caffe 利用Python API 做数据输入层

caffe （Convolutional Architecture for Fast Feature Embedding）

在caffe中，主要使用LMDB提供数据管理，将形形色色的原始数据转换为统一的Key-Value形式存储，便于数据输入层获得这些数据，而且提高了磁盘IO的利用率。
但是，有时我们可以使用python作为网络结构数据的输入层，毕竟python 简单易写。

参考网址：https://chrischoy.github.io/research/caffe-python-layer/

编译选择

想要使用Python Layer，我们需要在编译的时候修改Makefile.config中的pyhon 的选项。

WITH_PYTHON_LAYER :=1

修改完之后，重新编译：

#在caffe_root依次运行
make clean
make
make pycaffe

如果还是 import caffe错误，则将以下几句加入到Python文件的最前面，再导入caffe库：

import sys
sys.path.append("/home/yonghuming/caffe-master/python")
sys.path.append("/home/yonghuming/caffe-master/python/caffe")

或者是修改全局变量：

sudo vim /etc/profile

添加全局变量：

export PYTHONPATH=/home/yonghu/caffe/python

最后:source /etc/profile

先看一个官方例程

此历程所在位置为$caffe_root/examples/pycaffe下
配置文件linreg.prototxt

layer {type: 'Python'name: 'loss'top: 'loss'bottom: 'ipx'bottom: 'ipy'python_param {# the module name -- usually the filename -- that needs to be in $PYTHONPATHmodule: 'pyloss'# the layer name -- the class name in the modulelayer: 'EuclideanLossLayer'}# set loss weight so Caffe knows this is a loss layer.# since PythonLayer inherits directly from Layer, this isn't automatically# known to Caffeloss_weight: 1
}

此配置文件中的loss层就是用Python写的层。

 module: 'pyloss'layer: 'EuclideanLossLayer'

module指的是python文件的名字;layer值得是Python文件中的类。

    # the module name -- usually the filename -- that needs to be in $PYTHONPATH

其中$PYTHONPATH指的是$caffe_root/python , 只要把python文件放到此目录下就可以了。
再看一下pyloss.py文件

import caffe
import numpy as npclass EuclideanLossLayer(caffe.Layer):"""Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayerto demonstrate the class interface for developing layers in Python."""def setup(self, bottom, top):# check input pairif len(bottom) != 2:raise Exception("Need two inputs to compute distance.")def reshape(self, bottom, top):# check input dimensions matchif bottom[0].count != bottom[1].count:raise Exception("Inputs must have the same dimension.")# difference is shape of inputsself.diff = np.zeros_like(bottom[0].data, dtype=np.float32)# loss output is scalartop[0].reshape(1)def forward(self, bottom, top):self.diff[...] = bottom[0].data - bottom[1].datatop[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.def backward(self, top, propagate_down, bottom):for i in range(2):if not propagate_down[i]:continueif i == 0:sign = 1else:sign = -1bottom[i].diff[...] = sign * self.diff / bottom[i].num

使用Python layer 做数据输入层

此处我导入的数据为28*28*6的数组，也就是数据类型为：

print type(data)
print np.shape(data)# <type 'numpy.ndarray'>
# (28, 28, 6)

图片也是类似，利用opencv读入的图片也是数组结构：

import numpy as np
import cv2
data = cv2.imread('1.jpg')
print type(data)
#<type 'numpy.ndarray'>
print np.shape(data)
# <type 'numpy.ndarray'>
# (375, 500, 3)

但是一定要注意三色通道问题！
此时我的配置文件中有关Python层的定义：

name: "LeNet"
layer {name:"Data"type: "Python"top: "data"top: "label"include {phase: TRAIN}python_param {module:"dataLayer"layer: "Custom_Data_Layer"param_str: '{"batch_size":64, "im_shape":28, "src_file":"data/input/train"}'}
}

param_str为参数。
此时py文件：

import caffe
import numpy as np
import os
import random
def GetTupleList(src_file, dirtag):subDirTuples = []folder = os.path.join(src_file, dirtag)fns = os.listdir(folder)if dirtag=='pos':tag = 0elif dirtag=='neg':tag = 1else:raise Exception('Invalid dirtag {}'.format(str(dirtag)))for fn in fns:path = os.path.join(folder, fn)data = np.load(path)subDirTuples.append((data, np.array([tag])))return subDirTuplesdef readSrcFile(src_file):posTuples = GetTupleList(src_file, 'pos')print(len(posTuples))negTuples = GetTupleList(src_file, 'neg')print(len(negTuples))imgTuples = posTuples + negTuplesreturn imgTuplesclass Custom_Data_Layer(caffe.Layer):def setup(self, bottom, top):# Check top shapeif len(top) != 2:raise Exception("Need to define tops (data and label1)")# Check bottom shapeif len(bottom) != 0:raise Exception("Do not define a bottom")# Read parametersparams = eval(self.param_str)src_file = params["src_file"]self.batch_size = params["batch_size"]self.im_shape = params["im_shape"]top[0].reshape(self.batch_size, 6, self.im_shape, self.im_shape)top[1].reshape(self.batch_size, 1)self.imgTuples = readSrcFile(src_file)self._cur = 0 # use this to check if we need to restart the list of imagesdef forward(self, bottom, top):for itt in range(self.batch_size):# Use the batch loader to load the next imageim, label = self.load_next_image()# Here we could preprocess the image# Add directly to the top blobim_data = np.reshape(im, (6,28,28))#注意！！np.reshape()top[0].data[itt, ...] = im_data top[1].data[itt, ...] = labeldef load_next_image(self):# If we have finished forwarding all images, then an epoch has finished# and it is time to start a new oneif self._cur == len(self.imgTuples):self._cur = 0random.shuffle(self.imgTuples)im, label = self.imgTuples[self._cur]self._cur += 1return im, labeldef reshape(self, bottom, top):"""There is no need to reshape the data, since the input is of fixed size(img shape and batch size)"""passdef backward(self, bottom, top):"""This layer does not back propagate"""pass

完成！