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mcnn_Attri.prototxt
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mcnn_Attri.prototxt
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name: "MCNN_Attri"
layer {
name: "data"
type: "Data"
top: "data"
transform_param {
mean_file: "root/mean.binaryproto"
crop_size: 227
}
include {
phase: TRAIN
}
data_param {
source: "root/TrainDbImage"
batch_size: 128
backend: LEVELDB
}
}
layer {
name: "labels"
type: "Data"
top: "labels"
include {
phase: TRAIN
}
data_param {
source: "root/TrainDbLabel"
batch_size: 128
backend: LEVELDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
transform_param {
mean_file: "root/mean.binaryproto"
crop_size: 227
}
include {
phase: TEST
}
data_param {
source: "root/TestDbImage"
batch_size: 100
backend: LEVELDB
}
}
layer {
name: "labels"
type: "Data"
top: "labels"
include {
phase: TEST
}
data_param {
source: "root/TestDbLabel"
batch_size: 100
backend: LEVELDB
}
}
layer {
name: "sliceL"
type: "Slice"
bottom: "labels"
top: "label_attr1"
top: "label_attr2"
top: "label_attr3"
top: "label_attr4"
top: "label_attr5"
top: "label_attr6"
top: "label_attr7"
top: "label_attr8"
top: "label_attr9"
top: "label_attr10"
top: "label_attr11"
top: "label_attr12"
top: "label_attr13"
top: "label_attr14"
top: "label_attr15"
top: "label_attr16"
top: "label_attr17"
top: "label_attr18"
top: "label_attr19"
top: "label_attr20"
top: "label_attr21"
top: "label_attr22"
top: "label_attr23"
top: "label_attr24"
top: "label_attr25"
top: "label_attr26"
top: "label_attr27"
top: "label_attr28"
top: "label_attr29"
top: "label_attr30"
top: "label_attr31"
top: "label_attr32"
top: "label_attr33"
top: "label_attr34"
top: "label_attr35"
top: "label_attr36"
top: "label_attr37"
top: "label_attr38"
top: "label_attr39"
top: "label_attr40"
slice_param {
slice_dim: 1
slice_point: 1
slice_point: 2
slice_point: 3
slice_point: 4
slice_point: 5
slice_point: 6
slice_point: 7
slice_point: 8
slice_point: 9
slice_point: 10
slice_point: 11
slice_point: 12
slice_point: 13
slice_point: 14
slice_point: 15
slice_point: 16
slice_point: 17
slice_point: 18
slice_point: 19
slice_point: 20
slice_point: 21
slice_point: 22
slice_point: 23
slice_point: 24
slice_point: 25
slice_point: 26
slice_point: 27
slice_point: 28
slice_point: 29
slice_point: 30
slice_point: 31
slice_point: 32
slice_point: 33
slice_point: 34
slice_point: 35
slice_point: 36
slice_point: 37
slice_point: 38
slice_point: 39
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 75
kernel_size: 7
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer
{
name:"bn1"
type:"BatchNorm"
bottom:"pool1"
top:"bn1"
batch_norm_param:{
use_global_stats:false
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "bn1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 200
kernel_size: 5
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer
{
name:"bn2"
type:"BatchNorm"
bottom:"pool2"
top:"bn2"
batch_norm_param:{
use_global_stats:false
}
}
#for group1
layer {
name: "conv3_g1_gender"
type: "Convolution"
bottom: "bn2"
top: "conv3_g1_gender"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 300
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3_g1_gender"
type: "ReLU"
bottom: "conv3_g1_gender"
top: "conv3_g1_gender"
}
layer {
name: "pool3_g1_gender"
type: "Pooling"
bottom: "conv3_g1_gender"
top: "pool3_g1_gender"
pooling_param {
pool: MAX
kernel_size: 5
stride: 3
}
}
layer
{
name:"bn3_g1_gender"
type:"BatchNorm"
bottom:"pool3_g1_gender"
top:"bn3_g1_gender"
batch_norm_param:{
use_global_stats:false
}
}
layer {
name: "FC1_g1_gender"
type: "InnerProduct"
bottom: "bn3_g1_gender"
top: "FC1_g1_gender"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu4_g1_gender"
type: "ReLU"
bottom: "FC1_g1_gender"
top: "relu4_g1_gender"
}
layer
{
name:"drop1_g1"
type:"Dropout"
bottom:"relu4_g1_gender"
top:"relu4_g1_gender"
dropout_param
{
dropout_ratio:0.5
}
}
layer {
name: "FC2_g1_gender"
type: "InnerProduct"
bottom: "relu4_g1_gender"
top: "FC2_g1_gender"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu5_g1_gender"
type: "ReLU"
bottom: "FC2_g1_gender"
top: "relu5_g1_gender"
}
layer
{
name:"drop2_g1"
type:"Dropout"
bottom:"relu5_g1_gender"
top:"relu5_g1_gender"
dropout_param
{
dropout_ratio:0.5
}
}
layer {
name: "FC3_g1_gender"
type: "InnerProduct"
bottom: "relu5_g1_gender"
top: "FC3_g1_gender"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_Male"
type: "Accuracy"
bottom: "FC3_g1_gender"
bottom: "label_attr21"
top: "accuracy_Male"
include {
phase: TEST
}
}
layer {
name: "loss_Male"
type: "SoftmaxWithLoss"
bottom: "FC3_g1_gender"
bottom: "label_attr21"
top: "loss_Male"
loss_weight:0.025
}
#for group2
layer {
name: "conv3_g2_Nose"
type: "Convolution"
bottom: "bn2"
top: "conv3_g2_Nose"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 300
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3_g2_Nose"
type: "ReLU"
bottom: "conv3_g2_Nose"
top: "conv3_g2_Nose"
}
layer {
name: "pool3_g2_Nose"
type: "Pooling"
bottom: "conv3_g2_Nose"
top: "pool3_g2_Nose"
pooling_param {
pool: MAX
kernel_size: 5
stride: 3
}
}
layer
{
name:"bn3_g2_Nose"
type:"BatchNorm"
bottom:"pool3_g2_Nose"
top:"bn3_g2_Nose"
batch_norm_param:{
use_global_stats:false
}
}
layer {
name: "FC1_g2_Nose"
type: "InnerProduct"
bottom: "bn3_g2_Nose"
top: "FC1_g2_Nose"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu4_g2_Nose"
type: "ReLU"
bottom: "FC1_g2_Nose"
top: "relu4_g2_Nose"
}
layer
{
name:"drop1_g2"
type:"Dropout"
bottom:"relu4_g2_Nose"
top:"relu4_g2_Nose"
dropout_param
{
dropout_ratio:0.5
}
}
layer {
name: "FC2_g2_Nose"
type: "InnerProduct"
bottom: "relu4_g2_Nose"
top: "FC2_g2_Nose"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu5_g2_Nose"
type: "ReLU"
bottom: "FC2_g2_Nose"
top: "relu5_g2_Nose"
}
layer
{
name:"drop2_g2"
type:"Dropout"
bottom:"relu5_g2_Nose"
top:"relu5_g2_Nose"
dropout_param
{
dropout_ratio:0.5
}
}
#group2 big nose
layer {
name: "FC3_g2_BigNose"
type: "InnerProduct"
bottom: "relu5_g2_Nose"
top: "FC3_g2_BigNose"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_BigNose"
type: "Accuracy"
bottom: "FC3_g2_BigNose"
bottom: "label_attr8"
top: "accuracy_BigNose"
include {
phase: TEST
}
}
layer {
name: "loss_BigNose"
type: "SoftmaxWithLoss"
bottom: "FC3_g2_BigNose"
bottom: "label_attr8"
top: "loss_BigNose"
loss_weight:0.025
}
#group2 pointy nose
layer {
name: "FC3_g2_PointyNose"
type: "InnerProduct"
bottom: "relu5_g2_Nose"
top: "FC3_g2_PointyNose"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_PointyNose"
type: "Accuracy"
bottom: "FC3_g2_PointyNose"
bottom: "label_attr28"
top: "accuracy_PointyNose"
include {
phase: TEST
}
}
layer {
name: "loss_PointyNose"
type: "SoftmaxWithLoss"
bottom: "FC3_g2_PointyNose"
bottom: "label_attr28"
top: "loss_PointyNose"
loss_weight:0.025
}
#for group3
layer {
name: "conv3_g3_Mouth"
type: "Convolution"
bottom: "bn2"
top: "conv3_g3_Mouth"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 300
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3_g3_Mouth"
type: "ReLU"
bottom: "conv3_g3_Mouth"
top: "conv3_g3_Mouth"
}
layer {
name: "pool3_g3_Mouth"
type: "Pooling"
bottom: "conv3_g3_Mouth"
top: "pool3_g3_Mouth"
pooling_param {
pool: MAX
kernel_size: 5
stride: 3
}
}
layer
{
name:"bn3_g3_Mouth"
type:"BatchNorm"
bottom:"pool3_g3_Mouth"
top:"bn3_g3_Mouth"
batch_norm_param:{
use_global_stats:false
}
}
layer {
name: "FC1_g3_Mouth"
type: "InnerProduct"
bottom: "bn3_g3_Mouth"
top: "FC1_g3_Mouth"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu4_g3_Mouth"
type: "ReLU"
bottom: "FC1_g3_Mouth"
top: "relu4_g3_Mouth"
}
layer
{
name:"drop1_g3"
type:"Dropout"
bottom:"relu4_g3_Mouth"
top:"relu4_g3_Mouth"
dropout_param
{
dropout_ratio:0.5
}
}
layer {
name: "FC2_g3_Mouth"
type: "InnerProduct"
bottom: "relu4_g3_Mouth"
top: "FC2_g3_Mouth"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 512
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu5_g3_Mouth"
type: "ReLU"
bottom: "FC2_g3_Mouth"
top: "relu5_g3_Mouth"
}
layer
{
name:"drop2_g3"
type:"Dropout"
bottom:"relu5_g3_Mouth"
top:"relu5_g3_Mouth"
dropout_param
{
dropout_ratio:0.5
}
}
#group3 big lips
layer {
name: "FC3_g3_BigLips"
type: "InnerProduct"
bottom: "relu5_g3_Mouth"
top: "FC3_g3_BigLips"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_BigLips"
type: "Accuracy"
bottom: "FC3_g3_BigLips"
bottom: "label_attr7"
top: "accuracy_BigLips"
include {
phase: TEST
}
}
layer {
name: "loss_BigLips"
type: "SoftmaxWithLoss"
bottom: "FC3_g3_BigLips"
bottom: "label_attr7"
top: "loss_BigLips"
loss_weight:0.025
}
#group3 Smiling
layer {
name: "FC3_g3_Smiling"
type: "InnerProduct"
bottom: "relu5_g3_Mouth"
top: "FC3_g3_Smiling"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_Smiling"
type: "Accuracy"
bottom: "FC3_g3_Smiling"
bottom: "label_attr32"
top: "accuracy_Smiling"
include {
phase: TEST
}
}
layer {
name: "loss_Smiling"
type: "SoftmaxWithLoss"
bottom: "FC3_g3_Smiling"
bottom: "label_attr32"
top: "loss_Smiling"
loss_weight:0.025
}
#group3 Lipstick
layer {
name: "FC3_g3_Lipstick"
type: "InnerProduct"
bottom: "relu5_g3_Mouth"
top: "FC3_g3_Lipstick"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_Lipstick"
type: "Accuracy"
bottom: "FC3_g3_Lipstick"
bottom: "label_attr37"
top: "accuracy_Lipstick"
include {
phase: TEST
}
}
layer {
name: "loss_Lipstick"
type: "SoftmaxWithLoss"
bottom: "FC3_g3_Lipstick"
bottom: "label_attr37"
top: "loss_Lipstick"
loss_weight:0.025
}
#group3 Mouth_Slightly_Open
layer {
name: "FC3_g3_Mouth_Slightly_Open"
type: "InnerProduct"
bottom: "relu5_g3_Mouth"
top: "FC3_g3_Mouth_Slightly_Open"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy_Mouth_Slightly_Open"
type: "Accuracy"
bottom: "FC3_g3_Mouth_Slightly_Open"
bottom: "label_attr22"
top: "accuracy_Mouth_Slightly_Open"
include {
phase: TEST
}
}
layer {
name: "loss_Mouth_Slightly_Open"
type: "SoftmaxWithLoss"
bottom: "FC3_g3_Mouth_Slightly_Open"
bottom: "label_attr22"
top: "loss_Mouth_Slightly_Open"
loss_weight:0.025
}
#for group4
layer {
name: "conv3_g4_Eyes"
type: "Convolution"
bottom: "bn2"
top: "conv3_g4_Eyes"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 300
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu3_g4_Eyes"
type: "ReLU"
bottom: "conv3_g4_Eyes"
top: "conv3_g4_Eyes"
}
layer {
name: "pool3_g4_Eyes"
type: "Pooling"
bottom: "conv3_g4_Eyes"
top: "pool3_g4_Eyes"
pooling_param {
pool: MAX
kernel_size: 5
stride: 3
}