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私はtrain_caffenet.shを実行すると、私は次のエラーを取得：私はそれを解決しようとした使用カフェは自分のjpgデータセットを訓練する：タイプ「caffe.ImageDataParameterは、」「バックエンド」という名前のフィールドを持っていない
I0906 10:56:42.327703 21556 solver.cpp:91] Creating training net from net file: /home/pris/caffe-master/examples/myself/train_val.prototxt
[libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.NetParameter: 26:12: Message type "caffe.ImageDataParameter" has no field named "backend".
F0906 10:56:42.327837 21556 upgrade_proto.cpp:79] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: /home/pris/caffe-master/examples/myself/train_val.prototxt
*** Check failure stack trace: ***
@ 0x7f5013ca0daa (unknown)
@ 0x7f5013ca0ce4 (unknown)
@ 0x7f5013ca06e6 (unknown)
@ 0x7f5013ca3687 (unknown)
@ 0x7f50142b019e caffe::ReadNetParamsFromTextFileOrDie()
@ 0x7f501429e76b caffe::Solver<>::InitTrainNet()
@ 0x7f501429f83c caffe::Solver<>::Init()
@ 0x7f501429fb6a caffe::Solver<>::Solver()
@ 0x7f50143de663 caffe::Creator_SGDSolver<>()
@ 0x40e9be caffe::SolverRegistry<>::CreateSolver()
@ 0x407b62 train()
@ 0x4059ec main
@ 0x7f5012faef45 (unknown)
@ 0x406121 (unknown)
@ (nil) (unknown)
Aborted (core dumped)
を数日間はまだそれが間違っている方法を把握することはできません。誰かが私には固定助けることができれば、私は本当に感謝します
#!/usr/bin/env sh
/home/pris/caffe-master/build/tools/caffe train \
--solver=/home/pris/caffe-master/examples/myself/solver.prototxt
：ここ
は私のtrain_val.prototxt、主に$CAFFE_TOOT/models/bvlc_reference_caffenet
name: "CaffeNet"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/home/pris/caffe-master/data/myself/myimagenet_mean.binaryproto"
}
# mean pixel/channel-wise mean instead of mean image
# transform_param {
# crop_size: 227
# mean_value: 104
# mean_value: 117
# mean_value: 123
# mirror: true
# }
image_data_param {
source: "/home/pris/caffe-master/examples/myself/imagenet_train_leveldb"
batch_size: 256
backend: LEVELDB
}
}
layer
{
name: "data"
type: "ImageData"
top: "data"
top: "label"
include { phase: TEST }
transform_param
{
mirror: false
crop_size: 227
mean_file: "/home/pris/caffe-master/data/myself/myimagenet_mean.binaryproto"
}
# mean pixel/channel-wise mean instead of mean image
# transform_param {
# crop_size: 227
# mean_value: 104
# mean_value: 117
# mean_value: 123
# mirror: false
# }
image_data_param
{
source: "/home/pris/caffe-master/examples/myself/imagenet_val_leveldb"
batch_size: 50
backend: LEVELDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param
{
lr_mult: 1
decay_mult: 1
}
param
{
lr_mult: 2
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: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "norm1"
top: "conv2"
param
{
lr_mult: 1
decay_mult: 1
}
param
{
lr_mult: 2
decay_mult: 0
}
convolution_param
{
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler
{
type: "gaussian"
std: 0.01
}
bias_filler
{
type: "constant"
value: 1
}
}
}
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: "norm2"
type: "LRN"
bottom: "pool2"
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: 1
decay_mult: 1
}
param
{
lr_mult: 2
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: 1
decay_mult: 1
}
param
{
lr_mult: 2
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: 1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param
{
lr_mult: 1
decay_mult: 1
}
param
{
lr_mult: 2
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: 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
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param
{
lr_mult: 1
decay_mult: 1
}
param
{
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
とsovler.prototxt
net: "/home/pris/caffe-master/examples/myself/train_val.prototxt"
test_iter: 10
test_interval: 500
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 2000
snapshot_prefix:"/home/pris/caffe-master/examples/myself/result"
solver_mode: GPU
train_caffenet.shに1から変更されていますそれ。
ImageDataをDataに、image_data_paramをdata_paramに変更しています。本当にありがとうございます！ – StalkerMuse