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train_3d_denseseg.prototxt
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layer {
name: "data"
type: "HDF5Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
crop_size_w: 64
crop_size_h: 64
crop_size_l: 64
}
hdf5_data_param {
source: "./train_list.txt"
batch_size: 4
shuffle: true
}
}
layer {
name: "conv1a"
type: "Convolution"
bottom: "data"
top: "conv1a"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: -0.10000000149
}
axis: 1
}
}
layer {
name: "bnorm1a"
type: "BatchNorm"
bottom: "conv1a"
top: "bnorm1a"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "scale1a"
type: "Scale"
bottom: "bnorm1a"
top: "bnorm1a"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "relu1a"
type: "ReLU"
bottom: "bnorm1a"
top: "bnorm1a"
}
layer {
name: "conv1b"
type: "Convolution"
bottom: "bnorm1a"
top: "conv1b"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "bnorm1b"
type: "BatchNorm"
bottom: "conv1b"
top: "bnorm1b"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "scale1b"
type: "Scale"
bottom: "bnorm1b"
top: "bnorm1b"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "relu1b"
type: "ReLU"
bottom: "bnorm1b"
top: "bnorm1b"
}
layer {
name: "conv1c"
type: "Convolution"
bottom: "bnorm1b"
top: "conv1c"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "bnorm1c"
type: "BatchNorm"
bottom: "conv1c"
top: "bnorm1c"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "scale1c"
type: "Scale"
bottom: "bnorm1c"
top: "bnorm1c"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "relu1c"
type: "ReLU"
bottom: "bnorm1c"
top: "bnorm1c"
}
layer {
name: "Conv_down_1"
type: "Convolution"
bottom: "bnorm1c"
top: "Conv_down_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 2
kernel_size: 2
kernel_size: 2
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm1"
type: "BatchNorm"
bottom: "Conv_down_1"
top: "BatchNorm1"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale1"
type: "Scale"
bottom: "BatchNorm1"
top: "BatchNorm1"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU1"
type: "ReLU"
bottom: "BatchNorm1"
top: "BatchNorm1"
}
layer {
name: "Convolution1"
type: "Convolution"
bottom: "BatchNorm1"
top: "Convolution1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm2"
type: "BatchNorm"
bottom: "Convolution1"
top: "BatchNorm2"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale2"
type: "Scale"
bottom: "BatchNorm2"
top: "BatchNorm2"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU2"
type: "ReLU"
bottom: "BatchNorm2"
top: "BatchNorm2"
}
layer {
name: "Convolution2"
type: "Convolution"
bottom: "BatchNorm2"
top: "Convolution2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout1"
type: "Dropout"
bottom: "Convolution2"
top: "Dropout1"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_1"
type: "Concat"
bottom: "Conv_down_1"
bottom: "Dropout1"
top: "Concat_1"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm3"
type: "BatchNorm"
bottom: "Concat_1"
top: "BatchNorm3"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale3"
type: "Scale"
bottom: "BatchNorm3"
top: "BatchNorm3"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU3"
type: "ReLU"
bottom: "BatchNorm3"
top: "BatchNorm3"
}
layer {
name: "Convolution3"
type: "Convolution"
bottom: "BatchNorm3"
top: "Convolution3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm4"
type: "BatchNorm"
bottom: "Convolution3"
top: "BatchNorm4"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale4"
type: "Scale"
bottom: "BatchNorm4"
top: "BatchNorm4"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU4"
type: "ReLU"
bottom: "BatchNorm4"
top: "BatchNorm4"
}
layer {
name: "Convolution4"
type: "Convolution"
bottom: "BatchNorm4"
top: "Convolution4"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout2"
type: "Dropout"
bottom: "Convolution4"
top: "Dropout2"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_2"
type: "Concat"
bottom: "Concat_1"
bottom: "Dropout2"
top: "Concat_2"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm5"
type: "BatchNorm"
bottom: "Concat_2"
top: "BatchNorm5"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale5"
type: "Scale"
bottom: "BatchNorm5"
top: "BatchNorm5"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU5"
type: "ReLU"
bottom: "BatchNorm5"
top: "BatchNorm5"
}
layer {
name: "Convolution5"
type: "Convolution"
bottom: "BatchNorm5"
top: "Convolution5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm6"
type: "BatchNorm"
bottom: "Convolution5"
top: "BatchNorm6"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale6"
type: "Scale"
bottom: "BatchNorm6"
top: "BatchNorm6"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU6"
type: "ReLU"
bottom: "BatchNorm6"
top: "BatchNorm6"
}
layer {
name: "Convolution6"
type: "Convolution"
bottom: "BatchNorm6"
top: "Convolution6"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout3"
type: "Dropout"
bottom: "Convolution6"
top: "Dropout3"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_3"
type: "Concat"
bottom: "Concat_2"
bottom: "Dropout3"
top: "Concat_3"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm7"
type: "BatchNorm"
bottom: "Concat_3"
top: "BatchNorm7"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale7"
type: "Scale"
bottom: "BatchNorm7"
top: "BatchNorm7"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU7"
type: "ReLU"
bottom: "BatchNorm7"
top: "BatchNorm7"
}
layer {
name: "Convolution7"
type: "Convolution"
bottom: "BatchNorm7"
top: "Convolution7"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm8"
type: "BatchNorm"
bottom: "Convolution7"
top: "BatchNorm8"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale8"
type: "Scale"
bottom: "BatchNorm8"
top: "BatchNorm8"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU8"
type: "ReLU"
bottom: "BatchNorm8"
top: "BatchNorm8"
}
layer {
name: "Convolution8"
type: "Convolution"
bottom: "BatchNorm8"
top: "Convolution8"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout4"
type: "Dropout"
bottom: "Convolution8"
top: "Dropout4"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_4"
type: "Concat"
bottom: "Concat_3"
bottom: "Dropout4"
top: "Concat_4"
concat_param {
axis: 1
}
}
layer {
name: "Deconvolution_5"
type: "Deconvolution"
bottom: "Concat_4"
top: "Deconvolution_5"
param {
lr_mult: 0.10000000149
decay_mult: 1.0
}
convolution_param {
num_output: 4
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 4
kernel_size: 4
kernel_size: 4
group: 4
stride: 2
stride: 2
stride: 2
weight_filler {
type: "bilinear_3D"
}
}
}
layer {
name: "BatchNorm9"
type: "BatchNorm"
bottom: "Concat_4"
top: "BatchNorm9"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale9"
type: "Scale"
bottom: "BatchNorm9"
top: "BatchNorm9"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU9"
type: "ReLU"
bottom: "BatchNorm9"
top: "BatchNorm9"
}
layer {
name: "Convolution9"
type: "Convolution"
bottom: "BatchNorm9"
top: "Convolution9"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 48
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm10"
type: "BatchNorm"
bottom: "Convolution9"
top: "BatchNorm10"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale10"
type: "Scale"
bottom: "BatchNorm10"
top: "BatchNorm10"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU10"
type: "ReLU"
bottom: "BatchNorm10"
top: "BatchNorm10"
}
layer {
name: "Conv_down_5"
type: "Convolution"
bottom: "BatchNorm10"
top: "Conv_down_5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 48
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 2
kernel_size: 2
kernel_size: 2
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm11"
type: "BatchNorm"
bottom: "Conv_down_5"
top: "BatchNorm11"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale11"
type: "Scale"
bottom: "BatchNorm11"
top: "BatchNorm11"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU11"
type: "ReLU"
bottom: "BatchNorm11"
top: "BatchNorm11"
}
layer {
name: "Convolution10"
type: "Convolution"
bottom: "BatchNorm11"
top: "Convolution10"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm12"
type: "BatchNorm"
bottom: "Convolution10"
top: "BatchNorm12"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale12"
type: "Scale"
bottom: "BatchNorm12"
top: "BatchNorm12"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU12"
type: "ReLU"
bottom: "BatchNorm12"
top: "BatchNorm12"
}
layer {
name: "Convolution11"
type: "Convolution"
bottom: "BatchNorm12"
top: "Convolution11"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout5"
type: "Dropout"
bottom: "Convolution11"
top: "Dropout5"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_6"
type: "Concat"
bottom: "Conv_down_5"
bottom: "Dropout5"
top: "Concat_6"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm13"
type: "BatchNorm"
bottom: "Concat_6"
top: "BatchNorm13"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale13"
type: "Scale"
bottom: "BatchNorm13"
top: "BatchNorm13"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU13"
type: "ReLU"
bottom: "BatchNorm13"
top: "BatchNorm13"
}
layer {
name: "Convolution12"
type: "Convolution"
bottom: "BatchNorm13"
top: "Convolution12"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm14"
type: "BatchNorm"
bottom: "Convolution12"
top: "BatchNorm14"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale14"
type: "Scale"
bottom: "BatchNorm14"
top: "BatchNorm14"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU14"
type: "ReLU"
bottom: "BatchNorm14"
top: "BatchNorm14"
}
layer {
name: "Convolution13"
type: "Convolution"
bottom: "BatchNorm14"
top: "Convolution13"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout6"
type: "Dropout"
bottom: "Convolution13"
top: "Dropout6"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_7"
type: "Concat"
bottom: "Concat_6"
bottom: "Dropout6"
top: "Concat_7"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm15"
type: "BatchNorm"
bottom: "Concat_7"
top: "BatchNorm15"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale15"
type: "Scale"
bottom: "BatchNorm15"
top: "BatchNorm15"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU15"
type: "ReLU"
bottom: "BatchNorm15"
top: "BatchNorm15"
}
layer {
name: "Convolution14"
type: "Convolution"
bottom: "BatchNorm15"
top: "Convolution14"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm16"
type: "BatchNorm"
bottom: "Convolution14"
top: "BatchNorm16"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale16"
type: "Scale"
bottom: "BatchNorm16"
top: "BatchNorm16"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU16"
type: "ReLU"
bottom: "BatchNorm16"
top: "BatchNorm16"
}
layer {
name: "Convolution15"
type: "Convolution"
bottom: "BatchNorm16"
top: "Convolution15"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout7"
type: "Dropout"
bottom: "Convolution15"
top: "Dropout7"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_8"
type: "Concat"
bottom: "Concat_7"
bottom: "Dropout7"
top: "Concat_8"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm17"
type: "BatchNorm"
bottom: "Concat_8"
top: "BatchNorm17"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale17"
type: "Scale"
bottom: "BatchNorm17"
top: "BatchNorm17"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU17"
type: "ReLU"
bottom: "BatchNorm17"
top: "BatchNorm17"
}
layer {
name: "Convolution16"
type: "Convolution"
bottom: "BatchNorm17"
top: "Convolution16"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm18"
type: "BatchNorm"
bottom: "Convolution16"
top: "BatchNorm18"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale18"
type: "Scale"
bottom: "BatchNorm18"
top: "BatchNorm18"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU18"
type: "ReLU"
bottom: "BatchNorm18"
top: "BatchNorm18"
}
layer {
name: "Convolution17"
type: "Convolution"
bottom: "BatchNorm18"
top: "Convolution17"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout8"
type: "Dropout"
bottom: "Convolution17"
top: "Dropout8"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_9"
type: "Concat"
bottom: "Concat_8"
bottom: "Dropout8"
top: "Concat_9"
concat_param {
axis: 1
}
}
layer {
name: "Deconvolution_10"
type: "Deconvolution"
bottom: "Concat_9"
top: "Deconvolution_10"
param {
lr_mult: 0.10000000149
decay_mult: 1.0
}
convolution_param {
num_output: 4
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 6
kernel_size: 6
kernel_size: 6
group: 4
stride: 4
stride: 4
stride: 4
weight_filler {
type: "bilinear_3D"
}
}
}
layer {
name: "BatchNorm19"
type: "BatchNorm"
bottom: "Concat_9"
top: "BatchNorm19"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale19"
type: "Scale"
bottom: "BatchNorm19"
top: "BatchNorm19"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU19"
type: "ReLU"
bottom: "BatchNorm19"
top: "BatchNorm19"
}
layer {
name: "Convolution18"
type: "Convolution"
bottom: "BatchNorm19"
top: "Convolution18"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 56
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm20"
type: "BatchNorm"
bottom: "Convolution18"
top: "BatchNorm20"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale20"
type: "Scale"
bottom: "BatchNorm20"
top: "BatchNorm20"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU20"
type: "ReLU"
bottom: "BatchNorm20"
top: "BatchNorm20"
}
layer {
name: "Conv_down_10"
type: "Convolution"
bottom: "BatchNorm20"
top: "Conv_down_10"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 56
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 2
kernel_size: 2
kernel_size: 2
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm21"
type: "BatchNorm"
bottom: "Conv_down_10"
top: "BatchNorm21"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale21"
type: "Scale"
bottom: "BatchNorm21"
top: "BatchNorm21"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU21"
type: "ReLU"
bottom: "BatchNorm21"
top: "BatchNorm21"
}
layer {
name: "Convolution19"
type: "Convolution"
bottom: "BatchNorm21"
top: "Convolution19"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm22"
type: "BatchNorm"
bottom: "Convolution19"
top: "BatchNorm22"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale22"
type: "Scale"
bottom: "BatchNorm22"
top: "BatchNorm22"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU22"
type: "ReLU"
bottom: "BatchNorm22"
top: "BatchNorm22"
}
layer {
name: "Convolution20"
type: "Convolution"
bottom: "BatchNorm22"
top: "Convolution20"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout9"
type: "Dropout"
bottom: "Convolution20"
top: "Dropout9"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_11"
type: "Concat"
bottom: "Conv_down_10"
bottom: "Dropout9"
top: "Concat_11"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm23"
type: "BatchNorm"
bottom: "Concat_11"
top: "BatchNorm23"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale23"
type: "Scale"
bottom: "BatchNorm23"
top: "BatchNorm23"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU23"
type: "ReLU"
bottom: "BatchNorm23"
top: "BatchNorm23"
}
layer {
name: "Convolution21"
type: "Convolution"
bottom: "BatchNorm23"
top: "Convolution21"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm24"
type: "BatchNorm"
bottom: "Convolution21"
top: "BatchNorm24"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale24"
type: "Scale"
bottom: "BatchNorm24"
top: "BatchNorm24"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU24"
type: "ReLU"
bottom: "BatchNorm24"
top: "BatchNorm24"
}
layer {
name: "Convolution22"
type: "Convolution"
bottom: "BatchNorm24"
top: "Convolution22"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout10"
type: "Dropout"
bottom: "Convolution22"
top: "Dropout10"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_12"
type: "Concat"
bottom: "Concat_11"
bottom: "Dropout10"
top: "Concat_12"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm25"
type: "BatchNorm"
bottom: "Concat_12"
top: "BatchNorm25"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale25"
type: "Scale"
bottom: "BatchNorm25"
top: "BatchNorm25"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU25"
type: "ReLU"
bottom: "BatchNorm25"
top: "BatchNorm25"
}
layer {
name: "Convolution23"
type: "Convolution"
bottom: "BatchNorm25"
top: "Convolution23"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm26"
type: "BatchNorm"
bottom: "Convolution23"
top: "BatchNorm26"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale26"
type: "Scale"
bottom: "BatchNorm26"
top: "BatchNorm26"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU26"
type: "ReLU"
bottom: "BatchNorm26"
top: "BatchNorm26"
}
layer {
name: "Convolution24"
type: "Convolution"
bottom: "BatchNorm26"
top: "Convolution24"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout11"
type: "Dropout"
bottom: "Convolution24"
top: "Dropout11"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_13"
type: "Concat"
bottom: "Concat_12"
bottom: "Dropout11"
top: "Concat_13"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm27"
type: "BatchNorm"
bottom: "Concat_13"
top: "BatchNorm27"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale27"
type: "Scale"
bottom: "BatchNorm27"
top: "BatchNorm27"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU27"
type: "ReLU"
bottom: "BatchNorm27"
top: "BatchNorm27"
}
layer {
name: "Convolution25"
type: "Convolution"
bottom: "BatchNorm27"
top: "Convolution25"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm28"
type: "BatchNorm"
bottom: "Convolution25"
top: "BatchNorm28"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale28"
type: "Scale"
bottom: "BatchNorm28"
top: "BatchNorm28"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU28"
type: "ReLU"
bottom: "BatchNorm28"
top: "BatchNorm28"
}
layer {
name: "Convolution26"
type: "Convolution"
bottom: "BatchNorm28"
top: "Convolution26"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout12"
type: "Dropout"
bottom: "Convolution26"
top: "Dropout12"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_14"
type: "Concat"
bottom: "Concat_13"
bottom: "Dropout12"
top: "Concat_14"
concat_param {
axis: 1
}
}
layer {
name: "Deconvolution_15"
type: "Deconvolution"
bottom: "Concat_14"
top: "Deconvolution_15"
param {
lr_mult: 0.10000000149
decay_mult: 1.0
}
convolution_param {
num_output: 4
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 10
kernel_size: 10
kernel_size: 10
group: 4
stride: 8
stride: 8
stride: 8
weight_filler {
type: "bilinear_3D"
}
}
}
layer {
name: "BatchNorm29"
type: "BatchNorm"
bottom: "Concat_14"
top: "BatchNorm29"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale29"
type: "Scale"
bottom: "BatchNorm29"
top: "BatchNorm29"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU29"
type: "ReLU"
bottom: "BatchNorm29"
top: "BatchNorm29"
}
layer {
name: "Convolution27"
type: "Convolution"
bottom: "BatchNorm29"
top: "Convolution27"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 60
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm30"
type: "BatchNorm"
bottom: "Convolution27"
top: "BatchNorm30"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale30"
type: "Scale"
bottom: "BatchNorm30"
top: "BatchNorm30"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU30"
type: "ReLU"
bottom: "BatchNorm30"
top: "BatchNorm30"
}
layer {
name: "Conv_down_15"
type: "Convolution"
bottom: "BatchNorm30"
top: "Conv_down_15"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 60
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 2
kernel_size: 2
kernel_size: 2
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm31"
type: "BatchNorm"
bottom: "Conv_down_15"
top: "BatchNorm31"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale31"
type: "Scale"
bottom: "BatchNorm31"
top: "BatchNorm31"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU31"
type: "ReLU"
bottom: "BatchNorm31"
top: "BatchNorm31"
}
layer {
name: "Convolution28"
type: "Convolution"
bottom: "BatchNorm31"
top: "Convolution28"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm32"
type: "BatchNorm"
bottom: "Convolution28"
top: "BatchNorm32"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale32"
type: "Scale"
bottom: "BatchNorm32"
top: "BatchNorm32"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU32"
type: "ReLU"
bottom: "BatchNorm32"
top: "BatchNorm32"
}
layer {
name: "Convolution29"
type: "Convolution"
bottom: "BatchNorm32"
top: "Convolution29"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout13"
type: "Dropout"
bottom: "Convolution29"
top: "Dropout13"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_19"
type: "Concat"
bottom: "Conv_down_15"
bottom: "Dropout13"
top: "Concat_19"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm33"
type: "BatchNorm"
bottom: "Concat_19"
top: "BatchNorm33"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale33"
type: "Scale"
bottom: "BatchNorm33"
top: "BatchNorm33"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU33"
type: "ReLU"
bottom: "BatchNorm33"
top: "BatchNorm33"
}
layer {
name: "Convolution30"
type: "Convolution"
bottom: "BatchNorm33"
top: "Convolution30"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm34"
type: "BatchNorm"
bottom: "Convolution30"
top: "BatchNorm34"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale34"
type: "Scale"
bottom: "BatchNorm34"
top: "BatchNorm34"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU34"
type: "ReLU"
bottom: "BatchNorm34"
top: "BatchNorm34"
}
layer {
name: "Convolution31"
type: "Convolution"
bottom: "BatchNorm34"
top: "Convolution31"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout14"
type: "Dropout"
bottom: "Convolution31"
top: "Dropout14"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_20"
type: "Concat"
bottom: "Concat_19"
bottom: "Dropout14"
top: "Concat_20"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm35"
type: "BatchNorm"
bottom: "Concat_20"
top: "BatchNorm35"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale35"
type: "Scale"
bottom: "BatchNorm35"
top: "BatchNorm35"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU35"
type: "ReLU"
bottom: "BatchNorm35"
top: "BatchNorm35"
}
layer {
name: "Convolution32"
type: "Convolution"
bottom: "BatchNorm35"
top: "Convolution32"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm36"
type: "BatchNorm"
bottom: "Convolution32"
top: "BatchNorm36"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale36"
type: "Scale"
bottom: "BatchNorm36"
top: "BatchNorm36"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU36"
type: "ReLU"
bottom: "BatchNorm36"
top: "BatchNorm36"
}
layer {
name: "Convolution33"
type: "Convolution"
bottom: "BatchNorm36"
top: "Convolution33"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout15"
type: "Dropout"
bottom: "Convolution33"
top: "Dropout15"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_21"
type: "Concat"
bottom: "Concat_20"
bottom: "Dropout15"
top: "Concat_21"
concat_param {
axis: 1
}
}
layer {
name: "BatchNorm37"
type: "BatchNorm"
bottom: "Concat_21"
top: "BatchNorm37"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale37"
type: "Scale"
bottom: "BatchNorm37"
top: "BatchNorm37"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU37"
type: "ReLU"
bottom: "BatchNorm37"
top: "BatchNorm37"
}
layer {
name: "Convolution34"
type: "Convolution"
bottom: "BatchNorm37"
top: "Convolution34"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "BatchNorm38"
type: "BatchNorm"
bottom: "Convolution34"
top: "BatchNorm38"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "Scale38"
type: "Scale"
bottom: "BatchNorm38"
top: "BatchNorm38"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "ReLU38"
type: "ReLU"
bottom: "BatchNorm38"
top: "BatchNorm38"
}
layer {
name: "Convolution35"
type: "Convolution"
bottom: "BatchNorm38"
top: "Convolution35"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 3
kernel_size: 3
kernel_size: 3
stride: 1
stride: 1
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
axis: 1
}
}
layer {
name: "Dropout16"
type: "Dropout"
bottom: "Convolution35"
top: "Dropout16"
dropout_param {
dropout_ratio: 0.20000000298
}
}
layer {
name: "Concat_22"
type: "Concat"
bottom: "Concat_21"
bottom: "Dropout16"
top: "Concat_22"
concat_param {
axis: 1
}
}
layer {
name: "Deconvolution_20"
type: "Deconvolution"
bottom: "Concat_22"
top: "Deconvolution_20"
param {
lr_mult: 0.10000000149
decay_mult: 1.0
}
convolution_param {
num_output: 4
bias_term: false
pad: 1
pad: 1
pad: 1
kernel_size: 18
kernel_size: 18
kernel_size: 18
group: 4
stride: 16
stride: 16
stride: 16
weight_filler {
type: "bilinear_3D"
}
}
}
layer {
name: "Concat1"
type: "Concat"
bottom: "conv1c"
bottom: "Deconvolution_5"
bottom: "Deconvolution_10"
bottom: "Deconvolution_15"
bottom: "Deconvolution_20"
top: "Concat1"
concat_param {
axis: 1
}
}
layer {
name: "bnorm_concat"
type: "BatchNorm"
bottom: "Concat1"
top: "bnorm_concat"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
batch_norm_param {
use_global_stats: false
}
}
layer {
name: "scale_concat"
type: "Scale"
bottom: "bnorm_concat"
top: "bnorm_concat"
scale_param {
filler {
value: 1.0
}
bias_term: true
bias_filler {
value: 0.0
}
}
}
layer {
name: "relu_concat"
type: "ReLU"
bottom: "bnorm_concat"
top: "bnorm_concat"
}
layer {
name: "Convolution36"
type: "Convolution"
bottom: "bnorm_concat"
top: "Convolution36"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 4
pad: 0
pad: 0
pad: 0
kernel_size: 1
kernel_size: 1
kernel_size: 1
weight_filler {
type: "msra"
}
axis: 1
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "Convolution36"
bottom: "label"
top: "loss"
}