有谁知道如何追踪网络训练？我收到以下错误，不知道如何跟踪培训。似乎无法找到图像，但在某些早期阶段，它们被发现了。
root@samar-Dell-Precision-M3800:~/caffe# ./build/tools/caffe train -solver models/caltech101/caltech101_solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel
snapshot_prefix: "models/caltech101/caltech101"
solver_mode: CPU
net: "models/caltech101/caltech101_train.prototxt"
I0702 16:19:43.065757 20618 solver.cpp:91] Creating training net from net file: models/caltech101/caltech101_train.prototxt
I0702 16:19:43.066241 20618 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0702 16:19:43.066275 20618 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0702 16:19:43.066431 20618 net.cpp:49] Initializing net from parameters:
name: "CaffeNet"
state {
phase: TRAIN
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
}
image_data_param {
source: "data/caltech101/caltech101_train.txt"
batch_size: 50
new_height: 256
new_width: 256
}
}
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_caltech101"
type: "InnerProduct"
bottom: "fc7"
top: "fc8_caltech101"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 20
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8_caltech101"
bottom: "label"
top: "loss"
}
I0702 16:19:43.067663 20618 layer_factory.hpp:77] Creating layer data
I0702 16:19:43.067703 20618 net.cpp:91] Creating Layer data
I0702 16:19:43.067718 20618 net.cpp:399] data -> data
I0702 16:19:43.067746 20618 net.cpp:399] data -> label
I0702 16:19:43.067770 20618 data_transformer.cpp:25] Loading mean file from: data/ilsvrc12/imagenet_mean.binaryproto
I0702 16:19:43.069584 20618 image_data_layer.cpp:38] Opening file data/caltech101/caltech101_train.txt
I0702 16:19:43.069648 20618 image_data_layer.cpp:58] A total of 84 images.
I0702 16:19:43.071579 20618 image_data_layer.cpp:85] output data size: 50,3,227,227
I0702 16:19:43.081862 20618 net.cpp:141] Setting up data
I0702 16:19:43.081907 20618 net.cpp:148] Top shape: 50 3 227 227 (7729350)
I0702 16:19:43.081920 20618 net.cpp:148] Top shape: 50 (50)
I0702 16:19:43.081928 20618 net.cpp:156] Memory required for data: 30917600
I0702 16:19:43.081943 20618 layer_factory.hpp:77] Creating layer conv1
I0702 16:19:43.081975 20618 net.cpp:91] Creating Layer conv1
I0702 16:19:43.081989 20618 net.cpp:425] conv1 <- data
I0702 16:19:43.082007 20618 net.cpp:399] conv1 -> conv1
I0702 16:19:43.083432 20618 net.cpp:141] Setting up conv1
I0702 16:19:43.083454 20618 net.cpp:148] Top shape: 50 96 55 55 (14520000)
I0702 16:19:43.083464 20618 net.cpp:156] Memory required for data: 88997600
I0702 16:19:43.083483 20618 layer_factory.hpp:77] Creating layer relu1
I0702 16:19:43.083499 20618 net.cpp:91] Creating Layer relu1
I0702 16:19:43.083508 20618 net.cpp:425] relu1 <- conv1
I0702 16:19:43.083519 20618 net.cpp:386] relu1 -> conv1 (in-place)
I0702 16:19:43.083537 20618 net.cpp:141] Setting up relu1
I0702 16:19:43.083549 20618 net.cpp:148] Top shape: 50 96 55 55 (14520000)
I0702 16:19:43.083559 20618 net.cpp:156] Memory required for data: 147077600
I0702 16:19:43.083566 20618 layer_factory.hpp:77] Creating layer pool1
I0702 16:19:43.083578 20618 net.cpp:91] Creating Layer pool1
I0702 16:19:43.083587 20618 net.cpp:425] pool1 <- conv1
I0702 16:19:43.083598 20618 net.cpp:399] pool1 -> pool1
I0702 16:19:43.083622 20618 net.cpp:141] Setting up pool1
I0702 16:19:43.083636 20618 net.cpp:148] Top shape: 50 96 27 27 (3499200)
I0702 16:19:43.083645 20618 net.cpp:156] Memory required for data: 161074400
I0702 16:19:43.083654 20618 layer_factory.hpp:77] Creating layer norm1
I0702 16:19:43.083668 20618 net.cpp:91] Creating Layer norm1
I0702 16:19:43.083678 20618 net.cpp:425] norm1 <- pool1
I0702 16:19:43.083703 20618 net.cpp:399] norm1 -> norm1
I0702 16:19:43.083721 20618 net.cpp:141] Setting up norm1
I0702 16:19:43.083734 20618 net.cpp:148] Top shape: 50 96 27 27 (3499200)
I0702 16:19:43.083744 20618 net.cpp:156] Memory required for data: 175071200
I0702 16:19:43.083752 20618 layer_factory.hpp:77] Creating layer conv2
I0702 16:19:43.083768 20618 net.cpp:91] Creating Layer conv2
I0702 16:19:43.083777 20618 net.cpp:425] conv2 <- norm1
I0702 16:19:43.083789 20618 net.cpp:399] conv2 -> conv2
I0702 16:19:43.093122 20618 net.cpp:141] Setting up conv2
I0702 16:19:43.093155 20618 net.cpp:148] Top shape: 50 256 27 27 (9331200)
I0702 16:19:43.093164 20618 net.cpp:156] Memory required for data: 212396000
I0702 16:19:43.093183 20618 layer_factory.hpp:77] Creating layer relu2
I0702 16:19:43.093199 20618 net.cpp:91] Creating Layer relu2
I0702 16:19:43.093209 20618 net.cpp:425] relu2 <- conv2
I0702 16:19:43.093222 20618 net.cpp:386] relu2 -> conv2 (in-place)
I0702 16:19:43.093240 20618 net.cpp:141] Setting up relu2
I0702 16:19:43.093255 20618 net.cpp:148] Top shape: 50 256 27 27 (9331200)
I0702 16:19:43.093266 20618 net.cpp:156] Memory required for data: 249720800
I0702 16:19:43.093274 20618 layer_factory.hpp:77] Creating layer pool2
I0702 16:19:43.093287 20618 net.cpp:91] Creating Layer pool2
I0702 16:19:43.093297 20618 net.cpp:425] pool2 <- conv2
I0702 16:19:43.093308 20618 net.cpp:399] pool2 -> pool2
I0702 16:19:43.093325 20618 net.cpp:141] Setting up pool2
I0702 16:19:43.093338 20618 net.cpp:148] Top shape: 50 256 13 13 (2163200)
I0702 16:19:43.093345 20618 net.cpp:156] Memory required for data: 258373600
I0702 16:19:43.093354 20618 layer_factory.hpp:77] Creating layer norm2
I0702 16:19:43.093370 20618 net.cpp:91] Creating Layer norm2
I0702 16:19:43.093385 20618 net.cpp:425] norm2 <- pool2
I0702 16:19:43.093397 20618 net.cpp:399] norm2 -> norm2
I0702 16:19:43.093412 20618 net.cpp:141] Setting up norm2
I0702 16:19:43.093425 20618 net.cpp:148] Top shape: 50 256 13 13 (2163200)
I0702 16:19:43.093433 20618 net.cpp:156] Memory required for data: 267026400
I0702 16:19:43.093442 20618 layer_factory.hpp:77] Creating layer conv3
I0702 16:19:43.093458 20618 net.cpp:91] Creating Layer conv3
I0702 16:19:43.093468 20618 net.cpp:425] conv3 <- norm2
I0702 16:19:43.093480 20618 net.cpp:399] conv3 -> conv3
I0702 16:19:43.119555 20618 net.cpp:141] Setting up conv3
I0702 16:19:43.119588 20618 net.cpp:148] Top shape: 50 384 13 13 (3244800)
I0702 16:19:43.119598 20618 net.cpp:156] Memory required for data: 280005600
I0702 16:19:43.119616 20618 layer_factory.hpp:77] Creating layer relu3
I0702 16:19:43.119632 20618 net.cpp:91] Creating Layer relu3
I0702 16:19:43.119642 20618 net.cpp:425] relu3 <- conv3
I0702 16:19:43.119655 20618 net.cpp:386] relu3 -> conv3 (in-place)
I0702 16:19:43.119671 20618 net.cpp:141] Setting up relu3
I0702 16:19:43.119683 20618 net.cpp:148] Top shape: 50 384 13 13 (3244800)
I0702 16:19:43.119693 20618 net.cpp:156] Memory required for data: 292984800
I0702 16:19:43.119701 20618 layer_factory.hpp:77] Creating layer conv4
I0702 16:19:43.119719 20618 net.cpp:91] Creating Layer conv4
I0702 16:19:43.119735 20618 net.cpp:425] conv4 <- conv3
I0702 16:19:43.119750 20618 net.cpp:399] conv4 -> conv4
I0702 16:19:43.139026 20618 net.cpp:141] Setting up conv4
I0702 16:19:43.139058 20618 net.cpp:148] Top shape: 50 384 13 13 (3244800)
I0702 16:19:43.139066 20618 net.cpp:156] Memory required for data: 305964000
I0702 16:19:43.139080 20618 layer_factory.hpp:77] Creating layer relu4
I0702 16:19:43.139094 20618 net.cpp:91] Creating Layer relu4
I0702 16:19:43.139104 20618 net.cpp:425] relu4 <- conv4
I0702 16:19:43.139117 20618 net.cpp:386] relu4 -> conv4 (in-place)
I0702 16:19:43.139132 20618 net.cpp:141] Setting up relu4
I0702 16:19:43.139152 20618 net.cpp:148] Top shape: 50 384 13 13 (3244800)
I0702 16:19:43.139161 20618 net.cpp:156] Memory required for data: 318943200
I0702 16:19:43.139170 20618 layer_factory.hpp:77] Creating layer conv5
I0702 16:19:43.139188 20618 net.cpp:91] Creating Layer conv5
I0702 16:19:43.139217 20618 net.cpp:425] conv5 <- conv4
I0702 16:19:43.139231 20618 net.cpp:399] conv5 -> conv5
I0702 16:19:43.152601 20618 net.cpp:141] Setting up conv5
I0702 16:19:43.152634 20618 net.cpp:148] Top shape: 50 256 13 13 (2163200)
I0702 16:19:43.152643 20618 net.cpp:156] Memory required for data: 327596000
I0702 16:19:43.152662 20618 layer_factory.hpp:77] Creating layer relu5
I0702 16:19:43.152678 20618 net.cpp:91] Creating Layer relu5
I0702 16:19:43.152688 20618 net.cpp:425] relu5 <- conv5
I0702 16:19:43.152701 20618 net.cpp:386] relu5 -> conv5 (in-place)
I0702 16:19:43.152719 20618 net.cpp:141] Setting up relu5
I0702 16:19:43.152730 20618 net.cpp:148] Top shape: 50 256 13 13 (2163200)
I0702 16:19:43.152740 20618 net.cpp:156] Memory required for data: 336248800
I0702 16:19:43.152750 20618 layer_factory.hpp:77] Creating layer pool5
I0702 16:19:43.152761 20618 net.cpp:91] Creating Layer pool5
I0702 16:19:43.152770 20618 net.cpp:425] pool5 <- conv5
I0702 16:19:43.152782 20618 net.cpp:399] pool5 -> pool5
I0702 16:19:43.152801 20618 net.cpp:141] Setting up pool5
I0702 16:19:43.152817 20618 net.cpp:148] Top shape: 50 256 6 6 (460800)
I0702 16:19:43.152827 20618 net.cpp:156] Memory required for data: 338092000
I0702 16:19:43.152835 20618 layer_factory.hpp:77] Creating layer fc6
I0702 16:19:43.152858 20618 net.cpp:91] Creating Layer fc6
I0702 16:19:43.152869 20618 net.cpp:425] fc6 <- pool5
I0702 16:19:43.152881 20618 net.cpp:399] fc6 -> fc6
E0702 16:19:43.215560 20620 io.cpp:80] Could not open or find file
F0702 16:19:43.215747 20620 image_data_layer.cpp:143] Check failed: cv_img.data Could not load
*** Check failure stack trace: ***
@ 0x7fb695883daa (unknown)
@ 0x7fb695883ce4 (unknown)
@ 0x7fb6958836e6 (unknown)
@ 0x7fb695886687 (unknown)
@ 0x7fb695d1f8ec caffe::ImageDataLayer<>::load_batch()
@ 0x7fb695d2a048 caffe::BasePrefetchingDataLayer<>::InternalThreadEntry()
@ 0x7fb693024a4a (unknown)
@ 0x7fb6928dc182 start_thread
@ 0x7fb694c6a47d (unknown)
@ (nil) (unknown)
Aborted (core dumped)
感谢您的帮助。
答案 0 :(得分：0)
首先，查看文件$ CAFFEROOT / data / caltech101 / caltech101_train.txt（在caltech101_train.prototxt第16行中引用）。文件是否按照正确的路径列出（从$ CAFFEROOT看到）并启用了读取？要检查这一点，从运行build / tools / caffe命令的地方（应该是$ CAFFEROOT），尝试＆＃39; ls -l＆＃39;在他们的目录上（从* _train.txt文件剪切和粘贴）。如果您没有看到正确的阅读权限，请相应地调整权限或路径。
一旦您解决了访问问题： 内容是否合适？如果没有，则调整输入尺寸。
它们是image_data格式吗？如果没有，请从 image_data_param 切换到 data_param 。