def aggregate_scores(test_ref_pairs, evaluator=NiftiEvaluator, labels=None, use_label=None, nanmean=True, json_output_file=None, json_name="", json_description="", json_author="Fabian", json_task="", num_threads=2, **metric_kwargs): """ test = predicted image :param test_ref_pairs: :param evaluator: :param labels: must be a dict of int-> str or a list of int :param nanmean: :param json_output_file: :param json_name: :param json_description: :param json_author: :param json_task: :param metric_kwargs: :return: """ if type(evaluator) == type: evaluator = evaluator() if labels is not None: evaluator.set_labels(labels) if use_label is not None: evaluator.set_use_label(use_label) all_scores = OrderedDict() all_scores["all"] = [] all_scores["mean"] = OrderedDict() test = [i[0] for i in test_ref_pairs] ref = [i[1] for i in test_ref_pairs] p = Pool(num_threads) all_res = p.map( run_evaluation, zip(test, ref, [evaluator] * len(ref), [metric_kwargs] * len(ref))) p.close() p.join() for i in range(len(all_res)): all_scores["all"].append(all_res[i]) # append score list for mean for label, score_dict in all_res[i].items(): if label in ("test", "reference"): continue if label not in all_scores["mean"]: all_scores["mean"][label] = OrderedDict() for score, value in score_dict.items(): if score not in all_scores["mean"][label]: all_scores["mean"][label][score] = [] all_scores["mean"][label][score].append(value) for label in all_scores["mean"]: for score in all_scores["mean"][label]: if nanmean: all_scores["mean"][label][score] = float( np.nanmean(all_scores["mean"][label][score])) else: all_scores["mean"][label][score] = float( np.mean(all_scores["mean"][label][score])) # save to file if desired # we create a hopefully unique id by hashing the entire output dictionary if json_output_file is not None: json_dict = OrderedDict() json_dict["name"] = json_name json_dict["description"] = json_description timestamp = datetime.today() json_dict["timestamp"] = str(timestamp) json_dict["task"] = json_task json_dict["author"] = json_author json_dict["results"] = all_scores json_dict["id"] = hashlib.md5( json.dumps(json_dict).encode("utf-8")).hexdigest()[:12] save_json(json_dict, json_output_file) return all_scores
def validate(self, do_mirroring: bool = True, use_sliding_window: bool = True, step_size: float = 0.5, save_softmax: bool = True, use_gaussian: bool = True, overwrite: bool = True, validation_folder_name: str = 'validation_raw', debug: bool = False, all_in_gpu: bool = False, segmentation_export_kwargs: dict = None, run_postprocessing_on_folds: bool = True): if isinstance(self.network, DDP): net = self.network.module else: net = self.network ds = net.do_ds net.do_ds = False current_mode = self.network.training self.network.eval() assert self.was_initialized, "must initialize, ideally with checkpoint (or train first)" if self.dataset_val is None: self.load_dataset() self.do_split() if segmentation_export_kwargs is None: if 'segmentation_export_params' in self.plans.keys(): force_separate_z = self.plans['segmentation_export_params'][ 'force_separate_z'] interpolation_order = self.plans['segmentation_export_params'][ 'interpolation_order'] interpolation_order_z = self.plans[ 'segmentation_export_params']['interpolation_order_z'] else: force_separate_z = None interpolation_order = 1 interpolation_order_z = 0 else: force_separate_z = segmentation_export_kwargs['force_separate_z'] interpolation_order = segmentation_export_kwargs[ 'interpolation_order'] interpolation_order_z = segmentation_export_kwargs[ 'interpolation_order_z'] # predictions as they come from the network go here output_folder = join(self.output_folder, validation_folder_name) maybe_mkdir_p(output_folder) # this is for debug purposes my_input_args = { 'do_mirroring': do_mirroring, 'use_sliding_window': use_sliding_window, 'step_size': step_size, 'save_softmax': save_softmax, 'use_gaussian': use_gaussian, 'overwrite': overwrite, 'validation_folder_name': validation_folder_name, 'debug': debug, 'all_in_gpu': all_in_gpu, 'segmentation_export_kwargs': segmentation_export_kwargs, } save_json(my_input_args, join(output_folder, "validation_args.json")) if do_mirroring: if not self.data_aug_params['do_mirror']: raise RuntimeError( "We did not train with mirroring so you cannot do inference with mirroring enabled" ) mirror_axes = self.data_aug_params['mirror_axes'] else: mirror_axes = () pred_gt_tuples = [] export_pool = Pool(default_num_threads) results = [] all_keys = list(self.dataset_val.keys()) my_keys = all_keys[self.local_rank::dist.get_world_size()] # we cannot simply iterate over all_keys because we need to know pred_gt_tuples and valid_labels of all cases # for evaluation (which is done by local rank 0) for k in my_keys: properties = load_pickle(self.dataset[k]['properties_file']) fname = properties['list_of_data_files'][0].split("/")[-1][:-12] pred_gt_tuples.append([ join(output_folder, fname + ".nii.gz"), join(self.gt_niftis_folder, fname + ".nii.gz") ]) if k in my_keys: if overwrite or (not isfile(join(output_folder, fname + ".nii.gz"))) or \ (save_softmax and not isfile(join(output_folder, fname + ".npz"))): data = np.load(self.dataset[k]['data_file'])['data'] print(k, data.shape) data[-1][data[-1] == -1] = 0 softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax( data[:-1], do_mirroring=do_mirroring, mirror_axes=mirror_axes, use_sliding_window=use_sliding_window, step_size=step_size, use_gaussian=use_gaussian, all_in_gpu=all_in_gpu, mixed_precision=self.fp16)[1] softmax_pred = softmax_pred.transpose( [0] + [i + 1 for i in self.transpose_backward]) if save_softmax: softmax_fname = join(output_folder, fname + ".npz") else: softmax_fname = None """There is a problem with python process communication that prevents us from communicating obejcts larger than 2 GB between processes (basically when the length of the pickle string that will be sent is communicated by the multiprocessing.Pipe object then the placeholder (\%i I think) does not allow for long enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either filename or np.ndarray and will handle this automatically""" if np.prod(softmax_pred.shape) > ( 2e9 / 4 * 0.85): # *0.85 just to be save np.save(join(output_folder, fname + ".npy"), softmax_pred) softmax_pred = join(output_folder, fname + ".npy") results.append( export_pool.starmap_async( save_segmentation_nifti_from_softmax, ((softmax_pred, join(output_folder, fname + ".nii.gz"), properties, interpolation_order, self.regions_class_order, None, None, softmax_fname, None, force_separate_z, interpolation_order_z), ))) _ = [i.get() for i in results] self.print_to_log_file("finished prediction") distributed.barrier() if self.local_rank == 0: # evaluate raw predictions self.print_to_log_file("evaluation of raw predictions") task = self.dataset_directory.split("/")[-1] job_name = self.experiment_name _ = aggregate_scores(pred_gt_tuples, labels=list(range(self.num_classes)), json_output_file=join(output_folder, "summary.json"), json_name=job_name + " val tiled %s" % (str(use_sliding_window)), json_author="Fabian", json_task=task, num_threads=default_num_threads) if run_postprocessing_on_folds: # in the old nnunet we would stop here. Now we add a postprocessing. This postprocessing can remove everything # except the largest connected component for each class. To see if this improves results, we do this for all # classes and then rerun the evaluation. Those classes for which this resulted in an improved dice score will # have this applied during inference as well self.print_to_log_file("determining postprocessing") determine_postprocessing( self.output_folder, self.gt_niftis_folder, validation_folder_name, final_subf_name=validation_folder_name + "_postprocessed", debug=debug) # after this the final predictions for the vlaidation set can be found in validation_folder_name_base + "_postprocessed" # They are always in that folder, even if no postprocessing as applied! # detemining postprocesing on a per-fold basis may be OK for this fold but what if another fold finds another # postprocesing to be better? In this case we need to consolidate. At the time the consolidation is going to be # done we won't know what self.gt_niftis_folder was, so now we copy all the niftis into a separate folder to # be used later gt_nifti_folder = join(self.output_folder_base, "gt_niftis") maybe_mkdir_p(gt_nifti_folder) for f in subfiles(self.gt_niftis_folder, suffix=".nii.gz"): success = False attempts = 0 e = None while not success and attempts < 10: try: shutil.copy(f, gt_nifti_folder) success = True except OSError as e: attempts += 1 sleep(1) if not success: print("Could not copy gt nifti file %s into folder %s" % (f, gt_nifti_folder)) if e is not None: raise e self.network.train(current_mode) net.do_ds = ds