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train_val.prototxt
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700 lines (685 loc) · 10 KB
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name: "VGG_FACE_16_layer"
layer {
name: "data"
type: "Data"
include {
phase: TRAIN
}
transform_param {
mirror: false
scale: 0.003922
}
data_param {
source: "/home/nd/caffe_set/caffe-moon/examples/moon_paper/train_data"
batch_size: 128
backend: LMDB
}
top: "data"
}
layer {
name: "labels"
type: "Data"
top: "labels"
include: { phase: TRAIN }
data_param {
source: "/home/nd/caffe_set/caffe-moon/examples/moon_paper/train_label"
batch_size: 128
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
include {
phase: TEST
}
transform_param {
scale: 0.003922
mirror: false
}
data_param {
source: "/home/nd/caffe_set/caffe-moon/examples/moon_paper/val_data"
batch_size: 64
backend: LMDB
}
top: "data"
}
layer {
name: "labels"
type: "Data"
top: "labels"
include: { phase: TEST }
data_param {
source: "/home/nd/caffe_set/caffe-moon/examples/moon_paper/val_label"
batch_size: 64
backend: LMDB
}
}
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv1"
top: "bn_conv1"
name: "bn_conv1"
type: "BatchNorm"
}
layer {
name: "relu1"
type: "ReLU"
bottom: "bn_conv1"
top: "bn_conv1"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv1"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool1"
top: "conv2"
name: "conv2"
type: "Convolution"
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv2"
top: "bn_conv2"
name: "bn_conv2"
type: "BatchNorm"
}
layer {
name: "relu2"
type: "ReLU"
bottom: "bn_conv2"
top: "bn_conv2"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv2"
top: "pool2"
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool2"
top: "conv3"
name: "conv3"
type: "Convolution"
convolution_param {
num_output: 16
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv3"
top: "bn_conv3"
name: "bn_conv3"
type: "BatchNorm"
}
layer {
name: "relu3"
type: "ReLU"
bottom: "bn_conv3"
top: "bn_conv3"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv3"
top: "pool3"
name: "pool3"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: "Convolution"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv4_1"
top: "bn_conv4_1"
name: "bn_conv4_1"
type: "BatchNorm"
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "bn_conv4_1"
top: "bn_conv4_1"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv4_1"
top: "conv4_2"
name: "conv4_2"
type: "Convolution"
convolution_param {
num_output: 16
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv4_2"
top: "bn_conv4_2"
name: "bn_conv4_2"
type: "BatchNorm"
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "bn_conv4_2"
top: "bn_conv4_2"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv4_2"
top: "conv4_3"
name: "conv4_3"
type: "Convolution"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv4_3"
top: "bn_conv4_3"
name: "bn_conv4_3"
type: "BatchNorm"
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "bn_conv4_3"
top: "bn_conv4_3"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv4_3"
top: "pool4"
name: "pool4"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: "Convolution"
convolution_param {
num_output: 32
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv5_1"
top: "bn_conv5_1"
name: "bn_conv5_1"
type: "BatchNorm"
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "bn_conv5_1"
top: "bn_conv5_1"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv5_1"
top: "conv5_2"
name: "conv5_2"
type: "Convolution"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv5_2"
top: "bn_conv5_2"
name: "bn_conv5_2"
type: "BatchNorm"
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "bn_conv5_2"
top: "bn_conv5_2"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv5_2"
top: "conv5_3"
name: "conv5_3"
type: "Convolution"
convolution_param {
num_output: 32
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv5_3"
top: "bn_conv5_3"
name: "bn_conv5_3"
type: "BatchNorm"
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "bn_conv5_3"
top: "bn_conv5_3"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv5_3"
top: "conv5_4"
name: "conv5_4"
type: "Convolution"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv5_4"
top: "bn_conv5_4"
name: "bn_conv5_4"
type: "BatchNorm"
}
layer {
name: "relu5_4"
type: "ReLU"
bottom: "bn_conv5_4"
top: "bn_conv5_4"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv5_4"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool5"
top: "conv6_1"
name: "conv6_1"
type: "Convolution"
convolution_param {
num_output: 64
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv6_1"
top: "bn_conv6_1"
name: "bn_conv6_1"
type: "BatchNorm"
}
layer {
name: "relu6_1"
type: "ReLU"
bottom: "bn_conv6_1"
top: "bn_conv6_1"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv6_1"
top: "conv6_2"
name: "conv6_2"
type: "Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv6_2"
top: "bn_conv6_2"
name: "bn_conv6_2"
type: "BatchNorm"
}
layer {
name: "relu6_2"
type: "ReLU"
bottom: "bn_conv6_2"
top: "bn_conv6_2"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv6_2"
top: "conv6_3"
name: "conv6_3"
type: "Convolution"
convolution_param {
num_output: 64
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv6_3"
top: "bn_conv6_3"
name: "bn_conv6_3"
type: "BatchNorm"
}
layer {
name: "relu6_3"
type: "ReLU"
bottom: "bn_conv6_3"
top: "bn_conv6_3"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv6_3"
top: "conv6_4"
name: "conv6_4"
type: "Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv6_4"
top: "bn_conv6_4"
name: "bn_conv6_4"
type: "BatchNorm"
}
layer {
name: "relu6_4"
type: "ReLU"
bottom: "bn_conv6_4"
top: "bn_conv6_4"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv6_4"
top: "conv7"
name: "conv7"
type: "Convolution"
convolution_param {
num_output: 128
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv7"
top: "bn_conv7"
name: "bn_conv7"
type: "BatchNorm"
}
layer {
name: "relu7"
type: "ReLU"
bottom: "bn_conv7"
top: "bn_conv7"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv7"
top: "conv8"
name: "conv8"
type: "Convolution"
convolution_param {
num_output: 1000
pad: 1
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv8"
top: "bn_conv8"
name: "bn_conv8"
type: "BatchNorm"
}
layer {
name: "relu8"
type: "ReLU"
bottom: "bn_conv8"
top: "bn_conv8"
relu_param{
negative_slope: 0.1
}
}
layer {
bottom: "bn_conv8"
top: "global_pool8_max"
name: "global_pool8_max"
type: "Pooling"
pooling_param{
pool: MAX
global_pooling: true
}
}
layer {
bottom: "global_pool8_max"
top: "norm_conv8_max"
type: "Normalize"
name: "norm_conv8_max"
}
layer {
bottom: "bn_conv8"
top: "global_pool8_ave"
name: "global_pool8_ave"
type: "Pooling"
pooling_param{
pool: AVE
global_pooling: true
}
}
layer {
bottom: "global_pool8_ave"
top: "norm_conv8_ave"
type: "Normalize"
name: "norm_conv8_ave"
}
layer {
bottom: "norm_conv8_max"
bottom: "norm_conv8_ave"
top: "concat_data"
name: "concat_data"
type: "Concat"
}
layer {
bottom: "concat_data"
top: "moon-fc"
name: "moon-fc"
type: "InnerProduct"
inner_product_param {
num_output: 40
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "euclidean-loss"
type: "EuclideanLoss"
bottom: "moon-fc"
bottom: "labels"
top: "loss"
}