Fast RCNN 訓練自己的數據集（3訓練和檢測）

轉載請注明出處，樓燚(yì)航的blog，http://www.cnblogs.com/louyihang-loves-baiyan/

https://github.com/YihangLou/fast-rcnn-train-another-dataset?這是我在github上修改的幾個文件的鏈接，求星星啊，求星星啊（原諒我那么不要臉~~）

在之前兩篇文章中我介紹了怎么編譯Fast RCNN，和怎么修改Fast RCNN的讀取數據接口，接下來我來說明一下怎么來訓練網絡和之后的檢測過程
先給看一下極好的檢測效果

https://github.com/YihangLou/fast-rcnn-train-another-dataset

1.預訓練模型介紹

首先在data目錄下，有兩個目錄就是之前在1中解壓好

• fast_rcnn_models/
• imagenet_models/

fast_rcnn_model文件夾下面是作者用fast rcnn訓練好的三個網絡,分別對應著小、中、大型網絡，大家可以試用一下這幾個網絡，看一些檢測效果，他們訓練都迭代了40000次，數據集都是pascal_voc的數據集。

caffenet_fast_rcnn_iter_40000.caffemodel

vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel

vgg16_fast_rcnn_iter_40000.caffemodel

imagenet_model文件夾下面是在Imagenet上訓練好的通用模型，在這里用來初始化網絡的參數

CaffeNet.v2.caffemodel

VGG_CNN_M_1024.v2.caffemodel

VGG16.v2.caffemodel

在這里我比較推薦先用中型網絡訓練，中型網絡訓練和檢測的速度都比較快，效果也都比較理想，大型網絡的話訓練速度比較慢，我當時是5000多個標注信息，網絡配置默認，中型網絡訓練大概兩三個小時，大型網絡的話用十幾個小時，需要注意的是網絡訓練最好用GPU，CPU的話太慢了，我當時用的實驗室的服務器，有16塊Tesla K80，用起來真的是灰常爽！

2. 修改模型文件配置

模型文件在models下面對應的網絡文件夾下，在這里我用中型網絡的配置文件修改為例子
比如：我的檢測目標物是car ，那么我的類別就有兩個類別即 background 和 car
因此，首先打開網絡的模型文件夾，打開train.prototxt
修改的地方重要有三個
分別是個地方

首先在data層把num_classes 從原來的21類 20類+背景 ，改成 2類 車+背景

接在在cls_score層把num_output 從原來的21 改成 2

在bbox_pred層把num_output 從原來的84 改成8， 為檢測類別個數乘以4，比如這里是2類那就是2*4=8

OK，如果你要進一步修改網絡訓練中的學習速率，步長，gamma值，以及輸出模型的名字，需要在同目錄下的solver.prototxt中修改。
如下圖：

train_net: "models/VGG_CNN_M_1024/train.prototxt" base_lr: 0.001 lr_policy: "step" gamma: 0.1 stepsize: 30000 display: 20 average_loss: 100 momentum: 0.9 weight_decay: 0.0005 # We disable standard caffe solver snapshotting and implement our own snapshot # function snapshot: 0 # We still use the snapshot prefix, though snapshot_prefix: "vgg_cnn_m_1024_fast_rcnn" #debug_info: true

3.啟動Fast RCNN網絡訓練

啟動訓練：
./tools/train_net.py --gpu 11 --solver models/VGG_CNN_M_1024_LOUYIHANG/solver.prototxt --weights data/imagenet_models/VGG_CNN_M_1024.v2.caffemodel --imdb KakouTrain

參數講解：

• 這里的--是兩個-，markdown寫的，大家不要輸錯
• train_net.py是網絡的訓練文件，之后的參數都是附帶的輸入參數
• --gpu 代表機器上的GPU編號，如果是nvidia系列的tesla顯卡，可以在終端中輸入nvidia-smi來查看當前的顯卡負荷，選擇合適的顯卡
• --solver 代表模型的配置文件，train.prototxt的文件路徑已經包含在這個文件之中
• --weights 代表初始化的權重文件，這里用的是Imagenet上預訓練好的模型，中型的網絡我們選擇用VGG_CNN_M_1024.v2.caffemodel
• --imdb 這里給出的訓練的數據庫名字需要在factory.py的__sets中，我在文件里面有__sets['KakouTrain']，train_net.py這個文件會調用factory.py再生成kakou這個類，來讀取數據

4.啟動Fast RCNN網絡檢測

我修改了tools下面的demo.py這個文件，用來做檢測，并且將檢測的坐標結果輸出到相應的txt文件中
可以看到原始的demo.py 是用網絡測試了兩張圖像，并做可視化輸出，有具體的檢測效果，但是我是在Linux服務器的終端下，沒有display device，因此部分代碼要少做修改

下面是原始的demo.py：

#!/usr/bin/env python# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # --------------------------------------------------------""" Demo script showing detections in sample images.See README.md for installation instructions before running. """import _init_paths from fast_rcnn.config import cfg from fast_rcnn.test import im_detect from utils.cython_nms import nms from utils.timer import Timer import matplotlib.pyplot as plt import numpy as np import scipy.io as sio import caffe, os, sys, cv2 import argparseCLASSES = ('__background__','aeroplane', 'bicycle', 'bird', 'boat','bottle', 'bus', 'car', 'cat', 'chair','cow', 'diningtable', 'dog', 'horse','motorbike', 'person', 'pottedplant','sheep', 'sofa', 'train', 'tvmonitor')NETS = {'vgg16': ('VGG16','vgg16_fast_rcnn_iter_40000.caffemodel'),'vgg_cnn_m_1024': ('VGG_CNN_M_1024','vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel'),'caffenet': ('CaffeNet','caffenet_fast_rcnn_iter_40000.caffemodel')}def vis_detections(im, class_name, dets, thresh=0.5):"""Draw detected bounding boxes."""inds = np.where(dets[:, -1] >= thresh)[0]if len(inds) == 0:returnim = im[:, :, (2, 1, 0)]fig, ax = plt.subplots(figsize=(12, 12))ax.imshow(im, aspect='equal')for i in inds:bbox = dets[i, :4]score = dets[i, -1]ax.add_patch(plt.Rectangle((bbox[0], bbox[1]),bbox[2] - bbox[0],bbox[3] - bbox[1], fill=False,edgecolor='red', linewidth=3.5))ax.text(bbox[0], bbox[1] - 2,'{:s} {:.3f}'.format(class_name, score),bbox=dict(facecolor='blue', alpha=0.5),fontsize=14, color='white')ax.set_title(('{} detections with ''p({} | box) >= {:.1f}').format(class_name, class_name,thresh),fontsize=14)plt.axis('off')plt.tight_layout()plt.draw()def demo(net, image_name, classes):"""Detect object classes in an image using pre-computed object proposals."""# Load pre-computed Selected Search object proposalsbox_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo',image_name + '_boxes.mat')obj_proposals = sio.loadmat(box_file)['boxes']# Load the demo imageim_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name + '.jpg')im = cv2.imread(im_file)# Detect all object classes and regress object boundstimer = Timer()timer.tic()scores, boxes = im_detect(net, im, obj_proposals)timer.toc()print ('Detection took {:.3f}s for ''{:d} object proposals').format(timer.total_time, boxes.shape[0])# Visualize detections for each classCONF_THRESH = 0.8NMS_THRESH = 0.3for cls in classes:cls_ind = CLASSES.index(cls)cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]cls_scores = scores[:, cls_ind]dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)keep = nms(dets, NMS_THRESH)dets = dets[keep, :]print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,CONF_THRESH)vis_detections(im, cls, dets, thresh=CONF_THRESH)def parse_args():"""Parse input arguments."""parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',default=0, type=int)parser.add_argument('--cpu', dest='cpu_mode',help='Use CPU mode (overrides --gpu)',action='store_true')parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',choices=NETS.keys(), default='vgg16')args = parser.parse_args()return argsif __name__ == '__main__':args = parse_args()prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS[args.demo_net][0],'test.prototxt')caffemodel = os.path.join(cfg.ROOT_DIR, 'data', 'fast_rcnn_models',NETS[args.demo_net][1])if not os.path.isfile(caffemodel):raise IOError(('{:s} not found.\nDid you run ./data/script/''fetch_fast_rcnn_models.sh?').format(caffemodel))if args.cpu_mode:caffe.set_mode_cpu()else:caffe.set_mode_gpu()caffe.set_device(args.gpu_id)net = caffe.Net(prototxt, caffemodel, caffe.TEST)print '\n\nLoaded network {:s}'.format(caffemodel)print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'print 'Demo for data/demo/000004.jpg'demo(net, '000004', ('car',))print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'print 'Demo for data/demo/001551.jpg'demo(net, '001551', ('sofa', 'tvmonitor'))plt.show()

復制這個demo.py 修改成CarFaceTest.py，下面是修改后的文件
修改后的文件主要是添加了outputDetectionResult和runDetection兩個函數， 添加了部分注釋

#!/usr/bin/env python # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # --------------------------------------------------------""" Demo script showing detections in sample images.See README.md for installation instructions before running. """import _init_paths from fast_rcnn.config import cfg from fast_rcnn.test import im_detect from utils.cython_nms import nms from utils.timer import Timer import matplotlib.pyplot as plt import numpy as np import scipy.io as sio import caffe, os, sys, cv2 import argparse#CLASSES = ('__background__','aeroplane','bicycle','bird','boat', # 'bottle','bus','car','cat','chair','cow','diningtable','dog','horse' # 'motorbike','person','pottedplant','sheep','sofa','train','tvmonitor')CLASSES = ('__background__','car') #需要跟自己訓練的數據集中的類別一致，原來是21類的voc數據集，自己的數據集就是car和backgroundNETS = {'vgg16': ('VGG16','vgg16_fast_rcnn_iter_40000.caffemodel'),'vgg_cnn_m_1024': ('VGG_CNN_M_1024','vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel'),'vgg_cnn_m_1024_louyihang': ('VGG_CNN_M_1024_LOUYIHANG','vgg_cnn_m_1024_fast_rcnn_louyihang_iter_40000.caffemodel'),'caffenet': ('CaffeNet','caffenet_fast_rcnn_iter_40000.caffemodel'),'caffenet_louyihang':('CaffeNet_LOUYIHANG','caffenet_fast_rcnn_louyihang_iter_40000.caffemodel'),'vgg16_louyihang':('VGG16_LOUYIHANG','vgg16_fast_rcnn_louyihang_iter_40000.caffemodel')}#映射到對應的模型文件def outputDetectionResult(im, class_name, dets, thresh=0.5): #打開相應的輸出文件outputFile = open('CarDetectionResult.txt')inds = np.where(dets[:,-1] >= thresh)[0]if len(inds) == 0:return def runDetection (net, basePath, testFileName,classes):#這個函數是自己后加的，取代了demo函數，給定測試數據列表ftest = open(testFileName,'r')imageFileName = basePath+'/' + ftest.readline().strip()num = 1outputFile = open('CarDetectionResult.txt','w')while imageFileName:print imageFileNameprint 'now is ', numnum +=1imageFileBaseName = os.path.basename(imageFileName)imageFileDir = os.path.dirname(imageFileName)boxFileName = imageFileDir +'/'+imageFileBaseName.replace('.jpg','_boxes.mat')print boxFileNameobj_proposals = sio.loadmat(boxFileName)['boxes']#obj_proposals[:,2] = obj_proposals[:, 2] + obj_proposals[:, 0]#這里也需要注意，OP里面的坐標數據是否為x1y1x2y2還是x1y1wh#obj_proposals[:,3] = obj_proposals[:, 3] + obj_proposals[:, 1]im = cv2.imread(imageFileName)timer = Timer()timer.tic()scores, boxes = im_detect(net, im, obj_proposals)#檢測函數timer.toc()print ('Detection took {:.3f} for ''{:d} object proposals').format(timer.total_time, boxes.shape[0])CONF_THRESH = 0.8NMS_THRESH = 0.3#NMS參數用來控制非極大值抑制for cls in classes:cls_ind = CLASSES.index(cls)cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]cls_scores = scores[:, cls_ind]dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)keep = nms(dets, NMS_THRESH)dets = dets[keep, :]print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,CONF_THRESH)inds = np.where(dets[:, -1] >= CONF_THRESH)[0]print 'inds.size', inds.sizeif len(inds) != 0:outputFile.write(imageFileName+' ')outputFile.write(str(inds.size)+' ')將檢測的結果寫出相應的文件里for i in inds:bbox = dets[i, :4]outputFile.write(str(int(bbox[0]))+' '+ str(int(bbox[1]))+' '+ str(int(bbox[2]))+' '+ str(int(bbox[3]))+' ')outputFile.write('\n')else:outputFile.write(imageFileName +' 0' '\n')temp = ftest.readline().strip()if temp:imageFileName = basePath+'/' + tempelse:break def vis_detections(im, class_name, dets, thresh=0.5):#這個函數需要加以說明，這個函數雖然沒有用，但是我的服務器上沒有輸出設備"""Draw detected bounding boxes."""#因此要將部分用到顯示的函數給注釋掉，否則運行會報錯inds = np.where(dets[:, -1] >= thresh)[0]print 'inds.shape', inds.shapeprint indsprint 'inds.size', inds.sizeif len(inds) == 0:return#im = im[:, :, (2, 1, 0)]#fig, ax = plt.subplots(figsize=(12, 12))#ax.imshow(im, aspect='equal')#for i in inds:# bbox = dets[i, :4]# score = dets[i, -1]# ax.add_patch(# plt.Rectangle((bbox[0], bbox[1]),# bbox[2] - bbox[0],# bbox[3] - bbox[1], fill=False,# edgecolor='red', linewidth=3.5)# )# ax.text(bbox[0], bbox[1] - 2,# '{:s} {:.3f}'.format(class_name, score),# bbox=dict(facecolor='blue', alpha=0.5),# fontsize=14, color='white')#ax.set_title(('{} detections with '# 'p({} | box) >= {:.1f}').format(class_name, class_name,# thresh),# fontsize=14)#plt.axis('off')#plt.tight_layout()#plt.draw()def demo(net, image_name, classes):#原來的demo函數，沒有修改"""Detect object classes in an image using pre-computed object proposals."""# Load pre-computed Selected Search object proposals#box_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo',image_name + '_boxes.mat')basePath='/home/chenjie/DataSet/500CarTestDataSet2'box_file = os.path.join(basePath,image_name + '_boxes.mat')obj_proposals = sio.loadmat(box_file)['boxes']# Load the demo image#im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name + '.jpg')im_file = os.path.join(basePath, image_name + '.jpg')im = cv2.imread(im_file)# Detect all object classes and regress object boundstimer = Timer()timer.tic()scores, boxes = im_detect(net, im, obj_proposals)timer.toc()print ('Detection took {:.3f}s for ''{:d} object proposals').format(timer.total_time, boxes.shape[0])# Visualize detections for each classCONF_THRESH = 0.8NMS_THRESH = 0.3for cls in classes:cls_ind = CLASSES.index(cls)cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]cls_scores = scores[:, cls_ind]dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)keep = nms(dets, NMS_THRESH)dets = dets[keep, :]print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,CONF_THRESH)vis_detections(im, cls, dets, thresh=CONF_THRESH)def parse_args():"""Parse input arguments."""parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',default=0, type=int)parser.add_argument('--cpu', dest='cpu_mode',help='Use CPU mode (overrides --gpu)',action='store_true')parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',choices=NETS.keys(), default='vgg16')args = parser.parse_args()return argsif __name__ == '__main__':args = parse_args()prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS[args.demo_net][0],'test.prototxt')#caffemodel = os.path.join(cfg.ROOT_DIR, 'data', 'fast_rcnn_models',# NETS[args.demo_net][1])#caffemodel = '/home/chenjie/fast-rcnn/output/default/KakouTrain/vgg16_fast_rcnn_louyihang_iter_40000.caffemodel'#caffemodel = '/home/chenjie/louyihang/fast-rcnn/output/default/KakouTrain/caffenet_fast_rcnn_louyihang_iter_40000.caffemodel'caffemodel = '/home/chenjie/fast-rcnn/output/default/KakouTrain/vgg_cnn_m_1024_fast_rcnn_louyihang_iter_40000.caffemodel'#我在這里直接指定了訓練好的模型文件，訓練好的模型文件是在工程根目錄下的，output/default/對應的數據庫名字下面if not os.path.isfile(caffemodel):raise IOError(('{:s} not found.\nDid you run ./data/script/''fetch_fast_rcnn_models.sh?').format(caffemodel))if args.cpu_mode:caffe.set_mode_cpu()else:caffe.set_mode_gpu()caffe.set_device(args.gpu_id)net = caffe.Net(prototxt, caffemodel, caffe.TEST)print '\n\nLoaded network {:s}'.format(caffemodel)#demo(net, 'Target0/000001', ('car',))#輸入對應的測試圖像列表，需要在同級目錄下擺放同名的_boxes.mat文件，它會自動的替換后綴名！#runDetection(net, '/home/chenjie/DataSet/temptest','/home/chenjie/DataSet/temptest/Imagelist.txt',('car',))runDetection(net, '/home/chenjie/DataSet/500CarTestDataSet2','/home/chenjie/DataSet/500CarTestDataSet2/Imagelist.txt',('car',))#runDetection(net, '/home/chenjie/DataSet/Kakou_Test_Scale0.25/','/home/chenjie/DataSet/Kakou_Test_Scale0.25/imagelist.txt',('car',))#runDetection(net, '/home/chenjie/DataSet/Images_Version1_Test_Boxes','/home/chenjie/DataSet/Images_Version1_Test_Boxes/ImageList_Version1_List.txt',('car',))#plt.show()

5.檢測結果

訓練數據集

首先給出我的訓練數據集，其實我的訓練數據集并不是太復雜的

測試數據集

輸出檢測結果到txt文件中，

測試效果

**在復雜場景下的測試效果非常好，速度也非常快，中型網絡監測平均每張在K80顯卡下時0.1~0.2S左右，圖像的尺寸是480*640，6000張測試數據集下達到的準確率是98%!!!**

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