# image shape

VOLUME_ROWS = 256

VOLUME_COLS = 128

VOLUME_DEPS = 256

# number of classes

NUM_CLASSES = 4

# patch extract

PATCH_SIZE = 32

if PATCH_SIZE==64:

    EXTRACTTION_STEP = 20

    EXTRACTTION_STEP_CSF = 5

elif PATCH_SIZE==32:

    EXTRACTTION_STEP = 10

    EXTRACTTION_STEP_CSF = 5

elif PATCH_SIZE==16:

    EXTRACTTION_STEP = 9

    EXTRACTTION_STEP_CSF = 4

# training configs

UNET_MODEL = 0

if UNET_MODEL==0:

    MODEL = 'default'    

elif UNET_MODEL==1:

    MODEL = 'reduced'

elif UNET_MODEL==2:

    MODEL = 'extended'

elif UNET_MODEL==3:

    MODEL = 'extended2'    

BASE = PATCH_SIZE

SMOOTH = 1.

NUM_EPOCHS  = 500

BATCH_SIZE  = 16

DEPTH = 5

EXTRACTION_RECONSTRUCT_STEP = 32

if PATCH_SIZE==64:

    PATIENCE = 10  

elif PATCH_SIZE==32:

    PATIENCE = 20  

elif PATCH_SIZE==16:

    PATIENCE = 10

# output

IMAGE_TYPE = '3d_whole_image'