Source code for monailabel.tasks.activelearning.epistemic

# 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import logging
import time
from typing import Any, Dict

from monailabel.interfaces.datastore import Datastore
from monailabel.interfaces.tasks.strategy import Strategy

logger = logging.getLogger(__name__)


[docs]class Epistemic(Strategy): """ Epistemic as active learning strategy """ SECS_IN_DAY = 24 * 60 * 60 def __init__( self, k=0, reset=SECS_IN_DAY, key="epistemic_entropy", desc="Get First Sample Based on Epistemic score" ): self.k = k self.reset = reset # Reset previously served samples after N seconds (ex: every day) self.key = key super().__init__(desc) def __call__(self, request, datastore: Datastore): label_tag = request.get("label_tag") labels = request.get("labels") images = datastore.get_unlabeled_images(label_tag, labels) if not len(images): return None scores: Dict[str, Any] = {} current_ts = int(time.time()) strategy = request["strategy"] for image in images: info = datastore.get_image_info(image) score = info.get(self.key, 0) ts = min(current_ts - info.get("strategy", {}).get(strategy, {}).get("ts", 0), self.reset) scores[image] = {"score": score, "ts": ts} scores = {k: v for k, v in sorted(scores.items(), key=lambda item: item[1]["score"], reverse=True)} # type: ignore logger.info(f"{strategy}: Top-N: {scores}") # Pick Top-N based on epistemic scores top_k: Dict[str, Any] = {} max_len = self.k if 0 < self.k < len(scores) else len(scores) for k, v in scores.items(): if len(top_k) == max_len: break # Handle similar timestamps top_k[k] = { "score": v["score"], "ts": v["ts"] - (pow(10, len(top_k)) if v["ts"] == self.reset else len(top_k)) * 10, } logger.info(f"{strategy}: Top-K: {top_k}") # Pick the one which is least served recently among Top-N top_k = {k: v for k, v in sorted(scores.items(), key=lambda item: item[1]["ts"], reverse=True)} # type: ignore logger.info(f"{strategy}: Top-K (ts): {top_k};") image = next(iter(top_k)) logger.info(f"{strategy}: Selected Image: {image}; epistemic_entropy: {top_k[image]}") return {"id": image, "epistemic_entropy": top_k[image]}