在前面的一些文章中，我们经常使用飞桨的PaddleX来训练各种模型，然后导出部署模型后进行推理预测。比如我们的入门篇，猫狗分类项目，详细可以参看：2.人工智能-图像分类。

Paddle Inference是飞桨原推理库，来满足高性能服务器端和云端推理。

推理流程

本文主要通过具体实例来演示一下如何实现部署模型的推理。

这里我们下载ResNet50模型，https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz。解压后就得到Paddle推理格式的模型。

# 获得模型目录即文件如下
resnet50/
├── inference.pdmodel    （模型结构文件）
├── inference.pdiparams.info 
└── inference.pdiparams  （模型参数文件）

ResNet50是基于ImageNet 训练的1000分类模型，imagenet 类别映射表链接:https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a，部分对应的信息如下：

0:'tench,Tincatinca',
1:'goldfish,Carassiusauratus',
2:'greatwhiteshark,whiteshark,man-eater,man-eatingshark,Carcharodoncarcharias',
3:'tigershark,Galeocerdocuvieri',
4:'hammerhead,hammerheadshark',
5:'electricray,crampfish,numbfish,torpedo',
6:'stingray',
7:'cock',
8:'hen',
9:'ostrich,Struthiocamelus',
10:'brambling,Fringillamontifringilla',
11:'goldfinch,Cardueliscarduelis',
12:'housefinch,linnet,Carpodacusmexicanus',
13:'junco,snowbird',
14:'indigobunting,indigofinch,indigobird,Passerinacyanea',
15:'robin,Americanrobin,Turdusmigratorius',
16:'bulbul',
17:'jay',
18:'magpie',
19:'chickadee',
20:'waterouzel,dipper',
  …………

实现代码

import numpy as np
import cv2
# 引用 paddle inference 推理库
import paddle.inference as paddle_infer

def init_predector():
    # 引用 paddle 模型文件
    model_file="infer-model/resnet50/inference.pdmodel"
    params_file="infer-model/resnet50/inference.pdiparams"
    # 配置参数，创建推理对象
    config=paddle_infer.Config(model_file, params_file)
    #配置推理线程数
    config.enable_memory_optim()
    config.set_cpu_math_library_num_threads(4)
    config.enable_mkldnn()

    predictor=paddle_infer.create_predictor(config)
    return predictor

#图像缩放
def resize_short(img, target_size):
    """ resize_short """
    percent = float(target_size) / min(img.shape[0], img.shape[1])
    resized_width = int(round(img.shape[1] * percent))
    resized_height = int(round(img.shape[0] * percent))
    resized = cv2.resize(img, (resized_width, resized_height))
    return resized
#图像裁剪
def crop_image(img, target_size, center):
    """ crop_image """
    height, width = img.shape[:2]
    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[int(h_start):int(h_end), int(w_start):int(w_end), :]
    return img
#图像预处理
def preprocess(img):
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    img = resize_short(img, 224)
    img = crop_image(img, 224, True)
    # bgr-> rgb && hwc->chw
    img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
    img_mean = np.array(mean).reshape((3, 1, 1))
    img_std = np.array(std).reshape((3, 1, 1))
    img -= img_mean
    img /= img_std
    return img[np.newaxis, :]


def run(predictor, img):
    # copy img data to input tensor
    input_names = predictor.get_input_names()
    for i, name in enumerate(input_names):
        input_tensor = predictor.get_input_handle(name)
        input_tensor.reshape(img[i].shape)
        input_tensor.copy_from_cpu(img[i].copy())

    # do the inference
    predictor.run()

    results = []
    # get out data from output tensor
    output_names = predictor.get_output_names()
    for i, name in enumerate(output_names):
        output_tensor = predictor.get_output_handle(name)
        output_data = output_tensor.copy_to_cpu()
        results.append(output_data)

    return results

#读取imagenet1000_cls.txt文件,获取图像分类信息
def get_class_info(file_path):
    class_info = {}
    with open(file_path, 'r') as f:
        for line in f:
            line = line.strip()
            kv=line.split(":")
            class_info[kv[0]]= kv[1]
    return class_info
   
if __name__ == '__main__':
    class_info=get_class_info('infer-model/resnet50/imagenet1000_cls.txt')
    #print(class_info)

    pred=init_predector()
    #加载图片
    imgpath="img/16.jpg"
    img=cv2.imread(imgpath)
    img0=img.copy()
    img=preprocess(img)
    result=run(pred,[img])
    print("class index:",np.argmax(result[0][0]))
    print("class name:",class_info[str(np.argmax(result[0][0]))])
    
    cv2.putText(img0,class_info[str(np.argmax(result[0][0]))],(10,30),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)
    cv2.imshow("img",img0)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
  

推理1

推理2

参考资料：PaddlePaddle/Paddle-Inference-Demo (github.com)