【转载】faster-rcnn保存网络中间训练结果

转载自：http://blog.****.net/u010668907/article/details/51439503

faster rcnn用Python版本https://github.com/rbgirshick/py-faster-rcnn

以demo.py中默认网络VGG16.

原本demo.py地址https://github.com/rbgirshick/py-faster-rcnn/blob/master/tools/demo.py

图有点多，贴一个图的本分结果出来：

上图是原图，下面第一张是网络中命名为“conv1_1”的结果图；第二张是命名为“rpn_cls_prob_reshape”的结果图；第三张是“rpnoutput”的结果图

看一下我修改后的代码：

#!/usr/bin/env python



 



# --------------------------------------------------------



# Faster 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 fast_rcnn.nms_wrapper 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




import math



 



CLASSES = ('__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_faster_rcnn_final.caffemodel'),



        'zf': ('ZF',



                  'ZF_faster_rcnn_final.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:



        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 save_feature_picture(data, name, image_name=None, padsize = 1, padval = 1):



    data = data[0]



    #print "data.shape1: ", data.shape




    n = int(np.ceil(np.sqrt(data.shape[0])))



    padding = ((0, n ** 2 - data.shape[0]), (0, 0), (0, padsize)) + ((0, 0),) * (data.ndim - 3)



    #print "padding: ", padding




    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))



    #print "data.shape2: ", data.shape




    



    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))



    #print "data.shape3: ", data.shape, n




    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])



    #print "data.shape4: ", data.shape




    plt.figure()



    plt.imshow(data,cmap='gray')



    plt.axis('off')



    #plt.show()




    if image_name == None:



        img_path = './data/feature_picture/' 



    else:



        img_path = './data/feature_picture/' + image_name + "/"




        check_file(img_path)



    plt.savefig(img_path + name + ".jpg", dpi = 400, bbox_inches = "tight")



def check_file(path):



    if not os.path.exists(path):



        os.mkdir(path)



def demo(net, image_name):



    """Detect object classes in an image using pre-computed object proposals."""




 



    # Load the demo image




    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)



    im = cv2.imread(im_file)



 



    # Detect all object classes and regress object bounds




    timer = Timer()



    timer.tic()



    scores, boxes = im_detect(net, im)



    for k, v in net.blobs.items():



        if k.find("conv")>-1 or k.find("pool")>-1 or k.find("rpn")>-1:



            save_feature_picture(v.data, k.replace("/", ""), image_name)#net.blobs["conv1_1"].data, "conv1_1") 




    timer.toc()



    print ('Detection took {:.3f}s for '




           '{:d} object proposals').format(timer.total_time, boxes.shape[0])



 



    # Visualize detections for each class




    CONF_THRESH = 0.8




    NMS_THRESH = 0.3




    for cls_ind, cls in enumerate(CLASSES[1:]):



        cls_ind += 1 # because we skipped background




        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, :]



        vis_detections(im, cls, dets, thresh=CONF_THRESH)



 



def parse_args():



    """Parse input arguments."""




    parser = argparse.ArgumentParser(description='Faster R-CNN demo')



    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 args



 



def print_param(net):



    for k, v in net.blobs.items():



	print (k, v.data.shape)



    print ""




    for k, v in net.params.items():



	print (k, v[0].data.shape)  



 




if __name__ == '__main__':



    cfg.TEST.HAS_RPN = True  # Use RPN for proposals




 



    args = parse_args()



 



    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],



                            'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')



    #print "prototxt: ", prototxt




    caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',



                              NETS[args.demo_net][1])



 



    if not os.path.isfile(caffemodel):



        raise IOError(('{:s} not found.\nDid you run ./data/script/'




                       'fetch_faster_rcnn_models.sh?').format(caffemodel))



 



    if args.cpu_mode:



        caffe.set_mode_cpu()



    else:



        caffe.set_mode_gpu()



        caffe.set_device(args.gpu_id)



        cfg.GPU_ID = args.gpu_id



    net = caffe.Net(prototxt, caffemodel, caffe.TEST)



    



    #print_param(net)




 



    print '\n\nLoaded network {:s}'.format(caffemodel)



 



    # Warmup on a dummy image




    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)



    for i in xrange(2):



        _, _= im_detect(net, im)



 



    im_names = ['000456.jpg', '000542.jpg', '001150.jpg',



                '001763.jpg', '004545.jpg']



    for im_name in im_names:



        print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'




        print 'Demo for data/demo/{}'.format(im_name)



        demo(net, im_name)



 



    #plt.show()

1.在data下手动创建“feature_picture”文件夹就可以替换原来的demo使用了。

2.上面代码主要添加方法是：save_feature_picture，它会对网络测试的某些阶段的数据处理然后保存。

3.某些阶段是因为：if k.find("conv")>-1 or k.find("pool")>-1 or k.find("rpn")>-1这行代码（110行），保证网络层name有这三个词的才会被保存，因为其他层无法用图片

保存，如全连接（参数已经是二维的了）等层。

4.放开174行print_param(net)的注释，就可以看到网络参数的输出。

5.执行的最终结果 是在data/feature_picture产生以图片名字为文件夹名字的文件夹，文件夹下有以网络每层name为名字的图片。

6.另外部分网络的层name中有非法字符不能作为图片名字，我在代码的111行只是把‘字符/’剔除掉了，所以建议网络名字不要又其他字符。

图片下载和代码下载方式：