Я использую AlexNet для задачи попиксельной регрессии (оценка глубины). Теперь я хотел заменить AlexNet сетью VGG, так как она должна быть лучше.
Это AlexNet, который я использовал:
layer {
name: "train-data"
type: "Data"
top: "data"
include {
phase: TRAIN
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
transform_param {
mean_value: 127
}
}
layer {
name: "train-depth"
type: "Data"
top: "gt"
include {
phase: TRAIN
}
transform_param {
# feature scaling coefficient: this maps [0, 255] to [0, 1]
scale: 0.00390625
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
}
layer {
name: "val-data"
type: "Data"
top: "data"
include {
phase: TEST
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
transform_param {
mean_value: 127
}
}
layer {
name: "val-depth"
type: "Data"
top: "gt"
include {
phase: TEST
}
transform_param {
# feature scaling coefficient: this maps [0, 255] to [0, 1]
scale: 0.00390625
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
}
# CONVOLUTIONAL
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
engine: CAFFE
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# MAIN
layer {
name: "fc-main"
type: "InnerProduct"
bottom: "pool5"
top: "fc-main"
param {
decay_mult: 1
}
param {
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
std: 0.005
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc-main"
top: "fc-main"
relu_param {
engine: CAFFE
}
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc-main"
top: "fc-main"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc-depth"
type: "InnerProduct"
bottom: "fc-main"
top: "fc-depth"
param {
decay_mult: 1
lr_mult: 0.2
}
param {
lr_mult: 0.2
decay_mult: 0
}
inner_product_param {
num_output: 1369
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
name: "reshape"
type: "Reshape"
bottom: "fc-depth"
top: "depth"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: 37
dim: 37 # infer it from the other dimensions
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "depth"
bottom: "gt"
top: "loss"
loss_weight: 1
}
Это VGG, который я использую:
layer {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: "ReLU"
}
layer {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: "ReLU"
}
layer {
bottom: "conv1_2"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: "ReLU"
}
layer {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: "ReLU"
}
layer {
bottom: "conv2_2"
top: "pool2"
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: "ReLU"
}
layer {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: "ReLU"
}
layer {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: "ReLU"
}
layer {
bottom: "conv3_3"
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"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: "ReLU"
}
layer {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: "ReLU"
}
layer {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: "ReLU"
}
layer {
bottom: "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"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: "ReLU"
}
layer {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: "ReLU"
}
layer {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: "ReLU"
}
layer {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: "InnerProduct"
param {
lr_mult: 0.1
decay_mult: 1
}
param {
lr_mult: 0.1
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: "ReLU"
relu_param {
engine: CAFFE
}
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: "InnerProduct"
param {
lr_mult: 0.1
decay_mult: 1
}
param {
lr_mult: 0.1
decay_mult: 0
}
inner_product_param {
num_output: 1369
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
name: "reshape"
type: "Reshape"
bottom: "fc7"
top: "depth"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: 37
dim: 37 # infer it from the other dimensions
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "depth"
bottom: "gt"
top: "loss"
loss_weight: 1
}
скорость обучения: 0,0005
При обучении AlexNet потери сходятся примерно к 5, а при использовании VGG сеть вообще не сходится. Он всегда остается на уровне 30, хотя я уменьшал Learning_rate и даже уменьшал mult_lr. У кого-нибудь есть идеи, что еще может быть не так? Я на 100% уверен, что отличаются только файлы .prototxt, все остальное точно такое же.
