# Source code for monai.apps.deepgrow.interaction

```
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from collections.abc import Callable, Sequence
import torch
from monai.data import decollate_batch, list_data_collate
from monai.engines import SupervisedEvaluator, SupervisedTrainer
from monai.engines.utils import IterationEvents
from monai.transforms import Compose
from monai.utils.enums import CommonKeys
[docs]
class Interaction:
"""
Ignite process_function used to introduce interactions (simulation of clicks) for Deepgrow Training/Evaluation.
For more details please refer to: https://pytorch.org/ignite/generated/ignite.engine.engine.Engine.html.
This implementation is based on:
Sakinis et al., Interactive segmentation of medical images through
fully convolutional neural networks. (2019) https://arxiv.org/abs/1903.08205
Args:
transforms: execute additional transformation during every iteration (before train).
Typically, several Tensor based transforms composed by `Compose`.
max_interactions: maximum number of interactions per iteration
train: training or evaluation
key_probability: field name to fill probability for every interaction
"""
def __init__(
self,
transforms: Sequence[Callable] | Callable,
max_interactions: int,
train: bool,
key_probability: str = "probability",
) -> None:
if not isinstance(transforms, Compose):
transforms = Compose(transforms)
self.transforms: Compose = transforms
self.max_interactions = max_interactions
self.train = train
self.key_probability = key_probability
def __call__(self, engine: SupervisedTrainer | SupervisedEvaluator, batchdata: dict[str, torch.Tensor]) -> dict:
if batchdata is None:
raise ValueError("Must provide batch data for current iteration.")
for j in range(self.max_interactions):
inputs, _ = engine.prepare_batch(batchdata)
inputs = inputs.to(engine.state.device)
engine.fire_event(IterationEvents.INNER_ITERATION_STARTED)
engine.network.eval()
with torch.no_grad():
if engine.amp:
with torch.cuda.amp.autocast():
predictions = engine.inferer(inputs, engine.network)
else:
predictions = engine.inferer(inputs, engine.network)
engine.fire_event(IterationEvents.INNER_ITERATION_COMPLETED)
batchdata.update({CommonKeys.PRED: predictions})
# decollate batch data to execute click transforms
batchdata_list = decollate_batch(batchdata, detach=True)
for i in range(len(batchdata_list)):
batchdata_list[i][self.key_probability] = (
(1.0 - ((1.0 / self.max_interactions) * j)) if self.train else 1.0
)
batchdata_list[i] = self.transforms(batchdata_list[i])
# collate list into a batch for next round interaction
batchdata = list_data_collate(batchdata_list)
return engine._iteration(engine, batchdata) # type: ignore[arg-type]
```