Caffe编译(Win10+Vs2015+Cuda8.0)
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参考博客
- 编译
- Windows下VS2015编译caffe
- caffe window10 安装 Caffe在window10+VS2015安装
- 测试minist数据集
- CAFFE学习笔记(一)Caffe_Example之训练mnist
环境
- Win10
- Vs2015 or Vs2013
- cmake-3.7.2-win64-x64.zip
- cudnn-8.0-windows10-x64-v5.1
- Cuda8.0
- Anaconda2-4.3.0.1-Windows-x86_64 (Python2.7 or 3.5)
- Git-2.12.0-64-bit
- Caffe-Windows源码
编译步骤
- 安装上述所有环境
- 配置环境变量
- cmake的bin目录加入path
- cudnn的cuda下bin目录加入path
- Anaconda安装目录加入path(python就在根目录下)
- 解压Caffe-Windows,最好不要含有中文路径
进入…\caffe-windows\scripts,用文本编辑器如Notepad++打开build_win.cmd文件(我的目录F:\vs2015\caffe\caffe-windows\scripts\build_win.cmd),下面进行修改编译配置:
我的配置如下,如果你是Vs2013,把MSVC_VERSION=14改为12。WITH_NINJA=0,除非你想用ninja编译器来进行编译。Python版本设置为2或者3,取决于装的版本,这里编译只支持2.7或者3.5版本,其他不用怎么改变。else ( :: Change the settings here to match your setup :: Change MSVC_VERSION to 12 to use VS 2013 if NOT DEFINED MSVC_VERSION set MSVC_VERSION=14 :: Change to 1 to use Ninja generator (builds much faster) if NOT DEFINED WITH_NINJA set WITH_NINJA=0 :: Change to 1 to build caffe without CUDA support if NOT DEFINED CPU_ONLY set CPU_ONLY=0 :: Change to Debug to build Debug. This is only relevant for the Ninja generator the Visual Studio generator will generate both Debug and Release configs if NOT DEFINED CMAKE_CONFIG set CMAKE_CONFIG=Release :: Set to 1 to use NCCL if NOT DEFINED USE_NCCL set USE_NCCL=0 :: Change to 1 to build a caffe.dll if NOT DEFINED CMAKE_BUILD_SHARED_LIBS set CMAKE_BUILD_SHARED_LIBS=0 :: Change to 3 if using python 3.5 (only 2.7 and 3.5 are supported) if NOT DEFINED PYTHON_VERSION set PYTHON_VERSION=2 :: Change these options for your needs. if NOT DEFINED BUILD_PYTHON set BUILD_PYTHON=1 if NOT DEFINED BUILD_PYTHON_LAYER set BUILD_PYTHON_LAYER=1 if NOT DEFINED BUILD_MATLAB set BUILD_MATLAB=0 :: If python is on your path leave this alone if NOT DEFINED PYTHON_EXE set PYTHON_EXE=python :: Run the tests if NOT DEFINED RUN_TESTS set RUN_TESTS=0 :: Run lint if NOT DEFINED RUN_LINT set RUN_LINT=0 :: Build the install target if NOT DEFINED RUN_INSTALL set RUN_INSTALL=0)
修改完build_win.cmd保存好后,打开cmd命令提示符,进入该文件目录,执行
F:\vs2015\caffe\caffe-windows\scripts>build_win.cmd
等待编译,先下载依赖包,然后进行caffe编译,大约20-30分钟编译成功。最后结果大致:
CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-string-l1-1-0.dll' returning type'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-heap-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-heap-l1-1-0.dll' returning type 'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-stdio-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-stdio-l1-1-0.dll' returning type 'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-convert-l1-1-0.dll' [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-convert-l1-1-0.dll' returning type 'system' -- possibly incorrect [F:\vs2015\caffe\caffe-windows\scripts\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] 13407 个警告 0 个错误已用时间 00:19:12.74
- 编译完成后,会在…/caffe-windows/scripts目录下多出build文件夹,里面的Caffe.sln用Vs2015打开就可以了,如果想再编译,选择Release方式。在…/caffe-windows/scripts/build/tools/Release下有编译生成的Caffe.exe,使用它便可以测试数据集。
测试mnist数据集
- 下载mnist leveldb格式的数据集,包含mnist-train-leveldb以及mnist-test-leveldb。
- 在…\caffe-windows\examples\mnist目录下,有lenet_train.prototxt、lenet_solver.prototxt和train_lenet.sh文件。先明确作用:
- 在lenet_train.prototxt中:
- 给出用于训练的MNIST数据mnist-train-leveldb以及mnist-test-leveldb的路径;
- 在此定义这个网络都有哪些层,给出每一层的定义。
- 在lenet_solver.prototxt中:
- 在此指明我们要训练的网络是lenet_train.prototxt,给出lenet_train.prototxt的路径;
- 在此指定是用CPU还是GPU。
- 文件train_lenet.sh,在里面给出:
- caffe编译后生成的可执行文件exe的名称(带不带“.exe”都可以),后面跟“train”代表选择“训练”;
- 刚才写好的lenet_solver.prototxt的路径。
- 在lenet_train.prototxt中:
- 在…\caffe-windows\scripts\build\tools\Release下新建一个文件夹,我的叫mnist_test。将mnist-test-leveldb、 mnist-train-leveldb 和…\caffe-windows\examples\mnist目录下的lenet_train_test.prototxt、lenet_solver.prototxt这四个文件全部拷贝到mnist_test文件夹中去。
设定prototxt文件,给出相应的路径:
lenet_train_test.prototxt,这个文件即之前说的lenet_train.prototxt,为了便于区分在后面加了一个_test。路径改变如下(修改部分截取,未修改部分没有贴上来):
name: "LeNet"layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { scale: 0.00390625 } data_param { source: "mnist_test/mnist-train-leveldb" ---------》修改部分 batch_size: 64 backend: LEVELDB ---------》修改部分 }}layer { name: "mnist" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { scale: 0.00390625 } data_param { source: "mnist_test/mnist-test-leveldb" ---------》修改部分 batch_size: 100 backend: LEVELDB ---------》修改部分 }}...........
lenet_solver.prototxt 设置相对路径:
# The train/test net protocol buffer definitionnet: "mnist_test/lenet_train_test.prototxt" ---------》修改部分# test_iter specifies how many forward passes the test should carry out.# In the case of MNIST, we have test batch size 100 and 100 test iterations,# covering the full 10,000 testing images.test_iter: 100# Carry out testing every 500 training iterations.test_interval: 500# The base learning rate, momentum and the weight decay of the network.base_lr: 0.01momentum: 0.9weight_decay: 0.0005# The learning rate policylr_policy: "inv"gamma: 0.0001power: 0.75# Display every 100 iterationsdisplay: 100# The maximum number of iterationsmax_iter: 10000# snapshot intermediate resultssnapshot: 5000snapshot_prefix: "mnist_test/lenet" ---------》修改部分# solver mode: CPU or GPUsolver_mode: GPU ---------》使用GPU
打开cmd,进入caffe.exe的目录:
C:\Users\Mr.sorrow>f: F:\>cd F:\vs2015\caffe\caffe-windows\scripts\build\tools\Release F:\vs2015\caffe\caffe-windows\scripts\build\tools\Release>
运行:
caffe.exe train --solver=mnist_test/lenet_solver.prototxt
结果:
....... ....... I0309 13:46:14.446481 3708 solver.cpp:219] Iteration 9600 (165.724 iter/s, 0.603412s/100 iters), loss = 0.0029485 I0309 13:46:14.446481 3708 solver.cpp:238] Train net output #0: loss = 0.0029485 (* 1 = 0.0029485 loss) I0309 13:46:14.447450 3708 sgd_solver.cpp:105] Iteration 9600, lr = 0.00603682 I0309 13:46:15.059074 3708 solver.cpp:219] Iteration 9700 (164.181 iter/s, 0.609083s/100 iters), loss = 0.00178553 I0309 13:46:15.059074 3708 solver.cpp:238] Train net output #0: loss = 0.00178552 (* 1 = 0.00178552 loss) I0309 13:46:15.060080 3708 sgd_solver.cpp:105] Iteration 9700, lr = 0.00601382 I0309 13:46:15.664719 3708 solver.cpp:219] Iteration 9800 (165.979 iter/s, 0.602486s/100 iters), loss = 0.0119374 I0309 13:46:15.665698 3708 solver.cpp:238] Train net output #0: loss = 0.0119374 (* 1 = 0.0119374 loss) I0309 13:46:15.665698 3708 sgd_solver.cpp:105] Iteration 9800, lr = 0.00599102 I0309 13:46:16.286525 3708 solver.cpp:219] Iteration 9900 (161.895 iter/s, 0.617683s/100 iters), loss = 0.00379853 I0309 13:46:16.286990 3708 solver.cpp:238] Train net output #0: loss = 0.00379854 (* 1 = 0.00379854 loss) I0309 13:46:16.287490 3708 sgd_solver.cpp:105] Iteration 9900, lr = 0.00596843 I0309 13:46:16.890439 3708 solver.cpp:448] Snapshotting to binary proto file mnist_test/mnist/lenet_iter_10000.caffemodel I0309 13:46:16.903506 3708 sgd_solver.cpp:273] Snapshotting solver state to binary proto file mnist_test/mnist/lenet_iter_10000.solverstate I0309 13:46:16.910498 3708 solver.cpp:311] Iteration 10000, loss = 0.00267322 I0309 13:46:16.910498 3708 solver.cpp:331] Iteration 10000, Testing net (#0) I0309 13:46:17.131078 7880 data_layer.cpp:73] Restarting data prefetching from start. I0309 13:46:17.140102 3708 solver.cpp:398] Test net output #0: accuracy = 0.99 I0309 13:46:17.140102 3708 solver.cpp:398] Test net output #1: loss = 0.0297552 (* 1 = 0.0297552 loss) I0309 13:46:17.143108 3708 solver.cpp:316] Optimization Done. I0309 13:46:17.143108 3708 caffe.cpp:260] Optimization Done.
在….\caffe-windows\scripts\build\tools\Release\mnist_test目录下,即生成四个文件:
- lenet_iter_5000.caffemodel
- lenet_iter_5000.solverstate
- lenet_iter_10000.caffemodel
- lenet_iter_10000.solverstate
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