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import multiprocessing
import queue
from torch.multiprocessing import Event, Process, Queue, Manager
from time import sleep
from typing import Union, List
import numpy as np
import torch
from batchgenerators.dataloading.data_loader import DataLoader
from nnunetv2.preprocessing.preprocessors.default_preprocessor import DefaultPreprocessor
from nnunetv2.utilities.label_handling.label_handling import convert_labelmap_to_one_hot
from nnunetv2.utilities.plans_handling.plans_handler import PlansManager, ConfigurationManager
def preprocess_fromfiles_save_to_queue(list_of_lists: List[List[str]],
list_of_segs_from_prev_stage_files: Union[None, List[str]],
output_filenames_truncated: Union[None, List[str]],
plans_manager: PlansManager,
dataset_json: dict,
configuration_manager: ConfigurationManager,
target_queue: Queue,
done_event: Event,
abort_event: Event,
verbose: bool = False):
try:
label_manager = plans_manager.get_label_manager(dataset_json)
preprocessor = configuration_manager.preprocessor_class(verbose=verbose)
for idx in range(len(list_of_lists)):
data, seg, data_properties = preprocessor.run_case(list_of_lists[idx],
list_of_segs_from_prev_stage_files[
idx] if list_of_segs_from_prev_stage_files is not None else None,
plans_manager,
configuration_manager,
dataset_json)
if list_of_segs_from_prev_stage_files is not None and list_of_segs_from_prev_stage_files[idx] is not None:
seg_onehot = convert_labelmap_to_one_hot(seg[0], label_manager.foreground_labels, data.dtype)
data = np.vstack((data, seg_onehot))
data = torch.from_numpy(data).contiguous().float()
item = {'data': data, 'data_properties': data_properties,
'ofile': output_filenames_truncated[idx] if output_filenames_truncated is not None else None}
success = False
while not success:
try:
if abort_event.is_set():
return
target_queue.put(item, timeout=0.01)
success = True
except queue.Full:
pass
done_event.set()
except Exception as e:
# print(Exception, e)
abort_event.set()
raise e
def preprocessing_iterator_fromfiles(list_of_lists: List[List[str]],
list_of_segs_from_prev_stage_files: Union[None, List[str]],
output_filenames_truncated: Union[None, List[str]],
plans_manager: PlansManager,
dataset_json: dict,
configuration_manager: ConfigurationManager,
num_processes: int,
pin_memory: bool = False,
verbose: bool = False):
context = multiprocessing.get_context('spawn')
manager = Manager()
num_processes = min(len(list_of_lists), num_processes)
assert num_processes >= 1
processes = []
done_events = []
target_queues = []
abort_event = manager.Event()
for i in range(num_processes):
event = manager.Event()
queue = Manager().Queue(maxsize=1)
pr = context.Process(target=preprocess_fromfiles_save_to_queue,
args=(
list_of_lists[i::num_processes],
list_of_segs_from_prev_stage_files[
i::num_processes] if list_of_segs_from_prev_stage_files is not None else None,
output_filenames_truncated[
i::num_processes] if output_filenames_truncated is not None else None,
plans_manager,
dataset_json,
configuration_manager,
queue,
event,
abort_event,
verbose
), daemon=True)
pr.start()
target_queues.append(queue)
done_events.append(event)
processes.append(pr)
worker_ctr = 0
while (not done_events[worker_ctr].is_set()) or (not target_queues[worker_ctr].empty()):
# import IPython;IPython.embed()
if not target_queues[worker_ctr].empty():
item = target_queues[worker_ctr].get()
worker_ctr = (worker_ctr + 1) % num_processes
else:
all_ok = all(
[i.is_alive() or j.is_set() for i, j in zip(processes, done_events)]) and not abort_event.is_set()
if not all_ok:
raise RuntimeError('Background workers died. Look for the error message further up! If there is '
'none then your RAM was full and the worker was killed by the OS. Use fewer '
'workers or get more RAM in that case!')
sleep(0.01)
continue
if pin_memory:
[i.pin_memory() for i in item.values() if isinstance(i, torch.Tensor)]
yield item
[p.join() for p in processes]
class PreprocessAdapter(DataLoader):
def __init__(self, list_of_lists: List[List[str]],
list_of_segs_from_prev_stage_files: Union[None, List[str]],
preprocessor: DefaultPreprocessor,
output_filenames_truncated: Union[None, List[str]],
plans_manager: PlansManager,
dataset_json: dict,
configuration_manager: ConfigurationManager,
num_threads_in_multithreaded: int = 1):
self.preprocessor, self.plans_manager, self.configuration_manager, self.dataset_json = \
preprocessor, plans_manager, configuration_manager, dataset_json
self.label_manager = plans_manager.get_label_manager(dataset_json)
if list_of_segs_from_prev_stage_files is None:
list_of_segs_from_prev_stage_files = [None] * len(list_of_lists)
if output_filenames_truncated is None:
output_filenames_truncated = [None] * len(list_of_lists)
super().__init__(list(zip(list_of_lists, list_of_segs_from_prev_stage_files, output_filenames_truncated)),
1, num_threads_in_multithreaded,
seed_for_shuffle=1, return_incomplete=True,
shuffle=False, infinite=False, sampling_probabilities=None)
self.indices = list(range(len(list_of_lists)))
def generate_train_batch(self):
idx = self.get_indices()[0]
files = self._data[idx][0]
seg_prev_stage = self._data[idx][1]
ofile = self._data[idx][2]
# if we have a segmentation from the previous stage we have to process it together with the images so that we
# can crop it appropriately (if needed). Otherwise it would just be resized to the shape of the data after
# preprocessing and then there might be misalignments
data, seg, data_properties = self.preprocessor.run_case(files, seg_prev_stage, self.plans_manager,
self.configuration_manager,
self.dataset_json)
if seg_prev_stage is not None:
seg_onehot = convert_labelmap_to_one_hot(seg[0], self.label_manager.foreground_labels, data.dtype)
data = np.vstack((data, seg_onehot))
data = torch.from_numpy(data)
return {'data': data, 'data_properties': data_properties, 'ofile': ofile}
class PreprocessAdapterFromNpy(DataLoader):
def __init__(self, list_of_images: List[np.ndarray],
list_of_segs_from_prev_stage: Union[List[np.ndarray], None],
list_of_image_properties: List[dict],
truncated_ofnames: Union[List[str], None],
plans_manager: PlansManager, dataset_json: dict, configuration_manager: ConfigurationManager,
num_threads_in_multithreaded: int = 1, verbose: bool = False):
preprocessor = configuration_manager.preprocessor_class(verbose=verbose)
self.preprocessor, self.plans_manager, self.configuration_manager, self.dataset_json, self.truncated_ofnames = \
preprocessor, plans_manager, configuration_manager, dataset_json, truncated_ofnames
self.label_manager = plans_manager.get_label_manager(dataset_json)
if list_of_segs_from_prev_stage is None:
list_of_segs_from_prev_stage = [None] * len(list_of_images)
if truncated_ofnames is None:
truncated_ofnames = [None] * len(list_of_images)
super().__init__(
list(zip(list_of_images, list_of_segs_from_prev_stage, list_of_image_properties, truncated_ofnames)),
1, num_threads_in_multithreaded,
seed_for_shuffle=1, return_incomplete=True,
shuffle=False, infinite=False, sampling_probabilities=None)
self.indices = list(range(len(list_of_images)))
def generate_train_batch(self):
idx = self.get_indices()[0]
image = self._data[idx][0]
seg_prev_stage = self._data[idx][1]
props = self._data[idx][2]
ofname = self._data[idx][3]
# if we have a segmentation from the previous stage we have to process it together with the images so that we
# can crop it appropriately (if needed). Otherwise it would just be resized to the shape of the data after
# preprocessing and then there might be misalignments
data, seg = self.preprocessor.run_case_npy(image, seg_prev_stage, props,
self.plans_manager,
self.configuration_manager,
self.dataset_json)
if seg_prev_stage is not None:
seg_onehot = convert_labelmap_to_one_hot(seg[0], self.label_manager.foreground_labels, data.dtype)
data = np.vstack((data, seg_onehot))
data = torch.from_numpy(data)
return {'data': data, 'data_properties': props, 'ofile': ofname}
def preprocess_fromnpy_save_to_queue(list_of_images: List[np.ndarray],
list_of_segs_from_prev_stage: Union[List[np.ndarray], None],
list_of_image_properties: List[dict],
truncated_ofnames: Union[List[str], None],
plans_manager: PlansManager,
dataset_json: dict,
configuration_manager: ConfigurationManager,
target_queue: Queue,
done_event: Event,
abort_event: Event,
verbose: bool = False):
try:
label_manager = plans_manager.get_label_manager(dataset_json)
preprocessor = configuration_manager.preprocessor_class(verbose=verbose)
for idx in range(len(list_of_images)):
data, seg = preprocessor.run_case_npy(list_of_images[idx],
list_of_segs_from_prev_stage[
idx] if list_of_segs_from_prev_stage is not None else None,
list_of_image_properties[idx],
plans_manager,
configuration_manager,
dataset_json)
if list_of_segs_from_prev_stage is not None and list_of_segs_from_prev_stage[idx] is not None:
seg_onehot = convert_labelmap_to_one_hot(seg[0], label_manager.foreground_labels, data.dtype)
data = np.vstack((data, seg_onehot))
data = torch.from_numpy(data).contiguous().float()
item = {'data': data, 'data_properties': list_of_image_properties[idx],
'ofile': truncated_ofnames[idx] if truncated_ofnames is not None else None}
success = False
while not success:
try:
if abort_event.is_set():
return
target_queue.put(item, timeout=0.01)
success = True
except queue.Full:
pass
done_event.set()
except Exception as e:
abort_event.set()
raise e
def preprocessing_iterator_fromnpy(list_of_images: List[np.ndarray],
list_of_segs_from_prev_stage: Union[List[np.ndarray], None],
list_of_image_properties: List[dict],
truncated_ofnames: Union[List[str], None],
plans_manager: PlansManager,
dataset_json: dict,
configuration_manager: ConfigurationManager,
num_processes: int,
pin_memory: bool = False,
verbose: bool = False):
context = multiprocessing.get_context('spawn')
manager = Manager()
num_processes = min(len(list_of_images), num_processes)
assert num_processes >= 1
target_queues = []
processes = []
done_events = []
abort_event = manager.Event()
for i in range(num_processes):
event = manager.Event()
queue = manager.Queue(maxsize=1)
pr = context.Process(target=preprocess_fromnpy_save_to_queue,
args=(
list_of_images[i::num_processes],
list_of_segs_from_prev_stage[
i::num_processes] if list_of_segs_from_prev_stage is not None else None,
list_of_image_properties[i::num_processes],
truncated_ofnames[i::num_processes] if truncated_ofnames is not None else None,
plans_manager,
dataset_json,
configuration_manager,
queue,
event,
abort_event,
verbose
), daemon=True)
pr.start()
done_events.append(event)
processes.append(pr)
target_queues.append(queue)
worker_ctr = 0
while (not done_events[worker_ctr].is_set()) or (not target_queues[worker_ctr].empty()):
if not target_queues[worker_ctr].empty():
item = target_queues[worker_ctr].get()
worker_ctr = (worker_ctr + 1) % num_processes
else:
all_ok = all(
[i.is_alive() or j.is_set() for i, j in zip(processes, done_events)]) and not abort_event.is_set()
if not all_ok:
raise RuntimeError('Background workers died. Look for the error message further up! If there is '
'none then your RAM was full and the worker was killed by the OS. Use fewer '
'workers or get more RAM in that case!')
sleep(0.01)
continue
if pin_memory:
[i.pin_memory() for i in item.values() if isinstance(i, torch.Tensor)]
yield item
[p.join() for p in processes]
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