diff --git a/datasets/lidc/configs.py b/datasets/lidc/configs.py
index 738ecee..19b243f 100644
--- a/datasets/lidc/configs.py
+++ b/datasets/lidc/configs.py
@@ -1,444 +1,445 @@
#!/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
import os
from collections import namedtuple
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import numpy as np
sys.path.append(os.path.dirname(os.path.realpath(__file__))+"/../..")
from default_configs import DefaultConfigs
# legends, nested classes are not handled well in multiprocessing! hence, Label class def in outer scope
Label = namedtuple("Label", ['id', 'name', 'color', 'm_scores']) # m_scores = malignancy scores
binLabel = namedtuple("binLabel", ['id', 'name', 'color', 'm_scores', 'bin_vals'])
class Configs(DefaultConfigs):
def __init__(self, server_env=None):
super(Configs, self).__init__(server_env)
#########################
# Preprocessing #
#########################
self.root_dir = '/home/gregor/networkdrives/E130-Personal/Goetz/Datenkollektive/Lungendaten/Nodules_LIDC_IDRI'
self.raw_data_dir = '{}/new_nrrd'.format(self.root_dir)
self.pp_dir = '/media/gregor/HDD2TB/data/lidc/pp_20200309_dev'
# 'merged' for one gt per image, 'single_annotator' for four gts per image.
self.gts_to_produce = ["single_annotator", "merged"]
self.target_spacing = (0.7, 0.7, 1.25)
#########################
# I/O #
#########################
# path to preprocessed data.
self.pp_name = 'pp_20190805'
self.input_df_name = 'info_df.pickle'
self.data_sourcedir = '/media/gregor/HDD2TB/data/lidc/{}/'.format(self.pp_name)
#self.data_sourcedir = '/home/gregor/networkdrives/E132-Cluster-Projects/lidc/data/{}/'.format(self.pp_name)
# settings for deployment on cluster.
if server_env:
# path to preprocessed data.
self.data_sourcedir = '/datasets/datasets_ramien/lidc/data/{}_npz/'.format(self.pp_name)
# one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_fpn'].
- self.model = 'retina_unet'
+ self.model = 'mrcnn'
self.model_path = 'models/{}.py'.format(self.model if not 'retina' in self.model else 'retina_net')
self.model_path = os.path.join(self.source_dir, self.model_path)
#########################
# Architecture #
#########################
# dimension the model operates in. one out of [2, 3].
self.dim = 2
# 'class': standard object classification per roi, pairwise combinable with each of below tasks.
# if 'class' is omitted from tasks, object classes will be fg/bg (1/0) from RPN.
# 'regression': regress some vector per each roi
# 'regression_ken_gal': use kendall-gal uncertainty sigma
# 'regression_bin': classify each roi into a bin related to a regression scale
self.prediction_tasks = ['class']
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 = "instance_norm" # one of None, 'instance_norm', 'batch_norm'
# one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
self.weight_init = None
self.regression_n_features = 1
#########################
# Data Loader #
#########################
# distorted gt experiments: train on single-annotator gts in a random fashion to investigate network's
# handling of noisy gts.
# choose 'merged' for single, merged gt per image, or 'single_annotator' for four gts per image.
# validation is always performed on same gt kind as training, testing always on merged gt.
self.training_gts = "merged"
# 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.pre_crop_size_3D = [160, 160, 96]
self.patch_size_3D = [160, 160, 96]
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
- self.batch_random_ratio = 0.2
+ self.batch_random_ratio = 0.1
self.balance_target = "class_targets" if 'class' in self.prediction_tasks else 'rg_bin_targets'
# set 2D network to match 3D gt boxes.
self.merge_2D_to_3D_preds = self.dim==2
self.observables_rois = []
#self.rg_map = {1:1, 2:2, 3:3, 4:4, 5:5}
#########################
# Colors and Legends #
#########################
self.plot_frequency = 5
binary_cl_labels = [Label(1, 'benign', (*self.dark_green, 1.), (1, 2)),
Label(2, 'malignant', (*self.red, 1.), (3, 4, 5))]
quintuple_cl_labels = [Label(1, 'MS1', (*self.dark_green, 1.), (1,)),
Label(2, 'MS2', (*self.dark_yellow, 1.), (2,)),
Label(3, 'MS3', (*self.orange, 1.), (3,)),
Label(4, 'MS4', (*self.bright_red, 1.), (4,)),
Label(5, 'MS5', (*self.red, 1.), (5,))]
# choose here if to do 2-way or 5-way regression-bin classification
task_spec_cl_labels = quintuple_cl_labels
self.class_labels = [
# #id #name #color #malignancy score
Label( 0, 'bg', (*self.gray, 0.), (0,))]
if "class" in self.prediction_tasks:
self.class_labels += task_spec_cl_labels
else:
self.class_labels += [Label(1, 'lesion', (*self.orange, 1.), (1,2,3,4,5))]
if any(['regression' in task for task in self.prediction_tasks]):
self.bin_labels = [binLabel(0, 'MS0', (*self.gray, 1.), (0,), (0,))]
self.bin_labels += [binLabel(cll.id, cll.name, cll.color, cll.m_scores,
tuple([ms for ms in cll.m_scores])) for cll in task_spec_cl_labels]
self.bin_id2label = {label.id: label for label in self.bin_labels}
self.ms2bin_label = {ms: label for label in self.bin_labels for ms in label.m_scores}
bins = [(min(label.bin_vals), max(label.bin_vals)) for label in self.bin_labels]
self.bin_id2rg_val = {ix: [np.mean(bin)] for ix, bin in enumerate(bins)}
self.bin_edges = [(bins[i][1] + bins[i + 1][0]) / 2 for i in range(len(bins) - 1)]
if self.class_specific_seg:
self.seg_labels = self.class_labels
else:
self.seg_labels = [ # id #name #color
Label(0, 'bg', (*self.gray, 0.)),
Label(1, 'fg', (*self.orange, 1.))
]
self.class_id2label = {label.id: label for label in self.class_labels}
self.class_dict = {label.id: label.name for label in self.class_labels if label.id != 0}
# class_dict is used in evaluator / ap, auc, etc. statistics, and class 0 (bg) only needs to be
# evaluated in debugging
self.class_cmap = {label.id: label.color for label in self.class_labels}
self.seg_id2label = {label.id: label for label in self.seg_labels}
self.cmap = {label.id: label.color for label in self.seg_labels}
self.plot_prediction_histograms = True
self.plot_stat_curves = False
self.has_colorchannels = False
self.plot_class_ids = True
self.num_classes = len(self.class_dict) # for instance classification (excl background)
self.num_seg_classes = len(self.seg_labels) # incl background
#########################
# Data Augmentation #
#########################
self.da_kwargs={
'mirror': True,
'mirror_axes': tuple(np.arange(0, self.dim, 1)),
'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)
#################################
# Schedule / Selection / Optim #
#################################
self.num_epochs = 130 if self.dim == 2 else 150
self.num_train_batches = 200 if self.dim == 2 else 200
self.batch_size = 20 if self.dim == 2 else 8
# 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_sampling' # only 'val_sampling', 'val_patient' not implemented
if self.val_mode == 'val_patient':
raise NotImplementedError
if self.val_mode == 'val_sampling':
self.num_val_batches = 70
self.save_n_models = 4
# 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
# criteria to average over for saving epochs, 'criterion':weight.
if "class" in self.prediction_tasks:
# 'criterion': weight
if len(self.class_labels)==3:
self.model_selection_criteria = {"benign_ap": 0.5, "malignant_ap": 0.5}
elif len(self.class_labels)==6:
self.model_selection_criteria = {str(label.name)+"_ap": 1./5 for label in self.class_labels if label.id!=0}
elif any("regression" in task for task in self.prediction_tasks):
self.model_selection_criteria = {"lesion_ap": 0.2, "lesion_avp": 0.8}
- self.weight_decay = 3e-5
+ self.weight_decay = 1e-5
+ self.exclude_from_wd = []
self.clip_norm = 200 if 'regression_ken_gal' in self.prediction_tasks else None # number or None
# int in [0, dataset_size]. select n patients from dataset for prototyping. If None, all data is used.
self.select_prototype_subset = None #self.batch_size
#########################
# Testing #
#########################
# set the top-n-epochs to be saved for temporal averaging in testing.
self.test_n_epochs = self.save_n_models
self.test_aug_axes = (0,1,(0,1)) # None or list: choices are 0,1,(0,1) (0==spatial y, 1== spatial x).
self.held_out_test_set = False
self.max_test_patients = "all" # "all" or number
self.report_score_level = ['rois', 'patient'] # choose list from 'patient', 'rois'
self.patient_class_of_interest = 2 if 'class' in self.prediction_tasks else 1
self.metrics = ['ap', 'auc']
if any(['regression' in task for task in self.prediction_tasks]):
self.metrics += ['avp', 'rg_MAE_weighted', 'rg_MAE_weighted_tp',
'rg_bin_accuracy_weighted', 'rg_bin_accuracy_weighted_tp']
if 'aleatoric' in self.model:
self.metrics += ['rg_uncertainty', 'rg_uncertainty_tp', 'rg_uncertainty_tp_weighted']
self.evaluate_fold_means = True
self.ap_match_ious = [0.1] # list of ious to be evaluated for ap-scoring.
self.min_det_thresh = 0.1 # minimum confidence value to select predictions for evaluation.
# aggregation method for test and val_patient predictions.
# wbc = weighted box clustering as in https://arxiv.org/pdf/1811.08661.pdf,
# nms = standard non-maximum suppression, or None = no clustering
self.clustering = 'wbc'
# iou thresh (exclusive!) for regarding two preds as concerning the same ROI
self.clustering_iou = 0.1 # has to be larger than desired possible overlap iou of model predictions
self.plot_prediction_histograms = True
self.plot_stat_curves = False
self.n_test_plots = 1
#########################
# Assertions #
#########################
if not 'class' in self.prediction_tasks:
assert self.num_classes == 1
#########################
# Add model specifics #
#########################
{'detection_fpn': self.add_det_fpn_configs,
'mrcnn': self.add_mrcnn_configs, 'mrcnn_aleatoric': self.add_mrcnn_configs,
'retina_net': self.add_mrcnn_configs,
'retina_unet': self.add_mrcnn_configs,
}[self.model]()
def rg_val_to_bin_id(self, rg_val):
return float(np.digitize(np.mean(rg_val), self.bin_edges))
def add_det_fpn_configs(self):
- self.learning_rate = [1e-4] * self.num_epochs
+ self.learning_rate = [3e-4] * self.num_epochs
self.dynamic_lr_scheduling = False
# RoI score assigned to aggregation from pixel prediction (connected component). One of ['max', 'median'].
self.score_det = 'max'
# max number of roi candidates to identify per batch element and class.
self.n_roi_candidates = 10 if self.dim == 2 else 30
# loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
self.seg_loss_mode = 'wce'
# if <1, false positive predictions in foreground are penalized less.
self.fp_dice_weight = 1 if self.dim == 2 else 1
if len(self.class_labels)==3:
self.wce_weights = [1., 1., 1.] if self.seg_loss_mode=="dice_wce" else [0.1, 1., 1.]
elif len(self.class_labels)==6:
self.wce_weights = [1., 1., 1., 1., 1., 1.] if self.seg_loss_mode == "dice_wce" else [0.1, 1., 1., 1., 1., 1.]
else:
raise Exception("mismatch loss weights & nr of classes")
self.detection_min_confidence = self.min_det_thresh
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
self.dynamic_lr_scheduling = 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_train = False
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 network heads: n_foreground_classes + 1 (background)
self.head_classes = self.num_classes + 1
self.frcnn_mode = False
# 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 = 6 #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], 0, self.patch_size_3D[2]])
self.scale = np.array([self.patch_size[0], self.patch_size[1], self.patch_size[0], self.patch_size[1],
self.patch_size_3D[2], self.patch_size_3D[2]])
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 = 2500 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 == 'retina_net' or self.model == 'retina_unet':
self.focal_loss = True
# 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 128
# pre-selection of detections for NMS-speedup. per entire batch.
self.pre_nms_limit = (500 if self.dim == 2 else 6250) * self.batch_size
# 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 self.model == 'retina_unet':
self.operate_stride1 = True
diff --git a/datasets/lidc/data_loader.py b/datasets/lidc/data_loader.py
index 4f5b3b0..e9cd2a3 100644
--- a/datasets/lidc/data_loader.py
+++ b/datasets/lidc/data_loader.py
@@ -1,1025 +1,1026 @@
# 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
from utils.dataloader_utils import BatchGenerator as BatchGeneratorParent
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, 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=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 = 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 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 = 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)):
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)):
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_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(BatchGeneratorParent):
"""
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.
"""
+ # todo limit bg weights
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.plot_dir,
"train_gen_distr_"+str(self.cf.fold)+"_rater"+str(rater)+".png"))
return p_probs, unique_ts, sample_stats
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.p_probs = []
self.sample_stats = []
# todo resolve pickling error
# p = Pool(processes=min(self.rater_bsize, 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)
for r in range(self.rater_bsize):
# todo multiprocess. takes forever
p_probs, unique_ts, sample_stats = self.balance_target_distribution(r, plot=name == "train")
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_counts": np.zeros(len(self.unique_ts,), dtype='uint32')}
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.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 = 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 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 = 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)):
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)):
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_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
"""
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=data_gen.n_filled_threads,
seeds=range(data_gen.n_filled_threads))
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", 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/configs.py b/datasets/toy/configs.py
index 7d7d657..10d5889 100644
--- a/datasets/toy/configs.py
+++ b/datasets/toy/configs.py
@@ -1,490 +1,491 @@
#!/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
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import numpy as np
from default_configs import DefaultConfigs
from collections import namedtuple
boxLabel = namedtuple('boxLabel', ["name", "color"])
Label = namedtuple("Label", ['id', 'name', 'shape', 'radius', 'color', 'regression', 'ambiguities', 'gt_distortion'])
binLabel = namedtuple("binLabel", ['id', 'name', 'color', 'bin_vals'])
class Configs(DefaultConfigs):
def __init__(self, server_env=None):
super(Configs, self).__init__(server_env)
#########################
# Prepro #
#########################
self.pp_rootdir = os.path.join('/home/gregor/datasets/toy', "cyl1ps_dev")
self.pp_npz_dir = self.pp_rootdir+"_npz"
self.pre_crop_size = [320,320,8] #y,x,z; determines pp data shape (2D easily implementable, but only 3D for now)
self.min_2d_radius = 6 #in pixels
self.n_train_samples, self.n_test_samples = 1200, 1000
# not actually real one-hot encoding (ohe) but contains more info: roi-overlap only within classes.
self.pp_create_ohe_seg = False
self.pp_empty_samples_ratio = 0.1
self.pp_place_radii_mid_bin = True
self.pp_only_distort_2d = True
# outer-most intensity of blurred radii, relative to inner-object intensity. <1 for decreasing, > 1 for increasing.
# e.g.: setting 0.1 means blurred edge has min intensity 10% as large as inner-object intensity.
self.pp_blur_min_intensity = 0.2
self.max_instances_per_sample = 1 #how many max instances over all classes per sample (img if 2d, vol if 3d)
self.max_instances_per_class = self.max_instances_per_sample # how many max instances per image per class
self.noise_scale = 0. # std-dev of gaussian noise
self.ambigs_sampling = "gaussian" #"gaussian" or "uniform"
""" radius_calib: gt distort for calibrating uncertainty. Range of gt distortion is inferable from
image by distinguishing it from the rest of the object.
blurring width around edge will be shifted so that symmetric rel to orig radius.
blurring scale: if self.ambigs_sampling is uniform, distribution's non-zero range (b-a) will be sqrt(12)*scale
since uniform dist has variance (b-a)²/12. b,a will be placed symmetrically around unperturbed radius.
if sampling is gaussian, then scale parameter sets one std dev, i.e., blurring width will be orig_radius * std_dev * 2.
"""
self.ambiguities = {
#set which classes to apply which ambs to below in class labels
#choose out of: 'outer_radius', 'inner_radius', 'radii_relations'.
#kind #probability #scale (gaussian std, relative to unperturbed value)
#"outer_radius": (1., 0.5),
#"outer_radius_xy": (1., 0.5),
#"inner_radius": (0.5, 0.1),
#"radii_relations": (0.5, 0.1),
"radius_calib": (1., 1./6)
}
# shape choices: 'cylinder', 'block'
# id, name, shape, radius, color, regression, ambiguities, gt_distortion
self.pp_classes = [Label(1, 'cylinder', 'cylinder', ((6,6,1),(40,40,8)), (*self.blue, 1.), "radius_2d", (), ()),
#Label(2, 'block', 'block', ((6,6,1),(40,40,8)), (*self.aubergine,1.), "radii_2d", (), ('radius_calib',))
]
#########################
# I/O #
#########################
self.data_sourcedir = '/home/gregor/datasets/toy/cyl1ps_dev'
if server_env:
- self.data_sourcedir = '/datasets/data_ramien/toy/cyl1ps_dev_npz'
+ self.data_sourcedir = '/datasets/datasets_ramien/toy/data/cyl1ps_dev_npz'
self.test_data_sourcedir = os.path.join(self.data_sourcedir, 'test')
self.data_sourcedir = os.path.join(self.data_sourcedir, "train")
self.info_df_name = 'info_df.pickle'
# one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn', 'detection_fpn'].
self.model = 'mrcnn'
self.model_path = 'models/{}.py'.format(self.model if not 'retina' in self.model else 'retina_net')
self.model_path = os.path.join(self.source_dir, self.model_path)
#########################
# Architecture #
#########################
# one out of [2, 3]. dimension the model operates in.
- self.dim = 3
+ self.dim = 2
# 'class', 'regression', 'regression_bin', 'regression_ken_gal'
# currently only tested mode is a single-task at a time (i.e., only one task in below list)
# but, in principle, tasks could be combined (e.g., object classes and regression per class)
self.prediction_tasks = ['class', ]
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 = 'instance_norm' # one of None, 'instance_norm', 'batch_norm'
self.relu = 'relu'
# one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
self.weight_init = None
self.regression_n_features = 1 # length of regressor target vector
#########################
# Data Loader #
#########################
- self.num_epochs = 32
- self.num_train_batches = 120 if self.dim == 2 else 180
- self.batch_size = 8 if self.dim == 2 else 4
+ self.num_epochs = 24
+ self.num_train_batches = 100 if self.dim == 2 else 180
+ self.batch_size = 20 if self.dim == 2 else 8
self.n_cv_splits = 4
# select modalities from preprocessed data
self.channels = [0]
self.n_channels = len(self.channels)
# which channel (mod) to show as bg in plotting, will be extra added to batch if not in self.channels
self.plot_bg_chan = 0
self.crop_margin = [20, 20, 1] # has to be smaller than respective patch_size//2
self.patch_size_2D = self.pre_crop_size[:2]
self.patch_size_3D = self.pre_crop_size[:2]+[8]
# patch_size to be used for training. pre_crop_size is the patch_size before data augmentation.
self.patch_size = self.patch_size_2D if self.dim == 2 else self.patch_size_3D
# ratio of free sampled batch elements before class balancing is triggered
# (>0 to include "empty"/background patches.)
self.batch_random_ratio = 0.2
self.balance_target = "class_targets" if 'class' in self.prediction_tasks else "rg_bin_targets"
self.observables_patient = []
self.observables_rois = []
self.seed = 3 #for generating folds
#############################
# Colors, Classes, Legends #
#############################
self.plot_frequency = 4
binary_bin_labels = [binLabel(1, 'r<=25', (*self.green, 1.), (1,25)),
binLabel(2, 'r>25', (*self.red, 1.), (25,))]
quintuple_bin_labels = [binLabel(1, 'r2-10', (*self.green, 1.), (2,10)),
binLabel(2, 'r10-20', (*self.yellow, 1.), (10,20)),
binLabel(3, 'r20-30', (*self.orange, 1.), (20,30)),
binLabel(4, 'r30-40', (*self.bright_red, 1.), (30,40)),
binLabel(5, 'r>40', (*self.red, 1.), (40,))]
# choose here if to do 2-way or 5-way regression-bin classification
task_spec_bin_labels = quintuple_bin_labels
self.class_labels = [
# regression: regression-task label, either value or "(x,y,z)_radius" or "radii".
# ambiguities: name of above defined ambig to apply to image data (not gt); need to be iterables!
# gt_distortion: name of ambig to apply to gt only; needs to be iterable!
# #id #name #shape #radius #color #regression #ambiguities #gt_distortion
Label( 0, 'bg', None, (0, 0, 0), (*self.white, 0.), (0, 0, 0), (), ())]
if "class" in self.prediction_tasks:
self.class_labels += self.pp_classes
else:
self.class_labels += [Label(1, 'object', 'object', ('various',), (*self.orange, 1.), ('radius_2d',), ("various",), ('various',))]
if any(['regression' in task for task in self.prediction_tasks]):
self.bin_labels = [binLabel(0, 'bg', (*self.white, 1.), (0,))]
self.bin_labels += task_spec_bin_labels
self.bin_id2label = {label.id: label for label in self.bin_labels}
bins = [(min(label.bin_vals), max(label.bin_vals)) for label in self.bin_labels]
self.bin_id2rg_val = {ix: [np.mean(bin)] for ix, bin in enumerate(bins)}
self.bin_edges = [(bins[i][1] + bins[i + 1][0]) / 2 for i in range(len(bins) - 1)]
self.bin_dict = {label.id: label.name for label in self.bin_labels if label.id != 0}
if self.class_specific_seg:
self.seg_labels = self.class_labels
self.box_type2label = {label.name: label for label in self.box_labels}
self.class_id2label = {label.id: label for label in self.class_labels}
self.class_dict = {label.id: label.name for label in self.class_labels if label.id != 0}
self.seg_id2label = {label.id: label for label in self.seg_labels}
self.cmap = {label.id: label.color for label in self.seg_labels}
self.plot_prediction_histograms = True
self.plot_stat_curves = False
self.has_colorchannels = False
self.plot_class_ids = True
self.num_classes = len(self.class_dict)
self.num_seg_classes = len(self.seg_labels)
#########################
# Data Augmentation #
#########################
self.do_aug = True
self.da_kwargs = {
'mirror': True,
'mirror_axes': tuple(np.arange(0, self.dim, 1)),
'do_elastic_deform': False,
'alpha': (500., 1500.),
'sigma': (40., 45.),
'do_rotation': False,
'angle_x': (0., 2 * np.pi),
'angle_y': (0., 0),
'angle_z': (0., 0),
'do_scale': False,
'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)
#########################
# Schedule / Selection #
#########################
# 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_sampling' # one of 'val_sampling' , 'val_patient'
if self.val_mode == 'val_patient':
self.max_val_patients = 220 # if 'all' iterates over entire val_set once.
if self.val_mode == 'val_sampling':
self.num_val_batches = 35 if self.dim==2 else 25
self.save_n_models = 2
self.min_save_thresh = 1 if self.dim == 2 else 1 # =wait time in epochs
if "class" in self.prediction_tasks:
self.model_selection_criteria = {name + "_ap": 1. for name in self.class_dict.values()}
elif any("regression" in task for task in self.prediction_tasks):
self.model_selection_criteria = {name + "_ap": 0.2 for name in self.class_dict.values()}
self.model_selection_criteria.update({name + "_avp": 0.8 for name in self.class_dict.values()})
self.lr_decay_factor = 0.25
- self.scheduling_patience = int(self.num_epochs / 5)
+ self.scheduling_patience = np.ceil(1800 / (self.num_train_batches * self.batch_size))
self.weight_decay = 3e-5
+ self.exclude_from_wd = []
self.clip_norm = None # number or None
#########################
# Testing / Plotting #
#########################
self.test_aug_axes = (0,1,(0,1)) # None or list: choices are 0,1,(0,1)
self.held_out_test_set = True
self.max_test_patients = "all" # number or "all" for all
self.test_against_exact_gt = True # only True implemented
self.val_against_exact_gt = False # True is an unrealistic --> irrelevant scenario.
self.report_score_level = ['rois'] # 'patient' or 'rois' (incl)
self.patient_class_of_interest = 1
self.patient_bin_of_interest = 2
self.eval_bins_separately = False#"additionally" if not 'class' in self.prediction_tasks else False
self.metrics = ['ap', 'auc', 'dice']
if any(['regression' in task for task in self.prediction_tasks]):
self.metrics += ['avp', 'rg_MAE_weighted', 'rg_MAE_weighted_tp',
'rg_bin_accuracy_weighted', 'rg_bin_accuracy_weighted_tp']
if 'aleatoric' in self.model:
self.metrics += ['rg_uncertainty', 'rg_uncertainty_tp', 'rg_uncertainty_tp_weighted']
self.evaluate_fold_means = True
self.ap_match_ious = [0.5] # threshold(s) for considering a prediction as true positive
self.min_det_thresh = 0.3
self.model_max_iou_resolution = 0.2
# aggregation method for test and val_patient predictions.
# wbc = weighted box clustering as in https://arxiv.org/pdf/1811.08661.pdf,
# nms = standard non-maximum suppression, or None = no clustering
self.clustering = 'wbc'
# iou thresh (exclusive!) for regarding two preds as concerning the same ROI
self.clustering_iou = self.model_max_iou_resolution # has to be larger than desired possible overlap iou of model predictions
- self.merge_2D_to_3D_preds = False
+ self.merge_2D_to_3D_preds = self.dim==2
self.merge_3D_iou = self.model_max_iou_resolution
self.n_test_plots = 1 # per fold and rank
self.test_n_epochs = self.save_n_models # should be called n_test_ens, since is number of models to ensemble over during testing
# is multiplied by (1 + nr of test augs)
#########################
# Assertions #
#########################
if not 'class' in self.prediction_tasks:
assert self.num_classes == 1
#########################
# Add model specifics #
#########################
{'mrcnn': self.add_mrcnn_configs, 'mrcnn_aleatoric': self.add_mrcnn_configs,
'retina_net': self.add_mrcnn_configs, 'retina_unet': self.add_mrcnn_configs,
'detection_unet': self.add_det_unet_configs, 'detection_fpn': self.add_det_fpn_configs
}[self.model]()
def rg_val_to_bin_id(self, rg_val):
#only meant for isotropic radii!!
# only 2D radii (x and y dims) or 1D (x or y) are expected
return np.round(np.digitize(rg_val, self.bin_edges).mean())
def add_det_fpn_configs(self):
- self.learning_rate = [1 * 1e-4] * self.num_epochs
+ self.learning_rate = [3 * 1e-4] * self.num_epochs
self.dynamic_lr_scheduling = True
self.scheduling_criterion = 'torch_loss'
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
self.n_roi_candidates = 4 if self.dim == 2 else 6
# max number of roi candidates to identify per image (slice in 2D, volume in 3D)
# loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
self.seg_loss_mode = 'wce'
self.wce_weights = [1] * self.num_seg_classes if 'dice' in self.seg_loss_mode else [0.1, 1]
self.fp_dice_weight = 1 if self.dim == 2 else 1
# if <1, false positive predictions in foreground are penalized less.
self.detection_min_confidence = 0.05
# how to determine score of roi: 'max' or 'median'
self.score_det = 'max'
def add_det_unet_configs(self):
- self.learning_rate = [1 * 1e-4] * self.num_epochs
+ self.learning_rate = [3 * 1e-4] * self.num_epochs
self.dynamic_lr_scheduling = True
self.scheduling_criterion = "torch_loss"
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
# max number of roi candidates to identify per image (slice in 2D, volume in 3D)
self.n_roi_candidates = 4 if self.dim == 2 else 6
# loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
self.seg_loss_mode = 'wce'
self.wce_weights = [1] * self.num_seg_classes if 'dice' in self.seg_loss_mode else [0.1, 1]
# if <1, false positive predictions in foreground are penalized less.
self.fp_dice_weight = 1 if self.dim == 2 else 1
self.detection_min_confidence = 0.05
# how to determine score of roi: 'max' or 'median'
self.score_det = 'max'
self.init_filts = 32
self.kernel_size = 3 # ks for horizontal, normal convs
self.kernel_size_m = 2 # ks for max pool
self.pad = "same" # "same" or integer, padding of horizontal convs
def add_mrcnn_configs(self):
self.learning_rate = [3e-4] * self.num_epochs
self.dynamic_lr_scheduling = True # with scheduler set in exec
self.scheduling_criterion = max(self.model_selection_criteria, key=self.model_selection_criteria.get)
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
# number of classes for network heads: n_foreground_classes + 1 (background)
self.head_classes = self.num_classes + 1 if 'class' in self.prediction_tasks else 2
# 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)
self.detect_while_training = True
# 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_train = True
self.return_masks_in_val = True
self.return_masks_in_test = True
# feature map strides per pyramid level are inferred from architecture. anchor scales are set accordingly.
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': [[4], [8], [16], [32]], 'z': [[1], [2], [4], [8]]}
# 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 64
# 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 = max(0.8, self.model_max_iou_resolution)
+ self.rpn_nms_threshold = max(0.7, self.model_max_iou_resolution)
# loss sampling settings.
- self.rpn_train_anchors_per_image = 4
+ self.rpn_train_anchors_per_image = 32
self.train_rois_per_image = 6 # per batch_instance
self.roi_positive_ratio = 0.5
self.anchor_matching_iou = 0.8
# k negative example candidates are drawn from a pool of size k*shem_poolsize (stochastic hard-example mining),
# where k<=#positive examples.
- self.shem_poolsize = 2
+ self.shem_poolsize = 6
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], 0, self.patch_size_3D[2]])
self.scale = np.array([self.patch_size[0], self.patch_size[1], self.patch_size[0], self.patch_size[1],
self.patch_size_3D[2], self.patch_size_3D[2]]) # y1,x1,y2,x2,z1,z2
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]
self.plot_y_max = 1.5
self.n_plot_rpn_props = 5 if self.dim == 2 else 30 # per batch_instance (slice in 2D / patient in 3D)
# pre-selection in proposal-layer (stage 1) for NMS-speedup. applied per batch element.
self.pre_nms_limit = 2000 if self.dim == 2 else 4000
# 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 as one "batch".
self.roi_chunk_size = 1300 if self.dim == 2 else 500
self.post_nms_rois_training = 200 * (self.head_classes-1) if self.dim == 2 else 400
self.post_nms_rois_inference = 200 * (self.head_classes-1)
# Final selection of detections (refine_detections)
self.model_max_instances_per_batch_element = 9 if self.dim == 2 else 18 # per batch element and class.
self.detection_nms_threshold = self.model_max_iou_resolution # needs to be > 0, otherwise all predictions are one cluster.
self.model_min_confidence = 0.2 # iou for nms in box refining (directly after heads), should be >0 since ths>=x in mrcnn.py
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 == 'retina_net' or self.model == 'retina_unet':
# whether to use focal loss or SHEM for loss-sample selection
self.focal_loss = False
# implement extra anchor-scales according to https://arxiv.org/abs/1708.02002
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
# pre-selection of detections for NMS-speedup. per entire batch.
self.pre_nms_limit = (500 if self.dim == 2 else 6250) * self.batch_size
# anchor matching iou is lower than in Mask R-CNN according to https://arxiv.org/abs/1708.02002
self.anchor_matching_iou = 0.7
if self.model == 'retina_unet':
self.operate_stride1 = True
diff --git a/datasets/toy/data_loader.py b/datasets/toy/data_loader.py
index 3f5387b..f4bf28f 100644
--- a/datasets/toy/data_loader.py
+++ b/datasets/toy/data_loader.py
@@ -1,596 +1,595 @@
#!/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
-from multiprocessing import Pool
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)
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 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(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 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 = 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)):
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)):
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_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, **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, **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=data_gen.n_filled_threads,
seeds=range(data_gen.n_filled_threads))
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)
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'] = 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 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/datasets/toy_mdt/configs.py b/datasets/toy_mdt/configs.py
index c78225e..6533192 100644
--- a/datasets/toy_mdt/configs.py
+++ b/datasets/toy_mdt/configs.py
@@ -1,351 +1,355 @@
#!/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 collections import namedtuple
from default_configs import DefaultConfigs
Label = namedtuple("Label", ['id', 'name', 'color'])
class Configs(DefaultConfigs):
def __init__(self, server_env=None):
#########################
# Preprocessing #
#########################
self.root_dir = '/home/gregor/datasets/toy_mdt'
#########################
# I/O #
#########################
# one out of [2, 3]. dimension the model operates in.
self.dim = 2
DefaultConfigs.__init__(self, server_env, self.dim)
# one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn'].
self.model = 'mrcnn'
self.model_path = 'models/{}.py'.format(self.model if not 'retina' in self.model else 'retina_net')
self.model_path = os.path.join(self.source_dir, self.model_path)
# int [0 < dataset_size]. select n patients from dataset for prototyping.
self.select_prototype_subset = None
self.held_out_test_set = True
- self.n_train_data = 2500
+ # 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.info_df_name = 'info_df.pickle'
self.pp_name = os.path.join(toy_mode, 'train')
self.data_sourcedir = os.path.join(self.root_dir, self.pp_name)
self.pp_test_name = os.path.join(toy_mode, 'test')
self.test_data_sourcedir = 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.data_sourcedir = os.path.join(pp_root_dir, self.pp_name)
self.pp_test_name = os.path.join(toy_mode, 'test')
self.test_data_sourcedir = 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)
self.plot_bg_chan = 0
# 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_random_ratio = 0.2
# set 2D network to operate in 3D images.
self.merge_2D_to_3D_preds = False
#########################
# 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.norm = "instance_norm" # one of None, 'instance_norm', 'batch_norm'
# one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
self.weight_init = "xavier_uniform"
# compatibility
self.regression_n_features = 1
self.num_classes = 2 # excluding bg
self.num_seg_classes = 3 # incl bg
#########################
# Schedule / Selection #
#########################
self.num_epochs = 24
self.num_train_batches = 100 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 = "all" # if 'None' iterates over entire val_set once.
if self.val_mode == 'val_sampling':
self.num_val_batches = 50
self.optimizer = "ADAMW"
# set dynamic_lr_scheduling to True to apply LR scheduling with below settings.
self.dynamic_lr_scheduling = True
self.lr_decay_factor = 0.25
self.scheduling_patience = np.ceil(2400 / (self.num_train_batches * self.batch_size))
self.scheduling_criterion = 'donuts_ap'
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
self.weight_decay = 3e-5
+ self.exclude_from_wd = []
self.clip_norm = None
#########################
# 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
self.test_aug_axes = (0, 1, (0, 1))
self.n_test_plots = 2
self.clustering = "wbc"
self.clustering_iou = 1e-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_labels = [Label(0, 'bg', (*self.white, 0.)),
Label(1, 'circles', (*self.orange, .9)),
Label(2, 'donuts', (*self.blue, .9)),]
if self.class_specific_seg:
self.seg_labels = self.class_labels
self.box_type2label = {label.name: label for label in self.box_labels}
self.class_id2label = {label.id: label for label in self.class_labels}
self.class_dict = {label.id: label.name for label in self.class_labels if label.id != 0}
self.seg_id2label = {label.id: label for label in self.seg_labels}
self.cmap = {label.id: label.color for label in self.seg_labels}
- self.metrics = ["ap", "auc"]
+ self.metrics = ["ap", "auc", "dice"]
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 = {name + "_ap": 1. for name in self.class_dict.values()}# criteria to average over for saving epochs.
self.min_det_thresh = 0.1 # minimum confidence value to select predictions for evaluation.
self.plot_prediction_histograms = True
self.plot_stat_curves = False
self.plot_class_ids = True
#########################
# Data Augmentation #
#########################
self.do_aug = False
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_fpn': self.add_det_fpn_configs,
'mrcnn': self.add_mrcnn_configs,
'retina_net': self.add_mrcnn_configs,
'retina_unet': self.add_mrcnn_configs,
}[self.model]()
def add_det_fpn_configs(self):
- self.learning_rate = [1 * 1e-3] * self.num_epochs
+ self.learning_rate = [3 * 1e-4] * self.num_epochs
self.dynamic_lr_scheduling = True
self.scheduling_criterion = 'torch_loss'
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
self.n_roi_candidates = 4 if self.dim == 2 else 6
# max number of roi candidates to identify per image (slice in 2D, volume in 3D)
# loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
self.seg_loss_mode = 'wce'
self.wce_weights = [1] * self.num_seg_classes if 'dice' in self.seg_loss_mode else [0.1, 1., 1.]
self.fp_dice_weight = 1 if self.dim == 2 else 1
# if <1, false positive predictions in foreground are penalized less.
self.detection_min_confidence = 0.05
# how to determine score of roi: 'max' or 'median'
self.score_det = 'max'
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 = self.num_classes + 1
# 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.rpn_train_anchors_per_image = 32 #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 == 'retina_net' or self.model == 'retina_unet':
+ # whether to use focal loss or SHEM for loss-sample selection
+ self.focal_loss = False
# 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 self.model == 'retina_unet':
self.operate_stride1 = True
diff --git a/datasets/toy/data_loader.py b/datasets/toy_mdt/data_loader.py
similarity index 50%
copy from datasets/toy/data_loader.py
copy to datasets/toy_mdt/data_loader.py
index 3f5387b..bee309d 100644
--- a/datasets/toy/data_loader.py
+++ b/datasets/toy_mdt/data_loader.py
@@ -1,596 +1,379 @@
#!/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
-from multiprocessing import Pool
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]
+ seg_paths = [os.path.join(self.data_dir, '{}.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()
+ class_targets = p_df['class_id'].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.data[pid] = {'data': img_paths[ix], 'seg': seg_paths[ix], 'pid': pid}
+ self.data[pid]['class_targets'] = np.array([class_targets[ix]], dtype='uint8') + 1
+
+ cf.roi_items = ['class_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)
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 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(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 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]
+ all_data = np.load(patient['data'], mmap_mode='r')
+ data = all_data[0].astype('float16')[np.newaxis]
+ seg = all_data[1].astype('uint8')
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 = 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)):
- 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)):
- 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
+ for tix in range(len(self.unique_ts)):
+ 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_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]
+ all_data = np.load(patient['data'], mmap_mode='r')
+ data = all_data[0].astype('float16')[np.newaxis]
+ seg = all_data[1].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))})
+ out_data = data[None]
+ out_seg = seg[None]
- 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
+ batch_2D = {'data': out_data, 'seg': out_seg}
+ for o in self.cf.roi_items:
+ batch_2D[o] = np.repeat(np.array([patient[o]]), len(out_data), axis=0)
+ converter = ConvertSegToBoundingBoxCoordinates(2, self.cf.roi_items, False, self.cf.class_specific_seg)
+ batch_2D = converter(**batch_2D)
+
+ 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_2D
+ out_batch.update({'pid': np.array([patient['pid']] * len(out_data))})
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, **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, **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=data_gen.n_filled_threads,
seeds=range(data_gen.n_filled_threads))
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 = []
+ assert cf.n_train_val_data <= len(dataset.set_ids), \
+ "requested {} train val samples, but dataset only has {} train val samples.".format(
+ cf.n_train_val_data, len(dataset.set_ids))
+ train_ids = dataset.set_ids[:int(2*cf.n_train_val_data//3)]
+ val_ids = dataset.set_ids[int(np.ceil(2*cf.n_train_val_data//3)):cf.n_train_val_data]
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)))
+ logger.info("data set loaded with: {} train / {} val patients".format(len(train_ids), len(val_ids)))
if data_statistics:
- dataset.calc_statistics(subsets={"train": train_ids, "val": val_ids, "test": test_ids}, plot_dir=
+ dataset.calc_statistics(subsets={"train": train_ids, "val": val_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)
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'] = 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 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/evaluator.py b/evaluator.py
index 6d53912..2ac7fc6 100644
--- a/evaluator.py
+++ b/evaluator.py
@@ -1,980 +1,980 @@
#!/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 os
from multiprocessing import Pool
import pickle
import time
import numpy as np
import pandas as pd
from sklearn.metrics import roc_auc_score, average_precision_score
from sklearn.metrics import roc_curve, precision_recall_curve
from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
import torch
import utils.model_utils as mutils
import plotting as plg
import warnings
def get_roi_ap_from_df(inputs):
'''
:param df: data frame.
:param det_thresh: min_threshold for filtering out low confidence predictions.
:param per_patient_ap: boolean flag. evaluate average precision per patient id and average over per-pid results,
instead of computing one ap over whole data set.
:return: average_precision (float)
'''
df, det_thresh, per_patient_ap = inputs
if per_patient_ap:
pids_list = df.pid.unique()
aps = []
for match_iou in df.match_iou.unique():
iou_df = df[df.match_iou == match_iou]
for pid in pids_list:
pid_df = iou_df[iou_df.pid == pid]
all_p = len(pid_df[pid_df.class_label == 1])
pid_df = pid_df[(pid_df.det_type == 'det_fp') | (pid_df.det_type == 'det_tp')].sort_values('pred_score', ascending=False)
pid_df = pid_df[pid_df.pred_score > det_thresh]
if (len(pid_df) ==0 and all_p == 0):
pass
elif (len(pid_df) > 0 and all_p == 0):
aps.append(0)
else:
aps.append(compute_roi_ap(pid_df, all_p))
return np.mean(aps)
else:
aps = []
for match_iou in df.match_iou.unique():
iou_df = df[df.match_iou == match_iou]
# it's important to not apply the threshold before counting all_p in order to not lose the fn!
all_p = len(iou_df[(iou_df.det_type == 'det_tp') | (iou_df.det_type == 'det_fn')])
# sorting out all entries that are not fp or tp or have confidence(=pred_score) <= detection_threshold
iou_df = iou_df[(iou_df.det_type == 'det_fp') | (iou_df.det_type == 'det_tp')].sort_values('pred_score', ascending=False)
iou_df = iou_df[iou_df.pred_score > det_thresh]
if all_p>0:
aps.append(compute_roi_ap(iou_df, all_p))
return np.mean(aps)
def compute_roi_ap(df, all_p):
"""
adapted from: https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py
:param df: dataframe containing class labels of predictions sorted in descending manner by their prediction score.
:param all_p: number of all ground truth objects. (for denominator of recall.)
:return:
"""
tp = df.class_label.values
fp = (tp == 0) * 1
#recall thresholds, where precision will be measured
R = np.linspace(0., 1., np.round((1. - 0.) / .01).astype(int) + 1, endpoint=True)
tp_sum = np.cumsum(tp)
fp_sum = np.cumsum(fp)
n_dets = len(tp)
rc = tp_sum / all_p
pr = tp_sum / (fp_sum + tp_sum)
# initialize precision array over recall steps (q=queries).
q = [0. for _ in range(len(R))]
# numpy is slow without cython optimization for accessing elements
# python array gets significant speed improvement
pr = pr.tolist()
for i in range(n_dets - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
#--> pr[i]<=pr[i-1] for all i since we want to consider the maximum
#precision value for a queried interval
# discretize empiric recall steps with given bins.
assert np.all(rc[:-1]<=rc[1:]), "recall not sorted ascendingly"
inds = np.searchsorted(rc, R, side='left')
try:
for rc_ix, pr_ix in enumerate(inds):
q[rc_ix] = pr[pr_ix]
except IndexError: #now q is filled with pr values up to first non-available index
pass
return np.mean(q)
def roi_avp(inputs):
'''
:param df: data frame.
:param det_thresh: min_threshold for filtering out low confidence predictions.
:param per_patient_ap: boolean flag. evaluate average precision per patient id and average over per-pid results,
instead of computing one ap over whole data set.
:return: average_precision (float)
'''
df, det_thresh, per_patient_ap = inputs
if per_patient_ap:
pids_list = df.pid.unique()
aps = []
for match_iou in df.match_iou.unique():
iou_df = df[df.match_iou == match_iou]
for pid in pids_list:
pid_df = iou_df[iou_df.pid == pid]
all_p = len(pid_df[pid_df.class_label == 1])
mask = ((pid_df.rg_bins == pid_df.rg_bin_target) & (pid_df.det_type == 'det_tp')) | (pid_df.det_type == 'det_fp')
pid_df = pid_df[mask].sort_values('pred_score', ascending=False)
pid_df = pid_df[pid_df.pred_score > det_thresh]
if (len(pid_df) ==0 and all_p == 0):
pass
elif (len(pid_df) > 0 and all_p == 0):
aps.append(0)
else:
aps.append(compute_roi_ap(pid_df, all_p))
return np.mean(aps)
else:
aps = []
for match_iou in df.match_iou.unique():
iou_df = df[df.match_iou == match_iou]
#it's important to not apply the threshold before counting all_positives!
all_p = len(iou_df[(iou_df.det_type == 'det_tp') | (iou_df.det_type == 'det_fn')])
# filtering out tps which don't match rg_bin target at this point is same as reclassifying them as fn.
# also sorting out all entries that are not fp or have confidence(=pred_score) <= detection_threshold
mask = ((iou_df.rg_bins == iou_df.rg_bin_target) & (iou_df.det_type == 'det_tp')) | (iou_df.det_type == 'det_fp')
iou_df = iou_df[mask].sort_values('pred_score', ascending=False)
iou_df = iou_df[iou_df.pred_score > det_thresh]
if all_p>0:
aps.append(compute_roi_ap(iou_df, all_p))
return np.mean(aps)
def compute_prc(df):
"""compute precision-recall curve with maximum precision per recall interval.
:param df:
:param all_p: # of all positive samples in data.
:return: array: [precisions, recall query values]
"""
assert (df.class_label==1).any(), "cannot compute prc when no positives in data."
all_p = len(df[(df.det_type == 'det_tp') | (df.det_type == 'det_fn')])
df = df[(df.det_type=="det_tp") | (df.det_type=="det_fp")]
df = df.sort_values("pred_score", ascending=False)
# recall thresholds, where precision will be measured
scores = df.pred_score.values
labels = df.class_label.values
n_dets = len(scores)
pr = np.zeros((n_dets,))
rc = pr.copy()
for rank in range(n_dets):
tp = np.count_nonzero(labels[:rank+1]==1)
fp = np.count_nonzero(labels[:rank+1]==0)
pr[rank] = tp/(tp+fp)
rc[rank] = tp/all_p
#after obj detection convention/ coco-dataset template: take maximum pr within intervals:
# --> pr[i]<=pr[i-1] for all i since we want to consider the maximum
# precision value for a queried interval
for i in range(n_dets - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
R = np.linspace(0., 1., np.round((1. - 0.) / .01).astype(int) + 1, endpoint=True)#precision queried at R points
inds = np.searchsorted(rc, R, side='left')
queries = np.zeros((len(R),))
try:
for q_ix, rank in enumerate(inds):
queries[q_ix] = pr[rank]
except IndexError:
pass
return np.array((queries, R))
def RMSE(y_true, y_pred, weights=None):
if len(y_true)>0:
return np.sqrt(mean_squared_error(y_true, y_pred, sample_weight=weights))
else:
return np.nan
def MAE_w_std(y_true, y_pred, weights=None):
if len(y_true)>0:
y_true, y_pred = np.array(y_true), np.array(y_pred)
deltas = np.abs(y_true-y_pred)
mae = np.average(deltas, weights=weights, axis=0).item()
skmae = mean_absolute_error(y_true, y_pred, sample_weight=weights)
assert np.allclose(mae, skmae, atol=1e-6), "mae {}, sklearn mae {}".format(mae, skmae)
std = np.std(weights*deltas)
return mae, std
else:
return np.nan, np.nan
def MAE(y_true, y_pred, weights=None):
if len(y_true)>0:
return mean_absolute_error(y_true, y_pred, sample_weight=weights)
else:
return np.nan
def accuracy(y_true, y_pred, weights=None):
if len(y_true)>0:
return accuracy_score(y_true, y_pred, sample_weight=weights)
else:
return np.nan
# noinspection PyCallingNonCallable
class Evaluator():
""" Evaluates given results dicts. Can return results as updated monitor_metrics. Can save test data frames to
file.
"""
def __init__(self, cf, logger, mode='test'):
"""
:param mode: either 'train', 'val_sampling', 'val_patient' or 'test'. handles prediction lists of different forms.
"""
self.cf = cf
self.logger = logger
self.mode = mode
self.regress_flag = any(['regression' in task for task in self.cf.prediction_tasks])
- self.plot_dir = self.cf.plot_dir if not self.mode == "test" else self.cf.test_dir
+ self.plot_dir = self.cf.test_dir if self.mode == "test" else self.cf.plot_dir
if self.cf.plot_prediction_histograms:
self.hist_dir = os.path.join(self.plot_dir, 'histograms')
os.makedirs(self.hist_dir, exist_ok=True)
if self.cf.plot_stat_curves:
self.curves_dir = os.path.join(self.plot_dir, 'stat_curves')
os.makedirs(self.curves_dir, exist_ok=True)
def eval_losses(self, batch_res_dicts):
if hasattr(self.cf, "losses_to_monitor"):
loss_names = self.cf.losses_to_monitor
else:
loss_names = {name for b_res_dict in batch_res_dicts for name in b_res_dict if 'loss' in name}
self.epoch_losses = {l_name: torch.tensor([b_res_dict[l_name] for b_res_dict in batch_res_dicts if l_name
in b_res_dict.keys()]).mean().item() for l_name in loss_names}
def eval_segmentations(self, batch_res_dicts, pid_list):
batch_dices = [b_res_dict['batch_dices'] for b_res_dict in batch_res_dicts if
'batch_dices' in b_res_dict.keys()] # shape (n_batches, n_seg_classes)
if len(batch_dices) > 0:
batch_dices = np.array(batch_dices) # dims n_batches x 1 in sampling / n_test_epochs x n_classes
assert batch_dices.shape[1] == self.cf.num_seg_classes, "bdices shp {}, n seg cl {}, pid lst len {}".format(
batch_dices.shape, self.cf.num_seg_classes, len(pid_list))
self.seg_df = pd.DataFrame()
for seg_id in range(batch_dices.shape[1]):
self.seg_df[self.cf.seg_id2label[seg_id].name + "_dice"] = batch_dices[:,
seg_id] # one row== one batch, one column== one class
# self.seg_df[self.cf.seg_id2label[seg_id].name+"_dice"] = np.concatenate(batch_dices[:,:,seg_id])
self.seg_df['fold'] = self.cf.fold
if self.mode == "val_patient" or self.mode == "test":
# need to make it more conform between sampling and patient-mode
self.seg_df["pid"] = [pid for pix, pid in enumerate(pid_list)] # for b_inst in batch_inst_boxes[pix]]
else:
self.seg_df["pid"] = np.nan
def eval_boxes(self, batch_res_dicts, pid_list, obj_cl_dict,
obj_cl_identifiers={"gt":'class_targets', "pred":'box_pred_class_id'}):
"""
:param batch_res_dicts:
:param pid_list: [pid_0, pid_1, ...]
:return:
"""
if self.mode == 'train' or self.mode == 'val_sampling':
# one pid per batch element
# batch_size > 1, with varying patients across batch:
# [[[results_0, ...], [pid_0, ...]], [[results_n, ...], [pid_n, ...]], ...]
# -> [results_0, results_1, ..]
batch_inst_boxes = [b_res_dict['boxes'] for b_res_dict in batch_res_dicts] # len: nr of batches in epoch
batch_inst_boxes = [[b_inst_boxes] for whole_batch_boxes in batch_inst_boxes for b_inst_boxes in
whole_batch_boxes] # len: batch instances of whole epoch
assert np.all(len(b_boxes_list) == self.cf.batch_size for b_boxes_list in batch_inst_boxes)
elif self.mode == "val_patient" or self.mode == "test":
# patient processing, one element per batch = one patient.
# [[results_0, pid_0], [results_1, pid_1], ...] -> [results_0, results_1, ..]
# in patientbatchiterator there is only one pid per batch
batch_inst_boxes = [b_res_dict['boxes'] for b_res_dict in batch_res_dicts]
# in patient mode not actually per batch instance, but per whole batch!
if hasattr(self.cf, "eval_test_separately") and self.cf.eval_test_separately:
""" you could write your own routines to add GTs to raw predictions for evaluation.
implemented standard is: cf.eval_test_separately = False or not set --> GTs are saved at same time
and in same file as raw prediction results.
"""
raise NotImplementedError
assert len(batch_inst_boxes) == len(pid_list)
df_list_preds = []
df_list_labels = []
df_list_class_preds = []
df_list_pids = []
df_list_type = []
df_list_match_iou = []
df_list_n_missing = []
df_list_regressions = []
df_list_rg_targets = []
df_list_rg_bins = []
df_list_rg_bin_targets = []
df_list_rg_uncs = []
for match_iou in self.cf.ap_match_ious:
self.logger.info('evaluating with ap_match_iou: {}'.format(match_iou))
for cl in list(obj_cl_dict.keys()):
for pix, pid in enumerate(pid_list):
len_df_list_before_patient = len(df_list_pids)
# input of each batch element is a list of boxes, where each box is a dictionary.
for b_inst_ix, b_boxes_list in enumerate(batch_inst_boxes[pix]):
b_tar_boxes = []
b_cand_boxes, b_cand_scores, b_cand_n_missing = [], [], []
if self.regress_flag:
b_tar_regs, b_tar_rg_bins = [], []
b_cand_regs, b_cand_rg_bins, b_cand_rg_uncs = [], [], []
for box in b_boxes_list:
# each box is either gt or detection or proposal/anchor
# we need all gts in the same order & all dets in same order
if box['box_type'] == 'gt' and box[obj_cl_identifiers["gt"]] == cl:
b_tar_boxes.append(box["box_coords"])
if self.regress_flag:
b_tar_regs.append(np.array(box['regression_targets'], dtype='float32'))
b_tar_rg_bins.append(box['rg_bin_targets'])
if box['box_type'] == 'det' and box[obj_cl_identifiers["pred"]] == cl:
b_cand_boxes.append(box["box_coords"])
b_cand_scores.append(box["box_score"])
b_cand_n_missing.append(box["cluster_n_missing"] if 'cluster_n_missing' in box.keys() else np.nan)
if self.regress_flag:
b_cand_regs.append(box["regression"])
b_cand_rg_bins.append(box["rg_bin"])
b_cand_rg_uncs.append(box["rg_uncertainty"] if 'rg_uncertainty' in box.keys() else np.nan)
b_tar_boxes = np.array(b_tar_boxes)
b_cand_boxes, b_cand_scores, b_cand_n_missing = np.array(b_cand_boxes), np.array(b_cand_scores), np.array(b_cand_n_missing)
if self.regress_flag:
b_tar_regs, b_tar_rg_bins = np.array(b_tar_regs), np.array(b_tar_rg_bins)
b_cand_regs, b_cand_rg_bins, b_cand_rg_uncs = np.array(b_cand_regs), np.array(b_cand_rg_bins), np.array(b_cand_rg_uncs)
# check if predictions and ground truth boxes exist and match them according to match_iou.
if not 0 in b_cand_boxes.shape and not 0 in b_tar_boxes.shape:
assert np.all(np.round(b_cand_scores,6) <= 1.), "there is a box score>1: {}".format(b_cand_scores[~(b_cand_scores<=1.)])
#coords_check = np.array([len(coords)==self.cf.dim*2 for coords in b_cand_boxes])
#assert np.all(coords_check), "cand box with wrong bcoords dim: {}, mode: {}".format(b_cand_boxes[~coords_check], self.mode)
expected_dim = len(b_cand_boxes[0])
assert np.all([len(coords) == expected_dim for coords in b_tar_boxes]), \
"gt/cand box coords mismatch, expected dim: {}.".format(expected_dim)
# overlaps: shape len(cand_boxes) x len(tar_boxes)
overlaps = mutils.compute_overlaps(b_cand_boxes, b_tar_boxes)
# match_cand_ixs: shape (nr_of_matches,)
# theses indices are the indices of b_cand_boxes
match_cand_ixs = np.argwhere(np.max(overlaps, axis=1) > match_iou)[:, 0]
non_match_cand_ixs = np.argwhere(np.max(overlaps, 1) <= match_iou)[:, 0]
# the corresponding gt assigned to the pred boxes by highest iou overlap,
# i.e., match_gt_ixs holds index into b_tar_boxes for each entry in match_cand_ixs,
# i.e., gt_ixs and cand_ixs are paired via their position in their list
# (cand_ixs[j] corresponds to gt_ixs[j])
match_gt_ixs = np.argmax(overlaps[match_cand_ixs, :], axis=1) if \
not 0 in match_cand_ixs.shape else np.array([])
assert len(match_gt_ixs)==len(match_cand_ixs)
#match_gt_ixs: shape (nr_of_matches,) or 0
non_match_gt_ixs = np.array(
[ii for ii in np.arange(b_tar_boxes.shape[0]) if ii not in match_gt_ixs])
unique, counts = np.unique(match_gt_ixs, return_counts=True)
# check for double assignments, i.e. two predictions having been assigned to the same gt.
# according to the COCO-metrics, only one prediction counts as true positive, the rest counts as
# false positive. This case is supposed to be avoided by the model itself by,
# e.g. using a low enough NMS threshold.
if np.any(counts > 1):
double_match_gt_ixs = unique[np.argwhere(counts > 1)[:, 0]]
keep_max = []
double_match_list = []
for dg in double_match_gt_ixs:
double_match_cand_ixs = match_cand_ixs[np.argwhere(match_gt_ixs == dg)]
keep_max.append(double_match_cand_ixs[np.argmax(b_cand_scores[double_match_cand_ixs])])
double_match_list += [ii for ii in double_match_cand_ixs]
fp_ixs = np.array([ii for ii in match_cand_ixs if
(ii in double_match_list and ii not in keep_max)])
# count as fp: boxes that match gt above match_iou threshold but have not highest class confidence score
match_gt_ixs = np.array([gt_ix for ii, gt_ix in enumerate(match_gt_ixs) if match_cand_ixs[ii] not in fp_ixs])
match_cand_ixs = np.array([cand_ix for cand_ix in match_cand_ixs if cand_ix not in fp_ixs])
assert len(match_gt_ixs) == len(match_cand_ixs)
df_list_preds += [ii for ii in b_cand_scores[fp_ixs]]
df_list_labels += [0] * fp_ixs.shape[0] # means label==gt==0==bg for all these fp_ixs
df_list_class_preds += [cl] * fp_ixs.shape[0]
df_list_n_missing += [n for n in b_cand_n_missing[fp_ixs]]
if self.regress_flag:
df_list_regressions += [r for r in b_cand_regs[fp_ixs]]
df_list_rg_bins += [r for r in b_cand_rg_bins[fp_ixs]]
df_list_rg_uncs += [r for r in b_cand_rg_uncs[fp_ixs]]
df_list_rg_targets += [[0.]*self.cf.regression_n_features] * fp_ixs.shape[0]
df_list_rg_bin_targets += [0.] * fp_ixs.shape[0]
df_list_pids += [pid] * fp_ixs.shape[0]
df_list_type += ['det_fp'] * fp_ixs.shape[0]
# matched/tp:
if not 0 in match_cand_ixs.shape:
df_list_preds += list(b_cand_scores[match_cand_ixs])
df_list_labels += [1] * match_cand_ixs.shape[0]
df_list_class_preds += [cl] * match_cand_ixs.shape[0]
df_list_n_missing += list(b_cand_n_missing[match_cand_ixs])
if self.regress_flag:
df_list_regressions += list(b_cand_regs[match_cand_ixs])
df_list_rg_bins += list(b_cand_rg_bins[match_cand_ixs])
df_list_rg_uncs += list(b_cand_rg_uncs[match_cand_ixs])
assert len(match_cand_ixs)==len(match_gt_ixs)
df_list_rg_targets += list(b_tar_regs[match_gt_ixs])
df_list_rg_bin_targets += list(b_tar_rg_bins[match_gt_ixs])
df_list_pids += [pid] * match_cand_ixs.shape[0]
df_list_type += ['det_tp'] * match_cand_ixs.shape[0]
# rest fp:
if not 0 in non_match_cand_ixs.shape:
df_list_preds += list(b_cand_scores[non_match_cand_ixs])
df_list_labels += [0] * non_match_cand_ixs.shape[0]
df_list_class_preds += [cl] * non_match_cand_ixs.shape[0]
df_list_n_missing += list(b_cand_n_missing[non_match_cand_ixs])
if self.regress_flag:
df_list_regressions += list(b_cand_regs[non_match_cand_ixs])
df_list_rg_bins += list(b_cand_rg_bins[non_match_cand_ixs])
df_list_rg_uncs += list(b_cand_rg_uncs[non_match_cand_ixs])
df_list_rg_targets += [[0.]*self.cf.regression_n_features] * non_match_cand_ixs.shape[0]
df_list_rg_bin_targets += [0.] * non_match_cand_ixs.shape[0]
df_list_pids += [pid] * non_match_cand_ixs.shape[0]
df_list_type += ['det_fp'] * non_match_cand_ixs.shape[0]
# fn:
if not 0 in non_match_gt_ixs.shape:
df_list_preds += [0] * non_match_gt_ixs.shape[0]
df_list_labels += [1] * non_match_gt_ixs.shape[0]
df_list_class_preds += [cl] * non_match_gt_ixs.shape[0]
df_list_n_missing += [np.nan] * non_match_gt_ixs.shape[0]
if self.regress_flag:
df_list_regressions += [[0.]*self.cf.regression_n_features] * non_match_gt_ixs.shape[0]
df_list_rg_bins += [0.] * non_match_gt_ixs.shape[0]
df_list_rg_uncs += [np.nan] * non_match_gt_ixs.shape[0]
df_list_rg_targets += list(b_tar_regs[non_match_gt_ixs])
df_list_rg_bin_targets += list(b_tar_rg_bins[non_match_gt_ixs])
df_list_pids += [pid] * non_match_gt_ixs.shape[0]
df_list_type += ['det_fn'] * non_match_gt_ixs.shape[0]
# only fp:
if not 0 in b_cand_boxes.shape and 0 in b_tar_boxes.shape:
# means there is no gt in all samples! any preds have to be fp.
df_list_preds += list(b_cand_scores)
df_list_labels += [0] * b_cand_boxes.shape[0]
df_list_class_preds += [cl] * b_cand_boxes.shape[0]
df_list_n_missing += list(b_cand_n_missing)
if self.regress_flag:
df_list_regressions += list(b_cand_regs)
df_list_rg_bins += list(b_cand_rg_bins)
df_list_rg_uncs += list(b_cand_rg_uncs)
df_list_rg_targets += [[0.]*self.cf.regression_n_features] * b_cand_boxes.shape[0]
df_list_rg_bin_targets += [0.] * b_cand_boxes.shape[0]
df_list_pids += [pid] * b_cand_boxes.shape[0]
df_list_type += ['det_fp'] * b_cand_boxes.shape[0]
# only fn:
if 0 in b_cand_boxes.shape and not 0 in b_tar_boxes.shape:
df_list_preds += [0] * b_tar_boxes.shape[0]
df_list_labels += [1] * b_tar_boxes.shape[0]
df_list_class_preds += [cl] * b_tar_boxes.shape[0]
df_list_n_missing += [np.nan] * b_tar_boxes.shape[0]
if self.regress_flag:
df_list_regressions += [[0.]*self.cf.regression_n_features] * b_tar_boxes.shape[0]
df_list_rg_bins += [0.] * b_tar_boxes.shape[0]
df_list_rg_uncs += [np.nan] * b_tar_boxes.shape[0]
df_list_rg_targets += list(b_tar_regs)
df_list_rg_bin_targets += list(b_tar_rg_bins)
df_list_pids += [pid] * b_tar_boxes.shape[0]
df_list_type += ['det_fn'] * b_tar_boxes.shape[0]
# empty patient with 0 detections needs empty patient score, in order to not disappear from stats.
# filtered out for roi-level evaluation later. During training (and val_sampling),
# tn are assigned per sample independently of associated patients.
# i.e., patient_tn is also meant as sample_tn if a list of samples is evaluated instead of whole patient
if len(df_list_pids) == len_df_list_before_patient:
df_list_preds += [0]
df_list_labels += [0]
df_list_class_preds += [cl]
df_list_n_missing += [np.nan]
if self.regress_flag:
df_list_regressions += [[0.]*self.cf.regression_n_features]
df_list_rg_bins += [0.]
df_list_rg_uncs += [np.nan]
df_list_rg_targets += [[0.]*self.cf.regression_n_features]
df_list_rg_bin_targets += [0.]
df_list_pids += [pid]
df_list_type += ['patient_tn'] # true negative: no ground truth boxes, no detections.
df_list_match_iou += [match_iou] * (len(df_list_preds) - len(df_list_match_iou))
self.test_df = pd.DataFrame()
self.test_df['pred_score'] = df_list_preds
self.test_df['class_label'] = df_list_labels
# class labels are gt, 0,1, only indicate neg/pos (or bg/fg) remapped from all classes
self.test_df['pred_class'] = df_list_class_preds # can be diff than 0,1
self.test_df['pid'] = df_list_pids
self.test_df['det_type'] = df_list_type
self.test_df['fold'] = self.cf.fold
self.test_df['match_iou'] = df_list_match_iou
self.test_df['cluster_n_missing'] = df_list_n_missing
if self.regress_flag:
self.test_df['regressions'] = df_list_regressions
self.test_df['rg_targets'] = df_list_rg_targets
self.test_df['rg_uncertainties'] = df_list_rg_uncs
self.test_df['rg_bins'] = df_list_rg_bins
# super weird error: pandas does not properly add an attribute if column is named "rg_bin_targets" ... ?!?
self.test_df['rg_bin_target'] = df_list_rg_bin_targets
assert hasattr(self.test_df, "rg_bin_target")
#fn_df = self.test_df[self.test_df["det_type"] == "det_fn"]
pass
def evaluate_predictions(self, results_list, monitor_metrics=None):
"""
Performs the matching of predicted boxes and ground truth boxes. Loops over list of matching IoUs and foreground classes.
Resulting info of each prediction is stored as one line in an internal dataframe, with the keys:
det_type: 'tp' (true positive), 'fp' (false positive), 'fn' (false negative), 'tn' (true negative)
pred_class: foreground class which the object predicts.
pid: corresponding patient-id.
pred_score: confidence score [0, 1]
fold: corresponding fold of CV.
match_iou: utilized IoU for matching.
:param results_list: list of model predictions. Either from train/val_sampling (patch processing) for monitoring with form:
[[[results_0, ...], [pid_0, ...]], [[results_n, ...], [pid_n, ...]], ...]
Or from val_patient/testing (patient processing), with form: [[results_0, pid_0], [results_1, pid_1], ...])
:param monitor_metrics (optional): dict of dicts with all metrics of previous epochs.
:return monitor_metrics: if provided (during training), return monitor_metrics now including results of current epoch.
"""
# gets results_list = [[batch_instances_box_lists], [batch_instances_pids]]*n_batches
# we want to evaluate one batch_instance (= 2D or 3D image) at a time.
self.logger.info('evaluating in mode {}'.format(self.mode))
batch_res_dicts = [batch[0] for batch in results_list] # len: nr of batches in epoch
if self.mode == 'train' or self.mode=='val_sampling':
# one pid per batch element
# [[[results_0, ...], [pid_0, ...]], [[results_n, ...], [pid_n, ...]], ...]
# -> [pid_0, pid_1, ...]
# additional list wrapping to make conform with below per-patient batches, where one pid is linked to more than one batch instance
pid_list = [batch_instance_pid for batch in results_list for batch_instance_pid in batch[1]]
elif self.mode == "val_patient" or self.mode=="test":
# [[results_0, pid_0], [results_1, pid_1], ...] -> [pid_0, pid_1, ...]
# in patientbatchiterator there is only one pid per batch
pid_list = [np.unique(batch[1]) for batch in results_list]
assert np.all([len(pid)==1 for pid in pid_list]), "pid list in patient-eval mode, should only contain a single scalar per patient: {}".format(pid_list)
pid_list = [pid[0] for pid in pid_list]
else:
raise Exception("undefined run mode encountered")
self.eval_losses(batch_res_dicts)
self.eval_segmentations(batch_res_dicts, pid_list)
self.eval_boxes(batch_res_dicts, pid_list, self.cf.class_dict)
if monitor_metrics is not None:
# return all_stats, updated monitor_metrics
return self.return_metrics(self.test_df, self.cf.class_dict, monitor_metrics)
def return_metrics(self, df, obj_cl_dict, monitor_metrics=None, boxes_only=False):
"""
Calculates metric scores for internal data frame. Called directly from evaluate_predictions during training for
monitoring, or from score_test_df during inference (for single folds or aggregated test set).
Loops over foreground classes and score_levels ('roi' and/or 'patient'), gets scores and stores them.
Optionally creates plots of prediction histograms and ROC/PR curves.
:param df: Data frame that holds evaluated predictions.
:param obj_cl_dict: Dict linking object-class ids to object-class names. E.g., {1: "bikes", 2 : "cars"}. Set in
configs as cf.class_dict.
:param monitor_metrics: dict of dicts with all metrics of previous epochs. This function adds metrics for
current epoch and returns the same object.
:param boxes_only: whether to produce metrics only for the boxes, not the segmentations.
:return: all_stats: list. Contains dicts with resulting scores for each combination of foreground class and
score_level.
:return: monitor_metrics
"""
# -------------- monitoring independent of class, score level ------------
if monitor_metrics is not None:
for l_name in self.epoch_losses:
monitor_metrics[l_name] = [self.epoch_losses[l_name]]
# -------------- metrics calc dependent on class, score level ------------
all_stats = [] # all_stats: one entry per score_level per class
for cl in list(obj_cl_dict.keys()): # bg eval is neglected
cl_name = obj_cl_dict[cl]
cl_df = df[df.pred_class == cl]
if hasattr(self, "seg_df") and not boxes_only:
dice_col = self.cf.seg_id2label[cl].name+"_dice"
seg_cl_df = self.seg_df.loc[:,['pid', dice_col, 'fold']]
for score_level in self.cf.report_score_level:
stats_dict = {}
stats_dict['name'] = 'fold_{} {} {}'.format(self.cf.fold, score_level, cl_name)
# -------------- RoI-based -----------------
if score_level == 'rois':
stats_dict['auc'] = np.nan
stats_dict['roc'] = np.nan
if monitor_metrics is not None:
tn = len(cl_df[cl_df.det_type == "patient_tn"])
tp = len(cl_df[(cl_df.det_type == "det_tp")&(cl_df.pred_score>self.cf.min_det_thresh)])
fp = len(cl_df[(cl_df.det_type == "det_fp")&(cl_df.pred_score>self.cf.min_det_thresh)])
fn = len(cl_df[cl_df.det_type == "det_fn"])
sens = np.divide(tp, (fn + tp))
monitor_metrics.update({"Bin_Stats/" + cl_name + "_fp": [fp], "Bin_Stats/" + cl_name + "_tp": [tp],
"Bin_Stats/" + cl_name + "_fn": [fn], "Bin_Stats/" + cl_name + "_tn": [tn],
"Bin_Stats/" + cl_name + "_sensitivity": [sens]})
# list wrapping only needed bc other metrics are recorded over all epochs;
spec_df = cl_df[cl_df.det_type != 'patient_tn']
if self.regress_flag:
# filter false negatives out for regression-only eval since regressor didn't predict
truncd_df = spec_df[(((spec_df.det_type == "det_fp") | (
spec_df.det_type == "det_tp")) & spec_df.pred_score > self.cf.min_det_thresh)]
truncd_df_tp = truncd_df[truncd_df.det_type == "det_tp"]
weights, weights_tp = truncd_df.pred_score.tolist(), truncd_df_tp.pred_score.tolist()
y_true, y_pred = truncd_df.rg_targets.tolist(), truncd_df.regressions.tolist()
stats_dict["rg_RMSE"] = RMSE(y_true, y_pred)
stats_dict["rg_MAE"] = MAE(y_true, y_pred)
stats_dict["rg_RMSE_weighted"] = RMSE(y_true, y_pred, weights)
stats_dict["rg_MAE_weighted"] = MAE(y_true, y_pred, weights)
y_true, y_pred = truncd_df_tp.rg_targets.tolist(), truncd_df_tp.regressions.tolist()
stats_dict["rg_MAE_weighted_tp"] = MAE(y_true, y_pred, weights_tp)
stats_dict["rg_MAE_w_std_weighted_tp"] = MAE_w_std(y_true, y_pred, weights_tp)
y_true, y_pred = truncd_df.rg_bin_target.tolist(), truncd_df.rg_bins.tolist()
stats_dict["rg_bin_accuracy"] = accuracy(y_true, y_pred)
stats_dict["rg_bin_accuracy_weighted"] = accuracy(y_true, y_pred, weights)
y_true, y_pred = truncd_df_tp.rg_bin_target.tolist(), truncd_df_tp.rg_bins.tolist()
stats_dict["rg_bin_accuracy_weighted_tp"] = accuracy(y_true, y_pred, weights_tp)
if np.any(~truncd_df.rg_uncertainties.isna()):
# det_fn are expected to be NaN so they drop out in means
stats_dict.update({"rg_uncertainty": truncd_df.rg_uncertainties.mean(),
"rg_uncertainty_tp": truncd_df_tp.rg_uncertainties.mean(),
"rg_uncertainty_tp_weighted": (truncd_df_tp.rg_uncertainties * truncd_df_tp.pred_score).sum()
/ truncd_df_tp.pred_score.sum()
})
if (spec_df.class_label==1).any():
stats_dict['ap'] = get_roi_ap_from_df((spec_df, self.cf.min_det_thresh, self.cf.per_patient_ap))
stats_dict['prc'] = precision_recall_curve(spec_df.class_label.tolist(), spec_df.pred_score.tolist())
if self.regress_flag:
stats_dict['avp'] = roi_avp((spec_df, self.cf.min_det_thresh, self.cf.per_patient_ap))
else:
stats_dict['ap'] = np.nan
stats_dict['prc'] = np.nan
stats_dict['avp'] = np.nan
# np.nan is formattable by __format__ as a float, None-type is not
if hasattr(self, "seg_df") and not boxes_only:
stats_dict["dice"] = seg_cl_df.loc[:,dice_col].mean() # mean per all rois in this epoch
stats_dict["dice_std"] = seg_cl_df.loc[:,dice_col].std()
# for the aggregated test set case, additionally get the scores of averaging over fold results.
if self.cf.evaluate_fold_means and len(df.fold.unique()) > 1:
aps = []
for fold in df.fold.unique():
fold_df = spec_df[spec_df.fold == fold]
if (fold_df.class_label==1).any():
aps.append(get_roi_ap_from_df((fold_df, self.cf.min_det_thresh, self.cf.per_patient_ap)))
stats_dict['ap_folds_mean'] = np.mean(aps) if len(aps)>0 else np.nan
stats_dict['ap_folds_std'] = np.std(aps) if len(aps)>0 else np.nan
stats_dict['auc_folds_mean'] = np.nan
stats_dict['auc_folds_std'] = np.nan
if self.regress_flag:
avps, accuracies, MAEs = [], [], []
for fold in df.fold.unique():
fold_df = spec_df[spec_df.fold == fold]
if (fold_df.class_label == 1).any():
avps.append(roi_avp((fold_df, self.cf.min_det_thresh, self.cf.per_patient_ap)))
truncd_df_tp = fold_df[((fold_df.det_type == "det_tp") & fold_df.pred_score > self.cf.min_det_thresh)]
weights_tp = truncd_df_tp.pred_score.tolist()
y_true, y_pred = truncd_df_tp.rg_bin_target.tolist(), truncd_df_tp.rg_bins.tolist()
accuracies.append(accuracy(y_true, y_pred, weights_tp))
y_true, y_pred = truncd_df_tp.rg_targets.tolist(), truncd_df_tp.regressions.tolist()
MAEs.append(MAE_w_std(y_true, y_pred, weights_tp))
stats_dict['avp_folds_mean'] = np.mean(avps) if len(avps) > 0 else np.nan
stats_dict['avp_folds_std'] = np.std(avps) if len(avps) > 0 else np.nan
stats_dict['rg_bin_accuracy_weighted_tp_folds_mean'] = np.mean(accuracies) if len(accuracies) > 0 else np.nan
stats_dict['rg_bin_accuracy_weighted_tp_folds_std'] = np.std(accuracies) if len(accuracies) > 0 else np.nan
stats_dict['rg_MAE_w_std_weighted_tp_folds_mean'] = np.mean(MAEs, axis=0) if len(MAEs) > 0 else np.nan
stats_dict['rg_MAE_w_std_weighted_tp_folds_std'] = np.std(MAEs, axis=0) if len(MAEs) > 0 else np.nan
if hasattr(self, "seg_df") and not boxes_only and self.cf.evaluate_fold_means and len(seg_cl_df.fold.unique()) > 1:
fold_means = seg_cl_df.groupby(['fold'], as_index=True).agg({dice_col:"mean"})
stats_dict["dice_folds_mean"] = float(fold_means.mean())
stats_dict["dice_folds_std"] = float(fold_means.std())
# -------------- patient-based -----------------
# on patient level, aggregate predictions per patient (pid): The patient predicted score is the highest
# confidence prediction for this class. The patient class label is 1 if roi of this class exists in patient, else 0.
if score_level == 'patient':
#this is the critical part in patient scoring: only the max gt and max pred score are taken per patient!
#--> does mix up values from separate detections
spec_df = cl_df.groupby(['pid'], as_index=False)
agg_args = {'class_label': 'max', 'pred_score': 'max', 'fold': 'first'}
if self.regress_flag:
# pandas throws error if aggregated value is np.array, not if is list.
agg_args.update({'regressions': lambda series: list(series.iloc[np.argmax(series.apply(np.linalg.norm).values)]),
'rg_targets': lambda series: list(series.iloc[np.argmax(series.apply(np.linalg.norm).values)]),
'rg_bins': 'max', 'rg_bin_target': 'max',
'rg_uncertainties': 'max'
})
if hasattr(cl_df, "cluster_n_missing"):
agg_args.update({'cluster_n_missing': 'mean'})
spec_df = spec_df.agg(agg_args)
if len(spec_df.class_label.unique()) > 1:
stats_dict['auc'] = roc_auc_score(spec_df.class_label.tolist(), spec_df.pred_score.tolist())
stats_dict['roc'] = roc_curve(spec_df.class_label.tolist(), spec_df.pred_score.tolist())
else:
stats_dict['auc'] = np.nan
stats_dict['roc'] = np.nan
if (spec_df.class_label == 1).any():
patient_cl_labels = spec_df.class_label.tolist()
stats_dict['ap'] = average_precision_score(patient_cl_labels, spec_df.pred_score.tolist())
stats_dict['prc'] = precision_recall_curve(patient_cl_labels, spec_df.pred_score.tolist())
if self.regress_flag:
avp_scores = spec_df[spec_df.rg_bins == spec_df.rg_bin_target].pred_score.tolist()
avp_scores += [0.] * (len(patient_cl_labels) - len(avp_scores))
stats_dict['avp'] = average_precision_score(patient_cl_labels, avp_scores)
else:
stats_dict['ap'] = np.nan
stats_dict['prc'] = np.nan
stats_dict['avp'] = np.nan
if self.regress_flag:
y_true, y_pred = spec_df.rg_targets.tolist(), spec_df.regressions.tolist()
stats_dict["rg_RMSE"] = RMSE(y_true, y_pred)
stats_dict["rg_MAE"] = MAE(y_true, y_pred)
stats_dict["rg_bin_accuracy"] = accuracy(spec_df.rg_bin_target.tolist(), spec_df.rg_bins.tolist())
stats_dict["rg_uncertainty"] = spec_df.rg_uncertainties.mean()
if hasattr(self, "seg_df") and not boxes_only:
seg_cl_df = seg_cl_df.groupby(['pid'], as_index=False).agg(
{dice_col: "mean", "fold": "first"}) # mean of all rois per patient in this epoch
stats_dict["dice"] = seg_cl_df.loc[:,dice_col].mean() #mean of all patients
stats_dict["dice_std"] = seg_cl_df.loc[:, dice_col].std()
# for the aggregated test set case, additionally get the scores for averaging over fold results.
if self.cf.evaluate_fold_means and len(df.fold.unique()) > 1 and self.mode in ["test", "analysis"]:
aucs = []
aps = []
for fold in df.fold.unique():
fold_df = spec_df[spec_df.fold == fold]
if (fold_df.class_label==1).any():
aps.append(
average_precision_score(fold_df.class_label.tolist(), fold_df.pred_score.tolist()))
if len(fold_df.class_label.unique())>1:
aucs.append(roc_auc_score(fold_df.class_label.tolist(), fold_df.pred_score.tolist()))
stats_dict['auc_folds_mean'] = np.mean(aucs)
stats_dict['auc_folds_std'] = np.std(aucs)
stats_dict['ap_folds_mean'] = np.mean(aps)
stats_dict['ap_folds_std'] = np.std(aps)
if hasattr(self, "seg_df") and not boxes_only and self.cf.evaluate_fold_means and len(seg_cl_df.fold.unique()) > 1:
fold_means = seg_cl_df.groupby(['fold'], as_index=True).agg({dice_col:"mean"})
stats_dict["dice_folds_mean"] = float(fold_means.mean())
stats_dict["dice_folds_std"] = float(fold_means.std())
all_stats.append(stats_dict)
# -------------- monitoring, visualisation -----------------
# fill new results into monitor_metrics dict. for simplicity, only one class (of interest) is monitored on patient level.
patient_interests = [self.cf.class_dict[self.cf.patient_class_of_interest],]
if hasattr(self.cf, "bin_dict"):
patient_interests += [self.cf.bin_dict[self.cf.patient_bin_of_interest]]
if monitor_metrics is not None and (score_level != 'patient' or cl_name in patient_interests):
name = 'patient_'+cl_name if score_level == 'patient' else cl_name
for metric in self.cf.metrics:
if metric in stats_dict.keys():
monitor_metrics[name + '_'+metric].append(stats_dict[metric])
else:
print("WARNING: skipped monitor metric {}_{} since not avail".format(name, metric))
# histograms
if self.cf.plot_prediction_histograms:
out_filename = os.path.join(self.hist_dir, 'pred_hist_{}_{}_{}_{}'.format(
self.cf.fold, self.mode, score_level, cl_name))
plg.plot_prediction_hist(self.cf, spec_df, out_filename)
# analysis of the hyper-parameter cf.min_det_thresh, for optimization on validation set.
if self.cf.scan_det_thresh and "val" in self.mode:
conf_threshs = list(np.arange(0.8, 1, 0.02))
pool = Pool(processes=self.cf.n_workers)
mp_inputs = [[spec_df, ii, self.cf.per_patient_ap] for ii in conf_threshs]
aps = pool.map(get_roi_ap_from_df, mp_inputs, chunksize=1)
pool.close()
pool.join()
self.logger.info('results from scanning over det_threshs: {}'.format([[i, j] for i, j in zip(conf_threshs, aps)]))
class_means = pd.DataFrame(columns=self.cf.report_score_level)
for slevel in self.cf.report_score_level:
level_stats = pd.DataFrame([stats for stats in all_stats if slevel in stats["name"]])[self.cf.metrics]
class_means.loc[:, slevel] = level_stats.mean()
all_stats.extend([{"name": 'fold_{} {} {}'.format(self.cf.fold, slevel, "class_means"), **level_means} for
slevel, level_means in class_means.to_dict().items()])
if self.cf.plot_stat_curves:
out_filename = os.path.join(self.curves_dir, '{}_{}_stat_curves'.format(self.cf.fold, self.mode))
plg.plot_stat_curves(self.cf, all_stats, out_filename)
if self.cf.plot_prediction_histograms and hasattr(df, "cluster_n_missing") and df.cluster_n_missing.notna().any():
out_filename = os.path.join(self.hist_dir, 'n_missing_hist_{}_{}.png'.format(self.cf.fold, self.mode))
plg.plot_wbc_n_missing(self.cf, df, outfile=out_filename)
return all_stats, monitor_metrics
def score_test_df(self, max_fold=None, internal_df=True):
"""
Writes out resulting scores to text files: First checks for class-internal-df (typically current) fold,
gets resulting scores, writes them to a text file and pickles data frame. Also checks if data-frame pickles of
all folds of cross-validation exist in exp_dir. If true, loads all dataframes, aggregates test sets over folds,
and calculates and writes out overall metrics.
"""
# this should maybe be extended to auc, ap stds.
- metrics_to_score = self.cf.metrics # + [ m+ext for m in self.cf.metrics if "dice" in m for ext in ["_std"]]
+ metrics_to_score = self.cf.metrics.copy() # + [ m+ext for m in self.cf.metrics if "dice" in m for ext in ["_std"]]
if internal_df:
self.test_df.to_pickle(os.path.join(self.cf.test_dir, '{}_test_df.pkl'.format(self.cf.fold)))
if hasattr(self, "seg_df"):
self.seg_df.to_pickle(os.path.join(self.cf.test_dir, '{}_test_seg_df.pkl'.format(self.cf.fold)))
stats, _ = self.return_metrics(self.test_df, self.cf.class_dict)
with open(os.path.join(self.cf.test_dir, 'results.txt'), 'a') as handle:
handle.write('\n****************************\n')
handle.write('\nresults for fold {}, {} \n'.format(self.cf.fold, time.strftime("%d/%m/%y %H:%M:%S")))
handle.write('\n****************************\n')
handle.write('\nfold df shape {}\n \n'.format(self.test_df.shape))
for s in stats:
for metric in metrics_to_score:
if metric in s.keys(): #needed as long as no dice on patient level poss
if "accuracy" in metric:
handle.write('{} {:0.4f} '.format(metric, s[metric]))
else:
handle.write('{} {:0.3f} '.format(metric, s[metric]))
else:
print("WARNING: skipped metric {} since not avail".format(metric))
handle.write('{} \n'.format(s['name']))
fold_df_paths = sorted([ii for ii in os.listdir(self.cf.test_dir) if 'test_df.pkl' in ii])
fold_seg_df_paths = sorted([ii for ii in os.listdir(self.cf.test_dir) if 'test_seg_df.pkl' in ii])
for paths in [fold_df_paths, fold_seg_df_paths]:
assert len(paths)<= self.cf.n_cv_splits, "found {} > nr of cv splits results dfs in {}".format(len(paths), self.cf.test_dir)
if max_fold is None:
max_fold = self.cf.n_cv_splits-1
if self.cf.fold == max_fold:
print("max fold/overall stats triggered")
if self.cf.evaluate_fold_means:
metrics_to_score += [m + ext for m in self.cf.metrics for ext in ("_folds_mean", "_folds_std")]
with open(os.path.join(self.cf.test_dir, 'results.txt'), 'a') as handle:
self.cf.fold = 'overall'
dfs_list = [pd.read_pickle(os.path.join(self.cf.test_dir, ii)) for ii in fold_df_paths]
seg_dfs_list = [pd.read_pickle(os.path.join(self.cf.test_dir, ii)) for ii in fold_seg_df_paths]
self.test_df = pd.concat(dfs_list, sort=True)
if len(seg_dfs_list)>0:
self.seg_df = pd.concat(seg_dfs_list, sort=True)
stats, _ = self.return_metrics(self.test_df, self.cf.class_dict)
handle.write('\n****************************\n')
handle.write('\nOVERALL RESULTS \n')
handle.write('\n****************************\n')
handle.write('\ndf shape \n \n'.format(self.test_df.shape))
for s in stats:
for metric in metrics_to_score:
if metric in s.keys():
handle.write('{} {:0.3f} '.format(metric, s[metric]))
handle.write('{} \n'.format(s['name']))
results_table_path = os.path.join(self.cf.test_dir,"../../", 'results_table.csv')
with open(results_table_path, 'a') as handle:
#---column headers---
handle.write('\n{},'.format("Experiment Name"))
handle.write('{},'.format("Time Stamp"))
handle.write('{},'.format("Samples Seen"))
handle.write('{},'.format("Spatial Dim"))
handle.write('{},'.format("Patch Size"))
handle.write('{},'.format("CV Folds"))
handle.write('{},'.format("{}-clustering IoU".format(self.cf.clustering)))
handle.write('{},'.format("Merge-2D-to-3D IoU"))
if hasattr(self.cf, "test_against_exact_gt"):
handle.write('{},'.format('Exact GT'))
for s in stats:
assert "overall" in s['name'].split(" ")[0]
if self.cf.class_dict[self.cf.patient_class_of_interest] in s['name'] or "mean" in s["name"]:
for metric in metrics_to_score:
if metric in s.keys() and not np.isnan(s[metric]):
if metric=='ap':
handle.write('{}_{} : {}_{},'.format(*s['name'].split(" ")[1:], metric, int(np.mean(self.cf.ap_match_ious)*100)))
elif not "folds_std" in metric:
handle.write('{}_{} : {},'.format(*s['name'].split(" ")[1:], metric))
else:
print("WARNING: skipped metric {} since not avail".format(metric))
handle.write('\n')
#--- columns content---
handle.write('{},'.format(self.cf.exp_dir.split(os.sep)[-1]))
handle.write('{},'.format(time.strftime("%d%b%y %H:%M:%S")))
handle.write('{},'.format(self.cf.num_epochs*self.cf.num_train_batches*self.cf.batch_size))
handle.write('{}D,'.format(self.cf.dim))
handle.write('{},'.format("x".join([str(self.cf.patch_size[i]) for i in range(self.cf.dim)])))
handle.write('{},'.format(str(self.test_df.fold.unique().tolist()).replace(",", "")))
handle.write('{},'.format(self.cf.clustering_iou if self.cf.clustering else str("N/A")))
handle.write('{},'.format(self.cf.merge_3D_iou if self.cf.merge_2D_to_3D_preds else str("N/A")))
if hasattr(self.cf, "test_against_exact_gt"):
handle.write('{},'.format(self.cf.test_against_exact_gt))
for s in stats:
if self.cf.class_dict[self.cf.patient_class_of_interest] in s['name'] or "mean" in s["name"]:
for metric in metrics_to_score:
if metric in s.keys() and not np.isnan(s[metric]): # needed as long as no dice on patient level possible
if "folds_mean" in metric:
handle.write('{:0.3f}\u00B1{:0.3f}, '.format(s[metric], s["_".join((*metric.split("_")[:-1], "std"))]))
elif not "folds_std" in metric:
handle.write('{:0.3f}, '.format(s[metric]))
handle.write('\n')
with open(os.path.join(self.cf.test_dir, 'results_extr_scores.txt'), 'w') as handle:
handle.write('\n****************************\n')
handle.write('\nextremal scores for fold {} \n'.format(self.cf.fold))
handle.write('\n****************************\n')
# want: pid & fold (&other) of highest scoring tp & fp in test_df
for cl in self.cf.class_dict.keys():
print("\nClass {}".format(self.cf.class_dict[cl]), file=handle)
cl_df = self.test_df[self.test_df.pred_class == cl] #.dropna(axis=1)
for det_type in ['det_tp', 'det_fp']:
filtered_df = cl_df[cl_df.det_type==det_type]
print("\nHighest scoring {} of class {}".format(det_type, self.cf.class_dict[cl]), file=handle)
if len(filtered_df)>0:
print(filtered_df.loc[filtered_df.pred_score.idxmax()], file=handle)
else:
print("No detections of type {} for class {} in this df".format(det_type, self.cf.class_dict[cl]), file=handle)
handle.write('\n****************************\n')
diff --git a/exec.py b/exec.py
index c343d47..85e7716 100644
--- a/exec.py
+++ b/exec.py
@@ -1,343 +1,344 @@
#!/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.
# ==============================================================================
""" execution script. this where all routines come together and the only script you need to call.
refer to parse args below to see options for execution.
"""
import plotting as plg
import os
import warnings
import argparse
import time
import torch
import utils.exp_utils as utils
from evaluator import Evaluator
from predictor import Predictor
for msg in ["Attempting to set identical bottom==top results",
"This figure includes Axes that are not compatible with tight_layout",
"Data has no positive values, and therefore cannot be log-scaled.",
".*invalid value encountered in true_divide.*"]:
warnings.filterwarnings("ignore", msg)
def train(cf, logger):
"""
performs the training routine for a given fold. saves plots and selected parameters to the experiment dir
specified in the configs. logs to file and tensorboard.
"""
logger.info('performing training in {}D over fold {} on experiment {} with model {}'.format(
cf.dim, cf.fold, cf.exp_dir, cf.model))
logger.time("train_val")
# -------------- inits and settings -----------------
net = model.net(cf, logger).cuda()
if cf.optimizer == "ADAMW":
- optimizer = torch.optim.AdamW(utils.parse_params_for_optim(net, weight_decay=cf.weight_decay),
+ optimizer = torch.optim.AdamW(utils.parse_params_for_optim(net, weight_decay=cf.weight_decay,
+ exclude_from_wd=cf.exclude_from_wd),
lr=cf.learning_rate[0])
elif cf.optimizer == "SGD":
optimizer = torch.optim.SGD(utils.parse_params_for_optim(net, weight_decay=cf.weight_decay),
lr=cf.learning_rate[0], momentum=0.3)
if cf.dynamic_lr_scheduling:
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=cf.scheduling_mode, factor=cf.lr_decay_factor,
patience=cf.scheduling_patience)
model_selector = utils.ModelSelector(cf, logger)
starting_epoch = 1
if cf.resume:
checkpoint_path = os.path.join(cf.fold_dir, "last_state.pth")
starting_epoch, net, optimizer, model_selector = \
utils.load_checkpoint(checkpoint_path, net, optimizer, model_selector)
logger.info('resumed from checkpoint {} to epoch {}'.format(checkpoint_path, starting_epoch))
# prepare monitoring
monitor_metrics = utils.prepare_monitoring(cf)
logger.info('loading dataset and initializing batch generators...')
batch_gen = data_loader.get_train_generators(cf, logger)
# -------------- training -----------------
for epoch in range(starting_epoch, cf.num_epochs + 1):
logger.info('starting training epoch {}/{}'.format(epoch, cf.num_epochs))
logger.time("train_epoch")
net.train()
train_results_list = []
train_evaluator = Evaluator(cf, logger, mode='train')
for i in range(cf.num_train_batches):
logger.time("train_batch_loadfw")
batch = next(batch_gen['train'])
batch_gen['train'].generator.stats['roi_counts'] += batch['roi_counts']
batch_gen['train'].generator.stats['empty_counts'] += batch['empty_counts']
logger.time("train_batch_loadfw")
logger.time("train_batch_netfw")
results_dict = net.train_forward(batch)
logger.time("train_batch_netfw")
logger.time("train_batch_bw")
optimizer.zero_grad()
results_dict['torch_loss'].backward()
if cf.clip_norm:
torch.nn.utils.clip_grad_norm_(net.parameters(), cf.clip_norm, norm_type=2) # gradient clipping
optimizer.step()
train_results_list.append(({k:v for k,v in results_dict.items() if k != "seg_preds"}, batch["pid"])) # slim res dict
if not cf.server_env:
print("\rFinished training batch " +
"{}/{} in {:.1f}s ({:.2f}/{:.2f} forw load/net, {:.2f} backw).".format(i+1, cf.num_train_batches,
logger.get_time("train_batch_loadfw")+
logger.get_time("train_batch_netfw")
+logger.time("train_batch_bw"),
logger.get_time("train_batch_loadfw",reset=True),
logger.get_time("train_batch_netfw", reset=True),
logger.get_time("train_batch_bw", reset=True)), end="", flush=True)
print()
#--------------- train eval ----------------
if (epoch-1)%cf.plot_frequency==0:
# view an example batch
utils.split_off_process(plg.view_batch, cf, batch, results_dict, has_colorchannels=cf.has_colorchannels,
show_gt_labels=True, get_time="train-example plot",
out_file=os.path.join(cf.plot_dir, 'batch_example_train_{}.png'.format(cf.fold)))
logger.time("evals")
_, monitor_metrics['train'] = train_evaluator.evaluate_predictions(train_results_list, monitor_metrics['train'])
logger.time("evals")
logger.time("train_epoch", toggle=False)
del train_results_list
#----------- validation ------------
logger.info('starting validation in mode {}.'.format(cf.val_mode))
logger.time("val_epoch")
with torch.no_grad():
net.eval()
val_results_list = []
val_evaluator = Evaluator(cf, logger, mode=cf.val_mode)
val_predictor = Predictor(cf, net, logger, mode='val')
for i in range(batch_gen['n_val']):
logger.time("val_batch")
batch = next(batch_gen[cf.val_mode])
if cf.val_mode == 'val_patient':
results_dict = val_predictor.predict_patient(batch)
elif cf.val_mode == 'val_sampling':
results_dict = net.train_forward(batch, is_validation=True)
val_results_list.append([results_dict, batch["pid"]])
if not cf.server_env:
print("\rFinished validation {} {}/{} in {:.1f}s.".format('patient' if cf.val_mode=='val_patient' else 'batch',
i + 1, batch_gen['n_val'],
logger.time("val_batch")), end="", flush=True)
print()
#------------ val eval -------------
if (epoch - 1) % cf.plot_frequency == 0:
utils.split_off_process(plg.view_batch, cf, batch, results_dict, has_colorchannels=cf.has_colorchannels,
show_gt_labels=True, get_time="val-example plot",
out_file=os.path.join(cf.plot_dir, 'batch_example_val_{}.png'.format(cf.fold)))
logger.time("evals")
_, monitor_metrics['val'] = val_evaluator.evaluate_predictions(val_results_list, monitor_metrics['val'])
model_selector.run_model_selection(net, optimizer, monitor_metrics, epoch)
del val_results_list
#----------- monitoring -------------
monitor_metrics.update({"lr":
{str(g) : group['lr'] for (g, group) in enumerate(optimizer.param_groups)}})
logger.metrics2tboard(monitor_metrics, global_step=epoch)
logger.time("evals")
logger.info('finished epoch {}/{}, took {:.2f}s. train total: {:.2f}s, average: {:.2f}s. val total: {:.2f}s, average: {:.2f}s.'.format(
epoch, cf.num_epochs, logger.get_time("train_epoch")+logger.time("val_epoch"), logger.get_time("train_epoch"),
logger.get_time("train_epoch", reset=True)/cf.num_train_batches, logger.get_time("val_epoch"),
logger.get_time("val_epoch", reset=True)/batch_gen["n_val"]))
logger.info("time for evals: {:.2f}s".format(logger.get_time("evals", reset=True)))
#-------------- scheduling -----------------
if cf.dynamic_lr_scheduling:
scheduler.step(monitor_metrics["val"][cf.scheduling_criterion][-1])
else:
for param_group in optimizer.param_groups:
param_group['lr'] = cf.learning_rate[epoch-1]
logger.time("train_val")
logger.info("Training and validating over {} epochs took {}".format(cf.num_epochs, logger.get_time("train_val", format="hms", reset=True)))
batch_gen['train'].generator.print_stats(logger, plot=True)
def test(cf, logger, max_fold=None):
"""performs testing for a given fold (or held out set). saves stats in evaluator.
"""
logger.time("test_fold")
logger.info('starting testing model of fold {} in exp {}'.format(cf.fold, cf.exp_dir))
net = model.net(cf, logger).cuda()
batch_gen = data_loader.get_test_generator(cf, logger)
test_predictor = Predictor(cf, net, logger, mode='test')
test_results_list = test_predictor.predict_test_set(batch_gen, return_results = not hasattr(
cf, "eval_test_separately") or not cf.eval_test_separately)
if test_results_list is not None:
test_evaluator = Evaluator(cf, logger, mode='test')
test_evaluator.evaluate_predictions(test_results_list)
test_evaluator.score_test_df(max_fold=max_fold)
logger.info('Testing of fold {} took {}.\n'.format(cf.fold, logger.get_time("test_fold", reset=True, format="hms")))
if __name__ == '__main__':
stime = time.time()
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', type=str, default='toy',
help="path to the dataset-specific code in source_dir/datasets")
parser.add_argument('--exp_dir', type=str, default='/home/gregor/Documents/regrcnn/datasets/toy/experiments/dev',
help='path to experiment dir. will be created if non existent.')
parser.add_argument('-m', '--mode', type=str, default='train_test', help='one out of: create_exp, analysis, train, train_test, or test')
parser.add_argument('-f', '--folds', nargs='+', type=int, default=None, help='None runs over all folds in CV. otherwise specify list of folds.')
parser.add_argument('--server_env', default=False, action='store_true', help='change IO settings to deploy models on a cluster.')
parser.add_argument('--data_dest', type=str, default=None, help="path to final data folder if different from config")
parser.add_argument('--use_stored_settings', default=False, action='store_true',
help='load configs from existing exp_dir instead of source dir. always done for testing, '
'but can be set to true to do the same for training. useful in job scheduler environment, '
'where source code might change before the job actually runs.')
parser.add_argument('--resume', action="store_true", default=False,
help='if given, resume from checkpoint(s) of the specified folds.')
parser.add_argument('-d', '--dev', default=False, action='store_true', help="development mode: shorten everything")
args = parser.parse_args()
args.dataset_name = os.path.join("datasets", args.dataset_name) if not "datasets" in args.dataset_name else args.dataset_name
folds = args.folds
resume = None if args.resume in ['None', 'none'] else args.resume
if args.mode == 'create_exp':
cf = utils.prep_exp(args.dataset_name, args.exp_dir, args.server_env, use_stored_settings=False)
logger = utils.get_logger(cf.exp_dir, cf.server_env, -1)
logger.info('created experiment directory at {}'.format(args.exp_dir))
elif args.mode == 'train' or args.mode == 'train_test':
cf = utils.prep_exp(args.dataset_name, args.exp_dir, args.server_env, args.use_stored_settings)
if args.dev:
folds = [0,1]
cf.batch_size, cf.num_epochs, cf.min_save_thresh, cf.save_n_models = 3 if cf.dim==2 else 1, 2, 0, 2
cf.num_train_batches, cf.num_val_batches, cf.max_val_patients = 5, 1, 1
cf.test_n_epochs = cf.save_n_models
cf.max_test_patients = 1
torch.backends.cudnn.benchmark = cf.dim==3
else:
torch.backends.cudnn.benchmark = cf.cuda_benchmark
if args.data_dest is not None:
cf.data_dest = args.data_dest
logger = utils.get_logger(cf.exp_dir, cf.server_env, cf.sysmetrics_interval)
data_loader = utils.import_module('data_loader', os.path.join(args.dataset_name, 'data_loader.py'))
model = utils.import_module('model', cf.model_path)
logger.info("loaded model from {}".format(cf.model_path))
if folds is None:
folds = range(cf.n_cv_splits)
for fold in folds:
"""k-fold cross-validation: the dataset is split into k equally-sized folds, one used for validation,
one for testing, the rest for training. This loop iterates k-times over the dataset, cyclically moving the
splits. k==folds, fold in [0,folds) says which split is used for testing.
"""
cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(fold)); cf.fold = fold
logger.set_logfile(fold=fold)
cf.resume = resume
if not os.path.exists(cf.fold_dir):
os.mkdir(cf.fold_dir)
train(cf, logger)
cf.resume = None
if args.mode == 'train_test':
test(cf, logger)
elif args.mode == 'test':
cf = utils.prep_exp(args.dataset_name, args.exp_dir, args.server_env, use_stored_settings=True, is_training=False)
if args.data_dest is not None:
cf.data_dest = args.data_dest
logger = utils.get_logger(cf.exp_dir, cf.server_env, cf.sysmetrics_interval)
data_loader = utils.import_module('data_loader', os.path.join(args.dataset_name, 'data_loader.py'))
model = utils.import_module('model', cf.model_path)
logger.info("loaded model from {}".format(cf.model_path))
fold_dirs = sorted([os.path.join(cf.exp_dir, f) for f in os.listdir(cf.exp_dir) if
os.path.isdir(os.path.join(cf.exp_dir, f)) and f.startswith("fold")])
if folds is None:
folds = range(cf.n_cv_splits)
if args.dev:
folds = folds[:2]
cf.batch_size, cf.max_test_patients, cf.test_n_epochs = 1 if cf.dim==2 else 1, 2, 2
else:
torch.backends.cudnn.benchmark = cf.cuda_benchmark
for fold in folds:
cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(fold)); cf.fold = fold
logger.set_logfile(fold=fold)
if cf.fold_dir in fold_dirs:
test(cf, logger, max_fold=max([int(f[-1]) for f in fold_dirs]))
else:
logger.info("Skipping fold {} since no model parameters found.".format(fold))
# load raw predictions saved by predictor during testing, run aggregation algorithms and evaluation.
elif args.mode == 'analysis':
""" analyse already saved predictions.
"""
cf = utils.prep_exp(args.dataset_name, args.exp_dir, args.server_env, use_stored_settings=True, is_training=False)
logger = utils.get_logger(cf.exp_dir, cf.server_env, cf.sysmetrics_interval)
if cf.held_out_test_set and not cf.eval_test_fold_wise:
predictor = Predictor(cf, net=None, logger=logger, mode='analysis')
results_list = predictor.load_saved_predictions()
logger.info('starting evaluation...')
cf.fold = 0
evaluator = Evaluator(cf, logger, mode='test')
evaluator.evaluate_predictions(results_list)
evaluator.score_test_df(max_fold=0)
else:
fold_dirs = sorted([os.path.join(cf.exp_dir, f) for f in os.listdir(cf.exp_dir) if
os.path.isdir(os.path.join(cf.exp_dir, f)) and f.startswith("fold")])
if args.dev:
fold_dirs = fold_dirs[:1]
if folds is None:
folds = range(cf.n_cv_splits)
for fold in folds:
cf.fold = fold; cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(cf.fold))
logger.set_logfile(fold=fold)
if cf.fold_dir in fold_dirs:
predictor = Predictor(cf, net=None, logger=logger, mode='analysis')
results_list = predictor.load_saved_predictions()
# results_list[x][1] is pid, results_list[x][0] is list of len samples-per-patient, each entry hlds
# list of boxes per that sample, i.e., len(results_list[x][y][0]) would be nr of boxes in sample y of patient x
logger.info('starting evaluation...')
evaluator = Evaluator(cf, logger, mode='test')
evaluator.evaluate_predictions(results_list)
max_fold = max([int(f[-1]) for f in fold_dirs])
evaluator.score_test_df(max_fold=max_fold)
else:
logger.info("Skipping fold {} since no model parameters found.".format(fold))
else:
raise ValueError('mode "{}" specified in args is not implemented.'.format(args.mode))
mins, secs = divmod((time.time() - stime), 60)
h, mins = divmod(mins, 60)
t = "{:d}h:{:02d}m:{:02d}s".format(int(h), int(mins), int(secs))
logger.info("{} total runtime: {}".format(os.path.split(__file__)[1], t))
del logger
torch.cuda.empty_cache()
diff --git a/models/retina_net.py b/models/retina_net.py
index ee1b266..6b70cbc 100644
--- a/models/retina_net.py
+++ b/models/retina_net.py
@@ -1,778 +1,779 @@
#!/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.
# ==============================================================================
"""Retina Net. According to https://arxiv.org/abs/1708.02002"""
import utils.model_utils as mutils
import utils.exp_utils as utils
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
sys.path.append('..')
from custom_extensions.nms import nms
class Classifier(nn.Module):
def __init__(self, cf, conv):
"""
Builds the classifier sub-network.
"""
super(Classifier, self).__init__()
self.dim = conv.dim
self.n_classes = cf.head_classes
n_input_channels = cf.end_filts
n_features = cf.n_rpn_features
n_output_channels = cf.n_anchors_per_pos * cf.head_classes
anchor_stride = cf.rpn_anchor_stride
self.conv_1 = conv(n_input_channels, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_2 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_3 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_4 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_final = conv(n_features, n_output_channels, ks=3, stride=anchor_stride, pad=1, relu=None)
def forward(self, x):
"""
:param x: input feature map (b, in_c, y, x, (z))
:return: class_logits (b, n_anchors, n_classes)
"""
x = self.conv_1(x)
x = self.conv_2(x)
x = self.conv_3(x)
x = self.conv_4(x)
class_logits = self.conv_final(x)
axes = (0, 2, 3, 1) if self.dim == 2 else (0, 2, 3, 4, 1)
class_logits = class_logits.permute(*axes)
class_logits = class_logits.contiguous()
class_logits = class_logits.view(x.shape[0], -1, self.n_classes)
return [class_logits]
class BBRegressor(nn.Module):
def __init__(self, cf, conv):
"""
Builds the bb-regression sub-network.
"""
super(BBRegressor, self).__init__()
self.dim = conv.dim
n_input_channels = cf.end_filts
n_features = cf.n_rpn_features
n_output_channels = cf.n_anchors_per_pos * self.dim * 2
anchor_stride = cf.rpn_anchor_stride
self.conv_1 = conv(n_input_channels, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_2 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_3 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_4 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_final = conv(n_features, n_output_channels, ks=3, stride=anchor_stride, pad=1, relu=None)
def forward(self, x):
"""
:param x: input feature map (b, in_c, y, x, (z))
:return: bb_logits (b, n_anchors, dim * 2)
"""
x = self.conv_1(x)
x = self.conv_2(x)
x = self.conv_3(x)
x = self.conv_4(x)
bb_logits = self.conv_final(x)
axes = (0, 2, 3, 1) if self.dim == 2 else (0, 2, 3, 4, 1)
bb_logits = bb_logits.permute(*axes)
bb_logits = bb_logits.contiguous()
bb_logits = bb_logits.view(x.shape[0], -1, self.dim * 2)
return [bb_logits]
class RoIRegressor(nn.Module):
def __init__(self, cf, conv, rg_feats):
"""
Builds the RoI-item-regression sub-network. Regression items can be, e.g., malignancy scores of tumors.
"""
super(RoIRegressor, self).__init__()
self.dim = conv.dim
n_input_channels = cf.end_filts
n_features = cf.n_rpn_features
self.rg_feats = rg_feats
n_output_channels = cf.n_anchors_per_pos * self.rg_feats
anchor_stride = cf.rpn_anchor_stride
self.conv_1 = conv(n_input_channels, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_2 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_3 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_4 = conv(n_features, n_features, ks=3, stride=anchor_stride, pad=1, relu=cf.relu, norm=cf.norm)
self.conv_final = conv(n_features, n_output_channels, ks=3, stride=anchor_stride,
pad=1, relu=None)
def forward(self, x):
"""
:param x: input feature map (b, in_c, y, x, (z))
:return: bb_logits (b, n_anchors, dim * 2)
"""
x = self.conv_1(x)
x = self.conv_2(x)
x = self.conv_3(x)
x = self.conv_4(x)
x = self.conv_final(x)
axes = (0, 2, 3, 1) if self.dim == 2 else (0, 2, 3, 4, 1)
x = x.permute(*axes)
x = x.contiguous()
x = x.view(x.shape[0], -1, self.rg_feats)
return [x]
############################################################
# Loss Functions
############################################################
#
def compute_class_loss(anchor_matches, class_pred_logits, shem_poolsize=20):
"""
:param anchor_matches: (n_anchors). [-1, 0, 1] for negative, neutral, and positive matched anchors.
:param class_pred_logits: (n_anchors, n_classes). logits from classifier sub-network.
:param shem_poolsize: int. factor of top-k candidates to draw from per negative sample (online-hard-example-mining).
:return: loss: torch tensor
:return: np_neg_ix: 1D array containing indices of the neg_roi_logits, which have been sampled for training.
"""
# Positive and Negative anchors contribute to the loss,
# but neutral anchors (match value = 0) don't.
pos_indices = torch.nonzero(anchor_matches > 0)
neg_indices = torch.nonzero(anchor_matches == -1)
# get positive samples and calucalte loss.
if not 0 in pos_indices.size():
pos_indices = pos_indices.squeeze(1)
roi_logits_pos = class_pred_logits[pos_indices]
targets_pos = anchor_matches[pos_indices].detach()
pos_loss = F.cross_entropy(roi_logits_pos, targets_pos.long())
else:
pos_loss = torch.FloatTensor([0]).cuda()
# get negative samples, such that the amount matches the number of positive samples, but at least 1.
# get high scoring negatives by applying online-hard-example-mining.
if not 0 in neg_indices.size():
neg_indices = neg_indices.squeeze(1)
roi_logits_neg = class_pred_logits[neg_indices]
negative_count = np.max((1, pos_indices.cpu().data.numpy().size))
roi_probs_neg = F.softmax(roi_logits_neg, dim=1)
neg_ix = mutils.shem(roi_probs_neg, negative_count, shem_poolsize)
neg_loss = F.cross_entropy(roi_logits_neg[neg_ix], torch.LongTensor([0] * neg_ix.shape[0]).cuda())
# return the indices of negative samples, who contributed to the loss for monitoring plots.
np_neg_ix = neg_ix.cpu().data.numpy()
else:
neg_loss = torch.FloatTensor([0]).cuda()
np_neg_ix = np.array([]).astype('int32')
loss = (pos_loss + neg_loss) / 2
return loss, np_neg_ix
def compute_bbox_loss(target_deltas, pred_deltas, anchor_matches):
"""
:param target_deltas: (b, n_positive_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd)))).
Uses 0 padding to fill in unused bbox deltas.
:param pred_deltas: predicted deltas from bbox regression head. (b, n_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd))))
:param anchor_matches: tensor (n_anchors). value in [-1, 0, class_ids] for negative, neutral, and positive matched anchors.
i.e., positively matched anchors are marked by class_id >0
:return: loss: torch 1D tensor.
"""
if not 0 in torch.nonzero(anchor_matches>0).shape:
indices = torch.nonzero(anchor_matches>0).squeeze(1)
# Pick bbox deltas that contribute to the loss
pred_deltas = pred_deltas[indices]
# Trim target bounding box deltas to the same length as pred_deltas.
target_deltas = target_deltas[:pred_deltas.shape[0], :].detach()
# Smooth L1 loss
loss = F.smooth_l1_loss(pred_deltas, target_deltas)
else:
loss = torch.FloatTensor([0]).cuda()
return loss
def compute_rg_loss(tasks, target, pred, anchor_matches):
"""
:param target_deltas: (b, n_positive_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd)))).
Uses 0 padding to fill in unsed bbox deltas.
:param pred_deltas: predicted deltas from bbox regression head. (b, n_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd))))
:param anchor_matches: (n_anchors). [-1, 0, 1] for negative, neutral, and positive matched anchors.
:return: loss: torch 1D tensor.
"""
if not 0 in target.shape and not 0 in torch.nonzero(anchor_matches>0).shape:
indices = torch.nonzero(anchor_matches>0).squeeze(1)
# Pick rgs that contribute to the loss
pred = pred[indices]
# Trim target
target = target[:pred.shape[0]].detach()
if 'regression_bin' in tasks:
loss = F.cross_entropy(pred, target.long())
else:
loss = F.smooth_l1_loss(pred, target)
else:
loss = torch.FloatTensor([0]).cuda()
return loss
def compute_focal_class_loss(anchor_matches, class_pred_logits, gamma=2.):
- """ Focal Loss FL = -(1-q)^g log(q) with q = pred class probability.
+ """ Focal Loss :math:`FL = -(1-q)^g log(q)` with q = pred class probability, g = gamma hyperparameter.
:param anchor_matches: (n_anchors). [-1, 0, class] for negative, neutral, and positive matched anchors.
:param class_pred_logits: (n_anchors, n_classes). logits from classifier sub-network.
:param gamma: g in above formula, good results with g=2 in original paper.
:return: loss: torch tensor
:return: focal loss
"""
- # Positive and Negative anchors contribute to the loss,
- # but neutral anchors (match value = 0) don't.
+ # Positive and Negative anchors contribute to the loss but neutral anchors (match value = 0) don't.
pos_indices = torch.nonzero(anchor_matches > 0).squeeze(-1) # dim=-1 instead of 1 or 0 to cover empty matches.
neg_indices = torch.nonzero(anchor_matches == -1).squeeze(-1)
target_classes = torch.cat( (anchor_matches[pos_indices].long(), torch.LongTensor([0] * neg_indices.shape[0]).cuda()) )
non_neutral_indices = torch.cat( (pos_indices, neg_indices) )
- q = F.softmax(class_pred_logits[non_neutral_indices], dim=1) # q shape: (n_non_neutral_anchors, n_classes)
+ # q shape: (n_non_neutral_anchors, n_classes)
+ q = F.softmax(class_pred_logits[non_neutral_indices], dim=1)
- # one-hot encoded target classes: keep only the pred probs of the correct class. it will receive incentive to be maximized.
+ # one-hot encoded target classes: keep only the pred probs of the correct class.
+ # that class will receive the incentive to be maximized.
# log(q_i) where i = target class --> FL shape (n_anchors,)
# need to transform to indices into flattened tensor to use torch.take
target_locs_flat = q.shape[1] * torch.arange(q.shape[0]).cuda() + target_classes
q = torch.take(q, target_locs_flat)
FL = torch.log(q) # element-wise log
- FL *= -(1-q)**gamma
+ FL *= -(1.-q)**gamma
# take mean over all considered anchors
FL = FL.sum() / FL.shape[0]
return FL
def refine_detections(anchors, probs, deltas, regressions, batch_ixs, cf):
"""Refine classified proposals, filter overlaps and return final
detections. n_proposals here is typically a very large number: batch_size * n_anchors.
This function is hence optimized on trimming down n_proposals.
:param anchors: (n_anchors, 2 * dim)
:param probs: (n_proposals, n_classes) softmax probabilities for all rois as predicted by classifier head.
:param deltas: (n_proposals, n_classes, 2 * dim) box refinement deltas as predicted by bbox regressor head.
:param regressions: (n_proposals, n_classes, n_rg_feats)
:param batch_ixs: (n_proposals) batch element assignemnt info for re-allocation.
:return: result: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score, pred_regr))
"""
anchors = anchors.repeat(batch_ixs.unique().shape[0], 1)
#flatten foreground probabilities, sort and trim down to highest confidences by pre_nms limit.
fg_probs = probs[:, 1:].contiguous()
flat_probs, flat_probs_order = fg_probs.view(-1).sort(descending=True)
keep_ix = flat_probs_order[:cf.pre_nms_limit]
# reshape indices to 2D index array with shape like fg_probs.
keep_arr = torch.cat(((keep_ix / fg_probs.shape[1]).unsqueeze(1), (keep_ix % fg_probs.shape[1]).unsqueeze(1)), 1)
pre_nms_scores = flat_probs[:cf.pre_nms_limit]
pre_nms_class_ids = keep_arr[:, 1] + 1 # add background again.
pre_nms_batch_ixs = batch_ixs[keep_arr[:, 0]]
pre_nms_anchors = anchors[keep_arr[:, 0]]
pre_nms_deltas = deltas[keep_arr[:, 0]]
pre_nms_regressions = regressions[keep_arr[:, 0]]
keep = torch.arange(pre_nms_scores.size()[0]).long().cuda()
# apply bounding box deltas. re-scale to image coordinates.
std_dev = torch.from_numpy(np.reshape(cf.rpn_bbox_std_dev, [1, cf.dim * 2])).float().cuda()
scale = torch.from_numpy(cf.scale).float().cuda()
refined_rois = mutils.apply_box_deltas_2D(pre_nms_anchors / scale, pre_nms_deltas * std_dev) * scale \
if cf.dim == 2 else mutils.apply_box_deltas_3D(pre_nms_anchors / scale, pre_nms_deltas * std_dev) * scale
# round and cast to int since we're deadling with pixels now
refined_rois = mutils.clip_to_window(cf.window, refined_rois)
pre_nms_rois = torch.round(refined_rois)
for j, b in enumerate(mutils.unique1d(pre_nms_batch_ixs)):
bixs = torch.nonzero(pre_nms_batch_ixs == b)[:, 0]
bix_class_ids = pre_nms_class_ids[bixs]
bix_rois = pre_nms_rois[bixs]
bix_scores = pre_nms_scores[bixs]
for i, class_id in enumerate(mutils.unique1d(bix_class_ids)):
ixs = torch.nonzero(bix_class_ids == class_id)[:, 0]
# nms expects boxes sorted by score.
ix_rois = bix_rois[ixs]
ix_scores = bix_scores[ixs]
ix_scores, order = ix_scores.sort(descending=True)
ix_rois = ix_rois[order, :]
ix_scores = ix_scores
class_keep = nms.nms(ix_rois, ix_scores, cf.detection_nms_threshold)
# map indices back.
class_keep = keep[bixs[ixs[order[class_keep]]]]
# merge indices over classes for current batch element
b_keep = class_keep if i == 0 else mutils.unique1d(torch.cat((b_keep, class_keep)))
# only keep top-k boxes of current batch-element.
top_ids = pre_nms_scores[b_keep].sort(descending=True)[1][:cf.model_max_instances_per_batch_element]
b_keep = b_keep[top_ids]
# merge indices over batch elements.
batch_keep = b_keep if j == 0 else mutils.unique1d(torch.cat((batch_keep, b_keep)))
keep = batch_keep
# arrange output.
result = torch.cat((pre_nms_rois[keep],
pre_nms_batch_ixs[keep].unsqueeze(1).float(),
pre_nms_class_ids[keep].unsqueeze(1).float(),
pre_nms_scores[keep].unsqueeze(1),
pre_nms_regressions[keep]), dim=1)
return result
def gt_anchor_matching(cf, anchors, gt_boxes, gt_class_ids=None, gt_regressions=None):
"""Given the anchors and GT boxes, compute overlaps and identify positive
anchors and deltas to refine them to match their corresponding GT boxes.
anchors: [num_anchors, (y1, x1, y2, x2, (z1), (z2))]
gt_boxes: [num_gt_boxes, (y1, x1, y2, x2, (z1), (z2))]
gt_class_ids (optional): [num_gt_boxes] Integer class IDs for one stage detectors. in RPN case of Mask R-CNN,
set all positive matches to 1 (foreground)
gt_regressions: [num_gt_rgs, n_rg_feats], if None empty rg_targets are returned
Returns:
anchor_class_matches: [N] (int32) matches between anchors and GT boxes. class_id = positive anchor,
-1 = negative anchor, 0 = neutral. i.e., positively matched anchors are marked by class_id (which is >0).
anchor_delta_targets: [N, (dy, dx, (dz), log(dh), log(dw), (log(dd)))] Anchor bbox deltas.
anchor_rg_targets: [n_anchors, n_rg_feats]
"""
anchor_class_matches = np.zeros([anchors.shape[0]], dtype=np.int32)
anchor_delta_targets = np.zeros((cf.rpn_train_anchors_per_image, 2*cf.dim))
if gt_regressions is not None:
if 'regression_bin' in cf.prediction_tasks:
anchor_rg_targets = np.zeros((cf.rpn_train_anchors_per_image,))
else:
anchor_rg_targets = np.zeros((cf.rpn_train_anchors_per_image, cf.regression_n_features))
else:
anchor_rg_targets = np.array([])
anchor_matching_iou = cf.anchor_matching_iou
if gt_boxes is None:
anchor_class_matches = np.full(anchor_class_matches.shape, fill_value=-1)
return anchor_class_matches, anchor_delta_targets, anchor_rg_targets
# for mrcnn: anchor matching is done for RPN loss, so positive labels are all 1 (foreground)
if gt_class_ids is None:
gt_class_ids = np.array([1] * len(gt_boxes))
# Compute overlaps [num_anchors, num_gt_boxes]
overlaps = mutils.compute_overlaps(anchors, gt_boxes)
# Match anchors to GT Boxes
# If an anchor overlaps a GT box with IoU >= anchor_matching_iou then it's positive.
# If an anchor overlaps a GT box with IoU < 0.1 then it's negative.
# Neutral anchors are those that don't match the conditions above,
# and they don't influence the loss function.
# However, don't keep any GT box unmatched (rare, but happens). Instead,
# match it to the closest anchor (even if its max IoU is < 0.1).
# 1. Set negative anchors first. They get overwritten below if a GT box is
# matched to them. Skip boxes in crowd areas.
anchor_iou_argmax = np.argmax(overlaps, axis=1)
anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax]
if anchors.shape[1] == 4:
anchor_class_matches[(anchor_iou_max < 0.1)] = -1
elif anchors.shape[1] == 6:
anchor_class_matches[(anchor_iou_max < 0.01)] = -1
else:
raise ValueError('anchor shape wrong {}'.format(anchors.shape))
# 2. Set an anchor for each GT box (regardless of IoU value).
gt_iou_argmax = np.argmax(overlaps, axis=0)
for ix, ii in enumerate(gt_iou_argmax):
anchor_class_matches[ii] = gt_class_ids[ix]
# 3. Set anchors with high overlap as positive.
above_thresh_ixs = np.argwhere(anchor_iou_max >= anchor_matching_iou)
anchor_class_matches[above_thresh_ixs] = gt_class_ids[anchor_iou_argmax[above_thresh_ixs]]
# Subsample to balance positive anchors.
ids = np.where(anchor_class_matches > 0)[0]
extra = len(ids) - (cf.rpn_train_anchors_per_image // 2)
if extra > 0:
# Reset the extra ones to neutral
ids = np.random.choice(ids, extra, replace=False)
anchor_class_matches[ids] = 0
# Leave all negative proposals negative for now and sample from them later in online hard example mining.
# For positive anchors, compute shift and scale needed to transform them to match the corresponding GT boxes.
ids = np.where(anchor_class_matches > 0)[0]
ix = 0 # index into anchor_delta_targets
for i, a in zip(ids, anchors[ids]):
# closest gt box (it might have IoU < anchor_matching_iou)
gt = gt_boxes[anchor_iou_argmax[i]]
# convert coordinates to center plus width/height.
gt_h = gt[2] - gt[0]
gt_w = gt[3] - gt[1]
gt_center_y = gt[0] + 0.5 * gt_h
gt_center_x = gt[1] + 0.5 * gt_w
# Anchor
a_h = a[2] - a[0]
a_w = a[3] - a[1]
a_center_y = a[0] + 0.5 * a_h
a_center_x = a[1] + 0.5 * a_w
if cf.dim == 2:
anchor_delta_targets[ix] = [
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
np.log(gt_h / a_h),
np.log(gt_w / a_w)]
else:
gt_d = gt[5] - gt[4]
gt_center_z = gt[4] + 0.5 * gt_d
a_d = a[5] - a[4]
a_center_z = a[4] + 0.5 * a_d
anchor_delta_targets[ix] = [
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
(gt_center_z - a_center_z) / a_d,
np.log(gt_h / a_h),
np.log(gt_w / a_w),
np.log(gt_d / a_d)]
# normalize.
anchor_delta_targets[ix] /= cf.rpn_bbox_std_dev
if gt_regressions is not None:
anchor_rg_targets[ix] = gt_regressions[anchor_iou_argmax[i]]
ix += 1
return anchor_class_matches, anchor_delta_targets, anchor_rg_targets
############################################################
# RetinaNet Class
############################################################
class net(nn.Module):
"""Encapsulates the RetinaNet model functionality.
"""
def __init__(self, cf, logger):
"""
cf: A Sub-class of the cf class
model_dir: Directory to save training logs and trained weights
"""
super(net, self).__init__()
self.cf = cf
self.logger = logger
self.build()
if self.cf.weight_init is not None:
mutils.initialize_weights(self)
else:
logger.info("using default pytorch weight init")
self.debug_acm = []
def build(self):
"""Build Retina Net architecture."""
# Image size must be dividable by 2 multiple times.
h, w = self.cf.patch_size[:2]
if h / 2 ** 5 != int(h / 2 ** 5) or w / 2 ** 5 != int(w / 2 ** 5):
raise Exception("Image size must be divisible by 2 at least 5 times "
"to avoid fractions when downscaling and upscaling."
"For example, use 256, 320, 384, 448, 512, ... etc. ")
backbone = utils.import_module('bbone', self.cf.backbone_path)
self.logger.info("loaded backbone from {}".format(self.cf.backbone_path))
conv = backbone.ConvGenerator(self.cf.dim)
# build Anchors, FPN, Classifier / Bbox-Regressor -head
self.np_anchors = mutils.generate_pyramid_anchors(self.logger, self.cf)
self.anchors = torch.from_numpy(self.np_anchors).float().cuda()
self.fpn = backbone.FPN(self.cf, conv, operate_stride1=self.cf.operate_stride1).cuda()
self.classifier = Classifier(self.cf, conv).cuda()
self.bb_regressor = BBRegressor(self.cf, conv).cuda()
if 'regression' in self.cf.prediction_tasks:
self.roi_regressor = RoIRegressor(self.cf, conv, self.cf.regression_n_features).cuda()
elif 'regression_bin' in self.cf.prediction_tasks:
# classify into bins of regression values
self.roi_regressor = RoIRegressor(self.cf, conv, len(self.cf.bin_labels)).cuda()
else:
self.roi_regressor = lambda x: [torch.tensor([]).cuda()]
if self.cf.model == 'retina_unet':
self.final_conv = conv(self.cf.end_filts, self.cf.num_seg_classes, ks=1, pad=0, norm=None, relu=None)
def forward(self, img):
"""
:param img: input img (b, c, y, x, (z)).
"""
# Feature extraction
fpn_outs = self.fpn(img)
if self.cf.model == 'retina_unet':
seg_logits = self.final_conv(fpn_outs[0])
selected_fmaps = [fpn_outs[i + 1] for i in self.cf.pyramid_levels]
else:
seg_logits = None
selected_fmaps = [fpn_outs[i] for i in self.cf.pyramid_levels]
# Loop through pyramid layers
class_layer_outputs, bb_reg_layer_outputs, roi_reg_layer_outputs = [], [], [] # list of lists
for p in selected_fmaps:
class_layer_outputs.append(self.classifier(p))
bb_reg_layer_outputs.append(self.bb_regressor(p))
roi_reg_layer_outputs.append(self.roi_regressor(p))
# Concatenate layer outputs
# Convert from list of lists of level outputs to list of lists
# of outputs across levels.
# e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]
class_logits = list(zip(*class_layer_outputs))
class_logits = [torch.cat(list(o), dim=1) for o in class_logits][0]
bb_outputs = list(zip(*bb_reg_layer_outputs))
bb_outputs = [torch.cat(list(o), dim=1) for o in bb_outputs][0]
if not 0 == roi_reg_layer_outputs[0][0].shape[0]:
rg_outputs = list(zip(*roi_reg_layer_outputs))
rg_outputs = [torch.cat(list(o), dim=1) for o in rg_outputs][0]
else:
if self.cf.dim == 2:
n_feats = np.array([p.shape[-2] * p.shape[-1] * self.cf.n_anchors_per_pos for p in selected_fmaps]).sum()
else:
n_feats = np.array([p.shape[-3]*p.shape[-2]*p.shape[-1]*self.cf.n_anchors_per_pos for p in selected_fmaps]).sum()
rg_outputs = torch.zeros((selected_fmaps[0].shape[0], n_feats, self.cf.regression_n_features),
dtype=torch.float32).fill_(float('NaN')).cuda()
# merge batch_dimension and store info in batch_ixs for re-allocation.
batch_ixs = torch.arange(class_logits.shape[0]).unsqueeze(1).repeat(1, class_logits.shape[1]).view(-1).cuda()
flat_class_softmax = F.softmax(class_logits.view(-1, class_logits.shape[-1]), 1)
flat_bb_outputs = bb_outputs.view(-1, bb_outputs.shape[-1])
flat_rg_outputs = rg_outputs.view(-1, rg_outputs.shape[-1])
detections = refine_detections(self.anchors, flat_class_softmax, flat_bb_outputs, flat_rg_outputs, batch_ixs,
self.cf)
return detections, class_logits, bb_outputs, rg_outputs, seg_logits
def get_results(self, img_shape, detections, seg_logits, box_results_list=None):
"""
Restores batch dimension of merged detections, unmolds detections, creates and fills results dict.
:param img_shape:
:param detections: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score,
pred_regression)
:param box_results_list: None or list of output boxes for monitoring/plotting.
each element is a list of boxes per batch element.
:return: results_dict: dictionary with keys:
'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
[[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
'seg_preds': pixel-wise class predictions (b, 1, y, x, (z)) with values [0, 1] only fg. vs. bg for now.
class-specific return of masks will come with implementation of instance segmentation evaluation.
"""
detections = detections.cpu().data.numpy()
batch_ixs = detections[:, self.cf.dim*2]
detections = [detections[batch_ixs == ix] for ix in range(img_shape[0])]
if box_results_list == None: # for test_forward, where no previous list exists.
box_results_list = [[] for _ in range(img_shape[0])]
for ix in range(img_shape[0]):
if not 0 in detections[ix].shape:
boxes = detections[ix][:, :2 * self.cf.dim].astype(np.int32)
class_ids = detections[ix][:, 2 * self.cf.dim + 1].astype(np.int32)
scores = detections[ix][:, 2 * self.cf.dim + 2]
regressions = detections[ix][:, 2 * self.cf.dim + 3:]
# Filter out detections with zero area. Often only happens in early
# stages of training when the network weights are still a bit random.
if self.cf.dim == 2:
exclude_ix = np.where((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0]
else:
exclude_ix = np.where(
(boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 5] - boxes[:, 4]) <= 0)[0]
if exclude_ix.shape[0] > 0:
boxes = np.delete(boxes, exclude_ix, axis=0)
class_ids = np.delete(class_ids, exclude_ix, axis=0)
scores = np.delete(scores, exclude_ix, axis=0)
regressions = np.delete(regressions, exclude_ix, axis=0)
if not 0 in boxes.shape:
for ix2, score in enumerate(scores):
if score >= self.cf.model_min_confidence:
box = {'box_type': 'det', 'box_coords': boxes[ix2], 'box_score': score,
'box_pred_class_id': class_ids[ix2]}
if "regression_bin" in self.cf.prediction_tasks:
# in this case, regression preds are actually the rg_bin_ids --> map to rg value the bin stands for
box['rg_bin'] = regressions[ix2].argmax()
box['regression'] = self.cf.bin_id2rg_val[box['rg_bin']]
else:
box['regression'] = regressions[ix2]
if hasattr(self.cf, "rg_val_to_bin_id") and \
any(['regression' in task for task in self.cf.prediction_tasks]):
box['rg_bin'] = self.cf.rg_val_to_bin_id(regressions[ix2])
box_results_list[ix].append(box)
results_dict = {}
results_dict['boxes'] = box_results_list
if seg_logits is None:
# output dummy segmentation for retina_net.
out_logits_shape = list(img_shape)
out_logits_shape[1] = self.cf.num_seg_classes
results_dict['seg_preds'] = np.zeros(out_logits_shape, dtype=np.float16)
#todo: try with seg_preds=None? as to not carry heavy dummy preds.
else:
# output label maps for retina_unet.
results_dict['seg_preds'] = F.softmax(seg_logits, 1).cpu().data.numpy()
return results_dict
def train_forward(self, batch, is_validation=False):
"""
train method (also used for validation monitoring). wrapper around forward pass of network. prepares input data
for processing, computes losses, and stores outputs in a dictionary.
:param batch: dictionary containing 'data', 'seg', etc.
:return: results_dict: dictionary with keys:
'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
[[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
'seg_preds': pixelwise segmentation output (b, c, y, x, (z)) with values [0, .., n_classes].
'torch_loss': 1D torch tensor for backprop.
'class_loss': classification loss for monitoring.
"""
img = batch['data']
gt_class_ids = batch['class_targets']
gt_boxes = batch['bb_target']
if 'regression' in self.cf.prediction_tasks:
gt_regressions = batch["regression_targets"]
elif 'regression_bin' in self.cf.prediction_tasks:
gt_regressions = batch["rg_bin_targets"]
else:
gt_regressions = None
if self.cf.model == 'retina_unet':
var_seg_ohe = torch.FloatTensor(mutils.get_one_hot_encoding(batch['seg'], self.cf.num_seg_classes)).cuda()
var_seg = torch.LongTensor(batch['seg']).cuda()
img = torch.from_numpy(img).float().cuda()
torch_loss = torch.FloatTensor([0]).cuda()
# list of output boxes for monitoring/plotting. each element is a list of boxes per batch element.
box_results_list = [[] for _ in range(img.shape[0])]
detections, class_logits, pred_deltas, pred_rgs, seg_logits = self.forward(img)
# loop over batch
for b in range(img.shape[0]):
# add gt boxes to results dict for monitoring.
if len(gt_boxes[b]) > 0:
for tix in range(len(gt_boxes[b])):
gt_box = {'box_type': 'gt', 'box_coords': batch['bb_target'][b][tix]}
for name in self.cf.roi_items:
gt_box.update({name: batch[name][b][tix]})
box_results_list[b].append(gt_box)
# match gt boxes with anchors to generate targets.
anchor_class_match, anchor_target_deltas, anchor_target_rgs = gt_anchor_matching(
self.cf, self.np_anchors, gt_boxes[b], gt_class_ids[b], gt_regressions[b] if gt_regressions is not None else None)
# add positive anchors used for loss to results_dict for monitoring.
pos_anchors = mutils.clip_boxes_numpy(
self.np_anchors[np.argwhere(anchor_class_match > 0)][:, 0], img.shape[2:])
for p in pos_anchors:
box_results_list[b].append({'box_coords': p, 'box_type': 'pos_anchor'})
else:
anchor_class_match = np.array([-1]*self.np_anchors.shape[0])
anchor_target_deltas = np.array([])
anchor_target_rgs = np.array([])
anchor_class_match = torch.from_numpy(anchor_class_match).cuda()
anchor_target_deltas = torch.from_numpy(anchor_target_deltas).float().cuda()
anchor_target_rgs = torch.from_numpy(anchor_target_rgs).float().cuda()
if self.cf.focal_loss:
# compute class loss as focal loss as suggested in original publication, but multi-class.
class_loss = compute_focal_class_loss(anchor_class_match, class_logits[b], gamma=self.cf.focal_loss_gamma)
# sparing appendix of negative anchors for monitoring as not really relevant
else:
# compute class loss with SHEM.
class_loss, neg_anchor_ix = compute_class_loss(anchor_class_match, class_logits[b])
# add negative anchors used for loss to results_dict for monitoring.
neg_anchors = mutils.clip_boxes_numpy(
self.np_anchors[np.argwhere(anchor_class_match.cpu().numpy() == -1)][neg_anchor_ix, 0],
img.shape[2:])
for n in neg_anchors:
box_results_list[b].append({'box_coords': n, 'box_type': 'neg_anchor'})
rg_loss = compute_rg_loss(self.cf.prediction_tasks, anchor_target_rgs, pred_rgs[b], anchor_class_match)
bbox_loss = compute_bbox_loss(anchor_target_deltas, pred_deltas[b], anchor_class_match)
torch_loss += (class_loss + bbox_loss + rg_loss) / img.shape[0]
results_dict = self.get_results(img.shape, detections, seg_logits, box_results_list)
results_dict['seg_preds'] = results_dict['seg_preds'].argmax(axis=1).astype('uint8')[:, np.newaxis]
if self.cf.model == 'retina_unet':
seg_loss_dice = 1 - mutils.batch_dice(F.softmax(seg_logits, dim=1),var_seg_ohe)
seg_loss_ce = F.cross_entropy(seg_logits, var_seg[:, 0])
torch_loss += (seg_loss_dice + seg_loss_ce) / 2
#self.logger.info("loss: {0:.2f}, class: {1:.2f}, bbox: {2:.2f}, seg dice: {3:.3f}, seg ce: {4:.3f}, "
# "mean pixel preds: {5:.5f}".format(torch_loss.item(), batch_class_loss.item(), batch_bbox_loss.item(),
# seg_loss_dice.item(), seg_loss_ce.item(), np.mean(results_dict['seg_preds'])))
- if 'dice' in self.cf.metrics:
- results_dict['batch_dices'] = mutils.dice_per_batch_and_class(
- results_dict['seg_preds'], batch["seg"], self.cf.num_seg_classes, convert_to_ohe=True)
+ if 'dice' in self.cf.metrics:
+ results_dict['batch_dices'] = mutils.dice_per_batch_and_class(
+ results_dict['seg_preds'], batch["seg"], self.cf.num_seg_classes, convert_to_ohe=True)
#else:
#self.logger.info("loss: {0:.2f}, class: {1:.2f}, bbox: {2:.2f}".format(
# torch_loss.item(), class_loss.item(), bbox_loss.item()))
results_dict['torch_loss'] = torch_loss
results_dict['class_loss'] = class_loss.item()
return results_dict
def test_forward(self, batch, **kwargs):
"""
test method. wrapper around forward pass of network without usage of any ground truth information.
prepares input data for processing and stores outputs in a dictionary.
:param batch: dictionary containing 'data'
:return: results_dict: dictionary with keys:
'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
[[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
'seg_preds': actually contain seg probabilities since evaluated to seg_preds (via argmax) in predictor.
or dummy seg logits for real retina net (detection only)
"""
img = torch.from_numpy(batch['data']).float().cuda()
detections, _, _, _, seg_logits = self.forward(img)
results_dict = self.get_results(img.shape, detections, seg_logits)
return results_dict
\ No newline at end of file
diff --git a/predictor.py b/predictor.py
index 99035bd..2e2b699 100644
--- a/predictor.py
+++ b/predictor.py
@@ -1,1005 +1,1006 @@
#!/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 os
from multiprocessing import Pool
import pickle
import time
import numpy as np
import torch
from scipy.stats import norm
from collections import OrderedDict
import plotting as plg
import utils.model_utils as mutils
import utils.exp_utils as utils
def get_mirrored_patch_crops(patch_crops, org_img_shape):
mirrored_patch_crops = []
mirrored_patch_crops.append([[org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], ii[2], ii[3]]
if len(ii) == 4 else [org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], ii[2],
ii[3], ii[4], ii[5]]
for ii in patch_crops])
mirrored_patch_crops.append([[ii[0], ii[1], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2]]
if len(ii) == 4 else [ii[0], ii[1], org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2], ii[4], ii[5]]
for ii in patch_crops])
mirrored_patch_crops.append([[org_img_shape[2] - ii[1],
org_img_shape[2] - ii[0],
org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2]]
if len(ii) == 4 else
[org_img_shape[2] - ii[1],
org_img_shape[2] - ii[0],
org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2], ii[4], ii[5]]
for ii in patch_crops])
return mirrored_patch_crops
def get_mirrored_patch_crops_ax_dep(patch_crops, org_img_shape, mirror_axes):
mirrored_patch_crops = []
for ax_ix, axes in enumerate(mirror_axes):
if isinstance(axes, (int, float)) and int(axes) == 0:
mirrored_patch_crops.append([[org_img_shape[2] - ii[1], org_img_shape[2] - ii[0], ii[2], ii[3]]
if len(ii) == 4 else [org_img_shape[2] - ii[1], org_img_shape[2] - ii[0],
ii[2], ii[3], ii[4], ii[5]]
for ii in patch_crops])
elif isinstance(axes, (int, float)) and int(axes) == 1:
mirrored_patch_crops.append([[ii[0], ii[1], org_img_shape[3] - ii[3], org_img_shape[3] - ii[2]]
if len(ii) == 4 else [ii[0], ii[1], org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2], ii[4], ii[5]]
for ii in patch_crops])
elif hasattr(axes, "__iter__") and (tuple(axes) == (0, 1) or tuple(axes) == (1, 0)):
mirrored_patch_crops.append([[org_img_shape[2] - ii[1],
org_img_shape[2] - ii[0],
org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2]]
if len(ii) == 4 else
[org_img_shape[2] - ii[1],
org_img_shape[2] - ii[0],
org_img_shape[3] - ii[3],
org_img_shape[3] - ii[2], ii[4], ii[5]]
for ii in patch_crops])
else:
raise Exception("invalid mirror axes {} in get mirrored patch crops".format(axes))
return mirrored_patch_crops
def apply_wbc_to_patient(inputs):
"""
wrapper around prediction box consolidation: weighted box clustering (wbc). processes a single patient.
loops over batch elements in patient results (1 in 3D, slices in 2D) and foreground classes,
aggregates and stores results in new list.
:return. patient_results_list: list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D
predictions, and a dummy batch dimension of 1 for 3D predictions.
:return. pid: string. patient id.
"""
regress_flag, in_patient_results_list, pid, class_dict, clustering_iou, n_ens = inputs
out_patient_results_list = [[] for _ in range(len(in_patient_results_list))]
for bix, b in enumerate(in_patient_results_list):
for cl in list(class_dict.keys()):
boxes = [(ix, box) for ix, box in enumerate(b) if
(box['box_type'] == 'det' and box['box_pred_class_id'] == cl)]
box_coords = np.array([b[1]['box_coords'] for b in boxes])
box_scores = np.array([b[1]['box_score'] for b in boxes])
box_center_factor = np.array([b[1]['box_patch_center_factor'] for b in boxes])
box_n_overlaps = np.array([b[1]['box_n_overlaps'] for b in boxes])
try:
box_patch_id = np.array([b[1]['patch_id'] for b in boxes])
except KeyError: #backward compatibility for already saved pred results ... omg
box_patch_id = np.array([b[1]['ens_ix'] for b in boxes])
box_regressions = np.array([b[1]['regression'] for b in boxes]) if regress_flag else None
box_rg_bins = np.array([b[1]['rg_bin'] if 'rg_bin' in b[1].keys() else float('NaN') for b in boxes])
box_rg_uncs = np.array([b[1]['rg_uncertainty'] if 'rg_uncertainty' in b[1].keys() else float('NaN') for b in boxes])
if 0 not in box_scores.shape:
keep_scores, keep_coords, keep_n_missing, keep_regressions, keep_rg_bins, keep_rg_uncs = \
weighted_box_clustering(box_coords, box_scores, box_center_factor, box_n_overlaps, box_rg_bins, box_rg_uncs,
box_regressions, box_patch_id, clustering_iou, n_ens)
for boxix in range(len(keep_scores)):
clustered_box = {'box_type': 'det', 'box_coords': keep_coords[boxix],
'box_score': keep_scores[boxix], 'cluster_n_missing': keep_n_missing[boxix],
'box_pred_class_id': cl}
if regress_flag:
clustered_box.update({'regression': keep_regressions[boxix],
'rg_uncertainty': keep_rg_uncs[boxix],
'rg_bin': keep_rg_bins[boxix]})
out_patient_results_list[bix].append(clustered_box)
# add gt boxes back to new output list.
out_patient_results_list[bix].extend([box for box in b if box['box_type'] == 'gt'])
return [out_patient_results_list, pid]
def weighted_box_clustering(box_coords, scores, box_pc_facts, box_n_ovs, box_rg_bins, box_rg_uncs,
box_regress, box_patch_id, thresh, n_ens):
"""Consolidates overlapping predictions resulting from patch overlaps, test data augmentations and temporal ensembling.
clusters predictions together with iou > thresh (like in NMS). Output score and coordinate for one cluster are the
average weighted by individual patch center factors (how trustworthy is this candidate measured by how centered
its position within the patch is) and the size of the corresponding box.
The number of expected predictions at a position is n_data_aug * n_temp_ens * n_overlaps_at_position
(1 prediction per unique patch). Missing predictions at a cluster position are defined as the number of unique
patches in the cluster, which did not contribute any predict any boxes.
:param dets: (n_dets, (y1, x1, y2, x2, (z1), (z2), scores, box_pc_facts, box_n_ovs).
:param box_coords: y1, x1, y2, x2, (z1), (z2).
:param scores: confidence scores.
:param box_pc_facts: patch-center factors from position on patch tiles.
:param box_n_ovs: number of patch overlaps at box position.
:param box_rg_bins: regression bin predictions.
:param box_rg_uncs: (n_dets,) regression uncertainties (from model mrcnn_aleatoric).
:param box_regress: (n_dets, n_regression_features).
:param box_patch_id: ensemble index.
:param thresh: threshold for iou_matching.
:param n_ens: number of models, that are ensembled. (-> number of expected predictions per position).
:return: keep_scores: (n_keep) new scores of boxes to be kept.
:return: keep_coords: (n_keep, (y1, x1, y2, x2, (z1), (z2)) new coordinates of boxes to be kept.
"""
dim = 2 if box_coords.shape[1] == 4 else 3
y1 = box_coords[:,0]
x1 = box_coords[:,1]
y2 = box_coords[:,2]
x2 = box_coords[:,3]
areas = (y2 - y1 + 1) * (x2 - x1 + 1)
if dim == 3:
z1 = box_coords[:, 4]
z2 = box_coords[:, 5]
areas *= (z2 - z1 + 1)
# order is the sorted index. maps order to index o[1] = 24 (rank1, ix 24)
order = scores.argsort()[::-1]
keep_scores = []
keep_coords = []
keep_n_missing = []
keep_regress = []
keep_rg_bins = []
keep_rg_uncs = []
while order.size > 0:
i = order[0] # highest scoring element
yy1 = np.maximum(y1[i], y1[order])
xx1 = np.maximum(x1[i], x1[order])
yy2 = np.minimum(y2[i], y2[order])
xx2 = np.minimum(x2[i], x2[order])
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
inter = w * h
if dim == 3:
zz1 = np.maximum(z1[i], z1[order])
zz2 = np.minimum(z2[i], z2[order])
d = np.maximum(0, zz2 - zz1 + 1)
inter *= d
# overlap between currently highest scoring box and all boxes.
ovr = inter / (areas[i] + areas[order] - inter)
ovr_fl = inter.astype('float64') / (areas[i] + areas[order] - inter.astype('float64'))
assert np.all(ovr==ovr_fl), "ovr {}\n ovr_float {}".format(ovr, ovr_fl)
# get all the predictions that match the current box to build one cluster.
matches = np.nonzero(ovr > thresh)[0]
match_n_ovs = box_n_ovs[order[matches]]
match_pc_facts = box_pc_facts[order[matches]]
match_patch_id = box_patch_id[order[matches]]
match_ov_facts = ovr[matches]
match_areas = areas[order[matches]]
match_scores = scores[order[matches]]
# weight all scores in cluster by patch factors, and size.
match_score_weights = match_ov_facts * match_areas * match_pc_facts
match_scores *= match_score_weights
# for the weighted average, scores have to be divided by the number of total expected preds at the position
# of the current cluster. 1 Prediction per patch is expected. therefore, the number of ensembled models is
# multiplied by the mean overlaps of patches at this position (boxes of the cluster might partly be
# in areas of different overlaps).
n_expected_preds = n_ens * np.mean(match_n_ovs)
# the number of missing predictions is obtained as the number of patches,
# which did not contribute any prediction to the current cluster.
n_missing_preds = np.max((0, n_expected_preds - np.unique(match_patch_id).shape[0]))
# missing preds are given the mean weighting
# (expected prediction is the mean over all predictions in cluster).
denom = np.sum(match_score_weights) + n_missing_preds * np.mean(match_score_weights)
# compute weighted average score for the cluster
avg_score = np.sum(match_scores) / denom
# compute weighted average of coordinates for the cluster. now only take existing
# predictions into account.
avg_coords = [np.sum(y1[order[matches]] * match_scores) / np.sum(match_scores),
np.sum(x1[order[matches]] * match_scores) / np.sum(match_scores),
np.sum(y2[order[matches]] * match_scores) / np.sum(match_scores),
np.sum(x2[order[matches]] * match_scores) / np.sum(match_scores)]
if dim == 3:
avg_coords.append(np.sum(z1[order[matches]] * match_scores) / np.sum(match_scores))
avg_coords.append(np.sum(z2[order[matches]] * match_scores) / np.sum(match_scores))
if box_regress is not None:
# compute wt. avg. of regression vectors (component-wise average)
avg_regress = np.sum(box_regress[order[matches]] * match_scores[:, np.newaxis], axis=0) / np.sum(
match_scores)
avg_rg_bins = np.round(np.sum(box_rg_bins[order[matches]] * match_scores) / np.sum(match_scores))
avg_rg_uncs = np.sum(box_rg_uncs[order[matches]] * match_scores) / np.sum(match_scores)
else:
avg_regress = np.array(float('NaN'))
avg_rg_bins = np.array(float('NaN'))
avg_rg_uncs = np.array(float('NaN'))
# some clusters might have very low scores due to high amounts of missing predictions.
# filter out the with a conservative threshold, to speed up evaluation.
if avg_score > 0.01:
keep_scores.append(avg_score)
keep_coords.append(avg_coords)
keep_n_missing.append((n_missing_preds / n_expected_preds * 100)) # relative
keep_regress.append(avg_regress)
keep_rg_uncs.append(avg_rg_uncs)
keep_rg_bins.append(avg_rg_bins)
# get index of all elements that were not matched and discard all others.
inds = np.nonzero(ovr <= thresh)[0]
inds_where = np.where(ovr<=thresh)[0]
assert np.all(inds == inds_where), "inds_nonzero {} \ninds_where {}".format(inds, inds_where)
order = order[inds]
return keep_scores, keep_coords, keep_n_missing, keep_regress, keep_rg_bins, keep_rg_uncs
def apply_nms_to_patient(inputs):
in_patient_results_list, pid, class_dict, iou_thresh = inputs
out_patient_results_list = []
# collect box predictions over batch dimension (slices) and store slice info as slice_ids.
for batch in in_patient_results_list:
batch_el_boxes = []
for cl in list(class_dict.keys()):
det_boxes = [box for box in batch if (box['box_type'] == 'det' and box['box_pred_class_id'] == cl)]
box_coords = np.array([box['box_coords'] for box in det_boxes])
box_scores = np.array([box['box_score'] for box in det_boxes])
if 0 not in box_scores.shape:
keep_ix = mutils.nms_numpy(box_coords, box_scores, iou_thresh)
else:
keep_ix = []
batch_el_boxes += [det_boxes[ix] for ix in keep_ix]
batch_el_boxes += [box for box in batch if box['box_type'] == 'gt']
out_patient_results_list.append(batch_el_boxes)
assert len(in_patient_results_list) == len(out_patient_results_list), "batch dim needs to be maintained, in: {}, out {}".format(len(in_patient_results_list), len(out_patient_results_list))
return [out_patient_results_list, pid]
def nms_2to3D(dets, thresh):
"""
Merges 2D boxes to 3D cubes. For this purpose, boxes of all slices are regarded as lying in one slice.
An adaptation of Non-maximum suppression is applied where clusters are found (like in NMS) with the extra constraint
that suppressed boxes have to have 'connected' z coordinates w.r.t the core slice (cluster center, highest
scoring box, the prevailing box). 'connected' z-coordinates are determined
as the z-coordinates with predictions until the first coordinate for which no prediction is found.
example: a cluster of predictions was found overlap > iou thresh in xy (like NMS). The z-coordinate of the highest
scoring box is 50. Other predictions have 23, 46, 48, 49, 51, 52, 53, 56, 57.
Only the coordinates connected with 50 are clustered to one cube: 48, 49, 51, 52, 53. (46 not because nothing was
found in 47, so 47 is a 'hole', which interrupts the connection). Only the boxes corresponding to these coordinates
are suppressed. All others are kept for building of further clusters.
This algorithm works better with a certain min_confidence of predictions, because low confidence (e.g. noisy/cluttery)
predictions can break the relatively strong assumption of defining cubes' z-boundaries at the first 'hole' in the cluster.
:param dets: (n_detections, (y1, x1, y2, x2, scores, slice_id)
:param thresh: iou matchin threshold (like in NMS).
:return: keep: (n_keep,) 1D tensor of indices to be kept.
:return: keep_z: (n_keep, [z1, z2]) z-coordinates to be added to boxes, which are kept in order to form cubes.
"""
y1 = dets[:, 0]
x1 = dets[:, 1]
y2 = dets[:, 2]
x2 = dets[:, 3]
assert np.all(y1 <= y2) and np.all(x1 <= x2), """"the definition of the coordinates is crucially important here:
where maximum is taken needs to be the lower coordinate"""
scores = dets[:, -2]
slice_id = dets[:, -1]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
keep_z = []
while order.size > 0: # order is the sorted index. maps order to index: order[1] = 24 means (rank1, ix 24)
i = order[0] # highest scoring element
yy1 = np.maximum(y1[i], y1[order]) # highest scoring element still in >order<, is compared to itself: okay?
xx1 = np.maximum(x1[i], x1[order])
yy2 = np.minimum(y2[i], y2[order])
xx2 = np.minimum(x2[i], x2[order])
h = np.maximum(0.0, yy2 - yy1 + 1)
w = np.maximum(0.0, xx2 - xx1 + 1)
inter = h * w
iou = inter / (areas[i] + areas[order] - inter)
matches = np.argwhere(
iou > thresh) # get all the elements that match the current box and have a lower score
slice_ids = slice_id[order[matches]]
core_slice = slice_id[int(i)]
upper_holes = [ii for ii in np.arange(core_slice, np.max(slice_ids)) if ii not in slice_ids]
lower_holes = [ii for ii in np.arange(np.min(slice_ids), core_slice) if ii not in slice_ids]
max_valid_slice_id = np.min(upper_holes) if len(upper_holes) > 0 else np.max(slice_ids)
min_valid_slice_id = np.max(lower_holes) if len(lower_holes) > 0 else np.min(slice_ids)
z_matches = matches[(slice_ids <= max_valid_slice_id) & (slice_ids >= min_valid_slice_id)]
# expand by one z voxel since box content is surrounded w/o overlap, i.e., z-content computed as z2-z1
z1 = np.min(slice_id[order[z_matches]]) - 1
z2 = np.max(slice_id[order[z_matches]]) + 1
keep.append(i)
keep_z.append([z1, z2])
order = np.delete(order, z_matches, axis=0)
return keep, keep_z
def apply_2d_3d_merging_to_patient(inputs):
"""
wrapper around 2Dto3D merging operation. Processes a single patient. Takes 2D patient results (slices in batch dimension)
and returns 3D patient results (dummy batch dimension of 1). Applies an adaption of Non-Maximum Surpression
(Detailed methodology is described in nms_2to3D).
:return. results_dict_boxes: list over batch elements (1 in 3D). each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]].
:return. pid: string. patient id.
"""
in_patient_results_list, pid, class_dict, merge_3D_iou = inputs
out_patient_results_list = []
for cl in list(class_dict.keys()):
det_boxes, slice_ids = [], []
# collect box predictions over batch dimension (slices) and store slice info as slice_ids.
for batch_ix, batch in enumerate(in_patient_results_list):
batch_element_det_boxes = [(ix, box) for ix, box in enumerate(batch) if
(box['box_type'] == 'det' and box['box_pred_class_id'] == cl)]
det_boxes += batch_element_det_boxes
slice_ids += [batch_ix] * len(batch_element_det_boxes)
box_coords = np.array([batch[1]['box_coords'] for batch in det_boxes])
box_scores = np.array([batch[1]['box_score'] for batch in det_boxes])
slice_ids = np.array(slice_ids)
if 0 not in box_scores.shape:
keep_ix, keep_z = nms_2to3D(
np.concatenate((box_coords, box_scores[:, None], slice_ids[:, None]), axis=1), merge_3D_iou)
else:
keep_ix, keep_z = [], []
# store kept predictions in new results list and add corresponding z-dimension info to coordinates.
for kix, kz in zip(keep_ix, keep_z):
keep_box = det_boxes[kix][1]
keep_box['box_coords'] = list(keep_box['box_coords']) + kz
out_patient_results_list.append(keep_box)
gt_boxes = [box for b in in_patient_results_list for box in b if box['box_type'] == 'gt']
if len(gt_boxes) > 0:
assert np.all([len(box["box_coords"]) == 6 for box in gt_boxes]), "expanded preds to 3D but GT is 2D."
out_patient_results_list += gt_boxes
return [[out_patient_results_list], pid] # additional list wrapping is extra batch dim.
class Predictor:
"""
Prediction pipeline:
- receives a patched patient image (n_patches, c, y, x, (z)) from patient data loader.
- forwards patches through model in chunks of batch_size. (method: batch_tiling_forward)
- unmolds predictions (boxes and segmentations) to original patient coordinates. (method: spatial_tiling_forward)
Ensembling (mode == 'test'):
- for inference, forwards 4 mirrored versions of image to through model and unmolds predictions afterwards
accordingly (method: data_aug_forward)
- for inference, loads multiple parameter-sets of the trained model corresponding to different epochs. for each
parameter-set loops over entire test set, runs prediction pipeline for each patient. (method: predict_test_set)
Consolidation of predictions:
- consolidates a patient's predictions (boxes, segmentations) collected over patches, data_aug- and temporal ensembling,
performs clustering and weighted averaging (external function: apply_wbc_to_patient) to obtain consistent outptus.
- for 2D networks, consolidates box predictions to 3D cubes via clustering (adaption of non-maximum surpression).
(external function: apply_2d_3d_merging_to_patient)
Ground truth handling:
- dissmisses any ground truth boxes returned by the model (happens in validation mode, patch-based groundtruth)
- if provided by data loader, adds patient-wise ground truth to the final predictions to be passed to the evaluator.
"""
def __init__(self, cf, net, logger, mode):
self.cf = cf
self.batch_size = cf.batch_size
self.logger = logger
self.mode = mode
self.net = net
self.n_ens = 1
self.rank_ix = '0'
self.regress_flag = any(['regression' in task for task in self.cf.prediction_tasks])
if self.cf.merge_2D_to_3D_preds:
assert self.cf.dim == 2, "Merge 2Dto3D only valid for 2D preds, but current dim is {}.".format(self.cf.dim)
if self.mode == 'test':
last_state_path = os.path.join(self.cf.fold_dir, 'last_state.pth')
try:
self.model_index = torch.load(last_state_path)["model_index"]
self.model_index = self.model_index[self.model_index["rank"] <= self.cf.test_n_epochs]
except FileNotFoundError:
raise FileNotFoundError('no last_state/model_index file in fold directory. '
'seems like you are trying to run testing without prior training...')
self.n_ens = cf.test_n_epochs
if self.cf.test_aug_axes is not None:
self.n_ens *= (len(self.cf.test_aug_axes)+1)
self.example_plot_dir = os.path.join(cf.test_dir, "example_plots")
os.makedirs(self.example_plot_dir, exist_ok=True)
def batch_tiling_forward(self, batch):
"""
calls the actual network forward method. in patch-based prediction, the batch dimension might be overladed
with n_patches >> batch_size, which would exceed gpu memory. In this case, batches are processed in chunks of
batch_size. validation mode calls the train method to monitor losses (returned ground truth objects are discarded).
test mode calls the test forward method, no ground truth required / involved.
:return. results_dict: stores the results for one patient. dictionary with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions,
and a dummy batch dimension of 1 for 3D predictions.
- 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z))
- loss / class_loss (only in validation mode)
"""
img = batch['data']
if img.shape[0] <= self.batch_size:
if self.mode == 'val':
# call training method to monitor losses
results_dict = self.net.train_forward(batch, is_validation=True)
# discard returned ground-truth boxes (also training info boxes).
results_dict['boxes'] = [[box for box in b if box['box_type'] == 'det'] for b in results_dict['boxes']]
elif self.mode == 'test':
results_dict = self.net.test_forward(batch, return_masks=self.cf.return_masks_in_test)
else: # needs batch tiling
split_ixs = np.split(np.arange(img.shape[0]), np.arange(img.shape[0])[::self.batch_size])
chunk_dicts = []
for chunk_ixs in split_ixs[1:]: # first split is elements before 0, so empty
b = {k: batch[k][chunk_ixs] for k in batch.keys()
if (isinstance(batch[k], np.ndarray) and batch[k].shape[0] == img.shape[0])}
if self.mode == 'val':
chunk_dicts += [self.net.train_forward(b, is_validation=True)]
else:
chunk_dicts += [self.net.test_forward(b, return_masks=self.cf.return_masks_in_test)]
results_dict = {}
# flatten out batch elements from chunks ([chunk, chunk] -> [b, b, b, b, ...])
results_dict['boxes'] = [item for d in chunk_dicts for item in d['boxes']]
results_dict['seg_preds'] = np.array([item for d in chunk_dicts for item in d['seg_preds']])
if self.mode == 'val':
# if hasattr(self.cf, "losses_to_monitor"):
# loss_names = self.cf.losses_to_monitor
# else:
# loss_names = {name for dic in chunk_dicts for name in dic if 'loss' in name}
# estimate patient loss by mean over batch_chunks. Most similar to training loss.
results_dict['torch_loss'] = torch.mean(torch.cat([d['torch_loss'] for d in chunk_dicts]))
results_dict['class_loss'] = np.mean([d['class_loss'] for d in chunk_dicts])
# discard returned ground-truth boxes (also training info boxes).
results_dict['boxes'] = [[box for box in b if box['box_type'] == 'det'] for b in results_dict['boxes']]
return results_dict
def spatial_tiling_forward(self, batch, patch_crops = None, n_aug='0'):
"""
forwards batch to batch_tiling_forward method and receives and returns a dictionary with results.
if patch-based prediction, the results received from batch_tiling_forward will be on a per-patch-basis.
this method uses the provided patch_crops to re-transform all predictions to whole-image coordinates.
Patch-origin information of all box-predictions will be needed for consolidation, hence it is stored as
'patch_id', which is a unique string for each patch (also takes current data aug and temporal epoch instances
into account). all box predictions get additional information about the amount overlapping patches at the
respective position (used for consolidation).
:return. results_dict: stores the results for one patient. dictionary with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions,
and a dummy batch dimension of 1 for 3D predictions.
- 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z))
- monitor_values (only in validation mode)
returned dict is a flattened version with 1 batch instance (3D) or slices (2D)
"""
if patch_crops is not None:
#print("patch_crops not None, applying patch center factor")
patches_dict = self.batch_tiling_forward(batch)
results_dict = {'boxes': [[] for _ in range(batch['original_img_shape'][0])]}
#bc of ohe--> channel dim of seg has size num_classes
out_seg_shape = list(batch['original_img_shape'])
out_seg_shape[1] = patches_dict["seg_preds"].shape[1]
out_seg_preds = np.zeros(out_seg_shape, dtype=np.float16)
patch_overlap_map = np.zeros_like(out_seg_preds, dtype='uint8')
for pix, pc in enumerate(patch_crops):
if self.cf.dim == 3:
out_seg_preds[:, :, pc[0]:pc[1], pc[2]:pc[3], pc[4]:pc[5]] += patches_dict['seg_preds'][pix]
patch_overlap_map[:, :, pc[0]:pc[1], pc[2]:pc[3], pc[4]:pc[5]] += 1
elif self.cf.dim == 2:
out_seg_preds[pc[4]:pc[5], :, pc[0]:pc[1], pc[2]:pc[3], ] += patches_dict['seg_preds'][pix]
patch_overlap_map[pc[4]:pc[5], :, pc[0]:pc[1], pc[2]:pc[3], ] += 1
out_seg_preds[patch_overlap_map > 0] /= patch_overlap_map[patch_overlap_map > 0]
results_dict['seg_preds'] = out_seg_preds
for pix, pc in enumerate(patch_crops):
patch_boxes = patches_dict['boxes'][pix]
for box in patch_boxes:
# add unique patch id for consolidation of predictions.
box['patch_id'] = self.rank_ix + '_' + n_aug + '_' + str(pix)
# boxes from the edges of a patch have a lower prediction quality, than the ones at patch-centers.
# hence they will be down-weighted for consolidation, using the 'box_patch_center_factor', which is
# obtained by a gaussian distribution over positions in the patch and average over spatial dimensions.
# Also the info 'box_n_overlaps' is stored for consolidation, which represents the amount of
# overlapping patches at the box's position.
c = box['box_coords']
#box_centers = np.array([(c[ii] + c[ii+2])/2 for ii in range(len(c)//2)])
box_centers = [(c[ii] + c[ii + 2]) / 2 for ii in range(2)]
if self.cf.dim == 3:
box_centers.append((c[4] + c[5]) / 2)
box['box_patch_center_factor'] = np.mean(
[norm.pdf(bc, loc=pc, scale=pc * 0.8) * np.sqrt(2 * np.pi) * pc * 0.8 for bc, pc in
zip(box_centers, np.array(self.cf.patch_size) / 2)])
if self.cf.dim == 3:
c += np.array([pc[0], pc[2], pc[0], pc[2], pc[4], pc[4]])
int_c = [int(np.floor(ii)) if ix%2 == 0 else int(np.ceil(ii)) for ix, ii in enumerate(c)]
box['box_n_overlaps'] = np.mean(patch_overlap_map[:, :, int_c[1]:int_c[3], int_c[0]:int_c[2], int_c[4]:int_c[5]])
results_dict['boxes'][0].append(box)
else:
c += np.array([pc[0], pc[2], pc[0], pc[2]])
int_c = [int(np.floor(ii)) if ix % 2 == 0 else int(np.ceil(ii)) for ix, ii in enumerate(c)]
box['box_n_overlaps'] = np.mean(
patch_overlap_map[pc[4], :, int_c[1]:int_c[3], int_c[0]:int_c[2]])
results_dict['boxes'][pc[4]].append(box)
if self.mode == 'val':
results_dict['torch_loss'] = patches_dict['torch_loss']
results_dict['class_loss'] = patches_dict['class_loss']
else:
results_dict = self.batch_tiling_forward(batch)
for b in results_dict['boxes']:
for box in b:
box['box_patch_center_factor'] = 1
box['box_n_overlaps'] = 1
box['patch_id'] = self.rank_ix + '_' + n_aug
return results_dict
def data_aug_forward(self, batch):
"""
in val_mode: passes batch through to spatial_tiling method without data_aug.
in test_mode: if cf.test_aug is set in configs, createst 4 mirrored versions of the input image,
passes all of them to the next processing step (spatial_tiling method) and re-transforms returned predictions
to original image version.
:return. results_dict: stores the results for one patient. dictionary with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions,
and a dummy batch dimension of 1 for 3D predictions.
- 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z))
- loss / class_loss (only in validation mode)
"""
patch_crops = batch['patch_crop_coords'] if self.patched_patient else None
results_list = [self.spatial_tiling_forward(batch, patch_crops)]
org_img_shape = batch['original_img_shape']
if self.mode == 'test' and self.cf.test_aug_axes is not None:
if isinstance(self.cf.test_aug_axes, (int, float)):
self.cf.test_aug_axes = (self.cf.test_aug_axes,)
#assert np.all(np.array(self.cf.test_aug_axes)<self.cf.dim), "test axes {} need to be spatial axes".format(self.cf.test_aug_axes)
if self.patched_patient:
# apply mirror transformations to patch-crop coordinates, for correct tiling in spatial_tiling method.
mirrored_patch_crops = get_mirrored_patch_crops_ax_dep(patch_crops, batch['original_img_shape'],
self.cf.test_aug_axes)
self.logger.info("mirrored patch crop coords for patched patient in test augs!")
else:
mirrored_patch_crops = [None] * 3
img = np.copy(batch['data'])
for n_aug, sp_axis in enumerate(self.cf.test_aug_axes):
#sp_axis = np.array(axis) #-2 #spatial axis index
axis = np.array(sp_axis)+2
if isinstance(sp_axis, (int, float)):
# mirroring along one axis at a time
batch['data'] = np.flip(img, axis=axis).copy()
chunk_dict = self.spatial_tiling_forward(batch, mirrored_patch_crops[n_aug], n_aug=str(n_aug))
# re-transform coordinates.
for ix in range(len(chunk_dict['boxes'])):
for boxix in range(len(chunk_dict['boxes'][ix])):
coords = chunk_dict['boxes'][ix][boxix]['box_coords'].copy()
coords[sp_axis] = org_img_shape[axis] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis+2]
coords[sp_axis+2] = org_img_shape[axis] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis]
assert coords[2] >= coords[0], [coords, chunk_dict['boxes'][ix][boxix]['box_coords']]
assert coords[3] >= coords[1], [coords, chunk_dict['boxes'][ix][boxix]['box_coords']]
chunk_dict['boxes'][ix][boxix]['box_coords'] = coords
# re-transform segmentation predictions.
chunk_dict['seg_preds'] = np.flip(chunk_dict['seg_preds'], axis=axis)
elif hasattr(sp_axis, "__iter__") and tuple(sp_axis)==(0,1) or tuple(sp_axis)==(1,0):
#NEED: mirrored patch crops are given as [(y-axis), (x-axis), (y-,x-axis)], obey this order!
# mirroring along two axes at same time
batch['data'] = np.flip(np.flip(img, axis=axis[0]), axis=axis[1]).copy()
chunk_dict = self.spatial_tiling_forward(batch, mirrored_patch_crops[n_aug], n_aug=str(n_aug))
# re-transform coordinates.
for ix in range(len(chunk_dict['boxes'])):
for boxix in range(len(chunk_dict['boxes'][ix])):
coords = chunk_dict['boxes'][ix][boxix]['box_coords'].copy()
coords[sp_axis[0]] = org_img_shape[axis[0]] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis[0]+2]
coords[sp_axis[0]+2] = org_img_shape[axis[0]] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis[0]]
coords[sp_axis[1]] = org_img_shape[axis[1]] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis[1]+2]
coords[sp_axis[1]+2] = org_img_shape[axis[1]] - chunk_dict['boxes'][ix][boxix]['box_coords'][sp_axis[1]]
assert coords[2] >= coords[0], [coords, chunk_dict['boxes'][ix][boxix]['box_coords']]
assert coords[3] >= coords[1], [coords, chunk_dict['boxes'][ix][boxix]['box_coords']]
chunk_dict['boxes'][ix][boxix]['box_coords'] = coords
# re-transform segmentation predictions.
chunk_dict['seg_preds'] = np.flip(np.flip(chunk_dict['seg_preds'], axis=axis[0]), axis=axis[1]).copy()
else:
raise Exception("Invalid axis type {} in test augs".format(type(axis)))
results_list.append(chunk_dict)
batch['data'] = img
# aggregate all boxes/seg_preds per batch element from data_aug predictions.
results_dict = {}
results_dict['boxes'] = [[item for d in results_list for item in d['boxes'][batch_instance]]
for batch_instance in range(org_img_shape[0])]
# results_dict['seg_preds'] = np.array([[item for d in results_list for item in d['seg_preds'][batch_instance]]
# for batch_instance in range(org_img_shape[0])])
results_dict['seg_preds'] = np.stack([dic['seg_preds'] for dic in results_list], axis=1)
# needs segs probs in seg_preds entry:
results_dict['seg_preds'] = np.sum(results_dict['seg_preds'], axis=1) #add up seg probs from different augs per class
if self.mode == 'val':
results_dict['torch_loss'] = results_list[0]['torch_loss']
results_dict['class_loss'] = results_list[0]['class_loss']
return results_dict
def load_saved_predictions(self):
"""loads raw predictions saved by self.predict_test_set. aggregates and/or merges 2D boxes to 3D cubes for
evaluation (if model predicts 2D but evaluation is run in 3D), according to settings config.
:return: list_of_results_per_patient: list over patient results. each entry is a dict with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions
(if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions.
- 'batch_dices': dice scores as recorded in raw prediction results.
- 'seg_preds': not implemented yet. could replace dices by seg preds to have raw seg info available, however
would consume critically large memory amount. todo evaluation of instance/semantic segmentation.
"""
results_file = 'pred_results.pkl' if not self.cf.held_out_test_set else 'pred_results_held_out.pkl'
if not self.cf.held_out_test_set or self.cf.eval_test_fold_wise:
self.logger.info("loading saved predictions of fold {}".format(self.cf.fold))
with open(os.path.join(self.cf.fold_dir, results_file), 'rb') as handle:
results_list = pickle.load(handle)
box_results_list = [(res_dict["boxes"], pid) for res_dict, pid in results_list]
da_factor = len(self.cf.test_aug_axes)+1 if self.cf.test_aug_axes is not None else 1
self.n_ens = self.cf.test_n_epochs * da_factor
self.logger.info('loaded raw test set predictions with n_patients = {} and n_ens = {}'.format(
len(results_list), self.n_ens))
else:
self.logger.info("loading saved predictions of hold-out test set")
fold_dirs = sorted([os.path.join(self.cf.exp_dir, f) for f in os.listdir(self.cf.exp_dir) if
os.path.isdir(os.path.join(self.cf.exp_dir, f)) and f.startswith("fold")])
results_list = []
folds_loaded = 0
for fold in range(self.cf.n_cv_splits):
fold_dir = os.path.join(self.cf.exp_dir, 'fold_{}'.format(fold))
if fold_dir in fold_dirs:
with open(os.path.join(fold_dir, results_file), 'rb') as handle:
fold_list = pickle.load(handle)
results_list += fold_list
folds_loaded += 1
else:
self.logger.info("Skipping fold {} since no saved predictions found.".format(fold))
box_results_list = []
for res_dict, pid in results_list: #without filtering gt out:
box_results_list.append((res_dict['boxes'], pid))
#it's usually not right to filter out gts here, is it?
da_factor = len(self.cf.test_aug_axes)+1 if self.cf.test_aug_axes is not None else 1
self.n_ens = self.cf.test_n_epochs * da_factor * folds_loaded
# -------------- aggregation of boxes via clustering -----------------
if self.cf.clustering == "wbc":
self.logger.info('applying WBC to test-set predictions with iou {} and n_ens {} over {} patients'.format(
self.cf.clustering_iou, self.n_ens, len(box_results_list)))
mp_inputs = [[self.regress_flag, ii[0], ii[1], self.cf.class_dict, self.cf.clustering_iou, self.n_ens] for ii
in box_results_list]
del box_results_list
pool = Pool(processes=self.cf.n_workers)
box_results_list = pool.map(apply_wbc_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
elif self.cf.clustering == "nms":
self.logger.info('applying standard NMS to test-set predictions with iou {} over {} patients.'.format(
self.cf.clustering_iou, len(box_results_list)))
pool = Pool(processes=self.cf.n_workers)
mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.clustering_iou] for ii in box_results_list]
del box_results_list
box_results_list = pool.map(apply_nms_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
if self.cf.merge_2D_to_3D_preds:
self.logger.info('applying 2Dto3D merging to test-set predictions with iou = {}.'.format(self.cf.merge_3D_iou))
pool = Pool(processes=self.cf.n_workers)
mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.merge_3D_iou] for ii in box_results_list]
box_results_list = pool.map(apply_2d_3d_merging_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
for ix in range(len(results_list)):
assert np.all(results_list[ix][1] == box_results_list[ix][1]), "pid mismatch between loaded and aggregated results"
results_list[ix][0]["boxes"] = box_results_list[ix][0]
return results_list # holds (results_dict, pid)
def predict_patient(self, batch):
"""
predicts one patient.
called either directly via loop over validation set in exec.py (mode=='val')
or from self.predict_test_set (mode=='test).
in val mode: adds 3D ground truth info to predictions and runs consolidation and 2Dto3D merging of predictions.
in test mode: returns raw predictions (ground truth addition, consolidation, 2D to 3D merging are
done in self.predict_test_set, because patient predictions across several epochs might be needed
to be collected first, in case of temporal ensembling).
:return. results_dict: stores the results for one patient. dictionary with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions
(if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions.
- 'seg_preds': pixel-wise predictions. (b, 1, y, x, (z))
- loss / class_loss (only in validation mode)
"""
#if self.mode=="test":
# self.logger.info('predicting patient {} for fold {} '.format(np.unique(batch['pid']), self.cf.fold))
# True if patient is provided in patches and predictions need to be tiled.
self.patched_patient = 'patch_crop_coords' in list(batch.keys())
# forward batch through prediction pipeline.
results_dict = self.data_aug_forward(batch)
#has seg probs in entry 'seg_preds'
if self.mode == 'val':
for b in range(batch['patient_bb_target'].shape[0]):
for t in range(len(batch['patient_bb_target'][b])):
gt_box = {'box_type': 'gt', 'box_coords': batch['patient_bb_target'][b][t],
'class_targets': batch['patient_class_targets'][b][t]}
for name in self.cf.roi_items:
gt_box.update({name : batch['patient_'+name][b][t]})
results_dict['boxes'][b].append(gt_box)
if 'dice' in self.cf.metrics:
if self.patched_patient:
assert 'patient_seg' in batch.keys(), "Results_dict preds are in original patient shape."
results_dict['batch_dices'] = mutils.dice_per_batch_and_class(
results_dict['seg_preds'], batch["patient_seg"] if self.patched_patient else batch['seg'],
self.cf.num_seg_classes, convert_to_ohe=True)
if self.patched_patient and self.cf.clustering == "wbc":
wbc_input = [self.regress_flag, results_dict['boxes'], 'dummy_pid', self.cf.class_dict, self.cf.clustering_iou, self.n_ens]
results_dict['boxes'] = apply_wbc_to_patient(wbc_input)[0]
elif self.patched_patient:
nms_inputs = [results_dict['boxes'], 'dummy_pid', self.cf.class_dict, self.cf.clustering_iou]
results_dict['boxes'] = apply_nms_to_patient(nms_inputs)[0]
if self.cf.merge_2D_to_3D_preds:
results_dict['2D_boxes'] = results_dict['boxes']
merge_dims_inputs = [results_dict['boxes'], 'dummy_pid', self.cf.class_dict, self.cf.merge_3D_iou]
results_dict['boxes'] = apply_2d_3d_merging_to_patient(merge_dims_inputs)[0]
return results_dict
def predict_test_set(self, batch_gen, return_results=True):
"""
wrapper around test method, which loads multiple (or one) epoch parameters (temporal ensembling), loops through
the test set and collects predictions per patient. Also flattens the results per patient and epoch
and adds optional ground truth boxes for evaluation. Saves out the raw result list for later analysis and
optionally consolidates and returns predictions immediately.
:return: (optionally) list_of_results_per_patient: list over patient results. each entry is a dict with keys:
- 'boxes': list over batch elements. each element is a list over boxes, where each box is
one dictionary: [[box_0, ...], [box_n,...]]. batch elements are slices for 2D predictions
(if not merged to 3D), and a dummy batch dimension of 1 for 3D predictions.
- 'seg_preds': not implemented yet. todo evaluation of instance/semantic segmentation.
"""
# -------------- raw predicting -----------------
dict_of_patients_results = OrderedDict()
set_of_result_types = set()
self.model_index = self.model_index.sort_values(by="rank")
# get paths of all parameter sets to be loaded for temporal ensembling. (or just one for no temp. ensembling).
weight_paths = [os.path.join(self.cf.fold_dir, file_name) for file_name in self.model_index["file_name"]]
for rank_ix, weight_path in enumerate(weight_paths):
self.logger.info(('tmp ensembling over rank_ix:{} epoch:{}'.format(rank_ix, weight_path)))
self.net.load_state_dict(torch.load(weight_path))
self.net.eval()
self.rank_ix = str(rank_ix)
+ plot_batches = np.random.choice(np.arange(batch_gen['n_test']),
+ size=min(batch_gen['n_test'], self.cf.n_test_plots), replace=False)
with torch.no_grad():
- plot_batches = np.random.choice(np.arange(batch_gen['n_test']), size=self.cf.n_test_plots, replace=False)
- for i in range(batch_gen['n_test']):
+ for i in range(batch_gen['n_test']):
batch = next(batch_gen['test'])
pid = np.unique(batch['pid'])
assert len(pid)==1
pid = pid[0]
if not pid in dict_of_patients_results.keys(): # store batch info in patient entry of results dict.
dict_of_patients_results[pid] = {}
dict_of_patients_results[pid]['results_dicts'] = []
dict_of_patients_results[pid]['patient_bb_target'] = batch['patient_bb_target']
for name in self.cf.roi_items:
dict_of_patients_results[pid]["patient_"+name] = batch["patient_"+name]
stime = time.time()
results_dict = self.predict_patient(batch) #only holds "boxes", "seg_preds"
# needs ohe seg probs in seg_preds entry:
results_dict['seg_preds'] = np.argmax(results_dict['seg_preds'], axis=1)[:,np.newaxis]
- self.logger.info("predicting patient {} with weight rank {} (progress: {}/{}) took {:.2f}s".format(
- str(pid), rank_ix, (rank_ix)*batch_gen['n_test']+(i+1), len(weight_paths)*batch_gen['n_test'], time.time()-stime))
+ print("\rpredicting patient {} with weight rank {} (progress: {}/{}) took {:.2f}s".format(
+ str(pid), rank_ix, (rank_ix)*batch_gen['n_test']+(i+1), len(weight_paths)*batch_gen['n_test'],
+ time.time()-stime), end="", flush=True)
if i in plot_batches and (not self.patched_patient or 'patient_data' in batch.keys()):
try:
# view qualitative results of random test case
- self.logger.time("test_plot")
out_file = os.path.join(self.example_plot_dir,
'batch_example_test_{}_rank_{}.png'.format(self.cf.fold, rank_ix))
utils.split_off_process(plg.view_batch, self.cf, batch, results_dict,
has_colorchannels=self.cf.has_colorchannels,
show_gt_labels=True, show_seg_ids='dice' in self.cf.metrics,
get_time="test-example plot", out_file=out_file)
except Exception as e:
self.logger.info("WARNING: error in view_batch: {}".format(e))
if 'dice' in self.cf.metrics:
if self.patched_patient:
assert 'patient_seg' in batch.keys(), "Results_dict preds are in original patient shape."
results_dict['batch_dices'] = mutils.dice_per_batch_and_class( results_dict['seg_preds'],
batch["patient_seg"] if self.patched_patient else batch['seg'],
self.cf.num_seg_classes, convert_to_ohe=True)
dict_of_patients_results[pid]['results_dicts'].append({k:v for k,v in results_dict.items()
if k in ["boxes", "batch_dices"]})
# collect result types to know which ones to look for when saving
set_of_result_types.update(dict_of_patients_results[pid]['results_dicts'][-1].keys())
# -------------- re-order, save raw results -----------------
self.logger.info('finished predicting test set. starting aggregation of predictions.')
results_per_patient = []
for pid, p_dict in dict_of_patients_results.items():
# dict_of_patients_results[pid]['results_list'] has length batch['n_test']
results_dict = {}
# collect all boxes/seg_preds of same batch_instance over temporal instances.
b_size = len(p_dict['results_dicts'][0]["boxes"])
for res_type in [rtype for rtype in set_of_result_types if rtype in ["boxes", "batch_dices"]]:#, "seg_preds"]]:
if not 'batch' in res_type: #assume it's results on batch-element basis
results_dict[res_type] = [[item for rank_dict in p_dict['results_dicts'] for item in rank_dict[res_type][batch_instance]]
for batch_instance in range(b_size)]
else:
results_dict[res_type] = []
for dict in p_dict['results_dicts']:
if 'dice' in res_type:
item = dict[res_type] #dict['batch_dices'] has shape (num_seg_classes,)
assert len(item) == self.cf.num_seg_classes, \
"{}, {}".format(len(item), self.cf.num_seg_classes)
else:
raise NotImplementedError
results_dict[res_type].append(item)
# rdict[dice] shape (n_rank_epochs (n_saved_ranks), nsegclasses)
# calc mean over test epochs so inline with shape from sampling
results_dict[res_type] = np.mean(results_dict[res_type], axis=0) #maybe error type with other than dice
if not hasattr(self.cf, "eval_test_separately") or not self.cf.eval_test_separately:
# add unpatched 2D or 3D (if dim==3 or merge_2D_to_3D) ground truth boxes for evaluation.
for b in range(p_dict['patient_bb_target'].shape[0]):
for targ in range(len(p_dict['patient_bb_target'][b])):
gt_box = {'box_type': 'gt', 'box_coords':p_dict['patient_bb_target'][b][targ],
'class_targets': p_dict['patient_class_targets'][b][targ]}
for name in self.cf.roi_items:
gt_box.update({name: p_dict["patient_"+name][b][targ]})
results_dict['boxes'][b].append(gt_box)
results_per_patient.append([results_dict, pid])
out_string = 'pred_results_held_out' if self.cf.held_out_test_set else 'pred_results'
with open(os.path.join(self.cf.fold_dir, '{}.pkl'.format(out_string)), 'wb') as handle:
pickle.dump(results_per_patient, handle)
if return_results:
# -------------- results processing, clustering, etc. -----------------
final_patient_box_results = [ (res_dict["boxes"], pid) for res_dict,pid in results_per_patient ]
if self.cf.clustering == "wbc":
self.logger.info('applying WBC to test-set predictions with iou = {} and n_ens = {}.'.format(
self.cf.clustering_iou, self.n_ens))
mp_inputs = [[self.regress_flag, ii[0], ii[1], self.cf.class_dict, self.cf.clustering_iou, self.n_ens] for ii in final_patient_box_results]
del final_patient_box_results
pool = Pool(processes=self.cf.n_workers)
final_patient_box_results = pool.map(apply_wbc_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
elif self.cf.clustering == "nms":
self.logger.info('applying standard NMS to test-set predictions with iou = {}.'.format(self.cf.clustering_iou))
pool = Pool(processes=self.cf.n_workers)
mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.clustering_iou] for ii in final_patient_box_results]
del final_patient_box_results
final_patient_box_results = pool.map(apply_nms_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
if self.cf.merge_2D_to_3D_preds:
self.logger.info('applying 2D-to-3D merging to test-set predictions with iou = {}.'.format(self.cf.merge_3D_iou))
mp_inputs = [[ii[0], ii[1], self.cf.class_dict, self.cf.merge_3D_iou] for ii in final_patient_box_results]
del final_patient_box_results
pool = Pool(processes=self.cf.n_workers)
final_patient_box_results = pool.map(apply_2d_3d_merging_to_patient, mp_inputs, chunksize=1)
pool.close()
pool.join()
del mp_inputs
# final_patient_box_results holds [avg_boxes, pid] if wbc
for ix in range(len(results_per_patient)):
assert results_per_patient[ix][1] == final_patient_box_results[ix][1], "should be same pid"
results_per_patient[ix][0]["boxes"] = final_patient_box_results[ix][0]
# results_per_patient = [(res_dict["boxes"] = boxes, pid) for (boxes,pid) in final_patient_box_results]
return results_per_patient # holds list of (results_dict, pid)
diff --git a/utils/dataloader_utils.py b/utils/dataloader_utils.py
index f3e5850..45ba333 100644
--- a/utils/dataloader_utils.py
+++ b/utils/dataloader_utils.py
@@ -1,729 +1,727 @@
#!/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=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')
os.makedirs(self.plot_dir, exist_ok=True)
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.n_filled_threads = min(int(self.dataset_length/self.batch_size), self.number_of_threads_in_multithreaded)
if self.n_filled_threads != self.number_of_threads_in_multithreaded:
print("Adjusting nr of threads from {} to {}.".format(self.number_of_threads_in_multithreaded,
self.n_filled_threads))
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.n_filled_threads)
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.n_filled_threads >= self.batch_size, \
"at least one batch needed per thread. dataset size: {}, n_threads: {}, batch_size: {}.".format(
len(self.dataset_pids), self.n_filled_threads, self.batch_size)
self.lock = Lock()
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.
"""
+ # oversampling of fg: limit bg weights to 1 s.t. bg is weighted at max as heavily as it occurs.
+ max_bg_weight = 1.
+
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
+ mask = ["_bg" in ix for ix in self.fg_bg_weights.index]
+ self.fg_bg_weights.loc[mask] = self.fg_bg_weights.loc[mask].apply(lambda x: min(x, max_bg_weight))
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)
self.stats = {"roi_counts": np.zeros(len(self.unique_ts,), dtype='uint32'),
"empty_counts": np.zeros(len(self.unique_ts,), dtype='uint32')}
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.n_filled_threads \
+ 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
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, 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
diff --git a/utils/exp_utils.py b/utils/exp_utils.py
index ec20a53..c734481 100644
--- a/utils/exp_utils.py
+++ b/utils/exp_utils.py
@@ -1,726 +1,727 @@
#!/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.
# ==============================================================================
from typing import Union, Iterable
import sys
import os
import subprocess
from multiprocessing import Process
import threading
import pickle
import importlib.util
import psutil
import time
import nvidia_smi
import logging
from torch.utils.tensorboard import SummaryWriter
from collections import OrderedDict
import numpy as np
import pandas as pd
import torch
def import_module(name, path):
"""
correct way of importing a module dynamically in python 3.
:param name: name given to module instance.
:param path: path to module.
:return: module: returned module instance.
"""
spec = importlib.util.spec_from_file_location(name, path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def save_obj(obj, name):
"""Pickle a python object."""
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(file_path):
with open(file_path, 'rb') as handle:
return pickle.load(handle)
def IO_safe(func, *args, _tries=5, _raise=True, **kwargs):
""" Wrapper calling function func with arguments args and keyword arguments kwargs to catch input/output errors
on cluster.
:param func: function to execute (intended to be read/write operation to a problematic cluster drive, but can be
any function).
:param args: positional args of func.
:param kwargs: kw args of func.
:param _tries: how many attempts to make executing func.
"""
for _try in range(_tries):
try:
return func(*args, **kwargs)
except OSError as e: # to catch cluster issues with network drives
if _raise:
raise e
else:
print("After attempting execution {} time{}, following error occurred:\n{}".format(_try + 1,
"" if _try == 0 else "s",
e))
continue
def split_off_process(target, *args, daemon=False, **kwargs):
"""Start a process that won't block parent script.
No join(), no return value. If daemon=False: before parent exits, it waits for this to finish.
"""
p = Process(target=target, args=tuple(args), kwargs=kwargs, daemon=daemon)
p.start()
return p
def query_nvidia_gpu(device_id, d_keyword=None, no_units=False):
"""
:param device_id:
:param d_keyword: -d, --display argument (keyword(s) for selective display), all are selected if None
:return: dict of gpu-info items
"""
cmd = ['nvidia-smi', '-i', str(device_id), '-q']
if d_keyword is not None:
cmd += ['-d', d_keyword]
outp = subprocess.check_output(cmd).strip().decode('utf-8').split("\n")
outp = [x for x in outp if len(x) > 0]
headers = [ix for ix, item in enumerate(outp) if len(item.split(":")) == 1] + [len(outp)]
out_dict = {}
for lix, hix in enumerate(headers[:-1]):
head = outp[hix].strip().replace(" ", "_").lower()
out_dict[head] = {}
for lix2 in range(hix, headers[lix + 1]):
try:
key, val = [x.strip().lower() for x in outp[lix2].split(":")]
if no_units:
val = val.split()[0]
out_dict[head][key] = val
except:
pass
return out_dict
class _AnsiColorizer(object):
"""
A colorizer is an object that loosely wraps around a stream, allowing
callers to write text to the stream in a particular color.
Colorizer classes must implement C{supported()} and C{write(text, color)}.
"""
_colors = dict(black=30, red=31, green=32, yellow=33,
blue=34, magenta=35, cyan=36, white=37, default=39)
def __init__(self, stream):
self.stream = stream
@classmethod
def supported(cls, stream=sys.stdout):
"""
A class method that returns True if the current platform supports
coloring terminal output using this method. Returns False otherwise.
"""
if not stream.isatty():
return False # auto color only on TTYs
try:
import curses
except ImportError:
return False
else:
try:
try:
return curses.tigetnum("colors") > 2
except curses.error:
curses.setupterm()
return curses.tigetnum("colors") > 2
except:
raise
# guess false in case of error
return False
def write(self, text, color):
"""
Write the given text to the stream in the given color.
@param text: Text to be written to the stream.
@param color: A string label for a color. e.g. 'red', 'white'.
"""
color = self._colors[color]
self.stream.write('\x1b[%sm%s\x1b[0m' % (color, text))
class ColorHandler(logging.StreamHandler):
def __init__(self, stream=sys.stdout):
super(ColorHandler, self).__init__(_AnsiColorizer(stream))
def emit(self, record):
msg_colors = {
logging.DEBUG: "green",
logging.INFO: "default",
logging.WARNING: "red",
logging.ERROR: "red"
}
color = msg_colors.get(record.levelno, "blue")
self.stream.write(record.msg + "\n", color)
class CombinedPrinter(object):
"""combined print function.
prints to logger and/or file if given, to normal print if non given.
"""
def __init__(self, logger=None, file=None):
if logger is None and file is None:
self.out = [print]
elif logger is None:
self.out = [file.write]
elif file is None:
self.out = [logger.info]
else:
self.out = [logger.info, file.write]
def __call__(self, string):
for fct in self.out:
fct(string)
class Nvidia_GPU_Logger(object):
def __init__(self):
self.count = None
def get_vals(self):
# cmd = ['nvidia-settings', '-t', '-q', 'GPUUtilization']
# gpu_util = subprocess.check_output(cmd).strip().decode('utf-8').split(",")
# gpu_util = dict([f.strip().split("=") for f in gpu_util])
# cmd[-1] = 'UsedDedicatedGPUMemory'
# gpu_used_mem = subprocess.check_output(cmd).strip().decode('utf-8')
nvidia_smi.nvmlInit()
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate
self.gpu_handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
util_res = nvidia_smi.nvmlDeviceGetUtilizationRates(self.gpu_handle)
#mem_res = nvidia_smi.nvmlDeviceGetMemoryInfo(self.gpu_handle)
# current_vals = {"gpu_mem_alloc": mem_res.used / (1024**2), "gpu_graphics_util": int(gpu_util['graphics']),
# "gpu_mem_util": gpu_util['memory'], "time": time.time()}
current_vals = {"gpu_graphics_util": float(util_res.gpu),
"time": time.time()}
return current_vals
def loop(self, interval):
i = 0
while True:
current_vals = self.get_vals()
self.log["time"].append(time.time())
self.log["gpu_util"].append(current_vals["gpu_graphics_util"])
if self.count is not None:
i += 1
if i == self.count:
exit(0)
time.sleep(self.interval)
def start(self, interval=1.):
self.interval = interval
self.start_time = time.time()
self.log = {"time": [], "gpu_util": []}
if self.interval is not None:
thread = threading.Thread(target=self.loop)
thread.daemon = True
thread.start()
class CombinedLogger(object):
"""Combine console and tensorboard logger and record system metrics.
"""
def __init__(self, name, log_dir, server_env=True, fold="all", sysmetrics_interval=2):
self.pylogger = logging.getLogger(name)
self.tboard = SummaryWriter(log_dir=os.path.join(log_dir, "tboard"))
self.times = {}
self.log_dir = log_dir
self.fold = str(fold)
self.server_env = server_env
self.pylogger.setLevel(logging.DEBUG)
self.log_file = os.path.join(log_dir, "fold_"+self.fold, 'exec.log')
os.makedirs(os.path.dirname(self.log_file), exist_ok=True)
self.pylogger.addHandler(logging.FileHandler(self.log_file))
if not server_env:
self.pylogger.addHandler(ColorHandler())
else:
self.pylogger.addHandler(logging.StreamHandler())
self.pylogger.propagate = False
# monitor system metrics (cpu, mem, ...)
if not server_env and sysmetrics_interval > 0:
self.sysmetrics = pd.DataFrame(
columns=["global_step", "rel_time", r"CPU (%)", "mem_used (GB)", r"mem_used (%)",
r"swap_used (GB)", r"gpu_utilization (%)"], dtype="float16")
for device in range(torch.cuda.device_count()):
self.sysmetrics[
"mem_allocd (GB) by torch on {:10s}".format(torch.cuda.get_device_name(device))] = np.nan
self.sysmetrics[
"mem_cached (GB) by torch on {:10s}".format(torch.cuda.get_device_name(device))] = np.nan
self.sysmetrics_start(sysmetrics_interval)
pass
else:
print("NOT logging sysmetrics")
def __getattr__(self, attr):
"""delegate all undefined method requests to objects of
this class in order pylogger, tboard (first find first serve).
E.g., combinedlogger.add_scalars(...) should trigger self.tboard.add_scalars(...)
"""
for obj in [self.pylogger, self.tboard]:
if attr in dir(obj):
return getattr(obj, attr)
print("logger attr not found")
#raise AttributeError("CombinedLogger has no attribute {}".format(attr))
def set_logfile(self, fold=None, log_file=None):
if fold is not None:
self.fold = str(fold)
if log_file is None:
self.log_file = os.path.join(self.log_dir, "fold_"+self.fold, 'exec.log')
else:
self.log_file = log_file
os.makedirs(os.path.dirname(self.log_file), exist_ok=True)
for hdlr in self.pylogger.handlers:
hdlr.close()
self.pylogger.handlers = []
self.pylogger.addHandler(logging.FileHandler(self.log_file))
if not self.server_env:
self.pylogger.addHandler(ColorHandler())
else:
self.pylogger.addHandler(logging.StreamHandler())
def time(self, name, toggle=None):
"""record time-spans as with a stopwatch.
:param name:
:param toggle: True^=On: start time recording, False^=Off: halt rec. if None determine from current status.
:return: either start-time or last recorded interval
"""
if toggle is None:
if name in self.times.keys():
toggle = not self.times[name]["toggle"]
else:
toggle = True
if toggle:
if not name in self.times.keys():
self.times[name] = {"total": 0, "last": 0}
elif self.times[name]["toggle"] == toggle:
self.info("restarting running stopwatch")
self.times[name]["last"] = time.time()
self.times[name]["toggle"] = toggle
return time.time()
else:
if toggle == self.times[name]["toggle"]:
self.info("WARNING: tried to stop stopped stop watch: {}.".format(name))
self.times[name]["last"] = time.time() - self.times[name]["last"]
self.times[name]["total"] += self.times[name]["last"]
self.times[name]["toggle"] = toggle
return self.times[name]["last"]
def get_time(self, name=None, kind="total", format=None, reset=False):
"""
:param name:
:param kind: 'total' or 'last'
:param format: None for float, "hms"/"ms" for (hours), mins, secs as string
:param reset: reset time after retrieving
:return:
"""
if name is None:
times = self.times
if reset:
self.reset_time()
return times
else:
if self.times[name]["toggle"]:
self.time(name, toggle=False)
time = self.times[name][kind]
if format == "hms":
m, s = divmod(time, 60)
h, m = divmod(m, 60)
time = "{:d}h:{:02d}m:{:02d}s".format(int(h), int(m), int(s))
elif format == "ms":
m, s = divmod(time, 60)
time = "{:02d}m:{:02d}s".format(int(m), int(s))
if reset:
self.reset_time(name)
return time
def reset_time(self, name=None):
if name is None:
self.times = {}
else:
del self.times[name]
def sysmetrics_update(self, global_step=None):
if global_step is None:
global_step = time.strftime("%x_%X")
mem = psutil.virtual_memory()
mem_used = (mem.total - mem.available)
gpu_vals = self.gpu_logger.get_vals()
rel_time = time.time() - self.sysmetrics_start_time
self.sysmetrics.loc[len(self.sysmetrics)] = [global_step, rel_time,
psutil.cpu_percent(), mem_used / 1024 ** 3,
mem_used / mem.total * 100,
psutil.swap_memory().used / 1024 ** 3,
int(gpu_vals['gpu_graphics_util']),
*[torch.cuda.memory_allocated(d) / 1024 ** 3 for d in
range(torch.cuda.device_count())],
*[torch.cuda.memory_cached(d) / 1024 ** 3 for d in
range(torch.cuda.device_count())]
]
return self.sysmetrics.loc[len(self.sysmetrics) - 1].to_dict()
def sysmetrics2tboard(self, metrics=None, global_step=None, suptitle=None):
tag = "per_time"
if metrics is None:
metrics = self.sysmetrics_update(global_step=global_step)
tag = "per_epoch"
if suptitle is not None:
suptitle = str(suptitle)
elif self.fold != "":
suptitle = "Fold_" + str(self.fold)
if suptitle is not None:
self.tboard.add_scalars(suptitle + "/System_Metrics/" + tag,
{k: v for (k, v) in metrics.items() if (k != "global_step"
and k != "rel_time")}, global_step)
def sysmetrics_loop(self):
try:
os.nice(-19)
self.info("Logging system metrics with superior process priority.")
except:
self.info("Logging system metrics without superior process priority.")
while True:
metrics = self.sysmetrics_update()
self.sysmetrics2tboard(metrics, global_step=metrics["rel_time"])
# print("thread alive", self.thread.is_alive())
time.sleep(self.sysmetrics_interval)
def sysmetrics_start(self, interval):
if interval is not None and interval > 0:
self.sysmetrics_interval = interval
self.gpu_logger = Nvidia_GPU_Logger()
self.sysmetrics_start_time = time.time()
self.sys_metrics_process = split_off_process(target=self.sysmetrics_loop, daemon=True)
# self.thread = threading.Thread(target=self.sysmetrics_loop)
# self.thread.daemon = True
# self.thread.start()
def sysmetrics_save(self, out_file):
self.sysmetrics.to_pickle(out_file)
def metrics2tboard(self, metrics, global_step=None, suptitle=None):
"""
:param metrics: {'train': dataframe, 'val':df}, df as produced in
evaluator.py.evaluate_predictions
"""
# print("metrics", metrics)
if global_step is None:
global_step = len(metrics['train'][list(metrics['train'].keys())[0]]) - 1
if suptitle is not None:
suptitle = str(suptitle)
else:
suptitle = "Fold_" + str(self.fold)
for key in ['train', 'val']:
# series = {k:np.array(v[-1]) for (k,v) in metrics[key].items() if not np.isnan(v[-1]) and not 'Bin_Stats' in k}
loss_series = {}
unc_series = {}
bin_stat_series = {}
mon_met_series = {}
for tag, val in metrics[key].items():
val = val[-1] # maybe remove list wrapping, recording in evaluator?
if 'bin_stats' in tag.lower() and not np.isnan(val):
bin_stat_series["{}".format(tag.split("/")[-1])] = val
elif 'uncertainty' in tag.lower() and not np.isnan(val):
unc_series["{}".format(tag)] = val
elif 'loss' in tag.lower() and not np.isnan(val):
loss_series["{}".format(tag)] = val
elif not np.isnan(val):
mon_met_series["{}".format(tag)] = val
self.tboard.add_scalars(suptitle + "/Binary_Statistics/{}".format(key), bin_stat_series, global_step)
self.tboard.add_scalars(suptitle + "/Uncertainties/{}".format(key), unc_series, global_step)
self.tboard.add_scalars(suptitle + "/Losses/{}".format(key), loss_series, global_step)
self.tboard.add_scalars(suptitle + "/Monitor_Metrics/{}".format(key), mon_met_series, global_step)
self.tboard.add_scalars(suptitle + "/Learning_Rate", metrics["lr"], global_step)
return
def batchImgs2tboard(self, batch, results_dict, cmap, boxtype2color, img_bg=False, global_step=None):
raise NotImplementedError("not up-to-date, problem with importing plotting-file, torchvision dependency.")
if len(batch["seg"].shape) == 5: # 3D imgs
slice_ix = np.random.randint(batch["seg"].shape[-1])
seg_gt = plg.to_rgb(batch['seg'][:, 0, :, :, slice_ix], cmap)
seg_pred = plg.to_rgb(results_dict['seg_preds'][:, 0, :, :, slice_ix], cmap)
mod_img = plg.mod_to_rgb(batch["data"][:, 0, :, :, slice_ix]) if img_bg else None
elif len(batch["seg"].shape) == 4:
seg_gt = plg.to_rgb(batch['seg'][:, 0, :, :], cmap)
seg_pred = plg.to_rgb(results_dict['seg_preds'][:, 0, :, :], cmap)
mod_img = plg.mod_to_rgb(batch["data"][:, 0]) if img_bg else None
else:
raise Exception("batch content has wrong format: {}".format(batch["seg"].shape))
# from here on only works in 2D
seg_gt = np.transpose(seg_gt, axes=(0, 3, 1, 2)) # previous shp: b,x,y,c
seg_pred = np.transpose(seg_pred, axes=(0, 3, 1, 2))
seg = np.concatenate((seg_gt, seg_pred), axis=0)
# todo replace torchvision (tv) dependency
seg = tv.utils.make_grid(torch.from_numpy(seg), nrow=2)
self.tboard.add_image("Batch seg, 1st col: gt, 2nd: pred.", seg, global_step=global_step)
if img_bg:
bg_img = np.transpose(mod_img, axes=(0, 3, 1, 2))
else:
bg_img = seg_gt
box_imgs = plg.draw_boxes_into_batch(bg_img, results_dict["boxes"], boxtype2color)
box_imgs = tv.utils.make_grid(torch.from_numpy(box_imgs), nrow=4)
self.tboard.add_image("Batch bboxes", box_imgs, global_step=global_step)
return
def __del__(self): # otherwise might produce multiple prints e.g. in ipython console
#self.sys_metrics_process.terminate()
for hdlr in self.pylogger.handlers:
hdlr.close()
self.pylogger.handlers = []
del self.pylogger
self.tboard.flush()
# close holds up main script exit. maybe revise this issue with a later pytorch version.
#self.tboard.close()
def get_logger(exp_dir, server_env=False, sysmetrics_interval=2):
log_dir = os.path.join(exp_dir, "logs")
logger = CombinedLogger('Reg R-CNN', log_dir, server_env=server_env,
sysmetrics_interval=sysmetrics_interval)
print("logging to {}".format(logger.log_file))
return logger
def prepare_monitoring(cf):
"""
creates dictionaries, where train/val metrics are stored.
"""
metrics = {}
# first entry for loss dict accounts for epoch starting at 1.
metrics['train'] = OrderedDict() # [(l_name, [np.nan]) for l_name in cf.losses_to_monitor] )
metrics['val'] = OrderedDict() # [(l_name, [np.nan]) for l_name in cf.losses_to_monitor] )
metric_classes = []
if 'rois' in cf.report_score_level:
metric_classes.extend([v for k, v in cf.class_dict.items()])
if hasattr(cf, "eval_bins_separately") and cf.eval_bins_separately:
metric_classes.extend([v for k, v in cf.bin_dict.items()])
if 'patient' in cf.report_score_level:
metric_classes.extend(['patient_' + cf.class_dict[cf.patient_class_of_interest]])
if hasattr(cf, "eval_bins_separately") and cf.eval_bins_separately:
metric_classes.extend(['patient_' + cf.bin_dict[cf.patient_bin_of_interest]])
for cl in metric_classes:
for m in cf.metrics:
metrics['train'][cl + '_' + m] = [np.nan]
metrics['val'][cl + '_' + m] = [np.nan]
return metrics
class ModelSelector:
'''
saves a checkpoint after each epoch as 'last_state' (can be loaded to continue interrupted training).
saves the top-k (k=cf.save_n_models) ranked epochs. In inference, predictions of multiple epochs can be ensembled
to improve performance.
'''
def __init__(self, cf, logger):
self.cf = cf
self.logger = logger
self.model_index = pd.DataFrame(columns=["rank", "score", "criteria_values", "file_name"],
index=pd.RangeIndex(self.cf.min_save_thresh, self.cf.num_epochs, name="epoch"))
def run_model_selection(self, net, optimizer, monitor_metrics, epoch):
"""rank epoch via weighted mean from self.cf.model_selection_criteria: {criterion : weight}
:param net:
:param optimizer:
:param monitor_metrics:
:param epoch:
:return:
"""
crita = self.cf.model_selection_criteria # shorter alias
metrics = monitor_metrics['val']
epoch_score = np.sum([metrics[criterion][-1] * weight for criterion, weight in crita.items() if
not np.isnan(metrics[criterion][-1])])
if not self.cf.resume:
epoch_score_check = np.sum([metrics[criterion][epoch] * weight for criterion, weight in crita.items() if
not np.isnan(metrics[criterion][epoch])])
assert np.all(epoch_score == epoch_score_check)
self.model_index.loc[epoch, ["score", "criteria_values"]] = epoch_score, {cr: metrics[cr][-1] for cr in crita.keys()}
nonna_ics = self.model_index["score"].dropna(axis=0).index
order = np.argsort(self.model_index.loc[nonna_ics, "score"].to_numpy(), kind="stable")[::-1]
self.model_index.loc[nonna_ics, "rank"] = np.argsort(order) + 1 # no zero-indexing for ranks (best rank is 1).
rank = int(self.model_index.loc[epoch, "rank"])
if rank <= self.cf.save_n_models:
name = '{}_best_params.pth'.format(epoch)
if self.cf.server_env:
IO_safe(torch.save, net.state_dict(), os.path.join(self.cf.fold_dir, name))
else:
torch.save(net.state_dict(), os.path.join(self.cf.fold_dir, name))
self.model_index.loc[epoch, "file_name"] = name
self.logger.info("saved current epoch {} at rank {}".format(epoch, rank))
clean_up = self.model_index.dropna(axis=0, subset=["file_name"])
clean_up = clean_up[clean_up["rank"] > self.cf.save_n_models]
if clean_up.size > 0:
file_name = clean_up["file_name"].to_numpy().item()
subprocess.call("rm {}".format(os.path.join(self.cf.fold_dir, file_name)), shell=True)
self.logger.info("removed outranked epoch {} at {}".format(clean_up.index.values.item(),
os.path.join(self.cf.fold_dir, file_name)))
self.model_index.loc[clean_up.index, "file_name"] = np.nan
state = {
'epoch': epoch,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict(),
'model_index': self.model_index,
}
if self.cf.server_env:
IO_safe(torch.save, state, os.path.join(self.cf.fold_dir, 'last_state.pth'))
else:
torch.save(state, os.path.join(self.cf.fold_dir, 'last_state.pth'))
def parse_params_for_optim(net: torch.nn.Module, weight_decay: float = 0., exclude_from_wd: Iterable = ("norm", "bias")):
"""Format network parameters for the optimizer.
Convenience function to include options for group-specific settings like weight decay.
:param net:
:param weight_decay:
:param exclude_from_wd: List of strings of parameter-group names to exclude from weight decay. Options: "norm", "bias".
:return:
"""
# pytorch implements parameter groups as dicts {'params': ...} and
# weight decay as p.data.mul_(1 - group['lr'] * group['weight_decay'])
norm_types = [torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d,
torch.nn.InstanceNorm1d, torch.nn.InstanceNorm2d, torch.nn.InstanceNorm3d,
torch.nn.LayerNorm, torch.nn.GroupNorm, torch.nn.SyncBatchNorm, torch.nn.LocalResponseNorm
]
level_map = {"bias": "weight",
"norm": "module"}
type_map = {"norm": norm_types}
exclude_from_wd = [str(name).lower() for name in exclude_from_wd]
exclude_weight_names = [k for k, v in level_map.items() if k in exclude_from_wd and v == "weight"]
exclude_module_types = tuple([type_ for k, v in level_map.items() if (k in exclude_from_wd and v == "module")
for type_ in type_map[k]])
- print("excluding {} from weight decay.".format(exclude_from_wd))
+ if exclude_from_wd:
+ print("excluding {} from weight decay.".format(exclude_from_wd))
with_dec, no_dec = [], []
for name, module in net.named_modules():
if isinstance(module, exclude_module_types):
no_dec.extend(module.parameters())
else:
for param_name, param in module.named_parameters():
if np.any([ename in param_name for ename in exclude_weight_names]):
no_dec.append(param)
else:
with_dec.append(param)
groups = [{'params': gr, 'weight_decay': wd} for gr, wd in [(no_dec, 0.), (with_dec, weight_decay)] if len(gr) > 0]
return groups
def load_checkpoint(checkpoint_path, net, optimizer, model_selector):
checkpoint = torch.load(checkpoint_path)
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model_selector.model_index = checkpoint["model_index"]
return checkpoint['epoch'] + 1, net, optimizer, model_selector
def prep_exp(dataset_path, exp_path, server_env, use_stored_settings=True, is_training=True):
"""
I/O handling, creating of experiment folder structure. Also creates a snapshot of configs/model scripts and copies them to the exp_dir.
This way the exp_dir contains all info needed to conduct an experiment, independent to changes in actual source code. Thus, training/inference of this experiment can be started at anytime.
Therefore, the model script is copied back to the source code dir as tmp_model (tmp_backbone).
Provides robust structure for cloud deployment.
:param dataset_path: path to source code for specific data set. (e.g. medicaldetectiontoolkit/lidc_exp)
:param exp_path: path to experiment directory.
:param server_env: boolean flag. pass to configs script for cloud deployment.
:param use_stored_settings: boolean flag. When starting training: If True, starts training from snapshot in existing
experiment directory, else creates experiment directory on the fly using configs/model scripts from source code.
:param is_training: boolean flag. distinguishes train vs. inference mode.
:return: configs object.
"""
if is_training:
if use_stored_settings:
cf_file = import_module('cf', os.path.join(exp_path, 'configs.py'))
cf = cf_file.Configs(server_env)
# in this mode, previously saved model and backbone need to be found in exp dir.
if not os.path.isfile(os.path.join(exp_path, 'model.py')) or \
not os.path.isfile(os.path.join(exp_path, 'backbone.py')):
raise Exception(
"Selected use_stored_settings option but no model and/or backbone source files exist in exp dir.")
cf.model_path = os.path.join(exp_path, 'model.py')
cf.backbone_path = os.path.join(exp_path, 'backbone.py')
else: # this case overwrites settings files in exp dir, i.e., default_configs, configs, backbone, model
os.makedirs(exp_path, exist_ok=True)
# run training with source code info and copy snapshot of model to exp_dir for later testing (overwrite scripts if exp_dir already exists.)
subprocess.call('cp {} {}'.format('default_configs.py', os.path.join(exp_path, 'default_configs.py')),
shell=True)
subprocess.call(
'cp {} {}'.format(os.path.join(dataset_path, 'configs.py'), os.path.join(exp_path, 'configs.py')),
shell=True)
cf_file = import_module('cf_file', os.path.join(dataset_path, 'configs.py'))
cf = cf_file.Configs(server_env)
subprocess.call('cp {} {}'.format(cf.model_path, os.path.join(exp_path, 'model.py')), shell=True)
subprocess.call('cp {} {}'.format(cf.backbone_path, os.path.join(exp_path, 'backbone.py')), shell=True)
if os.path.isfile(os.path.join(exp_path, "fold_ids.pickle")):
subprocess.call('rm {}'.format(os.path.join(exp_path, "fold_ids.pickle")), shell=True)
else: # testing, use model and backbone stored in exp dir.
cf_file = import_module('cf', os.path.join(exp_path, 'configs.py'))
cf = cf_file.Configs(server_env)
cf.model_path = os.path.join(exp_path, 'model.py')
cf.backbone_path = os.path.join(exp_path, 'backbone.py')
cf.exp_dir = exp_path
cf.test_dir = os.path.join(cf.exp_dir, 'test')
cf.plot_dir = os.path.join(cf.exp_dir, 'plots')
if not os.path.exists(cf.test_dir):
os.mkdir(cf.test_dir)
if not os.path.exists(cf.plot_dir):
os.mkdir(cf.plot_dir)
cf.experiment_name = exp_path.split("/")[-1]
cf.dataset_name = dataset_path
cf.server_env = server_env
cf.created_fold_id_pickle = False
return cf
diff --git a/utils/model_utils.py b/utils/model_utils.py
index e951ec7..745cba2 100644
--- a/utils/model_utils.py
+++ b/utils/model_utils.py
@@ -1,1527 +1,1563 @@
#!/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.
# ==============================================================================
"""
Parts are based on https://github.com/multimodallearning/pytorch-mask-rcnn
published under MIT license.
"""
import warnings
warnings.filterwarnings('ignore', '.*From scipy 0.13.0, the output shape of zoom()*')
import numpy as np
import scipy.misc
import scipy.ndimage
import scipy.interpolate
from scipy.ndimage.measurements import label as lb
import torch
import tqdm
from custom_extensions.nms import nms
from custom_extensions.roi_align import roi_align
############################################################
# Segmentation Processing
############################################################
def sum_tensor(input, axes, keepdim=False):
axes = np.unique(axes)
if keepdim:
for ax in axes:
input = input.sum(ax, keepdim=True)
else:
for ax in sorted(axes, reverse=True):
input = input.sum(int(ax))
return input
def get_one_hot_encoding(y, n_classes):
"""
transform a numpy label array to a one-hot array of the same shape.
:param y: array of shape (b, 1, y, x, (z)).
:param n_classes: int, number of classes to unfold in one-hot encoding.
:return y_ohe: array of shape (b, n_classes, y, x, (z))
"""
dim = len(y.shape) - 2
if dim == 2:
y_ohe = np.zeros((y.shape[0], n_classes, y.shape[2], y.shape[3])).astype('int32')
elif dim == 3:
y_ohe = np.zeros((y.shape[0], n_classes, y.shape[2], y.shape[3], y.shape[4])).astype('int32')
else:
raise Exception("invalid dimensions {} encountered".format(y.shape))
for cl in np.arange(n_classes):
y_ohe[:, cl][y[:, 0] == cl] = 1
return y_ohe
def dice_per_batch_inst_and_class(pred, y, n_classes, convert_to_ohe=True, smooth=1e-8):
'''
computes dice scores per batch instance and class.
:param pred: prediction array of shape (b, 1, y, x, (z)) (e.g. softmax prediction with argmax over dim 1)
:param y: ground truth array of shape (b, 1, y, x, (z)) (contains int [0, ..., n_classes]
:param n_classes: int
:return: dice scores of shape (b, c)
'''
if convert_to_ohe:
pred = get_one_hot_encoding(pred, n_classes)
y = get_one_hot_encoding(y, n_classes)
axes = tuple(range(2, len(pred.shape)))
intersect = np.sum(pred*y, axis=axes)
denominator = np.sum(pred, axis=axes)+np.sum(y, axis=axes)
dice = (2.0*intersect + smooth) / (denominator + smooth)
return dice
def dice_per_batch_and_class(pred, targ, n_classes, convert_to_ohe=True, smooth=1e-8):
'''
computes dice scores per batch and class.
:param pred: prediction array of shape (b, 1, y, x, (z)) (e.g. softmax prediction with argmax over dim 1)
:param targ: ground truth array of shape (b, 1, y, x, (z)) (contains int [0, ..., n_classes])
:param n_classes: int
:param smooth: Laplacian smooth, https://en.wikipedia.org/wiki/Additive_smoothing
:return: dice scores of shape (b, c)
'''
if convert_to_ohe:
pred = get_one_hot_encoding(pred, n_classes)
targ = get_one_hot_encoding(targ, n_classes)
axes = (0, *list(range(2, len(pred.shape)))) #(0,2,3(,4))
intersect = np.sum(pred * targ, axis=axes)
denominator = np.sum(pred, axis=axes) + np.sum(targ, axis=axes)
dice = (2.0 * intersect + smooth) / (denominator + smooth)
assert dice.shape==(n_classes,), "dice shp {}".format(dice.shape)
return dice
def batch_dice(pred, y, false_positive_weight=1.0, smooth=1e-6):
'''
compute soft dice over batch. this is a differentiable score and can be used as a loss function.
only dice scores of foreground classes are returned, since training typically
does not benefit from explicit background optimization. Pixels of the entire batch are considered a pseudo-volume to compute dice scores of.
This way, single patches with missing foreground classes can not produce faulty gradients.
:param pred: (b, c, y, x, (z)), softmax probabilities (network output).
:param y: (b, c, y, x, (z)), one hote encoded segmentation mask.
:param false_positive_weight: float [0,1]. For weighting of imbalanced classes,
reduces the penalty for false-positive pixels. Can be beneficial sometimes in data with heavy fg/bg imbalances.
:return: soft dice score (float).This function discards the background score and returns the mena of foreground scores.
'''
if len(pred.size()) == 4:
axes = (0, 2, 3)
intersect = sum_tensor(pred * y, axes, keepdim=False)
denom = sum_tensor(false_positive_weight*pred + y, axes, keepdim=False)
return torch.mean(( (2*intersect + smooth) / (denom + smooth))[1:]) #only fg dice here.
elif len(pred.size()) == 5:
axes = (0, 2, 3, 4)
intersect = sum_tensor(pred * y, axes, keepdim=False)
denom = sum_tensor(false_positive_weight*pred + y, axes, keepdim=False)
return torch.mean(( (2*intersect + smooth) / (denom + smooth))[1:]) #only fg dice here.
else:
raise ValueError('wrong input dimension in dice loss')
############################################################
# Bounding Boxes
############################################################
def compute_iou_2D(box, boxes, box_area, boxes_area):
"""Calculates IoU of the given box with the array of the given boxes.
box: 1D vector [y1, x1, y2, x2] THIS IS THE GT BOX
boxes: [boxes_count, (y1, x1, y2, x2)]
box_area: float. the area of 'box'
boxes_area: array of length boxes_count.
Note: the areas are passed in rather than calculated here for
efficency. Calculate once in the caller to avoid duplicate work.
"""
# Calculate intersection areas
y1 = np.maximum(box[0], boxes[:, 0])
y2 = np.minimum(box[2], boxes[:, 2])
x1 = np.maximum(box[1], boxes[:, 1])
x2 = np.minimum(box[3], boxes[:, 3])
intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0)
union = box_area + boxes_area[:] - intersection[:]
iou = intersection / union
return iou
def compute_iou_3D(box, boxes, box_volume, boxes_volume):
"""Calculates IoU of the given box with the array of the given boxes.
box: 1D vector [y1, x1, y2, x2, z1, z2] (typically gt box)
boxes: [boxes_count, (y1, x1, y2, x2, z1, z2)]
box_area: float. the area of 'box'
boxes_area: array of length boxes_count.
Note: the areas are passed in rather than calculated here for
efficency. Calculate once in the caller to avoid duplicate work.
"""
# Calculate intersection areas
y1 = np.maximum(box[0], boxes[:, 0])
y2 = np.minimum(box[2], boxes[:, 2])
x1 = np.maximum(box[1], boxes[:, 1])
x2 = np.minimum(box[3], boxes[:, 3])
z1 = np.maximum(box[4], boxes[:, 4])
z2 = np.minimum(box[5], boxes[:, 5])
intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0) * np.maximum(z2 - z1, 0)
union = box_volume + boxes_volume[:] - intersection[:]
iou = intersection / union
return iou
def compute_overlaps(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)]. / 3D: (z1, z2))
For better performance, pass the largest set first and the smaller second.
:return: (#boxes1, #boxes2), ious of each box of 1 machted with each of 2
"""
# Areas of anchors and GT boxes
if boxes1.shape[1] == 4:
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
# Compute overlaps to generate matrix [boxes1 count, boxes2 count]
# Each cell contains the IoU value.
overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0]))
for i in range(overlaps.shape[1]):
box2 = boxes2[i] #this is the gt box
overlaps[:, i] = compute_iou_2D(box2, boxes1, area2[i], area1)
return overlaps
else:
# Areas of anchors and GT boxes
volume1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) * (boxes1[:, 5] - boxes1[:, 4])
volume2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) * (boxes2[:, 5] - boxes2[:, 4])
# Compute overlaps to generate matrix [boxes1 count, boxes2 count]
# Each cell contains the IoU value.
overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0]))
for i in range(boxes2.shape[0]):
box2 = boxes2[i] # this is the gt box
overlaps[:, i] = compute_iou_3D(box2, boxes1, volume2[i], volume1)
return overlaps
def box_refinement(box, gt_box):
"""Compute refinement needed to transform box to gt_box.
box and gt_box are [N, (y1, x1, y2, x2)] / 3D: (z1, z2))
"""
height = box[:, 2] - box[:, 0]
width = box[:, 3] - box[:, 1]
center_y = box[:, 0] + 0.5 * height
center_x = box[:, 1] + 0.5 * width
gt_height = gt_box[:, 2] - gt_box[:, 0]
gt_width = gt_box[:, 3] - gt_box[:, 1]
gt_center_y = gt_box[:, 0] + 0.5 * gt_height
gt_center_x = gt_box[:, 1] + 0.5 * gt_width
dy = (gt_center_y - center_y) / height
dx = (gt_center_x - center_x) / width
dh = torch.log(gt_height / height)
dw = torch.log(gt_width / width)
result = torch.stack([dy, dx, dh, dw], dim=1)
if box.shape[1] > 4:
depth = box[:, 5] - box[:, 4]
center_z = box[:, 4] + 0.5 * depth
gt_depth = gt_box[:, 5] - gt_box[:, 4]
gt_center_z = gt_box[:, 4] + 0.5 * gt_depth
dz = (gt_center_z - center_z) / depth
dd = torch.log(gt_depth / depth)
result = torch.stack([dy, dx, dz, dh, dw, dd], dim=1)
return result
def unmold_mask_2D(mask, bbox, image_shape):
"""Converts a mask generated by the neural network into a format similar
to it's original shape.
mask: [height, width] of type float. A small, typically 28x28 mask.
bbox: [y1, x1, y2, x2]. The box to fit the mask in.
Returns a binary mask with the same size as the original image.
"""
y1, x1, y2, x2 = bbox
out_zoom = [y2 - y1, x2 - x1]
zoom_factor = [i / j for i, j in zip(out_zoom, mask.shape)]
mask = scipy.ndimage.zoom(mask, zoom_factor, order=1).astype(np.float32)
# Put the mask in the right location.
full_mask = np.zeros(image_shape[:2]) #only y,x
full_mask[y1:y2, x1:x2] = mask
return full_mask
def unmold_mask_2D_torch(mask, bbox, image_shape):
"""Converts a mask generated by the neural network into a format similar
to it's original shape.
mask: [height, width] of type float. A small, typically 28x28 mask.
bbox: [y1, x1, y2, x2]. The box to fit the mask in.
Returns a binary mask with the same size as the original image.
"""
y1, x1, y2, x2 = bbox
out_zoom = [(y2 - y1).float(), (x2 - x1).float()]
zoom_factor = [i / j for i, j in zip(out_zoom, mask.shape)]
mask = mask.unsqueeze(0).unsqueeze(0)
mask = torch.nn.functional.interpolate(mask, scale_factor=zoom_factor)
mask = mask[0][0]
#mask = scipy.ndimage.zoom(mask.cpu().numpy(), zoom_factor, order=1).astype(np.float32)
#mask = torch.from_numpy(mask).cuda()
# Put the mask in the right location.
full_mask = torch.zeros(image_shape[:2]) # only y,x
full_mask[y1:y2, x1:x2] = mask
return full_mask
def unmold_mask_3D(mask, bbox, image_shape):
"""Converts a mask generated by the neural network into a format similar
to it's original shape.
mask: [height, width] of type float. A small, typically 28x28 mask.
bbox: [y1, x1, y2, x2, z1, z2]. The box to fit the mask in.
Returns a binary mask with the same size as the original image.
"""
y1, x1, y2, x2, z1, z2 = bbox
out_zoom = [y2 - y1, x2 - x1, z2 - z1]
zoom_factor = [i/j for i,j in zip(out_zoom, mask.shape)]
mask = scipy.ndimage.zoom(mask, zoom_factor, order=1).astype(np.float32)
# Put the mask in the right location.
full_mask = np.zeros(image_shape[:3])
full_mask[y1:y2, x1:x2, z1:z2] = mask
return full_mask
def nms_numpy(box_coords, scores, thresh):
""" non-maximum suppression on 2D or 3D boxes in numpy.
:param box_coords: [y1,x1,y2,x2 (,z1,z2)] with y1<=y2, x1<=x2, z1<=z2.
:param scores: ranking scores (higher score == higher rank) of boxes.
:param thresh: IoU threshold for clustering.
:return:
"""
y1 = box_coords[:, 0]
x1 = box_coords[:, 1]
y2 = box_coords[:, 2]
x2 = box_coords[:, 3]
assert np.all(y1 <= y2) and np.all(x1 <= x2), """"the definition of the coordinates is crucially important here:
coordinates of which maxima are taken need to be the lower coordinates"""
areas = (x2 - x1) * (y2 - y1)
is_3d = box_coords.shape[1] == 6
if is_3d: # 3-dim case
z1 = box_coords[:, 4]
z2 = box_coords[:, 5]
assert np.all(z1<=z2), """"the definition of the coordinates is crucially important here:
coordinates of which maxima are taken need to be the lower coordinates"""
areas *= (z2 - z1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0: # order is the sorted index. maps order to index: order[1] = 24 means (rank1, ix 24)
i = order[0] # highest scoring element
yy1 = np.maximum(y1[i], y1[order]) # highest scoring element still in >order<, is compared to itself, that is okay.
xx1 = np.maximum(x1[i], x1[order])
yy2 = np.minimum(y2[i], y2[order])
xx2 = np.minimum(x2[i], x2[order])
h = np.maximum(0.0, yy2 - yy1)
w = np.maximum(0.0, xx2 - xx1)
inter = h * w
if is_3d:
zz1 = np.maximum(z1[i], z1[order])
zz2 = np.minimum(z2[i], z2[order])
d = np.maximum(0.0, zz2 - zz1)
inter *= d
iou = inter / (areas[i] + areas[order] - inter)
non_matches = np.nonzero(iou <= thresh)[0] # get all elements that were not matched and discard all others.
order = order[non_matches]
keep.append(i)
return keep
############################################################
# M-RCNN
############################################################
def refine_proposals(rpn_pred_probs, rpn_pred_deltas, proposal_count, batch_anchors, cf):
"""
Receives anchor scores and selects a subset to pass as proposals
to the second stage. Filtering is done based on anchor scores and
non-max suppression to remove overlaps. It also applies bounding
- box refinment details to anchors.
+ box refinement details to anchors.
:param rpn_pred_probs: (b, n_anchors, 2)
:param rpn_pred_deltas: (b, n_anchors, (y, x, (z), log(h), log(w), (log(d))))
:return: batch_normalized_props: Proposals in normalized coordinates (b, proposal_count, (y1, x1, y2, x2, (z1), (z2), score))
:return: batch_out_proposals: Box coords + RPN foreground scores
for monitoring/plotting (b, proposal_count, (y1, x1, y2, x2, (z1), (z2), score))
"""
std_dev = torch.from_numpy(cf.rpn_bbox_std_dev[None]).float().cuda()
norm = torch.from_numpy(cf.scale).float().cuda()
anchors = batch_anchors.clone()
-
batch_scores = rpn_pred_probs[:, :, 1]
# norm deltas
batch_deltas = rpn_pred_deltas * std_dev
batch_normalized_props = []
batch_out_proposals = []
# loop over batch dimension.
for ix in range(batch_scores.shape[0]):
scores = batch_scores[ix]
deltas = batch_deltas[ix]
+ non_nans = deltas == deltas
+ assert torch.all(non_nans), "deltas have nans: {}".format(deltas[~non_nans])
+
+ non_nans = anchors == anchors
+ assert torch.all(non_nans), "anchors have nans: {}".format(anchors[~non_nans])
+
# improve performance by trimming to top anchors by score
# and doing the rest on the smaller subset.
pre_nms_limit = min(cf.pre_nms_limit, anchors.size()[0])
scores, order = scores.sort(descending=True)
order = order[:pre_nms_limit]
scores = scores[:pre_nms_limit]
deltas = deltas[order, :]
# apply deltas to anchors to get refined anchors and filter with non-maximum suppression.
if batch_deltas.shape[-1] == 4:
boxes = apply_box_deltas_2D(anchors[order, :], deltas)
+ non_nans = boxes == boxes
+ assert torch.all(non_nans), "unnormalized boxes before clip/after delta apply have nans: {}".format(boxes[~non_nans])
boxes = clip_boxes_2D(boxes, cf.window)
else:
boxes = apply_box_deltas_3D(anchors[order, :], deltas)
boxes = clip_boxes_3D(boxes, cf.window)
+
+ non_nans = boxes == boxes
+ assert torch.all(non_nans), "unnormalized boxes before nms/after clip have nans: {}".format(boxes[~non_nans])
# boxes are y1,x1,y2,x2, torchvision-nms requires x1,y1,x2,y2, but consistent swap x<->y is irrelevant.
keep = nms.nms(boxes, scores, cf.rpn_nms_threshold)
keep = keep[:proposal_count]
boxes = boxes[keep, :]
rpn_scores = scores[keep][:, None]
# pad missing boxes with 0.
if boxes.shape[0] < proposal_count:
n_pad_boxes = proposal_count - boxes.shape[0]
zeros = torch.zeros([n_pad_boxes, boxes.shape[1]]).cuda()
boxes = torch.cat([boxes, zeros], dim=0)
zeros = torch.zeros([n_pad_boxes, rpn_scores.shape[1]]).cuda()
rpn_scores = torch.cat([rpn_scores, zeros], dim=0)
# concat box and score info for monitoring/plotting.
batch_out_proposals.append(torch.cat((boxes, rpn_scores), 1).cpu().data.numpy())
# normalize dimensions to range of 0 to 1.
+ non_nans = boxes == boxes
+ assert torch.all(non_nans), "unnormalized boxes after nms have nans: {}".format(boxes[~non_nans])
normalized_boxes = boxes / norm
where = normalized_boxes <=1
- assert torch.all(where), "normalized box coords >1 found:\n {}\n".format(normalized_boxes[where])
- #assert torch.all(normalized_boxes <= 1), "normalized box coords >1 found"
+ assert torch.all(where), "normalized box coords >1 found:\n {}\n".format(normalized_boxes[~where])
# add again batch dimension
batch_normalized_props.append(torch.cat((normalized_boxes, rpn_scores), 1).unsqueeze(0))
batch_normalized_props = torch.cat(batch_normalized_props)
batch_out_proposals = np.array(batch_out_proposals)
return batch_normalized_props, batch_out_proposals
def pyramid_roi_align(feature_maps, rois, pool_size, pyramid_levels, dim):
"""
Implements ROI Pooling on multiple levels of the feature pyramid.
:param feature_maps: list of feature maps, each of shape (b, c, y, x , (z))
:param rois: proposals (normalized coords.) as returned by RPN. contain info about original batch element allocation.
(n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ixs)
:param pool_size: list of poolsizes in dims: [x, y, (z)]
:param pyramid_levels: list. [0, 1, 2, ...]
:return: pooled: pooled feature map rois (n_proposals, c, poolsize_y, poolsize_x, (poolsize_z))
Output:
Pooled regions in the shape: [num_boxes, height, width, channels].
The width and height are those specific in the pool_shape in the layer
constructor.
"""
boxes = rois[:, :dim*2]
batch_ixs = rois[:, dim*2]
# Assign each ROI to a level in the pyramid based on the ROI area.
if dim == 2:
y1, x1, y2, x2 = boxes.chunk(4, dim=1)
else:
y1, x1, y2, x2, z1, z2 = boxes.chunk(6, dim=1)
h = y2 - y1
w = x2 - x1
# Equation 1 in https://arxiv.org/abs/1612.03144. Account for
# the fact that our coordinates are normalized here.
# divide sqrt(h*w) by 1 instead image_area.
roi_level = (4 + torch.log2(torch.sqrt(h*w))).round().int().clamp(pyramid_levels[0], pyramid_levels[-1])
# if Pyramid contains additional level P6, adapt the roi_level assignment accordingly.
if len(pyramid_levels) == 5:
roi_level[h*w > 0.65] = 5
# Loop through levels and apply ROI pooling to each.
pooled = []
box_to_level = []
fmap_shapes = [f.shape for f in feature_maps]
for level_ix, level in enumerate(pyramid_levels):
ix = roi_level == level
if not ix.any():
continue
ix = torch.nonzero(ix)[:, 0]
level_boxes = boxes[ix, :]
# re-assign rois to feature map of original batch element.
ind = batch_ixs[ix].int()
# Keep track of which box is mapped to which level
box_to_level.append(ix)
# Stop gradient propogation to ROI proposals
level_boxes = level_boxes.detach()
if len(pool_size) == 2:
# remap to feature map coordinate system
y_exp, x_exp = fmap_shapes[level_ix][2:] # exp = expansion
level_boxes.mul_(torch.tensor([y_exp, x_exp, y_exp, x_exp], dtype=torch.float32).cuda())
pooled_features = roi_align.roi_align_2d(feature_maps[level_ix],
torch.cat((ind.unsqueeze(1).float(), level_boxes), dim=1),
pool_size)
else:
y_exp, x_exp, z_exp = fmap_shapes[level_ix][2:]
level_boxes.mul_(torch.tensor([y_exp, x_exp, y_exp, x_exp, z_exp, z_exp], dtype=torch.float32).cuda())
pooled_features = roi_align.roi_align_3d(feature_maps[level_ix],
torch.cat((ind.unsqueeze(1).float(), level_boxes), dim=1),
pool_size)
pooled.append(pooled_features)
# Pack pooled features into one tensor
pooled = torch.cat(pooled, dim=0)
# Pack box_to_level mapping into one array and add another
# column representing the order of pooled boxes
box_to_level = torch.cat(box_to_level, dim=0)
# Rearrange pooled features to match the order of the original boxes
_, box_to_level = torch.sort(box_to_level)
pooled = pooled[box_to_level, :, :]
return pooled
def roi_align_3d_numpy(input: np.ndarray, rois, output_size: tuple,
spatial_scale: float = 1., sampling_ratio: int = -1) -> np.ndarray:
""" This fct mainly serves as a verification method for 3D CUDA implementation of RoIAlign, it's highly
inefficient due to the nested loops.
:param input: (ndarray[N, C, H, W, D]): input feature map
:param rois: list (N,K(n), 6), K(n) = nr of rois in batch-element n, single roi of format (y1,x1,y2,x2,z1,z2)
:param output_size:
:param spatial_scale:
:param sampling_ratio:
:return: (List[N, K(n), C, output_size[0], output_size[1], output_size[2]])
"""
out_height, out_width, out_depth = output_size
coord_grid = tuple([np.linspace(0, input.shape[dim] - 1, num=input.shape[dim]) for dim in range(2, 5)])
pooled_rois = [[]] * len(rois)
assert len(rois) == input.shape[0], "batch dim mismatch, rois: {}, input: {}".format(len(rois), input.shape[0])
print("Numpy 3D RoIAlign progress:", end="\n")
for b in range(input.shape[0]):
for roi in tqdm.tqdm(rois[b]):
y1, x1, y2, x2, z1, z2 = np.array(roi) * spatial_scale
roi_height = max(float(y2 - y1), 1.)
roi_width = max(float(x2 - x1), 1.)
roi_depth = max(float(z2 - z1), 1.)
if sampling_ratio <= 0:
sampling_ratio_h = int(np.ceil(roi_height / out_height))
sampling_ratio_w = int(np.ceil(roi_width / out_width))
sampling_ratio_d = int(np.ceil(roi_depth / out_depth))
else:
sampling_ratio_h = sampling_ratio_w = sampling_ratio_d = sampling_ratio # == n points per bin
bin_height = roi_height / out_height
bin_width = roi_width / out_width
bin_depth = roi_depth / out_depth
n_points = sampling_ratio_h * sampling_ratio_w * sampling_ratio_d
pooled_roi = np.empty((input.shape[1], out_height, out_width, out_depth), dtype="float32")
for chan in range(input.shape[1]):
lin_interpolator = scipy.interpolate.RegularGridInterpolator(coord_grid, input[b, chan],
method="linear")
for bin_iy in range(out_height):
for bin_ix in range(out_width):
for bin_iz in range(out_depth):
bin_val = 0.
for i in range(sampling_ratio_h):
for j in range(sampling_ratio_w):
for k in range(sampling_ratio_d):
loc_ijk = [
y1 + bin_iy * bin_height + (i + 0.5) * (bin_height / sampling_ratio_h),
x1 + bin_ix * bin_width + (j + 0.5) * (bin_width / sampling_ratio_w),
z1 + bin_iz * bin_depth + (k + 0.5) * (bin_depth / sampling_ratio_d)]
# print("loc_ijk", loc_ijk)
if not (np.any([c < -1.0 for c in loc_ijk]) or loc_ijk[0] > input.shape[2] or
loc_ijk[1] > input.shape[3] or loc_ijk[2] > input.shape[4]):
for catch_case in range(3):
# catch on-border cases
if int(loc_ijk[catch_case]) == input.shape[catch_case + 2] - 1:
loc_ijk[catch_case] = input.shape[catch_case + 2] - 1
bin_val += lin_interpolator(loc_ijk)
pooled_roi[chan, bin_iy, bin_ix, bin_iz] = bin_val / n_points
pooled_rois[b].append(pooled_roi)
return np.array(pooled_rois)
def refine_detections(cf, batch_ixs, rois, deltas, scores, regressions):
"""
Refine classified proposals (apply deltas to rpn rois), filter overlaps (nms) and return final detections.
:param rois: (n_proposals, 2 * dim) normalized boxes as proposed by RPN. n_proposals = batch_size * POST_NMS_ROIS
:param deltas: (n_proposals, n_classes, 2 * dim) box refinement deltas as predicted by mrcnn bbox regressor.
:param batch_ixs: (n_proposals) batch element assignment info for re-allocation.
:param scores: (n_proposals, n_classes) probabilities for all classes per roi as predicted by mrcnn classifier.
:param regressions: (n_proposals, n_classes, regression_features (+1 for uncertainty if predicted) regression vector
:return: result: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score, *regression vector features))
"""
# class IDs per ROI. Since scores of all classes are of interest (not just max class), all are kept at this point.
class_ids = []
fg_classes = cf.head_classes - 1
# repeat vectors to fill in predictions for all foreground classes.
for ii in range(1, fg_classes + 1):
class_ids += [ii] * rois.shape[0]
class_ids = torch.from_numpy(np.array(class_ids)).cuda()
batch_ixs = batch_ixs.repeat(fg_classes)
rois = rois.repeat(fg_classes, 1)
deltas = deltas.repeat(fg_classes, 1, 1)
scores = scores.repeat(fg_classes, 1)
regressions = regressions.repeat(fg_classes, 1, 1)
# get class-specific scores and bounding box deltas
idx = torch.arange(class_ids.size()[0]).long().cuda()
# using idx instead of slice [:,] squashes first dimension.
#len(class_ids)>scores.shape[1] --> probs is broadcasted by expansion from fg_classes-->len(class_ids)
batch_ixs = batch_ixs[idx]
deltas_specific = deltas[idx, class_ids]
class_scores = scores[idx, class_ids]
regressions = regressions[idx, class_ids]
# apply bounding box deltas. re-scale to image coordinates.
std_dev = torch.from_numpy(np.reshape(cf.rpn_bbox_std_dev, [1, cf.dim * 2])).float().cuda()
scale = torch.from_numpy(cf.scale).float().cuda()
refined_rois = apply_box_deltas_2D(rois, deltas_specific * std_dev) * scale if cf.dim == 2 else \
apply_box_deltas_3D(rois, deltas_specific * std_dev) * scale
# round and cast to int since we're dealing with pixels now
refined_rois = clip_to_window(cf.window, refined_rois)
refined_rois = torch.round(refined_rois)
# filter out low confidence boxes
keep = idx
keep_bool = (class_scores >= cf.model_min_confidence)
if not 0 in torch.nonzero(keep_bool).size():
score_keep = torch.nonzero(keep_bool)[:, 0]
pre_nms_class_ids = class_ids[score_keep]
pre_nms_rois = refined_rois[score_keep]
pre_nms_scores = class_scores[score_keep]
pre_nms_batch_ixs = batch_ixs[score_keep]
for j, b in enumerate(unique1d(pre_nms_batch_ixs)):
bixs = torch.nonzero(pre_nms_batch_ixs == b)[:, 0]
bix_class_ids = pre_nms_class_ids[bixs]
bix_rois = pre_nms_rois[bixs]
bix_scores = pre_nms_scores[bixs]
for i, class_id in enumerate(unique1d(bix_class_ids)):
ixs = torch.nonzero(bix_class_ids == class_id)[:, 0]
# nms expects boxes sorted by score.
ix_rois = bix_rois[ixs]
ix_scores = bix_scores[ixs]
ix_scores, order = ix_scores.sort(descending=True)
ix_rois = ix_rois[order, :]
class_keep = nms.nms(ix_rois, ix_scores, cf.detection_nms_threshold)
# map indices back.
class_keep = keep[score_keep[bixs[ixs[order[class_keep]]]]]
# merge indices over classes for current batch element
b_keep = class_keep if i == 0 else unique1d(torch.cat((b_keep, class_keep)))
# only keep top-k boxes of current batch-element
top_ids = class_scores[b_keep].sort(descending=True)[1][:cf.model_max_instances_per_batch_element]
b_keep = b_keep[top_ids]
# merge indices over batch elements.
batch_keep = b_keep if j == 0 else unique1d(torch.cat((batch_keep, b_keep)))
keep = batch_keep
else:
keep = torch.tensor([0]).long().cuda()
# arrange output
output = [refined_rois[keep], batch_ixs[keep].unsqueeze(1)]
output += [class_ids[keep].unsqueeze(1).float(), class_scores[keep].unsqueeze(1)]
output += [regressions[keep]]
result = torch.cat(output, dim=1)
# shape: (n_keeps, catted feats), catted feats: [0:dim*2] are box_coords, [dim*2] are batch_ics,
# [dim*2+1] are class_ids, [dim*2+2] are scores, [dim*2+3:] are regression vector features (incl uncertainty)
return result
def loss_example_mining(cf, batch_proposals, batch_gt_boxes, batch_gt_masks, batch_roi_scores,
batch_gt_class_ids, batch_gt_regressions):
"""
Subsamples proposals for mrcnn losses and generates targets. Sampling is done per batch element, seems to have positive
effects on training, as opposed to sampling over entire batch. Negatives are sampled via stochastic hard-example mining
(SHEM), where a number of negative proposals is drawn from larger pool of highest scoring proposals for stochasticity.
Scoring is obtained here as the max over all foreground probabilities as returned by mrcnn_classifier (worked better than
loss-based class-balancing methods like "online hard-example mining" or "focal loss".)
Classification-regression duality: regressions can be given along with classes (at least fg/bg, only class scores
are used for ranking).
:param batch_proposals: (n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ixs).
boxes as proposed by RPN. n_proposals here is determined by batch_size * POST_NMS_ROIS.
:param mrcnn_class_logits: (n_proposals, n_classes)
:param batch_gt_boxes: list over batch elements. Each element is a list over the corresponding roi target coordinates.
:param batch_gt_masks: list over batch elements. Each element is binary mask of shape (n_gt_rois, c, y, x, (z))
:param batch_gt_class_ids: list over batch elements. Each element is a list over the corresponding roi target labels.
if no classes predicted (only fg/bg from RPN): expected as pseudo classes [0, 1] for bg, fg.
:param batch_gt_regressions: list over b elements. Each element is a regression target vector. if None--> pseudo
:return: sample_indices: (n_sampled_rois) indices of sampled proposals to be used for loss functions.
:return: target_class_ids: (n_sampled_rois)containing target class labels of sampled proposals.
:return: target_deltas: (n_sampled_rois, 2 * dim) containing target deltas of sampled proposals for box refinement.
:return: target_masks: (n_sampled_rois, y, x, (z)) containing target masks of sampled proposals.
"""
# normalization of target coordinates
#global sample_regressions
if cf.dim == 2:
h, w = cf.patch_size
scale = torch.from_numpy(np.array([h, w, h, w])).float().cuda()
else:
h, w, z = cf.patch_size
scale = torch.from_numpy(np.array([h, w, h, w, z, z])).float().cuda()
positive_count = 0
negative_count = 0
sample_positive_indices = []
sample_negative_indices = []
sample_deltas = []
sample_masks = []
sample_class_ids = []
if batch_gt_regressions is not None:
sample_regressions = []
else:
target_regressions = torch.FloatTensor().cuda()
std_dev = torch.from_numpy(cf.bbox_std_dev).float().cuda()
# loop over batch and get positive and negative sample rois.
for b in range(len(batch_gt_boxes)):
gt_masks = torch.from_numpy(batch_gt_masks[b]).float().cuda()
gt_class_ids = torch.from_numpy(batch_gt_class_ids[b]).int().cuda()
if batch_gt_regressions is not None:
gt_regressions = torch.from_numpy(batch_gt_regressions[b]).float().cuda()
#if np.any(batch_gt_class_ids[b] > 0): # skip roi selection for no gt images.
if np.any([len(coords)>0 for coords in batch_gt_boxes[b]]):
gt_boxes = torch.from_numpy(batch_gt_boxes[b]).float().cuda() / scale
else:
gt_boxes = torch.FloatTensor().cuda()
# get proposals and indices of current batch element.
proposals = batch_proposals[batch_proposals[:, -1] == b][:, :-1]
batch_element_indices = torch.nonzero(batch_proposals[:, -1] == b).squeeze(1)
# Compute overlaps matrix [proposals, gt_boxes]
if not 0 in gt_boxes.size():
if gt_boxes.shape[1] == 4:
assert cf.dim == 2, "gt_boxes shape {} doesnt match cf.dim{}".format(gt_boxes.shape, cf.dim)
overlaps = bbox_overlaps_2D(proposals, gt_boxes)
else:
assert cf.dim == 3, "gt_boxes shape {} doesnt match cf.dim{}".format(gt_boxes.shape, cf.dim)
overlaps = bbox_overlaps_3D(proposals, gt_boxes)
# Determine positive and negative ROIs
roi_iou_max = torch.max(overlaps, dim=1)[0]
# 1. Positive ROIs are those with >= 0.5 IoU with a GT box
positive_roi_bool = roi_iou_max >= (0.5 if cf.dim == 2 else 0.3)
# 2. Negative ROIs are those with < 0.1 with every GT box.
negative_roi_bool = roi_iou_max < (0.1 if cf.dim == 2 else 0.01)
else:
positive_roi_bool = torch.FloatTensor().cuda()
negative_roi_bool = torch.from_numpy(np.array([1]*proposals.shape[0])).cuda()
# Sample Positive ROIs
if not 0 in torch.nonzero(positive_roi_bool).size():
positive_indices = torch.nonzero(positive_roi_bool).squeeze(1)
positive_samples = int(cf.train_rois_per_image * cf.roi_positive_ratio)
rand_idx = torch.randperm(positive_indices.size()[0])
rand_idx = rand_idx[:positive_samples].cuda()
positive_indices = positive_indices[rand_idx]
positive_samples = positive_indices.size()[0]
positive_rois = proposals[positive_indices, :]
# Assign positive ROIs to GT boxes.
positive_overlaps = overlaps[positive_indices, :]
roi_gt_box_assignment = torch.max(positive_overlaps, dim=1)[1]
roi_gt_boxes = gt_boxes[roi_gt_box_assignment, :]
roi_gt_class_ids = gt_class_ids[roi_gt_box_assignment]
if batch_gt_regressions is not None:
roi_gt_regressions = gt_regressions[roi_gt_box_assignment]
# Compute bbox refinement targets for positive ROIs
deltas = box_refinement(positive_rois, roi_gt_boxes)
deltas /= std_dev
roi_masks = gt_masks[roi_gt_box_assignment]
assert roi_masks.shape[1] == 1, "gt masks have more than one channel --> is this desired?"
# Compute mask targets
boxes = positive_rois
box_ids = torch.arange(roi_masks.shape[0]).cuda().unsqueeze(1).float()
if len(cf.mask_shape) == 2:
y_exp, x_exp = roi_masks.shape[2:] # exp = expansion
boxes.mul_(torch.tensor([y_exp, x_exp, y_exp, x_exp], dtype=torch.float32).cuda())
masks = roi_align.roi_align_2d(roi_masks,
torch.cat((box_ids, boxes), dim=1),
cf.mask_shape)
else:
y_exp, x_exp, z_exp = roi_masks.shape[2:] # exp = expansion
boxes.mul_(torch.tensor([y_exp, x_exp, y_exp, x_exp, z_exp, z_exp], dtype=torch.float32).cuda())
masks = roi_align.roi_align_3d(roi_masks,
torch.cat((box_ids, boxes), dim=1),
cf.mask_shape)
masks = masks.squeeze(1)
# Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with
# binary cross entropy loss.
masks = torch.round(masks)
sample_positive_indices.append(batch_element_indices[positive_indices])
sample_deltas.append(deltas)
sample_masks.append(masks)
sample_class_ids.append(roi_gt_class_ids)
if batch_gt_regressions is not None:
sample_regressions.append(roi_gt_regressions)
positive_count += positive_samples
else:
positive_samples = 0
# Sample negative ROIs. Add enough to maintain positive:negative ratio, but at least 1. Sample via SHEM.
if not 0 in torch.nonzero(negative_roi_bool).size():
negative_indices = torch.nonzero(negative_roi_bool).squeeze(1)
r = 1.0 / cf.roi_positive_ratio
b_neg_count = np.max((int(r * positive_samples - positive_samples), 1))
roi_scores_neg = batch_roi_scores[batch_element_indices[negative_indices]]
raw_sampled_indices = shem(roi_scores_neg, b_neg_count, cf.shem_poolsize)
sample_negative_indices.append(batch_element_indices[negative_indices[raw_sampled_indices]])
negative_count += raw_sampled_indices.size()[0]
if len(sample_positive_indices) > 0:
target_deltas = torch.cat(sample_deltas)
target_masks = torch.cat(sample_masks)
target_class_ids = torch.cat(sample_class_ids)
if batch_gt_regressions is not None:
target_regressions = torch.cat(sample_regressions)
# Pad target information with zeros for negative ROIs.
if positive_count > 0 and negative_count > 0:
sample_indices = torch.cat((torch.cat(sample_positive_indices), torch.cat(sample_negative_indices)), dim=0)
zeros = torch.zeros(negative_count, cf.dim * 2).cuda()
target_deltas = torch.cat([target_deltas, zeros], dim=0)
zeros = torch.zeros(negative_count, *cf.mask_shape).cuda()
target_masks = torch.cat([target_masks, zeros], dim=0)
zeros = torch.zeros(negative_count).int().cuda()
target_class_ids = torch.cat([target_class_ids, zeros], dim=0)
if batch_gt_regressions is not None:
# regression targets need to have 0 as background/negative with below practice
if 'regression_bin' in cf.prediction_tasks:
zeros = torch.zeros(negative_count, dtype=torch.float).cuda()
else:
zeros = torch.zeros(negative_count, cf.regression_n_features, dtype=torch.float).cuda()
target_regressions = torch.cat([target_regressions, zeros], dim=0)
elif positive_count > 0:
sample_indices = torch.cat(sample_positive_indices)
elif negative_count > 0:
sample_indices = torch.cat(sample_negative_indices)
target_deltas = torch.zeros(negative_count, cf.dim * 2).cuda()
target_masks = torch.zeros(negative_count, *cf.mask_shape).cuda()
target_class_ids = torch.zeros(negative_count).int().cuda()
if batch_gt_regressions is not None:
if 'regression_bin' in cf.prediction_tasks:
target_regressions = torch.zeros(negative_count, dtype=torch.float).cuda()
else:
target_regressions = torch.zeros(negative_count, cf.regression_n_features, dtype=torch.float).cuda()
else:
sample_indices = torch.LongTensor().cuda()
target_class_ids = torch.IntTensor().cuda()
target_deltas = torch.FloatTensor().cuda()
target_masks = torch.FloatTensor().cuda()
target_regressions = torch.FloatTensor().cuda()
return sample_indices, target_deltas, target_masks, target_class_ids, target_regressions
############################################################
# Anchors
############################################################
def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride):
"""
scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
shape: [height, width] spatial shape of the feature map over which
to generate anchors.
feature_stride: Stride of the feature map relative to the image in pixels.
anchor_stride: Stride of anchors on the feature map. For example, if the
value is 2 then generate anchors for every other feature map pixel.
"""
# Get all combinations of scales and ratios
scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
scales = scales.flatten()
ratios = ratios.flatten()
# Enumerate heights and widths from scales and ratios
heights = scales / np.sqrt(ratios)
widths = scales * np.sqrt(ratios)
# Enumerate shifts in feature space
shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
# Enumerate combinations of shifts, widths, and heights
box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
# Reshape to get a list of (y, x) and a list of (h, w)
box_centers = np.stack([box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])
# Convert to corner coordinates (y1, x1, y2, x2)
boxes = np.concatenate([box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1)
return boxes
def generate_anchors_3D(scales_xy, scales_z, ratios, shape, feature_stride_xy, feature_stride_z, anchor_stride):
"""
scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
shape: [height, width] spatial shape of the feature map over which
to generate anchors.
feature_stride: Stride of the feature map relative to the image in pixels.
anchor_stride: Stride of anchors on the feature map. For example, if the
value is 2 then generate anchors for every other feature map pixel.
"""
# Get all combinations of scales and ratios
scales_xy, ratios_meshed = np.meshgrid(np.array(scales_xy), np.array(ratios))
scales_xy = scales_xy.flatten()
ratios_meshed = ratios_meshed.flatten()
# Enumerate heights and widths from scales and ratios
heights = scales_xy / np.sqrt(ratios_meshed)
widths = scales_xy * np.sqrt(ratios_meshed)
depths = np.tile(np.array(scales_z), len(ratios_meshed)//np.array(scales_z)[..., None].shape[0])
# Enumerate shifts in feature space
shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride_xy #translate from fm positions to input coords.
shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride_xy
shifts_z = np.arange(0, shape[2], anchor_stride) * (feature_stride_z)
shifts_x, shifts_y, shifts_z = np.meshgrid(shifts_x, shifts_y, shifts_z)
# Enumerate combinations of shifts, widths, and heights
box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
box_depths, box_centers_z = np.meshgrid(depths, shifts_z)
# Reshape to get a list of (y, x, z) and a list of (h, w, d)
box_centers = np.stack(
[box_centers_y, box_centers_x, box_centers_z], axis=2).reshape([-1, 3])
box_sizes = np.stack([box_heights, box_widths, box_depths], axis=2).reshape([-1, 3])
# Convert to corner coordinates (y1, x1, y2, x2, z1, z2)
boxes = np.concatenate([box_centers - 0.5 * box_sizes,
box_centers + 0.5 * box_sizes], axis=1)
boxes = np.transpose(np.array([boxes[:, 0], boxes[:, 1], boxes[:, 3], boxes[:, 4], boxes[:, 2], boxes[:, 5]]), axes=(1, 0))
return boxes
def generate_pyramid_anchors(logger, cf):
"""Generate anchors at different levels of a feature pyramid. Each scale
is associated with a level of the pyramid, but each ratio is used in
all levels of the pyramid.
from configs:
:param scales: cf.RPN_ANCHOR_SCALES , for conformity with retina nets: scale entries need to be list, e.g. [[4], [8], [16], [32]]
:param ratios: cf.RPN_ANCHOR_RATIOS , e.g. [0.5, 1, 2]
:param feature_shapes: cf.BACKBONE_SHAPES , e.g. [array of shapes per feature map] [80, 40, 20, 10, 5]
:param feature_strides: cf.BACKBONE_STRIDES , e.g. [2, 4, 8, 16, 32, 64]
:param anchors_stride: cf.RPN_ANCHOR_STRIDE , e.g. 1
:return anchors: (N, (y1, x1, y2, x2, (z1), (z2)). All generated anchors in one array. Sorted
with the same order of the given scales. So, anchors of scale[0] come first, then anchors of scale[1], and so on.
"""
scales = cf.rpn_anchor_scales
ratios = cf.rpn_anchor_ratios
feature_shapes = cf.backbone_shapes
anchor_stride = cf.rpn_anchor_stride
pyramid_levels = cf.pyramid_levels
feature_strides = cf.backbone_strides
logger.info("anchor scales {} and feature map shapes {}".format(scales, feature_shapes))
expected_anchors = [np.prod(feature_shapes[level]) * len(ratios) * len(scales['xy'][level]) for level in pyramid_levels]
anchors = []
for lix, level in enumerate(pyramid_levels):
if len(feature_shapes[level]) == 2:
anchors.append(generate_anchors(scales['xy'][level], ratios, feature_shapes[level],
feature_strides['xy'][level], anchor_stride))
elif len(feature_shapes[level]) == 3:
anchors.append(generate_anchors_3D(scales['xy'][level], scales['z'][level], ratios, feature_shapes[level],
feature_strides['xy'][level], feature_strides['z'][level], anchor_stride))
else:
raise Exception("invalid feature_shapes[{}] size {}".format(level, feature_shapes[level]))
logger.info("level {}: expected anchors {}, built anchors {}.".format(level, expected_anchors[lix], anchors[-1].shape))
out_anchors = np.concatenate(anchors, axis=0)
logger.info("Total: expected anchors {}, built anchors {}.".format(np.sum(expected_anchors), out_anchors.shape))
return out_anchors
def apply_box_deltas_2D(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, 4] where each row is y1, x1, y2, x2
deltas: [N, 4] where each row is [dy, dx, log(dh), log(dw)]
"""
# Convert to y, x, h, w
+ non_nans = boxes == boxes
+ assert torch.all(non_nans), "boxes at beginning of delta apply have nans: {}".format(
+ boxes[~non_nans])
+
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
height *= torch.exp(deltas[:, 2])
width *= torch.exp(deltas[:, 3])
+
+ non_nans = width == width
+ assert torch.all(non_nans), "inside delta apply, width has nans: {}".format(
+ width[~non_nans])
+
+ # 0.*inf results in nan. fix nans to zeros.
+ height[height!=height] = 0.
+ width[width!=width] = 0.
+
+ non_nans = height == height
+ assert torch.all(non_nans), "inside delta apply, height has nans directly after setting to zero: {}".format(
+ height[~non_nans])
+
+ non_nans = width == width
+ assert torch.all(non_nans), "inside delta apply, width has nans directly after setting to zero: {}".format(
+ width[~non_nans])
+
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
y2 = y1 + height
x2 = x1 + width
result = torch.stack([y1, x1, y2, x2], dim=1)
+
+ non_nans = result == result
+ assert torch.all(non_nans), "inside delta apply, result has nans: {}".format(result[~non_nans])
+
return result
def apply_box_deltas_3D(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, 6] where each row is y1, x1, y2, x2, z1, z2
deltas: [N, 6] where each row is [dy, dx, dz, log(dh), log(dw), log(dd)]
"""
# Convert to y, x, h, w
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
depth = boxes[:, 5] - boxes[:, 4]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
center_z = boxes[:, 4] + 0.5 * depth
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
center_z += deltas[:, 2] * depth
height *= torch.exp(deltas[:, 3])
width *= torch.exp(deltas[:, 4])
depth *= torch.exp(deltas[:, 5])
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
z1 = center_z - 0.5 * depth
y2 = y1 + height
x2 = x1 + width
z2 = z1 + depth
result = torch.stack([y1, x1, y2, x2, z1, z2], dim=1)
return result
def clip_boxes_2D(boxes, window):
"""
boxes: [N, 4] each col is y1, x1, y2, x2
window: [4] in the form y1, x1, y2, x2
"""
boxes = torch.stack( \
[boxes[:, 0].clamp(float(window[0]), float(window[2])),
boxes[:, 1].clamp(float(window[1]), float(window[3])),
boxes[:, 2].clamp(float(window[0]), float(window[2])),
boxes[:, 3].clamp(float(window[1]), float(window[3]))], 1)
return boxes
def clip_boxes_3D(boxes, window):
"""
boxes: [N, 6] each col is y1, x1, y2, x2, z1, z2
window: [6] in the form y1, x1, y2, x2, z1, z2
"""
boxes = torch.stack( \
[boxes[:, 0].clamp(float(window[0]), float(window[2])),
boxes[:, 1].clamp(float(window[1]), float(window[3])),
boxes[:, 2].clamp(float(window[0]), float(window[2])),
boxes[:, 3].clamp(float(window[1]), float(window[3])),
boxes[:, 4].clamp(float(window[4]), float(window[5])),
boxes[:, 5].clamp(float(window[4]), float(window[5]))], 1)
return boxes
from matplotlib import pyplot as plt
def clip_boxes_numpy(boxes, window):
"""
boxes: [N, 4] each col is y1, x1, y2, x2 / [N, 6] in 3D.
window: iamge shape (y, x, (z))
"""
if boxes.shape[1] == 4:
boxes = np.concatenate(
(np.clip(boxes[:, 0], 0, window[0])[:, None],
np.clip(boxes[:, 1], 0, window[0])[:, None],
np.clip(boxes[:, 2], 0, window[1])[:, None],
np.clip(boxes[:, 3], 0, window[1])[:, None]), 1
)
else:
boxes = np.concatenate(
(np.clip(boxes[:, 0], 0, window[0])[:, None],
np.clip(boxes[:, 1], 0, window[0])[:, None],
np.clip(boxes[:, 2], 0, window[1])[:, None],
np.clip(boxes[:, 3], 0, window[1])[:, None],
np.clip(boxes[:, 4], 0, window[2])[:, None],
np.clip(boxes[:, 5], 0, window[2])[:, None]), 1
)
return boxes
def bbox_overlaps_2D(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
"""
# 1. Tile boxes2 and repeate boxes1. This allows us to compare
# every boxes1 against every boxes2 without loops.
# TF doesn't have an equivalent to np.repeate() so simulate it
# using tf.tile() and tf.reshape.
boxes1_repeat = boxes2.size()[0]
boxes2_repeat = boxes1.size()[0]
boxes1 = boxes1.repeat(1,boxes1_repeat).view(-1,4)
boxes2 = boxes2.repeat(boxes2_repeat,1)
# 2. Compute intersections
b1_y1, b1_x1, b1_y2, b1_x2 = boxes1.chunk(4, dim=1)
b2_y1, b2_x1, b2_y2, b2_x2 = boxes2.chunk(4, dim=1)
y1 = torch.max(b1_y1, b2_y1)[:, 0]
x1 = torch.max(b1_x1, b2_x1)[:, 0]
y2 = torch.min(b1_y2, b2_y2)[:, 0]
x2 = torch.min(b1_x2, b2_x2)[:, 0]
#--> expects x1<x2 & y1<y2
zeros = torch.zeros(y1.size()[0], requires_grad=False)
if y1.is_cuda:
zeros = zeros.cuda()
intersection = torch.max(x2 - x1, zeros) * torch.max(y2 - y1, zeros)
# 3. Compute unions
b1_area = (b1_y2 - b1_y1) * (b1_x2 - b1_x1)
b2_area = (b2_y2 - b2_y1) * (b2_x2 - b2_x1)
union = b1_area[:,0] + b2_area[:,0] - intersection
# 4. Compute IoU and reshape to [boxes1, boxes2]
iou = intersection / union
assert torch.all(iou<=1), "iou score>1 produced in bbox_overlaps_2D"
overlaps = iou.view(boxes2_repeat, boxes1_repeat) #--> per gt box: ious of all proposal boxes with that gt box
return overlaps
def bbox_overlaps_3D(boxes1, boxes2):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2, z1, z2)].
"""
# 1. Tile boxes2 and repeate boxes1. This allows us to compare
# every boxes1 against every boxes2 without loops.
# TF doesn't have an equivalent to np.repeate() so simulate it
# using tf.tile() and tf.reshape.
boxes1_repeat = boxes2.size()[0]
boxes2_repeat = boxes1.size()[0]
boxes1 = boxes1.repeat(1,boxes1_repeat).view(-1,6)
boxes2 = boxes2.repeat(boxes2_repeat,1)
# 2. Compute intersections
b1_y1, b1_x1, b1_y2, b1_x2, b1_z1, b1_z2 = boxes1.chunk(6, dim=1)
b2_y1, b2_x1, b2_y2, b2_x2, b2_z1, b2_z2 = boxes2.chunk(6, dim=1)
y1 = torch.max(b1_y1, b2_y1)[:, 0]
x1 = torch.max(b1_x1, b2_x1)[:, 0]
y2 = torch.min(b1_y2, b2_y2)[:, 0]
x2 = torch.min(b1_x2, b2_x2)[:, 0]
z1 = torch.max(b1_z1, b2_z1)[:, 0]
z2 = torch.min(b1_z2, b2_z2)[:, 0]
zeros = torch.zeros(y1.size()[0], requires_grad=False)
if y1.is_cuda:
zeros = zeros.cuda()
intersection = torch.max(x2 - x1, zeros) * torch.max(y2 - y1, zeros) * torch.max(z2 - z1, zeros)
# 3. Compute unions
b1_volume = (b1_y2 - b1_y1) * (b1_x2 - b1_x1) * (b1_z2 - b1_z1)
b2_volume = (b2_y2 - b2_y1) * (b2_x2 - b2_x1) * (b2_z2 - b2_z1)
union = b1_volume[:,0] + b2_volume[:,0] - intersection
# 4. Compute IoU and reshape to [boxes1, boxes2]
iou = intersection / union
overlaps = iou.view(boxes2_repeat, boxes1_repeat)
return overlaps
def gt_anchor_matching(cf, anchors, gt_boxes, gt_class_ids=None):
"""Given the anchors and GT boxes, compute overlaps and identify positive
anchors and deltas to refine them to match their corresponding GT boxes.
anchors: [num_anchors, (y1, x1, y2, x2, (z1), (z2))]
gt_boxes: [num_gt_boxes, (y1, x1, y2, x2, (z1), (z2))]
gt_class_ids (optional): [num_gt_boxes] Integer class IDs for one stage detectors. in RPN case of Mask R-CNN,
set all positive matches to 1 (foreground)
Returns:
anchor_class_matches: [N] (int32) matches between anchors and GT boxes.
1 = positive anchor, -1 = negative anchor, 0 = neutral
anchor_delta_targets: [N, (dy, dx, (dz), log(dh), log(dw), (log(dd)))] Anchor bbox deltas.
"""
anchor_class_matches = np.zeros([anchors.shape[0]], dtype=np.int32)
anchor_delta_targets = np.zeros((cf.rpn_train_anchors_per_image, 2*cf.dim))
anchor_matching_iou = cf.anchor_matching_iou
if gt_boxes is None:
anchor_class_matches = np.full(anchor_class_matches.shape, fill_value=-1)
return anchor_class_matches, anchor_delta_targets
# for mrcnn: anchor matching is done for RPN loss, so positive labels are all 1 (foreground)
if gt_class_ids is None:
gt_class_ids = np.array([1] * len(gt_boxes))
# Compute overlaps [num_anchors, num_gt_boxes]
overlaps = compute_overlaps(anchors, gt_boxes)
# Match anchors to GT Boxes
# If an anchor overlaps a GT box with IoU >= anchor_matching_iou then it's positive.
# If an anchor overlaps a GT box with IoU < 0.1 then it's negative.
# Neutral anchors are those that don't match the conditions above,
# and they don't influence the loss function.
# However, don't keep any GT box unmatched (rare, but happens). Instead,
# match it to the closest anchor (even if its max IoU is < 0.1).
# 1. Set negative anchors first. They get overwritten below if a GT box is
# matched to them. Skip boxes in crowd areas.
anchor_iou_argmax = np.argmax(overlaps, axis=1)
anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax]
if anchors.shape[1] == 4:
anchor_class_matches[(anchor_iou_max < 0.1)] = -1
elif anchors.shape[1] == 6:
anchor_class_matches[(anchor_iou_max < 0.01)] = -1
else:
raise ValueError('anchor shape wrong {}'.format(anchors.shape))
# 2. Set an anchor for each GT box (regardless of IoU value).
gt_iou_argmax = np.argmax(overlaps, axis=0)
for ix, ii in enumerate(gt_iou_argmax):
anchor_class_matches[ii] = gt_class_ids[ix]
# 3. Set anchors with high overlap as positive.
above_thresh_ixs = np.argwhere(anchor_iou_max >= anchor_matching_iou)
anchor_class_matches[above_thresh_ixs] = gt_class_ids[anchor_iou_argmax[above_thresh_ixs]]
# Subsample to balance positive anchors.
ids = np.where(anchor_class_matches > 0)[0]
extra = len(ids) - (cf.rpn_train_anchors_per_image // 2)
if extra > 0:
# Reset the extra ones to neutral
ids = np.random.choice(ids, extra, replace=False)
anchor_class_matches[ids] = 0
# Leave all negative proposals negative for now and sample from them later in online hard example mining.
# For positive anchors, compute shift and scale needed to transform them to match the corresponding GT boxes.
ids = np.where(anchor_class_matches > 0)[0]
ix = 0 # index into anchor_delta_targets
for i, a in zip(ids, anchors[ids]):
# closest gt box (it might have IoU < anchor_matching_iou)
gt = gt_boxes[anchor_iou_argmax[i]]
# convert coordinates to center plus width/height.
gt_h = gt[2] - gt[0]
gt_w = gt[3] - gt[1]
gt_center_y = gt[0] + 0.5 * gt_h
gt_center_x = gt[1] + 0.5 * gt_w
# Anchor
a_h = a[2] - a[0]
a_w = a[3] - a[1]
a_center_y = a[0] + 0.5 * a_h
a_center_x = a[1] + 0.5 * a_w
if cf.dim == 2:
anchor_delta_targets[ix] = [
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
np.log(gt_h / a_h),
np.log(gt_w / a_w),
]
else:
gt_d = gt[5] - gt[4]
gt_center_z = gt[4] + 0.5 * gt_d
a_d = a[5] - a[4]
a_center_z = a[4] + 0.5 * a_d
anchor_delta_targets[ix] = [
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
(gt_center_z - a_center_z) / a_d,
np.log(gt_h / a_h),
np.log(gt_w / a_w),
np.log(gt_d / a_d)
]
# normalize.
anchor_delta_targets[ix] /= cf.rpn_bbox_std_dev
ix += 1
return anchor_class_matches, anchor_delta_targets
def clip_to_window(window, boxes):
"""
window: (y1, x1, y2, x2) / 3D: (z1, z2). The window in the image we want to clip to.
boxes: [N, (y1, x1, y2, x2)] / 3D: (z1, z2)
"""
boxes[:, 0] = boxes[:, 0].clamp(float(window[0]), float(window[2]))
boxes[:, 1] = boxes[:, 1].clamp(float(window[1]), float(window[3]))
boxes[:, 2] = boxes[:, 2].clamp(float(window[0]), float(window[2]))
boxes[:, 3] = boxes[:, 3].clamp(float(window[1]), float(window[3]))
if boxes.shape[1] > 5:
boxes[:, 4] = boxes[:, 4].clamp(float(window[4]), float(window[5]))
boxes[:, 5] = boxes[:, 5].clamp(float(window[4]), float(window[5]))
return boxes
############################################################
# Connected Componenent Analysis
############################################################
def get_coords(binary_mask, n_components, dim):
"""
loops over batch to perform connected component analysis on binary input mask. computes box coordinates around
n_components - biggest components (rois).
:param binary_mask: (b, y, x, (z)). binary mask for one specific foreground class.
:param n_components: int. number of components to extract per batch element and class.
:return: coords (b, n, (y1, x1, y2, x2 (,z1, z2))
:return: batch_components (b, n, (y1, x1, y2, x2, (z1), (z2))
"""
assert len(binary_mask.shape)==dim+1
binary_mask = binary_mask.astype('uint8')
batch_coords = []
batch_components = []
for ix,b in enumerate(binary_mask):
clusters, n_cands = lb(b) # performs connected component analysis.
uniques, counts = np.unique(clusters, return_counts=True)
keep_uniques = uniques[1:][np.argsort(counts[1:])[::-1]][:n_components] #only keep n_components largest components
p_components = np.array([(clusters == ii) * 1 for ii in keep_uniques]) # separate clusters and concat
p_coords = []
if p_components.shape[0] > 0:
for roi in p_components:
mask_ixs = np.argwhere(roi != 0)
# get coordinates around component.
roi_coords = [np.min(mask_ixs[:, 0]) - 1, np.min(mask_ixs[:, 1]) - 1, np.max(mask_ixs[:, 0]) + 1,
np.max(mask_ixs[:, 1]) + 1]
if dim == 3:
roi_coords += [np.min(mask_ixs[:, 2]), np.max(mask_ixs[:, 2])+1]
p_coords.append(roi_coords)
p_coords = np.array(p_coords)
#clip coords.
p_coords[p_coords < 0] = 0
p_coords[:, :4][p_coords[:, :4] > binary_mask.shape[-2]] = binary_mask.shape[-2]
if dim == 3:
p_coords[:, 4:][p_coords[:, 4:] > binary_mask.shape[-1]] = binary_mask.shape[-1]
batch_coords.append(p_coords)
batch_components.append(p_components)
return batch_coords, batch_components
# noinspection PyCallingNonCallable
def get_coords_gpu(binary_mask, n_components, dim):
"""
loops over batch to perform connected component analysis on binary input mask. computes box coordiantes around
n_components - biggest components (rois).
:param binary_mask: (b, y, x, (z)). binary mask for one specific foreground class.
:param n_components: int. number of components to extract per batch element and class.
:return: coords (b, n, (y1, x1, y2, x2 (,z1, z2))
:return: batch_components (b, n, (y1, x1, y2, x2, (z1), (z2))
"""
raise Exception("throws floating point exception")
assert len(binary_mask.shape)==dim+1
binary_mask = binary_mask.type(torch.uint8)
batch_coords = []
batch_components = []
for ix,b in enumerate(binary_mask):
clusters, n_cands = lb(b.cpu().data.numpy()) # peforms connected component analysis.
clusters = torch.from_numpy(clusters).cuda()
uniques = torch.unique(clusters)
counts = torch.stack([(clusters==unique).sum() for unique in uniques])
keep_uniques = uniques[1:][torch.sort(counts[1:])[1].flip(0)][:n_components] #only keep n_components largest components
p_components = torch.cat([(clusters == ii).unsqueeze(0) for ii in keep_uniques]).cuda() # separate clusters and concat
p_coords = []
if p_components.shape[0] > 0:
for roi in p_components:
mask_ixs = torch.nonzero(roi)
# get coordinates around component.
roi_coords = [torch.min(mask_ixs[:, 0]) - 1, torch.min(mask_ixs[:, 1]) - 1,
torch.max(mask_ixs[:, 0]) + 1,
torch.max(mask_ixs[:, 1]) + 1]
if dim == 3:
roi_coords += [torch.min(mask_ixs[:, 2]), torch.max(mask_ixs[:, 2])+1]
p_coords.append(roi_coords)
p_coords = torch.tensor(p_coords)
#clip coords.
p_coords[p_coords < 0] = 0
p_coords[:, :4][p_coords[:, :4] > binary_mask.shape[-2]] = binary_mask.shape[-2]
if dim == 3:
p_coords[:, 4:][p_coords[:, 4:] > binary_mask.shape[-1]] = binary_mask.shape[-1]
batch_coords.append(p_coords)
batch_components.append(p_components)
return batch_coords, batch_components
############################################################
# Pytorch Utility Functions
############################################################
def unique1d(tensor):
"""discard all elements of tensor that occur more than once; make tensor unique.
:param tensor:
:return:
"""
if tensor.size()[0] == 0 or tensor.size()[0] == 1:
return tensor
tensor = tensor.sort()[0]
unique_bool = tensor[1:] != tensor[:-1]
first_element = torch.tensor([True], dtype=torch.bool, requires_grad=False)
if tensor.is_cuda:
first_element = first_element.cuda()
unique_bool = torch.cat((first_element, unique_bool), dim=0)
return tensor[unique_bool.data]
def intersect1d(tensor1, tensor2):
aux = torch.cat((tensor1, tensor2), dim=0)
aux = aux.sort(descending=True)[0]
return aux[:-1][(aux[1:] == aux[:-1]).data]
def shem(roi_probs_neg, negative_count, poolsize):
"""
stochastic hard example mining: from a list of indices (referring to non-matched predictions),
determine a pool of highest scoring (worst false positives) of size negative_count*poolsize.
Then, sample n (= negative_count) predictions of this pool as negative examples for loss.
:param roi_probs_neg: tensor of shape (n_predictions, n_classes).
:param negative_count: int.
:param poolsize: int.
:return: (negative_count). indices refer to the positions in roi_probs_neg. If pool smaller than expected due to
limited negative proposals availabel, this function will return sampled indices of number < negative_count without
throwing an error.
"""
# sort according to higehst foreground score.
probs, order = roi_probs_neg[:, 1:].max(1)[0].sort(descending=True)
select = torch.tensor((poolsize * int(negative_count), order.size()[0])).min().int()
pool_indices = order[:select]
rand_idx = torch.randperm(pool_indices.size()[0])
return pool_indices[rand_idx[:negative_count].cuda()]
############################################################
# Weight Init
############################################################
def initialize_weights(net):
"""Initialize model weights. Current Default in Pytorch (version 0.4.1) is initialization from a uniform distriubtion.
Will expectably be changed to kaiming_uniform in future versions.
"""
init_type = net.cf.weight_init
for m in [module for module in net.modules() if type(module) in [torch.nn.Conv2d, torch.nn.Conv3d,
torch.nn.ConvTranspose2d,
torch.nn.ConvTranspose3d,
torch.nn.Linear]]:
if init_type == 'xavier_uniform':
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif init_type == 'xavier_normal':
torch.nn.init.xavier_normal_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif init_type == "kaiming_uniform":
torch.nn.init.kaiming_uniform_(m.weight.data, mode='fan_out', nonlinearity=net.cf.relu, a=0)
if m.bias is not None:
fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(m.weight.data)
bound = 1 / np.sqrt(fan_out)
torch.nn.init.uniform_(m.bias, -bound, bound)
elif init_type == "kaiming_normal":
torch.nn.init.kaiming_normal_(m.weight.data, mode='fan_out', nonlinearity=net.cf.relu, a=0)
if m.bias is not None:
fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(m.weight.data)
bound = 1 / np.sqrt(fan_out)
torch.nn.init.normal_(m.bias, -bound, bound)
net.logger.info("applied {} weight init.".format(init_type))
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