def test_image_pipeline_and_pin_memory(self):
'''
This just should not crash
:return:
'''
try:
import torch
except ImportError:
'''dont test if torch is not installed'''
return
tr_transforms = []
tr_transforms.append(MirrorTransform())
tr_transforms.append(
TransposeAxesTransform(transpose_any_of_these=(0, 1),
p_per_sample=0.5))
tr_transforms.append(NumpyToTensor(keys='data', cast_to='float'))
composed = Compose(tr_transforms)
dl = self.dl_images
mt = MultiThreadedAugmenter(dl, composed, 4, 1, None, True)
for _ in range(50):
res = mt.next()
assert isinstance(res['data'], torch.Tensor)
assert res['data'].is_pinned()
# let mt finish caching, otherwise it's going to print an error (which is not a problem and will not prevent
# the success of the test but it does not look pretty)
sleep(2)
def test_return_incomplete_multi_threaded(self):
data = list(range(123))
batch_size = 12
num_workers = 3
dl = DummyDataLoader(deepcopy(data), batch_size, num_workers, 1, return_incomplete=False, shuffle=False, infinite=False)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, False)
all_return = []
total = 0
ctr = 0
for i in mt:
ctr += 1
assert len(i) == batch_size
total += len(i)
all_return += i
self.assertTrue(total == 120)
self.assertTrue(ctr == 10)
self.assertTrue(len(np.unique(all_return)) == total)
dl = DummyDataLoader(deepcopy(data), batch_size, num_workers, 1, return_incomplete=True, shuffle=False, infinite=False)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, False)
all_return = []
total = 0
ctr = 0
for i in mt:
ctr += 1
total += len(i)
all_return += i
self.assertTrue(total == 123)
self.assertTrue(ctr == 11)
self.assertTrue(len(np.unique(all_return)) == len(data))
def crop_transform_random():
'''
从图片的任意位置剪切出一个固定的尺寸
:return:
'''
crop_size = (128, 128)
batchgen = my_data_loader.DataLoader(camera(), 4)
# 随机地从图片上剪切除（128，128）尺寸的图片块
randomCrop = RandomCropTransform(crop_size=crop_size)
spatial_transform = SpatialTransform(crop_size,
np.array(crop_size) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.5, 2),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
multithreaded_generator = MultiThreadedAugmenter(
batchgen, Compose([randomCrop, spatial_transform]), 4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def spatial_transforms():
'''
空间变换：变形、缩放、旋转
:return:
'''
# 变形+旋转+缩放
spatial_transform = SpatialTransform(camera().shape,
np.array(camera().shape) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.3, 3.),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
my_transforms = []
my_transforms.append(spatial_transform)
all_transforms = Compose(my_transforms)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transforms,
4, 2)
# 显示转换效果
my_data_loader.plot_batch(multithreaded_generator.__next__())
def random_transform():
'''
随机地对某些批次的数据进行变换
:return:
'''
spatial_transform = SpatialTransform(camera().shape,
np.array(camera().shape) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.3, 3.),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
sometimes_spatial_transform = RndTransform(spatial_transform, prob=0.5)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(
batchgen, Compose([sometimes_spatial_transform]), 4, 2)
for _ in range(4):
my_data_loader.plot_batch(multithreaded_generator.__next__())
def noise_transform():
'''
为图片加噪声点
:return:
'''
batchgen = my_data_loader.DataLoader(camera(), 4)
noise_transform = RicianNoiseTransform(noise_variance=(0, 200))
multithreaded_generator = MultiThreadedAugmenter(batchgen, noise_transform,
4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def resampling_transform():
'''
对图片进行重采样
:return:
'''
batchgen = my_data_loader.DataLoader(camera(), 4)
resample_transform = ResampleTransform(zoom_range=(0.05, 0.2))
multithreaded_generator = MultiThreadedAugmenter(batchgen,
resample_transform, 4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def test_thoroughly(self):
data_list = [list(range(123)),
list(range(1243)),
list(range(1)),
list(range(7)),
]
worker_list = (1, 4, 7)
batch_size_list = (1, 3, 32)
seed_list = [318, None]
epochs = 3
for data in data_list:
#print('data', len(data))
for num_workers in worker_list:
#print('num_workers', num_workers)
for batch_size in batch_size_list:
#print('batch_size', batch_size)
for return_incomplete in [True, False]:
#print('return_incomplete', return_incomplete)
for shuffle in [True, False]:
#print('shuffle', shuffle)
for seed_for_shuffle in seed_list:
#print('seed_for_shuffle', seed_for_shuffle)
if return_incomplete:
if len(data) % batch_size == 0:
expected_num_batches = len(data) // batch_size
else:
expected_num_batches = len(data) // batch_size + 1
else:
expected_num_batches = len(data) // batch_size
expected_num_items = len(data) if return_incomplete else expected_num_batches * batch_size
print("init")
dl = DummyDataLoader(deepcopy(data), batch_size, num_workers, seed_for_shuffle,
return_incomplete=return_incomplete, shuffle=shuffle,
infinite=False)
mt = MultiThreadedAugmenter(dl, None, num_workers, 5, None, False, wait_time=0)
mt._start()
for epoch in range(epochs):
print("sampling")
all_return = []
total = 0
ctr = 0
for i in mt:
ctr += 1
total += len(i)
all_return += i
print('asserting')
self.assertTrue(total == expected_num_items)
self.assertTrue(ctr == expected_num_batches)
self.assertTrue(len(np.unique(all_return)) == expected_num_items)
def test_no_crash(self):
"""
This one should just not crash, that's all
:return:
"""
dl = self.dl_images
mt_dl = MultiThreadedAugmenter(dl, None, self.num_threads, 1, None,
False)
for _ in range(20):
_ = mt_dl.next()
def crop_transform_center_seg():
'''
从图片的正正中心剪切分割
:return:
'''
crop_size = (128, 128)
batchgen = my_data_loader.DataLoader(camera(), 4)
centerCropSeg = CenterCropSegTransform(output_size=crop_size)
multithreaded_generator = MultiThreadedAugmenter(batchgen,
Compose([centerCropSeg]),
4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def _augment_data(Config, batch_generator, type=None):
batch_gen = MultiThreadedAugmenter(batch_generator,
Compose([]),
num_processes=1,
num_cached_per_queue=1,
seeds=None)
return batch_gen
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=cf.n_workers, seeds=range(cf.n_workers))
return multithreaded_generator
def get_batches(self,
batch_size=128,
type=None,
subjects=None,
num_batches=None):
data = type
seg = []
num_processes = 1 # 6 is a bit faster than 16
nr_of_samples = len(subjects) * self.HP.INPUT_DIM[0]
if num_batches is None:
num_batches_multithr = int(
nr_of_samples / batch_size /
num_processes) #number of batches for exactly one epoch
else:
num_batches_multithr = int(num_batches / num_processes)
batch_gen = SlicesBatchGeneratorPrecomputedBatches(
(data, seg),
BATCH_SIZE=batch_size,
num_batches=num_batches_multithr,
seed=None)
batch_gen.HP = self.HP
batch_gen = MultiThreadedAugmenter(batch_gen,
Compose([]),
num_processes=num_processes,
num_cached_per_queue=1,
seeds=None)
return batch_gen
def get_batches(self, batch_size=1):
data = np.nan_to_num(self.data)
# Use dummy mask in case we only want to predict on some data (where we do not have Ground Truth))
seg = np.zeros(
(self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0],
self.HP.NR_OF_CLASSES)).astype(self.HP.LABELS_TYPE)
num_processes = 1 # not not use more than 1 if you want to keep original slice order (Threads do return in random order)
batch_gen = SlicesBatchGenerator((data, seg), BATCH_SIZE=batch_size)
batch_gen.HP = self.HP
tfs = [] # transforms
if self.HP.NORMALIZE_DATA:
tfs.append(
ZeroMeanUnitVarianceTransform(
per_channel=self.HP.NORMALIZE_PER_CHANNEL))
tfs.append(ReorderSegTransform())
batch_gen = MultiThreadedAugmenter(
batch_gen,
Compose(tfs),
num_processes=num_processes,
num_cached_per_queue=2,
seeds=None
) # Only use num_processes=1, otherwise global_idx of SlicesBatchGenerator not working
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, x, y, channels)
def test_infinite_multi_threaded(self):
data = list(range(123))
num_workers = 3
dl = DummyDataLoader(deepcopy(data), 12, num_workers, 1, return_incomplete=True, shuffle=True, infinite=False)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, False)
# this should raise a StopIteration
with self.assertRaises(StopIteration):
for i in range(1000):
idx = next(mt)
dl = DummyDataLoader(deepcopy(data), 12, num_workers, 1, return_incomplete=True, shuffle=True, infinite=True)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, False)
# this should now not raise a StopIteration anymore
for i in range(1000):
idx = next(mt)
def train(train_loader, model, optimizer, criterion_ADC, criterion_T2,
final_transform, workers, seed, training_batches):
train_losses = AverageMeter()
np.random.seed(seed)
seeds = np.random.choice(seed, workers, False, None)
model.train()
multithreaded_generator = MultiThreadedAugmenter(train_loader,
final_transform,
workers,
2,
seeds=seeds)
torch.cuda.empty_cache()
for i in range(training_batches):
print('Batch: [{0}/{1}]'.format(i + 1, training_batches))
batch = multithreaded_generator.next()
TensorBatch = ToTensor(batch)
target = TensorBatch['seg'].cuda()
target_T2 = TensorBatch['seg_T2'].cuda()
input_var = torch.autograd.Variable(
TensorBatch['data'], requires_grad=True).cuda(async=True)
input_var = input_var.float()
target_var = torch.autograd.Variable(target)
target_var = target_var.long()
target_var_T2 = torch.autograd.Variable(target_T2)
target_var_T2 = target_var_T2.long()
optimizer.zero_grad()
output = model(input_var)
loss_ADC = criterion_ADC(output, target_var)
loss_T2 = criterion_T2(output, target_var_T2)
loss = (loss_ADC + loss_T2) / 2.
loss.backward()
optimizer.step()
train_losses.update(loss.item())
print 'train_loss', loss.item()
torch.cuda.empty_cache()
return train_losses.avg
def _augment_data(self, batch_generator, type=None):
if self.Config.DATA_AUGMENTATION:
num_processes = 16 # 2D: 8 is a bit faster than 16
# num_processes = 8
else:
num_processes = 6
tfs = [] #transforms
if self.Config.NORMALIZE_DATA:
tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.Config.NORMALIZE_PER_CHANNEL))
if self.Config.DATA_AUGMENTATION:
if type == "train":
# scale: inverted: 0.5 -> bigger; 2 -> smaller
# patch_center_dist_from_border: if 144/2=72 -> always exactly centered; otherwise a bit off center (brain can get off image and will be cut then)
if self.Config.DAUG_SCALE:
center_dist_from_border = int(self.Config.INPUT_DIM[0] / 2.) - 10 # (144,144) -> 62
tfs.append(SpatialTransform(self.Config.INPUT_DIM,
patch_center_dist_from_border=center_dist_from_border,
do_elastic_deform=self.Config.DAUG_ELASTIC_DEFORM,
alpha=(90., 120.), sigma=(9., 11.),
do_rotation=self.Config.DAUG_ROTATE,
angle_x=(-0.8, 0.8), angle_y=(-0.8, 0.8), angle_z=(-0.8, 0.8),
do_scale=True, scale=(0.9, 1.5), border_mode_data='constant',
border_cval_data=0,
order_data=3,
border_mode_seg='constant', border_cval_seg=0,
order_seg=0, random_crop=True, p_el_per_sample=0.2,
p_rot_per_sample=0.2, p_scale_per_sample=0.2))
if self.Config.DAUG_RESAMPLE:
tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2))
if self.Config.DAUG_NOISE:
tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.2))
if self.Config.DAUG_MIRROR:
tfs.append(MirrorTransform())
if self.Config.DAUG_FLIP_PEAKS:
tfs.append(FlipVectorAxisTransform())
tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))
#num_cached_per_queue 1 or 2 does not really make a difference
batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
num_cached_per_queue=1, seeds=None, pin_memory=True)
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)
def test_image_pipeline(self):
'''
This just should not crash
:return:
'''
tr_transforms = []
tr_transforms.append(MirrorTransform())
tr_transforms.append(
TransposeAxesTransform(transpose_any_of_these=(0, 1),
p_per_sample=0.5))
composed = Compose(tr_transforms)
dl = self.dl_images
mt = MultiThreadedAugmenter(dl, composed, 4, 1, None, False)
for _ in range(50):
res = mt.next()
# let mt finish caching, otherwise it's going to print an error (which is not a problem and will not prevent
# the success of the test but it does not look pretty)
sleep(2)
def combine_transform():
'''
组合变换：对比度+镜像
:return:
'''
my_transforms = []
# 对比度变换
brightness_transform = ContrastAugmentationTransform((0.3, 3.),
preserve_range=True)
my_transforms.append(brightness_transform)
# 镜像变换
mirror_transform = MirrorTransform(axes=(2, 3))
my_transforms.append(mirror_transform)
all_transform = Compose(my_transforms)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transform,
4, 2)
# 显示转换效果
my_data_loader.plot_batch(multithreaded_generator.__next__())
def test_return_all_indices_multi_threaded_shuffle_False(self):
data = list(range(123))
batch_sizes = [1, 3, 75, 12, 23]
num_workers = 3
for b in batch_sizes:
dl = DummyDataLoader(deepcopy(data), b, num_workers, 1, return_incomplete=True, shuffle=False, infinite=False)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, False)
for _ in range(3):
idx = []
for i in mt:
idx += i
self.assertTrue(len(idx) == len(data))
self.assertTrue(all([i == j for i,j in zip(idx, data)]))
def test_order(self):
"""
Coordinating workers in a multiprocessing envrionment is difficult. We want DummyDL in a multithreaded
environment to still give us the numbers from 0 to 99 in ascending order
:return:
"""
dl = self.dl
mt = MultiThreadedAugmenter(dl, None, self.num_threads, 1, None, False)
res = []
for i in mt:
res.append(i)
assert len(res) == 100
res_copy = deepcopy(res)
res.sort()
assert all((i == j for i, j in zip(res, res_copy)))
assert all((i == j for i, j in zip(res, np.arange(0, 100))))
def create_data_gen_pipeline(cf, cities=None, data_split='train', do_aug=True, random=True, n_batches=None):
"""
create mutli-threaded train/val/test batch generation and augmentation pipeline.
:param cities: list of strings or None
:param patient_data: dictionary containing one dictionary per patient in the train/test subset
:param test_pids: (optional) list of test patient ids, calls the test generator.
:param do_aug: (optional) whether to perform data augmentation (training) or not (validation/testing)
:param random: bool, whether to draw random batches or go through data linearly
:return: multithreaded_generator
"""
data_gen = BatchGenerator(cities=cities, batch_size=cf.batch_size, data_dir=cf.data_dir,
label_density=cf.label_density, data_split=data_split, resolution=cf.resolution,
gt_instances=cf.gt_instances, n_batches=n_batches, random=random)
my_transforms = []
if do_aug:
mirror_transform = MirrorTransform(axes=(3,))
my_transforms.append(mirror_transform)
spatial_transform = SpatialTransform(patch_size=cf.patch_size[-2:],
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'],
border_mode_data=cf.da_kwargs['border_mode_data'],
border_mode_seg=cf.da_kwargs['border_mode_seg'],
border_cval_seg=cf.da_kwargs['border_cval_seg'])
my_transforms.append(spatial_transform)
else:
my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[-2:]))
my_transforms.append(GammaTransform(cf.da_kwargs['gamma_range'], invert_image=False, per_channel=True,
retain_stats=cf.da_kwargs['gamma_retain_stats'],
p_per_sample=cf.da_kwargs['p_gamma']))
my_transforms.append(AddLossMask(cf.ignore_label))
if cf.label_switches is not None:
my_transforms.append(StochasticLabelSwitches(cf.name2trainId, cf.label_switches))
all_transforms = Compose(my_transforms)
multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers,
seeds=range(cf.n_workers))
return multithreaded_generator
def get_batches(self,
batch_size=128,
type=None,
subjects=None,
num_batches=None):
data = type
seg = []
num_processes = 1 # 6 is a bit faster than 16
batch_gen = SlicesBatchGeneratorPrecomputedBatches(
(data, seg), batch_size=batch_size)
batch_gen.HP = self.HP
batch_gen = MultiThreadedAugmenter(batch_gen,
Compose([]),
num_processes=num_processes,
num_cached_per_queue=1,
seeds=None)
return batch_gen
def create_data_gen_pipeline(patient_data, cf, test_pids=None, do_aug=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 test_pids: (optional) list of test patient ids, calls the test generator.
:param do_aug: (optional) whether to perform data augmentation (training) or not (validation/testing)
:return: multithreaded_generator
"""
if test_pids is None:
data_gen = BatchGenerator(patient_data, batch_size=cf.batch_size,
pre_crop_size=cf.pre_crop_size, dim=cf.dim)
else:
data_gen = TestGenerator(patient_data, batch_size=cf.batch_size, n_batches=None,
pre_crop_size=cf.pre_crop_size, test_pids=test_pids, dim=cf.dim)
cf.n_workers = 1
my_transforms = []
if do_aug:
mirror_transform = Mirror(axes=(2, 3))
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(ConvertSegToOnehotTransform(classes=(0, 1, 2)))
my_transforms.append(TransposeChannels())
all_transforms = Compose(my_transforms)
multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
return multithreaded_generator
def get_batches(self, batch_size=1):
num_processes = 1 # not not use more than 1 if you want to keep original slice order (Threads do return in random order)
if self.HP.TYPE == "combined":
# Load from Npy file for Fusion
data = self.subject
seg = []
nr_of_samples = len([self.subject]) * self.HP.INPUT_DIM[0]
num_batches = int(nr_of_samples / batch_size / num_processes)
batch_gen = SlicesBatchGeneratorNpyImg_fusion(
(data, seg),
BATCH_SIZE=batch_size,
num_batches=num_batches,
seed=None)
else:
# Load Features
if self.HP.FEATURES_FILENAME == "12g90g270g":
data_img = nib.load(
join(self.data_dir, "270g_125mm_peaks.nii.gz"))
else:
data_img = nib.load(
join(self.data_dir, self.HP.FEATURES_FILENAME + ".nii.gz"))
data = data_img.get_data()
data = np.nan_to_num(data)
data = DatasetUtils.scale_input_to_unet_shape(
data, self.HP.DATASET, self.HP.RESOLUTION)
# data = DatasetUtils.scale_input_to_unet_shape(data, "HCP_32g", "1.25mm") #If we want to test HCP_32g on HighRes net
#Load Segmentation
if self.use_gt_mask:
seg = nib.load(
join(self.data_dir,
self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
if self.HP.LABELS_FILENAME not in [
"bundle_peaks_11_808080", "bundle_peaks_20_808080",
"bundle_peaks_808080", "bundle_masks_20_808080",
"bundle_masks_72_808080", "bundle_peaks_Part1_808080",
"bundle_peaks_Part2_808080",
"bundle_peaks_Part3_808080",
"bundle_peaks_Part4_808080"
]:
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask
seg = DatasetUtils.scale_input_to_unet_shape(
seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(
seg, self.HP.DATASET, self.HP.RESOLUTION)
else:
# Use dummy mask in case we only want to predict on some data (where we do not have Ground Truth))
seg = np.zeros(
(self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0],
self.HP.INPUT_DIM[0],
self.HP.NR_OF_CLASSES)).astype(self.HP.LABELS_TYPE)
batch_gen = SlicesBatchGenerator((data, seg),
batch_size=batch_size)
batch_gen.HP = self.HP
tfs = [] # transforms
if self.HP.NORMALIZE_DATA:
tfs.append(ZeroMeanUnitVarianceTransform(per_channel=False))
if self.HP.TEST_TIME_DAUG:
center_dist_from_border = int(
self.HP.INPUT_DIM[0] / 2.) - 10 # (144,144) -> 62
tfs.append(
SpatialTransform(
self.HP.INPUT_DIM,
patch_center_dist_from_border=center_dist_from_border,
do_elastic_deform=True,
alpha=(90., 120.),
sigma=(9., 11.),
do_rotation=True,
angle_x=(-0.8, 0.8),
angle_y=(-0.8, 0.8),
angle_z=(-0.8, 0.8),
do_scale=True,
scale=(0.9, 1.5),
border_mode_data='constant',
border_cval_data=0,
order_data=3,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=0,
random_crop=True))
# tfs.append(ResampleTransform(zoom_range=(0.5, 1)))
# tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05)))
tfs.append(
ContrastAugmentationTransform(contrast_range=(0.7, 1.3),
preserve_range=True,
per_channel=False))
tfs.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.7, 1.3),
per_channel=False))
tfs.append(ReorderSegTransform())
batch_gen = MultiThreadedAugmenter(
batch_gen,
Compose(tfs),
num_processes=num_processes,
num_cached_per_queue=2,
seeds=None
) # Only use num_processes=1, otherwise global_idx of SlicesBatchGenerator not working
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, x, y, channels)
def get_default_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
pin_memory=True,
seeds_train=None,
seeds_val=None,
regions=None):
assert params.get(
'mirror') is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
tr_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
tr_transforms.append(Convert3DTo2DTransform())
patch_size_spatial = patch_size[1:]
else:
patch_size_spatial = patch_size
# Set order_data=0 and order_seg=0 for some more speed for cascade???
tr_transforms.append(
SpatialTransform(patch_size_spatial,
patch_center_dist_from_border=None,
do_elastic_deform=params.get("do_elastic"),
alpha=params.get("elastic_deform_alpha"),
sigma=params.get("elastic_deform_sigma"),
do_rotation=params.get("do_rotation"),
angle_x=params.get("rotation_x"),
angle_y=params.get("rotation_y"),
angle_z=params.get("rotation_z"),
do_scale=params.get("do_scaling"),
scale=params.get("scale_range"),
border_mode_data=params.get("border_mode_data"),
border_cval_data=0,
order_data=3,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=1,
random_crop=params.get("random_crop"),
p_el_per_sample=params.get("p_eldef"),
p_scale_per_sample=params.get("p_scale"),
p_rot_per_sample=params.get("p_rot"),
independent_scale_for_each_axis=params.get(
"independent_scale_factor_for_each_axis")))
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
if params.get("do_gamma"):
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
False,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=params["p_gamma"]))
if params.get("do_mirror"):
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
if params.get("mask_was_used_for_normalization") is not None:
mask_was_used_for_normalization = params.get(
"mask_was_used_for_normalization")
tr_transforms.append(
MaskTransform(mask_was_used_for_normalization,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
tr_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
if params.get("cascade_do_cascade_augmentations"
) and not None and params.get(
"cascade_do_cascade_augmentations"):
# Remove the following transforms to remove cascade DA ??
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
p_per_sample=params.get(
"cascade_random_binary_transform_p"),
key="data",
strel_size=params.get(
"cascade_random_binary_transform_size")))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
key="data",
p_per_sample=params.get("cascade_remove_conn_comp_p"),
fill_with_other_class_p=params.get(
"cascade_remove_conn_comp_max_size_percent_threshold"),
dont_do_if_covers_more_than_X_percent=params.get(
"cascade_remove_conn_comp_fill_with_other_class_p")))
tr_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
# from batchgenerators.dataloading import SingleThreadedAugmenter
# batchgenerator_train = SingleThreadedAugmenter(dataloader_train, tr_transforms)
# import IPython;IPython.embed()
batchgenerator_train = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=seeds_train,
pin_memory=pin_memory)
val_transforms = []
val_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("selected_data_channels") is not None:
val_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
val_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
val_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
val_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
# batchgenerator_val = SingleThreadedAugmenter(dataloader_val, val_transforms)
batchgenerator_val = MultiThreadedAugmenter(
dataloader_val,
val_transforms,
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
def get_batches(self,
batch_size=128,
type=None,
subjects=None,
num_batches=None):
data = subjects
seg = []
#6 -> >30GB RAM
if self.HP.DATA_AUGMENTATION:
num_processes = 8 # 6 is a bit faster than 16
else:
num_processes = 6
nr_of_samples = len(subjects) * self.HP.INPUT_DIM[0]
if num_batches is None:
num_batches_multithr = int(
nr_of_samples / batch_size /
num_processes) #number of batches for exactly one epoch
else:
num_batches_multithr = int(num_batches / num_processes)
if self.HP.TYPE == "combined":
# Simple with .npy -> just a little bit faster than Nifti (<10%) and f1 not better => use Nifti
# batch_gen = SlicesBatchGeneratorRandomNpyImg_fusion((data, seg), batch_size=batch_size)
batch_gen = SlicesBatchGeneratorRandomNpyImg_fusion(
(data, seg), batch_size=batch_size)
else:
batch_gen = SlicesBatchGeneratorRandomNiftiImg(
(data, seg), batch_size=batch_size)
# batch_gen = SlicesBatchGeneratorRandomNiftiImg_5slices((data, seg), batch_size=batch_size)
batch_gen.HP = self.HP
tfs = [] #transforms
if self.HP.NORMALIZE_DATA:
tfs.append(
ZeroMeanUnitVarianceTransform(
per_channel=self.HP.NORMALIZE_PER_CHANNEL))
if self.HP.DATASET == "Schizo" and self.HP.RESOLUTION == "2mm":
tfs.append(PadToMultipleTransform(16))
if self.HP.DATA_AUGMENTATION:
if type == "train":
# scale: inverted: 0.5 -> bigger; 2 -> smaller
# patch_center_dist_from_border: if 144/2=72 -> always exactly centered; otherwise a bit off center (brain can get off image and will be cut then)
if self.HP.DAUG_SCALE:
center_dist_from_border = int(
self.HP.INPUT_DIM[0] / 2.) - 10 # (144,144) -> 62
tfs.append(
SpatialTransform(
self.HP.INPUT_DIM,
patch_center_dist_from_border=
center_dist_from_border,
do_elastic_deform=self.HP.DAUG_ELASTIC_DEFORM,
alpha=(90., 120.),
sigma=(9., 11.),
do_rotation=self.HP.DAUG_ROTATE,
angle_x=(-0.8, 0.8),
angle_y=(-0.8, 0.8),
angle_z=(-0.8, 0.8),
do_scale=True,
scale=(0.9, 1.5),
border_mode_data='constant',
border_cval_data=0,
order_data=3,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=0,
random_crop=True))
if self.HP.DAUG_RESAMPLE:
tfs.append(ResampleTransform(zoom_range=(0.5, 1)))
if self.HP.DAUG_NOISE:
tfs.append(GaussianNoiseTransform(noise_variance=(0,
0.05)))
if self.HP.DAUG_MIRROR:
tfs.append(MirrorTransform())
if self.HP.DAUG_FLIP_PEAKS:
tfs.append(FlipVectorAxisTransform())
#num_cached_per_queue 1 or 2 does not really make a difference
batch_gen = MultiThreadedAugmenter(batch_gen,
Compose(tfs),
num_processes=num_processes,
num_cached_per_queue=1,
seeds=None)
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)
# during training I like to run a validation from time to time to see where I am standing. This is not a correct
# validation because just like training this is patch-based but it's good enough. We don't do augmentation for the
# validation, so patch_size is used as shape target here
dataloader_validation = BraTS2017DataLoader3D(
val, batch_size, patch_size, max(1,
num_threads_for_brats_example // 2))
tr_transforms = get_train_transform(patch_size)
# finally we can create multithreaded transforms that we can actually use for training
# we don't pin memory here because this is pytorch specific.
tr_gen = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
num_processes=num_threads_for_brats_example,
num_cached_per_queue=3,
seeds=None,
pin_memory=False)
# we need less processes for vlaidation because we dont apply transformations
val_gen = MultiThreadedAugmenter(dataloader_validation,
None,
num_processes=max(
1,
num_threads_for_brats_example // 2),
num_cached_per_queue=1,
seeds=None,
pin_memory=False)
# lets start the MultiThreadedAugmenter. This is not necessary but allows them to start generating training
# batches while other things run in the main thread
def _augment_data(self, batch_generator, type=None):
if self.Config.DATA_AUGMENTATION:
num_processes = 15 # 15 is a bit faster than 8 on cluster
# num_processes = multiprocessing.cpu_count() # on cluster: gives all cores, not only assigned cores
else:
num_processes = 6
tfs = []
if self.Config.NORMALIZE_DATA:
tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.Config.NORMALIZE_PER_CHANNEL))
if self.Config.SPATIAL_TRANSFORM == "SpatialTransformPeaks":
SpatialTransformUsed = SpatialTransformPeaks
elif self.Config.SPATIAL_TRANSFORM == "SpatialTransformCustom":
SpatialTransformUsed = SpatialTransformCustom
else:
SpatialTransformUsed = SpatialTransform
if self.Config.DATA_AUGMENTATION:
if type == "train":
# patch_center_dist_from_border:
# if 144/2=72 -> always exactly centered; otherwise a bit off center
# (brain can get off image and will be cut then)
if self.Config.DAUG_SCALE:
if self.Config.INPUT_RESCALING:
source_mm = 2 # for bb
target_mm = float(self.Config.RESOLUTION[:-2])
scale_factor = target_mm / source_mm
scale = (scale_factor, scale_factor)
else:
scale = (0.9, 1.5)
if self.Config.PAD_TO_SQUARE:
patch_size = self.Config.INPUT_DIM
else:
patch_size = None # keeps dimensions of the data
# spatial transform automatically crops/pads to correct size
center_dist_from_border = int(self.Config.INPUT_DIM[0] / 2.) - 10 # (144,144) -> 62
tfs.append(SpatialTransformUsed(patch_size,
patch_center_dist_from_border=center_dist_from_border,
do_elastic_deform=self.Config.DAUG_ELASTIC_DEFORM,
alpha=self.Config.DAUG_ALPHA, sigma=self.Config.DAUG_SIGMA,
do_rotation=self.Config.DAUG_ROTATE,
angle_x=self.Config.DAUG_ROTATE_ANGLE,
angle_y=self.Config.DAUG_ROTATE_ANGLE,
angle_z=self.Config.DAUG_ROTATE_ANGLE,
do_scale=True, scale=scale, border_mode_data='constant',
border_cval_data=0,
order_data=3,
border_mode_seg='constant', border_cval_seg=0,
order_seg=0, random_crop=True,
p_el_per_sample=self.Config.P_SAMP,
p_rot_per_sample=self.Config.P_SAMP,
p_scale_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_RESAMPLE:
tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2, per_channel=False))
if self.Config.DAUG_RESAMPLE_LEGACY:
tfs.append(ResampleTransformLegacy(zoom_range=(0.5, 1)))
if self.Config.DAUG_GAUSSIAN_BLUR:
tfs.append(GaussianBlurTransform(blur_sigma=self.Config.DAUG_BLUR_SIGMA,
different_sigma_per_channel=False,
p_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_NOISE:
tfs.append(GaussianNoiseTransform(noise_variance=self.Config.DAUG_NOISE_VARIANCE,
p_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_MIRROR:
tfs.append(MirrorTransform())
if self.Config.DAUG_FLIP_PEAKS:
tfs.append(FlipVectorAxisTransform())
tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))
#num_cached_per_queue 1 or 2 does not really make a difference
batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
num_cached_per_queue=1, seeds=None, pin_memory=True)
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)
print('pin_memory', pin_memory)
print('num_epochs', num_epochs)
tr_transforms = [
SpatialTransform((32, 32))
] * 1 # SpatialTransform is computationally expensive and we need some
# load on CPU so we just stack 5 of them on top of each other
tr_transforms.append(numpy_to_tensor)
tr_transforms = Compose(tr_transforms)
cifar_dataset = CifarDataset(dataset_dir,
train=True,
transform=tr_transforms)
dl = DataLoaderFromDataset(cifar_dataset, batch_size, num_workers, 1)
mt = MultiThreadedAugmenter(dl, None, num_workers, 1, None, pin_memory)
batches = 0
for _ in mt:
batches += 1
assert len(_['data'].shape) == 4
assert batches == len(
cifar_dataset
) / batch_size # this assertion only holds if len(datset) is divisible by
# batch size
start = time()
for _ in range(num_epochs):
batches = 0
for _ in mt:
