--- a
+++ b/experiments/toy_exp/configs.py
@@ -0,0 +1,351 @@
+#!/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 sys
+import os
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+import numpy as np
+from default_configs import DefaultConfigs
+
+class configs(DefaultConfigs):
+
+    def __init__(self, server_env=False):
+
+        #########################
+        #    Preprocessing      #
+        #########################
+
+        self.root_dir = '/home/gregor/datasets/toy_mdt'
+
+        #########################
+        #         I/O           #
+        #########################
+
+        # one out of [2, 3]. dimension the model operates in.
+        self.dim = 2
+
+        # one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn'].
+        self.model = 'retina_unet'
+
+        DefaultConfigs.__init__(self, self.model, server_env, self.dim)
+
+        # int [0 < dataset_size]. select n patients from dataset for prototyping.
+        self.select_prototype_subset = None
+        self.hold_out_test_set = True
+        # including val set. will be 3/4 train, 1/4 val.
+        self.n_train_val_data = 2500
+
+        # choose one of the 3 toy experiments described in https://arxiv.org/pdf/1811.08661.pdf
+        # one of ['donuts_shape', 'donuts_pattern', 'circles_scale'].
+        toy_mode = 'donuts_shape_noise'
+
+        # path to preprocessed data.
+        self.input_df_name = 'info_df.pickle'
+        self.pp_name = os.path.join(toy_mode, 'train')
+        self.pp_data_path = os.path.join(self.root_dir, self.pp_name)
+        self.pp_test_name = os.path.join(toy_mode, 'test')
+        self.pp_test_data_path = os.path.join(self.root_dir, self.pp_test_name)
+
+        # settings for deployment in cloud.
+        if server_env:
+            # path to preprocessed data.
+            pp_root_dir = '/datasets/datasets_ramien/toy_exp/data'
+            self.pp_name = os.path.join(toy_mode, 'train')
+            self.pp_data_path = os.path.join(pp_root_dir, self.pp_name)
+            self.pp_test_name = os.path.join(toy_mode, 'test')
+            self.pp_test_data_path = os.path.join(pp_root_dir, self.pp_test_name)
+            self.select_prototype_subset = None
+
+        #########################
+        #      Data Loader      #
+        #########################
+
+        # select modalities from preprocessed data
+        self.channels = [0]
+        self.n_channels = len(self.channels)
+
+        # patch_size to be used for training. pre_crop_size is the patch_size before data augmentation.
+        self.pre_crop_size_2D = [320, 320]
+        self.patch_size_2D = [320, 320]
+
+        self.patch_size = self.patch_size_2D if self.dim == 2 else self.patch_size_3D
+        self.pre_crop_size = self.pre_crop_size_2D if self.dim == 2 else self.pre_crop_size_3D
+
+        # ratio of free sampled batch elements before class balancing is triggered
+        # (>0 to include "empty"/background patches.)
+        self.batch_sample_slack = 0.2
+
+        # set 2D network to operate in 3D images.
+        self.merge_2D_to_3D_preds = False
+
+        # feed +/- n neighbouring slices into channel dimension. set to None for no context.
+        self.n_3D_context = None
+        if self.n_3D_context is not None and self.dim == 2:
+            self.n_channels *= (self.n_3D_context * 2 + 1)
+
+
+        #########################
+        #      Architecture      #
+        #########################
+
+        self.start_filts = 48 if self.dim == 2 else 18
+        self.end_filts = self.start_filts * 4 if self.dim == 2 else self.start_filts * 2
+        self.res_architecture = 'resnet50' # 'resnet101', 'resnet50'
+        self.norm = None # one of None, 'instance_norm', 'batch_norm'
+        # 0 for no weight decay
+        self.weight_decay = 3e-6
+        # which weights to exclude from weight decay, options: ["norm", "bias"].
+        self.exclude_from_wd = ("norm",)
+
+        # one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (= default = 'kaiming_uniform')
+        self.weight_init = None
+
+        #########################
+        #  Schedule / Selection #
+        #########################
+
+        self.num_epochs = 28
+        self.num_train_batches = 100 if self.dim == 2 else 200
+        self.batch_size = 16 if self.dim == 2 else 8
+
+        self.do_validation = True
+        # decide whether to validate on entire patient volumes (like testing) or sampled patches (like training)
+        # the former is more accurate, while the latter is faster (depending on volume size)
+        self.val_mode = 'val_patient' # one of 'val_sampling' , 'val_patient'
+        if self.val_mode == 'val_patient':
+            self.max_val_patients = None  # if 'None' iterates over entire val_set once.
+        if self.val_mode == 'val_sampling':
+            self.num_val_batches = 50
+
+        # set dynamic_lr_scheduling to True to apply LR scheduling with below settings.
+        self.dynamic_lr_scheduling = True
+        self.lr_decay_factor = 0.5
+        self.scheduling_patience = np.ceil(7200 / (self.num_train_batches * self.batch_size))
+        self.scheduling_criterion = 'malignant_ap'
+        self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
+
+        #########################
+        #   Testing / Plotting  #
+        #########################
+
+        # set the top-n epochs to be saved for temporal averaging in testing.
+        self.save_n_models = 5
+        self.test_n_epochs = 5
+
+        # set a minimum epoch number for saving in case of instabilities in the first phase of training.
+        self.min_save_thresh = 0 if self.dim == 2 else 0
+
+        self.report_score_level = ['patient', 'rois']  # choose list from 'patient', 'rois'
+        self.class_dict = {1: 'benign', 2: 'malignant'}  # 0 is background.
+        self.patient_class_of_interest = 2  # patient metrics are only plotted for one class.
+        self.ap_match_ious = [0.1]  # list of ious to be evaluated for ap-scoring.
+
+        self.model_selection_criteria = ['benign_ap', 'malignant_ap'] # criteria to average over for saving epochs.
+        self.min_det_thresh = 0.1  # minimum confidence value to select predictions for evaluation.
+
+        # threshold for clustering predictions together (wcs = weighted cluster scoring).
+        # needs to be >= the expected overlap of predictions coming from one model (typically NMS threshold).
+        # if too high, preds of the same object are separate clusters.
+        self.wcs_iou = 1e-5
+
+        self.plot_prediction_histograms = True
+        self.plot_stat_curves = False
+
+        #########################
+        #   Data Augmentation   #
+        #########################
+
+        self.da_kwargs={
+        'do_elastic_deform': True,
+        'alpha':(0., 1500.),
+        'sigma':(30., 50.),
+        'do_rotation':True,
+        'angle_x': (0., 2 * np.pi),
+        'angle_y': (0., 0),
+        'angle_z': (0., 0),
+        'do_scale': True,
+        'scale':(0.8, 1.1),
+        'random_crop':False,
+        'rand_crop_dist':  (self.patch_size[0] / 2. - 3, self.patch_size[1] / 2. - 3),
+        'border_mode_data': 'constant',
+        'border_cval_data': 0,
+        'order_data': 1
+        }
+
+        if self.dim == 3:
+            self.da_kwargs['do_elastic_deform'] = False
+            self.da_kwargs['angle_x'] = (0, 0.0)
+            self.da_kwargs['angle_y'] = (0, 0.0) #must be 0!!
+            self.da_kwargs['angle_z'] = (0., 2 * np.pi)
+
+
+        #########################
+        #   Add model specifics #
+        #########################
+
+        {'detection_unet': self.add_det_unet_configs,
+         'mrcnn': self.add_mrcnn_configs,
+         'ufrcnn': self.add_mrcnn_configs,
+         'retina_net': self.add_mrcnn_configs,
+         'retina_unet': self.add_mrcnn_configs,
+        }[self.model]()
+
+
+    def add_det_unet_configs(self):
+
+        self.learning_rate = [1e-4] * self.num_epochs
+
+        # aggregation from pixel perdiction to object scores (connected component). One of ['max', 'median']
+        self.aggregation_operation = 'max'
+
+        # max number of roi candidates to identify per image (slice in 2D, volume in 3D)
+        self.n_roi_candidates = 3 if self.dim == 2 else 8
+
+        # loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
+        self.seg_loss_mode = 'dice_wce'
+
+        # if <1, false positive predictions in foreground are penalized less.
+        self.fp_dice_weight = 1 if self.dim == 2 else 1
+
+        self.wce_weights = [0.3, 1, 1]
+        self.detection_min_confidence = self.min_det_thresh
+
+        # if 'True', loss distinguishes all classes, else only foreground vs. background (class agnostic).
+        self.class_specific_seg_flag = True
+        self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
+        self.head_classes = self.num_seg_classes
+
+    def add_mrcnn_configs(self):
+
+        # learning rate is a list with one entry per epoch.
+        self.learning_rate = [3e-4] * self.num_epochs
+
+        # disable mask head loss. (e.g. if no pixelwise annotations available)
+        self.frcnn_mode = False
+
+        # disable the re-sampling of mask proposals to original size for speed-up.
+        # since evaluation is detection-driven (box-matching) and not instance segmentation-driven (iou-matching),
+        # mask-outputs are optional.
+        self.return_masks_in_val = True
+        self.return_masks_in_test = False
+
+        # set number of proposal boxes to plot after each epoch.
+        self.n_plot_rpn_props = 0 if self.dim == 2 else 0
+
+        # number of classes for head networks: n_foreground_classes + 1 (background)
+        self.head_classes = 3
+
+        # seg_classes hier refers to the first stage classifier (RPN)
+        self.num_seg_classes = 2  # foreground vs. background
+
+        # feature map strides per pyramid level are inferred from architecture.
+        self.backbone_strides = {'xy': [4, 8, 16, 32], 'z': [1, 2, 4, 8]}
+
+        # anchor scales are chosen according to expected object sizes in data set. Default uses only one anchor scale
+        # per pyramid level. (outer list are pyramid levels (corresponding to BACKBONE_STRIDES), inner list are scales per level.)
+        self.rpn_anchor_scales = {'xy': [[8], [16], [32], [64]], 'z': [[2], [4], [8], [16]]}
+
+        # choose which pyramid levels to extract features from: P2: 0, P3: 1, P4: 2, P5: 3.
+        self.pyramid_levels = [0, 1, 2, 3]
+
+        # number of feature maps in rpn. typically lowered in 3D to save gpu-memory.
+        self.n_rpn_features = 512 if self.dim == 2 else 128
+
+        # anchor ratios and strides per position in feature maps.
+        self.rpn_anchor_ratios = [0.5, 1., 2.]
+        self.rpn_anchor_stride = 1
+
+        # Threshold for first stage (RPN) non-maximum suppression (NMS):  LOWER == HARDER SELECTION
+        self.rpn_nms_threshold = 0.7 if self.dim == 2 else 0.7
+
+        # loss sampling settings.
+        self.rpn_train_anchors_per_image = 64 #per batch element
+        self.train_rois_per_image = 2 #per batch element
+        self.roi_positive_ratio = 0.5
+        self.anchor_matching_iou = 0.7
+
+        # factor of top-k candidates to draw from  per negative sample (stochastic-hard-example-mining).
+        # poolsize to draw top-k candidates from will be shem_poolsize * n_negative_samples.
+        self.shem_poolsize = 10
+
+        self.pool_size = (7, 7) if self.dim == 2 else (7, 7, 3)
+        self.mask_pool_size = (14, 14) if self.dim == 2 else (14, 14, 5)
+        self.mask_shape = (28, 28) if self.dim == 2 else (28, 28, 10)
+
+        self.rpn_bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
+        self.bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
+        self.window = np.array([0, 0, self.patch_size[0], self.patch_size[1]])
+        self.scale = np.array([self.patch_size[0], self.patch_size[1], self.patch_size[0], self.patch_size[1]])
+
+        if self.dim == 2:
+            self.rpn_bbox_std_dev = self.rpn_bbox_std_dev[:4]
+            self.bbox_std_dev = self.bbox_std_dev[:4]
+            self.window = self.window[:4]
+            self.scale = self.scale[:4]
+
+        # pre-selection in proposal-layer (stage 1) for NMS-speedup. applied per batch element.
+        self.pre_nms_limit = 3000 if self.dim == 2 else 6000
+
+        # n_proposals to be selected after NMS per batch element. too high numbers blow up memory if "detect_while_training" is True,
+        # since proposals of the entire batch are forwarded through second stage in as one "batch".
+        self.roi_chunk_size = 800 if self.dim == 2 else 600
+        self.post_nms_rois_training = 500 if self.dim == 2 else 75
+        self.post_nms_rois_inference = 500
+
+        # Final selection of detections (refine_detections)
+        self.model_max_instances_per_batch_element = 10 if self.dim == 2 else 30  # per batch element and class.
+        self.detection_nms_threshold = 1e-5  # needs to be > 0, otherwise all predictions are one cluster.
+        self.model_min_confidence = 0.1
+
+        if self.dim == 2:
+            self.backbone_shapes = np.array(
+                [[int(np.ceil(self.patch_size[0] / stride)),
+                  int(np.ceil(self.patch_size[1] / stride))]
+                 for stride in self.backbone_strides['xy']])
+        else:
+            self.backbone_shapes = np.array(
+                [[int(np.ceil(self.patch_size[0] / stride)),
+                  int(np.ceil(self.patch_size[1] / stride)),
+                  int(np.ceil(self.patch_size[2] / stride_z))]
+                 for stride, stride_z in zip(self.backbone_strides['xy'], self.backbone_strides['z']
+                                             )])
+        if self.model == 'ufrcnn':
+            self.operate_stride1 = True
+            self.class_specific_seg_flag = True
+            self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
+            self.frcnn_mode = True
+
+        if self.model == 'retina_net' or self.model == 'retina_unet':
+            # implement extra anchor-scales according to retina-net publication.
+            self.rpn_anchor_scales['xy'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
+                                            self.rpn_anchor_scales['xy']]
+            self.rpn_anchor_scales['z'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
+                                           self.rpn_anchor_scales['z']]
+            self.n_anchors_per_pos = len(self.rpn_anchor_ratios) * 3
+
+            self.n_rpn_features = 256 if self.dim == 2 else 64
+
+            # pre-selection of detections for NMS-speedup. per entire batch.
+            self.pre_nms_limit = 10000 if self.dim == 2 else 50000
+
+            # anchor matching iou is lower than in Mask R-CNN according to https://arxiv.org/abs/1708.02002
+            self.anchor_matching_iou = 0.5
+
+            # if 'True', seg loss distinguishes all classes, else only foreground vs. background (class agnostic).
+            self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
+
+            if self.model == 'retina_unet':
+                self.operate_stride1 = True