caffe的python接口

python接口，我的理解主要是两个，一个是原来caffe官方给的，这里是接口介绍。另一个是SSD框架加的model_libs.py，主要是添加了一些base_network和一些相关的函数。这两个如果能够用得很好的话，兄弟，恭喜你！你已经精通caffe的python接口了。其实不用说的，这个接口很方便，从此你不再需要手动去写什么网络，单独去执行什么bat又或者是sh的命令训练测试了，接口的可移植性非常好，我们将这些必要的设置写在python文件里，完成上面的一套工作。因此，在我看来，SSD的python接口就是上面两个的完美结合。

第一个就是官方给的，官方是这么介绍的：

Python

The Python interface – pycaffe – is the caffe module and its scripts in caffe/python. import caffe to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.

• caffe.Net is the central interface for loading, configuring, and running models. caffe.Classifierand caffe.Detector provide convenience interfaces for common tasks.
• caffe.SGDSolver exposes the solving interface.
• caffe.io handles input / output with preprocessing and protocol buffers.
• caffe.draw visualizes network architectures.
• Caffe blobs are exposed as numpy ndarrays for ease-of-use and efficiency.

Tutorial IPython notebooks are found in caffe/examples: do ipython notebook caffe/examples to try them. For developer reference docstrings can be found throughout the code.

Compile pycaffe by make pycaffe. Add the module directory to your $PYTHONPATH by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH or the like for import caffe.

上面也说了，使用python接口应该要编译pycaffe，同时应该export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH ，避免no moudle named caffe这样类似的错误。这里给出了一个例子，地址在这：例子，这个例子最主要是说明了怎么查看一个CNN网络中间层的输出和特征可视化。对此有不少博客给出了代码注释，给一个参考：链接。

其实对于中间层的可视化应该是我们在第一个问题中比较关注的，另外py文件如何训练、测试应该参考SSD模型相应的代码，在此不再赘述。

下面主要来看看中间层的可视化，代码如下（需要修改必要的路径）：

import numpy as np
import matplotlib.pyplot as plt
import pylab
import os
import caffe
import sys
import pickle
import cv2

caffe_root = os.getcwd()
deploy =  'models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt'
weight = 'models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'

def initilize():
    print 'initilize ... '
    sys.path.insert(0, caffe_root + 'python')
    caffe.set_mode_gpu()
    caffe.set_device(0)
    net = caffe.Net(deploy, weight,caffe.TEST)
    return net

def getNetDetails(image, net):
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
    #changing blob from H*W*C to C*H*W
    transformer.set_transpose('data', (2,0,1))
    #ensure the pixel scale is range from (0,255)
    transformer.set_raw_scale('data', 255)  
    #change channel order from RGB to BGR 
    transformer.set_channel_swap('data', (2,1,0))  
    #reshape data 
    net.blobs['data'].reshape(1,3,300,300)
    #input data and preprocess
    net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(image))
    #testing model is just a forward process
    out = net.forward()
    
    filters = net.params['conv1_1'][0].data
    with open('FirstLayerFilter.pickle','wb') as f:
       pickle.dump(filters,f)
    vis_square(filters.transpose(0, 2, 3, 1))

    feat = net.blobs['conv4_4'].data[0, :36]
    with open('FirstLayerOutput.pickle','wb') as f:
       pickle.dump(feat,f)
    vis_square(feat,padval=1)
    pool = net.blobs['conv8_2'].data[0, :81]
    with open('pool1.pickle','wb') as f:
       pickle.dump(pool,f)
    vis_square(pool,padval=1)

def vis_square(data, padsize=1, padval=0 ):
    data -= data.min()
    data /= data.max()
    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    plt.imshow(data)
    pylab.show()
    print data.shape

if __name__ == "__main__":
    net = initilize()
    testimage = 'examples/images/person.jpg'
    getNetDetails(testimage, net)

另一个就是model_libs.py，之所以出现它是因为我们不想自己写好了prototxt，然后导入，更希望是在一个base_network的基础下去修改，更具有普适性，同时效率高移植性好，出于这样的目的，写了model_libs.py这个脚本，当然这些脚本都是可以根据自己需要修改的，另外可以自己写个脚本实现项目的其它需求。

两者一结合，那就实现的太完美了，屡试不爽，从此就多了这么一套有趣的工具。