Caffe CNN特征可视化

Caffe CNN特征可视化

转载请注明出处，楼燚(yì)航的blog，http://www.cnblogs.com/louyihang-loves-baiyan/

以下部分代码是根据caffe的python接口，从一次forword中取出param和blob里面的卷积核 和响应的卷积图。

import numpy as np import matplotlib.pyplot as plt import os import caffe import sys import pickle import cv2caffe_root = '../' deployPrototxt = '/home/chenjie/louyihang/caffe/models/bvlc_reference_caffenet/deploy_louyihang.prototxt' modelFile = '/home/chenjie/louyihang/caffe/models/bvlc_reference_caffenet/caffenet_carmodel_louyihang_iter_50000.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(4)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 pixeltransformer.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 50net.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].datawith 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.shapeplt.imshow(data)if __name__ == "__main__":net = initilize()testimage = '../data/MyTest/visualize_test.jpg'getNetDetails(testimage, net)

输入的测试图像

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

第一个Pooling层的特征图

第二层卷积特征图

第二层pooling的特征图，可以看到pooling之后，对conv的特征有部分强化，我网络中使用的max-pooling，但是到了pooling2已经出现一些离散的块了，已经有些抽象了，难以看出什么东西

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