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

GitHub 代码地址： https://github.com/HansRen1024/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 pycaffe
sudo 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 caffe

def 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()