如何优化推理脚本以获得更快的分类器预测？答案

【问题标题】：How to optimize the inference script to get a faster prediction of the classifier?如何优化推理脚本以获得更快的分类器预测？
【发布时间】：2019-05-26 01:12:18
【问题描述】：

我编写了以下预测代码，该代码根据经过训练的分类器模型进行预测。现在预测时间在40s左右，我想尽可能减少。

我可以对我的推理脚本进行任何优化，还是应该寻找训练脚本的发展？

import torch
import torch.nn as nn
from torchvision.models import resnet18
from torchvision.transforms import transforms
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import Variable
import torch.functional as F
from PIL import Image
import os
import sys
import argparse
import time 
import json

parser = argparse.ArgumentParser(description = 'To Predict from a trained model')

parser.add_argument('-i','--image', dest = 'image_name', required = True, help='Path to the image file')
args = parser.parse_args()

def predict_image(image_path):
    print("prediciton in progress")
    image = Image.open(image_path)

    transformation = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    image_tensor = transformation(image).float()
    image_tensor = image_tensor.unsqueeze_(0)

    if cuda:
        image_tensor.cuda()

    input = Variable(image_tensor)
    output = model(input)

    index = output.data.numpy().argmax()
    return index

def parameters():
    hyp_param = open('param_predict.txt','r')
    param = {}
    for line in hyp_param:
        l = line.strip('\n').split(':')

def class_mapping(index):
    with open("class_mapping.json") as cm:
        data = json.load(cm)
    if index == -1:
        return len(data)
    else:
        return data[str(index)]

def segregate():
    with open("class_mapping.json") as cm:
        data = json.load(cm)
    try:
        os.mkdir(seg_dir)
        print("Directory " , seg_dir ,  " Created ") 
    except OSError:
        print("Directory " , seg_dir ,  " already created")
    for x in range (0,len(data)):
        dir_path="./"+seg_dir+"/"+data[str(x)]
        try:
            os.mkdir(dir_path)
            print("Directory " , dir_path ,  " Created ") 
        except OSError:
            print("Directory " , dir_path ,  " already created")


path_to_model = "./models/"+'trained.model'
checkpoint = torch.load(path_to_model)
seg_dir="segregation_folder"

cuda = torch.cuda.is_available()

num_class = class_mapping(index=-1)
print num_class
model = resnet18(num_classes = num_class)

if cuda:
    model.load_state_dict(checkpoint)
else:
    model.load_state_dict(checkpoint, map_location = 'cpu')

model.eval()

if __name__ == "__main__":

    imagepath = "./Predict_Image/"+args.image_name
    since = time.time()
    img = Image.open(imagepath)
    prediction = predict_image(imagepath)
    name = class_mapping(prediction)
    print("Time taken = ",time.time()-since)

    print("Predicted Class: ",name)

整个项目可以在 https://github.com/amrit-das/custom_image_classifier_pytorch/

【问题讨论】：

脚本的哪一部分花费的时间最多？你试过分析它吗？
predict_image() 大约占总执行时间的 70%

标签： python image-processing neural-network classification pytorch

【解决方案1】：

如果没有分析器的输出，很难判断其中有多少是因为代码效率低下。话虽如此，PyTorch 的启动开销很大——换句话说，与单个图像的推理时间相比，初始化库、模型、负载权重并将其传输到 GPU 的速度很慢。这使得作为单图像预测的 CLI 实用程序非常糟糕。

如果您的用例确实需要处理单个图像而不是批处理，则优化的潜力不大。我看到的两个选项是

完全跳过 GPU 执行并节省 GPU 分配和传输可能是值得的。
使用LibTorch 在 C++ 中编写此代码将获得更好的性能。不过，这需要大量的开发工作。

【讨论】：