【Python】【Caffe】一、生成prototxt文件《python调用caffe模块》

GitHub代码地址：https://github.com/HandsomeHans/Use-Python-to-call-Caffe-module

前言

写这一系列博文前真的想了好久，有种无从下手的感觉。还是功力太浅，越是这样越要硬着头皮写。加油！

我先将将各个函数单独放出来，最后在放出完整代码。各个函数中单独用到的模块，我在函数中单独加载，这样方便将代码独立出来单独运行。

import caffe是全局通用模块，我就不在每个函数中单独加载了。

一、加载caffe模块

为了以后方便使用，把caffe模块放到python默认路径下，这样在任意目录下就都能加载caffe模块了。

caffe编译通过后运行：

make pycaffesudo cp -r python/caffe/ /usr/local/lib/python/dist-packages #有些朋友路径是site-packages,这个因人而异。

这个时候运行python，import caffe，会提示找不到caffe的动态库。

可以将 $CAFFE_ROOT/.build_release/lib/ 加到环境变量中去，

也可以将该动态库复制到/usr/lib/ 或者/usr/local/lib 目录下。

此时，应该就可以在任意目录下运行python，import caffe了。

二、生成训练和测试prototxt文件

生成网络结构文件我找到了两种方法，下面这种通过定义一个NetSpec()实例n的方法是比较好的，所以我先写的这个方法。这种方法生成的文件内，各个层的名字和输出blob的名字就是等号前面定义的n的方法。

def lenet(lmdb, batch_size, include_acc=False):    from caffe import layers as L    from caffe import params as P    n = caffe.NetSpec()    # 具体每个层内的参数，可以对照现有的prototxt文件。我这里写的并不全。    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,                             transform_param=dict(scale=1./255), ntop=2)              # ntop表示两个输出    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))    n.relu1 = L.ReLU(n.ip1, in_place=True)    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)    if include_acc: # 生成测试文件时，需要有计算准确率的层。        n.acc = L.Accuracy(n.ip2, n.label)    return n.to_proto() # 注意这里的to_proto()不用带参数。def write_lenet():    with open('./doc/train_lenet.prototxt','w') as f:        f.write(str(lenet('./doc/mnist_train_lmdb', 64)))        with open('./doc/test_lenet.prototxt', 'w') as f:        f.write(str(lenet('./doc/mnist_test_lmdb', 100, True)))

三、生成deploy文件

这里我放出生成网络结构文件的第二种方法，虽然我并不推荐使用这个方法。其实deploy直接拿上面生成好的文件改就行，很简单。

def deploy():    from caffe import layers as L    from caffe import params as P    from caffe import to_proto    # deploy文件没有数据层    conv1 = L.Convolution(bottom='data', kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    ip1 = L.InnerProduct(pool2, num_output=500, weight_filler=dict(type='xavier'))    relu1 = L.ReLU(ip1, in_place=True)    ip2 = L.InnerProduct(relu1, num_output=10, weight_filler=dict(type='xavier'))     prob = L.Softmax(ip2) # 最后一层不用计算loss，输出概率。    return to_proto(prob) # 这里需要带参数def write_deploy():    with open('doc/deploy_lenet.prototxt', 'w') as f:        f.write('name: "Lenet"\n')        f.write('input: "data"\n')        f.write('input_dim: 1\n')        f.write('input_dim: 3\n')        f.write('input_dim: 28\n')        f.write('input_dim: 28\n')        f.write(str(deploy()))

四、生成solver文件

方法一是用字典生成：

def solver_dict():    solver_file='doc/solver_lenet.prototxt'    sp={}    sp['train_net']='"doc/train_lenet.prototxt"'    sp['test_net']='"doc/test_lenet.prototxt"'    sp['test_iter']='100'    sp['test_interval']='500'    sp['display']='100'    sp['max_iter']='10000'    sp['base_lr']='0.01'    sp['lr_policy']='"inv"'    sp['gamma']='0.0001'    sp['power']='0.75'    sp['momentum']='0.9'    sp['weight_decay']='0.0005'    sp['snapshot']='5000'    sp['snapshot_prefix']='"models/lenet"'    sp['solver_mode']='GPU'    sp['solver_type']='SGD'    sp['device_id']='0'        with open(solver_file, 'w') as f:        for key, value in sp.items():            if not(type(value) is str):                raise TypeError('All solver parameters must be string')            f.write('%s: %s\n' %(key, value))

方法二是调用caffe模块生成，这种方法生成的文件内，小数部分会有很小的损失。处女座强迫症犯了！

def solver_caffe():    from caffe.proto import caffe_pb2    s = caffe_pb2.SolverParameter()    solver_file='doc/solver_lenet.prototxt'        s.train_net = 'doc/train_lenet.prototxt'    s.test_net.append('doc/test_lenet.prototxt')    s.test_interval = 500    s.test_iter.append(100)    s.display = 100    s.max_iter = 10000    s.base_lr = 0.01    s.lr_policy = "inv"    s.gamma = 0.0001    s.power = 0.75    s.momentum = 0.9    s.weight_decay = 0.0005    s.snapshot = 5000    s.snapshot_prefix = "models/lenet"    s.type = "SGD"    s.solver_mode = caffe_pb2.SolverParameter.GPU        with open(solver_file, 'w') as f:        f.write(str(s))

五、完整代码：

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Sat Jul 29 11:16:29 2017@author: hans"""import caffedef lenet(lmdb, batch_size, include_acc=False):    from caffe import layers as L    from caffe import params as P    n = caffe.NetSpec()    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,                             transform_param=dict(scale=1./255), ntop=2)    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))    n.relu1 = L.ReLU(n.ip1, in_place=True)    n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)    if include_acc:        n.acc = L.Accuracy(n.ip2, n.label)    return n.to_proto()def write_lenet():    with open('./doc/train_lenet.prototxt','w') as f:        f.write(str(lenet('./doc/mnist_train_lmdb', 64)))        with open('./doc/test_lenet.prototxt', 'w') as f:        f.write(str(lenet('./doc/mnist_test_lmdb', 100, True)))def deploy():    from caffe import layers as L    from caffe import params as P    from caffe import to_proto    conv1 = L.Convolution(bottom='data', kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)    ip1 = L.InnerProduct(pool2, num_output=500, weight_filler=dict(type='xavier'))    relu1 = L.ReLU(ip1, in_place=True)    ip2 = L.InnerProduct(relu1, num_output=10, weight_filler=dict(type='xavier'))    prob = L.Softmax(ip2)    return to_proto(prob)def write_deploy():    with open('doc/deploy_lenet.prototxt', 'w') as f:        f.write('name: "Lenet"\n')        f.write('input: "data"\n')        f.write('input_dim: 1\n')        f.write('input_dim: 3\n')        f.write('input_dim: 28\n')        f.write('input_dim: 28\n')        f.write(str(deploy()))    def solver_dict():    solver_file='doc/solver_lenet.prototxt'    sp={}    sp['train_net']='"doc/train_lenet.prototxt"'    sp['test_net']='"doc/test_lenet.prototxt"'    sp['test_iter']='100'    sp['test_interval']='500'    sp['display']='100'    sp['max_iter']='10000'    sp['base_lr']='0.01'    sp['lr_policy']='"inv"'    sp['gamma']='0.0001'    sp['power']='0.75'    sp['momentum']='0.9'    sp['weight_decay']='0.0005'    sp['snapshot']='5000'    sp['snapshot_prefix']='"models/lenet"'    sp['solver_mode']='GPU'    sp['solver_type']='SGD'    sp['device_id']='0'        with open(solver_file, 'w') as f:        for key, value in sp.items():            if not(type(value) is str):                raise TypeError('All solver parameters must be string')            f.write('%s: %s\n' %(key, value))            def solver_caffe():    from caffe.proto import caffe_pb2    s = caffe_pb2.SolverParameter()    solver_file='doc/solver_lenet.prototxt'        s.train_net = 'doc/train_lenet.prototxt'    s.test_net.append('doc/test_lenet.prototxt')    s.test_interval = 500    s.test_iter.append(100)    s.display = 100    s.max_iter = 10000    s.base_lr = 0.01    s.lr_policy = "inv"    s.gamma = 0.0001    s.power = 0.75    s.momentum = 0.9    s.weight_decay = 0.0005    s.snapshot = 5000    s.snapshot_prefix = "models/lenet"    s.type = "SGD"    s.solver_mode = caffe_pb2.SolverParameter.GPU        with open(solver_file, 'w') as f:        f.write(str(s))def train():    caffe.set_device(0)    caffe.set_mode_gpu()    solver = caffe.SGDSolver('doc/solver_lenet.prototxt')    solver.solve()if __name__ == '__main__':    write_lenet()#    write_deploy()#    solver_dict()#    solver_caffe()#    train()