def run_interaction(self, train, compose):
data = []
for i in range(5):
data.append({"image": torch.tensor([float(i)]), "label": torch.tensor([float(i)])})
network = torch.nn.Linear(1, 1)
lr = 1e-3
opt = torch.optim.SGD(network.parameters(), lr)
loss = torch.nn.L1Loss()
dataset = Dataset(data, transform=None)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [Activationsd(keys="pred", sigmoid=True), ToNumpyd(keys="pred")]
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), 2, "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.get("probability"), "Probability is missing")
self.assertEqual(engine.state.best_metric, 9)
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 run_interaction(self, train):
label_names = {"spleen": 1, "background": 0}
np.random.seed(0)
data = [
{
"image": np.random.randint(0, 256, size=(1, 15, 15, 15)).astype(np.float32),
"label": np.random.randint(0, 2, size=(1, 15, 15, 15)),
"label_names": label_names,
}
for _ in range(5)
]
network = torch.nn.Conv3d(3, len(label_names), 1)
lr = 1e-3
opt = torch.optim.Adam(network.parameters(), lr)
loss = DiceCELoss(to_onehot_y=True, softmax=True)
pre_transforms = Compose(
[
FindAllValidSlicesMissingLabelsd(keys="label", sids="sids"),
AddInitialSeedPointMissingLabelsd(keys="label", guidance="guidance", sids="sids"),
AddGuidanceSignalDeepEditd(keys="image", guidance="guidance", number_intensity_ch=1),
ToTensord(keys=("image", "label")),
]
)
dataset = Dataset(data, transform=pre_transforms)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
iteration_transforms = [
FindDiscrepancyRegionsDeepEditd(keys="label", pred="pred", discrepancy="discrepancy"),
AddRandomGuidanceDeepEditd(
keys="NA", guidance="guidance", discrepancy="discrepancy", probability="probability"
),
AddGuidanceSignalDeepEditd(keys="image", guidance="guidance", number_intensity_ch=1),
ToTensord(keys=("image", "label")),
]
post_transforms = [
Activationsd(keys="pred", softmax=True),
AsDiscreted(keys=("pred", "label"), argmax=(True, False), to_onehot=len(label_names)),
SplitPredsLabeld(keys="pred"),
ToTensord(keys=("image", "label")),
]
iteration_transforms = Compose(iteration_transforms)
post_transforms = Compose(post_transforms)
i = Interaction(
deepgrow_probability=1.0,
transforms=iteration_transforms,
click_probability_key="probability",
train=train,
label_names=label_names,
)
self.assertEqual(len(i.transforms.transforms), 4, "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,
postprocessing=post_transforms,
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, 1)