# result.append(item + ';' +model.ClassNameTest(item)+';'+ door + '\n') ground_truth=model.ClassNameTest(item) print ground_truth,door if not model.ClassNameTest(item)=='0': P_num +=1 if not door == ground_truth: error += 1 erro_ratio = float(error)/i print erro_ratio print i,P_num,len(result),error # result.append('error_ratio:'+str(erro_ratio)+' Positive_num:'+str(P_num)+' total_num:'+str(i)) # myreslut = sorted(result, key=lambda result:result[0]) # if P_num<2000: # my_result = file('myresult_p.txt', 'wb') # else: # my_result = file('myresult_n.txt', 'wb') # my_result.writelines(myreslut) # my_result.close() except (UnpickleError, ShowNetError, opt.GetoptError), e: print "----------------" print "Error:" print e print 'finish_8' op = ShowPredction.get_options_parser() op, load_dic = IGPUModel.parse_options(op) model = ShowPredction(op, load_dic) print os.path.exists("G:\\door_data_sampling\\posture\\data_pos\\test\\test_value_p\\") show_predict_dir('G:\\door_data_sampling\\posture\\test\\org_data\\')
"Queue key") op.add_option("ensemble-id", "ensemble_id", IntegerOptionParser, "Id of predict ensemble") op.add_option("iteration-id", "iteration_id", IntegerOptionParser, "Id of predict iteration") op.add_option("data-dir", "data_dir", StringOptionParser, "Id of predict ensemble") op.add_option("is-dataset", "is_dataset", BooleanOptionParser, "Format output as file and upload on s3", default=False) op.options['load_file'].default = None return op if __name__ == "__main__": try: op = ShowConvNet.get_options_parser() op, load_dic, batch_meta = IGPUModel.parse_options(op) model = ShowConvNet(op, load_dic, { 'batch_meta': batch_meta, 'data_dir': op.options['data_dir'].value }) model.start() except (UnpickleError, ShowNetError, opt.GetoptError), e: print "----------------" print "Error:" print e