AUGMENTATION_PARAMETERS = {
"scale": [1, 1, 1], # factor
"rotation": [180, 180, 180], # degrees (from -180 to 180)
"shear": [0, 0, 0], # degrees
"translation": [16, 16, 16], # mms (from -128 to 128)
"reflection": [0, 0, 0] #Bernoulli p
}
IMAGE_SIZE = 64
"Put in here the preprocessors for your data." \
"They will be run consequently on the datadict of the dataloader in the order of your list."
preprocessors = [
AugmentOnlyPositive(tags=["luna:3d", "luna:segmentation"],
output_shape=(IMAGE_SIZE,IMAGE_SIZE,IMAGE_SIZE), # in pixels
norm_patch_size=(IMAGE_SIZE,IMAGE_SIZE,IMAGE_SIZE), # in mms
augmentation_params=AUGMENTATION_PARAMETERS
),
ZMUV("luna:3d", bias = -648.59027, std = 679.21021),
]
#####################
# training #
#####################
"This is the train dataloader. We will train until this one stops loading data."
"You can set the number of epochs, the datasets and if you want it multiprocessed"
training_data = LunaDataLoader(
only_positive=True,
sets=TRAINING,
epochs=30,
preprocessors=preprocessors,
"shear": [0, 0, 0], # degrees
"translation": [5, 5, 5], # mm
"reflection": [0, 0, 0] #Bernoulli p
}
preprocessors = [
# LioAugment(tags=["luna:3d", "luna:segmentation"],
# output_shape=(128,128,128),
# norm_patch_size=(128,128,128),
# augmentation_params=AUGMENTATION_PARAMETERS
# )
# RescaleInput(input_scale=(0,255), output_scale=(0.0, 1.0)),
#AugmentInput(output_shape=(160,120),**augmentation_parameters),
#NormalizeInput(num_samples=100),
AugmentOnlyPositive(tags=["luna:3d", "luna:segmentation"],
output_shape=(128, 128, 128),
norm_patch_size=(32, 32, 32),
augmentation_params=AUGMENTATION_PARAMETERS),
ZMUV("luna:3d", bias=-648.59027, std=679.21021),
]
#####################
# training #
#####################
training_data = LunaDataLoader(only_positive=True,
sets=TRAINING,
epochs=1,
preprocessors=preprocessors,
multiprocess=False,
crash_on_exception=True)
chunk_size = 1