diff --git a/datasets/lidc/data_loader.py b/datasets/lidc/data_loader.py
index 8588173..222364d 100644
--- a/datasets/lidc/data_loader.py
+++ b/datasets/lidc/data_loader.py
@@ -1,971 +1,1014 @@
 # Copyright 2019 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
 #
 # 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.
 # ==============================================================================
 
 '''
 Data Loader for the LIDC data set. This dataloader expects preprocessed data in .npy or .npz files per patient and
 a pandas dataframe containing the meta info e.g. file paths, and some ground-truth info like labels, foreground slice ids.
 
 LIDC 4-fold annotations storage capacity problem: keep segmentation gts compressed (npz), unpack at each batch generation.
 
 '''
 
 import plotting as plg
 
 import os
 import pickle
 import time
+from multiprocessing import Pool
 
 import numpy as np
 import pandas as pd
 from collections import OrderedDict
 
 # batch generator tools from https://github.com/MIC-DKFZ/batchgenerators
 from batchgenerators.transforms.spatial_transforms import MirrorTransform as Mirror
 from batchgenerators.transforms.abstract_transforms import Compose
 from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
 from batchgenerators.transforms.spatial_transforms import SpatialTransform
 from batchgenerators.transforms.crop_and_pad_transforms import CenterCropTransform
 
 import utils.dataloader_utils as dutils
 from utils.dataloader_utils import ConvertSegToBoundingBoxCoordinates
 
 
 def save_obj(obj, name):
     """Pickle a python object."""
     with open(name + '.pkl', 'wb') as f:
         pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
 
 def vector(item):
     """ensure item is vector-like (list or array or tuple)
     :param item: anything
     """
     if not isinstance(item, (list, tuple, np.ndarray)):
         item = [item]
     return item
 
 
 class Dataset(dutils.Dataset):
     r"""Load a dict holding memmapped arrays and clinical parameters for each patient,
     evtly subset of those.
         If server_env: copy and evtly unpack (npz->npy) data in cf.data_rootdir to
         cf.data_dest.
     :param cf: config object.
     :param logger: logger.
     :param subset_ids: subset of patient/sample identifiers to load from whole set.
     :param data_sourcedir: directory in which to find data, defaults to cf.data_sourcedir if None.
     :return: dict with imgs, segs, pids, class_labels, observables
     """
 
     def __init__(self, cf, logger=None, subset_ids=None, data_sourcedir=None, mode='train'):
         super(Dataset,self).__init__(cf, data_sourcedir)
         if mode == 'train' and not cf.training_gts == "merged":
             self.gt_dir = "patient_gts_sa"
             self.gt_kind = cf.training_gts
         else:
             self.gt_dir = "patient_gts_merged"
             self.gt_kind = "merged"
         if logger is not None:
             logger.info("loading {} ground truths for {}".format(self.gt_kind, 'training and validation' if mode=='train'
         else 'testing'))
 
         p_df = pd.read_pickle(os.path.join(self.data_sourcedir, self.gt_dir, cf.input_df_name))
         #exclude_pids = ["0305a", "0447a"]  # due to non-bg segmentation but bg mal label in nodules 5728, 8840
         #p_df = p_df[~p_df.pid.isin(exclude_pids)]
 
         if subset_ids is not None:
             p_df = p_df[p_df.pid.isin(subset_ids)]
             if logger is not None:
                 logger.info('subset: selected {} instances from df'.format(len(p_df)))
         if cf.select_prototype_subset is not None:
             prototype_pids = p_df.pid.tolist()[:cf.select_prototype_subset]
             p_df = p_df[p_df.pid.isin(prototype_pids)]
             if logger is not None:
                 logger.warning('WARNING: using prototyping data subset of length {}!!!'.format(len(p_df)))
 
         pids = p_df.pid.tolist()
 
         # evtly copy data from data_sourcedir to data_dest
         if cf.server_env and not hasattr(cf, 'data_dir') and hasattr(cf, "data_dest"):
                 # copy and unpack images
                 file_subset = ["{}_img.npz".format(pid) for pid in pids if not
                 os.path.isfile(os.path.join(cf.data_dest,'{}_img.npy'.format(pid)))]
                 file_subset += [os.path.join(self.data_sourcedir, self.gt_dir, cf.input_df_name)]
                 self.copy_data(cf, file_subset=file_subset, keep_packed=False, del_after_unpack=True)
                 # copy and do not unpack segmentations
                 file_subset = [os.path.join(self.gt_dir, "{}_rois.np*".format(pid)) for pid in pids]
                 keep_packed = not cf.training_gts == "merged"
                 self.copy_data(cf, file_subset=file_subset, keep_packed=keep_packed, del_after_unpack=(not keep_packed))
         else:
             cf.data_dir = self.data_sourcedir
 
         ext = 'npy' if self.gt_kind == "merged" else 'npz'
         imgs = [os.path.join(self.data_dir, '{}_img.npy'.format(pid)) for pid in pids]
         segs = [os.path.join(self.data_dir, self.gt_dir, '{}_rois.{}'.format(pid, ext)) for pid in pids]
         orig_class_targets = p_df['class_target'].tolist()
 
         data = OrderedDict()
 
         if self.gt_kind == 'merged':
             for ix, pid in enumerate(pids):
                 data[pid] = {'data': imgs[ix], 'seg': segs[ix], 'pid': pid}
                 data[pid]['fg_slices'] = np.array(p_df['fg_slices'].tolist()[ix])
                 if 'class' in cf.prediction_tasks:
                     if len(cf.class_labels)==3:
                         # malignancy scores are binarized: (benign: 1-2 --> cl 1, malignant: 3-5 --> cl 2)
                         data[pid]['class_targets'] = np.array([2 if ii >= 3 else 1 for ii in orig_class_targets[ix]],
                                                               dtype='uint8')
                     elif len(cf.class_labels)==6:
                         # classify each malignancy score
                         data[pid]['class_targets'] = np.array([1 if ii==0.5 else np.round(ii) for ii in orig_class_targets[ix]], dtype='uint8')
                     else:
                         raise Exception("mismatch class labels and data-loading implementations.")
                 else:
                     data[pid]['class_targets'] = np.ones_like(np.array(orig_class_targets[ix]), dtype='uint8')
                 if any(['regression' in task for task in cf.prediction_tasks]):
                     data[pid]["regression_targets"] = np.array([vector(v) for v in orig_class_targets[ix]],
                                                                dtype='float16')
                     data[pid]["rg_bin_targets"] = np.array(
                         [cf.rg_val_to_bin_id(v) for v in data[pid]["regression_targets"]], dtype='uint8')
         else:
             for ix, pid in enumerate(pids):
                 data[pid] = {'data': imgs[ix], 'seg': segs[ix], 'pid': pid}
                 data[pid]['fg_slices'] = np.array(p_df['fg_slices'].values[ix])
                 if 'class' in cf.prediction_tasks:
                     # malignancy scores are binarized: (benign: 1-2 --> cl 1, malignant: 3-5 --> cl 2)
                     raise NotImplementedError
                     # todo need to consider bg
                     # data[pid]['class_targets'] = np.array(
                     #     [[2 if ii >= 3 else 1 for ii in four_fold_targs] for four_fold_targs in orig_class_targets[ix]])
                 else:
                     data[pid]['class_targets'] = np.array(
                         [[1 if ii > 0 else 0 for ii in four_fold_targs] for four_fold_targs in orig_class_targets[ix]], dtype='uint8')
                 if any(['regression' in task for task in cf.prediction_tasks]):
                     data[pid]["regression_targets"] = np.array(
                         [[vector(v) for v in four_fold_targs] for four_fold_targs in orig_class_targets[ix]], dtype='float16')
                     data[pid]["rg_bin_targets"] = np.array(
                         [[cf.rg_val_to_bin_id(v) for v in four_fold_targs] for four_fold_targs in data[pid]["regression_targets"]], dtype='uint8')
 
         cf.roi_items = cf.observables_rois[:]
         cf.roi_items += ['class_targets']
         if any(['regression' in task for task in cf.prediction_tasks]):
             cf.roi_items += ['regression_targets']
             cf.roi_items += ['rg_bin_targets']
 
         self.data = data
         self.set_ids = np.array(list(self.data.keys()))
         self.df = None
 
 # merged GTs
 class BatchGenerator_merged(dutils.BatchGenerator):
     """
     creates the training/validation batch generator. Samples n_batch_size patients (draws a slice from each patient if 2D)
     from the data set while maintaining foreground-class balance. Returned patches are cropped/padded to pre_crop_size.
     Actual patch_size is obtained after data augmentation.
     :param data: data dictionary as provided by 'load_dataset'.
     :param batch_size: number of patients to sample for the batch
     :return dictionary containing the batch data (b, c, x, y, (z)) / seg (b, 1, x, y, (z)) / pids / class_target
     """
-    def __init__(self, cf, data):
+    def __init__(self, cf, data, name="train"):
         super(BatchGenerator_merged, self).__init__(cf, data)
 
         self.crop_margin = np.array(self.cf.patch_size)/8. #min distance of ROI center to edge of cropped_patch.
         self.p_fg = 0.5
         self.empty_samples_max_ratio = 0.6
 
         self.random_count = int(cf.batch_random_ratio * cf.batch_size)
         self.class_targets = {k: v["class_targets"] for (k, v) in self._data.items()}
 
 
-        self.balance_target_distribution(plot=True)
-        self.stats = {"roi_counts": np.zeros((len(self.unique_ts),), dtype='uint32'), "empty_samples_count": 0}
-
+        self.balance_target_distribution(plot=name=="train")
 
     def generate_train_batch(self):
 
         # samples patients towards equilibrium of foreground classes on a roi-level after sampling a random ratio
         # fully random patients
         batch_patient_ids = list(np.random.choice(self.dataset_pids, size=self.random_count, replace=False))
         # target-balanced patients
         batch_patient_ids += list(np.random.choice(self.dataset_pids, size=self.batch_size-self.random_count,
                                                    replace=False, p=self.p_probs))
 
         batch_data, batch_segs, batch_pids, batch_patient_labels = [], [], [], []
         batch_roi_items = {name: [] for name in self.cf.roi_items}
         # record roi count of classes in batch
-        batch_roi_counts, empty_samples_count = np.zeros((len(self.unique_ts),), dtype='uint32'), 0
-        # empty count for full bg samples (empty slices in 2D/patients in 3D)
+        batch_roi_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
+        batch_empty_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
+        # empty count for full bg samples (empty slices in 2D/patients in 3D) per class
 
 
         for sample in range(self.batch_size):
             patient = self._data[batch_patient_ids[sample]]
 
             data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))[np.newaxis]
             seg = np.transpose(np.load(patient['seg'], mmap_mode='r'), axes=(1, 2, 0))
             batch_pids.append(patient['pid'])
             (c, y, x, z) = data.shape
 
             if self.cf.dim == 2:
 
                 elig_slices, choose_fg = [], False
                 if len(patient['fg_slices']) > 0:
-                    if empty_samples_count / self.batch_size >= self.empty_samples_max_ratio or np.random.rand(1)<=self.p_fg:
+                    if np.all(batch_empty_counts / self.batch_size >= self.empty_samples_max_ratio) or \
+                            np.random.rand(1)<=self.p_fg:
                         # fg is to be picked
                         for tix in np.argsort(batch_roi_counts):
                             # pick slices of patient that have roi of sought-for target
                             # np.unique(seg[...,sl_ix][seg[...,sl_ix]>0]) gives roi_ids (numbering) of rois in slice sl_ix
                             elig_slices = [sl_ix for sl_ix in np.arange(z) if np.count_nonzero(
                                 patient[self.balance_target][np.unique(seg[..., sl_ix][seg[..., sl_ix] > 0])-1] ==
                                 self.unique_ts[tix]) > 0]
                             if len(elig_slices) > 0:
                                 choose_fg = True
                                 break
                     else:
                         # pick bg
                         elig_slices = np.setdiff1d(np.arange(z), patient['fg_slices'])
 
                 if len(elig_slices)>0:
                     sl_pick_ix = np.random.choice(elig_slices, size=None)
                 else:
                     sl_pick_ix = np.random.choice(z, size=None)
 
                 data = data[..., sl_pick_ix]
                 seg = seg[..., sl_pick_ix]
 
             # pad data if smaller than pre_crop_size.
             if np.any([data.shape[dim + 1] < ps for dim, ps in enumerate(self.cf.pre_crop_size)]):
                 new_shape = [np.max([data.shape[dim + 1], ps]) for dim, ps in enumerate(self.cf.pre_crop_size)]
                 data = dutils.pad_nd_image(data, new_shape, mode='constant')
                 seg = dutils.pad_nd_image(seg, new_shape, mode='constant')
 
             # crop patches of size pre_crop_size, while sampling patches containing foreground with p_fg.
             crop_dims = [dim for dim, ps in enumerate(self.cf.pre_crop_size) if data.shape[dim + 1] > ps]
             if len(crop_dims) > 0:
                 if self.cf.dim == 3:
-                    choose_fg = (empty_samples_count/self.batch_size>=self.empty_samples_max_ratio) or np.random.rand(1) <= self.p_fg
+                    choose_fg = np.all(batch_empty_counts / self.batch_size >= self.empty_samples_max_ratio)\
+                                or np.random.rand(1) <= self.p_fg
                 if choose_fg and np.any(seg):
                     available_roi_ids = np.unique(seg)[1:]
                     for tix in np.argsort(batch_roi_counts):
                         elig_roi_ids = available_roi_ids[patient[self.balance_target][available_roi_ids-1] == self.unique_ts[tix]]
                         if len(elig_roi_ids)>0:
                             seg_ics = np.argwhere(seg == np.random.choice(elig_roi_ids, size=None))
                             break
                     roi_anchor_pixel = seg_ics[np.random.choice(seg_ics.shape[0], size=None)]
                     assert seg[tuple(roi_anchor_pixel)] > 0
                     # sample the patch center coords. constrained by edges of images - pre_crop_size /2. And by
                     # distance to the desired ROI < patch_size /2.
                     # (here final patch size to account for center_crop after data augmentation).
                     sample_seg_center = {}
                     for ii in crop_dims:
                         low = np.max((self.cf.pre_crop_size[ii]//2, roi_anchor_pixel[ii] - (self.cf.patch_size[ii]//2 - self.crop_margin[ii])))
                         high = np.min((data.shape[ii + 1] - self.cf.pre_crop_size[ii]//2,
                                        roi_anchor_pixel[ii] + (self.cf.patch_size[ii]//2 - self.crop_margin[ii])))
                         # happens if lesion on the edge of the image. dont care about roi anymore,
                         # just make sure pre-crop is inside image.
                         if low >= high:
                             low = data.shape[ii + 1] // 2 - (data.shape[ii + 1] // 2 - self.cf.pre_crop_size[ii] // 2)
                             high = data.shape[ii + 1] // 2 + (data.shape[ii + 1] // 2 - self.cf.pre_crop_size[ii] // 2)
                         sample_seg_center[ii] = np.random.randint(low=low, high=high)
 
                 else:
                     # not guaranteed to be empty. probability of emptiness depends on the data.
                     sample_seg_center = {ii: np.random.randint(low=self.cf.pre_crop_size[ii]//2,
                                                            high=data.shape[ii + 1] - self.cf.pre_crop_size[ii]//2) for ii in crop_dims}
 
                 for ii in crop_dims:
                     min_crop = int(sample_seg_center[ii] - self.cf.pre_crop_size[ii] // 2)
                     max_crop = int(sample_seg_center[ii] + self.cf.pre_crop_size[ii] // 2)
                     data = np.take(data, indices=range(min_crop, max_crop), axis=ii + 1)
                     seg = np.take(seg, indices=range(min_crop, max_crop), axis=ii)
 
             batch_data.append(data)
             batch_segs.append(seg[np.newaxis])
             for o in batch_roi_items: #after loop, holds every entry of every batchpatient per roi-item
                     batch_roi_items[o].append(patient[o])
 
             if self.cf.dim == 3:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient[self.balance_target] == self.unique_ts[tix])
+                    non_zero = np.count_nonzero(patient[self.balance_target] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero==0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
             elif self.cf.dim == 2:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient[self.balance_target][np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
-            if not np.any(seg):
-                empty_samples_count += 1
+                    non_zero = np.count_nonzero(patient[self.balance_target][np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero == 0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
 
 
         data = np.array(batch_data).astype(np.float16)
         seg = np.array(batch_segs).astype(np.uint8)
         batch = {'data': data, 'seg': seg, 'pid': batch_pids,
-                'roi_counts':batch_roi_counts, 'empty_samples_count': empty_samples_count}
+                'roi_counts':batch_roi_counts, 'empty_counts': batch_empty_counts}
         for key,val in batch_roi_items.items(): #extend batch dic by roi-wise items (obs, class ids, regression vectors...)
             batch[key] = np.array(val)
 
         return batch
 
 class PatientBatchIterator_merged(dutils.PatientBatchIterator):
     """
     creates a test generator that iterates over entire given dataset returning 1 patient per batch.
     Can be used for monitoring if cf.val_mode = 'patient_val' for a monitoring closer to actualy evaluation (done in 3D),
     if willing to accept speed-loss during training.
     :return: out_batch: dictionary containing one patient with batch_size = n_3D_patches in 3D or
     batch_size = n_2D_patches in 2D .
     """
 
     def __init__(self, cf, data):  # threads in augmenter
         super(PatientBatchIterator_merged, self).__init__(cf, data)
         self.patient_ix = 0
         self.patch_size = cf.patch_size + [1] if cf.dim == 2 else cf.patch_size
 
     def generate_train_batch(self, pid=None):
 
         if pid is None:
             pid = self.dataset_pids[self.patient_ix]
         patient = self._data[pid]
 
         data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))
         seg = np.transpose(np.load(patient['seg'], mmap_mode='r'), axes=(1, 2, 0))
 
         # pad data if smaller than patch_size seen during training.
         if np.any([data.shape[dim] < ps for dim, ps in enumerate(self.patch_size)]):
             new_shape = [np.max([data.shape[dim], self.patch_size[dim]]) for dim, ps in enumerate(self.patch_size)]
             data = dutils.pad_nd_image(data, new_shape)  # use 'return_slicer' to crop image back to original shape.
             seg = dutils.pad_nd_image(seg, new_shape)
 
         # get 3D targets for evaluation, even if network operates in 2D. 2D predictions will be merged to 3D in predictor.
         if self.cf.dim == 3 or self.cf.merge_2D_to_3D_preds:
             out_data = data[np.newaxis, np.newaxis]
             out_seg = seg[np.newaxis, np.newaxis]
             batch_3D = {'data': out_data, 'seg': out_seg}
             for o in self.cf.roi_items:
                 batch_3D[o] = np.array([patient[o]])
             converter = ConvertSegToBoundingBoxCoordinates(3, self.cf.roi_items, False, self.cf.class_specific_seg)
             batch_3D = converter(**batch_3D)
             batch_3D.update({'patient_bb_target': batch_3D['bb_target'], 'original_img_shape': out_data.shape})
             for o in self.cf.roi_items:
                 batch_3D["patient_" + o] = batch_3D[o]
 
         if self.cf.dim == 2:
             out_data = np.transpose(data, axes=(2, 0, 1))[:, np.newaxis]  # (z, c, x, y )
             out_seg = np.transpose(seg, axes=(2, 0, 1))[:, np.newaxis]
 
             batch_2D = {'data': out_data, 'seg': out_seg}
             for o in self.cf.roi_items:
                 batch_2D[o] = np.repeat(np.array([patient[o]]), out_data.shape[0], axis=0)
 
             converter = ConvertSegToBoundingBoxCoordinates(2, self.cf.roi_items, False, self.cf.class_specific_seg)
             batch_2D = converter(**batch_2D)
 
             if self.cf.merge_2D_to_3D_preds:
                 batch_2D.update({'patient_bb_target': batch_3D['patient_bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_3D[o]
             else:
                 batch_2D.update({'patient_bb_target': batch_2D['bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_2D[o]
 
         out_batch = batch_3D if self.cf.dim == 3 else batch_2D
         out_batch.update({'pid': np.array([patient['pid']] * len(out_data))})
 
         # crop patient-volume to patches of patch_size used during training. stack patches up in batch dimension.
         # in this case, 2D is treated as a special case of 3D with patch_size[z] = 1.
         if np.any([data.shape[dim] > self.patch_size[dim] for dim in range(3)]):
             patient_batch = out_batch
             patch_crop_coords_list = dutils.get_patch_crop_coords(data, self.patch_size)
             new_img_batch, new_seg_batch = [], []
 
             for cix, c in enumerate(patch_crop_coords_list):
 
                 seg_patch = seg[c[0]:c[1], c[2]: c[3], c[4]:c[5]]
                 new_seg_batch.append(seg_patch)
 
                 tmp_c_5 = c[5]
 
                 new_img_batch.append(data[c[0]:c[1], c[2]:c[3], c[4]:tmp_c_5])
 
             data = np.array(new_img_batch)[:, np.newaxis]  # (n_patches, c, x, y, z)
             seg = np.array(new_seg_batch)[:, np.newaxis]  # (n_patches, 1, x, y, z)
             if self.cf.dim == 2:
                 # all patches have z dimension 1 (slices). discard dimension
                 data = data[..., 0]
                 seg = seg[..., 0]
 
             patch_batch = {'data': data.astype('float32'), 'seg': seg.astype('uint8'),
                            'pid': np.array([patient['pid']] * data.shape[0])}
             for o in self.cf.roi_items:
                 patch_batch[o] = np.repeat(np.array([patient[o]]), len(patch_crop_coords_list), axis=0)
             # patient-wise (orig) batch info for putting the patches back together after prediction
             for o in self.cf.roi_items:
                 patch_batch["patient_" + o] = patient_batch['patient_' + o]
                 if self.cf.dim == 2:
                     # this could also be named "unpatched_2d_roi_items"
                     patch_batch["patient_" + o + "_2d"] = patient_batch[o]
             # adding patient-wise data and seg adds about 2 GB of additional RAM consumption to a batch 20x288x288
             # and enables calculating test-dice/viewing patient-wise results in test
             # remove, but also remove dice from metrics, when like to save memory
             patch_batch['patient_data'] = patient_batch['data']
             patch_batch['patient_seg'] = patient_batch['seg']
             patch_batch['patch_crop_coords'] = np.array(patch_crop_coords_list)
             patch_batch['patient_bb_target'] = patient_batch['patient_bb_target']
             if self.cf.dim == 2:
                 patch_batch['patient_bb_target_2d'] = patient_batch['bb_target']
             patch_batch['original_img_shape'] = patient_batch['original_img_shape']
 
             converter = ConvertSegToBoundingBoxCoordinates(self.cf.dim, self.cf.roi_items, False,
                                                            self.cf.class_specific_seg)
             patch_batch = converter(**patch_batch)
             out_batch = patch_batch
 
         self.patient_ix += 1
         if self.patient_ix == len(self.dataset_pids):
             self.patient_ix = 0
 
         return out_batch
 
 # single-annotator GTs
 class BatchGenerator_sa(dutils.BatchGenerator):
     """
     creates the training/validation batch generator. Samples n_batch_size patients (draws a slice from each patient if 2D)
     from the data set while maintaining foreground-class balance. Returned patches are cropped/padded to pre_crop_size.
     Actual patch_size is obtained after data augmentation.
     :param data: data dictionary as provided by 'load_dataset'.
     :param batch_size: number of patients to sample for the batch
     :return dictionary containing the batch data (b, c, x, y, (z)) / seg (b, 1, x, y, (z)) / pids / class_target
     """
 
     # noinspection PyMethodOverriding
     def balance_target_distribution(self, rater, plot=False):
         """
         :param rater: for which rater slot to generate the distribution
         :param self.targets:  dic holding {patient_specifier : patient-wise-unique ROI targets}
         :param plot: whether to plot the generated patient distributions
         :return: probability distribution over all pids. draw without replace from this.
         """
-        # get unique foreground targets per patient, assign -1 to an "empty" patient (has no foreground)
-        patient_ts = [[roi[rater] for roi in patient_rois_lst] for patient_rois_lst in self.targets.values()]
-        # assign [-1] to empty patients
-        patient_ts = [np.unique(lst) if len([t for t in lst if np.any(t>0)])>0 else [-1] for lst in patient_ts]
-        #bg_mask = np.array([np.all(lst == [-1]) for lst in patient_ts])
-        # sort out bg labels (are 0)
-        unique_ts, t_counts = np.unique([t for lst in patient_ts for t in lst if t>0], return_counts=True)
-        t_probs = t_counts.sum() / t_counts
-        t_probs /= t_probs.sum()
-        t_probs = {t : t_probs[ix] for ix, t in enumerate(unique_ts)}
-        t_probs[-1] = 0.
-        t_probs[0] = 0.
-        # fail if balance target is not a number (i.e., a vector)
-        p_probs = np.array([ max([t_probs[t] for t in lst]) for lst in patient_ts ])
-        #normalize
-        p_probs /= p_probs.sum()
+        unique_ts = np.unique([v[rater] for pat in self.targets.values() for v in pat])
+        sample_stats = pd.DataFrame(columns=[str(ix) + suffix for ix in unique_ts for suffix in ["", "_bg"]],
+                                         index=list(self.targets.keys()))
+        for pid in sample_stats.index:
+            for targ in unique_ts:
+                fg_count = 0 if len(self.targets[pid]) == 0 else np.count_nonzero(self.targets[pid][:, rater] == targ)
+                sample_stats.loc[pid, str(targ)] = int(fg_count > 0)
+                sample_stats.loc[pid, str(targ) + "_bg"] = int(fg_count == 0)
+
+        target_stats = sample_stats.agg(
+            ("sum", lambda col: col.sum() / len(self._data)), axis=0, sort=False).rename({"<lambda>": "relative"})
+
+        anchor = 1. - target_stats.loc["relative"].iloc[0]
+        fg_bg_weights = anchor / target_stats.loc["relative"]
+        cum_weights = anchor * len(fg_bg_weights)
+        fg_bg_weights /= cum_weights
+
+        p_probs = sample_stats.apply(self.sample_targets_to_weights, args=(fg_bg_weights,), axis=1).sum(axis=1)
+        p_probs = p_probs / p_probs.sum()
+        if plot:
+            print("Rater: {}. Applying class-weights:\n {}".format(rater, fg_bg_weights))
+        if len(sample_stats.columns) == 2:
+            # assert that probs are calc'd correctly:
+            # (p_probs * sample_stats["1"]).sum() == (p_probs * sample_stats["1_bg"]).sum()
+            # only works if one label per patient (multi-label expectations depend on multi-label occurences).
+            for rater in range(self.rater_bsize):
+                expectations = []
+                for targ in sample_stats.columns:
+                    expectations.append((p_probs[rater] * sample_stats[targ]).sum())
+                assert np.allclose(expectations, expectations[0], atol=1e-4), "expectation values for fgs/bgs: {}".format(
+                    expectations)
 
         if plot:
             plg.plot_batchgen_distribution(self.cf, self.dataset_pids, p_probs, self.balance_target,
-                                           out_file=os.path.join(self.cf.plot_dir,
+                                           out_file=os.path.join(self.plot_dir,
                                                                  "train_gen_distr_"+str(self.cf.fold)+"_rater"+str(rater)+".png"))
-        return p_probs, unique_ts
+        return p_probs, unique_ts, sample_stats
 
 
 
-    def __init__(self, cf, data):
+    def __init__(self, cf, data, name="train"):
         super(BatchGenerator_sa, self).__init__(cf, data)
-
+        self.name = name
         self.crop_margin = np.array(self.cf.patch_size) / 8.  # min distance of ROI center to edge of cropped_patch.
         self.p_fg = 0.5
         self.empty_samples_max_ratio = 0.6
 
         self.random_count = int(cf.batch_random_ratio * cf.batch_size)
 
         self.rater_bsize = 4
         unique_ts_total = set()
-        self.rater_p_probs = []
-        for r in range(self.rater_bsize):
-            p_probs, unique_ts = self.balance_target_distribution(r, plot=True)
-            self.rater_p_probs.append(p_probs)
+        self.p_probs = []
+        self.sample_stats = []
+        p = Pool(processes=cf.n_workers)
+        mp_res = p.starmap(self.balance_target_distribution, [(r, name=="train") for r in range(self.rater_bsize)])
+        p.close()
+        p.join()
+
+        for r, res in enumerate(mp_res):
+            p_probs, unique_ts, sample_stats = res
+            self.p_probs.append(p_probs)
+            self.sample_stats.append(sample_stats)
             unique_ts_total.update(unique_ts)
+
         self.unique_ts = sorted(list(unique_ts_total))
-        self.stats = {"roi_counts": np.zeros((len(self.unique_ts),), dtype='uint32'), "empty_samples_count": 0}
 
 
     def generate_train_batch(self):
 
         rater = np.random.randint(self.rater_bsize)
 
         # samples patients towards equilibrium of foreground classes on a roi-level (after randomly sampling the ratio batch_random_ratio).
         # random patients
         batch_patient_ids = list(np.random.choice(self.dataset_pids, size=self.random_count, replace=False))
         # target-balanced patients
         batch_patient_ids += list(np.random.choice(self.dataset_pids, size=self.batch_size-self.random_count, replace=False,
-                                             p=self.rater_p_probs[rater]))
+                                             p=self.p_probs[rater]))
 
         batch_data, batch_segs, batch_pids, batch_patient_labels = [], [], [], []
         batch_roi_items = {name: [] for name in self.cf.roi_items}
         # record roi count of classes in batch
-        batch_roi_counts, empty_samples_count = np.zeros((len(self.unique_ts),), dtype='uint32'), 0
+        batch_roi_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
+        batch_empty_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
         # empty count for full bg samples (empty slices in 2D/patients in 3D)
 
 
         for sample in range(self.batch_size):
 
             patient = self._data[batch_patient_ids[sample]]
 
             patient_balance_ts = np.array([roi[rater] for roi in patient[self.balance_target]])
             data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))[np.newaxis]
             seg = np.load(patient['seg'], mmap_mode='r')
             seg = np.transpose(seg[list(seg.keys())[0]][rater], axes=(1, 2, 0))
             batch_pids.append(patient['pid'])
             (c, y, x, z) = data.shape
 
             if self.cf.dim == 2:
 
                 elig_slices, choose_fg = [], False
                 if len(patient['fg_slices']) > 0:
-                    if empty_samples_count / self.batch_size >= self.empty_samples_max_ratio or np.random.rand(
-                            1) <= self.p_fg:
+                    if np.all(batch_empty_counts / self.batch_size >= self.empty_samples_max_ratio) or \
+                            np.random.rand(1) <= self.p_fg:
                         # fg is to be picked
                         for tix in np.argsort(batch_roi_counts):
                             # pick slices of patient that have roi of sought-for target
                             # np.unique(seg[...,sl_ix][seg[...,sl_ix]>0]) gives roi_ids (numbering) of rois in slice sl_ix
                             elig_slices = [sl_ix for sl_ix in np.arange(z) if np.count_nonzero(
                                 patient_balance_ts[np.unique(seg[..., sl_ix][seg[..., sl_ix] > 0]) - 1] ==
                                 self.unique_ts[tix]) > 0]
                             if len(elig_slices) > 0:
                                 choose_fg = True
                                 break
                     else:
                         # pick bg
                         elig_slices = np.setdiff1d(np.arange(z), patient['fg_slices'][rater])
 
                 if len(elig_slices) > 0:
                     sl_pick_ix = np.random.choice(elig_slices, size=None)
                 else:
                     sl_pick_ix = np.random.choice(z, size=None)
 
                 data = data[..., sl_pick_ix]
                 seg = seg[..., sl_pick_ix]
 
             # pad data if smaller than pre_crop_size.
             if np.any([data.shape[dim + 1] < ps for dim, ps in enumerate(self.cf.pre_crop_size)]):
                 new_shape = [np.max([data.shape[dim + 1], ps]) for dim, ps in enumerate(self.cf.pre_crop_size)]
                 data = dutils.pad_nd_image(data, new_shape, mode='constant')
                 seg = dutils.pad_nd_image(seg, new_shape, mode='constant')
 
             # crop patches of size pre_crop_size, while sampling patches containing foreground with p_fg.
             crop_dims = [dim for dim, ps in enumerate(self.cf.pre_crop_size) if data.shape[dim + 1] > ps]
             if len(crop_dims) > 0:
                 if self.cf.dim == 3:
-                    choose_fg = (empty_samples_count/self.batch_size>=self.empty_samples_max_ratio) or np.random.rand(1) <= self.p_fg
+                    choose_fg = np.all(batch_empty_counts / self.batch_size >= self.empty_samples_max_ratio) or \
+                                np.random.rand(1) <= self.p_fg
                 if choose_fg and np.any(seg):
                     available_roi_ids = np.unique(seg[seg>0])
                     assert np.all(patient_balance_ts[available_roi_ids-1]>0), "trying to choose roi with rating 0"
                     for tix in np.argsort(batch_roi_counts):
                         elig_roi_ids = available_roi_ids[ patient_balance_ts[available_roi_ids-1] == self.unique_ts[tix] ]
                         if len(elig_roi_ids)>0:
                             seg_ics = np.argwhere(seg == np.random.choice(elig_roi_ids, size=None))
                             roi_anchor_pixel = seg_ics[np.random.choice(seg_ics.shape[0], size=None)]
                             break
 
                     assert seg[tuple(roi_anchor_pixel)] > 0, "roi_anchor_pixel not inside roi: {}, pb_ts {}, elig ids {}".format(tuple(roi_anchor_pixel), patient_balance_ts, elig_roi_ids)
                     # sample the patch center coords. constrained by edges of images - pre_crop_size /2. And by
                     # distance to the desired ROI < patch_size /2.
                     # (here final patch size to account for center_crop after data augmentation).
                     sample_seg_center = {}
                     for ii in crop_dims:
                         low = np.max((self.cf.pre_crop_size[ii]//2, roi_anchor_pixel[ii] - (self.cf.patch_size[ii]//2 - self.crop_margin[ii])))
                         high = np.min((data.shape[ii + 1] - self.cf.pre_crop_size[ii]//2,
                                        roi_anchor_pixel[ii] + (self.cf.patch_size[ii]//2 - self.crop_margin[ii])))
                         # happens if lesion on the edge of the image. dont care about roi anymore,
                         # just make sure pre-crop is inside image.
                         if low >= high:
                             low = data.shape[ii + 1] // 2 - (data.shape[ii + 1] // 2 - self.cf.pre_crop_size[ii] // 2)
                             high = data.shape[ii + 1] // 2 + (data.shape[ii + 1] // 2 - self.cf.pre_crop_size[ii] // 2)
                         sample_seg_center[ii] = np.random.randint(low=low, high=high)
                 else:
                     # not guaranteed to be empty. probability of emptiness depends on the data.
                     sample_seg_center = {ii: np.random.randint(low=self.cf.pre_crop_size[ii]//2,
                                                            high=data.shape[ii + 1] - self.cf.pre_crop_size[ii]//2) for ii in crop_dims}
 
                 for ii in crop_dims:
                     min_crop = int(sample_seg_center[ii] - self.cf.pre_crop_size[ii] // 2)
                     max_crop = int(sample_seg_center[ii] + self.cf.pre_crop_size[ii] // 2)
                     data = np.take(data, indices=range(min_crop, max_crop), axis=ii + 1)
                     seg = np.take(seg, indices=range(min_crop, max_crop), axis=ii)
 
             batch_data.append(data)
             batch_segs.append(seg[np.newaxis])
             for o in batch_roi_items: #after loop, holds every entry of every batchpatient per roi-item
                 batch_roi_items[o].append([roi[rater] for roi in patient[o]])
 
             if self.cf.dim == 3:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient_balance_ts == self.unique_ts[tix])
+                    non_zero = np.count_nonzero(patient[self.balance_target] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero==0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
             elif self.cf.dim == 2:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient_balance_ts[np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
-            if not np.any(seg):
-                empty_samples_count += 1
+                    non_zero = np.count_nonzero(patient[self.balance_target][np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero == 0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
 
 
         data = np.array(batch_data).astype('float16')
         seg = np.array(batch_segs).astype('uint8')
         batch = {'data': data, 'seg': seg, 'pid': batch_pids, 'rater_id': rater,
-                'roi_counts':batch_roi_counts, 'empty_samples_count': empty_samples_count}
+                'roi_counts':batch_roi_counts, 'empty_counts': batch_empty_counts}
         for key,val in batch_roi_items.items(): #extend batch dic by roi-wise items (obs, class ids, regression vectors...)
             batch[key] = np.array(val)
 
         return batch
 
 class PatientBatchIterator_sa(dutils.PatientBatchIterator):
     """
     creates a test generator that iterates over entire given dataset returning 1 patient per batch.
     Can be used for monitoring if cf.val_mode = 'patient_val' for a monitoring closer to actual evaluation (done in 3D),
     if willing to accept speed loss during training.
     :return: out_batch: dictionary containing one patient with batch_size = n_3D_patches in 3D or
     batch_size = n_2D_patches in 2D .
 
     This is the data & gt loader for the 4-fold single-annotator GTs: each data input has separate annotations of 4 annotators.
     the way the pipeline is currently setup, the single-annotator GTs are only used if training with validation mode
     val_patient; during testing the Iterator with the merged GTs is used.
     # todo mode val_patient not implemented yet (since very slow). would need to sample from all available rater GTs.
     """
     def __init__(self, cf, data): #threads in augmenter
         super(PatientBatchIterator_sa, self).__init__(cf, data)
         self.cf = cf
         self.patient_ix = 0
         self.dataset_pids = list(self._data.keys())
         self.patch_size =  cf.patch_size+[1] if cf.dim==2 else cf.patch_size
 
         self.rater_bsize = 4
 
 
     def generate_train_batch(self, pid=None):
 
         if pid is None:
             pid = self.dataset_pids[self.patient_ix]
         patient = self._data[pid]
 
         data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))
         # all gts are 4-fold and npz!
         seg = np.load(patient['seg'], mmap_mode='r')
         seg = np.transpose(seg[list(seg.keys())[0]], axes=(0, 2, 3, 1))
 
         # pad data if smaller than patch_size seen during training.
         if np.any([data.shape[dim] < ps for dim, ps in enumerate(self.patch_size)]):
             new_shape = [np.max([data.shape[dim], self.patch_size[dim]]) for dim, ps in enumerate(self.patch_size)]
             data = dutils.pad_nd_image(data, new_shape) # use 'return_slicer' to crop image back to original shape.
             seg = dutils.pad_nd_image(seg, new_shape)
 
         # get 3D targets for evaluation, even if network operates in 2D. 2D predictions will be merged to 3D in predictor.
         if self.cf.dim == 3 or self.cf.merge_2D_to_3D_preds:
             out_data = data[np.newaxis, np.newaxis]
             out_seg = seg[:, np.newaxis]
             batch_3D = {'data': out_data, 'seg': out_seg}
 
             for item in self.cf.roi_items:
                 batch_3D[item] = []
             for r in range(self.rater_bsize):
                 for item in self.cf.roi_items:
                     batch_3D[item].append(np.array([roi[r] for roi in patient[item]]))
 
             converter = ConvertSegToBoundingBoxCoordinates(3, self.cf.roi_items, False, self.cf.class_specific_seg)
             batch_3D = converter(**batch_3D)
             batch_3D.update({'patient_bb_target': batch_3D['bb_target'], 'original_img_shape': out_data.shape})
             for o in self.cf.roi_items:
                 batch_3D["patient_" + o] = batch_3D[o]
 
         if self.cf.dim == 2:
             out_data = np.transpose(data, axes=(2, 0, 1))[:, np.newaxis]  # (z, c, y, x )
             out_seg = np.transpose(seg, axes=(0, 3, 1, 2))[:, :, np.newaxis] # (n_raters, z, 1, y,x)
 
             batch_2D = {'data': out_data}
 
             for item in ["seg", "bb_target"]+self.cf.roi_items:
                 batch_2D[item] = []
 
             converter = ConvertSegToBoundingBoxCoordinates(2, self.cf.roi_items, False, self.cf.class_specific_seg)
             for r in range(self.rater_bsize):
                 tmp_batch = {"seg": out_seg[r]}
                 for item in self.cf.roi_items:
                     tmp_batch[item] = np.repeat(np.array([[roi[r] for roi in patient[item]]]), out_data.shape[0], axis=0)
                 tmp_batch = converter(**tmp_batch)
                 for item in ["seg", "bb_target"]+self.cf.roi_items:
                     batch_2D[item].append(tmp_batch[item])
             # for item in ["seg", "bb_target"]+self.cf.roi_items:
             #     batch_2D[item] = np.array(batch_2D[item])
 
             if self.cf.merge_2D_to_3D_preds:
                 batch_2D.update({'patient_bb_target': batch_3D['patient_bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_3D[o]
             else:
                 batch_2D.update({'patient_bb_target': batch_2D['bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_2D[o]
 
         out_batch = batch_3D if self.cf.dim == 3 else batch_2D
         out_batch.update({'pid': np.array([patient['pid']] * out_data.shape[0])})
 
         # crop patient-volume to patches of patch_size used during training. stack patches up in batch dimension.
         # in this case, 2D is treated as a special case of 3D with patch_size[z] = 1.
         if np.any([data.shape[dim] > self.patch_size[dim] for dim in range(3)]):
             patient_batch = out_batch
             patch_crop_coords_list = dutils.get_patch_crop_coords(data, self.patch_size)
             new_img_batch  = []
             new_seg_batch = []
 
             for cix, c in enumerate(patch_crop_coords_list):
                 seg_patch = seg[:, c[0]:c[1], c[2]: c[3], c[4]:c[5]]
                 new_seg_batch.append(seg_patch)
                 tmp_c_5 = c[5]
 
                 new_img_batch.append(data[c[0]:c[1], c[2]:c[3], c[4]:tmp_c_5])
 
             data = np.array(new_img_batch)[:, np.newaxis] # (n_patches, c, x, y, z)
             seg = np.transpose(np.array(new_seg_batch), axes=(1,0,2,3,4))[:,:,np.newaxis] # (n_raters, n_patches, x, y, z)
 
             if self.cf.dim == 2:
                 # all patches have z dimension 1 (slices). discard dimension
                 data = data[..., 0]
                 seg = seg[..., 0]
 
             patch_batch = {'data': data.astype('float32'),
                            'pid': np.array([patient['pid']] * data.shape[0])}
             # for o in self.cf.roi_items:
             #     patch_batch[o] = np.repeat(np.array([patient[o]]), len(patch_crop_coords_list), axis=0)
 
             converter = ConvertSegToBoundingBoxCoordinates(self.cf.dim, self.cf.roi_items, False,
                                                            self.cf.class_specific_seg)
 
             for item in ["seg", "bb_target"]+self.cf.roi_items:
                 patch_batch[item] = []
             # coord_list = [np.min(seg_ixs[:, 1]) - 1, np.min(seg_ixs[:, 2]) - 1, np.max(seg_ixs[:, 1]) + 1,
             # IndexError: index 2 is out of bounds for axis 1 with size 2
             for r in range(self.rater_bsize):
                 tmp_batch = {"seg": seg[r]}
                 for item in self.cf.roi_items:
                     tmp_batch[item] = np.repeat(np.array([[roi[r] for roi in patient[item]]]), len(patch_crop_coords_list), axis=0)
                 tmp_batch = converter(**tmp_batch)
                 for item in ["seg", "bb_target"]+self.cf.roi_items:
                     patch_batch[item].append(tmp_batch[item])
 
             # patient-wise (orig) batch info for putting the patches back together after prediction
             for o in self.cf.roi_items:
                 patch_batch["patient_" + o] = patient_batch['patient_'+o]
                 if self.cf.dim==2:
                     # this could also be named "unpatched_2d_roi_items"
                     patch_batch["patient_"+o+"_2d"] = patient_batch[o]
             # adding patient-wise data and seg adds about 2 GB of additional RAM consumption to a batch 20x288x288
             # and enables calculating test-dice/viewing patient-wise results in test
             # remove, but also remove dice from metrics, if you like to save memory
             patch_batch['patient_data'] =  patient_batch['data']
             patch_batch['patient_seg'] = patient_batch['seg']
             patch_batch['patch_crop_coords'] = np.array(patch_crop_coords_list)
             patch_batch['patient_bb_target'] = patient_batch['patient_bb_target']
             if self.cf.dim==2:
                 patch_batch['patient_bb_target_2d'] = patient_batch['bb_target']
             patch_batch['original_img_shape'] = patient_batch['original_img_shape']
 
             out_batch = patch_batch
 
         self.patient_ix += 1
         if self.patient_ix == len(self.dataset_pids):
             self.patient_ix = 0
 
         return out_batch
 
 
 def create_data_gen_pipeline(cf, patient_data, is_training=True):
     """ create multi-threaded train/val/test batch generation and augmentation pipeline.
     :param cf: configs object.
     :param patient_data: dictionary containing one dictionary per patient in the train/test subset.
     :param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
     :return: multithreaded_generator
     """
-
-    data_gen = BatchGenerator_merged(cf, patient_data) if cf.training_gts=='merged' else BatchGenerator_sa(cf, patient_data)
+    BG_name = "train" if is_training else "val"
+    data_gen = BatchGenerator_merged(cf, patient_data, name=BG_name) if cf.training_gts=='merged' else \
+        BatchGenerator_sa(cf, patient_data, name=BG_name)
 
     # add transformations to pipeline.
     my_transforms = []
     if is_training:
         if cf.da_kwargs["mirror"]:
             mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
             my_transforms.append(mirror_transform)
         spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                              patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                              do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                              alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                              do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                              angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                              do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                              random_crop=cf.da_kwargs['random_crop'])
 
         my_transforms.append(spatial_transform)
     else:
         my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))
 
     if cf.create_bounding_box_targets:
         my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, cf.roi_items, False, cf.class_specific_seg))
     all_transforms = Compose(my_transforms)
 
     multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
     return multithreaded_generator
 
 def get_train_generators(cf, logger,  data_statistics=True):
     """
     wrapper function for creating the training batch generator pipeline. returns the train/val generators.
     selects patients according to cv folds (generated by first run/fold of experiment):
     splits the data into n-folds, where 1 split is used for val, 1 split for testing and the rest for training. (inner loop test set)
     If cf.held_out_test_set is True, adds the test split to the training data.
     """
     dataset = Dataset(cf, logger)
 
     dataset.init_FoldGenerator(cf.seed, cf.n_cv_splits)
     dataset.generate_splits(check_file=os.path.join(cf.exp_dir, 'fold_ids.pickle'))
     set_splits = dataset.fg.splits
 
     test_ids, val_ids = set_splits.pop(cf.fold), set_splits.pop(cf.fold - 1)
     train_ids = np.concatenate(set_splits, axis=0)
 
     if cf.held_out_test_set:
         train_ids = np.concatenate((train_ids, test_ids), axis=0)
         test_ids = []
 
     train_data = {k: v for (k, v) in dataset.data.items() if k in train_ids}
     val_data = {k: v for (k, v) in dataset.data.items() if k in val_ids}
 
     logger.info("data set loaded with: {} train / {} val / {} test patients".format(len(train_ids), len(val_ids),
                                                                                     len(test_ids)))
     if data_statistics:
         dataset.calc_statistics(subsets={"train": train_ids, "val": val_ids, "test": test_ids},
                                 plot_dir=os.path.join(cf.plot_dir,"dataset"))
 
     batch_gen = {}
     batch_gen['train'] = create_data_gen_pipeline(cf, train_data, is_training=True)
     batch_gen['val_sampling'] = create_data_gen_pipeline(cf, val_data, is_training=False)
     if cf.val_mode == 'val_patient':
         assert cf.training_gts == 'merged', 'val_patient not yet implemented for sa gts'
         batch_gen['val_patient'] = PatientBatchIterator_merged(cf, val_data) if cf.training_gts=='merged' \
             else PatientBatchIterator_sa(cf, val_data)
         batch_gen['n_val'] = len(val_data) if cf.max_val_patients=="all" else min(len(val_data), cf.max_val_patients)
     else:
         batch_gen['n_val'] = cf.num_val_batches
 
     return batch_gen
 
 def get_test_generator(cf, logger):
     """
     wrapper function for creating the test batch generator pipeline.
     selects patients according to cv folds (generated by first run/fold of experiment)
     If cf.held_out_test_set is True, gets the data from an external folder instead.
     """
     if cf.held_out_test_set:
         sourcedir = cf.test_data_sourcedir
         test_ids = None
     else:
         sourcedir = None
         with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'rb') as handle:
             set_splits = pickle.load(handle)
         test_ids = set_splits[cf.fold]
 
     test_data = Dataset(cf, logger, subset_ids=test_ids, data_sourcedir=sourcedir, mode="test").data
     logger.info("data set loaded with: {} test patients".format(len(test_ids)))
     batch_gen = {}
     batch_gen['test'] = PatientBatchIterator_merged(cf, test_data)
     batch_gen['n_test'] = len(test_ids) if cf.max_test_patients == "all" else min(cf.max_test_patients, len(test_ids))
     return batch_gen
 
 
 if __name__ == "__main__":
     import sys
     sys.path.append('../')
     import plotting as plg
     import utils.exp_utils as utils
     from configs import Configs
 
     cf = Configs()
     cf.batch_size = 3
     #dataset_path = os.path.dirname(os.path.realpath(__file__))
     #exp_path = os.path.join(dataset_path, "experiments/dev")
     #cf = utils.prep_exp(dataset_path, exp_path, server_env=False, use_stored_settings=False, is_training=True)
     cf.created_fold_id_pickle = False
     total_stime = time.time()
     times = {}
 
     # cf.server_env = True
     # cf.data_dir = "experiments/dev_data"
 
     # dataset = Dataset(cf)
     # patient = dataset['Master_00018']
     cf.exp_dir = "experiments/dev/"
     cf.plot_dir = cf.exp_dir + "plots"
     os.makedirs(cf.exp_dir, exist_ok=True)
     cf.fold = 0
     logger = utils.get_logger(cf.exp_dir)
     gens = get_train_generators(cf, logger)
     train_loader = gens['train']
 
 
 
     for i in range(1):
         stime = time.time()
         #ex_batch = next(train_loader)
         print("train batch", i)
         times["train_batch"] = time.time() - stime
         #plg.view_batch(cf, ex_batch, out_file="experiments/dev/dev_exbatch.png", show_gt_labels=True)
     #
     # # with open(os.path.join(cf.exp_dir, "fold_"+str(cf.fold), "BatchGenerator_stats.txt"), mode="w") as file:
     # #    train_loader.generator.print_stats(logger, file)
     #
     val_loader = gens['val_sampling']
     stime = time.time()
     ex_batch = next(val_loader)
     times["val_batch"] = time.time() - stime
     stime = time.time()
     #plg.view_batch(cf, ex_batch, out_file="experiments/dev/dev_exvalbatch.png", show_gt_labels=True, plot_mods=False,
     #               show_info=False)
     times["val_plot"] = time.time() - stime
     #
     test_loader = get_test_generator(cf, logger)["test"]
     stime = time.time()
     ex_batch = test_loader.generate_train_batch()
     times["test_batch"] = time.time() - stime
     stime = time.time()
-    plg.view_batch(cf, ex_batch, show_gt_labels=True, out_file="experiments/dev/dev_expatchbatch.png")#, sample_picks=[0,1,2,3])
+    plg.view_batch(cf, ex_batch, show_gt_labels=True, out_file="experiments/dev/dev_expatchbatch.png", get_time=False)#, sample_picks=[0,1,2,3])
     times["test_patchbatch_plot"] = time.time() - stime
 
     # ex_batch['data'] = ex_batch['patient_data']
     # ex_batch['seg'] = ex_batch['patient_seg']
     # ex_batch['bb_target'] = ex_batch['patient_bb_target']
     # for item in cf.roi_items:
     #     ex_batch[]
     # stime = time.time()
     # #ex_batch = next(test_loader)
     # ex_batch = next(test_loader)
     # plg.view_batch(cf, ex_batch, show_gt_labels=False, show_gt_boxes=True, patient_items=True,# vol_slice_picks=[146,148, 218,220],
     #                 out_file="experiments/dev/dev_expatientbatch.png")  # , sample_picks=[0,1,2,3])
     # times["test_patient_batch_plot"] = time.time() - stime
 
 
 
     print("Times recorded throughout:")
     for (k, v) in times.items():
         print(k, "{:.2f}".format(v))
 
     mins, secs = divmod((time.time() - total_stime), 60)
     h, mins = divmod(mins, 60)
     t = "{:d}h:{:02d}m:{:02d}s".format(int(h), int(mins), int(secs))
     print("{} total runtime: {}".format(os.path.split(__file__)[1], t))
diff --git a/datasets/toy/data_loader.py b/datasets/toy/data_loader.py
index 78fb673..e77b3db 100644
--- a/datasets/toy/data_loader.py
+++ b/datasets/toy/data_loader.py
@@ -1,582 +1,594 @@
 #!/usr/bin/env python
 # Copyright 2019 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
 #
 # 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 sys
 sys.path.append('../') # works on cluster indep from where sbatch job is started
 import plotting as plg
 
 import numpy as np
 import os
 from multiprocessing import Lock
 from collections import OrderedDict
 import pandas as pd
 import pickle
 import time
 
 # batch generator tools from https://github.com/MIC-DKFZ/batchgenerators
 from batchgenerators.transforms.spatial_transforms import MirrorTransform as Mirror
 from batchgenerators.transforms.abstract_transforms import Compose
 from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
 from batchgenerators.transforms.spatial_transforms import SpatialTransform
 from batchgenerators.transforms.crop_and_pad_transforms import CenterCropTransform
 
 sys.path.append(os.path.dirname(os.path.realpath(__file__)))
 import utils.dataloader_utils as dutils
 from utils.dataloader_utils import ConvertSegToBoundingBoxCoordinates
 
 
 def load_obj(file_path):
     with open(file_path, 'rb') as handle:
         return pickle.load(handle)
 
 class Dataset(dutils.Dataset):
     r""" Load a dict holding memmapped arrays and clinical parameters for each patient,
     evtly subset of those.
         If server_env: copy and evtly unpack (npz->npy) data in cf.data_rootdir to
         cf.data_dir.
     :param cf: config file
     :param folds: number of folds out of @params n_cv folds to include
     :param n_cv: number of total folds
     :return: dict with imgs, segs, pids, class_labels, observables
     """
 
     def __init__(self, cf, logger, subset_ids=None, data_sourcedir=None, mode='train'):
         super(Dataset,self).__init__(cf, data_sourcedir=data_sourcedir)
 
         load_exact_gts = (mode=='test' or cf.val_mode=="val_patient") and self.cf.test_against_exact_gt
 
         p_df = pd.read_pickle(os.path.join(self.data_dir, cf.info_df_name))
 
         if subset_ids is not None:
             p_df = p_df[p_df.pid.isin(subset_ids)]
             logger.info('subset: selected {} instances from df'.format(len(p_df)))
 
         pids = p_df.pid.tolist()
         #evtly copy data from data_sourcedir to data_dest
         if cf.server_env and not hasattr(cf, "data_dir"):
             file_subset = [os.path.join(self.data_dir, '{}.*'.format(pid)) for pid in pids]
             file_subset += [os.path.join(self.data_dir, '{}_seg.*'.format(pid)) for pid in pids]
             file_subset += [cf.info_df_name]
             if load_exact_gts:
                 file_subset += [os.path.join(self.data_dir, '{}_exact_seg.*'.format(pid)) for pid in pids]
             self.copy_data(cf, file_subset=file_subset)
 
         img_paths = [os.path.join(self.data_dir, '{}.npy'.format(pid)) for pid in pids]
         seg_paths = [os.path.join(self.data_dir, '{}_seg.npy'.format(pid)) for pid in pids]
         if load_exact_gts:
             exact_seg_paths = [os.path.join(self.data_dir, '{}_exact_seg.npy'.format(pid)) for pid in pids]
 
         class_targets = p_df['class_ids'].tolist()
         rg_targets = p_df['regression_vectors'].tolist()
         if load_exact_gts:
             exact_rg_targets = p_df['undistorted_rg_vectors'].tolist()
         fg_slices = p_df['fg_slices'].tolist()
 
         self.data = OrderedDict()
         for ix, pid in enumerate(pids):
             self.data[pid] = {'data': img_paths[ix], 'seg': seg_paths[ix], 'pid': pid,
                               'fg_slices': np.array(fg_slices[ix])}
             if load_exact_gts:
                 self.data[pid]['exact_seg'] = exact_seg_paths[ix]
             if 'class' in self.cf.prediction_tasks:
                 self.data[pid]['class_targets'] = np.array(class_targets[ix], dtype='uint8')
             else:
                 self.data[pid]['class_targets'] = np.ones_like(np.array(class_targets[ix]), dtype='uint8')
             if load_exact_gts:
                 self.data[pid]['exact_class_targets'] = self.data[pid]['class_targets']
             if any(['regression' in task for task in self.cf.prediction_tasks]):
                 self.data[pid]['regression_targets'] = np.array(rg_targets[ix], dtype='float16')
                 self.data[pid]["rg_bin_targets"] = np.array([cf.rg_val_to_bin_id(v) for v in rg_targets[ix]], dtype='uint8')
                 if load_exact_gts:
                     self.data[pid]['exact_regression_targets'] = np.array(exact_rg_targets[ix], dtype='float16')
                     self.data[pid]["exact_rg_bin_targets"] = np.array([cf.rg_val_to_bin_id(v) for v in exact_rg_targets[ix]],
                                                                 dtype='uint8')
 
 
         cf.roi_items = cf.observables_rois[:]
         cf.roi_items += ['class_targets']
         if any(['regression' in task for task in self.cf.prediction_tasks]):
             cf.roi_items += ['regression_targets']
             cf.roi_items += ['rg_bin_targets']
 
         self.set_ids = np.array(list(self.data.keys()))
         self.df = None
 
 class BatchGenerator(dutils.BatchGenerator):
     """
     creates the training/validation batch generator. Samples n_batch_size patients (draws a slice from each patient if 2D)
     from the data set while maintaining foreground-class balance. Returned patches are cropped/padded to pre_crop_size.
     Actual patch_size is obtained after data augmentation.
     :param data: data dictionary as provided by 'load_dataset'.
     :param batch_size: number of patients to sample for the batch
     :return dictionary containing the batch data (b, c, x, y, (z)) / seg (b, 1, x, y, (z)) / pids / class_target
     """
     def __init__(self, cf, data, sample_pids_w_replace=True, max_batches=None, raise_stop_iteration=False, seed=0):
         super(BatchGenerator, self).__init__(cf, data, sample_pids_w_replace=sample_pids_w_replace,
                                              max_batches=max_batches, raise_stop_iteration=raise_stop_iteration,
                                              seed=seed)
 
         self.chans = cf.channels if cf.channels is not None else np.index_exp[:]
         assert hasattr(self.chans, "__iter__"), "self.chans has to be list-like to maintain dims when slicing"
 
         self.crop_margin = np.array(self.cf.patch_size) / 8.  # min distance of ROI center to edge of cropped_patch.
         self.p_fg = 0.5
         self.empty_samples_max_ratio = 0.6
 
         self.balance_target_distribution(plot=sample_pids_w_replace)
-        self.stats = {"roi_counts": np.zeros((len(self.unique_ts),), dtype='uint32'), "empty_samples_count": 0}
 
     def generate_train_batch(self):
         # everything done in here is per batch
         # print statements in here get confusing due to multithreading
 
         batch_pids = self.get_batch_pids()
 
         batch_data, batch_segs, batch_patient_targets = [], [], []
         batch_roi_items = {name: [] for name in self.cf.roi_items}
-        # record roi count of classes in batch
-        # empty count for full bg samples (empty slices in 2D/patients in 3D) in slot num_classes (last)
-        batch_roi_counts, empty_samples_count = np.zeros((len(self.unique_ts),), dtype='uint32'), 0
+        # record roi count and empty count of classes in batch
+        # empty count for no presence of resp. class in whole sample (empty slices in 2D/patients in 3D)
+        batch_roi_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
+        batch_empty_counts = np.zeros((len(self.unique_ts),), dtype='uint32')
 
-        for b in range(self.batch_size):
+        for b in range(len(batch_pids)):
             patient = self._data[batch_pids[b]]
 
             data = np.load(patient['data'], mmap_mode='r').astype('float16')[np.newaxis]
             seg =  np.load(patient['seg'], mmap_mode='r').astype('uint8')
 
             (c, y, x, z) = data.shape
             if self.cf.dim == 2:
                 elig_slices, choose_fg = [], False
                 if len(patient['fg_slices']) > 0:
-                    if empty_samples_count / self.batch_size >= self.empty_samples_max_ratio or np.random.rand(
+                    if np.all(batch_empty_counts / self.batch_size >= self.empty_samples_max_ratio) or np.random.rand(
                             1) <= self.p_fg:
                         # fg is to be picked
                         for tix in np.argsort(batch_roi_counts):
                             # pick slices of patient that have roi of sought-for target
                             # np.unique(seg[...,sl_ix][seg[...,sl_ix]>0]) gives roi_ids (numbering) of rois in slice sl_ix
                             elig_slices = [sl_ix for sl_ix in np.arange(z) if np.count_nonzero(
                                 patient[self.balance_target][np.unique(seg[..., sl_ix][seg[..., sl_ix] > 0]) - 1] ==
                                 self.unique_ts[tix]) > 0]
                             if len(elig_slices) > 0:
                                 choose_fg = True
                                 break
                     else:
                         # pick bg
                         elig_slices = np.setdiff1d(np.arange(z), patient['fg_slices'])
                 if len(elig_slices) > 0:
                     sl_pick_ix = np.random.choice(elig_slices, size=None)
                 else:
                     sl_pick_ix = np.random.choice(z, size=None)
                 data = data[..., sl_pick_ix]
                 seg = seg[..., sl_pick_ix]
 
             spatial_shp = data[0].shape
             assert spatial_shp == seg.shape, "spatial shape incongruence betw. data and seg"
             if np.any([spatial_shp[ix] < self.cf.pre_crop_size[ix] for ix in range(len(spatial_shp))]):
                 new_shape = [np.max([spatial_shp[ix], self.cf.pre_crop_size[ix]]) for ix in range(len(spatial_shp))]
                 data = dutils.pad_nd_image(data, (len(data), *new_shape))
                 seg = dutils.pad_nd_image(seg, new_shape)
 
             # eventual cropping to pre_crop_size: sample pixel from random ROI and shift center,
             # if possible, to that pixel, so that img still contains ROI after pre-cropping
             dim_cropflags = [spatial_shp[i] > self.cf.pre_crop_size[i] for i in range(len(spatial_shp))]
             if np.any(dim_cropflags):
                 # sample pixel from random ROI and shift center, if possible, to that pixel
                 if self.cf.dim==3:
-                    choose_fg = (empty_samples_count/self.batch_size>=self.empty_samples_max_ratio) or np.random.rand(1) <= self.p_fg
+                    choose_fg = np.any(batch_empty_counts/self.batch_size>=self.empty_samples_max_ratio) or \
+                                np.random.rand(1) <= self.p_fg
                 if choose_fg and np.any(seg):
                     available_roi_ids = np.unique(seg)[1:]
                     for tix in np.argsort(batch_roi_counts):
                         elig_roi_ids = available_roi_ids[patient[self.balance_target][available_roi_ids-1] == self.unique_ts[tix]]
                         if len(elig_roi_ids)>0:
                             seg_ics = np.argwhere(seg == np.random.choice(elig_roi_ids, size=None))
                             break
                     roi_anchor_pixel = seg_ics[np.random.choice(seg_ics.shape[0], size=None)]
                     assert seg[tuple(roi_anchor_pixel)] > 0
 
                     # sample the patch center coords. constrained by edges of image - pre_crop_size /2 and
                     # distance to the selected ROI < patch_size /2
                     def get_cropped_centercoords(dim):
                         low = np.max((self.cf.pre_crop_size[dim] // 2,
                                       roi_anchor_pixel[dim] - (
                                                   self.cf.patch_size[dim] // 2 - self.cf.crop_margin[dim])))
                         high = np.min((spatial_shp[dim] - self.cf.pre_crop_size[dim] // 2,
                                        roi_anchor_pixel[dim] + (
                                                    self.cf.patch_size[dim] // 2 - self.cf.crop_margin[dim])))
                         if low >= high:  # happens if lesion on the edge of the image.
                             low = self.cf.pre_crop_size[dim] // 2
                             high = spatial_shp[dim] - self.cf.pre_crop_size[dim] // 2
 
                         assert low < high, 'low greater equal high, data dimension {} too small, shp {}, patient {}, low {}, high {}'.format(
                             dim,
                             spatial_shp, patient['pid'], low, high)
                         return np.random.randint(low=low, high=high)
                 else:
                     # sample crop center regardless of ROIs, not guaranteed to be empty
                     def get_cropped_centercoords(dim):
                         return np.random.randint(low=self.cf.pre_crop_size[dim] // 2,
                                                  high=spatial_shp[dim] - self.cf.pre_crop_size[dim] // 2)
 
                 sample_seg_center = {}
                 for dim in np.where(dim_cropflags)[0]:
                     sample_seg_center[dim] = get_cropped_centercoords(dim)
                     min_ = int(sample_seg_center[dim] - self.cf.pre_crop_size[dim] // 2)
                     max_ = int(sample_seg_center[dim] + self.cf.pre_crop_size[dim] // 2)
                     data = np.take(data, indices=range(min_, max_), axis=dim + 1)  # +1 for channeldim
                     seg = np.take(seg, indices=range(min_, max_), axis=dim)
 
             batch_data.append(data)
             batch_segs.append(seg[np.newaxis])
 
             for o in batch_roi_items: #after loop, holds every entry of every batchpatient per observable
                     batch_roi_items[o].append(patient[o])
 
             if self.cf.dim == 3:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient[self.balance_target] == self.unique_ts[tix])
+                    non_zero = np.count_nonzero(patient[self.balance_target] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero==0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
             elif self.cf.dim == 2:
                 for tix in range(len(self.unique_ts)):
-                    batch_roi_counts[tix] += np.count_nonzero(patient[self.balance_target][np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
-            if not np.any(seg):
-                empty_samples_count += 1
+                    non_zero = np.count_nonzero(patient[self.balance_target][np.unique(seg[seg>0]) - 1] == self.unique_ts[tix])
+                    batch_roi_counts[tix] += non_zero
+                    batch_empty_counts[tix] += int(non_zero == 0)
+                    # todo remove assert when checked
+                    if not np.any(seg):
+                        assert non_zero==0
 
         batch = {'data': np.array(batch_data), 'seg': np.array(batch_segs).astype('uint8'),
                  'pid': batch_pids,
-                 'roi_counts': batch_roi_counts, 'empty_samples_count': empty_samples_count}
+                 'roi_counts': batch_roi_counts, 'empty_counts': batch_empty_counts}
         for key,val in batch_roi_items.items(): #extend batch dic by entries of observables dic
             batch[key] = np.array(val)
 
         return batch
 
 class PatientBatchIterator(dutils.PatientBatchIterator):
     """
     creates a test generator that iterates over entire given dataset returning 1 patient per batch.
     Can be used for monitoring if cf.val_mode = 'patient_val' for a monitoring closer to actually evaluation (done in 3D),
     if willing to accept speed-loss during training.
     Specific properties of toy data set: toy data may be created with added ground-truth noise. thus, there are
     exact ground truths (GTs) and noisy ground truths available. the normal or noisy GTs are used in training by
     the BatchGenerator. The PatientIterator, however, may use the exact GTs if set in configs.
 
     :return: out_batch: dictionary containing one patient with batch_size = n_3D_patches in 3D or
     batch_size = n_2D_patches in 2D .
     """
 
     def __init__(self, cf, data, mode='test'):
         super(PatientBatchIterator, self).__init__(cf, data)
 
         self.patch_size = cf.patch_size_2D + [1] if cf.dim == 2 else cf.patch_size_3D
         self.chans = cf.channels if cf.channels is not None else np.index_exp[:]
         assert hasattr(self.chans, "__iter__"), "self.chans has to be list-like to maintain dims when slicing"
 
         if (mode=="validation" and hasattr(self.cf, 'val_against_exact_gt') and self.cf.val_against_exact_gt) or \
                 (mode == 'test' and self.cf.test_against_exact_gt):
             self.gt_prefix = 'exact_'
             print("PatientIterator: Loading exact Ground Truths.")
         else:
             self.gt_prefix = ''
 
         self.patient_ix = 0  # running index over all patients in set
 
     def generate_train_batch(self, pid=None):
 
         if pid is None:
             pid = self.dataset_pids[self.patient_ix]
         patient = self._data[pid]
 
         # already swapped dimensions in pp from (c,)z,y,x to c,y,x,z or h,w,d to ease 2D/3D-case handling
         data = np.load(patient['data'], mmap_mode='r').astype('float16')[np.newaxis]
         seg =  np.load(patient[self.gt_prefix+'seg']).astype('uint8')[np.newaxis]
 
         data_shp_raw = data.shape
         plot_bg = data[self.cf.plot_bg_chan] if self.cf.plot_bg_chan not in self.chans else None
         data = data[self.chans]
         discarded_chans = len(
             [c for c in np.setdiff1d(np.arange(data_shp_raw[0]), self.chans) if c < self.cf.plot_bg_chan])
         spatial_shp = data[0].shape  # spatial dims need to be in order x,y,z
         assert spatial_shp == seg[0].shape, "spatial shape incongruence betw. data and seg"
 
         if np.any([spatial_shp[i] < ps for i, ps in enumerate(self.patch_size)]):
             new_shape = [np.max([spatial_shp[i], self.patch_size[i]]) for i in range(len(self.patch_size))]
             data = dutils.pad_nd_image(data, new_shape)  # use 'return_slicer' to crop image back to original shape.
             seg = dutils.pad_nd_image(seg, new_shape)
             if plot_bg is not None:
                 plot_bg = dutils.pad_nd_image(plot_bg, new_shape)
 
         if self.cf.dim == 3 or self.cf.merge_2D_to_3D_preds:
             # adds the batch dim here bc won't go through MTaugmenter
             out_data = data[np.newaxis]
             out_seg = seg[np.newaxis]
             if plot_bg is not None:
                out_plot_bg = plot_bg[np.newaxis]
             # data and seg shape: (1,c,x,y,z), where c=1 for seg
 
             batch_3D = {'data': out_data, 'seg': out_seg}
             for o in self.cf.roi_items:
                 batch_3D[o] = np.array([patient[self.gt_prefix+o]])
             converter = ConvertSegToBoundingBoxCoordinates(3, self.cf.roi_items, False, self.cf.class_specific_seg)
             batch_3D = converter(**batch_3D)
             batch_3D.update({'patient_bb_target': batch_3D['bb_target'], 'original_img_shape': out_data.shape})
             for o in self.cf.roi_items:
                 batch_3D["patient_" + o] = batch_3D[o]
 
         if self.cf.dim == 2:
             out_data = np.transpose(data, axes=(3, 0, 1, 2)).astype('float32')  # (c,y,x,z) to (b=z,c,x,y), use z=b as batchdim
             out_seg = np.transpose(seg, axes=(3, 0, 1, 2)).astype('uint8')  # (c,y,x,z) to (b=z,c,x,y)
 
             batch_2D = {'data': out_data, 'seg': out_seg}
             for o in self.cf.roi_items:
                 batch_2D[o] = np.repeat(np.array([patient[self.gt_prefix+o]]), len(out_data), axis=0)
             converter = ConvertSegToBoundingBoxCoordinates(2, self.cf.roi_items, False, self.cf.class_specific_seg)
             batch_2D = converter(**batch_2D)
 
             if plot_bg is not None:
                 out_plot_bg = np.transpose(plot_bg, axes=(2, 0, 1)).astype('float32')
 
             if self.cf.merge_2D_to_3D_preds:
                 batch_2D.update({'patient_bb_target': batch_3D['patient_bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_3D[o]
             else:
                 batch_2D.update({'patient_bb_target': batch_2D['bb_target'],
                                  'original_img_shape': out_data.shape})
                 for o in self.cf.roi_items:
                     batch_2D["patient_" + o] = batch_2D[o]
 
         out_batch = batch_3D if self.cf.dim == 3 else batch_2D
         out_batch.update({'pid': np.array([patient['pid']] * len(out_data))})
 
         if self.cf.plot_bg_chan in self.chans and discarded_chans > 0:  # len(self.chans[:self.cf.plot_bg_chan])<data_shp_raw[0]:
             assert plot_bg is None
             plot_bg = int(self.cf.plot_bg_chan - discarded_chans)
             out_plot_bg = plot_bg
         if plot_bg is not None:
             out_batch['plot_bg'] = out_plot_bg
 
         # eventual tiling into patches
         spatial_shp = out_batch["data"].shape[2:]
         if np.any([spatial_shp[ix] > self.patch_size[ix] for ix in range(len(spatial_shp))]):
             patient_batch = out_batch
             print("patientiterator produced patched batch!")
             patch_crop_coords_list = dutils.get_patch_crop_coords(data[0], self.patch_size)
             new_img_batch, new_seg_batch = [], []
 
             for c in patch_crop_coords_list:
                 new_img_batch.append(data[:, c[0]:c[1], c[2]:c[3], c[4]:c[5]])
                 seg_patch = seg[:, c[0]:c[1], c[2]: c[3], c[4]:c[5]]
                 new_seg_batch.append(seg_patch)
             shps = []
             for arr in new_img_batch:
                 shps.append(arr.shape)
 
             data = np.array(new_img_batch)  # (patches, c, x, y, z)
             seg = np.array(new_seg_batch)
             if self.cf.dim == 2:
                 # all patches have z dimension 1 (slices). discard dimension
                 data = data[..., 0]
                 seg = seg[..., 0]
             patch_batch = {'data': data.astype('float32'), 'seg': seg.astype('uint8'),
                            'pid': np.array([patient['pid']] * data.shape[0])}
             for o in self.cf.roi_items:
                 patch_batch[o] = np.repeat(np.array([patient[self.gt_prefix+o]]), len(patch_crop_coords_list), axis=0)
             #patient-wise (orig) batch info for putting the patches back together after prediction
             for o in self.cf.roi_items:
                 patch_batch["patient_"+o] = patient_batch["patient_"+o]
                 if self.cf.dim == 2:
                     # this could also be named "unpatched_2d_roi_items"
                     patch_batch["patient_" + o + "_2d"] = patient_batch[o]
             patch_batch['patch_crop_coords'] = np.array(patch_crop_coords_list)
             patch_batch['patient_bb_target'] = patient_batch['patient_bb_target']
             if self.cf.dim == 2:
                 patch_batch['patient_bb_target_2d'] = patient_batch['bb_target']
             patch_batch['patient_data'] = patient_batch['data']
             patch_batch['patient_seg'] = patient_batch['seg']
             patch_batch['original_img_shape'] = patient_batch['original_img_shape']
             if plot_bg is not None:
                 patch_batch['patient_plot_bg'] = patient_batch['plot_bg']
 
             converter = ConvertSegToBoundingBoxCoordinates(self.cf.dim, self.cf.roi_items, get_rois_from_seg=False,
                                                            class_specific_seg=self.cf.class_specific_seg)
 
             patch_batch = converter(**patch_batch)
             out_batch = patch_batch
 
         self.patient_ix += 1
         if self.patient_ix == len(self.dataset_pids):
             self.patient_ix = 0
 
         return out_batch
 
 
-def create_data_gen_pipeline(cf, patient_data, do_aug=True, sample_pids_w_replace=True):
+def create_data_gen_pipeline(cf, patient_data, do_aug=True, **kwargs):
     """
     create mutli-threaded train/val/test batch generation and augmentation pipeline.
     :param patient_data: dictionary containing one dictionary per patient in the train/test subset.
     :param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
     :return: multithreaded_generator
     """
 
     # create instance of batch generator as first element in pipeline.
-    data_gen = BatchGenerator(cf, patient_data, sample_pids_w_replace=sample_pids_w_replace)
+    data_gen = BatchGenerator(cf, patient_data, **kwargs)
 
     my_transforms = []
     if do_aug:
         if cf.da_kwargs["mirror"]:
             mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
             my_transforms.append(mirror_transform)
 
         spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                              patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                              do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                              alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                              do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                              angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                              do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                              random_crop=cf.da_kwargs['random_crop'])
 
         my_transforms.append(spatial_transform)
     else:
         my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))
 
     my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, cf.roi_items, False, cf.class_specific_seg))
     all_transforms = Compose(my_transforms)
     # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
     multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
     return multithreaded_generator
 
 def get_train_generators(cf, logger, data_statistics=False):
     """
     wrapper function for creating the training batch generator pipeline. returns the train/val generators.
     selects patients according to cv folds (generated by first run/fold of experiment):
     splits the data into n-folds, where 1 split is used for val, 1 split for testing and the rest for training. (inner loop test set)
     If cf.hold_out_test_set is True, adds the test split to the training data.
     """
     dataset = Dataset(cf, logger)
     dataset.init_FoldGenerator(cf.seed, cf.n_cv_splits)
     dataset.generate_splits(check_file=os.path.join(cf.exp_dir, 'fold_ids.pickle'))
     set_splits = dataset.fg.splits
 
     test_ids, val_ids = set_splits.pop(cf.fold), set_splits.pop(cf.fold - 1)
     train_ids = np.concatenate(set_splits, axis=0)
 
     if cf.held_out_test_set:
         train_ids = np.concatenate((train_ids, test_ids), axis=0)
         test_ids = []
 
     train_data = {k: v for (k, v) in dataset.data.items() if str(k) in train_ids}
     val_data = {k: v for (k, v) in dataset.data.items() if str(k) in val_ids}
 
     logger.info("data set loaded with: {} train / {} val / {} test patients".format(len(train_ids), len(val_ids),
                                                                                     len(test_ids)))
     if data_statistics:
         dataset.calc_statistics(subsets={"train": train_ids, "val": val_ids, "test": test_ids}, plot_dir=
         os.path.join(cf.plot_dir,"dataset"))
 
+
+
     batch_gen = {}
     batch_gen['train'] = create_data_gen_pipeline(cf, train_data, do_aug=cf.do_aug, sample_pids_w_replace=True)
-    batch_gen['val_sampling'] = create_data_gen_pipeline(cf, val_data, do_aug=False, sample_pids_w_replace=False)
-
     if cf.val_mode == 'val_patient':
         batch_gen['val_patient'] = PatientBatchIterator(cf, val_data, mode='validation')
         batch_gen['n_val'] = len(val_ids) if cf.max_val_patients=="all" else min(len(val_ids), cf.max_val_patients)
     elif cf.val_mode == 'val_sampling':
-        batch_gen['n_val'] = cf.num_val_batches if cf.num_val_batches != "all" else len(val_data)
+        batch_gen['n_val'] = int(np.ceil(len(val_data)/cf.batch_size)) if cf.num_val_batches == "all" else cf.num_val_batches
+        # in current setup, val loader is used like generator. with max_batches being applied in train routine.
+        batch_gen['val_sampling'] = create_data_gen_pipeline(cf, val_data, do_aug=False, sample_pids_w_replace=False,
+                                                             max_batches=None, raise_stop_iteration=False)
 
     return batch_gen
 
 def get_test_generator(cf, logger):
     """
     if get_test_generators is possibly called multiple times in server env, every time of
     Dataset initiation rsync will check for copying the data; this should be okay
     since rsync will not copy if files already exist in destination.
     """
 
     if cf.held_out_test_set:
         sourcedir = cf.test_data_sourcedir
         test_ids = None
     else:
         sourcedir = None
         with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'rb') as handle:
             set_splits = pickle.load(handle)
         test_ids = set_splits[cf.fold]
 
     test_set = Dataset(cf, logger, subset_ids=test_ids, data_sourcedir=sourcedir, mode='test')
     logger.info("data set loaded with: {} test patients".format(len(test_set.set_ids)))
     batch_gen = {}
     batch_gen['test'] = PatientBatchIterator(cf, test_set.data)
     batch_gen['n_test'] = len(test_set.set_ids) if cf.max_test_patients=="all" else \
         min(cf.max_test_patients, len(test_set.set_ids))
 
     return batch_gen
 
 
 if __name__=="__main__":
 
     import utils.exp_utils as utils
-    from configs import Configs
+    from datasets.toy.configs import Configs
 
     cf = Configs()
 
     total_stime = time.time()
     times = {}
 
     # cf.server_env = True
     # cf.data_dir = "experiments/dev_data"
 
     cf.exp_dir = "experiments/dev/"
     cf.plot_dir = cf.exp_dir + "plots"
     os.makedirs(cf.exp_dir, exist_ok=True)
     cf.fold = 0
     logger = utils.get_logger(cf.exp_dir)
     gens = get_train_generators(cf, logger)
     train_loader = gens['train']
     for i in range(0):
         stime = time.time()
         print("producing training batch nr ", i)
         ex_batch = next(train_loader)
         times["train_batch"] = time.time() - stime
         #experiments/dev/dev_exbatch_{}.png".format(i)
         plg.view_batch(cf, ex_batch, out_file="experiments/dev/dev_exbatch_{}.png".format(i), show_gt_labels=True, vmin=0, show_info=False)
 
 
     val_loader = gens['val_sampling']
     stime = time.time()
     for i in range(1):
         ex_batch = next(val_loader)
         times["val_batch"] = time.time() - stime
         stime = time.time()
         #"experiments/dev/dev_exvalbatch_{}.png"
         plg.view_batch(cf, ex_batch, out_file="experiments/dev/dev_exvalbatch_{}.png".format(i), show_gt_labels=True, vmin=0, show_info=True)
         times["val_plot"] = time.time() - stime
     #
     test_loader = get_test_generator(cf, logger)["test"]
     stime = time.time()
     ex_batch = test_loader.generate_train_batch(pid=None)
     times["test_batch"] = time.time() - stime
     stime = time.time()
     plg.view_batch(cf, ex_batch, show_gt_labels=True, out_file="experiments/dev/dev_expatchbatch.png", vmin=0)
     times["test_patchbatch_plot"] = time.time() - stime
 
 
 
     print("Times recorded throughout:")
     for (k, v) in times.items():
         print(k, "{:.2f}".format(v))
 
     mins, secs = divmod((time.time() - total_stime), 60)
     h, mins = divmod(mins, 60)
     t = "{:d}h:{:02d}m:{:02d}s".format(int(h), int(mins), int(secs))
     print("{} total runtime: {}".format(os.path.split(__file__)[1], t))
\ No newline at end of file
diff --git a/utils/dataloader_utils.py b/utils/dataloader_utils.py
index 4806fc2..cdd92e1 100644
--- a/utils/dataloader_utils.py
+++ b/utils/dataloader_utils.py
@@ -1,699 +1,743 @@
 #!/usr/bin/env python
 # Copyright 2019 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
 #
 # 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 plotting as plg
 
 import os
 from multiprocessing import Pool, Lock
 import pickle
 import warnings
 
 import numpy as np
 import pandas as pd
 from batchgenerators.transforms.abstract_transforms import AbstractTransform
 from scipy.ndimage.measurements import label as lb
 from torch.utils.data import Dataset as torchDataset
 from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
 
 import utils.exp_utils as utils
 import data_manager as dmanager
 
 
 for msg in ["This figure includes Axes that are not compatible with tight_layout",
             "Data has no positive values, and therefore cannot be log-scaled."]:
     warnings.filterwarnings("ignore", msg)
 
 
 class AttributeDict(dict):
     __getattr__ = dict.__getitem__
     __setattr__ = dict.__setitem__
 
 ##################################
 #  data loading, organisation  #
 ##################################
 
 
 class fold_generator:
     """
     generates splits of indices for a given length of a dataset to perform n-fold cross-validation.
     splits each fold into 3 subsets for training, validation and testing.
     This form of cross validation uses an inner loop test set, which is useful if test scores shall be reported on a
     statistically reliable amount of patients, despite limited size of a dataset.
     If hold out test set is provided and hence no inner loop test set needed, just add test_idxs to the training data in the dataloader.
     This creates straight-forward train-val splits.
     :returns names list: list of len n_splits. each element is a list of len 3 for train_ix, val_ix, test_ix.
     """
     def __init__(self, seed, n_splits, len_data):
         """
         :param seed: Random seed for splits.
         :param n_splits: number of splits, e.g. 5 splits for 5-fold cross-validation
         :param len_data: number of elements in the dataset.
         """
         self.tr_ix = []
         self.val_ix = []
         self.te_ix = []
         self.slicer = None
         self.missing = 0
         self.fold = 0
         self.len_data = len_data
         self.n_splits = n_splits
         self.myseed = seed
         self.boost_val = 0
 
     def init_indices(self):
 
         t = list(np.arange(self.l))
         # round up to next splittable data amount.
         split_length = int(np.ceil(len(t) / float(self.n_splits)))
         self.slicer = split_length
         self.mod = len(t) % self.n_splits
         if self.mod > 0:
             # missing is the number of folds, in which the new splits are reduced to account for missing data.
             self.missing = self.n_splits - self.mod
 
         self.te_ix = t[:self.slicer]
         self.tr_ix = t[self.slicer:]
         self.val_ix = self.tr_ix[:self.slicer]
         self.tr_ix = self.tr_ix[self.slicer:]
 
     def new_fold(self):
 
         slicer = self.slicer
         if self.fold < self.missing :
             slicer = self.slicer - 1
 
         temp = self.te_ix
 
         # catch exception mod == 1: test set collects 1+ data since walk through both roudned up splits.
         # account for by reducing last fold split by 1.
         if self.fold == self.n_splits-2 and self.mod ==1:
             temp += self.val_ix[-1:]
             self.val_ix = self.val_ix[:-1]
 
         self.te_ix = self.val_ix
         self.val_ix = self.tr_ix[:slicer]
         self.tr_ix = self.tr_ix[slicer:] + temp
 
 
     def get_fold_names(self):
         names_list = []
         rgen = np.random.RandomState(self.myseed)
         cv_names = np.arange(self.len_data)
 
         rgen.shuffle(cv_names)
         self.l = len(cv_names)
         self.init_indices()
 
         for split in range(self.n_splits):
             train_names, val_names, test_names = cv_names[self.tr_ix], cv_names[self.val_ix], cv_names[self.te_ix]
             names_list.append([train_names, val_names, test_names, self.fold])
             self.new_fold()
             self.fold += 1
 
         return names_list
 
 
 
 class FoldGenerator():
     r"""takes a set of elements (identifiers) and randomly splits them into the specified amt of subsets.
     """
 
     def __init__(self, identifiers, seed, n_splits=5):
         self.ids = np.array(identifiers)
         self.n_splits = n_splits
         self.seed = seed
 
     def generate_splits(self, n_splits=None):
         if n_splits is None:
             n_splits = self.n_splits
 
         rgen = np.random.RandomState(self.seed)
         rgen.shuffle(self.ids)
         self.splits = list(np.array_split(self.ids, n_splits, axis=0))  # already returns list, but to be sure
         return self.splits
 
 
 class Dataset(torchDataset):
     r"""Parent Class for actual Dataset classes to inherit from!
     """
     def __init__(self, cf, data_sourcedir=None):
         super(Dataset, self).__init__()
         self.cf = cf
 
         self.data_sourcedir = cf.data_sourcedir if data_sourcedir is None else data_sourcedir
         self.data_dir = cf.data_dir if hasattr(cf, 'data_dir') else self.data_sourcedir
 
         self.data_dest = cf.data_dest if hasattr(cf, "data_dest") else self.data_sourcedir
 
         self.data = {}
         self.set_ids = []
 
     def copy_data(self, cf, file_subset, keep_packed=False, del_after_unpack=False):
         if os.path.normpath(self.data_sourcedir) != os.path.normpath(self.data_dest):
             self.data_sourcedir = os.path.join(self.data_sourcedir, '')
             args = AttributeDict({
                     "source" :  self.data_sourcedir,
                     "destination" : self.data_dest,
                     "recursive" : True,
                     "cp_only_npz" : False,
                     "keep_packed" : keep_packed,
                     "del_after_unpack" : del_after_unpack,
                     "threads" : 16 if self.cf.server_env else os.cpu_count()
                     })
             dmanager.copy(args, file_subset=file_subset)
             self.data_dir = self.data_dest
 
 
 
     def __len__(self):
         return len(self.data)
     def __getitem__(self, id):
         """Return a sample of the dataset, i.e.,the dict of the id
         """
         return self.data[id]
     def __iter__(self):
         return self.data.__iter__()
 
     def init_FoldGenerator(self, seed, n_splits):
         self.fg = FoldGenerator(self.set_ids, seed=seed, n_splits=n_splits)
 
     def generate_splits(self, check_file):
         if not os.path.exists(check_file):
             self.fg.generate_splits()
             with open(check_file, 'wb') as handle:
                 pickle.dump(self.fg.splits, handle)
         else:
             with open(check_file, 'rb') as handle:
                 self.fg.splits = pickle.load(handle)
 
     def calc_statistics(self, subsets=None, plot_dir=None, overall_stats=True):
 
         if self.df is None:
             self.df = pd.DataFrame()
             balance_t = self.cf.balance_target if hasattr(self.cf, "balance_target") else "class_targets"
             self.df._metadata.append(balance_t)
             if balance_t=="class_targets":
                 mapper = lambda cl_id: self.cf.class_id2label[cl_id]
                 labels = self.cf.class_id2label.values()
             elif balance_t=="rg_bin_targets":
                 mapper = lambda rg_bin: self.cf.bin_id2label[rg_bin]
                 labels = self.cf.bin_id2label.values()
             # elif balance_t=="regression_targets":
             #     # todo this wont work
             #     mapper = lambda rg_val: AttributeDict({"name":rg_val}) #self.cf.bin_id2label[self.cf.rg_val_to_bin_id(rg_val)]
             #     labels = self.cf.bin_id2label.values()
             elif balance_t=="lesion_gleasons":
                 mapper = lambda gs: self.cf.gs2label[gs]
                 labels = self.cf.gs2label.values()
             else:
                 mapper = lambda x: AttributeDict({"name":x})
                 labels = None
             for pid, subj_data in self.data.items():
                 unique_ts, counts = np.unique(subj_data[balance_t], return_counts=True)
                 self.df = self.df.append(pd.DataFrame({"pid": [pid],
                                                        **{mapper(unique_ts[i]).name: [counts[i]] for i in
                                                           range(len(unique_ts))}}), ignore_index=True, sort=True)
             self.df = self.df.fillna(0)
 
         if overall_stats:
             df = self.df.drop("pid", axis=1)
             df = df.reindex(sorted(df.columns), axis=1).astype('uint32')
             print("Overall dataset roi counts per target kind:"); print(df.sum())
         if subsets is not None:
             self.df["subset"] = np.nan
             self.df["display_order"] = np.nan
             for ix, (subset, pids) in enumerate(subsets.items()):
                 self.df.loc[self.df.pid.isin(pids), "subset"] = subset
                 self.df.loc[self.df.pid.isin(pids), "display_order"] = ix
             df = self.df.groupby("subset").agg("sum").drop("pid", axis=1, errors='ignore').astype('int64')
             df = df.sort_values(by=['display_order']).drop('display_order', axis=1)
             df = df.reindex(sorted(df.columns), axis=1)
 
             print("Fold {} dataset roi counts per target kind:".format(self.cf.fold)); print(df)
         if plot_dir is not None:
             os.makedirs(plot_dir, exist_ok=True)
             if subsets is not None:
                 plg.plot_fold_stats(self.cf, df, labels, os.path.join(plot_dir, "data_stats_fold_" + str(self.cf.fold))+".pdf")
             if overall_stats:
                 plg.plot_data_stats(self.cf, df, labels, os.path.join(plot_dir, 'data_stats_overall.pdf'))
 
         return df, labels
 
 
 def get_class_balanced_patients(all_pids, class_targets, batch_size, num_classes, random_ratio=0):
     '''
     samples towards equilibrium of classes (on basis of total RoI counts). for highly imbalanced dataset, this might be a too strong requirement.
     :param class_targets: dic holding {patient_specifier : ROI class targets}, list position of ROI target corresponds to respective seg label - 1
     :param batch_size:
     :param num_classes:
     :return:
     '''
     # assert len(all_pids)>=batch_size, "not enough eligible pids {} to form a single batch of size {}".format(len(all_pids), batch_size)
     class_counts = {k: 0 for k in range(1,num_classes+1)}
     not_picked = np.array(all_pids)
     batch_patients = np.empty((batch_size,), dtype=not_picked.dtype)
     rarest_class = np.random.randint(1,num_classes+1)
 
     for ix in range(batch_size):
         if len(not_picked) == 0:
             warnings.warn("Dataset too small to generate batch with unique samples; => recycling.")
             not_picked = np.array(all_pids)
 
         np.random.shuffle(not_picked) #this could actually go outside(above) the loop.
         pick = not_picked[0]
         for cand in not_picked:
             if np.count_nonzero(class_targets[cand] == rarest_class) > 0:
                 pick = cand
                 cand_rarest_class = np.argmin([np.count_nonzero(class_targets[cand] == cl) for cl in
                                                range(1,num_classes+1)])+1
                 # if current batch already bigger than the batch random ratio, then
                 # check that weakest class in this patient is not the weakest in current batch (since needs to be boosted)
                 # also that at least one roi of this patient belongs to weakest class. If True, keep patient, else keep looking.
                 if (cand_rarest_class != rarest_class and np.count_nonzero(class_targets[cand] == rarest_class) > 0) \
                         or ix < int(batch_size * random_ratio):
                     break
 
         for c in range(1,num_classes+1):
             class_counts[c] += np.count_nonzero(class_targets[pick] == c)
         if not ix < int(batch_size * random_ratio) and class_counts[rarest_class] == 0:  # means searched thru whole set without finding rarest class
             print("Class {} not represented in current dataset.".format(rarest_class))
         rarest_class = np.argmin(([class_counts[c] for c in range(1,num_classes+1)]))+1
         batch_patients[ix] = pick
         not_picked = not_picked[not_picked != pick]  # removes pick
 
     return batch_patients
 
 
 class BatchGenerator(SlimDataLoaderBase):
     """
     create the training/validation batch generator. Randomly sample batch_size patients
     from the data set, (draw a random slice if 2D), pad-crop them to equal sizes and merge to an array.
     :param data: data dictionary as provided by 'load_dataset'
     :param img_modalities: list of strings ['adc', 'b1500'] from config
     :param batch_size: number of patients to sample for the batch
     :param pre_crop_size: equal size for merging the patients to a single array (before the final random-crop in data aug.)
     :return dictionary containing the batch data / seg / pids as lists; the augmenter will later concatenate them into an array.
     """
 
-    def __init__(self, cf, data, sample_pids_w_replace, max_batches=None, raise_stop_iteration=False, n_threads=None, seed=0):
+    def __init__(self, cf, data, sample_pids_w_replace=True, max_batches=None, raise_stop_iteration=False, n_threads=None, seed=0):
         if n_threads is None:
             n_threads = cf.n_workers
         super(BatchGenerator, self).__init__(data, cf.batch_size, number_of_threads_in_multithreaded=n_threads)
         self.cf = cf
         self.random_count = int(cf.batch_random_ratio * cf.batch_size)
         self.plot_dir = os.path.join(self.cf.plot_dir, 'train_generator')
         self.max_batches = max_batches
         self.raise_stop = raise_stop_iteration
         self.thread_id = 0
         self.batches_produced = 0
 
         self.dataset_length = len(self._data)
         self.dataset_pids = list(self._data.keys())
         self.rgen = np.random.RandomState(seed=seed)
         self.eligible_pids = self.rgen.permutation(self.dataset_pids.copy())
         self.eligible_pids = np.array_split(self.eligible_pids, self.number_of_threads_in_multithreaded)
         self.eligible_pids = sorted(self.eligible_pids, key=len, reverse=True)
         self.sample_pids_w_replace = sample_pids_w_replace
         if not self.sample_pids_w_replace:
             assert len(self.dataset_pids) / self.number_of_threads_in_multithreaded >= self.batch_size, \
                 "at least one batch needed per thread. dataset size: {}, n_threads: {}, batch_size: {}.".format(
                     len(self.dataset_pids), self.number_of_threads_in_multithreaded, self.batch_size)
             self.lock = Lock()
 
-        self.stats = {"roi_counts": np.zeros((self.cf.num_classes,), dtype='uint32'), "empty_samples_count": 0}
+        self.stats = {"roi_counts": np.zeros((self.cf.num_classes,), dtype='uint32'),
+                      "empty_counts": np.zeros((self.cf.num_classes,), dtype='uint32')}
 
         if hasattr(cf, "balance_target"):
             # WARNING: "balance targets are only implemented for 1-d targets (or 1-component vectors)"
             self.balance_target = cf.balance_target
         else:
             self.balance_target = "class_targets"
         self.targets = {k:v[self.balance_target] for (k,v) in self._data.items()}
 
     def set_thread_id(self, thread_id):
         self.thread_ids = self.eligible_pids[thread_id]
         self.thread_id  = thread_id
 
     def reset(self):
         self.batches_produced = 0
         self.thread_ids = self.rgen.permutation(self.eligible_pids[self.thread_id])
 
+    @staticmethod
+    def sample_targets_to_weights(targets, fg_bg_weights):
+        weights = targets * fg_bg_weights
+        return weights
+
     def balance_target_distribution(self, plot=False):
-        """
+        """Impose a drawing distribution over samples.
+         Distribution should be designed so that classes' fg and bg examples are (as good as possible) shown in
+         equal frequency. Since we are dealing with rois, fg/bg weights count a sample (e.g., a patient) with
+         **at least** one occurrence as fg, otherwise bg. For fg weights among classes, each RoI counts.
+
         :param all_pids:
         :param self.targets:  dic holding {patient_specifier : patient-wise-unique ROI targets}
         :return: probability distribution over all pids. draw without replace from this.
         """
+        self.unique_ts = np.unique([v for pat in self.targets.values() for v in pat])
+        self.sample_stats = pd.DataFrame(columns=[str(ix)+suffix for ix in self.unique_ts for suffix in ["", "_bg"]], index=list(self.targets.keys()))
+        for pid in self.sample_stats.index:
+            for targ in self.unique_ts:
+                fg_count = np.count_nonzero(self.targets[pid] == targ)
+                self.sample_stats.loc[pid, str(targ)] = int(fg_count > 0)
+                self.sample_stats.loc[pid, str(targ)+"_bg"] = int(fg_count == 0)
+
+        self.targ_stats = self.sample_stats.agg(
+            ("sum", lambda col: col.sum() / len(self._data)), axis=0, sort=False).rename({"<lambda>": "relative"})
+
+        anchor = 1. - self.targ_stats.loc["relative"].iloc[0]
+        self.fg_bg_weights = anchor / self.targ_stats.loc["relative"]
+        cum_weights = anchor * len(self.fg_bg_weights)
+        self.fg_bg_weights /= cum_weights
+
+        self.p_probs = self.sample_stats.apply(self.sample_targets_to_weights, args=(self.fg_bg_weights,), axis=1).sum(axis=1)
+        self.p_probs = self.p_probs / self.p_probs.sum()
+        if plot:
+            print("Applying class-weights:\n {}".format(self.fg_bg_weights))
+        if len(self.sample_stats.columns) == 2:
+            # assert that probs are calc'd correctly:
+            # (self.p_probs * self.sample_stats["1"]).sum() == (self.p_probs * self.sample_stats["1_bg"]).sum()
+            # only works if one label per patient (multi-label expectations depend on multi-label occurences).
+            expectations = []
+            for targ in self.sample_stats.columns:
+                expectations.append((self.p_probs * self.sample_stats[targ]).sum())
+            assert np.allclose(expectations, expectations[0], atol=1e-4), "expectation values for fgs/bgs: {}".format(expectations)
+
         # get unique foreground targets per patient, assign -1 to an "empty" patient (has no foreground)
-        patient_ts = [np.unique(lst) if len([t for t in lst if np.any(t>0)])>0 else [-1] for lst in self.targets.values()]
+        #patient_ts = [np.unique(lst) if len([t for t in lst if np.any(t>0)])>0 else [-1] for lst in self.targets.values()]
         #bg_mask = np.array([np.all(lst == [-1]) for lst in patient_ts])
-        unique_ts, t_counts = np.unique([t for lst in patient_ts for t in lst if t!=-1], return_counts=True)
-        t_probs = t_counts.sum() / t_counts
-        t_probs /= t_probs.sum()
-        t_probs = {t : t_probs[ix] for ix, t in enumerate(unique_ts)}
-        t_probs[-1] = 0.
-        # fail if balance target is not a number (i.e., a vector)
-        self.p_probs = np.array([ max([t_probs[t] for t in lst]) for lst in patient_ts ])
-        #normalize
-        self.p_probs /= self.p_probs.sum()
+        #unique_ts, t_counts = np.unique([t for lst in patient_ts for t in lst if t!=-1], return_counts=True)
+        # t_probs = t_counts.sum() / t_counts
+        # t_probs /= t_probs.sum()
+        # t_probs = {t : t_probs[ix] for ix, t in enumerate(unique_ts)}
+        # t_probs[-1] = 0.
+        # # fail if balance target is not a number (i.e., a vector)
+        # self.p_probs = np.array([ max([t_probs[t] for t in lst]) for lst in patient_ts ])
+        # #normalize
+        # self.p_probs /= self.p_probs.sum()
         # rescale probs of empty samples
         # if not 0 == self.p_probs[bg_mask].shape[0]:
         #     #rescale_f = (1 - self.cf.empty_samples_ratio) / self.p_probs[~bg_mask].sum()
         #     rescale_f = 1 / self.p_probs[~bg_mask].sum()
         #     self.p_probs *= rescale_f
         #     self.p_probs[bg_mask] = 0. #self.cf.empty_samples_ratio/self.p_probs[bg_mask].shape[0]
 
-        self.unique_ts = unique_ts
+        #self.unique_ts = unique_ts
 
         if plot:
             os.makedirs(self.plot_dir, exist_ok=True)
             plg.plot_batchgen_distribution(self.cf, self.dataset_pids, self.p_probs, self.balance_target,
                                            out_file=os.path.join(self.plot_dir,
                                                                  "train_gen_distr_"+str(self.cf.fold)+".png"))
         return self.p_probs
 
     def get_batch_pids(self):
         if self.max_batches is not None and self.batches_produced * self.number_of_threads_in_multithreaded \
                 + self.thread_id >= self.max_batches:
             self.reset()
             raise StopIteration
 
         if self.sample_pids_w_replace:
             # fully random patients
             batch_pids = list(np.random.choice(self.dataset_pids, size=self.random_count, replace=False))
             # target-balanced patients
             batch_pids += list(np.random.choice(
                 self.dataset_pids, size=self.batch_size - self.random_count, replace=False, p=self.p_probs))
         else:
             with self.lock:
                 if len(self.thread_ids) == 0:
                     if self.raise_stop:
                         self.reset()
                         raise StopIteration
                     else:
                         self.thread_ids = self.rgen.permutation(self.eligible_pids[self.thread_id])
                 batch_pids = self.thread_ids[:self.batch_size]
                 # batch_pids = np.random.choice(self.thread_ids, size=self.batch_size, replace=False)
                 self.thread_ids = [pid for pid in self.thread_ids if pid not in batch_pids]
         self.batches_produced += 1
 
         return batch_pids
 
     def generate_train_batch(self):
         # to be overriden by child
         # everything done in here is per batch
         # print statements in here get confusing due to multithreading
         raise NotImplementedError
 
     def print_stats(self, logger=None, file=None, plot_file=None, plot=True):
         print_f = utils.CombinedPrinter(logger, file)
 
         print_f('\n***Final Training Stats***')
         total_count = np.sum(self.stats['roi_counts'])
         for tix, count in enumerate(self.stats['roi_counts']):
             #name = self.cf.class_dict[tix] if self.balance_target=="class_targets" else str(self.unique_ts[tix])
             name=str(self.unique_ts[tix])
             print_f('{}: {} rois seen ({:.1f}%).'.format(name, count, count / total_count * 100))
         total_samples = self.cf.num_epochs*self.cf.num_train_batches*self.cf.batch_size
-        print_f('empty samples seen: {} ({:.1f}%).\n'.format(self.stats['empty_samples_count'],
-                                                         self.stats['empty_samples_count']/total_samples*100))
+        empties = [
+        '{}: {} ({:.1f}%)'.format(str(name), self.stats['empty_counts'][tix],
+                                    self.stats['empty_counts'][tix]/total_samples*100)
+            for tix, name in enumerate(self.unique_ts)
+        ]
+        empties = ", ".join(empties)
+        print_f('empty samples seen: {}\n'.format(empties))
         if plot:
             if plot_file is None:
                 plot_file = os.path.join(self.plot_dir, "train_gen_stats_{}.png".format(self.cf.fold))
                 os.makedirs(self.plot_dir, exist_ok=True)
-            plg.plot_batchgen_stats(self.cf, self.stats, self.balance_target, self.unique_ts, plot_file)
+            plg.plot_batchgen_stats(self.cf, self.stats, empties, self.balance_target, self.unique_ts, plot_file)
 
 class PatientBatchIterator(SlimDataLoaderBase):
     """
     creates a val/test generator. Step through the dataset and return dictionaries per patient.
     2D is a special case of 3D patching with patch_size[2] == 1 (slices)
     Creates whole Patient batch and targets, and - if necessary - patchwise batch and targets.
     Appends patient targets anyway for evaluation.
     For Patching, shifts all patches into batch dimension. batch_tiling_forward will take care of exceeding batch dimensions.
 
     This iterator/these batches are not intended to go through MTaugmenter afterwards
     """
 
     def __init__(self, cf, data):
         super(PatientBatchIterator, self).__init__(data, 0)
         self.cf = cf
 
         self.dataset_length = len(self._data)
         self.dataset_pids = list(self._data.keys())
 
     def generate_train_batch(self, pid=None):
         # to be overriden by child
 
         return
 
 ###################################
 #  transforms, image manipulation #
 ###################################
 
 def get_patch_crop_coords(img, patch_size, min_overlap=30):
     """
     _:param img (y, x, (z))
     _:param patch_size: list of len 2 (2D) or 3 (3D).
     _:param min_overlap: minimum required overlap of patches.
     If too small, some areas are poorly represented only at edges of single patches.
     _:return ndarray: shape (n_patches, 2*dim). crop coordinates for each patch.
     """
     crop_coords = []
     for dim in range(len(img.shape)):
         n_patches = int(np.ceil(img.shape[dim] / patch_size[dim]))
 
         # no crops required in this dimension, add image shape as coordinates.
         if n_patches == 1:
             crop_coords.append([(0, img.shape[dim])])
             continue
 
         # fix the two outside patches to coords patchsize/2 and interpolate.
         center_dists = (img.shape[dim] - patch_size[dim]) / (n_patches - 1)
 
         if (patch_size[dim] - center_dists) < min_overlap:
             n_patches += 1
             center_dists = (img.shape[dim] - patch_size[dim]) / (n_patches - 1)
 
         patch_centers = np.round([(patch_size[dim] / 2 + (center_dists * ii)) for ii in range(n_patches)])
         dim_crop_coords = [(center - patch_size[dim] / 2, center + patch_size[dim] / 2) for center in patch_centers]
         crop_coords.append(dim_crop_coords)
 
     coords_mesh_grid = []
     for ymin, ymax in crop_coords[0]:
         for xmin, xmax in crop_coords[1]:
             if len(crop_coords) == 3 and patch_size[2] > 1:
                 for zmin, zmax in crop_coords[2]:
                     coords_mesh_grid.append([ymin, ymax, xmin, xmax, zmin, zmax])
             elif len(crop_coords) == 3 and patch_size[2] == 1:
                 for zmin in range(img.shape[2]):
                     coords_mesh_grid.append([ymin, ymax, xmin, xmax, zmin, zmin + 1])
             else:
                 coords_mesh_grid.append([ymin, ymax, xmin, xmax])
     return np.array(coords_mesh_grid).astype(int)
 
 def pad_nd_image(image, new_shape=None, mode="edge", kwargs=None, return_slicer=False, shape_must_be_divisible_by=None):
     """
     one padder to pad them all. Documentation? Well okay. A little bit. by Fabian Isensee
 
     :param image: nd image. can be anything
     :param new_shape: what shape do you want? new_shape does not have to have the same dimensionality as image. If
     len(new_shape) < len(image.shape) then the last axes of image will be padded. If new_shape < image.shape in any of
     the axes then we will not pad that axis, but also not crop! (interpret new_shape as new_min_shape)
     Example:
     image.shape = (10, 1, 512, 512); new_shape = (768, 768) -> result: (10, 1, 768, 768). Cool, huh?
     image.shape = (10, 1, 512, 512); new_shape = (364, 768) -> result: (10, 1, 512, 768).
 
     :param mode: see np.pad for documentation
     :param return_slicer: if True then this function will also return what coords you will need to use when cropping back
     to original shape
     :param shape_must_be_divisible_by: for network prediction. After applying new_shape, make sure the new shape is
     divisibly by that number (can also be a list with an entry for each axis). Whatever is missing to match that will
     be padded (so the result may be larger than new_shape if shape_must_be_divisible_by is not None)
     :param kwargs: see np.pad for documentation
     """
     if kwargs is None:
         kwargs = {}
 
     if new_shape is not None:
         old_shape = np.array(image.shape[-len(new_shape):])
     else:
         assert shape_must_be_divisible_by is not None
         assert isinstance(shape_must_be_divisible_by, (list, tuple, np.ndarray))
         new_shape = image.shape[-len(shape_must_be_divisible_by):]
         old_shape = new_shape
 
     num_axes_nopad = len(image.shape) - len(new_shape)
 
     new_shape = [max(new_shape[i], old_shape[i]) for i in range(len(new_shape))]
 
     if not isinstance(new_shape, np.ndarray):
         new_shape = np.array(new_shape)
 
     if shape_must_be_divisible_by is not None:
         if not isinstance(shape_must_be_divisible_by, (list, tuple, np.ndarray)):
             shape_must_be_divisible_by = [shape_must_be_divisible_by] * len(new_shape)
         else:
             assert len(shape_must_be_divisible_by) == len(new_shape)
 
         for i in range(len(new_shape)):
             if new_shape[i] % shape_must_be_divisible_by[i] == 0:
                 new_shape[i] -= shape_must_be_divisible_by[i]
 
         new_shape = np.array([new_shape[i] + shape_must_be_divisible_by[i] - new_shape[i] % shape_must_be_divisible_by[i] for i in range(len(new_shape))])
 
     difference = new_shape - old_shape
     pad_below = difference // 2
     pad_above = difference // 2 + difference % 2
     pad_list = [[0, 0]]*num_axes_nopad + list([list(i) for i in zip(pad_below, pad_above)])
     res = np.pad(image, pad_list, mode, **kwargs)
     if not return_slicer:
         return res
     else:
         pad_list = np.array(pad_list)
         pad_list[:, 1] = np.array(res.shape) - pad_list[:, 1]
         slicer = list(slice(*i) for i in pad_list)
         return res, slicer
 
 def convert_seg_to_bounding_box_coordinates(data_dict, dim, roi_item_keys, get_rois_from_seg=False,
                                                 class_specific_seg=False):
     '''adapted from batchgenerators
 
     :param data_dict: seg: segmentation with labels indicating roi_count (get_rois_from_seg=False) or classes (get_rois_from_seg=True),
         class_targets: list where list index corresponds to roi id (roi_count)
     :param dim:
     :param roi_item_keys: keys of the roi-wise items in data_dict to process
     :param n_rg_feats: nr of regression vector features
     :param get_rois_from_seg:
     :return: coords (y1,x1,y2,x2 (,z1,z2)) where the segmentation GT is framed by +1 voxel, i.e., for an object with
         z-extensions z1=0 through z2=5, bbox target coords will be z1=-1, z2=6. (analogically for x,y).
         data_dict['roi_masks']: (b, n(b), c, h(n), w(n) (z(n))) list like roi_labels but with arrays (masks) inplace of
         integers. c==1 if segmentation not one-hot encoded.
     '''
 
     bb_target = []
     roi_masks = []
     roi_items = {name:[] for name in roi_item_keys}
     out_seg = np.copy(data_dict['seg'])
     for b in range(data_dict['seg'].shape[0]):
 
         p_coords_list = [] #p for patient?
         p_roi_masks_list = []
         p_roi_items_lists = {name:[] for name in roi_item_keys}
 
         if np.sum(data_dict['seg'][b] != 0) > 0:
             if get_rois_from_seg:
                 clusters, n_cands = lb(data_dict['seg'][b])
                 data_dict['class_targets'][b] = [data_dict['class_targets'][b]] * n_cands
             else:
                 n_cands = int(np.max(data_dict['seg'][b]))
 
             rois = np.array(
                 [(data_dict['seg'][b] == ii) * 1 for ii in range(1, n_cands + 1)], dtype='uint8')  # separate clusters
 
             for rix, r in enumerate(rois):
                 if np.sum(r != 0) > 0:  # check if the roi survived slicing (3D->2D) and data augmentation (cropping etc.)
                     seg_ixs = np.argwhere(r != 0)
                     coord_list = [np.min(seg_ixs[:, 1]) - 1, np.min(seg_ixs[:, 2]) - 1, np.max(seg_ixs[:, 1]) + 1,
                                   np.max(seg_ixs[:, 2]) + 1]
                     if dim == 3:
                         coord_list.extend([np.min(seg_ixs[:, 3]) - 1, np.max(seg_ixs[:, 3]) + 1])
 
                     p_coords_list.append(coord_list)
                     p_roi_masks_list.append(r)
                     # add background class = 0. rix is a patient wide index of lesions. since 'class_targets' is
                     # also patient wide, this assignment is not dependent on patch occurrences.
                     for name in roi_item_keys:
                         p_roi_items_lists[name].append(data_dict[name][b][rix])
 
                     assert data_dict["class_targets"][b][rix]>=1, "convertsegtobbox produced bg roi w cl targ {} and unique roi seg {}".format(data_dict["class_targets"][b][rix], np.unique(r))
 
 
                 if class_specific_seg:
                     out_seg[b][data_dict['seg'][b] == rix + 1] = data_dict['class_targets'][b][rix]
 
             if not class_specific_seg:
                 out_seg[b][data_dict['seg'][b] > 0] = 1
 
             bb_target.append(np.array(p_coords_list))
             roi_masks.append(np.array(p_roi_masks_list))
             for name in roi_item_keys:
                 roi_items[name].append(np.array(p_roi_items_lists[name]))
 
 
         else:
             bb_target.append([])
             roi_masks.append(np.zeros_like(data_dict['seg'][b], dtype='uint8')[None])
             for name in roi_item_keys:
                 roi_items[name].append(np.array([]))
 
     if get_rois_from_seg:
         data_dict.pop('class_targets', None)
 
     data_dict['bb_target'] = np.array(bb_target)
     data_dict['roi_masks'] = np.array(roi_masks)
     data_dict['seg'] = out_seg
     for name in roi_item_keys:
         data_dict[name] = np.array(roi_items[name])
 
 
     return data_dict
 
 class ConvertSegToBoundingBoxCoordinates(AbstractTransform):
     """ Converts segmentation masks into bounding box coordinates.
     """
 
     def __init__(self, dim, roi_item_keys, get_rois_from_seg=False, class_specific_seg=False):
         self.dim = dim
         self.roi_item_keys = roi_item_keys
         self.get_rois_from_seg = get_rois_from_seg
         self.class_specific_seg = class_specific_seg
 
     def __call__(self, **data_dict):
         return convert_seg_to_bounding_box_coordinates(data_dict, self.dim, self.roi_item_keys, self.get_rois_from_seg,
                                                        self.class_specific_seg)
 
 
 
 
 
 #############################
 #  data packing / unpacking # not used, data_manager.py used instead
 #############################
 
 def get_case_identifiers(folder):
     case_identifiers = [i[:-4] for i in os.listdir(folder) if i.endswith("npz")]
     return case_identifiers
 
 
 def convert_to_npy(npz_file):
     if not os.path.isfile(npz_file[:-3] + "npy"):
         a = np.load(npz_file)['data']
         np.save(npz_file[:-3] + "npy", a)
 
 
 def unpack_dataset(folder, threads=8):
     case_identifiers = get_case_identifiers(folder)
     p = Pool(threads)
     npz_files = [os.path.join(folder, i + ".npz") for i in case_identifiers]
     p.map(convert_to_npy, npz_files)
     p.close()
     p.join()
 
 
 def delete_npy(folder):
     case_identifiers = get_case_identifiers(folder)
     npy_files = [os.path.join(folder, i + ".npy") for i in case_identifiers]
     npy_files = [i for i in npy_files if os.path.isfile(i)]
     for n in npy_files:
         os.remove(n)
\ No newline at end of file