def run_interaction(self, train, compose):
data = [{
"image": np.ones((1, 2, 2, 2)).astype(np.float32),
"label": np.ones((1, 2, 2, 2))
} for _ in range(5)]
network = torch.nn.Linear(2, 2)
lr = 1e-3
opt = torch.optim.SGD(network.parameters(), lr)
loss = torch.nn.L1Loss()
train_transforms = Compose([
FindAllValidSlicesd(label="label", sids="sids"),
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
])
dataset = Dataset(data, transform=train_transforms)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=["image", "label", "pred"]),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(guidance="guidance",
discrepancy="discrepancy",
probability="probability"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
]
iteration_transforms = Compose(
iteration_transforms) if compose else iteration_transforms
i = Interaction(transforms=iteration_transforms,
train=train,
max_interactions=5)
self.assertEqual(len(i.transforms.transforms), 6,
"Mismatch in expected transforms")
# set up engine
engine = SupervisedTrainer(
device=torch.device("cpu"),
max_epochs=1,
train_data_loader=data_loader,
network=network,
optimizer=opt,
loss_function=loss,
iteration_update=i,
)
engine.add_event_handler(IterationEvents.INNER_ITERATION_STARTED,
add_one)
engine.add_event_handler(IterationEvents.INNER_ITERATION_COMPLETED,
add_one)
engine.run()
self.assertIsNotNone(engine.state.batch[0].get("guidance"),
"guidance is missing")
self.assertEqual(engine.state.best_metric, 9)
def train_pre_transforms(self, context: Context):
# Dataset preparation
t: List[Any] = [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
SpatialCropForegroundd(keys=("image", "label"),
source_key="label",
spatial_size=self.roi_size),
Resized(keys=("image", "label"),
spatial_size=self.model_size,
mode=("area", "nearest")),
NormalizeIntensityd(keys="image", subtrahend=208.0,
divisor=388.0), # type: ignore
]
if self.dimension == 3:
t.append(FindAllValidSlicesd(label="label", sids="sids"))
t.extend([
AddInitialSeedPointd(label="label",
guidance="guidance",
sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label"), device=context.device),
SelectItemsd(keys=("image", "label", "guidance")),
])
return t
def train_pre_transforms(self, context: Context):
return [
LoadImaged(keys=("image", "label"), dtype=np.uint8),
FilterImaged(keys="image", min_size=5),
AsChannelFirstd(keys="image"),
AddChanneld(keys="label"),
ToTensord(keys="image"),
TorchVisiond(keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04),
ToNumpyd(keys="image"),
RandRotate90d(keys=("image", "label"),
prob=0.5,
spatial_axes=(0, 1)),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
AddInitialSeedPointExd(label="label", guidance="guidance"),
AddGuidanceSignald(image="image",
guidance="guidance",
number_intensity_ch=3),
EnsureTyped(keys=("image", "label")),
]
def pre_transforms(self, data=None) -> Sequence[Callable]:
t = [
LoadImaged(keys="image"),
AsChannelFirstd(keys="image"),
Spacingd(keys="image",
pixdim=[1.0] * self.dimension,
mode="bilinear"),
AddGuidanceFromPointsd(ref_image="image",
guidance="guidance",
dimensions=self.dimension),
]
if self.dimension == 2:
t.append(Fetch2DSliced(keys="image", guidance="guidance"))
t.extend([
AddChanneld(keys="image"),
SpatialCropGuidanced(keys="image",
guidance="guidance",
spatial_size=self.spatial_size),
Resized(keys="image", spatial_size=self.model_size, mode="area"),
ResizeGuidanced(guidance="guidance", ref_image="image"),
NormalizeIntensityd(keys="image", subtrahend=208,
divisor=388), # type: ignore
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys="image",
device=data.get("device") if data else None),
])
return t
def pre_transforms(self, data):
return [
LoadImaged(keys="image"),
AsChannelFirstd(keys="image"),
Spacingd(keys="image", pixdim=[1.0, 1.0, 1.0], mode="bilinear"),
AddGuidanceFromPointsd(ref_image="image", guidance="guidance", dimensions=3),
AddChanneld(keys="image"),
SpatialCropGuidanced(keys="image", guidance="guidance", spatial_size=self.spatial_size),
Resized(keys="image", spatial_size=self.model_size, mode="area"),
ResizeGuidanced(guidance="guidance", ref_image="image"),
NormalizeIntensityd(keys="image", subtrahend=208, divisor=388),
AddGuidanceSignald(image="image", guidance="guidance"),
]
def get_click_transforms(self, context: Context):
return [
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=("image", "label", "pred")),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(guidance="guidance",
discrepancy="discrepancy",
probability="probability"),
AddGuidanceSignald(image="image", guidance="guidance"),
ToTensord(keys=("image", "label")),
]
def get_click_transforms():
return Compose([
Activationsd(keys='pred', sigmoid=True),
ToNumpyd(keys=('image', 'label', 'pred', 'probability', 'guidance')),
FindDiscrepancyRegionsd(label='label',
pred='pred',
discrepancy='discrepancy',
batched=True),
AddRandomGuidanced(guidance='guidance',
discrepancy='discrepancy',
probability='probability',
batched=True),
AddGuidanceSignald(image='image', guidance='guidance', batched=True),
ToTensord(keys=('image', 'label'))
])
def get_click_transforms():
return Compose([
Activationsd(keys="pred", sigmoid=True),
ToNumpyd(keys=("image", "label", "pred")),
FindDiscrepancyRegionsd(label="label",
pred="pred",
discrepancy="discrepancy"),
AddRandomGuidanced(
guidance="guidance",
discrepancy="discrepancy",
probability="probability",
),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label")),
])
def pre_transforms(self, data=None):
return [
LoadImagePatchd(keys="image", conversion="RGB", dtype=np.uint8),
FilterImaged(keys="image"),
AsChannelFirstd(keys="image"),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
AddClickGuidanced(image="image", guidance="guidance"),
AddGuidanceSignald(image="image",
guidance="guidance",
number_intensity_ch=3),
EnsureTyped(keys="image",
device=data.get("device") if data else None),
]
def pre_transforms(self, data=None):
return [
LoadImaged(keys="image"),
AsChannelFirstd(keys="image"),
Spacingd(keys="image", pixdim=[1.0, 1.0], mode="bilinear"),
AddGuidanceFromPointsd(ref_image="image",
guidance="guidance",
dimensions=2),
Fetch2DSliced(keys="image", guidance="guidance"),
AddChanneld(keys="image"),
SpatialCropGuidanced(keys="image",
guidance="guidance",
spatial_size=[256, 256]),
Resized(keys="image", spatial_size=[256, 256], mode="area"),
ResizeGuidanced(guidance="guidance", ref_image="image"),
NormalizeIntensityd(keys="image", subtrahend=208,
divisor=388), # type: ignore
AddGuidanceSignald(image="image", guidance="guidance"),
]
def get_pre_transforms(roi_size, model_size, dimensions):
t = [
LoadImaged(keys=('image', 'label')),
AddChanneld(keys=('image', 'label')),
SpatialCropForegroundd(keys=('image', 'label'),
source_key='label',
spatial_size=roi_size),
Resized(keys=('image', 'label'),
spatial_size=model_size,
mode=('area', 'nearest')),
NormalizeIntensityd(keys='image', subtrahend=208.0, divisor=388.0)
]
if dimensions == 3:
t.append(FindAllValidSlicesd(label='label', sids='sids'))
t.extend([
AddInitialSeedPointd(label='label', guidance='guidance', sids='sids'),
AddGuidanceSignald(image='image', guidance='guidance'),
ToTensord(keys=('image', 'label'))
])
return Compose(t)
def get_pre_transforms(roi_size, model_size, dimensions):
t = [
LoadImaged(keys=("image", "label")),
AddChanneld(keys=("image", "label")),
SpatialCropForegroundd(keys=("image", "label"),
source_key="label",
spatial_size=roi_size),
Resized(keys=("image", "label"),
spatial_size=model_size,
mode=("area", "nearest")),
NormalizeIntensityd(keys="image", subtrahend=208.0, divisor=388.0),
]
if dimensions == 3:
t.append(FindAllValidSlicesd(label="label", sids="sids"))
t.extend([
AddInitialSeedPointd(label="label", guidance="guidance", sids="sids"),
AddGuidanceSignald(image="image", guidance="guidance"),
EnsureTyped(keys=("image", "label")),
])
return Compose(t)
def test_correct_results(self, arguments, input_data, expected_result):
result = AddGuidanceSignald(**arguments)(input_data)
np.testing.assert_allclose(result["image"], expected_result, rtol=1e-5)