diff --git a/experiments/toy_exp/configs.py b/experiments/toy_exp/configs.py
index 2e8c5bf..2365ecf 100644
--- a/experiments/toy_exp/configs.py
+++ b/experiments/toy_exp/configs.py
@@ -1,344 +1,344 @@
#!/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=None):
#########################
# Preprocessing #
#########################
- self.root_dir = '/mnt/HDD2TB/Documents/data/mdt_toy'
+ self.root_dir = '/media/gregor/HDD2TB/data/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', 'detection_unet'].
- self.model = 'detection_unet'
+ self.model = 'mrcnn'
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
self.n_train_data = 1000
# 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'
# 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 = '/path/to/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'
self.weight_decay = 0
# one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
self.weight_init = None
#########################
# Schedule / Selection #
#########################
self.num_epochs = 100
self.num_train_batches = 200 if self.dim == 2 else 200
self.batch_size = 20 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 morge 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
#########################
# 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,
'ufrcnn_surrounding': self.add_mrcnn_configs,
'retina_net': self.add_mrcnn_configs,
'retina_unet': self.add_mrcnn_configs,
'prob_detector': 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 = [1, 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 = [1e-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 = 5 if self.dim == 2 else 30
# 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 = 2 #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' or self.model == 'prob_detector':
# 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
diff --git a/experiments/toy_exp/generate_toys.py b/experiments/toy_exp/generate_toys.py
index 4f44768..cee3341 100644
--- a/experiments/toy_exp/generate_toys.py
+++ b/experiments/toy_exp/generate_toys.py
@@ -1,107 +1,107 @@
#!/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 os
import numpy as np
import pandas as pd
import pickle
from multiprocessing import Pool
import configs as cf
def multi_processing_create_image(inputs):
out_dir, six, foreground_margin, class_diameters, mode = inputs
- print('proceesing {} {}'.format(out_dir, six))
+ print('processing {} {}'.format(out_dir, six))
img = np.random.rand(320, 320)
seg = np.zeros((320, 320)).astype('uint8')
center_x = np.random.randint(foreground_margin, img.shape[0] - foreground_margin)
center_y = np.random.randint(foreground_margin, img.shape[1] - foreground_margin)
class_id = np.random.randint(0, 2)
for y in range(img.shape[0]):
for x in range(img.shape[0]):
if ((x - center_x) ** 2 + (y - center_y) ** 2 - class_diameters[class_id] ** 2) < 0:
img[y][x] += 0.2
seg[y][x] = 1
if 'donuts' in mode:
whole_diameter = 4
if class_id == 1:
for y in range(img.shape[0]):
for x in range(img.shape[0]):
if ((x - center_x) ** 2 + (y - center_y) ** 2 - whole_diameter ** 2) < 0:
img[y][x] -= 0.2
if mode == 'donuts_shape':
seg[y][x] = 0
out = np.concatenate((img[None], seg[None]))
out_path = os.path.join(out_dir, '{}.npy'.format(six))
np.save(out_path, out)
with open(os.path.join(out_dir, 'meta_info_{}.pickle'.format(six)), 'wb') as handle:
pickle.dump([out_path, class_id, str(six)], handle)
def generate_experiment(exp_name, n_train_images, n_test_images, mode, class_diameters=(20, 20)):
train_dir = os.path.join(cf.root_dir, exp_name, 'train')
test_dir = os.path.join(cf.root_dir, exp_name, 'test')
if not os.path.exists(train_dir):
os.makedirs(train_dir)
if not os.path.exists(test_dir):
os.makedirs(test_dir)
# enforced distance between object center and image edge.
foreground_margin = np.max(class_diameters) // 2
info = []
info += [[train_dir, six, foreground_margin, class_diameters, mode] for six in range(n_train_images)]
info += [[test_dir, six, foreground_margin, class_diameters, mode] for six in range(n_test_images)]
print('starting creating {} images'.format(len(info)))
pool = Pool(processes=12)
pool.map(multi_processing_create_image, info, chunksize=1)
pool.close()
pool.join()
aggregate_meta_info(train_dir)
aggregate_meta_info(test_dir)
def aggregate_meta_info(exp_dir):
files = [os.path.join(exp_dir, f) for f in os.listdir(exp_dir) if 'meta_info' in f]
df = pd.DataFrame(columns=['path', 'class_id', 'pid'])
for f in files:
with open(f, 'rb') as handle:
df.loc[len(df)] = pickle.load(handle)
df.to_pickle(os.path.join(exp_dir, 'info_df.pickle'))
print ("aggregated meta info to df with length", len(df))
if __name__ == '__main__':
cf = cf.configs()
- generate_experiment('donuts_shape_threads', n_train_images=1500, n_test_images=1000, mode='donuts_shape')
+ generate_experiment('donuts_shape', n_train_images=1500, n_test_images=1000, mode='donuts_shape')
generate_experiment('donuts_pattern', n_train_images=1500, n_test_images=1000, mode='donuts_pattern')
generate_experiment('circles_scale', n_train_images=1500, n_test_images=1000, mode='circles_scale', class_diameters=(19, 20))