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model_defs.py
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"""
This module is for storing different network architectures.
Each architecture is saved as an instance of the class ModelDef,
which is basically just a wrapper around a list of dictionaries
called layer_vars.
layer_vars represents the layers in a neural network. For every layer
we have a dictionary that stores all the hyperparameters of that layer.
Every model has an id. For instance, the one used in the final results
reported in the paper has the id fcn_rffc4. The following code acquires
the definition of fcn_rffc4:
from model_defs import get_model
model_def = get_model('fcn_rffc4')
"""
import ash
import copy
from transformations import random_geometric_transformation, random_geometric_transformation, full_rotation, RandomSectionSelection
class ModelDef(object):
def __init__(self, alpha, layer_vars,
batchnorm, out_transfer,
loss_id=None, loss_layer_def=None,
size_reduction=0, bn_version='new',
regularize=False, l1=None, l2=None,
perform_transform=None, trans_for_valid=None,
optimizer=('adam', {}), alternate_size=None,
initializer='standard'):
self.alpha = alpha
self.layer_vars = layer_vars
self.batchnorm = batchnorm
self.loss_id = loss_id
self.loss_layer_def = loss_layer_def
self.out_transfer = out_transfer
self.size_reduction = size_reduction
self.regularize = regularize
self.l1 = l1
self.l2 = l2
self.bn_version = bn_version
self.perform_transform = perform_transform
self.trans_for_valid = trans_for_valid
self.optimizer = optimizer
self.alternate_size = alternate_size
self.initializer = initializer
for lv in self.layer_vars:
if lv['type'] == 'batch_norm':
lv['alpha'] = self.alpha
if self.bn_version is not None:
lv['version'] = self.bn_version
if loss_id is None:
lf = self.loss_layer_def['loss_fun']
if isinstance(lf, str):
self.loss_name = lf
else:
self.loss_name = lf.__name__
else:
if isinstance(self.loss_id, str):
self.loss_name = self.loss_id
else:
self.loss_name = self.loss_id.__name__
alpha = 0.9
alpha9 = 0.9
alpha1 = 0.1
alpha5 = 0.5
#random_brain_sections_plus = RandomSectionSelection(data_mode='brain', followed_by=random_geometric_transformation)
#random_hand_sections_plus = RandomSectionSelection(data_mode='hand', followed_by=random_geometric_transformation)
#random_brain_sections = RandomSectionSelection(data_mode='brain', followed_by=None)
#random_hand_sections = RandomSectionSelection(data_mode='hand', followed_by=None)
#fix_brain_sections = RandomSectionSelection(data_mode='brain_fix', followed_by=None)
fcn48 = ModelDef(
alpha = alpha,
layer_vars = [
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 48
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 24
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 12
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 6
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'shortcut', 'src': 11, 'dst': 16},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 12
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 24
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 2, 'up': (2, 2, 2)} # 48
],
batchnorm = True,
loss_id = ash.tanimoto,
out_transfer = ash.TransFun(ash.tensor_softmax, 2),
size_reduction = 0,
)
### COLOR MODELS ###
xcnn = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'input'},
{'i':1, 'type': 'gate', 'src': 0, 'take':[0,3]},
{'i':2, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 4}, # 3
{'i':3, 'type': 'batch_norm'},
{'i':4, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':5, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':6, 'type': 'gate', 'src': 0, 'take':[1,2]},
{'i':7, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8},
{'i':8, 'type': 'batch_norm'},
{'i':9, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':10, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':11, 'type': 'f-concat', 'left': 5, 'right':10, 'tl': 4, 'tr': 4},
{'i':12, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 10}, # 7
{'i':13, 'type': 'batch_norm'},
{'i':14, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':15, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 10}, # 11
{'i':16, 'type': 'shortcut', 'src': 11, 'dst': 15},
{'i':17, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':18, 'type': 'f-concat', 'left': 5, 'right':10, 'tl': 2, 'tr': 6},
{'i':19, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 12},
{'i':20, 'type': 'batch_norm'},
{'i':21, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':22, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 12},
{'i':23, 'type': 'shortcut', 'src': 17, 'dst': 21},
{'i':24, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':25, 'type': 'f-concat', 'left': 17, 'right':24, 'tl': 6, 'tr': 6},
{'i':26, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns':16}, # 19
{'i':27, 'type': 'batch_norm'},
{'i':28, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':29, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 27
{'i':30, 'type': 'shortcut', 'src': 25, 'dst': 29},
{'i':31, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':32, 'type': 'f-concat', 'left': 17, 'right':24, 'tl': 4, 'tr': 12},
{'i':33, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 24},
{'i':34, 'type': 'batch_norm'},
{'i':35, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':36, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 24},
{'i':37, 'type': 'shortcut', 'src': 32, 'dst': 36},
{'i':38, 'type': 'pool', 'ps': (2, 2, 2)},
{'i':39, 'type': 'f-concat', 'left': 31, 'right':38, 'tl': 12, 'tr': 12},
{'i':40, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns':24}, # 19
{'i':41, 'type': 'batch_norm'},
{'i':42, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':43, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 24}, # 27
{'i':44, 'type': 'shortcut', 'src': 39, 'dst': 43},
{'i':45, 'type': 'f-concat', 'left': 31, 'right':38, 'tl': 8, 'tr': 24},
{'i':46, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32},
{'i':47, 'type': 'batch_norm'},
{'i':48, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':49, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32},
{'i':50, 'type': 'shortcut', 'src': 45, 'dst': 49},
{'i':51, 'type': 'f-concat', 'left': 44, 'right': 50, 'tl': 10, 'tr': 22},
{'i':52, 'type': 'batch_norm'},
{'i':53, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':54, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)},
{'i':55, 'type': 'batch_norm'},
{'i':56, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':57, 'type': 'f-concat', 'left': 30, 'right': 37, 'tl': 6, 'tr': 18},
{'i':58, 'type': 'concat', 'left': 56, 'right': 57},
{'i':59, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 56},
{'i':60, 'type': 'batch_norm'},
{'i':61, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':62, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 12},
{'i':63, 'type': 'batch_norm'},
{'i':64, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':65, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 12, 'up': (2, 2, 2)},
{'i':66, 'type': 'batch_norm'},
{'i':67, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':68, 'type': 'f-concat', 'left': 16, 'right': 23, 'tl': 6, 'tr': 10},
{'i':69, 'type': 'concat', 'left': 67, 'right': 68},
{'i':70, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 28},
{'i':71, 'type': 'batch_norm'},
{'i':72, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':73, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 8},
{'i':74, 'type': 'batch_norm'},
{'i':75, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':76, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)},
{'i':77, 'type': 'batch_norm'},
{'i':78, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':79, 'type': 'f-concat', 'left': 4, 'right': 9, 'tl': 2, 'tr': 6},
{'i':80, 'type': 'concat', 'left': 78, 'right': 79},
{'i':81, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16},
{'i':82, 'type': 'batch_norm'},
{'i':83, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':84, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
plain = ModelDef(
alpha = alpha5,
layer_vars = [
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'pool', 'ps': (2, 2, 2)},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 64, 'up': (2, 2, 2)},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'rectifier'},
{'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
fcn_rffc5 = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 128*128*96*8 - 3
{'i':1, 'type': 'batch_norm', 'alpha': alpha},
{'i':2, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 64*64*48*8 - 5
{'i':4, 'type': 'batch_norm', 'alpha': alpha},
{'i':5, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 64*64*48*16 - 9
{'i':7, 'type': 'shortcut', 'src': 3, 'dst': 6},
{'i':8, 'type': 'batch_norm', 'alpha': alpha},
{'i':9, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':10, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 32*32*24*32 - 13
{'i':11, 'type': 'batch_norm', 'alpha': alpha},
{'i':12, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':13, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 21
{'i':14, 'type': 'shortcut', 'src': 10, 'dst': 13},
{'i':15, 'type': 'batch_norm', 'alpha': alpha},
{'i':16, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 16*16*12*64 - 29
{'i':18, 'type': 'batch_norm', 'alpha': alpha},
{'i':19, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':20, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 16*16*12*64 - 45
{'i':21, 'type': 'shortcut', 'src': 17, 'dst': 20},
{'i':22, 'type': 'batch_norm', 'alpha': alpha},
{'i':23, 'type': 'non_linearity', 'transfer': 'prelu'}, # 16*16*12*128
{'i':24, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':25, 'type': 'batch_norm', 'alpha': alpha},
{'i':26, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':27, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 32*32*24*32 - 53
{'i':28, 'type': 'batch_norm', 'alpha': alpha},
{'i':29, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':30, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 32*32*24*64 - 61
{'i':31, 'type': 'batch_norm', 'alpha': alpha},
{'i':32, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':33, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32}, # 32*32*24*16
{'i':34, 'type': 'batch_norm', 'alpha': alpha},
{'i':35, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':36, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 64*64*48*16 - 65
{'i':37, 'type': 'batch_norm', 'alpha': alpha},
{'i':38, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':39, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 64*64*48*32 - 69
{'i':40, 'type': 'batch_norm', 'alpha': alpha},
{'i':41, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':42, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16}, # 64*64*48*8
{'i':43, 'type': 'batch_norm', 'alpha': alpha},
{'i':44, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':45, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 71
{'i':46, 'type': 'batch_norm', 'alpha': alpha},
{'i':47, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':48, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 73
{'i':49, 'type': 'batch_norm', 'alpha': alpha},
{'i':50, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':51, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}, # 128*128*96*2
{'i':52, 'type': 'skip', 'src': 32},
{'i':53, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':54, 'type': 'bint', 'up': 2},
{'i':55, 'type': 'skip', 'src': 41},
{'i':56, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':57, 'type': 'shortcut', 'src': 54, 'dst': 56},
{'i':58, 'type': 'bint', 'up': 2},
{'i':59, 'type': 'shortcut', 'src': 51, 'dst': 58}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
fcn_rffc4 = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 128*128*96*8 - 3
{'i':1, 'type': 'batch_norm', 'alpha': alpha},
{'i':2, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 64*64*48*8 - 5
{'i':4, 'type': 'batch_norm', 'alpha': alpha},
{'i':5, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 64*64*48*16 - 9
{'i':7, 'type': 'shortcut', 'src': 3, 'dst': 6},
{'i':8, 'type': 'batch_norm', 'alpha': alpha},
{'i':9, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':10, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 32*32*24*32 - 13
{'i':11, 'type': 'batch_norm', 'alpha': alpha},
{'i':12, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':13, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 21
{'i':14, 'type': 'shortcut', 'src': 10, 'dst': 13},
{'i':15, 'type': 'batch_norm', 'alpha': alpha},
{'i':16, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 16*16*12*64 - 29
{'i':18, 'type': 'batch_norm', 'alpha': alpha},
{'i':19, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':20, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 16*16*12*64 - 45
{'i':21, 'type': 'shortcut', 'src': 17, 'dst': 20},
{'i':22, 'type': 'batch_norm', 'alpha': alpha},
{'i':23, 'type': 'non_linearity', 'transfer': 'prelu'}, # 16*16*12*128
{'i':24, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':25, 'type': 'batch_norm', 'alpha': alpha},
{'i':26, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':27, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 32*32*24*32 - 53
{'i':28, 'type': 'batch_norm', 'alpha': alpha},
{'i':29, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':30, 'type': 'concat', 'left':16, 'right':29}, # 32*32*24*64
{'i':31, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 32*32*24*64 - 61
{'i':32, 'type': 'batch_norm', 'alpha': alpha},
{'i':33, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':34, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32}, # 32*32*24*16
{'i':35, 'type': 'batch_norm', 'alpha': alpha},
{'i':36, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':37, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 64*64*48*16 - 65
{'i':38, 'type': 'batch_norm', 'alpha': alpha},
{'i':39, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':40, 'type': 'concat', 'left': 9, 'right': 39}, # 64*64*48*32
{'i':41, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 64*64*48*32 - 69
{'i':42, 'type': 'batch_norm', 'alpha': alpha},
{'i':43, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':44, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16}, # 64*64*48*8
{'i':45, 'type': 'batch_norm', 'alpha': alpha},
{'i':46, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':47, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 71
{'i':48, 'type': 'batch_norm', 'alpha': alpha},
{'i':49, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':50, 'type': 'concat', 'left': 2, 'right': 49}, # 128*128*96*16
{'i':51, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 73
{'i':52, 'type': 'batch_norm', 'alpha': alpha},
{'i':53, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':54, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}, # 128*128*96*2
{'i':55, 'type': 'skip', 'src': 33},
{'i':56, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':57, 'type': 'bint', 'up': 2},
{'i':58, 'type': 'skip', 'src': 43},
{'i':59, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':60, 'type': 'shortcut', 'src': 57, 'dst': 59},
{'i':61, 'type': 'bint', 'up': 2},
{'i':62, 'type': 'shortcut', 'src': 54, 'dst': 61}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
fcn_rffc3 = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 128*128*96*8 - 3
{'i':1, 'type': 'batch_norm', 'alpha': alpha},
{'i':2, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 64*64*48*8 - 5
{'i':4, 'type': 'batch_norm', 'alpha': alpha},
{'i':5, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 64*64*48*16 - 9
{'i':7, 'type': 'shortcut', 'src': 3, 'dst': 6},
{'i':8, 'type': 'batch_norm', 'alpha': alpha},
{'i':9, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':10, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 32*32*24*32 - 13
{'i':11, 'type': 'batch_norm', 'alpha': alpha},
{'i':12, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':13, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 21
{'i':14, 'type': 'shortcut', 'src': 10, 'dst': 13},
{'i':15, 'type': 'batch_norm', 'alpha': alpha},
{'i':16, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 16*16*12*64 - 29
{'i':18, 'type': 'batch_norm', 'alpha': alpha},
{'i':19, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':20, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 16*16*12*64 - 45
{'i':21, 'type': 'shortcut', 'src': 17, 'dst': 20},
{'i':22, 'type': 'batch_norm', 'alpha': alpha},
{'i':23, 'type': 'non_linearity', 'transfer': 'prelu'}, # 16*16*12*128
{'i':24, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':25, 'type': 'batch_norm', 'alpha': alpha},
{'i':26, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':27, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 32*32*24*32 - 53
{'i':28, 'type': 'batch_norm', 'alpha': alpha},
{'i':29, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':30, 'type': 'shortcut', 'src': 16, 'dst': 29}, # 32*32*24*64
{'i':31, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 32*32*24*64 - 61
{'i':32, 'type': 'batch_norm', 'alpha': alpha},
{'i':33, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':34, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32}, # 32*32*24*16
{'i':35, 'type': 'batch_norm', 'alpha': alpha},
{'i':36, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':37, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 64*64*48*16 - 65
{'i':38, 'type': 'batch_norm', 'alpha': alpha},
{'i':39, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':40, 'type': 'shortcut', 'src': 9, 'dst': 39}, # 64*64*48*32
{'i':41, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 64*64*48*32 - 69
{'i':42, 'type': 'batch_norm', 'alpha': alpha},
{'i':43, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':44, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16}, # 64*64*48*8
{'i':45, 'type': 'batch_norm', 'alpha': alpha},
{'i':46, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':47, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 71
{'i':48, 'type': 'batch_norm', 'alpha': alpha},
{'i':49, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':50, 'type': 'shortcut', 'src': 2, 'dst': 49}, # 128*128*96*16
{'i':51, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 73
{'i':52, 'type': 'batch_norm', 'alpha': alpha},
{'i':53, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':54, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}, # 128*128*96*2
{'i':55, 'type': 'skip', 'src': 33},
{'i':56, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':57, 'type': 'bint', 'up': 2},
{'i':58, 'type': 'skip', 'src': 43},
{'i':59, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':60, 'type': 'shortcut', 'src': 57, 'dst': 59},
{'i':61, 'type': 'bint', 'up': 2},
{'i':62, 'type': 'shortcut', 'src': 54, 'dst': 61}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
fcn_rffc3x = ModelDef(
alpha=fcn_rffc3.alpha,
layer_vars=copy.deepcopy(fcn_rffc3.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5}),
initializer='xavier'
)
fcn_rffc3c = ModelDef(
alpha=fcn_rffc3.alpha,
layer_vars=copy.deepcopy(fcn_rffc3.layer_vars),
batchnorm=True,
loss_id=ash.fcn_cat_ce,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5}),
)
fcn_rffc = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 128*128*96*8 - 3
{'i':1, 'type': 'batch_norm', 'alpha': alpha},
{'i':2, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 64*64*48*8 - 5
{'i':4, 'type': 'batch_norm', 'alpha': alpha},
{'i':5, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 64*64*48*16 - 9
{'i':7, 'type': 'shortcut', 'src': 3, 'dst': 6},
{'i':8, 'type': 'batch_norm', 'alpha': alpha},
{'i':9, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':10, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 32*32*24*32 - 13
{'i':11, 'type': 'batch_norm', 'alpha': alpha},
{'i':12, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':13, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 21
{'i':14, 'type': 'shortcut', 'src': 10, 'dst': 13},
{'i':15, 'type': 'batch_norm', 'alpha': alpha},
{'i':16, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 16*16*12*64 - 29
{'i':18, 'type': 'batch_norm', 'alpha': alpha},
{'i':19, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':20, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 16*16*12*64 - 45
{'i':21, 'type': 'shortcut', 'src': 17, 'dst': 20},
{'i':22, 'type': 'batch_norm', 'alpha': alpha},
{'i':23, 'type': 'non_linearity', 'transfer': 'prelu'}, # 16*16*12*128
{'i':24, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':25, 'type': 'batch_norm', 'alpha': alpha},
{'i':26, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':27, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 32*32*24*32 - 53
{'i':28, 'type': 'batch_norm', 'alpha': alpha},
{'i':29, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':30, 'type': 'concat', 'left':16, 'right':29}, # 32*32*24*64
{'i':31, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 32*32*24*64 - 61
{'i':32, 'type': 'shortcut', 'src': 16, 'dst': 31},
{'i':33, 'type': 'batch_norm', 'alpha': alpha},
{'i':34, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':35, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32}, # 32*32*24*16
{'i':36, 'type': 'batch_norm', 'alpha': alpha},
{'i':37, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':38, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 64*64*48*16 - 65
{'i':39, 'type': 'batch_norm', 'alpha': alpha},
{'i':40, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':41, 'type': 'concat', 'left': 9, 'right': 40}, # 64*64*48*32
{'i':42, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 64*64*48*32 - 69
{'i':43, 'type': 'shortcut', 'src': 9, 'dst': 42},
{'i':44, 'type': 'batch_norm', 'alpha': alpha},
{'i':45, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':46, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16}, # 64*64*48*8
{'i':47, 'type': 'batch_norm', 'alpha': alpha},
{'i':48, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':49, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 71
{'i':50, 'type': 'batch_norm', 'alpha': alpha},
{'i':51, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':52, 'type': 'concat', 'left': 2, 'right': 51}, # 128*128*96*16
{'i':53, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 73
{'i':54, 'type': 'shortcut', 'src': 2, 'dst': 53},
{'i':55, 'type': 'batch_norm', 'alpha': alpha},
{'i':56, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':57, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}, # 128*128*96*2
{'i':58, 'type': 'skip', 'src': 34},
{'i':59, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':60, 'type': 'bint', 'up': 2},
{'i':61, 'type': 'skip', 'src': 45},
{'i':62, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':63, 'type': 'shortcut', 'src': 60, 'dst': 62},
{'i':64, 'type': 'bint', 'up': 2},
{'i':65, 'type': 'shortcut', 'src': 57, 'dst': 64}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
fcn_rffc2 = ModelDef(
alpha=fcn_rffc.alpha,
layer_vars=copy.deepcopy(fcn_rffc.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-6})
)
fcn_rffc_mean = ModelDef(
alpha=fcn_rffc.alpha,
layer_vars=copy.deepcopy(fcn_rffc.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate':5e-5})
)
res_fcn_add = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 3
{'i':1, 'type': 'batch_norm'},
{'i':2, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 5
{'i':4, 'type': 'batch_norm'},
{'i':5, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 9
{'i':7, 'type': 'batch_norm'},
{'i':8, 'type': 'non_linearity', 'transfer': 'rectifier'}, # 13
{'i':9, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16},
{'i':10, 'type': 'shortcut', 'src': 3, 'dst': 9},
{'i':11, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 17
{'i':12, 'type': 'batch_norm'},
{'i':13, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':14, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 25
{'i':15, 'type': 'batch_norm'},
{'i':16, 'type': 'non_linearity', 'transfer': 'rectifier'}, # 33
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32},
{'i':18, 'type': 'shortcut', 'src': 11, 'dst': 17},
{'i':19, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 41
{'i':20, 'type': 'batch_norm'},
{'i':21, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':22, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 57
{'i':23, 'type': 'batch_norm'},
{'i':24, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':25, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 73
{'i':26, 'type': 'shortcut', 'src': 19, 'dst': 25},
{'i':27, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':28, 'type': 'batch_norm'},
{'i':29, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':30, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 81
{'i':31, 'type': 'shortcut', 'src': 18, 'dst': 30},
{'i':32, 'type': 'batch_norm'},
{'i':33, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':34, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 89
{'i':35, 'type': 'batch_norm'},
{'i':36, 'type': 'non_linearity', 'transfer': 'rectifier'}, # 97
{'i':37, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64},
{'i':38, 'type': 'shortcut', 'src': 31, 'dst': 37},
{'i':39, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16},
{'i':40, 'type': 'batch_norm'},
{'i':41, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':42, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 101
{'i':43, 'type': 'shortcut', 'src': 10, 'dst': 42},
{'i':44, 'type': 'batch_norm'},
{'i':45, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':46, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 105
{'i':47, 'type': 'batch_norm'},
{'i':48, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':49, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 109
{'i':50, 'type': 'shortcut', 'src': 43, 'dst': 49},
{'i':51, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 8},
{'i':52, 'type': 'batch_norm'},
{'i':53, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':54, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 111
{'i':55, 'type': 'shortcut', 'src': 2, 'dst': 54},
{'i':56, 'type': 'batch_norm'},
{'i':57, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':58, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 113
{'i':59, 'type': 'batch_norm'},
{'i':60, 'type': 'non_linearity', 'transfer': 'rectifier'},
{'i':61, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 115
{'i':62, 'type': 'shortcut', 'src': 55, 'dst': 61},
{'i':63, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':64, 'type': 'skip', 'src': 38},
{'i':65, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':66, 'type': 'bint', 'up': 2},
{'i':67, 'type': 'skip', 'src': 50},
{'i':68, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':69, 'type': 'shortcut', 'src': 66, 'dst': 68},
{'i':70, 'type': 'bint', 'up': 2},
{'i':71, 'type': 'shortcut', 'src': 63, 'dst': 70}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate': 2e-5})
)
res_fcn_concat = ModelDef(
alpha=alpha5,
layer_vars=[
{'i':0, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8}, # 3
{'i':1, 'type': 'batch_norm'},
{'i':2, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':3, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 8, 'stride': (2, 2, 2)}, # 5
{'i':4, 'type': 'batch_norm'},
{'i':5, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':6, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 9
{'i':7, 'type': 'batch_norm'},
{'i':8, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':9, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 13
{'i':10, 'type': 'shortcut', 'src': 3, 'dst': 9},
{'i':11, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32, 'stride': (2, 2, 2)}, # 17
{'i':12, 'type': 'batch_norm'},
{'i':13, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':14, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 25
{'i':15, 'type': 'batch_norm'},
{'i':16, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':17, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 33
{'i':18, 'type': 'shortcut', 'src': 11, 'dst': 17},
{'i':19, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64, 'stride': (2, 2, 2)}, # 41
{'i':20, 'type': 'batch_norm'},
{'i':21, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':22, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 57
{'i':23, 'type': 'batch_norm'},
{'i':24, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':25, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 73
{'i':26, 'type': 'shortcut', 'src': 19, 'dst': 25},
{'i':27, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 32},
{'i':28, 'type': 'batch_norm'},
{'i':29, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':30, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 32, 'up': (2, 2, 2)}, # 81
{'i':31, 'type': 'concat', 'left': 18, 'right': 30},
{'i':32, 'type': 'batch_norm'},
{'i':33, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':34, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 89
{'i':35, 'type': 'batch_norm'},
{'i':36, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':37, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 64}, # 97
{'i':38, 'type': 'shortcut', 'src': 31, 'dst': 37},
{'i':39, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 16},
{'i':40, 'type': 'batch_norm'},
{'i':41, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':42, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 16, 'up': (2, 2, 2)}, # 101
{'i':43, 'type': 'concat', 'left': 10, 'right': 42},
{'i':44, 'type': 'batch_norm'},
{'i':45, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':46, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 105
{'i':47, 'type': 'batch_norm'},
{'i':48, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':49, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 32}, # 109
{'i':50, 'type': 'shortcut', 'src': 43, 'dst': 49},
{'i':51, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 8},
{'i':52, 'type': 'batch_norm'},
{'i':53, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':54, 'type': 'deconv', 'fs': (3, 3, 3), 'nkerns': 8, 'up': (2, 2, 2)}, # 111
{'i':55, 'type': 'concat', 'left': 2, 'right': 54},
{'i':56, 'type': 'batch_norm'},
{'i':57, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':58, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 113
{'i':59, 'type': 'batch_norm'},
{'i':60, 'type': 'non_linearity', 'transfer': 'prelu'},
{'i':61, 'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 16}, # 115
{'i':62, 'type': 'shortcut', 'src': 55, 'dst': 61},
{'i':63, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':64, 'type': 'skip', 'src': 38},
{'i':65, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':66, 'type': 'bint', 'up': 2},
{'i':67, 'type': 'skip', 'src': 50},
{'i':68, 'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5},
{'i':69, 'type': 'shortcut', 'src': 66, 'dst': 68},
{'i':70, 'type': 'bint', 'up': 2},
{'i':71, 'type': 'shortcut', 'src': 63, 'dst': 70}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=ash.TransFun(ash.tensor_softmax, 5),
size_reduction=0,
bn_version='new',
regularize=False,
l1=None, # val like 30171.708
l2=None, #0.001 val like 736.552
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate': 5e-5})
)
res_hand_section = ModelDef(
alpha=res_fcn_add.alpha,
layer_vars=copy.deepcopy(res_fcn_add.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=res_fcn_add.out_transfer,
size_reduction=0,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
trans_for_valid=random_geometric_transformation,
optimizer=('adam', {'step_rate': 0.0001, 'decay_mom1': 0.1, 'decay_mom2': 0.001}),
alternate_size=(144, 120, 96)
)
res_hand_section_concat = ModelDef(
alpha=res_fcn_concat.alpha,
layer_vars=copy.deepcopy(res_fcn_concat.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=res_fcn_concat.out_transfer,
size_reduction=0,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
trans_for_valid=random_geometric_transformation,
optimizer=('adam', {'step_rate': 0.0001, 'decay_mom1': 0.1, 'decay_mom2': 0.001}),
alternate_size=(144, 120, 96)
)
res_brain_section = ModelDef(
alpha=res_fcn_add.alpha,
layer_vars=copy.deepcopy(res_fcn_add.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=res_fcn_add.out_transfer,
size_reduction=0,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
trans_for_valid=random_geometric_transformation,
optimizer=('adam', {'step_rate': 0.0001, 'decay_mom1': 0.1, 'decay_mom2': 0.001}),
alternate_size=(80, 72, 64)
)
res_brain_section_concat = ModelDef(
alpha=res_fcn_concat.alpha,
layer_vars=copy.deepcopy(res_fcn_concat.layer_vars),
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer=res_fcn_concat.out_transfer,
size_reduction=0,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
trans_for_valid=random_geometric_transformation,
optimizer=('adam', {'step_rate': 0.0001, 'decay_mom1': 0.1, 'decay_mom2': 0.001}),
alternate_size=(80, 72, 64)
)
ez = ModelDef(
alpha = alpha5,
layer_vars = [
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 5},
{'type': 'batch_norm', 'alpha': alpha},
{'type': 'non_linearity', 'transfer': 'prelu'},
{'type': 'conv', 'fs': (1, 1, 1), 'nkerns': 5}
],
batchnorm=True,
loss_id=ash.tanimoto,
out_transfer = ash.TransFun(ash.tensor_softmax, 5),
size_reduction = 0,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate': 5e-5}),
initializer='xavier'
)
single = ModelDef(
alpha = alpha,
layer_vars= [
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 5}
],
batchnorm=False,
loss_id=ash.tanimoto,
out_transfer = ash.TransFun(ash.tensor_softmax, 5),
size_reduction = 0,
bn_version = 'new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
optimizer=('adam', {'step_rate': 0.0001})
)
single2 = ModelDef(
alpha = alpha,
layer_vars= [
{'type': 'conv', 'fs': (3, 3, 3), 'nkerns': 5}
],
batchnorm=False,
loss_id=ash.tanimoto,
out_transfer = ash.TransFun(ash.tensor_softmax, 5),
size_reduction = 0,
bn_version = 'new',
regularize=False,
l1=None,
l2=None,
perform_transform=random_geometric_transformation,
optimizer= ('adam', {'step_rate': .0002})
)
## TRANSFORM TESTS: ##
res_fcn_add_rot = ModelDef(
alpha=res_fcn_add.alpha,
layer_vars=copy.deepcopy(res_fcn_add.layer_vars),
batchnorm=True,
loss_id=res_fcn_add.loss_id,
out_transfer=res_fcn_add.out_transfer,
size_reduction=res_fcn_add.size_reduction,
bn_version='new',
regularize=False,
l1=None,
l2=None,
perform_transform=full_rotation
)
res_fcn_concat_rot = ModelDef(