while (True): x_test, y_test, test_num = read_labels_batch_out( labels_path, (224, 224), (224, 224), root_images_folder, batch_size=batch_size, iteration_num=i, random_horizontal_flip=False) if (test_num == 0): break predicts = model.predict(x_test, batch_size=batch_size) for j, value in enumerate(predicts): predict = np.argmax(value) truth_val = y_test[j] confusion_mat.AddValueToConfusionMat(val=predict, label_val=truth_val) file_writer.Write("[[" + str(j + (batch_size * i)) + "]] " + str(truth_val) + " {" + str(predict) + "}") print( "====================================================================") file_writer.Write( "====================================================================") print("result of iteration number : " + str(i)) file_writer.Write("result of iteration number : " + str(i)) print(" ") file_writer.Write(" ") print("---Show Current Score---\n") file_writer.Write("---Show Current Score---\n")
else: net.blobs['data'].reshape(batch_size, image_color_channel, image_size_h, image_size_w) net.blobs['data'].data[j_0, :, :, :] = transformer.preprocess( 'data', caffe_img) #Run Model out = net.forward() for j_1, raw_predict_val in enumerate(out['score']): predict_val = raw_predict_val.argmax() #Storage Value prop_dict = sub_list[j_1] confusion_mat.AddValueToConfusionMat( val=predict_val, label_val=prop_dict['truth_label']) file_writer.Write("[[" + str(prop_dict['real_line_pos']) + "]] " + " [" + str(prop_dict['iteration_id']) + "]" + " [" + str(prop_dict['batch_id']) + "] " + prop_dict['img_path'] + " " + str(prop_dict['truth_label']) + " {" + str(predict_val) + "}") #Show confusion matrix every X iteration if (i < iteration_count): print( "====================================================================" ) file_writer.Write( "====================================================================" )