caffe使用过程+digits在windows下的安装和运行

一。模型基本组成

想要训练一个caffe模型，需要配置两个文件，包含两个部分：网络模型，参数配置，分别对应*.prototxt , ****_solver.prototxt文件。

Caffe模型文件解析：

预处理图像的leveldb构建

输入：一批图像和label （2和3）
输出：leveldb （4）
指令里包含如下信息：

1. conver_imageset （构建leveldb的可运行程序）
2. train/ （此目录放处理的jpg或者其他格式的图像)
3. label.txt (图像文件名及其label信息)
4. 输出的leveldb文件夹的名字
5. CPU/GPU (指定是在cpu上还是在gpu上运行code)

---

CNN网络配置文件

1. Imagenet_solver.prototxt （包含全局参数的配置的文件）
2. Imagenet.prototxt （包含训练网络的配置的文件）
3. Imagenet_val.prototxt （包含测试网络的配置文件）

网络模型：

DATA:一般包括训练数据和测试数据层两种类型。 一般指输入层，包含source：数据路径，批处理数据大小batch_size，scale表示数据表示在[0,1]，0.00390625即 1/255

训练数据层：

layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    scale: 0.00390625  }  data_param {    source: "examples/mnist/mnist_train_lmdb"    batch_size: 64    backend: LMDB  }}

测试数据层：

layer {  name: "mnist"  type: "Data"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    scale: 0.00390625  }  data_param {    source: "examples/mnist/mnist_test_lmdb"    batch_size: 100    backend: LMDB  }}

CONVOLUATION：卷积层，blobs_lr:1 , blobs_lr:2分别表示weight 及bias更新时的学习率，这里权重的学习率为solver.prototxt文件中定义的学习率真，bias的学习率真是权重学习率的2倍，这样一般会得到很好的收敛速度。

num_output表示滤波的个数，kernelsize表示滤波的大小，stride表示步长，weight_filter表示滤波的类型

layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1 //weight学习率  }  param {    lr_mult: 2 //bias学习率，一般为weight的两倍  }  convolution_param {    num_output: 20  //滤波器个数    kernel_size: 5    stride: 1  //步长    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}

POOLING: 池化层

layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2     stride: 2  }}

INNER_PRODUCT: 其实表示全连接，不要被名字误导

layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool2"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 500     weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}

RELU：激活函数，非线性变化层 max( 0 ,x )，一般与CONVOLUTION层成对出现

layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}

SOFTMAX:

layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"  top: "loss"}

参数配置文件：

***_solver.prototxt文件定义一些模型训练过程中需要到的参数，比较学习率，权重衰减系数，迭代次数，使用GPU还是CPU等等.

# The train/test net protocol buffer definitionnet: "examples/mnist/lenet_train_test.prototxt"# 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: "examples/mnist/lenet"# solver mode: CPU or GPUsolver_mode: GPUdevice_id: 0  #在cmdcaffe接口下，GPU序号从0开始，如果有一个GPU，则device_id:0

训练出的模型被存为*.caffemodel，可供以后使用。
一个完整的网络应该是：

---

步骤

• 数据准备
  准备三组数据：
  
  1. Training Set：用于训练网络
  2. Validation Set：用于训练时测试网络准确率
  3. Test Set：用于测试网络训练完成后的最终正确率
• 构建lmdb/leveldb文件，caffe支持三种数据格式输入：images, levelda, lmdb

虽然lmdb的内存消耗是leveldb的1.1倍，但是lmdb的速度比leveldb快10%至15%，更重要的是lmdb允许多种训练模型同时读取同一组数据集。
因此lmdb取代了leveldb成为Caffe默认的数据集生成格式。

• 定义name.prototxt , name_solver.prototxt文件

• 训练模型

  在windows下训练巨麻烦，要在win下使用.sh文件才行。

在windows使用.sh

安装一波
cygwin
在软件下可以安装，如果出现package不存在的情况可以重新打开setup执行包下载，一般没问题碰到什么问题解决什么问题。

用.bat来测试

1. 去官网http://yann.lecun.com/exdb/mnist/下载mnist数据集。下载后解压到C:\caffe-master\data\mnist

2. 在caffe根目录下，新建一个create_mnist.bat，里面写入如下的脚本。此处可能出错，因为train-images.idx3-ubyte 在解压的时候可能是train-images-idx3-ubyte要注意修改。

   .\Build\x64\Release\convert_mnist_data.exe .\data\mnist\mnist_train_lmdb\train-images.idx3-ubyte .\data\mnist\mnist_train_lmdb\train-labels.idx1-ubyte .\examples\mnist\mnist_train_lmdb
   echo.
   .\Build\x64\Release\convert_mnist_data.exe .\data\mnist\mnist_test_lmdb\t10k-images.idx3-ubyte .\data\mnist\mnist_test_lmdb\t10k-labels.idx1-ubyte .\examples\mnist\mnist_test_lmdb
   pause
   `

   然后双击该脚本运行，即可在E:\caffe\examples\mnist下面生成相应的lmdb数据文件。

3. 在caffe根目录下，新建train_mnist.bat，然后输入如下的脚本，

.\Build\x64\Release\caffe.exe train –solver=.\examples\mnist\lenet_solver.prototxt
pause

然后双击运行，就会开始训练，训练完毕后会得到相应的准确率和损失率。
、

接下来安装digits：

按照这里装就好了

https://github.com/NVIDIA/DIGITS/blob/digits-5.0/docs/BuildDigitsWindows.md

最后在digits目录下 执行python -m digits就可以了
出现了不少bug

bug1

出现找不到pycaffe的情况，
这种情况一般是因为python没有导入caffe的包只需要将CAFFE_ROOT\Build\x64\Release\pycaffe\caffe文件夹复制到anaconda的sitepackages中就可以了。

bug2

出现pkg_resources._vendor.packaging.version.InvalidVersion: Invalid version: 'CAFFE_VERSION'
找到\DIGITS-master\digits\config下的caffe.py
按照下面的中文部分修改。

from __future__ import absolute_importimport impimport osimport platformimport reimport subprocessimport sysfrom . import option_listfrom digits import device_queryfrom digits.utils import parse_versiondef load_from_envvar(envvar):    """    Load information from an installation indicated by an environment variable    """    value = os.environ[envvar].strip().strip("\"' ")#此处需要修改路径，于CAFFE_HOME对应    if platform.system() == 'Windows':        #executable_dir = os.path.join(value, 'install', 'bin')        executable_dir = os.path.join(value)        #python_dir = os.path.join(value, 'install', 'python')        python_dir = os.path.join(value, 'pycaffe')    else:        executable_dir = os.path.join(value, 'build', 'tools')        python_dir = os.path.join(value, 'python')    try:        executable = find_executable_in_dir(executable_dir)        if executable is None:            raise ValueError('Caffe executable not found at "%s"'                             % executable_dir)        if not is_pycaffe_in_dir(python_dir):            raise ValueError('Pycaffe not found in "%s"'                             % python_dir)        import_pycaffe(python_dir)        version, flavor = get_version_and_flavor(executable)    except:        print ('"%s" from %s does not point to a valid installation of Caffe.'               % (value, envvar))        print 'Use the envvar CAFFE_ROOT to indicate a valid installation.'        raise    return executable, version, flavordef load_from_path():    """    Load information from an installation on standard paths (PATH and PYTHONPATH)    """    try:        executable = find_executable_in_dir()        if executable is None:            raise ValueError('Caffe executable not found in PATH')        if not is_pycaffe_in_dir():            raise ValueError('Pycaffe not found in PYTHONPATH')        import_pycaffe()        version, flavor = get_version_and_flavor(executable)    except:        print 'A valid Caffe installation was not found on your system.'        print 'Use the envvar CAFFE_ROOT to indicate a valid installation.'        raise    return executable, version, flavordef find_executable_in_dir(dirname=None):    """    Returns the path to the caffe executable at dirname    If dirname is None, search all directories in sys.path    Returns None if not found    """    if platform.system() == 'Windows':        exe_name = 'caffe.exe'    else:        exe_name = 'caffe'    if dirname is None:        dirnames = [path.strip("\"' ") for path in os.environ['PATH'].split(os.pathsep)]    else:        dirnames = [dirname]    for dirname in dirnames:        path = os.path.join(dirname, exe_name)        if os.path.isfile(path) and os.access(path, os.X_OK):            return path    return Nonedef is_pycaffe_in_dir(dirname=None):    """    Returns True if you can "import caffe" from dirname    If dirname is None, search all directories in sys.path    """    old_path = sys.path    if dirname is not None:        sys.path = [dirname]  # temporarily replace sys.path    try:        imp.find_module('caffe')    except ImportError:        return False    finally:        sys.path = old_path    return Truedef import_pycaffe(dirname=None):    """    Imports caffe    If dirname is not None, prepend it to sys.path first    """    if dirname is not None:        sys.path.insert(0, dirname)        # Add to PYTHONPATH so that build/tools/caffe is aware of python layers there        os.environ['PYTHONPATH'] = '%s%s%s' % (            dirname, os.pathsep, os.environ.get('PYTHONPATH'))    # Suppress GLOG output for python bindings    GLOG_minloglevel = os.environ.pop('GLOG_minloglevel', None)    # Show only "ERROR" and "FATAL"    os.environ['GLOG_minloglevel'] = '2'    # for Windows environment, loading h5py before caffe solves the issue mentioned in    # https://github.com/NVIDIA/DIGITS/issues/47#issuecomment-206292824    import h5py  # noqa    try:        import caffe    except ImportError:        print 'Did you forget to "make pycaffe"?'        raise    # Strange issue with protocol buffers and pickle - see issue #32    sys.path.insert(0, os.path.join(        os.path.dirname(caffe.__file__), 'proto'))    # Turn GLOG output back on for subprocess calls    if GLOG_minloglevel is None:        del os.environ['GLOG_minloglevel']    else:        os.environ['GLOG_minloglevel'] = GLOG_minlogleveldef get_version_and_flavor(executable):    """    Returns (version, flavor)    Should be called after import_pycaffe()    """    version_string = get_version_from_pycaffe()    if version_string is None:        version_string = get_version_from_cmdline(executable)    if version_string is None:        version_string = get_version_from_soname(executable)    if version_string is None:        raise ValueError('Could not find version information for Caffe build ' +                         'at "%s". Upgrade your installation' % executable)    #这部分代码没用，但是会出现bug，我就注释了    #version = parse_version(version_string)    #if parse_version(0, 99, 0) > version > parse_version(0, 9, 0):    #    flavor = 'NVIDIA'    #    minimum_version = '0.11.0'    #    if version < parse_version(minimum_version):    #        raise ValueError(    #            'Required version "%s" is greater than "%s". Upgrade your installation.'    #            % (minimum_version, version_string))    #else:    #    flavor = 'BVLC'    flavor = 'BVLC'    return version_string, flavordef get_version_from_pycaffe():    try:        from caffe import __version__ as version        return version    except ImportError:        return Nonedef get_version_from_cmdline(executable):    command = [executable, '-version']    p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)    if p.wait():        print p.stderr.read().strip()        raise RuntimeError('"%s" returned error code %s' % (command, p.returncode))    pattern = 'version'    for line in p.stdout:        if pattern in line:            return line[line.find(pattern) + len(pattern) + 1:].strip()    return Nonedef get_version_from_soname(executable):    command = ['ldd', executable]    p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)    if p.wait():        print p.stderr.read().strip()        raise RuntimeError('"%s" returned error code %s' % (command, p.returncode))    # Search output for caffe library    libname = 'libcaffe'    caffe_line = None    for line in p.stdout:        if libname in line:            caffe_line = line            break    if caffe_line is None:        raise ValueError('libcaffe not found in linked libraries for "%s"'                         % executable)    # Read the symlink for libcaffe from ldd output    symlink = caffe_line.split()[2]    filename = os.path.basename(os.path.realpath(symlink))    # parse the version string    match = re.match(r'%s(.*)\.so\.(\S+)$' % (libname), filename)    if match:        return match.group(2)    else:        return None#看这里，看这里，一个路径问题#我们需要在环境变量里声明一下，CAFFE_ROOT 或者 CAFFE_HOME都可以，指向caffe编译后的 ./Build/x64/Releaseif 'CAFFE_ROOT' in os.environ:    executable, version, flavor = load_from_envvar('CAFFE_ROOT')elif 'CAFFE_HOME' in os.environ:    executable, version, flavor = load_from_envvar('CAFFE_HOME')else:    executable, version, flavor = load_from_path()option_list['caffe'] = {    'executable': executable,    'version': version,    'flavor': flavor,    'multi_gpu': (flavor == 'BVLC' or parse_version(version) >= parse_version(0, 12)),    'cuda_enabled': (len(device_query.get_devices()) > 0),}

再次运行：

训练：
官方教程

完美~