def setup_augmentation(self):
c = self.config
transforms_train = []
for t in sorted(c.transforms_train.keys()):
if c.transforms_train[t]["active"]:
cls = c.transforms_train[t]["type"]
kwargs = c.transforms_train[t]["kwargs"]
transforms_train.append(cls(**kwargs))
self.augmenter_train = c.augmenter_train(self.generator_train,
Compose(transforms_train),
**c.augmenter_train_kwargs)
transforms_val = []
for t in sorted(c.transforms_val.keys()):
if c.transforms_val[t]["active"]:
cls = c.transforms_val[t]["type"]
kwargs = c.transforms_val[t]["kwargs"]
transforms_val.append(cls(**kwargs))
self.augmenter_val = c.augmenter_val(self.generator_val,
Compose(transforms_val),
**c.augmenter_val_kwargs)
self.augmenter_test = c.augmenter_val(self.generator_test,
Compose(transforms_val),
**c.augmenter_val_kwargs)
def create_data_gen_train(patient_data_train, BATCH_SIZE, num_classes,
num_workers=5, num_cached_per_worker=2,
do_elastic_transform=False, alpha=(0., 1300.), sigma=(10., 13.),
do_rotation=False, a_x=(0., 2*np.pi), a_y=(0., 2*np.pi), a_z=(0., 2*np.pi),
do_scale=True, scale_range=(0.75, 1.25), seeds=None):
if seeds is None:
seeds = [None]*num_workers
elif seeds == 'range':
seeds = range(num_workers)
else:
assert len(seeds) == num_workers
data_gen_train = BatchGenerator_2D(patient_data_train, BATCH_SIZE, num_batches=None, seed=False,
PATCH_SIZE=(352, 352))
tr_transforms = []
tr_transforms.append(Mirror((2, 3)))
tr_transforms.append(RndTransform(SpatialTransform((352, 352), list(np.array((352, 352))//2),
do_elastic_transform, alpha,
sigma,
do_rotation, a_x, a_y,
a_z,
do_scale, scale_range, 'constant', 0, 3, 'constant',
0, 0,
random_crop=False), prob=0.67,
alternative_transform=RandomCropTransform((352, 352))))
tr_transforms.append(ConvertSegToOnehotTransform(range(num_classes), seg_channel=0, output_key='seg_onehot'))
tr_composed = Compose(tr_transforms)
tr_mt_gen = MultiThreadedAugmenter(data_gen_train, tr_composed, num_workers, num_cached_per_worker, seeds)
tr_mt_gen.restart()
return tr_mt_gen
def get_transforms(mode="train", target_size=128):
tranform_list = []
if mode == "train":
tranform_list = [# CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
MirrorTransform(axes=(1,)),
SpatialTransform(patch_size=(target_size,target_size), random_crop=False,
patch_center_dist_from_border=target_size // 2,
do_elastic_deform=True, alpha=(0., 1000.), sigma=(40., 60.),
do_rotation=True, p_rot_per_sample=0.5,
angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8),
scale=(0.5, 1.9), p_scale_per_sample=0.5,
border_mode_data="nearest", border_mode_seg="nearest"),
]
elif mode == "val":
tranform_list = [CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
]
elif mode == "test":
tranform_list = [CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
]
tranform_list.append(NumpyToTensor())
return Compose(tranform_list)
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
from batchgenerators.transforms import MirrorTransform, NumpyToTensor, TransposeAxesTransform, Compose
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 get_train_transform(patch_size):
tr_transforms = []
tr_transforms.append(
SpatialTransform_2(
None, [i // 2 for i in patch_size],
do_elastic_deform=False,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_x=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_y=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_z=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
do_scale=False,
scale=(0.8, 1.2),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
order_data=3,
random_crop=False,
p_el_per_sample=0.3,
p_rot_per_sample=0.3,
p_scale_per_sample=0.3))
tr_transforms.append(
RndTransform(MirrorTransform(axes=(0, 1, 2)), prob=0.3))
tr_transforms = Compose(transforms=tr_transforms)
return tr_transforms
def get_train_val_generators(fold):
tr_keys, te_keys = get_split(fold, split_seed)
train_data = {i: dataset[i] for i in tr_keys}
val_data = {i: dataset[i] for i in te_keys}
data_gen_train = create_data_gen_train(
train_data,
BATCH_SIZE,
num_classes,
INPUT_PATCH_SIZE,
num_workers=num_workers,
do_elastic_transform=True,
alpha=(0., 350.),
sigma=(14., 17.),
do_rotation=True,
a_x=(0, 2. * np.pi),
a_y=(-0.000001, 0.00001),
a_z=(-0.000001, 0.00001),
do_scale=True,
scale_range=(0.7, 1.3),
seeds=workers_seeds) # new se has no brain mask
data_gen_validation = BatchGenerator(val_data,
BATCH_SIZE,
num_batches=None,
seed=False,
PATCH_SIZE=INPUT_PATCH_SIZE)
val_transforms = []
val_transforms.append(
ConvertSegToOnehotTransform(range(4), 0, 'seg_onehot'))
data_gen_validation = MultiThreadedAugmenter(data_gen_validation,
Compose(val_transforms), 1, 2,
[0])
return data_gen_train, data_gen_validation
def test_future(self):
info = self.validate_make_default_info()
info["coords"]["Metric"] = info["coords"]["Metric"] + [
"Future KL", "Future Reconstruction NLL",
"Future Reconstruction Dice", "Prior Maximum Dice",
"Prior Best Volume Dice"
]
# our regular generators only produce 1 timestep, so we create this here manually
if self.config.test_on_val:
test_data = self.data_val
else:
test_data = self.data_test
generator = self.config.generator_val(
test_data,
self.config.batch_size_val,
3,
number_of_threads_in_multithreaded=self.config.
augmenter_val_kwargs.num_processes)
transforms = []
for t in sorted(self.config.transforms_val.keys()):
if self.config.transforms_val[t]["active"]:
cls = self.config.transforms_val[t]["type"]
kwargs = self.config.transforms_val[t]["kwargs"]
transforms.append(cls(**kwargs))
augmenter = self.config.augmenter_val(
generator, Compose(transforms), **self.config.augmenter_val_kwargs)
test_scores, info = self.test_inner(augmenter, [], info, future=True)
test_scores = np.array(test_scores)
self.elog.save_numpy_data(test_scores, "test_future.npy")
self.elog.save_dict(info, "test_future.json")
def get_train_transform(patch_size):
"""
data augmentation for training data, inspired by:
https://github.com/MIC-DKFZ/batchgenerators/blob/master/batchgenerators/examples/brats2017/brats2017_dataloader_3D.py
:param patch_size: shape of network's input
:return list of transformations
"""
train_transforms = []
def rad(deg):
return (-deg / 360 * 2 * np.pi, deg / 360 * 2 * np.pi)
train_transforms.append(
SpatialTransform_2(
patch_size,
(10, 10, 10),
do_elastic_deform=True,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_z=rad(15),
angle_x=(0, 0),
angle_y=(0, 0),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
random_crop=False,
p_el_per_sample=0.2,
p_rot_per_sample=0.2,
p_scale_per_sample=0.2,
))
train_transforms.append(MirrorTransform(axes=(0, 1)))
train_transforms.append(
BrightnessMultiplicativeTransform((0.7, 1.5),
per_channel=True,
p_per_sample=0.2))
train_transforms.append(
GammaTransform(gamma_range=(0.2, 1.0),
invert_image=False,
per_channel=False,
p_per_sample=0.2))
train_transforms.append(
GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.2))
train_transforms.append(
GaussianBlurTransform(blur_sigma=(0.2, 1.0),
different_sigma_per_channel=False,
p_per_channel=0.0,
p_per_sample=0.2))
return Compose(train_transforms)
def get_no_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params, border_val_seg=-1):
"""
use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation
:param dataloader_train:
:param dataloader_val:
:param patch_size:
:param params:
:param border_val_seg:
:return:
"""
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")))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
batchgenerator_train = MultiThreadedAugmenter(dataloader_train, tr_transforms, params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=range(params.get('num_threads')), pin_memory=True)
batchgenerator_train.restart()
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")))
val_transforms.append(RenameTransform('seg', 'target', True))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = MultiThreadedAugmenter(dataloader_val, val_transforms, max(params.get('num_threads')//2, 1),
params.get("num_cached_per_thread"),
seeds=range(max(params.get('num_threads')//2, 1)), pin_memory=True)
batchgenerator_val.restart()
return batchgenerator_train, batchgenerator_val
def get_train_transform(patch_size):
# we now create a list of transforms. These are not necessarily the best transforms to use for BraTS, this is just
# to showcase some things
tr_transforms = []
# the first thing we want to run is the SpatialTransform. It reduces the size of our data to patch_size and thus
# also reduces the computational cost of all subsequent operations. All subsequent operations do not modify the
# shape and do not transform spatially, so no border artifacts will be introduced
# Here we use the new SpatialTransform_2 which uses a new way of parameterizing elastic_deform
# We use all spatial transformations with a probability of 0.2 per sample. This means that 1 - (1 - 0.1) ** 3 = 27%
# of samples will be augmented, the rest will just be cropped
tr_transforms.append(
SpatialTransform_2(
patch_size, [i // 2 for i in patch_size],
do_elastic_deform=True,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_x=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_y=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_z=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
order_data=3,
random_crop=True,
p_el_per_sample=0.1,
p_rot_per_sample=0.1,
p_scale_per_sample=0.1))
# now we mirror along all axes
tr_transforms.append(MirrorTransform(axes=(0, 1, 2)))
# gamma transform. This is a nonlinear transformation of intensity values
# (https://en.wikipedia.org/wiki/Gamma_correction)
tr_transforms.append(
GammaTransform(gamma_range=(0.5, 2),
invert_image=False,
per_channel=True,
p_per_sample=0.15))
# we can also invert the image, apply the transform and then invert back
tr_transforms.append(
GammaTransform(gamma_range=(0.5, 2),
invert_image=True,
per_channel=True,
p_per_sample=0.15))
# Gaussian Noise
tr_transforms.append(
GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.15))
# now we compose these transforms together
tr_transforms = Compose(tr_transforms)
return tr_transforms
def setUp(self) -> None:
from delira.data_loading.numba_transform import NumbaTransform, \
NumbaCompose
self._basic_zoom_trafo = ZoomTransform(3)
self._numba_zoom_trafo = NumbaTransform(ZoomTransform, zoom_factors=3)
self._basic_pad_trafo = PadTransform(new_size=(30, 30))
self._numba_pad_trafo = NumbaTransform(PadTransform, new_size=(30, 30))
self._basic_compose_trafo = Compose(
[self._basic_pad_trafo, self._basic_zoom_trafo])
self._numba_compose_trafo = NumbaCompose(
[self._basic_pad_trafo, self._basic_zoom_trafo])
self._input = {"data": np.random.rand(10, 1, 24, 24)}
def get_validation_transforms(self):
val_transforms = []
if self.params.get("selected_data_channels"):
val_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
val_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
val_transforms.append(CenterCropTransform(self.patch_size))
val_transforms.append(RemoveLabelTransform(-1, 0))
val_transforms.append(RenameTransform('seg', 'target', True))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(val_transforms)
def test_image_pipeline(self):
'''
This just should not crash
:return:
'''
from batchgenerators.transforms import MirrorTransform, TransposeAxesTransform, Compose
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 get_augmentation(patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1):
print(f'patch size after augmentation {patch_size}')
tr_transforms = []
tr_transforms.append(
SpatialTransform(patch_size,
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")))
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"]))
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
tr_transforms = Compose(tr_transforms)
return tr_transforms
def create_data_gen_train(patient_data_train, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE, contrast_range=(0.75, 1.5),
gamma_range = (0.6, 2),
num_workers=5, num_cached_per_worker=3,
do_elastic_transform=False, alpha=(0., 1300.), sigma=(10., 13.),
do_rotation=False, a_x=(0., 2*np.pi), a_y=(0., 2*np.pi), a_z=(0., 2*np.pi),
do_scale=True, scale_range=(0.75, 1.25), seeds=None):
if seeds is None:
seeds = [None]*num_workers
elif seeds == 'range':
seeds = range(num_workers)
else:
assert len(seeds) == num_workers
data_gen_train = BatchGenerator3D_random_sampling(patient_data_train, BATCH_SIZE, num_batches=None, seed=False,
patch_size=(160, 192, 160), convert_labels=True)
tr_transforms = []
tr_transforms.append(DataChannelSelectionTransform([0, 1, 2, 3]))
tr_transforms.append(GenerateBrainMaskTransform())
tr_transforms.append(MirrorTransform())
tr_transforms.append(SpatialTransform(INPUT_PATCH_SIZE, list(np.array(INPUT_PATCH_SIZE)//2.),
do_elastic_deform=do_elastic_transform, alpha=alpha, sigma=sigma,
do_rotation=do_rotation, angle_x=a_x, angle_y=a_y, angle_z=a_z,
do_scale=do_scale, scale=scale_range, border_mode_data='nearest',
border_cval_data=0, order_data=3, border_mode_seg='constant', border_cval_seg=0,
order_seg=0, random_crop=True))
tr_transforms.append(BrainMaskAwareStretchZeroOneTransform((-5, 5), True))
tr_transforms.append(ContrastAugmentationTransform(contrast_range, True))
tr_transforms.append(GammaTransform(gamma_range, False))
tr_transforms.append(BrainMaskAwareStretchZeroOneTransform(per_channel=True))
tr_transforms.append(BrightnessTransform(0.0, 0.1, True))
tr_transforms.append(SegChannelSelectionTransform([0]))
tr_transforms.append(ConvertSegToOnehotTransform(range(num_classes), 0, "seg_onehot"))
gen_train = MultiThreadedAugmenter(data_gen_train, Compose(tr_transforms), num_workers, num_cached_per_worker,
seeds)
gen_train.restart()
return gen_train
def get_data_augmenter(data, batch_size=1,
mode=DataLoader.Mode.NORMAL,
volumetric=True,
normalization_range=None,
vector_generator=None,
input_shape=None,
sample_count=1,
transforms=None,
threads=1,
seed=None):
transforms = [] if transforms is None else transforms
threads = min(int(np.ceil(len(data) / batch_size)), threads)
loader = DataLoader(data=data,
batch_size=batch_size,
mode=mode,
volumetric=volumetric,
normalization_range=normalization_range,
vector_generator=vector_generator,
input_shape=input_shape,
sample_count=sample_count,
number_of_threads_in_multithreaded=threads,
seed=seed)
transforms = transforms + [PrepareForTF()]
return MultiThreadedAugmenter(loader, Compose(transforms), threads)
def get_valid_transform(patch_size):
"""
data augmentation for validation data
inspired by:
https://github.com/MIC-DKFZ/batchgenerators/blob/master/batchgenerators/examples/brats2017/brats2017_dataloader_3D.py
:param patch_size: shape of network's input
:return list of transformations
"""
train_transforms = []
train_transforms.append(
SpatialTransform_2(patch_size,
patch_size,
do_elastic_deform=False,
deformation_scale=(0, 0),
do_rotation=False,
angle_x=(0, 0),
angle_y=(0, 0),
angle_z=(0, 0),
do_scale=False,
scale=(1.0, 1.0),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
order_data=3,
random_crop=True,
p_el_per_sample=0.1,
p_rot_per_sample=0.1,
p_scale_per_sample=0.1))
train_transforms.append(MirrorTransform(axes=(0, 1)))
return Compose(train_transforms)
def get_transforms(mode="train", target_size=128):
tranform_list = []
if mode == "train":
tranform_list = [ # CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=(target_size, target_size), order=1),
MirrorTransform(axes=(1, )),
]
elif mode == "val":
tranform_list = [ #CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
MirrorTransform(axes=(1, )),
]
elif mode == "test":
tranform_list = [ #CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
MirrorTransform(axes=(1, )),
]
tranform_list.append(NumpyToTensor())
return Compose(tranform_list)
from delira.training.backends import convert_torch_to_numpy
from functools import partial
from delira.data_loading import DataManager, SequentialSampler
from delira_unet import UNetTorch
from delira import set_debug_mode
if __name__ == "__main__":
checkpoint_path = ""
data_path = ""
save_path = ""
set_debug_mode(True)
transforms = Compose([
CopyTransform("data", "data_orig"),
# HistogramEqualization(),
RangeTransform((-1, 1)),
# AddGridTransform(),
])
img_size = (1024, 256)
thresh = 0.5
print("Load Model")
torch.jit.loat(checkpoint_path)
model.eval()
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
def get_training_transforms(self):
assert self.params.get(
'mirror'
) is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if self.params.get("selected_data_channels"):
tr_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
tr_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
if self.params.get("dummy_2D", False):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(
self._spatial_transform_patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=self.params.get("do_elastic"),
alpha=self.params.get("elastic_deform_alpha"),
sigma=self.params.get("elastic_deform_sigma"),
do_rotation=self.params.get("do_rotation"),
angle_x=self.params.get("rotation_x"),
angle_y=self.params.get("rotation_y"),
angle_z=self.params.get("rotation_z"),
do_scale=self.params.get("do_scaling"),
scale=self.params.get("scale_range"),
order_data=self.params.get("order_data"),
border_mode_data=self.params.get("border_mode_data"),
border_cval_data=self.params.get("border_cval_data"),
order_seg=self.params.get("order_seg"),
border_mode_seg=self.params.get("border_mode_seg"),
border_cval_seg=self.params.get("border_cval_seg"),
random_crop=self.params.get("random_crop"),
p_el_per_sample=self.params.get("p_eldef"),
p_scale_per_sample=self.params.get("p_scale"),
p_rot_per_sample=self.params.get("p_rot"),
independent_scale_for_each_axis=self.params.get(
"independent_scale_factor_for_each_axis"),
))
if self.params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
# we need to put the color augmentations after the dummy 2d part (if applicable). Otherwise the overloaded color
# channel gets in the way
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.15))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5), )
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.3),
p_per_sample=0.15))
if self.params.get("do_additive_brightness"):
tr_transforms.append(
BrightnessTransform(
self.params.get("additive_brightness_mu"),
self.params.get("additive_brightness_sigma"),
True,
p_per_sample=self.params.get(
"additive_brightness_p_per_sample"),
p_per_channel=self.params.get(
"additive_brightness_p_per_channel")))
tr_transforms.append(
ContrastAugmentationTransform(contrast_range=(0.65, 1.5),
p_per_sample=0.15))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.5, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.25,
ignore_axes=ignore_axes), )
tr_transforms.append(
GammaTransform(self.params.get("gamma_range"),
True,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
if self.params.get("do_gamma"):
tr_transforms.append(
GammaTransform(
self.params.get("gamma_range"),
False,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=self.params["p_gamma"]))
if self.params.get("do_mirror") or self.params.get("mirror"):
tr_transforms.append(
MirrorTransform(self.params.get("mirror_axes")))
if self.params.get("use_mask_for_norm"):
use_mask_for_norm = self.params.get("use_mask_for_norm")
tr_transforms.append(
MaskTransform(use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(tr_transforms)
def get_training_transforms(self):
assert self.params.get(
'mirror'
) is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if self.params.get("selected_data_channels"):
tr_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
tr_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
if self.params.get("dummy_2D", False):
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
tr_transforms.append(Convert3DTo2DTransform())
tr_transforms.append(
SpatialTransform(
self._spatial_transform_patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=self.params.get("do_elastic"),
alpha=self.params.get("elastic_deform_alpha"),
sigma=self.params.get("elastic_deform_sigma"),
do_rotation=self.params.get("do_rotation"),
angle_x=self.params.get("rotation_x"),
angle_y=self.params.get("rotation_y"),
angle_z=self.params.get("rotation_z"),
do_scale=self.params.get("do_scaling"),
scale=self.params.get("scale_range"),
order_data=self.params.get("order_data"),
border_mode_data=self.params.get("border_mode_data"),
border_cval_data=self.params.get("border_cval_data"),
order_seg=self.params.get("order_seg"),
border_mode_seg=self.params.get("border_mode_seg"),
border_cval_seg=self.params.get("border_cval_seg"),
random_crop=self.params.get("random_crop"),
p_el_per_sample=self.params.get("p_eldef"),
p_scale_per_sample=self.params.get("p_scale"),
p_rot_per_sample=self.params.get("p_rot"),
independent_scale_for_each_axis=self.params.get(
"independent_scale_factor_for_each_axis"),
))
if self.params.get("dummy_2D", False):
tr_transforms.append(Convert2DTo3DTransform())
if self.params.get("do_gamma", False):
tr_transforms.append(
GammaTransform(
self.params.get("gamma_range"),
False,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=self.params["p_gamma"]))
if self.params.get("do_mirror", False):
tr_transforms.append(
MirrorTransform(self.params.get("mirror_axes")))
if self.params.get("use_mask_for_norm"):
use_mask_for_norm = self.params.get("use_mask_for_norm")
tr_transforms.append(
MaskTransform(use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(tr_transforms)
def get_insaneDA_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
seeds_train=None,
seeds_val=None,
order_seg=1,
order_data=3,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True):
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"):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(patch_size,
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=order_data,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=order_seg,
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"):
tr_transforms.append(Convert2DTo3DTransform())
# we need to put the color augmentations after the dummy 2d part (if applicable). Otherwise the overloaded color
# channel gets in the way
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.15))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.70, 1.3),
p_per_sample=0.15))
tr_transforms.append(
ContrastAugmentationTransform(contrast_range=(0.65, 1.5),
p_per_sample=0.15))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.5, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.25,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
True,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
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") or params.get("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"):
if params.get("cascade_random_binary_transform_p") > 0:
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")))
if params.get("cascade_remove_conn_comp_p") > 0:
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 deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
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 deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(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_no_augmentation(dataloader_train,
dataloader_val,
params=default_3D_augmentation_params,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True,
regions=None):
"""
use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation
"""
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")))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
batchgenerator_train = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=range(params.get('num_threads')),
pin_memory=pin_memory)
batchgenerator_train.restart()
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")))
val_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = MultiThreadedAugmenter(
dataloader_val,
val_transforms,
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=range(max(params.get('num_threads') // 2, 1)),
pin_memory=pin_memory)
batchgenerator_val.restart()
return batchgenerator_train, batchgenerator_val
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())
tr_transforms.append(SpatialTransform(
patch_size, 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"):
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
train_transforms = [MirrorTransform(axes=(0, 1))]
post_transforms = [
ToTensor(keys=('data', 'label'),
dtypes=(('data', torch.float32), ('label', torch.int64)))
]
def init_fn(worker_id):
seed = torch.utils.data._utils.worker._worker_info.seed
import numpy as np
seed32 = np.array(seed).astype(np.uint32)
np.random.seed(seed32)
train_dset = PytorchDatasetWrapper(ImageFolder(train_path))
train_transforms = Compose(pre_transforms + train_transforms + post_transforms)
train_data = DataLoader(train_dset,
shuffle=True,
batch_size=32,
num_workers=4,
batch_transforms=train_transforms,
collate_fn=numpy_collate,
pin_memory=False,
worker_init_fn=init_fn)
val_dset = PytorchDatasetWrapper(ImageFolder(val_path))
val_transforms = Compose(pre_transforms + post_transforms)
val_data = DataLoader(val_dset,
shuffle=False,
batch_size=32,
num_workers=4,
def run(fold=0):
print fold
# =================================================================================================================
I_AM_FOLD = fold
np.random.seed(65432)
lasagne.random.set_rng(np.random.RandomState(98765))
sys.setrecursionlimit(2000)
BATCH_SIZE = 2
INPUT_PATCH_SIZE =(128, 128, 128)
num_classes=4
EXPERIMENT_NAME = "final"
results_dir = os.path.join(paths.results_folder)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
results_dir = os.path.join(results_dir, EXPERIMENT_NAME)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
results_dir = os.path.join(results_dir, "fold%d"%I_AM_FOLD)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
n_epochs = 300
lr_decay = np.float32(0.985)
base_lr = np.float32(0.0005)
n_batches_per_epoch = 100
n_test_batches = 10
n_feedbacks_per_epoch = 10.
num_workers = 6
workers_seeds = [123, 1234, 12345, 123456, 1234567, 12345678]
# =================================================================================================================
all_data = load_dataset()
keys_sorted = np.sort(all_data.keys())
crossval_folds = KFold(len(all_data.keys()), n_folds=5, shuffle=True, random_state=123456)
ctr = 0
for train_idx, test_idx in crossval_folds:
print len(train_idx), len(test_idx)
if ctr == I_AM_FOLD:
train_keys = [keys_sorted[i] for i in train_idx]
test_keys = [keys_sorted[i] for i in test_idx]
break
ctr += 1
train_data = {i:all_data[i] for i in train_keys}
test_data = {i:all_data[i] for i in test_keys}
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.75, 1.5), gamma_range = (0.8, 1.5),
num_workers=num_workers, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 1300.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(0., 2*np.pi), a_z=(0., 2*np.pi),
do_scale=True, scale_range=(0.75, 1.25), seeds=workers_seeds)
data_gen_validation = BatchGenerator3D_random_sampling(test_data, BATCH_SIZE, num_batches=None, seed=False,
patch_size=INPUT_PATCH_SIZE, convert_labels=True)
val_transforms = []
val_transforms.append(GenerateBrainMaskTransform())
val_transforms.append(BrainMaskAwareStretchZeroOneTransform(clip_range=(-5, 5), per_channel=True))
val_transforms.append(SegChannelSelectionTransform([0]))
val_transforms.append(ConvertSegToOnehotTransform(range(4), 0, "seg_onehot"))
val_transforms.append(DataChannelSelectionTransform([0, 1, 2, 3]))
data_gen_validation = MultiThreadedAugmenter(data_gen_validation, Compose(val_transforms), 2, 2)
x_sym = T.tensor5()
seg_sym = T.matrix()
net, seg_layer = build_net(x_sym, INPUT_PATCH_SIZE, num_classes, 4, 16, batch_size=BATCH_SIZE,
do_instance_norm=True)
output_layer_for_loss = net
# add some weight decay
l2_loss = lasagne.regularization.regularize_network_params(output_layer_for_loss, lasagne.regularization.l2) * 1e-5
# the distinction between prediction_train and test is important only if we enable dropout (batch norm/inst norm
# does not use or save moving averages)
prediction_train = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=False,
batch_norm_update_averages=False, batch_norm_use_averages=False)
loss_vec = - soft_dice_per_img_in_batch(prediction_train, seg_sym, BATCH_SIZE)[:, 1:]
loss = loss_vec.mean()
loss += l2_loss
acc_train = T.mean(T.eq(T.argmax(prediction_train, axis=1), seg_sym.argmax(-1)), dtype=theano.config.floatX)
prediction_test = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=True,
batch_norm_update_averages=False, batch_norm_use_averages=False)
loss_val = - soft_dice_per_img_in_batch(prediction_test, seg_sym, BATCH_SIZE)[:, 1:]
loss_val = loss_val.mean()
loss_val += l2_loss
acc = T.mean(T.eq(T.argmax(prediction_test, axis=1), seg_sym.argmax(-1)), dtype=theano.config.floatX)
# learning rate has to be a shared variable because we decrease it with every epoch
params = lasagne.layers.get_all_params(output_layer_for_loss, trainable=True)
learning_rate = theano.shared(base_lr)
updates = lasagne.updates.adam(T.grad(loss, params), params, learning_rate=learning_rate, beta1=0.9, beta2=0.999)
dc = hard_dice_per_img_in_batch(prediction_test, seg_sym.argmax(1), num_classes, BATCH_SIZE).mean(0)
train_fn = theano.function([x_sym, seg_sym], [loss, acc_train, loss_vec], updates=updates)
val_fn = theano.function([x_sym, seg_sym], [loss_val, acc, dc])
all_val_dice_scores=None
all_training_losses = []
all_validation_losses = []
all_validation_accuracies = []
all_training_accuracies = []
val_dice_scores = []
epoch = 0
while epoch < n_epochs:
if epoch == 100:
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.85, 1.25), gamma_range = (0.8, 1.5),
num_workers=6, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 1000.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(-np.pi/8., np.pi/8.),
a_z=(-np.pi/8., np.pi/8.), do_scale=True, scale_range=(0.85, 1.15),
seeds=workers_seeds)
if epoch == 175:
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.9, 1.1), gamma_range = (0.85, 1.3),
num_workers=6, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 750.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(-0.00001, 0.00001),
a_z=(-0.00001, 0.00001), do_scale=True, scale_range=(0.85, 1.15),
seeds=workers_seeds)
epoch_start_time = time.time()
learning_rate.set_value(np.float32(base_lr* lr_decay**(epoch)))
print "epoch: ", epoch, " learning rate: ", learning_rate.get_value()
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data_dict in data_gen_train:
data = data_dict["data"].astype(np.float32)
seg = data_dict["seg_onehot"].astype(np.float32).transpose(0, 2, 3, 4, 1).reshape((-1, num_classes))
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/n_feedbacks_per_epoch)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", \
train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch), " train accuracy: ", \
train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
all_training_accuracies.append(train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
train_loss_tmp = 0
train_acc_tmp = 0
if len(val_dice_scores) > 0:
all_val_dice_scores = np.concatenate(val_dice_scores, axis=0).reshape((-1, num_classes))
try:
printLosses(all_training_losses, all_training_accuracies, all_validation_losses,
all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME),
n_feedbacks_per_epoch, val_dice_scores=all_val_dice_scores,
val_dice_scores_labels=["brain", "1", "2", "3", "4", "5"])
except:
pass
loss_vec, acc, l = train_fn(data, seg)
loss = loss_vec.mean()
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr >= n_batches_per_epoch:
break
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
all_training_accuracies.append(train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
train_loss /= n_batches_per_epoch
print "training loss average on epoch: ", train_loss
val_loss = 0
accuracies = []
valid_batch_ctr = 0
all_dice = []
for data_dict in data_gen_validation:
data = data_dict["data"].astype(np.float32)
seg = data_dict["seg_onehot"].astype(np.float32).transpose(0, 2, 3, 4, 1).reshape((-1, num_classes))
w = np.zeros(num_classes, dtype=np.float32)
w[np.unique(seg.argmax(-1))] = 1
loss, acc, dice = val_fn(data, seg)
dice[w==0] = 2
all_dice.append(dice)
val_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
if valid_batch_ctr >= n_test_batches:
break
all_dice = np.vstack(all_dice)
dice_means = np.zeros(num_classes)
for i in range(num_classes):
dice_means[i] = all_dice[all_dice[:, i]!=2, i].mean()
val_loss /= n_test_batches
print "val loss: ", val_loss
print "val acc: ", np.mean(accuracies), "\n"
print "val dice: ", dice_means
print "This epoch took %f sec" % (time.time()-epoch_start_time)
val_dice_scores.append(dice_means)
all_validation_losses.append(val_loss)
all_validation_accuracies.append(np.mean(accuracies))
all_val_dice_scores = np.concatenate(val_dice_scores, axis=0).reshape((-1, num_classes))
try:
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies,
os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch,
val_dice_scores=all_val_dice_scores, val_dice_scores_labels=["brain", "1", "2", "3", "4", "5"])
except:
pass
with open(os.path.join(results_dir, "%s_Params.pkl" % (EXPERIMENT_NAME)), 'w') as f:
cPickle.dump(lasagne.layers.get_all_param_values(output_layer_for_loss), f)
with open(os.path.join(results_dir, "%s_allLossesNAccur.pkl"% (EXPERIMENT_NAME)), 'w') as f:
cPickle.dump([all_training_losses, all_training_accuracies, all_validation_losses,
all_validation_accuracies, val_dice_scores], f)
epoch += 1
p_rot_per_axis=params.get("rotation_p_per_axis"),
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=order_data,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=order_seg,
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")))
tr_transforms = Compose(tr_transforms)
batchgenerator_train = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
pin_memory=True)
train_loader = batchgenerator_train
train_batch = next(train_loader)
print(
train_batch.keys()
) # dict_keys(['data', 'target']), each with torch.Size([2, 1, 112, 240, 272])
print((train_batch['seg'] - train_batch['weak_label']).sum())
train_batch = next(train_loader)
print((train_batch['seg'] - train_batch['weak_label']).sum())
ipdb.set_trace()
dataset_test = ConditionalGanDataset(path_test_real, load_sample_cgan_test, ['.PNG', '.png'], ['.PNG', '.png'])
### Transforms applied to data
from batchgenerators.transforms import RandomCropTransform, Compose
from batchgenerators.transforms.spatial_transforms import ResizeTransform,SpatialTransform
transforms = Compose([
#SpatialTransform(patch_size=(1024, 1024), do_rotation=True, patch_center_dist_from_border=1024, border_mode_data='reflect',
# border_mode_seg='reflect', angle_x=(args.rot_angle, args.rot_angle), angle_y=(0, 0), angle_z=(0, 0),
# do_elastic_deform=False, order_data=1, order_seg=1)
ResizeTransform((int(args.resize_size), int(args.resize_size)), order=1),
RandomCropTransform((params.nested_get("image_size"), params.nested_get("image_size"))),
])
from delira.data_loading import BaseDataManager, SequentialSampler, RandomSampler
manager_test = BaseDataManager(dataset_test, params.nested_get("batch_size"),
transforms=transforms,
sampler_cls=SequentialSampler,
n_process_augmentation=1)
import warnings
warnings.simplefilter("ignore", UserWarning) # ignore UserWarnings raised by dependency code
warnings.simplefilter("ignore", FutureWarning) # ignore FutureWarnings raised by dependency code
def get_default_augmentation_withEDT(dataloader_train,
dataloader_val,
patch_size,
idx_of_edts,
params=default_3D_augmentation_params,
border_val_seg=-1,
pin_memory=True,
seeds_train=None,
seeds_val=None):
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())
tr_transforms.append(
SpatialTransform(patch_size,
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")))
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
"""
##############################################################
##############################################################
Here we insert moving the EDT to a different key so that it does not get intensity transformed
##############################################################
##############################################################
"""
tr_transforms.append(
AppendChannelsTransform("data",
"bound",
idx_of_edts,
remove_from_input=True))
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"]))
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(
"advanced_pyramid_augmentations") and not None and params.get(
"advanced_pyramid_augmentations"):
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
p_per_sample=0.4,
key="data",
strel_size=(1, 8)))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
key="data",
p_per_sample=0.2,
fill_with_other_class_p=0.0,
dont_do_if_covers_more_than_X_percent=0.15))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target', 'bound'], 'float'))
tr_transforms = Compose(tr_transforms)
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")))
"""
##############################################################
##############################################################
Here we insert moving the EDT to a different key
##############################################################
##############################################################
"""
val_transforms.append(
AppendChannelsTransform("data",
"bound",
idx_of_edts,
remove_from_input=True))
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))
val_transforms.append(NumpyToTensor(['data', 'target', 'bound'], 'float'))
val_transforms = Compose(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 run(self, img_data, seg_data):
# Create a parser for the batchgenerators module
data_generator = DataParser(img_data, seg_data)
# Initialize empty transform list
transforms = []
# Add mirror augmentation
if self.mirror:
aug_mirror = MirrorTransform(axes=self.config_mirror_axes)
transforms.append(aug_mirror)
# Add contrast augmentation
if self.contrast:
aug_contrast = ContrastAugmentationTransform(
self.config_contrast_range,
preserve_range=True,
per_channel=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_contrast)
# Add brightness augmentation
if self.brightness:
aug_brightness = BrightnessMultiplicativeTransform(
self.config_brightness_range,
per_channel=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_brightness)
# Add gamma augmentation
if self.gamma:
aug_gamma = GammaTransform(self.config_gamma_range,
invert_image=False,
per_channel=True,
retain_stats=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_gamma)
# Add gaussian noise augmentation
if self.gaussian_noise:
aug_gaussian_noise = GaussianNoiseTransform(
self.config_gaussian_noise_range,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_gaussian_noise)
# Add spatial transformations as augmentation
# (rotation, scaling, elastic deformation)
if self.rotations or self.scaling or self.elastic_deform or \
self.cropping:
# Identify patch shape (full image or cropping)
if self.cropping: patch_shape = self.cropping_patch_shape
else: patch_shape = img_data[0].shape[0:-1]
# Assembling the spatial transformation
aug_spatial_transform = SpatialTransform(
patch_shape, [i // 2 for i in patch_shape],
do_elastic_deform=self.elastic_deform,
alpha=self.config_elastic_deform_alpha,
sigma=self.config_elastic_deform_sigma,
do_rotation=self.rotations,
angle_x=self.config_rotations_angleX,
angle_y=self.config_rotations_angleY,
angle_z=self.config_rotations_angleZ,
do_scale=self.scaling,
scale=self.config_scaling_range,
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_data=3,
order_seg=0,
p_el_per_sample=self.config_p_per_sample,
p_rot_per_sample=self.config_p_per_sample,
p_scale_per_sample=self.config_p_per_sample,
random_crop=self.cropping)
# Append spatial transformation to transformation list
transforms.append(aug_spatial_transform)
# Compose the batchgenerators transforms
all_transforms = Compose(transforms)
# Assemble transforms into a augmentation generator
augmentation_generator = SingleThreadedAugmenter(
data_generator, all_transforms)
# Perform the data augmentation x times (x = cycles)
aug_img_data = None
aug_seg_data = None
for i in range(0, self.cycles):
# Run the computation process for the data augmentations
augmentation = next(augmentation_generator)
# Access augmentated data from the batchgenerators data structure
if aug_img_data is None and aug_seg_data is None:
aug_img_data = augmentation["data"]
aug_seg_data = augmentation["seg"]
# Concatenate the new data augmentated data with the cached data
else:
aug_img_data = np.concatenate(
(augmentation["data"], aug_img_data), axis=0)
aug_seg_data = np.concatenate(
(augmentation["seg"], aug_seg_data), axis=0)
# Transform data from channel-first back to channel-last structure
# Data structure channel-first 3D: (batch, channel, x, y, z)
# Data structure channel-last 3D: (batch, x, y, z, channel)
aug_img_data = np.moveaxis(aug_img_data, 1, -1)
aug_seg_data = np.moveaxis(aug_seg_data, 1, -1)
# Return augmentated image and segmentation data
return aug_img_data, aug_seg_data
