def build_model():
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:sax"]
input_size_mask = data_sizes["sliced:data:sax:is_not_padded"]
input_size_locations = data_sizes["sliced:data:sax:locations"]
l0 = nn.layers.InputLayer(input_size)
lin_slice_mask = nn.layers.InputLayer(input_size_mask)
lin_slice_locations = nn.layers.InputLayer(input_size_locations)
# PREPROCESS SLICES SEPERATELY
# Convolutional layers and some dense layers are defined in a submodel
l0_slices = nn.layers.ReshapeLayer(l0, (-1, [2], [3], [4]))
import je_ss_jonisc64small_360_gauss_longer
submodel = je_ss_jonisc64small_360_gauss_longer.build_model(l0_slices)
# Systole Dense layers
l_sys_mu = submodel["meta_outputs"]["systole:mu"]
l_sys_sigma = submodel["meta_outputs"]["systole:sigma"]
# Diastole Dense layers
l_dia_mu = submodel["meta_outputs"]["diastole:mu"]
l_dia_sigma = submodel["meta_outputs"]["diastole:sigma"]
# AGGREGATE SLICES PER PATIENT
l_scaled_slice_locations = layers.TrainableScaleLayer(lin_slice_locations, scale=nn.init.Constant(0.1), trainable=False)
# Systole
l_pat_sys_ss_mu = nn.layers.ReshapeLayer(l_sys_mu, (-1, nr_slices))
l_pat_sys_ss_sigma = nn.layers.ReshapeLayer(l_sys_sigma, (-1, nr_slices))
l_pat_sys_aggr_mu_sigma = layers.JeroenLayer([l_pat_sys_ss_mu, l_pat_sys_ss_sigma, lin_slice_mask, l_scaled_slice_locations], rescale_input=100.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
# Diastole
l_pat_dia_ss_mu = nn.layers.ReshapeLayer(l_dia_mu, (-1, nr_slices))
l_pat_dia_ss_sigma = nn.layers.ReshapeLayer(l_dia_sigma, (-1, nr_slices))
l_pat_dia_aggr_mu_sigma = layers.JeroenLayer([l_pat_dia_ss_mu, l_pat_dia_ss_sigma, lin_slice_mask, l_scaled_slice_locations], rescale_input=100.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
submodels = [submodel]
return {
"inputs":{
"sliced:data:sax": l0,
"sliced:data:sax:is_not_padded": lin_slice_mask,
"sliced:data:sax:locations": lin_slice_locations,
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable": dict(
{},
**{
k: v
for d in [model["regularizable"] for model in submodels if "regularizable" in model]
for k, v in d.items() }
),
"pretrained":{
je_ss_jonisc64small_360_gauss_longer.__name__: submodel["outputs"],
}
}
def build_model():
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:sax"]
input_size_mask = data_sizes["sliced:data:sax:is_not_padded"]
input_size_locations = data_sizes["sliced:data:sax:locations"]
l0 = nn.layers.InputLayer(input_size)
lin_slice_mask = nn.layers.InputLayer(input_size_mask)
lin_slice_locations = nn.layers.InputLayer(input_size_locations)
# PREPROCESS SLICES SEPERATELY
# Convolutional layers and some dense layers are defined in a submodel
l0_slices = nn.layers.ReshapeLayer(l0, (-1, [2], [3], [4]))
import je_ss_jonisc64small_360_gauss_longer
submodel = je_ss_jonisc64small_360_gauss_longer.build_model(l0_slices)
# Systole Dense layers
l_sys_mu = submodel["meta_outputs"]["systole:mu"]
l_sys_sigma = submodel["meta_outputs"]["systole:sigma"]
# Diastole Dense layers
l_dia_mu = submodel["meta_outputs"]["diastole:mu"]
l_dia_sigma = submodel["meta_outputs"]["diastole:sigma"]
# AGGREGATE SLICES PER PATIENT
l_scaled_slice_locations = layers.TrainableScaleLayer(
lin_slice_locations, scale=nn.init.Constant(0.1), trainable=False)
# Systole
l_pat_sys_ss_mu = nn.layers.ReshapeLayer(l_sys_mu, (-1, nr_slices))
l_pat_sys_ss_sigma = nn.layers.ReshapeLayer(l_sys_sigma, (-1, nr_slices))
l_pat_sys_aggr_mu_sigma = layers.JeroenLayer([
l_pat_sys_ss_mu, l_pat_sys_ss_sigma, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=100.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
# Diastole
l_pat_dia_ss_mu = nn.layers.ReshapeLayer(l_dia_mu, (-1, nr_slices))
l_pat_dia_ss_sigma = nn.layers.ReshapeLayer(l_dia_sigma, (-1, nr_slices))
l_pat_dia_aggr_mu_sigma = layers.JeroenLayer([
l_pat_dia_ss_mu, l_pat_dia_ss_sigma, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=100.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
submodels = [submodel]
return {
"inputs": {
"sliced:data:sax": l0,
"sliced:data:sax:is_not_padded": lin_slice_mask,
"sliced:data:sax:locations": lin_slice_locations,
},
"outputs": {
"systole": l_systole,
"diastole": l_diastole,
},
"regularizable":
dict({}, **{
k: v
for d in [
model["regularizable"] for model in submodels
if "regularizable" in model
] for k, v in d.items()
}),
"pretrained": {
je_ss_jonisc64small_360_gauss_longer.__name__: submodel["outputs"],
}
}
def build_model():
import j6_2ch_gauss, j6_4ch_gauss
meta_2ch = j6_2ch_gauss.build_model()
meta_4ch = j6_4ch_gauss.build_model()
l_meta_2ch_systole = nn.layers.DenseLayer(
meta_2ch["meta_outputs"]["systole"],
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_meta_2ch_diastole = nn.layers.DenseLayer(
meta_2ch["meta_outputs"]["diastole"],
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_systole = nn.layers.DenseLayer(
meta_4ch["meta_outputs"]["systole"],
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_diastole = nn.layers.DenseLayer(
meta_4ch["meta_outputs"]["diastole"],
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:sax"]
input_size_mask = data_sizes["sliced:data:sax:is_not_padded"]
input_size_locations = data_sizes["sliced:data:sax:locations"]
l0 = nn.layers.InputLayer(input_size)
lin_slice_mask = nn.layers.InputLayer(input_size_mask)
lin_slice_locations = nn.layers.InputLayer(input_size_locations)
# PREPROCESS SLICES SEPERATELY
# Convolutional layers and some dense layers are defined in a submodel
l0_slices = nn.layers.ReshapeLayer(l0, (-1, [2], [3], [4]))
import je_ss_jonisc64small_360_gauss_longer
submodel = je_ss_jonisc64small_360_gauss_longer.build_model(l0_slices)
# Systole Dense layers
l_sys_mu = submodel["meta_outputs"]["systole:mu"]
l_sys_sigma = submodel["meta_outputs"]["systole:sigma"]
l_sys_meta = submodel["meta_outputs"]["systole"]
# Diastole Dense layers
l_dia_mu = submodel["meta_outputs"]["diastole:mu"]
l_dia_sigma = submodel["meta_outputs"]["diastole:sigma"]
l_dia_meta = submodel["meta_outputs"]["diastole"]
# AGGREGATE SLICES PER PATIENT
l_scaled_slice_locations = layers.TrainableScaleLayer(
lin_slice_locations, scale=nn.init.Constant(0.1), trainable=False)
# Systole
l_pat_sys_ss_mu = nn.layers.ReshapeLayer(l_sys_mu, (-1, nr_slices))
l_pat_sys_ss_sigma = nn.layers.ReshapeLayer(l_sys_sigma, (-1, nr_slices))
l_pat_sys_aggr_mu_sigma = layers.JeroenLayer([
l_pat_sys_ss_mu, l_pat_sys_ss_sigma, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=100.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
l_sys_meta = nn.layers.DenseLayer(nn.layers.ReshapeLayer(
l_sys_meta, (-1, nr_slices, 512)),
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_meta_systole = nn.layers.ConcatLayer(
[l_meta_2ch_systole, l_meta_4ch_systole, l_sys_meta])
l_weights = nn.layers.DenseLayer(l_meta_systole,
num_units=512,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_weights = nn.layers.DenseLayer(l_weights,
num_units=3,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
systole_output = layers.WeightedMeanLayer(l_weights, [
l_systole, meta_2ch["outputs"]["systole"],
meta_4ch["outputs"]["systole"]
])
# Diastole
l_pat_dia_ss_mu = nn.layers.ReshapeLayer(l_dia_mu, (-1, nr_slices))
l_pat_dia_ss_sigma = nn.layers.ReshapeLayer(l_dia_sigma, (-1, nr_slices))
l_pat_dia_aggr_mu_sigma = layers.JeroenLayer([
l_pat_dia_ss_mu, l_pat_dia_ss_sigma, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=100.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
l_dia_meta = nn.layers.DenseLayer(nn.layers.ReshapeLayer(
l_dia_meta, (-1, nr_slices, 512)),
num_units=64,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_meta_diastole = nn.layers.ConcatLayer(
[l_meta_2ch_diastole, l_meta_4ch_diastole, l_dia_meta])
l_weights = nn.layers.DenseLayer(l_meta_diastole,
num_units=512,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
l_weights = nn.layers.DenseLayer(l_weights,
num_units=3,
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.identity)
diastole_output = layers.WeightedMeanLayer(l_weights, [
l_diastole, meta_2ch["outputs"]["diastole"],
meta_4ch["outputs"]["diastole"]
])
submodels = [submodel, meta_2ch, meta_4ch]
return {
"inputs":
dict(
{
"sliced:data:sax": l0,
"sliced:data:sax:is_not_padded": lin_slice_mask,
"sliced:data:sax:locations": lin_slice_locations,
}, **{
k: v
for d in [model["inputs"] for model in [meta_2ch, meta_4ch]]
for k, v in d.items()
}),
"outputs": {
"systole": systole_output,
"diastole": diastole_output,
},
"regularizable":
dict({}, **{
k: v
for d in [
model["regularizable"] for model in submodels
if "regularizable" in model
] for k, v in d.items()
}),
"pretrained": {
je_ss_jonisc64small_360_gauss_longer.__name__: submodel["outputs"],
j6_2ch_gauss.__name__: meta_2ch["outputs"],
j6_4ch_gauss.__name__: meta_4ch["outputs"],
},
#"cutoff_gradients": [
#] + [ v for d in [model["meta_outputs"] for model in [meta_2ch, meta_4ch] if "meta_outputs" in model]
# for v in d.values() ]
}
def build_model():
import j6_2ch_gauss, j6_4ch_gauss
meta_2ch = j6_2ch_gauss.build_model()
meta_4ch = j6_4ch_gauss.build_model()
l_meta_2ch_systole = nn.layers.DenseLayer(meta_2ch["meta_outputs"]["systole"], num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_2ch_diastole = nn.layers.DenseLayer(meta_2ch["meta_outputs"]["diastole"], num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_systole = nn.layers.DenseLayer(meta_4ch["meta_outputs"]["systole"], num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_4ch_diastole = nn.layers.DenseLayer(meta_4ch["meta_outputs"]["diastole"], num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
#################
# Regular model #
#################
input_size = data_sizes["sliced:data:sax"]
input_size_mask = data_sizes["sliced:data:sax:is_not_padded"]
input_size_locations = data_sizes["sliced:data:sax:locations"]
l0 = nn.layers.InputLayer(input_size)
lin_slice_mask = nn.layers.InputLayer(input_size_mask)
lin_slice_locations = nn.layers.InputLayer(input_size_locations)
# PREPROCESS SLICES SEPERATELY
# Convolutional layers and some dense layers are defined in a submodel
l0_slices = nn.layers.ReshapeLayer(l0, (-1, [2], [3], [4]))
import je_ss_jonisc64small_360_gauss_longer
submodel = je_ss_jonisc64small_360_gauss_longer.build_model(l0_slices)
# Systole Dense layers
l_sys_mu = submodel["meta_outputs"]["systole:mu"]
l_sys_sigma = submodel["meta_outputs"]["systole:sigma"]
l_sys_meta = submodel["meta_outputs"]["systole"]
# Diastole Dense layers
l_dia_mu = submodel["meta_outputs"]["diastole:mu"]
l_dia_sigma = submodel["meta_outputs"]["diastole:sigma"]
l_dia_meta = submodel["meta_outputs"]["diastole"]
# AGGREGATE SLICES PER PATIENT
l_scaled_slice_locations = layers.TrainableScaleLayer(lin_slice_locations, scale=nn.init.Constant(0.1), trainable=False)
# Systole
l_pat_sys_ss_mu = nn.layers.ReshapeLayer(l_sys_mu, (-1, nr_slices))
l_pat_sys_ss_sigma = nn.layers.ReshapeLayer(l_sys_sigma, (-1, nr_slices))
l_pat_sys_aggr_mu_sigma = layers.JeroenLayer([l_pat_sys_ss_mu, l_pat_sys_ss_sigma, lin_slice_mask, l_scaled_slice_locations], rescale_input=100.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
l_sys_meta = nn.layers.DenseLayer(nn.layers.ReshapeLayer(l_sys_meta, (-1, nr_slices, 512)), num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_systole = nn.layers.ConcatLayer([l_meta_2ch_systole, l_meta_4ch_systole, l_sys_meta])
l_weights = nn.layers.DenseLayer(l_meta_systole, num_units=512, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_weights = nn.layers.DenseLayer(l_weights, num_units=3, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
systole_output = layers.WeightedMeanLayer(l_weights, [l_systole, meta_2ch["outputs"]["systole"], meta_4ch["outputs"]["systole"]])
# Diastole
l_pat_dia_ss_mu = nn.layers.ReshapeLayer(l_dia_mu, (-1, nr_slices))
l_pat_dia_ss_sigma = nn.layers.ReshapeLayer(l_dia_sigma, (-1, nr_slices))
l_pat_dia_aggr_mu_sigma = layers.JeroenLayer([l_pat_dia_ss_mu, l_pat_dia_ss_sigma, lin_slice_mask, l_scaled_slice_locations], rescale_input=100.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
l_dia_meta = nn.layers.DenseLayer(nn.layers.ReshapeLayer(l_dia_meta, (-1, nr_slices, 512)), num_units=64, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_meta_diastole = nn.layers.ConcatLayer([l_meta_2ch_diastole, l_meta_4ch_diastole, l_dia_meta])
l_weights = nn.layers.DenseLayer(l_meta_diastole, num_units=512, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
l_weights = nn.layers.DenseLayer(l_weights, num_units=3, W=nn.init.Orthogonal(), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.identity)
diastole_output = layers.WeightedMeanLayer(l_weights, [l_diastole, meta_2ch["outputs"]["diastole"], meta_4ch["outputs"]["diastole"]])
submodels = [submodel, meta_2ch, meta_4ch]
return {
"inputs":dict({
"sliced:data:sax": l0,
"sliced:data:sax:is_not_padded": lin_slice_mask,
"sliced:data:sax:locations": lin_slice_locations,
}, **{ k: v for d in [model["inputs"] for model in [meta_2ch, meta_4ch]]
for k, v in d.items() }
),
"outputs": {
"systole": systole_output,
"diastole": diastole_output,
},
"regularizable": dict(
{},
**{
k: v
for d in [model["regularizable"] for model in submodels if "regularizable" in model]
for k, v in d.items() }
),
"pretrained":{
je_ss_jonisc64small_360_gauss_longer.__name__: submodel["outputs"],
j6_2ch_gauss.__name__: meta_2ch["outputs"],
j6_4ch_gauss.__name__: meta_4ch["outputs"],
},
#"cutoff_gradients": [
#] + [ v for d in [model["meta_outputs"] for model in [meta_2ch, meta_4ch] if "meta_outputs" in model]
# for v in d.values() ]
}
