Paddle Inference——基于python API实现Windows端模型部署图像分类

部署环境准备

python环境准备

（1）PaddlePaddle

请参考飞桨官网安装最新版本的paddlepaddle-gpu。

需要按照自己cuda的版本进行不同命令安装。

（2）opencv

pip install opencv-python

模型文件准备

首先你得有 图像分类 / 目标检测的分类模型，模型文件一般为两种，如图所示：

• 模型格式一： __model__和__params__文件

• 模型格式二：Paddle的原生模型文件

python API预测

在这里，我使用了一个我自己训练的四分类的垃圾分类模型进行部署。采用GPU预测的方式加速预测速度：

import cv2
import numpy as np
from paddle.inference import Config
from paddle.inference import create_predictor

# ————————————————图像预处理函数————————————————
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

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

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, :]

#——————————————————————模型配置、预测相关函数——————————————————————————
def predict_config(model_file, params_file):
    # 根据预测部署的实际情况，设置Config
    config = Config()
    # 读取模型文件
    config.set_prog_file(model_file)
    config.set_params_file(params_file)
    # Config默认是使用CPU预测，若要使用GPU预测，需要手动开启，设置运行的GPU卡号和分配的初始显存。
    config.enable_use_gpu(500, 0)
    # 可以设置开启IR优化、开启内存优化。
    config.switch_ir_optim()
    config.enable_memory_optim()
    predictor = create_predictor(config)
    return predictor

def predict(image, predictor):
    img = preprocess(image)
    input_names = predictor.get_input_names()
    input_tensor = predictor.get_input_handle(input_names[0])
    input_tensor.reshape(img.shape)
    input_tensor.copy_from_cpu(img.copy())
    # 执行Predictor
    predictor.run()
    # 获取输出
    output_names = predictor.get_output_names()
    output_tensor = predictor.get_output_handle(output_names[0])
    output_data = output_tensor.copy_to_cpu()
    print("output_names", output_names)
    print("output_tensor", output_tensor)
    print("output_data", output_data)
    return output_data

# 展示结果
def post_res(label_dict, res):
    res = res.tolist()
    # print(type(res))
    # print(max(res))
    target_index = res.index(max(res))
    print("结果是:" + "   " + label_dict[target_index])

if __name__ == '__main__':
    label_dict = {0:"metal", 1:"paper", 2:"plastic", 3:"glass"}
    model_file = "PaddleInfence\model\ResNet50_trashClas_x86_model\__model__"
    params_file = "PaddleInfence\model\ResNet50_trashClas_x86_model\__params__"
    
    image = cv2.imread("PaddleInfence\python_demo\metal1.jpg")
    predictor = predict_config(model_file, params_file)
    res = predict(image, predictor)
    post_res(label_dict, res)

    cv2.imshow("image", image)
    cv2.waitKey()

运行结果：

参考文档：

• Windows上GPU预测部署示例