--- a
+++ b/experiments/toy_exp/data_loader.py
@@ -0,0 +1,312 @@
+#!/usr/bin/env python
+# Copyright 2018 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 numpy as np
+import os
+from collections import OrderedDict
+import pandas as pd
+import pickle
+import time
+import subprocess
+import utils.dataloader_utils as dutils
+
+# batch generator tools from https://github.com/MIC-DKFZ/batchgenerators
+from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
+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.dataloading import SingleThreadedAugmenter
+from batchgenerators.transforms.spatial_transforms import SpatialTransform
+from batchgenerators.transforms.crop_and_pad_transforms import CenterCropTransform
+from batchgenerators.transforms.utility_transforms import ConvertSegToBoundingBoxCoordinates
+
+
+
+def get_train_generators(cf, logger):
+    """
+    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.
+    """
+    all_data = load_dataset(cf, logger)
+    all_pids_list = np.unique([v['pid'] for (k, v) in all_data.items()])
+
+    assert cf.n_train_val_data <= len(all_pids_list), \
+        "requested {} train val samples, but dataset only has {} train val samples.".format(
+            cf.n_train_val_data, len(all_pids_list))
+    train_pids = all_pids_list[:int(2*cf.n_train_val_data//3)]
+    val_pids = all_pids_list[int(np.ceil(2*cf.n_train_val_data//3)):cf.n_train_val_data]
+
+    train_data = {k: v for (k, v) in all_data.items() if any(p == v['pid'] for p in train_pids)}
+    val_data = {k: v for (k, v) in all_data.items() if any(p == v['pid'] for p in val_pids)}
+
+    logger.info("data set loaded with: {} train / {} val patients".format(len(train_pids), len(val_pids)))
+    batch_gen = {}
+    batch_gen['train'] = create_data_gen_pipeline(train_data, cf=cf, do_aug=False)
+    batch_gen['val_sampling'] = create_data_gen_pipeline(val_data, cf=cf, do_aug=False)
+    if cf.val_mode == 'val_patient':
+        batch_gen['val_patient'] = PatientBatchIterator(val_data, cf=cf)
+        batch_gen['n_val'] = len(val_pids) if cf.max_val_patients is None else min(len(val_pids), 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.hold_out_test_set is True, gets the data from an external folder instead.
+    """
+    if cf.hold_out_test_set:
+        pp_name = cf.pp_test_name
+        test_ix = None
+    else:
+        pp_name = None
+        with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'rb') as handle:
+            fold_list = pickle.load(handle)
+        _, _, test_ix, _ = fold_list[cf.fold]
+        # warnings.warn('WARNING: using validation set for testing!!!')
+
+    test_data = load_dataset(cf, logger, test_ix, pp_data_path=cf.pp_test_data_path, pp_name=pp_name)
+    logger.info("data set loaded with: {} test patients from {}".format(len(test_data.keys()), cf.pp_test_data_path))
+    batch_gen = {}
+    batch_gen['test'] = PatientBatchIterator(test_data, cf=cf)
+    batch_gen['n_test'] = len(test_data.keys()) if cf.max_test_patients=="all" else \
+        min(cf.max_test_patients, len(test_data.keys()))
+
+    return batch_gen
+
+
+
+def load_dataset(cf, logger, subset_ixs=None, pp_data_path=None, pp_name=None):
+    """
+    loads the dataset. if deployed in cloud also copies and unpacks the data to the working directory.
+    :param subset_ixs: subset indices to be loaded from the dataset. used e.g. for testing to only load the test folds.
+    :return: data: dictionary with one entry per patient (in this case per patient-breast, since they are treated as
+    individual images for training) each entry is a dictionary containing respective meta-info as well as paths to the preprocessed
+    numpy arrays to be loaded during batch-generation
+    """
+    if pp_data_path is None:
+        pp_data_path = cf.pp_data_path
+    if pp_name is None:
+        pp_name = cf.pp_name
+    if cf.server_env:
+        copy_data = True
+        target_dir = os.path.join(cf.data_dest, pp_name)
+        if not os.path.exists(target_dir):
+            cf.data_source_dir = pp_data_path
+            os.makedirs(target_dir)
+            subprocess.call('rsync -av {} {}'.format(
+                os.path.join(cf.data_source_dir, cf.input_df_name), os.path.join(target_dir, cf.input_df_name)), shell=True)
+            logger.info('created target dir and info df at {}'.format(os.path.join(target_dir, cf.input_df_name)))
+
+        elif subset_ixs is None:
+            copy_data = False
+
+        pp_data_path = target_dir
+
+
+    p_df = pd.read_pickle(os.path.join(pp_data_path, cf.input_df_name))
+
+
+    if subset_ixs is not None:
+        subset_pids = [np.unique(p_df.pid.tolist())[ix] for ix in subset_ixs]
+        p_df = p_df[p_df.pid.isin(subset_pids)]
+        logger.info('subset: selected {} instances from df'.format(len(p_df)))
+
+    if cf.server_env:
+        if copy_data:
+            copy_and_unpack_data(logger, p_df.pid.tolist(), cf.fold_dir, cf.data_source_dir, target_dir)
+
+    class_targets = p_df['class_id'].tolist()
+    pids = p_df.pid.tolist()
+    imgs = [os.path.join(pp_data_path, '{}.npy'.format(pid)) for pid in pids]
+    segs = [os.path.join(pp_data_path,'{}.npy'.format(pid)) for pid in pids]
+
+    data = OrderedDict()
+    for ix, pid in enumerate(pids):
+
+        data[pid] = {'data': imgs[ix], 'seg': segs[ix], 'pid': pid, 'class_target': [class_targets[ix]]}
+
+    return data
+
+
+
+def create_data_gen_pipeline(patient_data, cf, do_aug=True):
+    """
+    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(patient_data, batch_size=cf.batch_size, cf=cf)
+
+    # add transformations to pipeline.
+    my_transforms = []
+    if do_aug:
+        mirror_transform = Mirror(axes=np.arange(2, cf.dim+2, 1))
+        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, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
+    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
+
+
+class BatchGenerator(SlimDataLoaderBase):
+    """
+    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, y, x, (z)) / seg (b, 1, y, x, (z)) / pids / class_target
+    """
+    def __init__(self, data, batch_size, cf):
+        super(BatchGenerator, self).__init__(data, batch_size)
+
+        self.cf = cf
+
+    def generate_train_batch(self):
+
+        batch_data, batch_segs, batch_pids, batch_targets = [], [], [], []
+        class_targets_list =  [v['class_target'] for (k, v) in self._data.items()]
+
+        #samples patients towards equilibrium of foreground classes on a roi-level (after randomly sampling the ratio "batch_sample_slack).
+        batch_ixs = dutils.get_class_balanced_patients(
+            class_targets_list, self.batch_size, self.cf.head_classes - 1, slack_factor=self.cf.batch_sample_slack)
+        patients = list(self._data.items())
+
+        for b in batch_ixs:
+
+            patient = patients[b][1]
+            all_data = np.load(patient['data'], mmap_mode='r')
+            data = all_data[0]
+            seg = all_data[1].astype('uint8')
+            batch_pids.append(patient['pid'])
+            batch_targets.append(patient['class_target'])
+            batch_data.append(data[np.newaxis])
+            batch_segs.append(seg[np.newaxis])
+
+        data = np.array(batch_data)
+        seg = np.array(batch_segs).astype(np.uint8)
+        class_target = np.array(batch_targets)
+        return {'data': data, 'seg': seg, 'pid': batch_pids, 'class_target': class_target}
+
+
+
+class PatientBatchIterator(SlimDataLoaderBase):
+    """
+    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, data, cf): #threads in augmenter
+        super(PatientBatchIterator, self).__init__(data, 0)
+        self.cf = cf
+        self.patient_ix = 0
+        self.dataset_pids = [v['pid'] for (k, v) in data.items()]
+        self.patch_size = cf.patch_size
+        if len(self.patch_size) == 2:
+            self.patch_size = self.patch_size + [1]
+
+
+    def generate_train_batch(self):
+
+        pid = self.dataset_pids[self.patient_ix]
+        patient = self._data[pid]
+        all_data = np.load(patient['data'], mmap_mode='r')
+        data = all_data[0]
+        seg = all_data[1].astype('uint8')
+        batch_class_targets = np.array([patient['class_target']])
+
+        out_data = data[None, None]
+        out_seg = seg[None, None]
+
+        #print('check patient data loader', out_data.shape, out_seg.shape)
+        batch_2D = {'data': out_data, 'seg': out_seg, 'class_target': batch_class_targets, 'pid': pid}
+        converter = ConvertSegToBoundingBoxCoordinates(dim=2, get_rois_from_seg_flag=False, class_specific_seg_flag=self.cf.class_specific_seg_flag)
+        batch_2D = converter(**batch_2D)
+
+        batch_2D.update({'patient_bb_target': batch_2D['bb_target'],
+                         'patient_roi_labels': batch_2D['class_target'],
+                         'original_img_shape': out_data.shape})
+
+        self.patient_ix += 1
+        if self.patient_ix == len(self.dataset_pids):
+            self.patient_ix = 0
+
+        return batch_2D
+
+def copy_and_unpack_data(logger, pids, fold_dir, source_dir, target_dir):
+
+
+    start_time = time.time()
+    with open(os.path.join(fold_dir, 'file_list.txt'), 'w') as handle:
+        for pid in pids:
+            handle.write('{}.npy\n'.format(pid))
+
+    subprocess.call('rsync -ahv --files-from {} {} {}'.format(os.path.join(fold_dir, 'file_list.txt'),
+        source_dir, target_dir), shell=True)
+    # dutils.unpack_dataset(target_dir)
+    copied_files = os.listdir(target_dir)
+    logger.info("copying data set finished : {} files in target dir: {}. took {} sec".format(
+        len(copied_files), target_dir, np.round(time.time() - start_time, 0)))
+
+if __name__=="__main__":
+    import utils.exp_utils as utils
+
+    total_stime = time.time()
+    cf_file = utils.import_module("cf", "configs.py")
+    cf = cf_file.configs()
+
+    logger = utils.get_logger("dev")
+    batch_gen = get_train_generators(cf, logger)
+
+    train_batch = next(batch_gen["train"])
+    pids = []
+    total = 100
+    for i in range(total):
+        print("\r producing batch {}/{}.".format(i, total), end="", flush=True)
+        train_batch = next(batch_gen["train"])
+        pids.append(train_batch["pid"])
+    print()
+
+
+    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))
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