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name:"UNET"
layer {
name: "data"
type: "Python"
top: "img"
top: "mask"
python_param{
module: 'mydatalayer'
layer: 'DataLayer'
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "img"
top: "conv1_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu1"
type:"ReLU"
bottom:"conv1_1"
top:"conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu2"
type:"ReLU"
bottom:"conv1_2"
top:"conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu3"
type:"ReLU"
bottom:"conv2_1"
top:"conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu4"
type:"ReLU"
bottom:"conv2_2"
top:"conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu5"
type:"ReLU"
bottom:"conv3_1"
top:"conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu6"
type:"ReLU"
bottom:"conv3_2"
top:"conv3_2"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_2"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu7"
type:"ReLU"
bottom:"conv4_1"
top:"conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu8"
type:"ReLU"
bottom:"conv4_2"
top:"conv4_2"
}
layer {
name: "drop4"
type: "Dropout"
bottom: "conv4_2"
top: "conv4_2"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_2"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu9"
type:"ReLU"
bottom:"conv5_1"
top:"conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 1024
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu10"
type:"ReLU"
bottom:"conv5_2"
top:"conv5_2"
}
layer {
name: "drop5"
type: "Dropout"
bottom: "conv5_2"
top: "conv5_2"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "upscore1"
type: "Deconvolution"
bottom: "conv5_2"
top: "upscore1"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 512
bias_term: false
kernel_size: 2
stride: 2
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu11"
type:"ReLU"
bottom:"upscore1"
top:"upscore1"
}
layer {
name: 'crop1'
type: 'Crop'
bottom: 'conv4_2'
bottom: 'upscore1'
top: 'crop1'
crop_param {
axis: 2
offset: 4
offset: 4
}
}
layer {
name: "concat1"
type: "Concat"
bottom: "crop1"
bottom: "upscore1"
top: "concat1"
concat_param {
axis: 1
}
}
layer {
name: "conv6_1"
type: "Convolution"
bottom: "concat1"
top: "conv6_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu12"
type:"ReLU"
bottom:"conv6_1"
top:"conv6_1"
}
layer {
name: "conv6_2"
type: "Convolution"
bottom: "conv6_1"
top: "conv6_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu13"
type:"ReLU"
bottom:"conv6_2"
top:"conv6_2"
}
layer {
name: "upscore2"
type: "Deconvolution"
bottom: "conv6_2"
top: "upscore2"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 256
bias_term: false
kernel_size: 2
stride: 2
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu14"
type:"ReLU"
bottom:"upscore2"
top:"upscore2"
}
layer {
name: 'crop2'
type: 'Crop'
bottom: 'conv3_2'
bottom: 'upscore2'
top: 'crop2'
crop_param {
axis: 2
offset: 16
offset: 16
}
}
layer {
name: "concat2"
type: "Concat"
bottom: "crop2"
bottom: "upscore2"
top: "concat2"
concat_param {
axis: 1
}
}
layer {
name: "conv7_1"
type: "Convolution"
bottom: "concat2"
top: "conv7_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu15"
type:"ReLU"
bottom:"conv7_1"
top:"conv7_1"
}
layer {
name: "conv7_2"
type: "Convolution"
bottom: "conv7_1"
top: "conv7_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu16"
type:"ReLU"
bottom:"conv7_2"
top:"conv7_2"
}
layer {
name: "upscore3"
type: "Deconvolution"
bottom: "conv7_2"
top: "upscore3"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 128
bias_term: false
kernel_size: 2
stride: 2
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu17"
type:"ReLU"
bottom:"upscore3"
top:"upscore3"
}
layer {
name: 'crop3'
type: 'Crop'
bottom: 'conv2_2'
bottom: 'upscore3'
top: 'crop3'
crop_param {
axis: 2
offset: 40
offset: 40
}
}
layer {
name: "concat3"
type: "Concat"
bottom: "crop3"
bottom: "upscore3"
top: "concat3"
concat_param {
axis: 1
}
}
layer {
name: "conv8_1"
type: "Convolution"
bottom: "concat3"
top: "conv8_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu18"
type:"ReLU"
bottom:"conv8_1"
top:"conv8_1"
}
layer {
name: "conv8_2"
type: "Convolution"
bottom: "conv8_1"
top: "conv8_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu19"
type:"ReLU"
bottom:"conv8_2"
top:"conv8_2"
}
layer {
name: "upscore4"
type: "Deconvolution"
bottom: "conv8_2"
top: "upscore4"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 2
stride: 2
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu20"
type:"ReLU"
bottom:"upscore4"
top:"upscore4"
}
layer {
name: 'crop4'
type: 'Crop'
bottom: 'conv1_2'
bottom: 'upscore4'
top: 'crop4'
crop_param {
axis: 2
offset: 88
offset: 88
}
}
layer {
name: "concat4"
type: "Concat"
bottom: "crop4"
bottom: "upscore4"
top: "concat4"
concat_param {
axis: 1
}
}
layer {
name: "conv9_1"
type: "Convolution"
bottom: "concat4"
top: "conv9_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu21"
type:"ReLU"
bottom:"conv9_1"
top:"conv9_1"
}
layer {
name: "conv9_2"
type: "Convolution"
bottom: "conv9_1"
top: "conv9_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 3
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"relu22"
type:"ReLU"
bottom:"conv9_2"
top:"conv9_2"
}
layer {
name: "score"
type: "Convolution"
bottom: "conv9_2"
top: "score"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 2
pad: 0
kernel_size: 1
weight_filler{
type:"gaussian"
std:0.01
}
}
}
layer{
name:"sigmoid1"
type:"Sigmoid"
bottom:"score"
top:"score"
}
layer {
name: 'crop5'
type: 'Crop'
bottom: 'mask'
bottom: 'score'
top: 'crop5'
crop_param {
axis: 2
offset: 92
offset: 92
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "score"
bottom: "crop5"
top: "loss"
propagate_down: true
propagate_down: false
loss_param {
normalize: false
}
}
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