SlidingWindowInferer(roi_shape,
sw_batch_size,
overlap=0.5,
mode="gaussian",
sigma_scale=[1.0, 1.0])(inputs,
_Pred().compute)
np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
