caffe特征层可视化

#参考1:https://blog.csdn.net/sushiqian/article/details/78614133
#参考2:https://blog.csdn.net/thy_2014/article/details/51659300
# coding=utf-8

import numpy as np
import matplotlib.pyplot as plt
import os
import sys
sys.path.append("/home/wit/caffe/python")
sys.path.append("/home/wit/caffe/python/caffe")
import caffe

deploy_file_name = \'/home/wit/wjx/MobileNetSSD_deploy.prototxt\'
model_file_name  = \'/home/wit/wjx/mobilenet_iter_25000.caffemodel\'
test_img   = "/home/wit/wjx/src.jpg"
#编写一个函数，用于显示各层的参数,padsize用于设置图片间隔空隙,padval用于调整亮度 
def show_data(data, padsize=1, padval=0, name = \'conv0\'):
    #归一化
    data -= data.min()
    data /= data.max()
 
    #根据data中图片数量data.shape[0]，计算最后输出时每行每列图片数n
    n = int(np.ceil(np.sqrt(data.shape[0])))
    # padding = ((图片个数维度的padding),(图片高的padding), (图片宽的padding), ....)
    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))
 
    # 先将padding后的data分成n*n张图像
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    # 再将（n, W, n, H）变换成(n*w, n*H)
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    plt.set_cmap(\'gray\')
    plt.imshow(data)
    plt.imsave(name+\'.jpg\',data)
    
 
if __name__ == \'__main__\':
 
    deploy_file = deploy_file_name
    model_file  = model_file_name
    #如果是用了GPU
    #caffe.set_mode_gpu()
 
    #初始化caffe 
    net = caffe.Net(deploy_file, model_file, caffe.TEST)
 
    #数据输入预处理
    # \'data\'对应于deploy文件：
    # input: "data"
    transformer = caffe.io.Transformer({\'data\': net.blobs[\'data\'].data.shape})
 
    # python读取的图片文件格式为H×W×K，需转化为K×H×W
    transformer.set_transpose(\'data\', (2, 0, 1))
 
    # python中将图片存储为[0, 1]
    # 如果模型输入用的是0~255的原始格式，则需要做以下转换
    transformer.set_raw_scale(\'data\', 255)
 
    # caffe中图片是BGR格式，而原始格式是RGB，所以要转化
    transformer.set_channel_swap(\'data\', (2, 1, 0))
 
    # 将输入图片格式转化为合适格式（与deploy文件相同）
    net.blobs[\'data\'].reshape(1, 3, 300, 300)
 
    #读取图片
    #参数color: True(default)是彩色图，False是灰度图
    img = caffe.io.load_image(test_img,color=True)
 
    # 数据输入、预处理
    net.blobs[\'data\'].data[...] = transformer.preprocess(\'data\', img)
 
    # 前向迭代，即分类
    out = net.forward()
 
    # 输出结果为各个可能分类的概率分布(deploy中最后一层)
    predicts = out[\'detection_out\']
    print "Prob:"
    print predicts

    #最可能分类
    predict = predicts.argmax()
    print "Result:"
    print predict
 
    for layer_name, blob in net.blobs.iteritems():
        print layer_name + \'\t\' + str(blob.data.shape)

    #---------------------------- 显示特征图 -------------------------------
    feature = net.blobs[\'conv1\'].data
    print(feature.shape)
    feature = feature.reshape(64,150,150)   
    show_data(feature, padsize=2, padval=0, name=\'conv1\')