diff --git a/experiments/lidc_exp/data_loader.py b/experiments/lidc_exp/data_loader.py
index 87aad82..163d413 100644
--- a/experiments/lidc_exp/data_loader.py
+++ b/experiments/lidc_exp/data_loader.py
@@ -1,485 +1,485 @@
#!/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.
# ==============================================================================
'''
Example Data Loader for the LIDC data set. This dataloader expects preprocessed data in .npy or .npz files per patient and
a pandas dataframe in the same directory containing the meta-info e.g. file paths, labels, foregound slice-ids.
'''
import numpy as np
import os
from collections import OrderedDict
import pandas as pd
import pickle
import time
import subprocess
import utils.dataloader_utils as dutils
# batch generator tools from https://github.com/MIC-DKFZ/batchgenerators
from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
from batchgenerators.transforms.spatial_transforms import MirrorTransform as Mirror
from batchgenerators.transforms.abstract_transforms import Compose
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
from batchgenerators.dataloading import SingleThreadedAugmenter
from batchgenerators.transforms.spatial_transforms import SpatialTransform
from batchgenerators.transforms.crop_and_pad_transforms import CenterCropTransform
from batchgenerators.transforms.utility_transforms import ConvertSegToBoundingBoxCoordinates
def get_train_generators(cf, logger):
"""
wrapper function for creating the training batch generator pipeline. returns the train/val generators.
selects patients according to cv folds (generated by first run/fold of experiment):
splits the data into n-folds, where 1 split is used for val, 1 split for testing and the rest for training. (inner loop test set)
If cf.hold_out_test_set is True, adds the test split to the training data.
"""
all_data = load_dataset(cf, logger)
all_pids_list = np.unique([v['pid'] for (k, v) in all_data.items()])
if not cf.created_fold_id_pickle:
fg = dutils.fold_generator(seed=cf.seed, n_splits=cf.n_cv_splits, len_data=len(all_pids_list)).get_fold_names()
with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'wb') as handle:
pickle.dump(fg, handle)
cf.created_fold_id_pickle = True
else:
with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'rb') as handle:
fg = pickle.load(handle)
train_ix, val_ix, test_ix, _ = fg[cf.fold]
train_pids = [all_pids_list[ix] for ix in train_ix]
val_pids = [all_pids_list[ix] for ix in val_ix]
if cf.hold_out_test_set:
train_pids += [all_pids_list[ix] for ix in test_ix]
train_data = {k: v for (k, v) in all_data.items() if any(p == v['pid'] for p in train_pids)}
val_data = {k: v for (k, v) in all_data.items() if any(p == v['pid'] for p in val_pids)}
logger.info("data set loaded with: {} train / {} val / {} test patients".format(len(train_ix), len(val_ix), len(test_ix)))
batch_gen = {}
batch_gen['train'] = create_data_gen_pipeline(train_data, cf=cf, is_training=True)
batch_gen['val_sampling'] = create_data_gen_pipeline(val_data, cf=cf, is_training=False)
if cf.val_mode == 'val_patient':
batch_gen['val_patient'] = PatientBatchIterator(val_data, cf=cf)
batch_gen['n_val'] = len(val_ix) if cf.max_val_patients is None else min(len(val_ix), cf.max_val_patients)
else:
batch_gen['n_val'] = cf.num_val_batches
return batch_gen
def get_test_generator(cf, logger):
"""
wrapper function for creating the test batch generator pipeline.
selects patients according to cv folds (generated by first run/fold of experiment)
If cf.hold_out_test_set is True, gets the data from an external folder instead.
"""
if cf.hold_out_test_set:
pp_name = cf.pp_test_name
test_ix = None
else:
pp_name = None
with open(os.path.join(cf.exp_dir, 'fold_ids.pickle'), 'rb') as handle:
fold_list = pickle.load(handle)
_, _, test_ix, _ = fold_list[cf.fold]
# warnings.warn('WARNING: using validation set for testing!!!')
test_data = load_dataset(cf, logger, test_ix, pp_data_path=cf.pp_test_data_path, pp_name=pp_name)
logger.info("data set loaded with: {} test patients".format(len(test_ix)))
batch_gen = {}
batch_gen['test'] = PatientBatchIterator(test_data, cf=cf)
batch_gen['n_test'] = len(test_ix) if cf.max_test_patients=="all" else \
min(cf.max_test_patients, len(test_ix))
return batch_gen
def load_dataset(cf, logger, subset_ixs=None, pp_data_path=None, pp_name=None):
"""
loads the dataset. if deployed in cloud also copies and unpacks the data to the working directory.
:param subset_ixs: subset indices to be loaded from the dataset. used e.g. for testing to only load the test folds.
:return: data: dictionary with one entry per patient (in this case per patient-breast, since they are treated as
individual images for training) each entry is a dictionary containing respective meta-info as well as paths to the preprocessed
numpy arrays to be loaded during batch-generation
"""
if pp_data_path is None:
pp_data_path = cf.pp_data_path
if pp_name is None:
pp_name = cf.pp_name
if cf.server_env:
copy_data = True
- target_dir = os.path.join(cf.data_dest, pp_name, cf.crop_name)
+ target_dir = os.path.join(cf.data_dest, pp_name)
if not os.path.exists(target_dir):
cf.data_source_dir = pp_data_path
os.makedirs(target_dir)
subprocess.call('rsync -av {} {}'.format(
os.path.join(cf.data_source_dir, cf.input_df_name), os.path.join(target_dir, cf.input_df_name)), shell=True)
logger.info('created target dir and info df at {}'.format(os.path.join(target_dir, cf.input_df_name)))
elif subset_ixs is None:
copy_data = False
pp_data_path = target_dir
p_df = pd.read_pickle(os.path.join(pp_data_path, cf.input_df_name))
if 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)]
logger.warning('WARNING: using prototyping data subset!!!')
if subset_ixs is not None:
subset_pids = [np.unique(p_df.pid.tolist())[ix] for ix in subset_ixs]
p_df = p_df[p_df.pid.isin(subset_pids)]
logger.info('subset: selected {} instances from df'.format(len(p_df)))
if cf.server_env:
if copy_data:
copy_and_unpack_data(logger, p_df.pid.tolist(), cf.fold_dir, cf.data_source_dir, target_dir)
class_targets = p_df['class_target'].tolist()
pids = p_df.pid.tolist()
imgs = [os.path.join(pp_data_path, '{}_img.npy'.format(pid)) for pid in pids]
segs = [os.path.join(pp_data_path,'{}_rois.npy'.format(pid)) for pid in pids]
data = OrderedDict()
for ix, pid in enumerate(pids):
# for the experiment conducted here, malignancy scores are binarized: (benign: 1-2, malignant: 3-5)
targets = [1 if ii >= 3 else 0 for ii in class_targets[ix]]
data[pid] = {'data': imgs[ix], 'seg': segs[ix], 'pid': pid, 'class_target': targets}
data[pid]['fg_slices'] = p_df['fg_slices'].tolist()[ix]
return data
def create_data_gen_pipeline(patient_data, cf, is_training=True):
"""
create mutli-threaded train/val/test batch generation and augmentation pipeline.
:param patient_data: dictionary containing one dictionary per patient in the train/test subset.
:param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
:return: multithreaded_generator
"""
# create instance of batch generator as first element in pipeline.
data_gen = BatchGenerator(patient_data, batch_size=cf.batch_size, cf=cf)
# add transformations to pipeline.
my_transforms = []
if is_training:
mirror_transform = Mirror(axes=np.arange(cf.dim))
my_transforms.append(mirror_transform)
spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
random_crop=cf.da_kwargs['random_crop'])
my_transforms.append(spatial_transform)
else:
my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))
my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
all_transforms = Compose(my_transforms)
# multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
return multithreaded_generator
class BatchGenerator(SlimDataLoaderBase):
"""
creates the training/validation batch generator. Samples n_batch_size patients (draws a slice from each patient if 2D)
from the data set while maintaining foreground-class balance. Returned patches are cropped/padded to pre_crop_size.
Actual patch_size is obtained after data augmentation.
:param data: data dictionary as provided by 'load_dataset'.
:param batch_size: number of patients to sample for the batch
:return dictionary containing the batch data (b, c, x, y, (z)) / seg (b, 1, x, y, (z)) / pids / class_target
"""
def __init__(self, data, batch_size, cf):
super(BatchGenerator, self).__init__(data, batch_size)
self.cf = cf
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
def generate_train_batch(self):
batch_data, batch_segs, batch_pids, batch_targets, batch_patient_labels = [], [], [], [], []
class_targets_list = [v['class_target'] for (k, v) in self._data.items()]
if self.cf.head_classes > 2:
# samples patients towards equilibrium of foreground classes on a roi-level (after randomly sampling the ratio "batch_sample_slack).
batch_ixs = dutils.get_class_balanced_patients(
class_targets_list, self.batch_size, self.cf.head_classes - 1, slack_factor=self.cf.batch_sample_slack)
else:
batch_ixs = np.random.choice(len(class_targets_list), self.batch_size)
patients = list(self._data.items())
for b in batch_ixs:
patient = patients[b][1]
data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))[np.newaxis] # (c, y, x, z)
seg = np.transpose(np.load(patient['seg'], mmap_mode='r'), axes=(1, 2, 0))
batch_pids.append(patient['pid'])
batch_targets.append(patient['class_target'])
if self.cf.dim == 2:
# draw random slice from patient while oversampling slices containing foreground objects with p_fg.
if len(patient['fg_slices']) > 0:
fg_prob = self.p_fg / len(patient['fg_slices'])
bg_prob = (1 - self.p_fg) / (data.shape[3] - len(patient['fg_slices']))
slices_prob = [fg_prob if ix in patient['fg_slices'] else bg_prob for ix in range(data.shape[3])]
slice_id = np.random.choice(data.shape[3], p=slices_prob)
else:
slice_id = np.random.choice(data.shape[3])
# if set to not None, add neighbouring slices to each selected slice in channel dimension.
if self.cf.n_3D_context is not None:
padded_data = dutils.pad_nd_image(data[0], [(data.shape[-1] + (self.cf.n_3D_context*2))], mode='constant')
padded_slice_id = slice_id + self.cf.n_3D_context
data = (np.concatenate([padded_data[..., ii][np.newaxis] for ii in range(
padded_slice_id - self.cf.n_3D_context, padded_slice_id + self.cf.n_3D_context + 1)], axis=0))
else:
data = data[..., slice_id]
seg = seg[..., slice_id]
# 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:
fg_prob_sample = np.random.rand(1)
# with p_fg: sample random pixel from random ROI and shift center by random value.
if fg_prob_sample < self.p_fg and np.sum(seg) > 0:
seg_ixs = np.argwhere(seg == np.random.choice(np.unique(seg)[1:], 1))
roi_anchor_pixel = seg_ixs[np.random.choice(seg_ixs.shape[0], 1)][0]
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])
data = np.array(batch_data)
seg = np.array(batch_segs).astype(np.uint8)
class_target = np.array(batch_targets)
return {'data': data, 'seg': seg, 'pid': batch_pids, 'class_target': class_target}
class PatientBatchIterator(SlimDataLoaderBase):
"""
creates a test generator that iterates over entire given dataset returning 1 patient per batch.
Can be used for monitoring if cf.val_mode = 'patient_val' for a monitoring closer to actualy evaluation (done in 3D),
if willing to accept speed-loss during training.
:return: out_batch: dictionary containing one patient with batch_size = n_3D_patches in 3D or
batch_size = n_2D_patches in 2D .
"""
def __init__(self, data, cf): #threads in augmenter
super(PatientBatchIterator, self).__init__(data, 0)
self.cf = cf
self.patient_ix = 0
self.dataset_pids = [v['pid'] for (k, v) in data.items()]
self.patch_size = cf.patch_size
if len(self.patch_size) == 2:
self.patch_size = self.patch_size + [1]
def generate_train_batch(self):
pid = self.dataset_pids[self.patient_ix]
patient = self._data[pid]
data = np.transpose(np.load(patient['data'], mmap_mode='r'), axes=(1, 2, 0))[np.newaxis] # (c, y, x, z)
seg = np.transpose(np.load(patient['seg'], mmap_mode='r'), axes=(1, 2, 0))
batch_class_targets = np.array([patient['class_target']])
# pad data if smaller than patch_size seen during training.
if np.any([data.shape[dim + 1] < ps for dim, ps in enumerate(self.patch_size)]):
new_shape = [data.shape[0]] + [np.max([data.shape[dim + 1], 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]
out_seg = seg[np.newaxis, np.newaxis]
out_targets = batch_class_targets
batch_3D = {'data': out_data, 'seg': out_seg, 'class_target': out_targets, 'pid': pid}
converter = ConvertSegToBoundingBoxCoordinates(dim=3, get_rois_from_seg_flag=False, class_specific_seg_flag=self.cf.class_specific_seg_flag)
batch_3D = converter(**batch_3D)
batch_3D.update({'patient_bb_target': batch_3D['bb_target'],
'patient_roi_labels': batch_3D['roi_labels'],
'original_img_shape': out_data.shape})
if self.cf.dim == 2:
out_data = np.transpose(data, axes=(3, 0, 1, 2)) # (z, c, x, y )
out_seg = np.transpose(seg, axes=(2, 0, 1))[:, np.newaxis]
out_targets = np.array(np.repeat(batch_class_targets, out_data.shape[0], axis=0))
# if set to not None, add neighbouring slices to each selected slice in channel dimension.
if self.cf.n_3D_context is not None:
slice_range = range(self.cf.n_3D_context, out_data.shape[0] + self.cf.n_3D_context)
out_data = np.pad(out_data, ((self.cf.n_3D_context, self.cf.n_3D_context), (0, 0), (0, 0), (0, 0)), 'constant', constant_values=0)
out_data = np.array(
[np.concatenate([out_data[ii] for ii in range(
slice_id - self.cf.n_3D_context, slice_id + self.cf.n_3D_context + 1)], axis=0) for slice_id in
slice_range])
batch_2D = {'data': out_data, 'seg': out_seg, 'class_target': out_targets, 'pid': pid}
converter = ConvertSegToBoundingBoxCoordinates(dim=2, get_rois_from_seg_flag=False, class_specific_seg_flag=self.cf.class_specific_seg_flag)
batch_2D = converter(**batch_2D)
if self.cf.merge_2D_to_3D_preds:
batch_2D.update({'patient_bb_target': batch_3D['patient_bb_target'],
'patient_roi_labels': batch_3D['patient_roi_labels'],
'original_img_shape': out_data.shape})
else:
batch_2D.update({'patient_bb_target': batch_2D['bb_target'],
'patient_roi_labels': batch_2D['roi_labels'],
'original_img_shape': out_data.shape})
out_batch = batch_3D if self.cf.dim == 3 else batch_2D
patient_batch = out_batch
# 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 + 1] > self.patch_size[dim] for dim in range(3)]):
patch_crop_coords_list = dutils.get_patch_crop_coords(data[0], self.patch_size)
new_img_batch, new_seg_batch, new_class_targets_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)
# if set to not None, add neighbouring slices to each selected slice in channel dimension.
# correct patch_crop coordinates by added slices of 3D context.
if self.cf.dim == 2 and self.cf.n_3D_context is not None:
tmp_c_5 = c[5] + (self.cf.n_3D_context * 2)
if cix == 0:
data = np.pad(data, ((0, 0), (0, 0), (0, 0), (self.cf.n_3D_context, self.cf.n_3D_context)), 'constant', constant_values=0)
else:
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) # (n_patches, c, x, y, z)
seg = np.array(new_seg_batch)[:, np.newaxis] # (n_patches, 1, x, y, z)
batch_class_targets = np.repeat(batch_class_targets, len(patch_crop_coords_list), axis=0)
if self.cf.dim == 2:
if self.cf.n_3D_context is not None:
data = np.transpose(data[:, 0], axes=(0, 3, 1, 2))
else:
# all patches have z dimension 1 (slices). discard dimension
data = data[..., 0]
seg = seg[..., 0]
patch_batch = {'data': data, 'seg': seg, 'class_target': batch_class_targets, 'pid': pid}
patch_batch['patch_crop_coords'] = np.array(patch_crop_coords_list)
patch_batch['patient_bb_target'] = patient_batch['patient_bb_target']
patch_batch['patient_roi_labels'] = patient_batch['patient_roi_labels']
patch_batch['original_img_shape'] = patient_batch['original_img_shape']
converter = ConvertSegToBoundingBoxCoordinates(self.cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=self.cf.class_specific_seg_flag)
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 copy_and_unpack_data(logger, pids, fold_dir, source_dir, target_dir):
start_time = time.time()
with open(os.path.join(fold_dir, 'file_list.txt'), 'w') as handle:
for pid in pids:
handle.write('{}_img.npz\n'.format(pid))
handle.write('{}_rois.npz\n'.format(pid))
subprocess.call('rsync -av --files-from {} {} {}'.format(os.path.join(fold_dir, 'file_list.txt'),
source_dir, target_dir), shell=True)
dutils.unpack_dataset(target_dir)
copied_files = os.listdir(target_dir)
logger.info("copying and unpacking data set finsihed : {} files in target dir: {}. took {} sec".format(
len(copied_files), target_dir, np.round(time.time() - start_time, 0)))
if __name__=="__main__":
import utils.exp_utils as utils
from configs import configs
total_stime = time.time()
cf = configs()
cf.created_fold_id_pickle = False
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)
batch_gen = get_train_generators(cf, logger)
train_batch = next(batch_gen["train"])
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