HerokuR15 使用多线程 python Flask 应用程序时出错

Question

我正在做一个输出增强图像的网络应用程序。它在本地系统中运行良好。但在服务器端引发 R15 错误。

我使用多线程来平衡工作流程。

app.py

from __future__ import division, print_function
# coding=utf-8
import os,cv2,uuid,threading
import numpy as np
from gan import *
from splits import Splits
from deliver import Deliver
# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename

app=Flask(__name__)

@app.route('/', methods=['GET','POST'])
def index():
    print("index Loaded")
    return render_template('index.html')

@app.route('/submit', methods=['GET', 'POST'])
def submit():
    if request.method == 'POST':
        f = request.files['file-ip-1']
        email=request.form['email']
        print(email)
        user_root=f'{email}_{uuid.uuid4()}'
        print(user_root)
        os.makedirs('web',exist_ok=True)
        os.mkdir(os.path.join('web', user_root))
        file_path = os.path.join('web',user_root, secure_filename(f.filename))
        print(file_path)
        f.save(file_path)
        t=threading.Thread(target=runn,args=(file_path,email,user_root)).start()
    return render_template('submit.html')

def runn(file_path,email,user_root):
    print("runn Loaded")
    s=Splits()
    tokens,h,w,ext = s.get_tokens(file_path,user_root)
    print('Tokens created')
    print(len(tokens),w,h,ext)
    d=gan.main(user_root)
    if d:
        print('resoultions created')
        i=s.get_image('outputs/',w,ext,user_root)
        print(i,email)

        D=Deliver().send_email(email,i,user_root)

    else:
        print("Error")



if __name__ == '__main__':
    app.run(debug=False,threaded=True)

从使用 srgan 执行图像增强操作的 d=gan.main(user_root) 行中获取错误。 gan.py

import argparse
import cv2
import glob
import os
from basicsr.archs.rrdbnet_arch import RRDBNet

from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact

class gan:
        
    def main(user_root):
        try:
            

            
            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
            netscale = 4
            model_name = 'RealESRGAN_x4plus'
            # determine model paths
            model_path = os.path.join('experiments/pretrained_models', model_name + '.pth')
            if not os.path.isfile(model_path):
                model_path = os.path.join('realesrgan/weights', model_name + '.pth')
        
            # restorer
            upsampler = RealESRGANer(
                scale=netscale,
                model_path=model_path,
                model=model,
                tile=0,
                tile_pad=10,
                pre_pad=0,
                )

            os.makedirs(f'outputs', exist_ok=True)
            os.makedirs(f'outputs/{user_root}', exist_ok=True)

            if os.path.isfile(f'inputs/{user_root}'):
                paths = [f'inputs/{user_root}/']
            else:
                paths = sorted(glob.glob(os.path.join(f'inputs/{user_root}', '*')))

            for idx, path in enumerate(paths):
                imgname, extension = os.path.splitext(os.path.basename(path))
                print('Processing {}.{}'.format(imgname, extension))
                print('Testing', idx, imgname)

                img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
                if len(img.shape) == 3 and img.shape[2] == 4:
                    img_mode = 'RGBA'
                else:
                    img_mode = None

                try:
                    output, _ = upsampler.enhance(img, outscale=4)

                except RuntimeError as error:
                    print('Error', error)
                    print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
                else:
                    ext='auto'
                    if ext == 'auto':
                        extension = extension[1:]
                    else:
                        extension = ext
                    if img_mode == 'RGBA':  # RGBA images should be saved in png format
                        extension = 'png'
                    save_path = os.path.join('outputs',user_root, f'{imgname}.{extension}')
                    cv2.imwrite(save_path, output)
                    print('Saved', save_path)
                    print('Done')
            return True
        except Exception as e:
            print(e)
            return False

if __name__ == '__main__':
    gan.main()

请帮助我避免运行时出现 R14 和 R15 错误。