def check_inverse(self, name, keys, orig_d, fwd_bck_d, unmodified_d,
acceptable_diff):
for key in keys:
orig = orig_d[key]
fwd_bck = fwd_bck_d[key]
if isinstance(fwd_bck, torch.Tensor):
fwd_bck = fwd_bck.cpu().numpy()
unmodified = unmodified_d[key]
if isinstance(orig, np.ndarray):
mean_diff = np.mean(np.abs(orig - fwd_bck))
resized = ResizeWithPadOrCrop(orig.shape[1:])(unmodified)
if isinstance(resized, torch.Tensor):
resized = resized.detach().cpu().numpy()
unmodded_diff = np.mean(np.abs(orig - resized))
try:
self.assertLessEqual(mean_diff, acceptable_diff)
except AssertionError:
print(
f"Failed: {name}. Mean diff = {mean_diff} (expected <= {acceptable_diff}), unmodified diff: {unmodded_diff}"
)
if orig[0].ndim == 1:
print("orig", orig[0])
print("fwd_bck", fwd_bck[0])
print("unmod", unmodified[0])
raise
def test_inverse_inferred_seg(self, extra_transform):
test_data = []
for _ in range(20):
image, label = create_test_image_2d(100, 101)
test_data.append({
"image": image,
"label": label.astype(np.float32)
})
batch_size = 10
# num workers = 0 for mac
num_workers = 2 if sys.platform == "linux" else 0
transforms = Compose([
AddChanneld(KEYS),
SpatialPadd(KEYS, (150, 153)), extra_transform
])
dataset = CacheDataset(test_data, transform=transforms, progress=False)
loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = UNet(spatial_dims=2,
in_channels=1,
out_channels=1,
channels=(2, 4),
strides=(1, )).to(device)
data = first(loader)
self.assertEqual(data["image"].shape[0], batch_size * NUM_SAMPLES)
labels = data["label"].to(device)
self.assertIsInstance(labels, MetaTensor)
segs = model(labels).detach().cpu()
segs_decollated = decollate_batch(segs)
self.assertIsInstance(segs_decollated[0], MetaTensor)
# inverse of individual segmentation
seg_metatensor = first(segs_decollated)
# test to convert interpolation mode for 1 data of model output batch
convert_applied_interp_mode(seg_metatensor.applied_operations,
mode="nearest",
align_corners=None)
# manually invert the last crop samples
xform = seg_metatensor.applied_operations.pop(-1)
shape_before_extra_xform = xform["orig_size"]
resizer = ResizeWithPadOrCrop(spatial_size=shape_before_extra_xform)
with resizer.trace_transform(False):
seg_metatensor = resizer(seg_metatensor)
with allow_missing_keys_mode(transforms):
inv_seg = transforms.inverse({"label": seg_metatensor})["label"]
self.assertEqual(inv_seg.shape[1:], test_data[0]["label"].shape)
def __call__(self, image, label, params=None):
if params is None:
params = self._random()
self.params = params
if self.params['width'] > 0 and self.params['height'] > 0:
image = ResizeWithPadOrCrop(
[self.params['width'], self.params['height']])(image)
label = ResizeWithPadOrCrop(
[self.params['width'], self.params['height']])(label)
return image, label
def __call__(
self,
img: np.ndarray,
msk: Optional[np.ndarray] = None,
center: Optional[tuple] = None,
z_axis: Optional[int] = None,
):
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't apply to the channel dim.
"""
if self.mask_data is None and msk is None:
raise ValueError("Unknown mask_data.")
mask_data_ = np.array([[1]])
if self.mask_data is not None and msk is None:
mask_data_ = self.mask_data > 0
if msk is not None:
mask_data_ = msk > 0
mask_data_ = np.asarray(mask_data_)
if mask_data_.shape[0] != 1 and mask_data_.shape[0] != img.shape[0]:
raise ValueError(
"When mask_data is not single channel, mask_data channels must match img, "
f"got img={img.shape[0]} mask_data={mask_data_.shape[0]}.")
z_axis_ = z_axis if z_axis is not None else self.z_axis
if center is None:
center = self.get_center_pos(mask_data_, z_axis_)
if self.crop_mode in ["single", "parallel"]:
size_ = self.get_new_spatial_size(z_axis_)
size_ = list(map(int, size_))
slice_ = SpatialCrop(roi_center=center, roi_size=size_)(img)
if np.any(slice_.shape[1:] != size_):
slice_ = ResizeWithPadOrCrop(spatial_size=size_)(slice_)
return np.moveaxis(slice_.squeeze(0), z_axis_, 0)
else:
cross_slices = np.zeros(shape=(3, ) + self.roi_size)
for k in range(3):
size_ = np.insert(self.roi_size, k, 1)
slice_ = SpatialCrop(roi_center=center, roi_size=size_)(img)
if np.any(slice_.shape[1:] != size_):
slice_ = ResizeWithPadOrCrop(spatial_size=size_)(slice_)
cross_slices[k] = slice_.squeeze()
return cross_slices
def __call__(self, data):
d = dict(data)
current_shape = d[self.ref_image].shape[1:]
dims = len(current_shape)
croppad = ResizeWithPadOrCrop(spatial_size=self.spatial_size)
# get guidance following pad or crop to spatial_size
new_guidance = []
for guidance in d[self.guidance]:
if guidance:
signal = np.zeros(current_shape)
for point in guidance:
if dims == 2:
signal[point[0], point[1]] = 1.0
else:
signal[point[0], point[1], point[2]] = 1.0
signal = croppad(signal[np.newaxis, :]).squeeze(
0) # croppad requires channel dim
new_guidance.append(
np.argwhere(signal == 1.0).astype(int).tolist())
else:
new_guidance.append([])
d[self.guidance] = new_guidance
return d
def test_pad_shape(self, input_param, input_shape, expected_shape):
for p in TEST_NDARRAYS_ALL:
if isinstance(
p(0),
torch.Tensor) and ("constant_values" in input_param
or input_param["mode"] == "reflect"):
continue
padcropper = ResizeWithPadOrCrop(**input_param)
result = padcropper(p(np.zeros(input_shape)))
np.testing.assert_allclose(result.shape, expected_shape)
result = padcropper(p(np.zeros(input_shape)), mode="constant")
np.testing.assert_allclose(result.shape, expected_shape)
self.assertIsInstance(result, MetaTensor)
self.assertEqual(len(result.applied_operations), 1)
inv = padcropper.inverse(result)
self.assertTupleEqual(inv.shape, input_shape)
self.assertIsInstance(inv, MetaTensor)
self.assertEqual(inv.applied_operations, [])
def test_pad_shape(self, input_param, input_shape, expected_shape):
for p in TEST_NDARRAYS:
if isinstance(
p(0),
torch.Tensor) and ("constant_values" in input_param
or input_param["mode"] == "reflect"):
continue
paddcroper = ResizeWithPadOrCrop(**input_param)
result = paddcroper(p(np.zeros(input_shape)))
np.testing.assert_allclose(result.shape, expected_shape)
result = paddcroper(p(np.zeros(input_shape)), mode="constant")
np.testing.assert_allclose(result.shape, expected_shape)
def __call__(
self,
img: np.ndarray,
label: Optional[np.ndarray] = None,
image: Optional[np.ndarray] = None,
fg_indices: Optional[np.ndarray] = None,
bg_indices: Optional[np.ndarray] = None,
) -> List[np.ndarray]:
"""
Args:
img: input data to crop samples from based on the pos/neg ratio of `label` and `image`.
Assumes `img` is a channel-first array.
label: the label image that is used for finding foreground/background, if None, use `self.label`.
image: optional image data to help select valid area, can be same as `img` or another image array.
use ``label == 0 & image > image_threshold`` to select the negative sample(background) center.
so the crop center will only exist on valid image area. if None, use `self.image`.
fg_indices: foreground indices to randomly select crop centers,
need to provide `fg_indices` and `bg_indices` together.
bg_indices: background indices to randomly select crop centers,
need to provide `fg_indices` and `bg_indices` together.
"""
if label is None:
label = self.label
if label is None:
raise ValueError("label should be provided.")
if image is None:
image = self.image
if fg_indices is None or bg_indices is None:
if self.fg_indices is not None and self.bg_indices is not None:
fg_indices = self.fg_indices
bg_indices = self.bg_indices
else:
fg_indices, bg_indices = map_binary_to_indices(
label, image, self.image_threshold)
if self.target_label is not None:
label = (label == self.target_label).astype(np.uint8)
self.randomize(label, fg_indices, bg_indices, image)
results: List[np.ndarray] = []
if self.centers is not None:
for center in self.centers:
if np.any(np.greater(self.spatial_size, img.shape[1:])):
cropper = ResizeWithPadOrCrop(
spatial_size=self.spatial_size)
else:
cropper = SpatialCrop(
roi_center=tuple(center),
spatial_size=self.spatial_size) # type: ignore
results.append(cropper(img))
return results
def test_invert(self):
set_determinism(seed=0)
im_fname = make_nifti_image(create_test_image_3d(101, 100, 107, noise_max=100)[1]) # label image, discrete
data = [im_fname for _ in range(12)]
transform = Compose(
[
LoadImage(image_only=True),
EnsureChannelFirst(),
Orientation("RPS"),
Spacing(pixdim=(1.2, 1.01, 0.9), mode="bilinear", dtype=np.float32),
RandFlip(prob=0.5, spatial_axis=[1, 2]),
RandAxisFlip(prob=0.5),
RandRotate90(prob=0, spatial_axes=(1, 2)),
RandZoom(prob=0.5, min_zoom=0.5, max_zoom=1.1, keep_size=True),
RandRotate(prob=0.5, range_x=np.pi, mode="bilinear", align_corners=True, dtype=np.float64),
RandAffine(prob=0.5, rotate_range=np.pi, mode="nearest"),
ResizeWithPadOrCrop(100),
CastToType(dtype=torch.uint8),
]
)
# num workers = 0 for mac or gpu transforms
num_workers = 0 if sys.platform != "linux" or torch.cuda.is_available() else 2
dataset = Dataset(data, transform=transform)
self.assertIsInstance(transform.inverse(dataset[0]), MetaTensor)
loader = DataLoader(dataset, num_workers=num_workers, batch_size=1)
inverter = Invert(transform=transform, nearest_interp=True, device="cpu")
for d in loader:
d = decollate_batch(d)
for item in d:
orig = deepcopy(item)
i = inverter(item)
self.assertTupleEqual(orig.shape[1:], (100, 100, 100))
# check the nearest interpolation mode
torch.testing.assert_allclose(i.to(torch.uint8).to(torch.float), i.to(torch.float))
self.assertTupleEqual(i.shape[1:], (100, 101, 107))
# check labels match
reverted = i.detach().cpu().numpy().astype(np.int32)
original = LoadImage(image_only=True)(data[-1])
n_good = np.sum(np.isclose(reverted, original.numpy(), atol=1e-3))
reverted_name = i.meta["filename_or_obj"]
original_name = original.meta["filename_or_obj"]
self.assertEqual(reverted_name, original_name)
print("invert diff", reverted.size - n_good)
self.assertTrue((reverted.size - n_good) < 300000, f"diff. {reverted.size - n_good}")
set_determinism(seed=None)
def __call__(
self, data: Mapping[Hashable,
np.ndarray]) -> List[Dict[Hashable, np.ndarray]]:
d = dict(data)
label = d[self.label_key]
image = d[self.image_key] if self.image_key else None
fg_indices = d.get(
self.fg_indices_key) if self.fg_indices_key is not None else None
bg_indices = d.get(
self.bg_indices_key) if self.bg_indices_key is not None else None
if self.target_label is not None:
label = (label == self.target_label).astype(np.uint8)
self.randomize(label, fg_indices, bg_indices, image)
if not isinstance(self.spatial_size, tuple):
raise TypeError(
f"Expect spatial_size to be tuple, but got {type(self.spatial_size)}"
)
if self.centers is None:
raise AssertionError
results: List[Dict[Hashable,
np.ndarray]] = [{} for _ in range(self.num_samples)]
for i, center in enumerate(self.centers):
for key in self.key_iterator(d):
img = d[key]
if np.greater(self.spatial_size, img.shape[1:]).any():
cropper = ResizeWithPadOrCrop(
spatial_size=self.spatial_size)
else:
cropper = SpatialCrop(
roi_center=tuple(center),
roi_size=self.spatial_size) # type: ignore
results[i][key] = cropper(img)
# fill in the extra keys with unmodified data
for key in set(data.keys()).difference(set(self.keys)):
results[i][key] = data[key]
# add `patch_index` to the meta data
for key in self.key_iterator(d):
meta_data_key = f"{key}_{self.meta_key_postfix}"
if meta_data_key not in results[i]:
results[i][meta_data_key] = {} # type: ignore
results[i][meta_data_key][Key.PATCH_INDEX] = i
return results
def test_pad_shape(self, input_param, input_shape, expected_shape):
paddcroper = ResizeWithPadOrCrop(**input_param)
result = paddcroper(np.zeros(input_shape))
np.testing.assert_allclose(result.shape, expected_shape)
result = paddcroper(np.zeros(input_shape), mode="constant")
np.testing.assert_allclose(result.shape, expected_shape)
