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): 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'] output_folder = join(self.output_folder, validation_folder_name) maybe_mkdir_p(output_folder) if do_mirroring: mirror_axes = self.data_aug_params['mirror_axes'] else: mirror_axes = () pred_gt_tuples = [] export_pool = Pool(2) results = [] transpose_backward = self.plans.get('transpose_backward') for k in self.dataset_val.keys(): properties = load_pickle(self.dataset[k]['properties_file']) data = np.load(self.dataset[k]['data_file'])['data'] # concat segmentation of previous step seg_from_prev_stage = np.load( join(self.folder_with_segs_from_prev_stage, k + "_segFromPrevStage.npz"))['data'][None] print(data.shape) data[-1][data[-1] == -1] = 0 data_for_net = np.concatenate( (data[:-1], to_one_hot(seg_from_prev_stage[0], range(1, self.num_classes)))) softmax_pred = self.predict_preprocessed_data_return_seg_and_softmax( data_for_net, 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] if transpose_backward is not None: transpose_backward = self.plans.get('transpose_backward') softmax_pred = softmax_pred.transpose( [0] + [i + 1 for i in transpose_backward]) fname = properties['list_of_data_files'][0].split("/")[-1][:-12] 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(fname + ".npy", softmax_pred) softmax_pred = 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), ))) pred_gt_tuples.append([ join(output_folder, fname + ".nii.gz"), join(self.gt_niftis_folder, fname + ".nii.gz") ]) _ = [i.get() for i in results] 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, json_author="Fabian", json_description="", json_task=task) # 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 while not success and attempts < 10: try: shutil.copy(f, gt_nifti_folder) success = True except OSError: attempts += 1 sleep(1) self.network.train(current_mode) export_pool.close() export_pool.join()
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 debug=True then the temporary files generated for postprocessing determination will be kept """ 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 = [] for k in self.dataset_val.keys(): properties = load_pickle(self.dataset[k]['properties_file']) fname = properties['list_of_data_files'][0].split("/")[-1][:-12] 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), ))) pred_gt_tuples.append([ join(output_folder, fname + ".nii.gz"), join(self.gt_niftis_folder, fname + ".nii.gz") ]) _ = [i.get() for i in results] self.print_to_log_file("finished prediction") # 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)