标签:random [] files 制作 math ref 中心 include 经典
一直在研究如何用caffe做行人检测问题,然而参考那些经典结构比如faster-rcnn等,都是自定义的caffe层来完成的检测任务。这些都要求对caffe框架有一定程度的了解。最近看到了如何用caffe完成回归的任务,就想把检测问题当成回归问题来解决。
我们把行人检测问题当成回归来看待,就需要限制检出目标的个数,因为我们的输出个数是固定的。所以,这里我假定每张图片最多检出的目标个数为2。即每个目标用4个值来表示其位置信息(中心位置坐标x,y。BBox的宽和高),则网络的最后输出是8个值。
这里我们使用HDF5格式的数据来完成我们的回归任务,那么首先我们需要的是制作h5格式的数据。这里以VOC数据集为例。下面是制作HDF5格式数据的python代码。
import h5py import caffe import os import xml.etree.ElementTree as ET import cv2 import time import math from os.path import join, exists import numpy as np def convert(size, box): dw = 1./size[0] dh = 1./size[1] x = (box[0] + box[1])/2.0 y = (box[2] + box[3])/2.0 w = box[1] - box[0] h = box[3] - box[2] x = x*dw w = w*dw y = y*dh h = h*dh return (x,y,w,h) def shuffle_in_unison_scary(a, b): rng_state = np.random.get_state() np.random.shuffle(a) np.random.set_state(rng_state) np.random.shuffle(b) def processImage(imgs): imgs = imgs.astype(np.float32) for i, img in enumerate(imgs): m = img.mean() s = img.std() imgs[i] = (img - m) / s return imgs TrainImgDir = ‘F:/GenerateHDF5/trainImage‘ TrainLabelDir = ‘F:/GenerateHDF5/trainLabels‘ TestImgDir = ‘F:/GenerateHDF5/testImg‘ TestLabelDir = ‘F:/GenerateHDF5/testLabels‘ InImg = [] InBBox = [] for rootDir,dirs,files in os.walk(TestLabelDir): ##### for file in files: file_name = file.split(‘.‘)[0] full_file_name = ‘%s%s‘%(file_name,‘.jpg‘) full_file_dir = ‘%s/%s‘%(TestImgDir,full_file_name) ##### Img = cv2.imread(full_file_dir,cv2.CV_LOAD_IMAGE_GRAYSCALE) xml_file = open("%s/%s"%(rootDir,file)) tree = ET.parse(xml_file) root = tree.getroot() size = root.find(‘size‘) w = int(size.find(‘width‘).text) h = int(size.find(‘height‘).text) landmark = np.zeros(8) count = 0 for obj in root.iter(‘object‘): count = count + 1 if count == 3: break xmlbox = obj.find(‘bndbox‘) b = (float(xmlbox.find(‘xmin‘).text), float(xmlbox.find(‘xmax‘).text), float(xmlbox.find(‘ymin‘).text), float(xmlbox.find(‘ymax‘).text)) bb = convert((w,h), b) landmark[(count-1)*4+0]=bb[0] landmark[(count-1)*4+1]=bb[1] landmark[(count-1)*4+2]=bb[2] landmark[(count-1)*4+3]=bb[3] InBBox.append(landmark.reshape(8)) Img = cv2.resize(Img,(h,w)) InImg.append(Img.reshape((1,h,w))) InImg, InBBox = np.asarray(InImg), np.asarray(InBBox) InImg = processImage(InImg) shuffle_in_unison_scary(InImg, InBBox) outputDir = ‘hdf5/‘ HDF5_file_name = ‘hdf5_test.h5‘ ##### if not os.path.exists(outputDir): os.makedirs(outputDir) output = join(outputDir,HDF5_file_name) with h5py.File(output, ‘w‘) as h5: h5[‘data‘] = InImg.astype(np.float32) h5[‘labels‘] = InBBox.astype(np.float32) h5.close()
制作好了HDF5数据后,注意每个H5文件大小不能超过2G(这是caffe的规定,如果一个文件超过2G,请分开制作多个)。然后建立一个TXT文件,文件里写上所有H5文件的绝对路径,比如我这里建立的文件是list_train.txt。然后我只有一个H5文件,即hdf5_train.h5。所以我的list_train.txt文件中的内容就是/home/XXX/caffe/model/hdf5/hdf5_train.h5
接下来是caffe的solver文件,这个文件没有什么区别,
test_iter: 20 test_interval: 70 base_lr: 0.0000000005 display: 9 max_iter: 210000 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0001 stepsize: 700 snapshot: 500 snapshot_prefix: "snapshot" solver_mode: GPU net: "train_val.prototxt" solver_type: SGD
接下来是网络的train_val.prototxt文件。这是caffe的网络结构文件,我们这里以LeNet网络为例,我这里是这样的:
name: "LeNet" layer { name: "data" type: "HDF5Data" top: "data" top: "labels" include { phase: TRAIN } hdf5_data_param { source: "list_train.txt" batch_size: 50 } } layer { name: "data" type: "HDF5Data" top: "data" top: "labels" include { phase: TEST } hdf5_data_param { source: "list_test.txt" batch_size: 50 } } layer { name: "conv1" type: "Convolution" bottom: "scaled" top: "conv1" param { lr_mult: 1.0 } param { lr_mult: 2.0 } convolution_param { num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1.0 } param { lr_mult: 2.0 } convolution_param { num_output: 50 kernel_size: 5 stride: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool2" top: "ip1" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 500 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "relu1" type: "ReLU" bottom: "ip1" top: "ip1" } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" param { lr_mult: 1.0 } param { lr_mult: 2.0 } inner_product_param { num_output: 8 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "error" type: "EuclideanLoss" bottom: "ip2" bottom: "labels" top: "error" include { phase: TEST } } layer { name: "loss" type: "EuclideanLoss" bottom: "ip2" bottom: "labels" top: "loss" include { phase: TRAIN } }
按照以上步骤配置好了,最后就是训练了。在控制台中输入以下指令来训练我们的数据:
./cafferoot/caffe/tools/caffe train --solver=solver.prototxt
标签:random [] files 制作 math ref 中心 include 经典
原文地址:http://blog.csdn.net/q6324266/article/details/54891321