# 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 typing import TYPE_CHECKING
from monai.config import IgniteInfo, KeysCollection
from monai.engines.utils import IterationEvents
from monai.transforms import Decollated
from monai.utils import min_version, optional_import
Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
if TYPE_CHECKING:
from ignite.engine import Engine
else:
Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine")
[docs]
class DecollateBatch:
"""
Ignite handler to execute the `decollate batch` logic for `engine.state.batch` and `engine.state.output`.
Typical usage is to set `decollate=False` in the engine and execute some postprocessing logic first
then decollate the batch, otherwise, engine will decollate batch before the postprocessing.
Args:
event: expected EVENT to attach the handler, should be "MODEL_COMPLETED" or "ITERATION_COMPLETED".
default to "MODEL_COMPLETED".
detach: whether to detach the tensors. scalars tensors will be detached into number types
instead of torch tensors.
decollate_batch: whether to decollate `engine.state.batch` of ignite engine.
batch_keys: if `decollate_batch=True`, specify the keys of the corresponding items to decollate
in `engine.state.batch`, note that it will delete other keys not specified. if None,
will decollate all the keys. it replicates the scalar values to every item of the decollated list.
decollate_output: whether to decollate `engine.state.output` of ignite engine.
output_keys: if `decollate_output=True`, specify the keys of the corresponding items to decollate
in `engine.state.output`, note that it will delete other keys not specified. if None,
will decollate all the keys. it replicates the scalar values to every item of the decollated list.
allow_missing_keys: don't raise exception if key is missing.
"""
def __init__(
self,
event: str = "MODEL_COMPLETED",
detach: bool = True,
decollate_batch: bool = True,
batch_keys: KeysCollection | None = None,
decollate_output: bool = True,
output_keys: KeysCollection | None = None,
allow_missing_keys: bool = False,
):
event = event.upper()
if event not in ("MODEL_COMPLETED", "ITERATION_COMPLETED"):
raise ValueError("event should be `MODEL_COMPLETED` or `ITERATION_COMPLETED`.")
self.event = event
self.batch_transform = (
Decollated(keys=batch_keys, detach=detach, allow_missing_keys=allow_missing_keys)
if decollate_batch
else None
)
self.output_transform = (
Decollated(keys=output_keys, detach=detach, allow_missing_keys=allow_missing_keys)
if decollate_output
else None
)
[docs]
def attach(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
if self.event == "MODEL_COMPLETED":
engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self)
else:
engine.add_event_handler(Events.ITERATION_COMPLETED, self)
def __call__(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
if self.batch_transform is not None and isinstance(engine.state.batch, (list, dict)):
engine.state.batch = self.batch_transform(engine.state.batch)
if self.output_transform is not None and isinstance(engine.state.output, (list, dict)):
engine.state.output = self.output_transform(engine.state.output)
