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