python问题，如何最后调用infer函数，修改代码

Question

 
SAVE_DIRNAME = 'models/step_2_model'  # 保存好的 inference model 的路径
abs_path = r'./cat_12_test' # 测试文件夹的真实路径
#########################################################################
# coding:utf-8
#from __future__ import print_function
import os
import json
import paddle
import paddle.fluid as fluid
import numpy as np
from PIL import Image
import sys
TOP_K = 1
DATA_DIM = 224
use_cuda = True
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
[inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model( SAVE_DIRNAME, exe
                # model_filename='model',
                # params_filename='params'
                # model_filename = 'fc_0.w_0',
                # params_filename = 'params'
                )
 
def real_infer_one_img(im):
    infer_result = exe.run(
        inference_program,
        feed={feed_target_names[0]: im},
        fetch_list=fetch_targets)
    # print(infer_result)
    # 打印预测结果
    mini_batch_result = np.argsort(infer_result)  # 找出可能性最大的列标，升序排列
    # print(mini_batch_result.shape)
    mini_batch_result = mini_batch_result[0][:, -TOP_K:]  # 把这些列标拿出来
    mini_batch_result = mini_batch_result.flatten() #拉平了，只吐出一个 array
    mini_batch_result = mini_batch_result[::-1] #逆序
    return mini_batch_result
 
def resize_short(img, target_size):
    percent = float(target_size) / min(img.size[0], img.size[1])
    resized_width = int(round(img.size[0] * percent))
    resized_height = int(round(img.size[1] * percent))
    img = img.resize((resized_width, resized_height), Image.LANCZOS)
    return img
 
def crop_image(img, target_size, center):
    width, height = img.size
    size = target_size
    if center == True:
        w_start = (width - size) / 2
        h_start = (height - size) / 2
    else:
        w_start = np.random.randint(0, width - size + 1)
        h_start = np.random.randint(0, height - size + 1)
    w_end = w_start + size
    h_end = h_start + size
    img = img.crop((w_start, h_start, w_end, h_end))
    return img
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
def process_image(img_path):
    img = Image.open(img_path)
    img = resize_short(img, target_size=256)
    img = crop_image(img, target_size=DATA_DIM, center=True)
    if img.mode != 'RGB':
        img = img.convert('RGB')
    img = np.array(img).astype(np.float32).transpose((2, 0, 1)) / 255
    img -= img_mean
    img /= img_std
    img = np.expand_dims(img, axis=0)
    return img
 
def convert_list(my_list):
    my_list = list(my_list)
    my_list = map(lambda x:str(x), my_list)
    # print('_'.join(my_list))
    return '_'.join(my_list)
 
def infer(file_path):
    im = process_image(file_path)
    result = real_infer_one_img(im)
    result = convert_list(result)
    return result
 
 
def createCSVFile(logs/test.log):
    lines = []
    # 获取所有的文件名
    img_paths = os.listdir(logs/test.log_path)
    for file_name in img_paths:
        file_name = file_name
        file_abs_path = os.path.join(logs/test.log_path, file_name)
        result_classes = infer(file_abs_path)
        file_predict_classes = result_classes
        line = '%s,%s\n'%(file_name, file_predict_classes)
        lines.append(line)
    with open('result.csv', 'w') as f:
        f.writelines(lines)
 
createCSVFile(abs_path)
print("成功输出结果文件")