caffe特征可视化---python实现

妖狐艹你老母 2022-05-27 10:27 279阅读 0赞

这里主要测试了一下如何对利用caffe的python接口对特征进行可视化，从一次forword中取出param和blob里面的卷积核 和响应的卷积图。我主要是对caffe/models/bvlc\_reference\_caffenet/路径下的模型和网络配置文件进行了测试，模型为bvlc\_reference\_caffenet.caffemodel，配置文件为：deploy.prototxt，模型可能需要你自己下载，下载地址为[http://dl.caffe.berkeleyvision.org/][http_dl.caffe.berkeleyvision.org]，当然你也可以用你自己训练得到的模型和网络配置文件，参考代码如下，注意下相应的文件路径：

caffe\_visual.py

# -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import os
    import caffe
    import sys
    import pickle
    import cv2
    
    caffe_root = '/home/rongsong/Downloads/caffe-caffe-0.15/'   # Your caffe diretory path
    
    deployPrototxt = '/home/rongsong/Downloads/caffe-caffe-0.15/models/bvlc_reference_caffenet/deploy.prototxt'
    modelFile = '/home/rongsong/Downloads/caffe-caffe-0.15/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
    meanFile = 'python/caffe/imagenet/ilsvrc_2012_mean.npy'
    #imageListFile = '/home/chenjie/DataSet/CompCars/data/train_test_split/classification/test_model431_label_start0.txt'
    #imageBasePath = '/home/chenjie/DataSet/CompCars/data/cropped_image'
    #resultFile = 'PredictResult.txt'
    
    #网络初始化
    def initilize():
        print 'initilize ... '
        sys.path.insert(0, caffe_root + 'python')
        caffe.set_mode_gpu()
        caffe.set_device(0)
        net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)
        return net
    
    #取出网络中的params和net.blobs的中的数据
    def getNetDetails(image, net):
        # input preprocessing: 'data' is the name of the input blob == net.inputs[0]
        transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
        transformer.set_transpose('data', (2,0,1))
        transformer.set_mean('data', np.load(caffe_root + meanFile ).mean(1).mean(1)) # mean pixel
        transformer.set_raw_scale('data', 255)  
        # the reference model operates on images in [0,255] range instead of [0,1]
        transformer.set_channel_swap('data', (2,1,0))  
        # the reference model has channels in BGR order instead of RGB
        # set net to batch size of 50
        net.blobs['data'].reshape(1,3,227,227)
    
        net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(image))
        out = net.forward()
        
        #网络提取conv1的卷积核
        filters = net.params['conv1'][0].data
        with open('FirstLayerFilter.pickle','wb') as f:
           pickle.dump(filters,f)
        vis_square(filters.transpose(0, 2, 3, 1))
        #conv1的特征图
        feat = net.blobs['conv1'].data[0, :36]
        with open('FirstLayerOutput.pickle','wb') as f:
           pickle.dump(feat,f)
        vis_square(feat,padval=1)
        pool = net.blobs['pool1'].data[0,:36]
        with open('pool1.pickle','wb') as f:
           pickle.dump(pool,f)
        vis_square(pool,padval=1)
    
    
    # 此处将卷积图和进行显示，
    def vis_square(data, padsize=1, padval=0 ):
        data -= data.min()
        data /= data.max()
        
        #让合成图为方
        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))
        #合并卷积图到一个图像中
        
        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:])
        print data.shape
        plt.imshow(data)
        plt.show()
    
    if __name__ == "__main__":
       net = initilize()
       testimage = '/home/rongsong/Pictures/car0.jpg'     # Your test picture path
       getNetDetails(testimage, net)

测试图片及结果如下：

（a）输入的测试图像

![20180417105022310][]

（b）第一层的卷积核和卷积图，可以看到一些明显的边缘轮廓，左侧是相应的卷积核

![20180417105456477][]![20180417105101564][]

（c）第一个Pooling层的特征图

![20180417105111103][]

（d）第二层卷积特征图

![20180417105122172][]

参考链接：[https://www.cnblogs.com/louyihang-loves-baiyan/p/5134671.html][https_www.cnblogs.com_louyihang-loves-baiyan_p_5134671.html]

[http_dl.caffe.berkeleyvision.org]: http://dl.caffe.berkeleyvision.org/
[20180417105022310]: /images/20220527/4b3e9a1ccc5f4c6ab2faefa913bb736e.png
[20180417105456477]: https://img-blog.csdn.net/20180417105456477
[20180417105101564]: /images/20220527/eaae3467b663484292bf493b7a74fb24.png
[20180417105111103]: /images/20220527/f5052775721b44b4a7375fbf83341069.png
[20180417105122172]: /images/20220527/ecec07bf758c4948ae3c029018d26178.png
[https_www.cnblogs.com_louyihang-loves-baiyan_p_5134671.html]: https://www.cnblogs.com/louyihang-loves-baiyan/p/5134671.html