""" Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 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 abc import ABC, abstractmethod from pathlib import Path import time from typing import Callable, Tuple, Dict, Sequence, Any, Optional, TypeVar import numpy as np from loguru import logger from nndet.io.paths import Pathlike from nndet.io.load import save_json from nndet.utils.info import maybe_verbose_iterable from nndet.utils import to_numpy from nndet.evaluator.registry import BoxEvaluator class Sweeper(ABC): def __init__(self, classes: Sequence[str], pred_dir: Pathlike, gt_dir: Pathlike, target_metric: str, save_dir: Optional[Pathlike] = None, ): """ Sweep multiple parameters and compute evaluation metrics to determine the best set of parameters Args: evaluation: reference to an evaluation objects pred_dir: directory where predicted data is saved device: device to use for internal computations """ self.classes = classes self.save_dir = save_dir if save_dir is None else Path(save_dir) if self.save_dir is not None: self.save_dir.mkdir(parents=True, exist_ok=True) self.target_metric = target_metric self.device = "cpu" self.pred_dir = Path(pred_dir) self.gt_dir = Path(gt_dir) @abstractmethod def run_postprocessing_sweep(self, restore: bool = True, ) -> Tuple[Dict, Dict]: """ Run parameter sweeps to determine best parameters accoring to target metric Args: target_metric: metric to optimize Returns: Dict: determined parameters Dict: final results with parameters """ raise NotImplementedError class BoxSweeper(Sweeper): def __init__(self, classes: Sequence[str], pred_dir: Pathlike, gt_dir: Pathlike, target_metric: str, ensembler_cls: Callable, save_dir: Optional[Pathlike] = None, ) -> None: """ Run sweep over parameters and select the best Args: classes: classes present in dataset pred_dir: directory where predictions are saved gt_dir: directory where ground truth is saved target_metric: metric to optimize ensembler_cls: ensembler class used during prediction save_dir: Directory to save results. Defaults to None. """ super().__init__(classes=classes, pred_dir=pred_dir, gt_dir=gt_dir, target_metric=target_metric, save_dir=save_dir, ) self.evaluator_cls = BoxEvaluator self.ensembler_cls = ensembler_cls def run_postprocessing_sweep(self): """ Sequentially search for the best parameters Returns: Dict: final parameters to run inference on new cases Dict: `det_scores`: detection score metrics `det_curves`: detection curves """ state, sweep_params = self.ensembler_cls.sweep_parameters() num_cases = self.ensembler_cls.get_case_ids(self.pred_dir) logger.info(f"Running parameter sweep on {num_cases} cases to optimize " f"{self.target_metric} with initial state {state}.") best_score = float('-inf') for param_name, values in sweep_params.items(): best_value, _best_score = self.run_parameter( values=values, param_name=param_name, state=state, ) state[param_name] = best_value if _best_score < best_score: logger.error("ERROR: Something went wrong during sweeping. " "Results were modified inplace! " f"Previous: {best_score} now {_best_score}") best_score = _best_score logger.info(f"\n\n Determined {state} with best sweeping score {best_score} {self.target_metric}\n\n") return state def run_parameter(self, values: Sequence[Any], param_name: str, state: Dict[str, Any], ): """ Evaluate parameters and select the best Args: values: values to evaluate param_name: name of parameter state: different state parameters """ cache = [] overview = {} for value in values: logger.info(f"Running sweep {param_name}={value}") tic = time.perf_counter() metric_scores = self._evaluate_value(state=state, **{param_name: value}) overview[f"{param_name}_{value}".replace(".", "_")] = { "state": str(state), "overwrite": {param_name: str(value)}, "scores": str(metric_scores), } cache.append(metric_scores[self.target_metric]) toc = time.perf_counter() logger.info(f"Sweep took {toc - tic} s") best_idx = np.argmax(cache) best_value = values[best_idx] best_score = cache[best_idx] if self.save_dir is not None: overview[f"best_{param_name}"] = {"value": str(best_value), "score": str(best_score)} save_json(overview, self.save_dir / f"sweep_{param_name}.json") return best_value, best_score def _evaluate_value(self, state: Dict[str, Any], **overwrite, ): """ Evalaute a single value Args: state: state for ensembler overwrite: state overwrites Returns: Dict: scalar metrics """ evaluator = self.evaluator_cls.create(classes=self.classes, fast=True, verbose=False, save_dir=None, ) for case_id in maybe_verbose_iterable(self.ensembler_cls.get_case_ids(self.pred_dir)): ensembler = self.ensembler_cls.from_checkpoint( base_dir=self.pred_dir, case_id=case_id, device=self.device, ) ensembler.update_parameters(**state) ensembler.update_parameters(**overwrite) pred = to_numpy(ensembler.get_case_result(restore=False)) gt = np.load(str(self.gt_dir / f"{case_id}_boxes_gt.npz"), allow_pickle=True) evaluator.run_online_evaluation( pred_boxes=[pred["pred_boxes"]], pred_classes=[pred["pred_labels"]], pred_scores=[pred["pred_scores"]], gt_boxes=[gt["boxes"]], gt_classes=[gt["classes"]], gt_ignore=None, ) metric_scores, _ = evaluator.finish_online_evaluation() return metric_scores SweeperType = TypeVar('SweeperType', bound=Sweeper)