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_jonisc80_leaky_convroll_augzoombright
submodel = je_ss_jonisc80_leaky_convroll_augzoombright.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_jonisc80_leaky_convroll_augzoombright.__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
l0_slices = nn.layers.ReshapeLayer(l0, (batch_size * nr_slices, 30, patch_px, patch_px)) # (bxs, t, i, j)
subsample_factor = 2
l0_slices_subsampled = nn.layers.SliceLayer(l0_slices, axis=1, indices=slice(0, 30, subsample_factor))
nr_frames_subsampled = 30 / subsample_factor
# PREPROCESS FRAMES SEPERATELY
l0_frames = nn.layers.ReshapeLayer(l0_slices_subsampled, (batch_size * nr_slices * nr_frames_subsampled, 1, patch_px, patch_px)) # (bxsxt, 1, i, j)
l1a = nn.layers.dnn.Conv2DDNNLayer(l0_frames, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=16, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l1b = nn.layers.dnn.Conv2DDNNLayer(l1a, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=16, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l1c = nn.layers.dnn.Conv2DDNNLayer(l1b, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=16, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l1 = nn.layers.dnn.MaxPool2DDNNLayer(l1c, pool_size=(2,2), stride=(2,2))
l2a = nn.layers.dnn.Conv2DDNNLayer(l1, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=32, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l2b = nn.layers.dnn.Conv2DDNNLayer(l2a, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=32, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l2c = nn.layers.dnn.Conv2DDNNLayer(l2b, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=32, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l2 = nn.layers.dnn.MaxPool2DDNNLayer(l2c, pool_size=(2,2), stride=(2,2))
l3a = nn.layers.dnn.Conv2DDNNLayer(l2, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=64, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l3b = nn.layers.dnn.Conv2DDNNLayer(l3a, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=64, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l3c = nn.layers.dnn.Conv2DDNNLayer(l3b, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=64, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l3d = nn.layers.dnn.Conv2DDNNLayer(l3c, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=64, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l3 = nn.layers.dnn.MaxPool2DDNNLayer(l3d, pool_size=(2,2), stride=(2,2))
l4a = nn.layers.dnn.Conv2DDNNLayer(l3, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l4b = nn.layers.dnn.Conv2DDNNLayer(l4a, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l4c = nn.layers.dnn.Conv2DDNNLayer(l4b, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l4d = nn.layers.dnn.Conv2DDNNLayer(l4c, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l4 = nn.layers.dnn.MaxPool2DDNNLayer(l4d, pool_size=(2,2), stride=(2,2))
l5a = nn.layers.dnn.Conv2DDNNLayer(l4, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l5b = nn.layers.dnn.Conv2DDNNLayer(l5a, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l5c = nn.layers.dnn.Conv2DDNNLayer(l5b, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l5d = nn.layers.dnn.Conv2DDNNLayer(l5c, W=nn.init.Orthogonal("relu"), filter_size=(3,3), num_filters=128, stride=(1,1), pad="same", nonlinearity=nn.nonlinearities.rectify)
l5 = nn.layers.dnn.MaxPool2DDNNLayer(l5d, pool_size=(2,2), stride=(2,2))
l5drop = nn.layers.dropout(l5, p=0.5)
ld1 = nn.layers.DenseLayer(l5drop, num_units=256, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
ld1drop = nn.layers.dropout(ld1, p=0.5)
ld2 = nn.layers.DenseLayer(ld1drop, num_units=256, W=nn.init.Orthogonal("relu"),b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.rectify)
ld2drop = nn.layers.dropout(ld2, p=0.5)
ld3mu = nn.layers.DenseLayer(ld2drop, num_units=1, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(15.0), nonlinearity=None)
ld3sigma = nn.layers.DenseLayer(ld2drop, num_units=1, W=nn.init.Orthogonal("relu"), b=nn.init.Constant(5.0), nonlinearity=lb_softplus(.3))
ld3musigma = nn.layers.ConcatLayer([ld3mu, ld3sigma], axis=1)
# Go back to a per slice model
l_slices_musigma = nn.layers.ReshapeLayer(ld3musigma, (batch_size * nr_slices, nr_frames_subsampled, 2)) # (bxs, t, 2)
l_slices_musigma_sys = layers.ArgmaxAndMaxLayer(l_slices_musigma, mode='min') # (bxs, 2)
l_slices_musigma_dia = layers.ArgmaxAndMaxLayer(l_slices_musigma, mode='max') # (bxs, 2)
# 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_musigma = nn.layers.ReshapeLayer(l_slices_musigma_sys, (batch_size, nr_slices, 2))
l_pat_sys_ss_mu = nn.layers.SliceLayer(l_pat_sys_ss_musigma, indices=0, axis=-1)
l_pat_sys_ss_sigma = nn.layers.SliceLayer(l_pat_sys_ss_musigma, indices=1, axis=-1)
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=1.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
# Diastole
l_pat_dia_ss_musigma = nn.layers.ReshapeLayer(l_slices_musigma_dia, (batch_size, nr_slices, 2))
l_pat_dia_ss_mu = nn.layers.SliceLayer(l_pat_dia_ss_musigma, indices=0, axis=-1)
l_pat_dia_ss_sigma = nn.layers.SliceLayer(l_pat_dia_ss_musigma, indices=1, axis=-1)
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=1.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
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": {
},
}
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]))
l1a = nn.layers.dnn.Conv2DDNNLayer(l0_slices,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=64,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l1b = nn.layers.dnn.Conv2DDNNLayer(l1a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=64,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l1 = nn.layers.dnn.MaxPool2DDNNLayer(l1b, pool_size=(2, 2), stride=(2, 2))
l2a = nn.layers.dnn.Conv2DDNNLayer(l1,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=128,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l2b = nn.layers.dnn.Conv2DDNNLayer(l2a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=128,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l2 = nn.layers.dnn.MaxPool2DDNNLayer(l2b, pool_size=(2, 2), stride=(2, 2))
l3a = nn.layers.dnn.Conv2DDNNLayer(l2,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=256,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l3b = nn.layers.dnn.Conv2DDNNLayer(l3a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=256,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l3c = nn.layers.dnn.Conv2DDNNLayer(l3b,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=256,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l3 = nn.layers.dnn.MaxPool2DDNNLayer(l3c, pool_size=(2, 2), stride=(2, 2))
l4a = nn.layers.dnn.Conv2DDNNLayer(l3,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l4b = nn.layers.dnn.Conv2DDNNLayer(l4a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l4c = nn.layers.dnn.Conv2DDNNLayer(l4b,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l4 = nn.layers.dnn.MaxPool2DDNNLayer(l4c, pool_size=(2, 2), stride=(2, 2))
l5a = nn.layers.dnn.Conv2DDNNLayer(l4,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l5b = nn.layers.dnn.Conv2DDNNLayer(l5a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l5c = nn.layers.dnn.Conv2DDNNLayer(l5b,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=512,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
l5 = nn.layers.dnn.MaxPool2DDNNLayer(l5c, pool_size=(2, 2), stride=(2, 2))
# Systole Dense layers
ldsys1 = nn.layers.DenseLayer(l5,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
ldsys1drop = nn.layers.dropout(ldsys1, p=0.5)
ldsys2 = nn.layers.DenseLayer(ldsys1drop,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
ldsys2drop = nn.layers.dropout(ldsys2, p=0.5)
l_sys_mu = nn.layers.DenseLayer(ldsys2drop,
num_units=1,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(10.0),
nonlinearity=None)
l_sys_sigma = nn.layers.DenseLayer(ldsys2drop,
num_units=1,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(3.),
nonlinearity=lb_softplus(.1))
# Diastole Dense layers
lddia1 = nn.layers.DenseLayer(l5,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
lddia1drop = nn.layers.dropout(lddia1, p=0.5)
lddia2 = nn.layers.DenseLayer(lddia1drop,
num_units=512,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.rectify)
lddia2drop = nn.layers.dropout(lddia2, p=0.5)
l_dia_mu = nn.layers.DenseLayer(lddia2drop,
num_units=1,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(10.0),
nonlinearity=None)
l_dia_sigma = nn.layers.DenseLayer(lddia2drop,
num_units=1,
W=nn.init.Orthogonal("relu"),
b=nn.init.Constant(3.),
nonlinearity=lb_softplus(.1))
# 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=1.)
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=1.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
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": {
ldsys1: l2_weight,
ldsys2: l2_weight,
l_sys_mu: l2_weight_out,
l_sys_sigma: l2_weight_out,
lddia1: l2_weight,
lddia2: l2_weight,
l_dia_mu: l2_weight_out,
l_dia_sigma: l2_weight_out,
},
}
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
l0_slices = nn.layers.ReshapeLayer(
l0, (batch_size * nr_slices, 30, patch_px, patch_px)) # (bxs, t, i, j)
subsample_factor = 2
l0_slices_subsampled = nn.layers.SliceLayer(l0_slices,
axis=1,
indices=slice(
0, 30, subsample_factor))
nr_frames_subsampled = 30 / subsample_factor
# PREPROCESS FRAMES SEPERATELY
l0_frames = nn.layers.ReshapeLayer(
l0_slices_subsampled, (batch_size * nr_slices * nr_frames_subsampled,
1, patch_px, patch_px)) # (bxsxt, 1, i, j)
# downsample
downsample = lambda incoming: nn.layers.dnn.Pool2DDNNLayer(
incoming, pool_size=(2, 2), stride=(2, 2), mode='average_inc_pad')
upsample = lambda incoming: nn.layers.Upscale2DLayer(incoming,
scale_factor=2)
l0_frames_d0 = l0_frames
l0_frames_d1 = downsample(l0_frames_d0)
l0_frames_d2 = downsample(l0_frames_d1)
l0_frames_d3 = downsample(l0_frames_d2)
ld3a = nn.layers.dnn.Conv2DDNNLayer(l0_frames_d3,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=16,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld3b = nn.layers.dnn.Conv2DDNNLayer(ld3a,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=16,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld3c = nn.layers.dnn.Conv2DDNNLayer(ld3b,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=16,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld3o = nn.layers.dnn.Conv2DDNNLayer(ld3c,
W=nn.init.Orthogonal("relu"),
filter_size=(3, 3),
num_filters=16,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld2i = nn.layers.ConcatLayer([l0_frames_d2, upsample(ld3o)], axis=1)
ld2a = nn.layers.dnn.Conv2DDNNLayer(ld2i,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld2b = nn.layers.dnn.Conv2DDNNLayer(ld2a,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld2c = nn.layers.dnn.Conv2DDNNLayer(ld2b,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld2d = nn.layers.dnn.Conv2DDNNLayer(ld2c,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld2o = nn.layers.dnn.Conv2DDNNLayer(ld2d,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld1i = nn.layers.ConcatLayer([l0_frames_d1, upsample(ld2o)], axis=1)
ld1a = nn.layers.dnn.Conv2DDNNLayer(ld1i,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld1b = nn.layers.dnn.Conv2DDNNLayer(ld1a,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld1c = nn.layers.dnn.Conv2DDNNLayer(ld1b,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld1d = nn.layers.dnn.Conv2DDNNLayer(ld1c,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld1o = nn.layers.dnn.Conv2DDNNLayer(ld1d,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld0i = nn.layers.ConcatLayer([l0_frames_d0, upsample(ld1o)], axis=1)
ld0a = nn.layers.dnn.Conv2DDNNLayer(ld0i,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld0b = nn.layers.dnn.Conv2DDNNLayer(ld0a,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld0c = nn.layers.dnn.Conv2DDNNLayer(ld0b,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld0d = nn.layers.dnn.Conv2DDNNLayer(ld0c,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=32,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.rectify)
ld0o = nn.layers.dnn.Conv2DDNNLayer(ld0d,
W=nn.init.Orthogonal("relu"),
filter_size=(5, 5),
num_filters=1,
stride=(1, 1),
pad="same",
nonlinearity=nn.nonlinearities.sigmoid)
ld0r = nn.layers.ReshapeLayer(
ld0o,
(batch_size * nr_slices * nr_frames_subsampled, patch_px, patch_px))
l_frames_musigma = layers.IntegrateAreaLayer(ld0r,
sigma_mode='scale',
sigma_scale=.1)
area_per_pixel_cm = (float(patch_mm) / float(patch_px))**2 / 100.0
l_frames_musigma_cm = layers.TrainableScaleLayer(
l_frames_musigma,
scale=nn.init.Constant(area_per_pixel_cm),
trainable=False)
# Go back to a per slice model
l_slices_musigma_cm = nn.layers.ReshapeLayer(
l_frames_musigma_cm,
(batch_size * nr_slices, nr_frames_subsampled, 2)) # (bxs, t, 2)
l_slices_musigma_cm_sys = layers.ArgmaxAndMaxLayer(l_slices_musigma_cm,
mode='min') # (bxs, 2)
l_slices_musigma_cm_dia = layers.ArgmaxAndMaxLayer(l_slices_musigma_cm,
mode='max') # (bxs, 2)
l_slices_musigma_cm_avg = layers.ArgmaxAndMaxLayer(l_slices_musigma_cm,
mode='mean')
# 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_musigma_cm = nn.layers.ReshapeLayer(
l_slices_musigma_cm_sys, (batch_size, nr_slices, 2))
l_pat_sys_ss_mu_cm = nn.layers.SliceLayer(l_pat_sys_ss_musigma_cm,
indices=0,
axis=-1)
l_pat_sys_ss_sigma_cm = nn.layers.SliceLayer(l_pat_sys_ss_musigma_cm,
indices=1,
axis=-1)
l_pat_sys_ss_sigma_cm = nn.layers.TrainableScaleLayer(
l_pat_sys_ss_sigma_cm)
l_pat_sys_aggr_mu_sigma = layers.JeroenLayer([
l_pat_sys_ss_mu_cm, l_pat_sys_ss_sigma_cm, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=1.)
l_systole = layers.MuSigmaErfLayer(l_pat_sys_aggr_mu_sigma)
# Diastole
l_pat_dia_ss_musigma_cm = nn.layers.ReshapeLayer(
l_slices_musigma_cm_dia, (batch_size, nr_slices, 2))
l_pat_dia_ss_mu_cm = nn.layers.SliceLayer(l_pat_dia_ss_musigma_cm,
indices=0,
axis=-1)
l_pat_dia_ss_sigma_cm = nn.layers.SliceLayer(l_pat_dia_ss_musigma_cm,
indices=1,
axis=-1)
l_pat_dia_ss_sigma_cm = nn.layers.TrainableScaleLayer(
l_pat_dia_ss_sigma_cm)
l_pat_dia_aggr_mu_sigma = layers.JeroenLayer([
l_pat_dia_ss_mu_cm, l_pat_dia_ss_sigma_cm, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=1.)
l_diastole = layers.MuSigmaErfLayer(l_pat_dia_aggr_mu_sigma)
# Average
l_pat_avg_ss_musigma_cm = nn.layers.ReshapeLayer(
l_slices_musigma_cm_avg, (batch_size, nr_slices, 2))
l_pat_avg_ss_mu_cm = nn.layers.SliceLayer(l_pat_avg_ss_musigma_cm,
indices=0,
axis=-1)
l_pat_avg_ss_sigma_cm = nn.layers.SliceLayer(l_pat_avg_ss_musigma_cm,
indices=1,
axis=-1)
l_pat_avg_aggr_mu_sigma = layers.JeroenLayer([
l_pat_avg_ss_mu_cm, l_pat_avg_ss_sigma_cm, lin_slice_mask,
l_scaled_slice_locations
],
rescale_input=1.)
l_mean = layers.MuSigmaErfLayer(l_pat_avg_aggr_mu_sigma)
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,
"average": l_mean,
},
"regularizable": {},
}
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() ]
}