# 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
