caffe (10) 使用python测试多张图片统计分类结果

直接上代码啦， test.py

#coding=utf-8      import osimport numpy as npimport caffe   import matplotlib.pyplot as plt root='/home/xxx/caffe/'deploy=root + 'examples/myfile2/cifar10_quick.prototxt'    print("deploy = %s\n" %deploy)  caffe_model=root + 'examples/myfile2/cifar10_quick_iter_300.caffemodel' print("caffe_model = %s\n" %caffe_model)  mean_file = root + 'examples/myfile2/mean.npy'print("mean_file = %s\n" %mean_file)  labels_filename = root+'examples/myfile2/test/label.txt'print("labels_filename = %s\n" %labels_filename)  #######################################################3net = caffe.Net(deploy,caffe_model,caffe.TEST) caffe.set_mode_cpu()transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) transformer.set_transpose('data', (2,0,1))  transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))  transformer.set_raw_scale('data', 255)#transformer.set_channel_swap(‘data‘, (2,1,0))      ########################################################path=root + 'examples/myfile2/test/images.txt'print("path = %s\n" %path) fp = open(labels_filename)fout = open(root+'examples/myfile2/test/badClassification.txt', 'w')num = 0 for line in open(path):    #print line   line=line.strip('\n')     fullfilename = root+'examples/myfile2/test/'+line   #print("fullfilename = %s" %fullfilename)    #fullfilename.replace("\n","")      im=caffe.io.load_image(fullfilename, False)      net.blobs['data'].data[...] = transformer.preprocess('data',im)      #执行测试     out = net.forward()     labels = np.loadtxt(labels_filename, str, delimiter='\t')    prob= net.blobs['prob'].data[0].flatten()    order=prob.argsort()[2]     line2 = fp.readline()     line2.replace("\n", "")   line2.replace("\t", "")      #print("label = %s  predict = %s\n" %(line2, order))   if int(line2) != int(order):   num = num+1   print fullfilename   fout.write(fullfilename)   fout.write('\n')print num   

cifar10_quick.prototxt is as follows:

name: "CIFAR10_quick_test"layer {  name: "data"  type: "Input"  top: "data"  input_param { shape: { dim: 1 dim: 1 dim: 64 dim: 64 } }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "relu1"  type: "ReLU"  bottom: "pool1"  top: "pool1"}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1  }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 64    pad: 2    kernel_size: 5    stride: 1  }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "pool3"  type: "Pooling"  bottom: "conv3"  top: "pool3"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool3"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 64  }}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 3  }}layer {  name: "prob"  type: "Softmax"  bottom: "ip2"  top: "prob"}

cifar10_quick_train_test.prototxt is as follows:

name: "CIFAR10_quick"layer {  name: "cifar"  type: "Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    mean_file: "examples/myfile2/mean.binaryproto"  }  data_param {    source: "examples/myfile2/img_train_lmdb"    batch_size: 50    backend: LMDB  }}layer {  name: "cifar"  type: "Data"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    mean_file: "examples/myfile2/mean.binaryproto"  }  data_param {    source: "examples/myfile2/img_test_lmdb"    batch_size: 50    backend: LMDB  }}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.0001    }    bias_filler {      type: "constant"    }  }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "relu1"  type: "ReLU"  bottom: "pool1"  top: "pool1"}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param {    num_output: 64    pad: 2    kernel_size: 5    stride: 1    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "pool3"  type: "Pooling"  bottom: "conv3"  top: "pool3"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool3"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 64    weight_filler {      type: "gaussian"      std: 0.1    }    bias_filler {      type: "constant"    }  }}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 3    weight_filler {      type: "gaussian"      std: 0.1    }    bias_filler {      type: "constant"    }  }}layer {  name: "accuracy"  type: "Accuracy"  bottom: "ip2"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "ip2"  bottom: "label"  top: "loss"}