# Copyright 2020 - 2021 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.
import warnings
from typing import TYPE_CHECKING, Callable
from monai.config import IgniteInfo
from monai.engines.utils import IterationEvents, engine_apply_transform
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 PostProcessing:
"""
Ignite handler to execute additional post processing after the post processing in engines.
So users can insert other handlers between engine postprocessing and this post processing handler.
"""
def __init__(self, transform: Callable) -> None:
"""
Args:
transform: callable function to execute on the `engine.state.batch` and `engine.state.output`.
can also be composed transforms.
"""
self.transform = transform
[docs] def attach(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self)
def __call__(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
if not isinstance(engine.state.batch, list) or not isinstance(engine.state.output, list):
warnings.warn("postprocessing requires `engine.state.batch` and `engine.state.outout` to be lists.")
else:
for i, (b, o) in enumerate(zip(engine.state.batch, engine.state.output)):
engine.state.batch[i], engine.state.output[i] = engine_apply_transform(b, o, self.transform)