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generator_val=data.LinearBatchGenerator,
transforms_train={
0: {
"type": SpatialTransform,
"kwargs": {
"patch_size": patch_size,
"patch_center_dist_from_border": patch_size[0] // 2,
"do_elastic_deform": False,
"p_el_per_sample": 0.2,
"p_rot_per_sample": 0.3,
"p_scale_per_sample": 0.3
},
"active": True
},
1: {
"type": MirrorTransform,
"kwargs": {"axes": (0, 1, 2)},
"active": True
},
2: {
"type": SegLabelSelectionBinarizeTransform,
"kwargs": {"label": [1, 2, 3]},
"active": False
}
},
transforms_val={
0: {
"type": CenterCropTransform,
"kwargs": {"crop_size": patch_size},
"active": False
},
1: {
def get_transforms(mode="train", target_size=128):
transform_list = []
if mode == "train":
transform_list = [CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
MirrorTransform(axes=(2,)),
SpatialTransform(patch_size=(target_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,
angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8),
scale=(0.9, 1.4),
border_mode_data="nearest", border_mode_seg="nearest"),
]
elif mode == "val":
transform_list = [CenterCropTransform(crop_size=target_size),
ResizeTransform(target_size=target_size, order=1),
]
elif mode == "test":
transform_list = [CenterCropTransform(crop_size=target_size),
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,)),
SpatialTransform(patch_size=(target_size, target_size), random_crop=False,
patch_center_dist_from_border=target_size // 2,
do_elastic_deform=True, alpha=(0., 900.), sigma=(20., 30.),
do_rotation=True, p_rot_per_sample=0.8,
angle_x=(-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi), angle_y=(0, 1e-8), angle_z=(0, 1e-8),
scale=(0.85, 1.25), p_scale_per_sample=0.8,
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":
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=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)
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)