def _augment_data(self, batch_generator, type=None):
tfs = [] # transforms
if self.Config.NORMALIZE_DATA:
tfs.append(
ZeroMeanUnitVarianceTransform_Standalone(
per_channel=self.Config.NORMALIZE_PER_CHANNEL))
# Not used, because those transformations are not easily invertible with batchgenerators framework:
# Mirroring would be the only easy test time DAug, but not trained with this DAug
# if self.Config.TEST_TIME_DAUG:
# from batchgenerators.transforms.spatial_transforms import SpatialTransform
# 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=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(NumpyToTensor(keys=["data", "seg"], cast_to="float"))
batch_gen = SingleThreadedAugmenter(batch_generator, Compose(tfs))
return batch_gen
def _augment_data(self, batch_generator, type=None):
tfs = []
if self.Config.NORMALIZE_DATA:
tfs.append(
ZeroMeanUnitVarianceTransform_Standalone(
per_channel=self.Config.NORMALIZE_PER_CHANNEL))
tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))
batch_gen = SingleThreadedAugmenter(batch_generator, Compose(tfs))
return batch_gen
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:
# todo: Use original transform as soon as bug fixed in batchgenerators
# tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.Config.NORMALIZE_PER_CHANNEL))
tfs.append(
ZeroMeanUnitVarianceTransform_Standalone(
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)