Pytorch Executable在从Anaconda提示符运行时可以工作,但不能从Cmd或.exe运行?

2024-03-29 13:09:10 发布

您现在位置:Python中文网/ 问答频道 /正文

我(使用Pyinstaller)打包了一个简约的Yolo github repo的小变体,发现Here,打包是使用Pyinstaller完成的,使用Flask作为服务器运行对象检测

因此,在尝试运行服务器时,它仅在从Anaconda提示符(我编写pyinstaller命令的地方)运行时工作,除此之外,还会发生以下错误

从(exe、Cmd、PowerShell)运行时出现的错误是:

Traceback (most recent call last):
File "flask\app.py", line 2446, in wsgi_app
File "flask\app.py", line 1951, in full_dispatch_request
File "flask\app.py", line 1820, in handle_user_exception
File "flask\_compat.py", line 39, in reraise
File "flask\app.py", line 1949, in full_dispatch_request
File "flask\app.py", line 1935, in dispatch_request
File "FlaskServerV2.py", line 53, in Hello
File "torch\nn\modules\module.py", line 532, in __call__
File "models.py", line 259, in forward
File "torch\nn\modules\module.py", line 532, in __call__
File "models.py", line 177, in forward
RuntimeError: error in LoadLibraryA
127.0.0.1 - - [19/Nov/2020 10:28:53] "GET /detect HTTP/1.1" 500 -

但是,当在conda中运行时,代码运行良好。 所以我怀疑这是PyTorch依赖的问题

当前代码:

from __future__ import division

from flask import Flask, Response, jsonify
app = Flask(__name__)

from models import *
from utils.utils import *
from utils.datasets import *

import os
import sys
import time
import datetime
import argparse

from PIL import Image

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.ticker import NullLocator

import cv2 
import time 
import json


@app.route('/CheckIfRunning')
def CheckIfRunning():
    return '1'

@app.route('/detect')
def Hello():
    global device
    global model
    global classes
    global colors
    global Tensor
    global a
    img=cv2.imread("temp.jpg")
    PILimg = np.array(Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB)))
    imgTensor = transforms.ToTensor()(PILimg)
    imgTensor, _ = pad_to_square(imgTensor, 0)
    imgTensor = resize(imgTensor, 416)
    #add the batch size
    imgTensor = imgTensor.unsqueeze(0)
    imgTensor = Variable(imgTensor.type(Tensor))
    with torch.no_grad():
        detections = model(imgTensor)
        detections = non_max_suppression(detections,0.8, 0.4)
    a.clear()

    Return={}
    ReturnCounter=0
    if detections is not None:
            a.extend(detections)
            b=len(a)
            if len(a)  :
                for detections in a:
                    if detections is not None:
                        detections = rescale_boxes(detections, 416, PILimg.shape[:2])
                        unique_labels = detections[:, -1].cpu().unique()
                        n_cls_preds = len(unique_labels)
                        for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
                            box_w = x2 - x1
                            box_h = y2 - y1
                            color = [int(c) for c in colors[int(cls_pred)]]
                            img = cv2.rectangle(img, (x1, y1 + box_h), (x2, y1), color, 2)
                            cv2.putText(img, classes[int(cls_pred)], (x1, y1),     cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
                            cv2.putText(img, str("%.2f" % float(conf)), (x2, y2 - box_h), cv2.FONT_HERSHEY_SIMPLEX, 0.5,color, 2)
                            Return[ReturnCounter]=    [x1.item(),y1.item(),x2.item(),y2.item(),conf.item(),cls_conf.item(),classes[int(cls_pred)]]
                            ReturnCounter+=1
                        cv2.imwrite("Temp2.jpg",img)
                        return Return
                

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Set up model
model = Darknet("config/yolov3.cfg", img_size=416).to(device)

model.load_darknet_weights("weights/yolov3.weights")

model.eval()  # Set in evaluation mode

classes = load_classes("data/coco.names")  # Extracts class labels from file
colors = np.random.randint(0, 255, size=(len(classes), 3), dtype="uint8")
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor

a=[]
app.run(threaded=True) 

Tags: infrompyimportappflaskimgline