# 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.
import logging
import os
import threading
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
from monai.config import DtypeLike, IgniteInfo
from monai.utils import ProbMapKeys, 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 ProbMapProducer:
"""
Event handler triggered on completing every iteration to calculate and save the probability map
"""
[docs] def __init__(
self,
output_dir: str = "./",
output_postfix: str = "",
prob_key: str = "pred",
dtype: DtypeLike = np.float64,
name: Optional[str] = None,
) -> None:
"""
Args:
output_dir: output directory to save probability maps.
output_postfix: a string appended to all output file names.
prob_key: the key associated to the probability output of the model
dtype: the data type in which the probability map is stored. Default np.float64.
name: identifier of logging.logger to use, defaulting to `engine.logger`.
"""
self.logger = logging.getLogger(name)
self._name = name
self.output_dir = output_dir
self.output_postfix = output_postfix
self.prob_key = prob_key
self.dtype = dtype
self.prob_map: Dict[str, np.ndarray] = {}
self.counter: Dict[str, int] = {}
self.num_done_images: int = 0
self.num_images: int = 0
self.lock = threading.Lock()
[docs] def attach(self, engine: Engine) -> None:
"""
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
image_data = engine.data_loader.dataset.image_data # type: ignore
self.num_images = len(image_data)
# Initialized probability maps for all the images
for sample in image_data:
name = sample[ProbMapKeys.NAME]
self.counter[name] = sample[ProbMapKeys.COUNT]
self.prob_map[name] = np.zeros(sample[ProbMapKeys.SIZE], dtype=self.dtype)
if self._name is None:
self.logger = engine.logger
if not engine.has_event_handler(self, Events.ITERATION_COMPLETED):
engine.add_event_handler(Events.ITERATION_COMPLETED, self)
if not engine.has_event_handler(self.finalize, Events.COMPLETED):
engine.add_event_handler(Events.COMPLETED, self.finalize)
def __call__(self, engine: Engine) -> None:
"""
This method assumes self.batch_transform will extract metadata from the input batch.
Args:
engine: Ignite Engine, it can be a trainer, validator or evaluator.
"""
if not isinstance(engine.state.batch, dict) or not isinstance(engine.state.output, dict):
raise ValueError("engine.state.batch and engine.state.output must be dictionaries.")
names = engine.state.batch["metadata"][ProbMapKeys.NAME]
locs = engine.state.batch["metadata"][ProbMapKeys.LOCATION]
probs = engine.state.output[self.prob_key]
for name, loc, prob in zip(names, locs, probs):
self.prob_map[name][loc] = prob
with self.lock:
self.counter[name] -= 1
if self.counter[name] == 0:
self.save_prob_map(name)
[docs] def save_prob_map(self, name: str) -> None:
"""
This method save the probability map for an image, when its inference is finished,
and delete that probability map from memory.
Args:
name: the name of image to be saved.
"""
file_path = os.path.join(self.output_dir, name)
np.save(file_path + self.output_postfix + ".npy", self.prob_map[name])
self.num_done_images += 1
self.logger.info(f"Inference of '{name}' is done [{self.num_done_images}/{self.num_images}]!")
del self.prob_map[name]
del self.counter[name]
def finalize(self, engine: Engine):
self.logger.info(f"Probability map is created for {self.num_done_images}/{self.num_images} images!")