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 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_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
batchgen = DataLoader(data.camera(), 1, None, False)
#batch = next(batchgen)
#print(batch['data'].shape)
def plot_batch(batch):
batch_size = batch['data'].shape[0]
for i in range(batch_size):
plt.subplot(1, batch_size, i+1)
plt.imshow(batch['data'][i, 0], cmap="gray")
plt.show()
#plot_batch(batch)
my_transforms = []
brightness_transform = ContrastAugmentationTransform((0.3, 3.), preserve_range=True)
my_transforms.append(brightness_transform)
noise_transform = GaussianNoiseTransform(noise_variance=(0, 20)) ##
my_transforms.append(noise_transform)
spatial_transform = SpatialTransform_2(data.camera().shape, np.array(data.camera().shape)//2,
do_elastic_deform=True, deformation_scale=(0,0.05),
do_rotation=True, angle_z=(0, 2*np.pi),
do_scale=True, scale=(0.8, 1.2),
border_mode_data='constant', border_cval_data=0, order_data=1,
random_crop=False)
my_transforms.append(spatial_transform)
all_transforms = Compose(my_transforms)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transforms, 4, 2, seeds=None)
plot_batch(next(multithreaded_generator))
def get_train_transforms(self) -> List[AbstractTransform]:
# used for transpost and rot90
matching_axes = np.array(
[sum([i == j for j in self.patch_size]) for i in self.patch_size])
valid_axes = list(np.where(matching_axes == np.max(matching_axes))[0])
tr_transforms = []
if self.data_aug_params['selected_seg_channels'] is not None:
tr_transforms.append(
SegChannelSelectionTransform(
self.data_aug_params['selected_seg_channels']))
if self.do_dummy_2D_aug:
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
patch_size_spatial = self.patch_size[1:]
else:
patch_size_spatial = self.patch_size
ignore_axes = None
tr_transforms.append(
SpatialTransform(
patch_size_spatial,
patch_center_dist_from_border=None,
do_elastic_deform=False,
do_rotation=True,
angle_x=self.data_aug_params["rotation_x"],
angle_y=self.data_aug_params["rotation_y"],
angle_z=self.data_aug_params["rotation_z"],
p_rot_per_axis=0.5,
do_scale=True,
scale=self.data_aug_params['scale_range'],
border_mode_data="constant",
border_cval_data=0,
order_data=3,
border_mode_seg="constant",
border_cval_seg=-1,
order_seg=1,
random_crop=False,
p_el_per_sample=0.2,
p_scale_per_sample=0.2,
p_rot_per_sample=0.4,
independent_scale_for_each_axis=True,
))
if self.do_dummy_2D_aug:
tr_transforms.append(Convert2DTo3DTransform())
if np.any(matching_axes > 1):
tr_transforms.append(
Rot90Transform((0, 1, 2, 3),
axes=valid_axes,
data_key='data',
label_key='seg',
p_per_sample=0.5), )
if np.any(matching_axes > 1):
tr_transforms.append(
TransposeAxesTransform(valid_axes,
data_key='data',
label_key='seg',
p_per_sample=0.5))
tr_transforms.append(
OneOfTransform([
MedianFilterTransform((2, 8),
same_for_each_channel=False,
p_per_sample=0.2,
p_per_channel=0.5),
GaussianBlurTransform((0.3, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5)
]))
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1))
tr_transforms.append(
BrightnessTransform(0,
0.5,
per_channel=True,
p_per_sample=0.1,
p_per_channel=0.5))
tr_transforms.append(
OneOfTransform([
ContrastAugmentationTransform(contrast_range=(0.5, 2),
preserve_range=True,
per_channel=True,
data_key='data',
p_per_sample=0.2,
p_per_channel=0.5),
ContrastAugmentationTransform(contrast_range=(0.5, 2),
preserve_range=False,
per_channel=True,
data_key='data',
p_per_sample=0.2,
p_per_channel=0.5),
]))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.25, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.15,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform((0.7, 1.5),
invert_image=True,
per_channel=True,
retain_stats=True,
p_per_sample=0.1))
tr_transforms.append(
GammaTransform((0.7, 1.5),
invert_image=True,
per_channel=True,
retain_stats=True,
p_per_sample=0.1))
if self.do_mirroring:
tr_transforms.append(MirrorTransform(self.mirror_axes))
tr_transforms.append(
BlankRectangleTransform([[max(1, p // 10), p // 3]
for p in self.patch_size],
rectangle_value=np.mean,
num_rectangles=(1, 5),
force_square=False,
p_per_sample=0.4,
p_per_channel=0.5))
tr_transforms.append(
BrightnessGradientAdditiveTransform(
lambda x, y: np.exp(
np.random.uniform(np.log(x[y] // 6), np.log(x[y]))),
(-0.5, 1.5),
max_strength=lambda x, y: np.random.uniform(-5, -1)
if np.random.uniform() < 0.5 else np.random.uniform(1, 5),
mean_centered=False,
same_for_all_channels=False,
p_per_sample=0.3,
p_per_channel=0.5))
tr_transforms.append(
LocalGammaTransform(
lambda x, y: np.exp(
np.random.uniform(np.log(x[y] // 6), np.log(x[y]))),
(-0.5, 1.5),
lambda: np.random.uniform(0.01, 0.8)
if np.random.uniform() < 0.5 else np.random.uniform(1.5, 4),
same_for_all_channels=False,
p_per_sample=0.3,
p_per_channel=0.5))
tr_transforms.append(
SharpeningTransform(strength=(0.1, 1),
same_for_each_channel=False,
p_per_sample=0.2,
p_per_channel=0.5))
if any(self.use_mask_for_norm.values()):
tr_transforms.append(
MaskTransform(self.use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
if self.data_aug_params["move_last_seg_chanel_to_data"]:
all_class_labels = np.arange(1, self.num_classes)
tr_transforms.append(
MoveSegAsOneHotToData(1, all_class_labels, 'seg', 'data'))
if self.data_aug_params["cascade_do_cascade_augmentations"]:
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(channel_idx=list(
range(-len(all_class_labels), 0)),
p_per_sample=0.4,
key="data",
strel_size=(1, 8),
p_per_label=1))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(range(-len(all_class_labels), 0)),
key="data",
p_per_sample=0.2,
fill_with_other_class_p=0.15,
dont_do_if_covers_more_than_X_percent=0))
tr_transforms.append(RenameTransform('seg', 'target', True))
if self.regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(self.regions, 'target',
'target'))
if self.deep_supervision_scales is not None:
tr_transforms.append(
DownsampleSegForDSTransform2(self.deep_supervision_scales,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return tr_transforms
up_kwargs={
'attention': True
},
encode_block=ResBlockStack,
encode_kwargs_fn=encode_kwargs_fn,
decode_block=ResBlock).cuda()
patch_size = (160, 160, 80)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.2,
patience=30)
tr_transform = Compose([
GammaTransform((0.9, 1.1)),
ContrastAugmentationTransform((0.9, 1.1)),
BrightnessMultiplicativeTransform((0.9, 1.1)),
MirrorTransform(axes=[0]),
SpatialTransform_2(
patch_size,
(90, 90, 50),
random_crop=True,
do_elastic_deform=True,
deformation_scale=(0, 0.05),
do_rotation=True,
angle_x=(-0.1 * np.pi, 0.1 * np.pi),
angle_y=(0, 0),
angle_z=(0, 0),
do_scale=True,
scale=(0.9, 1.1),
border_mode_data='constant',
# export NEPTUNE_API_TOKEN = '...' !!!
logging.getLogger().setLevel('INFO')
source_files = [__file__]
if hparams.config:
source_files.append(hparams.config)
neptune_logger = NeptuneLogger(project_name=hparams.neptune_project,
params=vars(hparams),
experiment_name=hparams.experiment_name,
tags=[hparams.experiment_name],
upload_source_files=source_files)
tb_logger = loggers.TensorBoardLogger(hparams.log_dir)
transform = Compose([
BrightnessTransform(mu=0.0, sigma=0.3, data_key='data'),
GammaTransform(gamma_range=(0.7, 1.3), data_key='data'),
ContrastAugmentationTransform(contrast_range=(0.3, 1.7), data_key='data')
])
with open(hparams.train_set, 'r') as keyfile:
train_keys = [l.strip() for l in keyfile.readlines()]
print(train_keys)
with open(hparams.val_set, 'r') as keyfile:
val_keys = [l.strip() for l in keyfile.readlines()]
print(val_keys)
train_ds = MedDataset(hparams.data_path,
train_keys,
hparams.patches_per_subject,
hparams.patch_size,
image_group=hparams.image_group,
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,
regions=None):
assert params.get(
'mirror') is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
# 'patch_size': array([288, 320]),
# 'border_val_seg': -1,
# 'seeds_train': None,
# 'seeds_val': None,
# 'order_seg': 1,
# 'order_data': 3,
# 'deep_supervision_scales': [[1, 1, 1],
# [1.0, 0.5, 0.5],
# [1.0, 0.25, 0.25],
# [0.5, 0.125, 0.125],
# [0.5, 0.0625, 0.0625]],
# 'soft_ds': False,
# 'classes': None,
# 'pin_memory': True,
# 'regions': None
# params
# {'selected_data_channels': None,
# 'selected_seg_channels': [0],
# 'do_elastic': True,
# 'elastic_deform_alpha': (0.0, 300.0),
# 'elastic_deform_sigma': (9.0, 15.0),
# 'p_eldef': 0.1,
# 'do_scaling': True,
# 'scale_range': (0.65, 1.6),
# 'independent_scale_factor_for_each_axis': True,
# 'p_independent_scale_per_axis': 0.3,
# 'p_scale': 0.3,
# 'do_rotation': True,
# 'rotation_x': (-3.141592653589793, 3.141592653589793),
# 'rotation_y': (-0.5235987755982988, 0.5235987755982988),
# 'rotation_z': (-0.5235987755982988, 0.5235987755982988),
# 'rotation_p_per_axis': 1,
# 'p_rot': 0.7,
# 'random_crop': False,
# 'random_crop_dist_to_border': None,
# 'do_gamma': True,
# 'gamma_retain_stats': True,
# 'gamma_range': (0.5, 1.6),
# 'p_gamma': 0.3,
# 'do_mirror': True,
# 'mirror_axes': (0, 1, 2),
# 'dummy_2D': True,
# 'mask_was_used_for_normalization': OrderedDict([(0, False)]),
# 'border_mode_data': 'constant',
# 'all_segmentation_labels': None,
# 'move_last_seg_chanel_to_data': False,
# 'cascade_do_cascade_augmentations': False,
# 'cascade_random_binary_transform_p': 0.4,
# 'cascade_random_binary_transform_p_per_label': 1,
# 'cascade_random_binary_transform_size': (1, 8),
# 'cascade_remove_conn_comp_p': 0.2,
# 'cascade_remove_conn_comp_max_size_percent_threshold': 0.15,
# 'cascade_remove_conn_comp_fill_with_other_class_p': 0.0,
# 'do_additive_brightness': True,
# 'additive_brightness_p_per_sample': 0.3,
# 'additive_brightness_p_per_channel': 1,
# 'additive_brightness_mu': 0,
# 'additive_brightness_sigma': 0.2,
# 'num_threads': 12,
# 'num_cached_per_thread': 1,
# 'patch_size_for_spatialtransform': array([288, 320])}
# selected_data_channels is None
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
# selected_seg_channels is [0]
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!!
# dummy_2D is True
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"),
p_independent_scale_per_axis=params.get(
"p_independent_scale_per_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
# do_additive_brightness is True
if params.get("do_additive_brightness"):
tr_transforms.append(
BrightnessTransform(
params.get("additive_brightness_mu"),
params.get("additive_brightness_sigma"),
True,
p_per_sample=params.get("additive_brightness_p_per_sample"),
p_per_channel=params.get("additive_brightness_p_per_channel")))
# do_gamma is 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"]))
# do_mirror is True
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
# mask_was_used_for_normalization is OrderedDict([(0, False)]),
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))
# move_last_seg_chanel_to_data is False
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))
# regions is None
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
# deep_supervision_scales is a not None
if deep_supervision_scales is not None:
# soft_ds is False
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))
# selected_data_channels is None
if params.get("selected_data_channels") is not None:
val_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
# selected_seg_channels is [0]
if params.get("selected_seg_channels") is not None:
val_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
# move_last_seg_chanel_to_data is False
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))
# regions is None
if regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
# deep_supervision_scales is not None
if deep_supervision_scales is not None:
# soft_ds is False
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_moreDA_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,
anisotropy=False,
extra_label_keys=None,
val_mode=False,
use_conf=False,
):
'''
Work as Dataloader with augmentation
:return: train_loader, val_loader
for each iterator, return {'data': (B, D, H, W), 'target': (B, D, H, W)}
'''
if not val_mode:
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")))
# anistropic setting
if anisotropy or params.get("dummy_2D"):
ignore_axes = (0, )
tr_transforms.append(
Convert3DTo2DTransform(extra_label_keys=extra_label_keys))
patch_size = patch_size[1:] # 2D patch size
print('Using dummy2d data augmentation')
params["elastic_deform_alpha"] = (0., 200.)
params["elastic_deform_sigma"] = (9., 13.)
params["rotation_x"] = (-180. / 360 * 2. * np.pi,
180. / 360 * 2. * np.pi)
params["rotation_y"] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
params["rotation_z"] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
else:
ignore_axes = None
# 1. Spatial Transform: rotation, scaling
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"),
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"),
extra_label_keys=extra_label_keys))
if anisotropy or params.get("dummy_2D"):
tr_transforms.append(
Convert2DTo3DTransform(extra_label_keys=extra_label_keys))
# 2. Noise Augmentation: gaussian noise, gaussian blur
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
# 3. Color Augmentation: brightness, constrast, low resolution, gamma_transform
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25),
p_per_sample=0.15))
if params.get("do_additive_brightness"):
tr_transforms.append(
BrightnessTransform(params.get("additive_brightness_mu"),
params.get("additive_brightness_sigma"),
True,
p_per_sample=params.get(
"additive_brightness_p_per_sample"),
p_per_channel=params.get(
"additive_brightness_p_per_channel")))
tr_transforms.append(ContrastAugmentationTransform(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=0.1)) # 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"]))
# 4. Mirror Transform
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(
MirrorTransform(params.get("mirror_axes"),
extra_label_keys=extra_label_keys))
# 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, extra_label_keys=extra_label_keys))
tr_transforms.append(RenameTransform('data', 'image', True))
tr_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
toTensorKeys = [
'image', 'gt'
] + extra_label_keys if extra_label_keys is not None else [
'image', 'gt'
]
tr_transforms.append(NumpyToTensor(toTensorKeys, 'float'))
tr_transforms = Compose(tr_transforms)
if seeds_train is not None:
seeds_train = [seeds_train] * params.get('num_threads')
if use_conf:
num_threads = 1
num_cached_per_thread = 1
else:
num_threads, num_cached_per_thread = params.get(
'num_threads'), params.get("num_cached_per_thread")
batchgenerator_train = MultiThreadedAugmenter(dataloader_train,
tr_transforms,
num_threads,
num_cached_per_thread,
seeds=seeds_train,
pin_memory=pin_memory)
val_transforms = []
val_transforms.append(
RemoveLabelTransform(-1, 0, extra_label_keys=extra_label_keys))
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('data', 'image', True))
val_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
val_transforms.append(NumpyToTensor(toTensorKeys, '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)
if seeds_val is not None:
seeds_val = [seeds_val] * 1
# batchgenerator_val = MultiThreadedAugmenter(dataloader_val, val_transforms, 1,
# params.get("num_cached_per_thread"),
# seeds=seeds_val, pin_memory=False)
batchgenerator_val = SingleThreadedAugmenter(dataloader_val,
val_transforms)
else:
val_transforms = []
val_transforms.append(
RemoveLabelTransform(-1, 0, extra_label_keys=extra_label_keys))
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('data', 'image', True))
val_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
toTensorKeys = [
'image', 'gt'
] + extra_label_keys if extra_label_keys is not None else [
'image', 'gt'
]
val_transforms.append(NumpyToTensor(toTensorKeys, 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = SingleThreadedAugmenter(dataloader_val,
val_transforms)
if dataloader_train is not None:
batchgenerator_train = SingleThreadedAugmenter(
dataloader_train, val_transforms)
else:
batchgenerator_train = None
return batchgenerator_train, batchgenerator_val
def run(self, img_data, seg_data):
# Define label for segmentation for segmentation augmentation
if self.seg_augmentation: seg_label = "seg"
else: seg_label = "class"
# Create a parser for the batchgenerators module
data_generator = DataParser(img_data, seg_data, seg_label)
# 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=self.config_contrast_preserverange,
per_channel=self.coloraug_per_channel,
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=self.coloraug_per_channel,
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=self.coloraug_per_channel,
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_label]
# 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_label], 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
def main():
# assign global args
global args
args = parser.parse_args()
# make a folder for the experiment
general_folder_name = args.output_path
try:
os.mkdir(general_folder_name)
except OSError:
pass
# create train, test split, return the indices, patients in test_split wont be seen during whole training
train_idx, val_idx, test_idx = CreateTrainValTestSplit(
HistoFile_path=args.input_file,
num_splits=args.num_splits,
num_test_folds=args.num_test_folds,
num_val_folds=args.num_val_folds,
seed=args.seed)
IDs = train_idx + val_idx
print('size of training set {}'.format(len(train_idx)))
print('size of validation set {}'.format(len(val_idx)))
print('size of test set {}'.format(len(test_idx)))
# data loading
Data = ProstataData(args.input_file) #For details on this class see README
# train and validate
for cv in range(args.cv_start, args.cv_end):
best_epoch = 0
train_loss = []
val_loss = []
# define patients for training and validation
train_idx, val_idx = split_training(IDs,
len_val=62,
cv=cv,
cv_runs=args.cv_number)
oversampling_factor, Slices_total, Natural_probability_tu_slice, Natural_probability_PRO_slice = get_oversampling(
Data,
train_idx=sorted(train_idx),
Batch_Size=args.b,
patch_size=args.patch_size)
training_batches = Slices_total / args.b
lr = args.lr
base_lr = args.lr
args.seed += 1
print('train_idx', train_idx, len(train_idx))
print('val_idx', val_idx, len(val_idx))
# get class frequencies
print('calculating class frequencie')
Tumor_frequencie_ADC, Prostate_frequencie_ADC, Background_frequencie_ADC,\
Tumor_frequencie_T2, Prostate_frequencie_T2, Background_frequencie_T2\
, ADC_mean, ADC_std, BVAL_mean, BVAL_std, T2_mean, T2_std \
= get_class_frequencies(Data, train_idx, patch_size=args.patch_size)
print ADC_mean, ADC_std, BVAL_mean, BVAL_std, T2_mean, T2_std
print('ADC', Tumor_frequencie_ADC, Prostate_frequencie_ADC,
Background_frequencie_ADC)
print('T2', Tumor_frequencie_T2, Prostate_frequencie_T2,
Background_frequencie_T2)
all_ADC = np.float(Background_frequencie_ADC +
Prostate_frequencie_ADC + Tumor_frequencie_ADC)
all_T2 = np.float(Background_frequencie_T2 + Prostate_frequencie_T2 +
Tumor_frequencie_T2)
print all_ADC
print all_T2
W1_ADC = 1 / (Background_frequencie_ADC / all_ADC)**0.25
W2_ADC = 1 / (Prostate_frequencie_ADC / all_ADC)**0.25
W3_ADC = 1 / (Tumor_frequencie_ADC / all_ADC)**0.25
Wa_ADC = W1_ADC / (W1_ADC + W2_ADC + W3_ADC)
Wb_ADC = W2_ADC / (W1_ADC + W2_ADC + W3_ADC)
Wc_ADC = W3_ADC / (W1_ADC + W2_ADC + W3_ADC)
print 'Weights ADC', Wa_ADC, Wb_ADC, Wc_ADC
weight_ADC = (Wa_ADC, Wb_ADC, Wc_ADC)
W1_T2 = 1 / (Background_frequencie_T2 / all_T2)**0.25
W2_T2 = 1 / (Prostate_frequencie_T2 / all_T2)**0.25
W3_T2 = 1 / (Tumor_frequencie_T2 / all_T2)**0.25
Wa_T2 = W1_T2 / (W1_T2 + W2_T2 + W3_T2)
Wb_T2 = W2_T2 / (W1_T2 + W2_T2 + W3_T2)
Wc_T2 = W3_T2 / (W1_T2 + W2_T2 + W3_T2)
print 'Weights T2', Wa_T2, Wb_T2, Wc_T2
weight_T2 = (Wa_T2, Wb_T2, Wc_T2)
# define model
Net = UNetPytorch(in_shape=(3, args.patch_size[0], args.patch_size[1]))
Net_Name = 'UNetPytorch'
model = Net.cuda()
# model parameter
optimizer = torch.optim.Adam(model.parameters(),
lr,
weight_decay=args.weight_decay)
criterion_ADC = CrossEntropyLoss2d(
weight=torch.FloatTensor(weight_ADC)).cuda()
criterion_T2 = CrossEntropyLoss2d(
weight=torch.FloatTensor(weight_T2)).cuda()
# new folder name for cv
folder_name = general_folder_name + '/CV_{}'.format(cv)
try:
os.mkdir(folder_name)
except OSError:
pass
checkpoint_file = folder_name + '/checkpoint_' + '{}.pth.tar'.format(
Net_Name)
# augmentation
for epoch in range(args.epochs):
torch.manual_seed(args.seed + epoch + cv)
np.random.seed(epoch + cv)
np.random.shuffle(train_idx)
if epoch == 0:
my_transforms = []
spatial_transform = SpatialTransform(
args.patch_size,
np.array(args.patch_size) // 2,
do_elastic_deform=True,
alpha=(100., 450.),
sigma=(13., 17.),
do_rotation=True,
angle_z=(-np.pi / 2., np.pi / 2.),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
order_data=3,
random_crop=True)
resample_transform = ResampleTransform(zoom_range=(0.7, 1.3))
brightness_transform = BrightnessTransform(0.0, 0.1, True)
my_transforms.append(resample_transform)
my_transforms.append(
ContrastAugmentationTransform((0.75, 1.25), True))
my_transforms.append(brightness_transform)
my_transforms.append(Mirror((2, 3)))
all_transforms = Compose(my_transforms)
sometimes_spatial_transforms = RndTransform(
spatial_transform,
prob=args.p,
alternative_transform=CenterCropTransform(args.patch_size))
sometimes_other_transforms = RndTransform(all_transforms,
prob=1.0)
final_transform = Compose(
[sometimes_spatial_transforms, sometimes_other_transforms])
Center_Crop = CenterCropTransform(args.patch_size)
if epoch == 30:
my_transforms = []
spatial_transform = SpatialTransform(
args.patch_size,
np.array(args.patch_size) // 2,
do_elastic_deform=True,
alpha=(0., 250.),
sigma=(11., 14.),
do_rotation=True,
angle_z=(-np.pi / 2., np.pi / 2.),
do_scale=True,
scale=(0.85, 1.15),
border_mode_data='constant',
border_cval_data=0,
order_data=3,
random_crop=True)
resample_transform = ResampleTransform(zoom_range=(0.8, 1.2))
brightness_transform = BrightnessTransform(0.0, 0.1, True)
my_transforms.append(resample_transform)
my_transforms.append(
ContrastAugmentationTransform((0.85, 1.15), True))
my_transforms.append(brightness_transform)
all_transforms = Compose(my_transforms)
sometimes_spatial_transforms = RndTransform(
spatial_transform,
prob=args.p,
alternative_transform=CenterCropTransform(args.patch_size))
sometimes_other_transforms = RndTransform(all_transforms,
prob=1.0)
final_transform = Compose(
[sometimes_spatial_transforms, sometimes_other_transforms])
Center_Crop = CenterCropTransform(args.patch_size)
if epoch == 50:
my_transforms = []
spatial_transform = SpatialTransform(
args.patch_size,
np.array(args.patch_size) // 2,
do_elastic_deform=True,
alpha=(0., 150.),
sigma=(10., 12.),
do_rotation=True,
angle_z=(-np.pi / 2., np.pi / 2.),
do_scale=True,
scale=(0.85, 1.15),
border_mode_data='constant',
border_cval_data=0,
order_data=3,
random_crop=False)
resample_transform = ResampleTransform(zoom_range=(0.9, 1.1))
brightness_transform = BrightnessTransform(0.0, 0.1, True)
my_transforms.append(resample_transform)
my_transforms.append(
ContrastAugmentationTransform((0.95, 1.05), True))
my_transforms.append(brightness_transform)
all_transforms = Compose(my_transforms)
sometimes_spatial_transforms = RndTransform(
spatial_transform,
prob=args.p,
alternative_transform=CenterCropTransform(args.patch_size))
sometimes_other_transforms = RndTransform(all_transforms,
prob=1.0)
final_transform = Compose(
[sometimes_spatial_transforms, sometimes_other_transforms])
Center_Crop = CenterCropTransform(args.patch_size)
train_loader = BatchGenerator(
Data,
BATCH_SIZE=args.b,
split_idx=train_idx,
seed=args.seed,
ProbabilityTumorSlices=oversampling_factor,
epoch=epoch,
ADC_mean=ADC_mean,
ADC_std=ADC_std,
BVAL_mean=BVAL_mean,
BVAL_std=BVAL_std,
T2_mean=T2_mean,
T2_std=T2_std)
val_loader = BatchGenerator(Data,
BATCH_SIZE=0,
split_idx=val_idx,
seed=args.seed,
ProbabilityTumorSlices=None,
epoch=epoch,
test=True,
ADC_mean=ADC_mean,
ADC_std=ADC_std,
BVAL_mean=BVAL_mean,
BVAL_std=BVAL_std,
T2_mean=T2_mean,
T2_std=T2_std)
#train on training set
train_losses = train(train_loader=train_loader,
model=model,
optimizer=optimizer,
criterion_ADC=criterion_ADC,
criterion_T2=criterion_T2,
final_transform=final_transform,
workers=args.workers,
seed=args.seed,
training_batches=training_batches)
train_loss.append(train_losses)
# evaluate on validation set
val_losses = validate(val_loader=val_loader,
model=model,
folder_name=folder_name,
criterion_ADC=criterion_ADC,
criterion_T2=criterion_T2,
split_ixs=val_idx,
epoch=epoch,
workers=1,
Center_Crop=Center_Crop,
seed=args.seed)
val_loss.append(val_losses)
# write TrainingsCSV to folder name
TrainingsCSV = pd.DataFrame({
'train_loss': train_loss,
'val_loss': val_loss
})
TrainingsCSV.to_csv(folder_name + '/TrainingsCSV.csv')
if val_losses <= min(val_loss):
best_epoch = epoch
print 'best epoch', epoch
save_checkpoint(
{
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
},
filename=checkpoint_file)
optimizer, lr = adjust_learning_rate(optimizer, base_lr, epoch)
# delete all output except best epoch
clear_image_data(folder_name, best_epoch, epoch)
def main():
image_data_file = [
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_data_1.npy',
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_data_2.npy',
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_data_3.npy'
]
label_data_file = [
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/gt_data_1.npy',
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/gt_data_2.npy',
'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/gt_data_3.npy'
]
## Using batchgenerators
transform_list = []
## Spatial transform
# transform_list.append(SpatialTransform((600, 600), np.array((600, 600)) // 2,
# do_elastic_deform=False, 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))
# transform_list.append(Mirror(axes=(2, 3)))
## Noise transforms
# transform_list.append(GaussianNoiseTransform(noise_variance=(0.1, 0.5)))
# transform_list.append(RicianNoiseTransform(noise_variance=(0, 0.3)))
## Color transforms
transform_list.append(
ContrastAugmentationTransform((0.3, 0.5), preserve_range=True))
transformations = Compose(transform_list)
giana_dataset = GianaDataset(image_data_file, label_data_file)
giana_dataloader = GianaDataGenerator(giana_dataset, 4, 4)
multithreaded_generator = MultiThreadedAugmenter(giana_dataloader,
transformations,
4,
2,
seeds=None)
#
#for data_dict in multithreaded_generator:
#data_dict = transformations(**data_dict)
## Using pytorch build-in transformations
# transformations = transforms.Compose([#transforms.ToPILImage(),
# # transforms.RandomApply([transforms.RandomVerticalFlip(),
# # transforms.RandomHorizontalFlip()]),
# #transforms.ToTensor(),
# #Permute(),
# RicianNoiseTransform(noise_variance=(0, 200))
# #transforms.Normalize(mean=[0.485, 0.456, 0.406],
# # std=[0.229, 0.224, 0.225])
# ])
# giana_dataset = GianaDataset(image_data_file, label_data_file)#, transform=transformations)
# giana_dataloader = DataLoader(giana_dataset, batch_size=4, shuffle=True)
## Using torchsample
# transformations = Compose([ToTensor(),
# TypeCast('float'),
# #ChannelsFirst(),
# RangeNormalize(0,1),
# RandomGamma(0.2,1.8),
# Brightness(0.4),
# RandomSaturation(0.5,0.9)
# ])
# giana_dataset = GianaDataset(image_data_file, label_data_file, transform=transformations)
# giana_dataloader = DataLoader(giana_dataset, batch_size=4, shuffle=True)
# for data_dict in giana_dataloader.next():
# print(data_dict)
# break
for i in range(4):
data_dict = next(giana_dataloader)
data_dict = transformations(**data_dict)
print("Dataset selected: {0}".format(
giana_dataloader.dataset.selected))
images, labels = data_dict['data'], data_dict['seg']
plt.figure()
idx = 0
for image, label in zip(images, labels):
# mylog.show_image(image, name="img plot"+str(idx), title="image title"+str(idx))
# mylog.show_image(label, name="label plot"+str(idx), title="label title"+str(idx))
# print(image.shape, label.shape)
plot_image_label(np.rollaxis(image, 0, 3), np.squeeze(label,
axis=0), idx)
# print(idx, image.shape, label.shape, type(image))
idx += 1
# plt.show()
# break
plt.show()