caffe的简单python脚本
caffe的简单python脚本
#-*-coding:utf-8-*- import caffe import matplotlib.pyplot as plt #网络构建 caffe.set_mode_cpu() # 设置caffe为cpu模式,也可设成gpu模式 model_def = 'my_alexnet_test.prototxt' model_weights = 'models_rist1/lrcn_four_words_iter_10000.caffemodel' net = caffe.Net(model_def, # 定义模型结构 model_weights, # 包含模型训练权重 caffe.TEST) # 使用测试模式(训练中不能执行dropout) #读取图像 image = caffe.io.load_image('./images/COCO_train2014_000000246146.jpg') #图像处理 transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) transformer.set_transpose('data', (2,0,1)) # 将图像通道数设置为outermost的维数 transformer.set_raw_scale('data', 255) # 像素值从[0,1]变换为[0,255] transformer.set_channel_swap('data', (2,1,0)) # 交换通道,RGB->BGR #transformer.set_mean('data', channel_mean) transformed_image = transformer.preprocess('data', image) #图像可视化 plt.imshow(image) plt.show() #前向 net.blobs['data'].data[...] = transformed_image output = net.forward() net.forward() # 循环打印每一层名字和相应维度 #for layer_name, blob in net.blobs.iteritems(): # print layer_name + '\t' + str(blob.data.shape) #打印输出 #for layer_name, param in net.params.iteritems(): # print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape) #print '**********' #filters = net.params['conv1'][0].data #打印层的输出 print transformed_image print '**********' #print transformed_image #feat = net.blobs['conv1'].data[0, :36] #print feat