Detect.py Error:Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument tensors in method wrapper___cat)

[Miaonika]

The code is as follows, it seems to report the CPU GPU confusion error, is it a problem with the code？how to solve it，please！

use command ：python tools/detect.py /home/3090/MiaoJxi/Project/CLRNet/configs/clrnet/clr_resnet18_tusimple.py --img /home/3090/MiaoJxi/Data/lanetest/43010777140972086.jpg --load_from /home/3090/MiaoJxi/Project/CLRNet/modules/tusimple_r18.pth --savedir ./vis

import numpy as np import torch import cv2 import os import os.path as osp import glob import argparse from clrnet.datasets.process import Process from clrnet.models.registry import build_net from clrnet.utils.config import Config from clrnet.utils.visualization import imshow_lanes from clrnet.utils.net_utils import load_network from pathlib import Path from tqdm import tqdm

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

class Detect(object): def init(self, cfg): self.cfg = cfg self.processes = Process(cfg.val_process, cfg) self.net = build_net(self.cfg) self.net = torch.nn.parallel.DataParallel(self.net, device_ids = range(1)).cuda() self.net.eval() load_network(self.net, self.cfg.load_from)

def preprocess(self, img_path):
    ori_img = cv2.imread(img_path)
    img = ori_img[self.cfg.cut_height:, :, :].astype(np.float32)
    data = {'img': img, 'lanes': []}
    data = self.processes(data)
    data['img'] = data['img'].unsqueeze(0)
    data.update({'img_path':img_path, 'ori_img':ori_img})
    return data

def inference(self, data):
    with torch.no_grad():
        data = self.net(data)
        data = self.net.module.get_lanes(data)
        # data = self.net.module.heads.get_lanes(data)
    return data

def show(self, data):
    out_file = self.cfg.savedir 
    if out_file:
        out_file = osp.join(out_file, osp.basename(data['img_path']))
    lanes = [lane.to_array(self.cfg) for lane in data['lanes']]
    imshow_lanes(data['ori_img'], lanes, show=self.cfg.show, out_file=out_file)

def run(self, data):
    data = self.preprocess(data)
    data['lanes'] = self.inference(data)[0]
    if self.cfg.show or self.cfg.savedir:
        self.show(data)
    return data

def get_img_paths(path): p = str(Path(path).absolute()) # os-agnostic absolute path if '' in p: paths = sorted(glob.glob(p, recursive=True)) # glob elif os.path.isdir(p): paths = sorted(glob.glob(os.path.join(p, '.*'))) # dir elif os.path.isfile(p): paths = [p] # files else: raise Exception(f'ERROR: {p} does not exist') return paths

def process(args): cfg = Config.fromfile(args.config) cfg.show = args.show cfg.savedir = args.savedir cfg.load_from = args.load_from detect = Detect(cfg) paths = get_img_paths(args.img) for p in tqdm(paths): detect.run(p)

if name == 'main': parser = argparse.ArgumentParser() parser.add_argument('config', help='The path of config file') parser.add_argument('--img', help='The path of the img (img file or img_folder), for example: data/*.png') parser.add_argument('--show', action='store_true', help='Whether to show the image') parser.add_argument('--savedir', type=str, default=None, help='The root of save directory') parser.add_argument('--load_from', type=str, default='best.pth', help='The path of model') args = parser.parse_args() process(args)