Ubuntu16.04下caffe CPU版的图片训练和测试
二、转换为lmdb格式
1、首先，在examples下面创建一个myfile的文件夹，来用存放配置文件和脚本文件。然后编写一个脚本create_filelist.sh，用来生成train.txt和test.txt清单文件
(caffe_src) root@ranxf-TEST:/workdisk/caffe/examples# mkdir myfile
(caffe_src) root@ranxf-TEST:/workdisk/caffe/examples/myfile# vim create_filelist.sh
#!/usr/bin/env sh DATA=data/re/ MY=examples/myfile echo "Create train.txt..." rm -rf $MY/train.txt for i in 3 4 5 6 7 do find $DATA/train -name $i*.jpg | cut -d '/' -f4-5 | sed "s/$/ $i/">>$MY/train.txt done echo “done” echo "Create test.txt..." rm -rf $MY/test.txt for i in 3 4 5 6 7 do find $DATA/test -name $i*.jpg | cut -d '/' -f4-5 | sed "s/$/ $i/">>$MY/test.txt done echo "All done"
然后，运行此脚本（注意是在caffe根目录下）
(caffe_src) root@ranxf-TEST:/workdisk/caffe# sh examples/myfile/create_filelist.sh Create train.txt... done Create test.txt... All done
成功的话，就会在examples/myfile/ 文件夹下生成train.txt和test.txt两个文本文件，里面就是图片的列表清单。
2、接着再编写一个脚本文件，调用convert_imageset命令来转换数据格式。
# sudo vi examples/myfile/create_lmdb.sh
#!/usr/bin/env sh MY=examples/myfile echo "Create train lmdb.." rm -rf $MY/img_train_lmdb build/tools/convert_imageset \ --shuffle \ --resize_height=256 \ --resize_width=256 \ /workdisk/caffe/data/re/ \ $MY/train.txt \ $MY/img_train_lmdb echo "done" echo "Create test lmdb.." rm -rf $MY/img_test_lmdb build/tools/convert_imageset \ --shuffle \ --resize_width=256 \ --resize_height=256 \ /workdisk/caffe/data/re/ \ $MY/test.txt \ $MY/img_test_lmdb echo "All Done.."
(caffe_src) root@ranxf-TEST:/workdisk/caffe# ./examples/myfile/create_lmdb.sh Create train lmdb.. I0910 15:49:20.354158 7404 convert_imageset.cpp:86] Shuffling data I0910 15:49:20.354992 7404 convert_imageset.cpp:89] A total of 400 images. I0910 15:49:20.355206 7404 db_lmdb.cpp:35] Opened lmdb examples/myfile/img_train_lmdb I0910 15:49:21.807344 7404 convert_imageset.cpp:153] Processed 400 files. done Create test lmdb.. I0910 15:49:21.852502 7407 convert_imageset.cpp:86] Shuffling data I0910 15:49:21.852725 7407 convert_imageset.cpp:89] A total of 100 images. I0910 15:49:21.852886 7407 db_lmdb.cpp:35] Opened lmdb examples/myfile/img_test_lmdb I0910 15:49:22.201551 7407 convert_imageset.cpp:153] Processed 100 files. All Done..
因为图片大小不一，因此统一转换成256*256大小。运行成功后，会在 examples/myfile下面生成两个文件夹img_train_lmdb和img_test_lmdb，分别用于保存图片转换后的lmdb文件。
(caffe_src) root@ranxf-TEST:/workdisk/caffe/examples/myfile# ls create_filelist.sh create_lmdb.sh img_test_lmdb img_train_lmdb test.txt train.txt
三、计算均值并保存
图片减去均值再训练，会提高训练速度和精度。因此，一般都会有这个操作。
caffe程序提供了一个计算均值的文件compute_image_mean.cpp，我们直接使用就可以了
(caffe_src) root@ranxf-TEST:/workdisk/caffe# build/tools/compute_image_mean examples/myfile/img_train_lmdb examples/myfile/mean.binaryproto I0910 15:56:26.287912 7824 db_lmdb.cpp:35] Opened lmdb examples/myfile/img_train_lmdb I0910 15:56:26.288938 7824 compute_image_mean.cpp:70] Starting iteration I0910 15:56:26.352404 7824 compute_image_mean.cpp:101] Processed 400 files. I0910 15:56:26.352833 7824 compute_image_mean.cpp:108] Write to examples/myfile/mean.binaryproto I0910 15:56:26.354002 7824 compute_image_mean.cpp:114] Number of channels: 3 I0910 15:56:26.354115 7824 compute_image_mean.cpp:119] mean_value channel [0]: 100.254 I0910 15:56:26.365298 7824 compute_image_mean.cpp:119] mean_value channel [1]: 114.454 I0910 15:56:26.365384 7824 compute_image_mean.cpp:119] mean_value channel [2]: 121.707 (caffe_src) root@ranxf-TEST:/workdisk/caffe#
compute_image_mean带两个参数，第一个参数是lmdb训练数据位置，第二个参数设定均值文件的名字及保存路径。
运行成功后，会在 examples/myfile/ 下面生成一个mean.binaryproto的均值文件。
(caffe_src) root@ranxf-TEST:/workdisk/caffe/examples/myfile# ls create_filelist.sh create_lmdb.sh img_test_lmdb img_train_lmdb mean.binaryproto test.txt train.txt (caffe_src) root@ranxf-TEST:/workdisk/caffe/examples/myfile#
四、创建模型并编写配置文件
模型就用程序自带的caffenet模型，位置在 models/bvlc_reference_caffenet/文件夹下, 将需要的两个配置文件，复制到myfile文件夹内
(caffe_src) root@ranxf-TEST:/workdisk/caffe# cp models/bvlc_reference_caffenet/solver.prototxt examples/myfile/ (caffe_src) root@ranxf-TEST:/workdisk/caffe# (caffe_src) root@ranxf-TEST:/workdisk/caffe# cp models/bvlc_reference_caffenet/train_val.prototxt examples/myfile/
修改其中的solver.prototxt
net: "examples/myfile/train_val.prototxt" test_iter: 2 test_interval: 50 base_lr: 0.01 lr_policy: "step" gamma: 0.1 stepsize: 100 display: 20 max_iter: 500 momentum: 0.9 weight_decay: 0.0005 solver_mode: CPU
原始配置文件内容为：
net: "models/bvlc_reference_caffenet/train_val.prototxt" test_iter: 1000 test_interval: 1000 base_lr: 0.01 lr_policy: "step" gamma: 0.1 stepsize: 100000 display: 20 max_iter: 450000 momentum: 0.9 weight_decay: 0.0005 snapshot: 10000 snapshot_prefix: "models/bvlc_reference_caffenet/caffenet_train" solver_mode: GPU
100个测试数据，batch_size为50，因此test_iter设置为2，就能全cover了。在训练过程中，调整学习率，逐步变小。
修改train_val.protxt，只需要修改两个阶段的data层就可以了，其它可以不用管。就是修改两个data layer的mean_file和source这两个地方，其它都没有变化 。
name: "CaffeNet"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "examples/myfile/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
# }
data_param {
source: "examples/myfile/img_train_lmdb"
batch_size: 256
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 227
mean_file: "examples/myfile/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
# }
data_param {
source: "examples/myfile/img_train_lmdb"
batch_size: 50
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
……………………如果前面都没有问题，数据准备好了，配置文件也配置好了，这一步就比较简单了。
运行时间和最后的精确度，会根据机器配置，参数设置的不同而不同。我的是CPU运行500次10个小时20分钟，准确性69%，吐槽机器配置。
I0911 02:42:50.312186 9113 solver.cpp:464] Snapshotting to binary proto file examples/myfile/solver_iter_500.caffemodel I0911 02:42:52.477775 9113 sgd_solver.cpp:284] Snapshotting solver state to binary proto file examples/myfile/solver_iter_500.solverstate I0911 02:42:53.719158 9116 data_layer.cpp:73] Restarting data prefetching from start. I0911 02:43:23.561343 9113 solver.cpp:327] Iteration 500, loss = 0.689866 I0911 02:43:23.648788 9113 solver.cpp:347] Iteration 500, Testing net (#0) I0911 02:43:23.693032 9119 data_layer.cpp:73] Restarting data prefetching from start. I0911 02:43:35.412401 9113 solver.cpp:414] Test net output #0: accuracy = 0.69 I0911 02:43:35.412444 9113 solver.cpp:414] Test net output #1: loss = 0.66485 (* 1 = 0.66485 loss) I0911 02:43:35.412451 9113 solver.cpp:332] Optimization Done. I0911 02:43:35.425511 9113 caffe.cpp:250] Optimization Done.