# figure out the user's intent). If the code does not seem to terminate, then the issue is most likely
# a wrong number of feature maps / channels in either the MemoryData-layers or the network output.
# This function takes as input:
# - The network
# - A list of other inputs to test (note: the nhood input is static and not spatially testable, thus excluded here)
# - A list of the maximal shapes for each input
# - A list of spatial dependencies; here [-1, 0] means the Y axis is a free parameter, and the X axis should be identical to the Y axis.
fix_input_dims(
net,
[
net.data,
net.aff_label,
net.comp_label,
net.scale
],
max_shapes=[
input_shape,
output_shape,
output_shape,
output_shape
],
shape_coupled=[-1, -1, 1]
)
protonet = net.to_proto()
protonet.name = 'net'
# Store the network as prototxt
with open(protonet.name + '.prototxt', 'w') as f:
print(protonet, file=f)
# Choose output activation functions
net.aff_pred = L.Sigmoid(net.aff_out, ntop=1, in_place=False)
# Choose a loss function and input data, label and scale inputs. Only include it during the training phase (phase = 0)
net.euclid_loss = L.EuclideanLoss(net.aff_pred, net.aff_label, net.scale, ntop=0, loss_weight=1.0, include=[dict(phase=0, stage='euclid')])
net.malis_loss = L.MalisLoss(net.aff_pred, net.aff_label, net.comp_label, net.nhood, ntop=0, loss_weight=1.0, include=[dict(phase=0, stage='malis')])
# Fix the spatial input dimensions. Note that only spatial dimensions get modified, the minibatch size
# and the channels/feature maps must be set correctly by the user (since this code can definitely not
# figure out the user's intent). If the code does not seem to terminate, then the issue is most likely
# a wrong number of feature maps / channels in either the MemoryData-layers or the network output.
# This function takes as input:
# - The network
# - A list of other inputs to test (note: the nhood input is static and not spatially testable, thus excluded here)
# - A list of the maximal shapes for each input
# - A list of spatial dependencies; here [-1, 0] means the Y axis is a free parameter, and the X axis should be identical to the Y axis.
caffe.fix_input_dims(net,
[net.data, net.aff_label, net.comp_label, net.scale],
max_shapes = [[200,200,200],[100,100,100],[100,100,100],[100,100,100]],
shape_coupled = [-1, -1, 1])
protonet = net.to_proto()
protonet.name = 'net';
# Store the network as prototxt
with open(protonet.name + '.prototxt', 'w') as f:
print(protonet, file=f)
def long_range_unet(name):
# Start a network
net = caffe.NetSpec()
# Data input layer
net.data = L.MemoryData(dim=[1, 1], ntop=1)
n_channels = 12
# TODO
# Label input layer
# I guess the second number is the number of channels
net.aff_label = L.MemoryData(dim=[1, n_channels],
ntop=1,
include=[dict(phase=0)])
# Components label layer
# No idea about this one...
net.comp_label = L.MemoryData(dim=[1, 2],
ntop=1,
include=[dict(phase=0, stage='malis')])
# Scale input layer
# again second = channels ?!
net.scale = L.MemoryData(dim=[1, n_channels],
ntop=1,
include=[dict(phase=0, stage='euclid')])
# Silence the not needed data and label integer values
# is this correct ????
net.nhood = L.MemoryData(dim=[1, 1, n_channels, 3],
ntop=1,
include=[dict(phase=0, stage='malis')])
# USK-Net metalayer
net.unet = ML.UNet(
net.data,
fmap_start=12,
depth=3,
fmap_inc_rule=lambda fmaps: int(math.ceil(float(fmaps) * 5)),
fmap_dec_rule=lambda fmaps: int(math.ceil(float(fmaps) / 5)),
downsampling_strategy=[[1, 3, 3], [1, 3, 3], [1, 3, 3]],
dropout=0.0,
use_deconv_uppath=False,
use_stable_upconv=True)
net.aff_out = L.Convolution(net.unet,
kernel_size=[1],
num_output=n_channels,
param=[dict(lr_mult=1),
dict(lr_mult=2)],
weight_filler=dict(type='msra'),
bias_filler=dict(type='constant'))
# Choose output activation functions
net.aff_pred = L.Sigmoid(net.aff_out, ntop=1, in_place=False)
# Choose a loss function and input data, label and scale inputs. Only include it during the training phase (phase = 0)
net.euclid_loss = L.EuclideanLoss(net.aff_pred,
net.aff_label,
net.scale,
ntop=0,
loss_weight=1.0,
include=[dict(phase=0, stage='euclid')])
net.malis_loss = L.MalisLoss(net.aff_pred,
net.aff_label,
net.comp_label,
net.nhood,
ntop=0,
loss_weight=1.0,
include=[dict(phase=0, stage='malis')])
# Fix the spatial input dimensions. Note that only spatial dimensions get modified, the minibatch size
# and the channels/feature maps must be set correctly by the user (since this code can definitely not
# figure out the user's intent). If the code does not seem to terminate, then the issue is most likely
# a wrong number of feature maps / channels in either the MemoryData-layers or the network output.
# This function takes as input:
# - The network
# - A list of other inputs to test (note: the nhood input is static and not spatially testable, thus excluded here)
# - A list of the maximal shapes for each input
# - A list of spatial dependencies; here [-1, 0] means the Y axis is a free parameter, and the X axis should be identical to the Y axis.
caffe.fix_input_dims(net,
[net.data, net.aff_label, net.comp_label, net.scale],
max_shapes=[[84, 268, 268], [100, 100, 100],
[100, 100, 100], [100, 100, 100]],
shape_coupled=[-1, -1, 1])
protonet = net.to_proto()
protonet.name = name
# Store the network as prototxt
with open(protonet.name + '.prototxt', 'w') as f:
print(protonet, file=f)