caffe可视化

test_extract_data

# -*- coding: utf-8 -*-# file:test_extract_data.pyimport numpy as npimport matplotlib.pyplot as pltimport osimport sysimport caffedeploy_file = "./mnist_deploy.prototxt"model_file  = "./lenet_iter_10000.caffemodel"test_data   = "./5.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):    #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("conv1_data.jpg",data)    plt.axis('off')if __name__ == '__main__':    #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"    # input_dim: 1    # input_dim: 1    # input_dim: 28    # input_dim: 28    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, 1, 28, 28)    #读取图片    #参数color: True(default)是彩色图，False是灰度图    img = caffe.io.load_image(test_data,color=False)    # 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    predicts = out['prob']    print "Prob:"    print predicts    # 上述'prob'来源于deploy文件：    # layer {    # name: "prob"    # type: "Softmax"    # bottom: "ip2"    # top: "prob"    # }    #最可能分类    predict = predicts.argmax()    print "Result:"    print predict    #---------------------------- 显示特征图 -------------------------------    feature = net.blobs['conv1'].data    show_data(feature.reshape(20,24,24))

test_extract_weights

# -*- coding: utf-8 -*-# file:test_extract_weights.pyimport numpy as npimport matplotlib.pyplot as pltimport osimport sysimport caffedeploy_file = "./mnist_deploy.prototxt"model_file  = "./lenet_iter_10000.caffemodel"#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_weight(data, padsize=1, padval=0):    #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("conv2.jpg",data)    plt.axis('off')if __name__ == '__main__':    #初始化caffe           net = caffe.Net(deploy_file,model_file,caffe.TEST)    print [(k, v[0].data.shape) for k, v in net.params.items()]    #第一个卷积层，参数规模为(50,20,5,5)，即50个5*5的1通道filter    weight = net.params["conv2"][0].data    print weight.shape    show_weight(weight.reshape(50*20,5,5)) # [!!!]参数取决于weight.shape