Zero Copy I: User-Mode Perspective (2)

本文介绍了Linux下零拷贝技术的原理及其实现方式,通过sendfile系统调用减少数据复制和上下文切换,提高网络数据传输效率。

Zero Copy I: User-Mode Perspective


Explaining what is zero-copy functionality for Linux, why it's useful and where it needs work.
Sendfile

In kernel version 2.1, the sendfile system call was introduced to simplify the transmission of data over the network and between two local files. Introduction of sendfile not only reduces data copying, it also reduces context switches. Use it like this:

sendfile(socket, file, len);

To get a better idea of the process involved, take a look at Figure 3.

Figure 3. Replacing Read and Write with Sendfile

Step one: the sendfile system call causes the file contents to be copied into a kernel buffer by the DMA engine. Then the data is copied by the kernel into the kernel buffer associated with sockets.

Step two: the third copy happens as the DMA engine passes the data from the kernel socket buffers to the protocol engine.

You are probably wondering what happens if another process truncates the file we are transmitting with the sendfile system call. If we don't register any signal handlers, the sendfile call simply returns with the number of bytes it transferred before it got interrupted, and the errno will be set to success.

If we get a lease from the kernel on the file before we call sendfile, however, the behavior and the return status are exactly the same. We also get the RT_SIGNAL_LEASE signal before the sendfile call returns.

So far, we have been able to avoid having the kernel make several copies, but we are still left with one copy. Can that be avoided too? Absolutely, with a little help from the hardware. To eliminate all the data duplication done by the kernel, we need a network interface that supports gather operations. This simply means that data awaiting transmission doesn't need to be in consecutive memory; it can be scattered through various memory locations. In kernel version 2.4, the socket buffer descriptor was modified to accommodate those requirements—what is known as zero copy under Linux. This approach not only reduces multiple context switches, it also eliminates data duplication done by the processor. For user-level applications nothing has changed, so the code still looks like this:

sendfile(socket, file, len);

To get a better idea of the process involved, take a look at Figure 4.

Figure 4. Hardware that supports gather can assemble data from multiple memory locations, eliminating another copy.

Step one: the sendfile system call causes the file contents to be copied into a kernel buffer by the DMA engine.

Step two: no data is copied into the socket buffer. Instead, only descriptors with information about the whereabouts and length of the data are appended to the socket buffer. The DMA engine passes data directly from the kernel buffer to the protocol engine, thus eliminating the remaining final copy.

Because data still is actually copied from the disk to the memory and from the memory to the wire, some might argue this is not a true zero copy. This is zero copy from the operating system standpoint, though, because the data is not duplicated between kernel buffers. When using zero copy, other performance benefits can be had besides copy avoidance, such as fewer context switches, less CPU data cache pollution and no CPU checksum calculations.

Now that we know what zero copy is, let's put theory into practice and write some code. You can download the full source code fromwww.xalien.org/articles/source/sfl-src.tgz. To unpack the source code, typetar -zxvf sfl-src.tgz at the prompt. To compile the code and create the random data file data.bin, run make.

Looking at the code starting with header files:

/* sfl.c sendfile example program
Dragan Stancevic <
header name                 function / variable
-------------------------------------------------*/
#include <stdio.h>          /* printf, perror */
#include <fcntl.h>          /* open */
#include <unistd.h>         /* close */
#include <errno.h>          /* errno */
#include <string.h>         /* memset */
#include <sys/socket.h>     /* socket */
#include <netinet/in.h>     /* sockaddr_in */
#include <sys/sendfile.h>   /* sendfile */
#include <arpa/inet.h>      /* inet_addr */
#define BUFF_SIZE (10*1024) /* size of the tmp
                               buffer */

Besides the regular <sys/socket.h> and <netinet/in.h> required for basic socket operation, we need a prototype definition of the sendfile system call. This can be found in the <sys/sendfile.h> server flag:

/* are we sending or receiving */
if(argv[1][0] == 's') is_server++;
/* open descriptors */
sd = socket(PF_INET, SOCK_STREAM, 0);
if(is_server) fd = open("data.bin", O_RDONLY);
The same program can act as either a server/sender or a client/receiver. We have to check one of the command-prompt parameters, and then set the flag is_server to run in sender mode. We also open a stream socket of the INET protocol family. As part of running in server mode we need some type of data to transmit to a client, so we open our data file. We are using the system call sendfile to transmit data, so we don't have to read the actual contents of the file and store it in our program memory buffer. Here's the server address:
/* clear the memory */
memset(&sa, 0, sizeof(struct sockaddr_in));
/* initialize structure */
sa.sin_family = PF_INET;
sa.sin_port = htons(1033);
sa.sin_addr.s_addr = inet_addr(argv[2]);
We clear the server address structure and assign the protocol family, port and IP address of the server. The address of the server is passed as a command-line parameter. The port number is hard coded to unassigned port 1033. This port number was chosen because it is above the port range requiring root access to the system.

Here is the server execution branch:

if(is_server){
    int client; /* new client socket */
    printf("Server binding to [%s]\n", argv[2]);
    if(bind(sd, (struct sockaddr *)&sa,
                      sizeof(sa)) < 0){
        perror("bind");
        exit(errno);
    }

As a server, we need to assign an address to our socket descriptor. This is achieved by the system call bind, which assigns the socket descriptor (sd) a server address (sa):

if(listen(sd,1) < 0){
    perror("listen");
    exit(errno);
}
Because we are using a stream socket, we have to advertise our willingness to accept incoming connections and set the connection queue size. I've set the backlog queue to 1, but it is common to set the backlog a bit higher for established connections waiting to be accepted. In older versions of the kernel, the backlog queue was used to prevent syn flood attacks. Because the system call listen changed to set parameters for only established connections, the backlog queue feature has been deprecated for this call. The kernel parameter tcp_max_syn_backlog has taken over the role of protecting the system from syn flood attacks:
if((client = accept(sd, NULL, NULL)) < 0){
    perror("accept");
    exit(errno);
}
The system call accept creates a new connected socket from the first connection request on the pending connections queue. The return value from the call is a descriptor for a newly created connection; the socket is now ready for read, write or poll/select system calls:
if((cnt = sendfile(client,fd,&off,
                          BUFF_SIZE)) < 0){
    perror("sendfile");
    exit(errno);
}
printf("Server sent %d bytes.\n", cnt);
close(client);
A connection is established on the client socket descriptor, so we can start transmitting data to the remote system. We do this by calling the sendfile system call, which is prototyped under Linux in the following manner:
extern ssize_t
sendfile (int __out_fd, int __in_fd, off_t *offset,
          size_t __count) __THROW;
The first two parameters are file descriptors. The third parameter points to an offset from which sendfile should start sending data. The fourth parameter is the number of bytes we want to transmit. In order for the sendfile transmit to use zero-copy functionality, you need memory gather operation support from your networking card. You also need checksum capabilities for protocols that implement checksums, such as TCP or UDP. If your NIC is outdated and doesn't support those features, you still can use sendfile to transmit files. The difference is the kernel will merge the buffers before transmitting them.

original link:http://www.linuxjournal.com/article/6345?page=0,1

Portability Issues

One of the problems with the sendfile system call, in general, is the lack of a standard implementation, as there is for the open system call. Sendfile implementations in Linux, Solaris or HP-UX are quite different. This poses a problem for developers who wish to use zero copy in their network data transmission code.

One of the implementation differences is Linux provides a sendfile that defines an interface for transmitting data between two file descriptors (file-to-file) and (file-to-socket). HP-UX and Solaris, on the other hand, can be used only for file-to-socket submissions.

The second difference is Linux doesn't implement vectored transfers. Solaris sendfile and HP-UX sendfile have extra parameters that eliminate overhead associated with prepending headers to the data being transmitted.

Looking Ahead

The implementation of zero copy under Linux is far from finished and is likely to change in the near future. More functionality should be added. For example, the sendfile call doesn't support vectored transfers, and servers such as Samba and Apache have to use multiple sendfile calls with the TCP_CORK flag set. This flag tells the system more data is coming through in the next sendfile calls. TCP_CORK also is incompatible with TCP_NODELAY and is used when we want to prepend or append headers to the data. This is a perfect example of where a vectored call would eliminate the need for multiple sendfile calls and delays mandated by the current implementation.

One rather unpleasant limitation in the current sendfile is it cannot be used when transferring files greater than 2GB. Files of such size are not all that uncommon today, and it's rather disappointing having to duplicate all that data on its way out. Because both sendfile and mmap methods are unusable in this case, a sendfile64 would be really handy in a future kernel version.

Conclusion

Despite some drawbacks, zero-copy sendfile is a useful feature, and I hope you have found this article informative enough to start using it in your programs. If you have a more in-depth interest in the subject, keep an eye out for my second article, titled “Zero Copy II: Kernel Perspective”, where I will dig a bit more into the kernel internals of zero copy.

Further Information

email:  visitor@xalien.org

Dragan Stancevic is a kernel and hardware bring-up engineer in his late twenties. He is a software engineer by profession but has a deep interest in applied physics and has been known to play with extremely high voltages in his free time.



original link: http://www.linuxjournal.com/article/6345?page=0,2
import argparse import logging import math import os import random import time from copy import deepcopy from pathlib import Path from threading import Thread import ckpt import numpy as np import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.optim.lr_scheduler as lr_scheduler import torch.utils.data import yaml from torch.cuda import amp from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm import test # import test.py to get mAP after each epoch from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.datasets import create_dataloader from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ check_requirements, print_mutation, set_logging, one_cycle, colorstr from utils.google_utils import attempt_download from utils.loss import ComputeLoss from utils.plots import plot_images, plot_labels, plot_results, plot_evolution from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume import chardet logger = logging.getLogger(__name__) def train(hyp, opt, device, tb_writer=None): logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) save_dir, epochs, batch_size, total_batch_size, weights, rank = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank # Directories wdir = save_dir / 'weights' wdir.mkdir(parents=True, exist_ok=True) # make dir last = wdir / 'last.pt' best = wdir / 'best.pt' results_file = save_dir / 'results.txt' # Save run settings with open(save_dir / 'hyp.yaml', 'w') as f: yaml.dump(hyp, f, sort_keys=False) with open(save_dir / 'opt.yaml', 'w') as f: yaml.dump(vars(opt), f, sort_keys=False) # Configure plots = not opt.evolve # create plots cuda = device.type != 'cpu' init_seeds(2 + rank) with open(opt.data) as f: data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict is_coco = opt.data.endswith('coco.yaml') # Logging- Doing this before checking the dataset. Might update data_dict loggers = {'wandb': None} # loggers dict if rank in [-1, 0]: opt.hyp = hyp # add hyperparameters run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict) loggers['wandb'] = wandb_logger.wandb data_dict = wandb_logger.data_dict if wandb_logger.wandb: weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check # Model pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally # ckpt = torch.load(weights, map_location=device) # load checkpoint ckpt = torch.load(weights, map_location=device, weights_only=False) # load checkpoint model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(state_dict, strict=False) # load logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report else: model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create with torch_distributed_zero_first(rank): check_dataset(data_dict) # check train_path = data_dict['train'] test_path = data_dict['val'] # Freeze freeze = [] # parameter names to freeze (full or partial) for k, v in model.named_parameters(): v.requires_grad = True # train all layers if any(x in k for x in freeze): print('freezing %s' % k) v.requires_grad = False # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay logger.info(f"Scaled weight_decay = {hyp['weight_decay']}") pg0, pg1, pg2 = [], [], [] # optimizer parameter groups for k, v in model.named_modules(): if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): pg2.append(v.bias) # biases if isinstance(v, nn.BatchNorm2d): pg0.append(v.weight) # no decay elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): pg1.append(v.weight) # apply decay if opt.adam: optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum else: optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg2}) # add pg2 (biases) logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) del pg0, pg1, pg2 # Scheduler https://arxiv.org/pdf/1812.01187.pdf # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR if opt.linear_lr: lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear else: lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA ema = ModelEMA(model) if rank in [-1, 0] else None # Resume start_epoch, best_fitness = 0, 0.0 if pretrained: # Optimizer if ckpt['optimizer'] is not None: optimizer.load_state_dict(ckpt['optimizer']) best_fitness = ckpt['best_fitness'] # EMA if ema and ckpt.get('ema'): ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) ema.updates = ckpt['updates'] # Results if ckpt.get('training_results') is not None: results_file.write_text(ckpt['training_results']) # write results.txt # Epochs start_epoch = ckpt['epoch'] + 1 if opt.resume: assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) if epochs < start_epoch: logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % (weights, ckpt['epoch'], epochs)) epochs += ckpt['epoch'] # finetune additional epochs del ckpt, state_dict # Image sizes gs = max(int(model.stride.max()), 32) # grid size (max stride) nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples # DP mode if cuda and rank == -1 and torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and rank != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) logger.info('Using SyncBatchNorm()') # Trainloader dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, world_size=opt.world_size, workers=opt.workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class nb = len(dataloader) # number of batches assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) # Process 0 if rank in [-1, 0]: testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5, prefix=colorstr('val: '))[0] if not opt.resume: labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency # model._initialize_biases(cf.to(device)) if plots: plot_labels(labels, names, save_dir, loggers) if tb_writer: tb_writer.add_histogram('classes', c, 0) # Anchors if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) model.half().float() # pre-reduce anchor precision # DDP mode if cuda and rank != -1: model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) # Model parameters hyp['box'] *= 3. / nl # scale to layers hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = amp.GradScaler(enabled=cuda) compute_loss = ComputeLoss(model) # init loss class logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' f'Using {dataloader.num_workers} dataloader workers\n' f'Logging results to {save_dir}\n' f'Starting training for {epochs} epochs...') for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ model.train() # Update image weights (optional) if opt.image_weights: # Generate indices if rank in [-1, 0]: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Broadcast if DDP if rank != -1: indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() dist.broadcast(indices, 0) if rank != 0: dataset.indices = indices.cpu().numpy() # Update mosaic border # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(4, device=device) # mean losses if rank != -1: dataloader.sampler.set_epoch(epoch) pbar = enumerate(dataloader) logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) if rank in [-1, 0]: pbar = tqdm(pbar, total=nb) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) # Multi-scale if opt.multi_scale: sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Forward with amp.autocast(enabled=cuda): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if rank != -1: loss *= opt.world_size # gradient averaged between devices in DDP mode if opt.quad: loss *= 4. # Backward scaler.scale(loss).backward() # Optimize if ni % accumulate == 0: scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) # Print if rank in [-1, 0]: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) s = ('%10s' * 2 + '%10.4g' * 6) % ( '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) pbar.set_description(s) # Plot if plots and ni < 3: f = save_dir / f'train_batch{ni}.jpg' # filename Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() # if tb_writer: # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) # tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph elif plots and ni == 10 and wandb_logger.wandb: wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg') if x.exists()]}) # end batch ------------------------------------------------------------------------------------------------ # end epoch ---------------------------------------------------------------------------------------------------- # Scheduler lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard scheduler.step() # DDP process 0 or single-GPU if rank in [-1, 0]: # mAP ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP wandb_logger.current_epoch = epoch + 1 results, maps, times = test.test(data_dict, batch_size=batch_size * 2, imgsz=imgsz_test, model=ema.ema, single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, verbose=nc < 50 and final_epoch, plots=plots and final_epoch, wandb_logger=wandb_logger, compute_loss=compute_loss, is_coco=is_coco) # Write with open(results_file, 'a') as f: f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss if len(opt.name) and opt.bucket: os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) # Log tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss 'x/lr0', 'x/lr1', 'x/lr2'] # params for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): if tb_writer: tb_writer.add_scalar(tag, x, epoch) # tensorboard if wandb_logger.wandb: wandb_logger.log({tag: x}) # W&B # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] if fi > best_fitness: best_fitness = fi wandb_logger.end_epoch(best_result=best_fitness == fi) # Save model if (not opt.nosave) or (final_epoch and not opt.evolve): # if save ckpt = {'epoch': epoch, 'best_fitness': best_fitness, 'training_results': results_file.read_text(), 'model': deepcopy(model.module if is_parallel(model) else model).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if wandb_logger.wandb: if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: wandb_logger.log_model( last.parent, opt, epoch, fi, best_model=best_fitness == fi) del ckpt # end epoch ---------------------------------------------------------------------------------------------------- # end training if rank in [-1, 0]: # Plots if plots: plot_results(save_dir=save_dir) # save as results.png if wandb_logger.wandb: files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files if (save_dir / f).exists()]}) # Test best.pt logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) if opt.data.endswith('coco.yaml') and nc == 80: # if COCO for m in (last, best) if best.exists() else (last): # speed, mAP tests results, _, _ = test.test(opt.data, batch_size=batch_size * 2, imgsz=imgsz_test, conf_thres=0.001, iou_thres=0.7, model=attempt_load(m, device).half(), single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, save_json=True, plots=False, is_coco=is_coco) # Strip optimizers final = best if best.exists() else last # final model for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if opt.bucket: os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload if wandb_logger.wandb and not opt.evolve: # Log the stripped model wandb_logger.wandb.log_artifact(str(final), type='model', name='run_' + wandb_logger.wandb_run.id + '_model', aliases=['last', 'best', 'stripped']) wandb_logger.finish_run() else: dist.destroy_process_group() torch.cuda.empty_cache() return results if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='v5Lite-s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='models/v5Lite-s.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default='data/mydata.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=3, help='total batch size for all GPUs') parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='[train, test] image sizes') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') parser.add_argument('--project', default='runs/train', help='save to project/name') parser.add_argument('--entity', default=None, help='W&B entity') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--linear-lr', action='store_true', help='linear LR') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') opt = parser.parse_args() # Set DDP variables opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 set_logging(opt.global_rank) if opt.global_rank in [-1, 0]: check_git_status() check_requirements() # Resume wandb_run = check_wandb_resume(opt) if opt.resume and not wandb_run: # resume an interrupted run # 修改后的加载代码 ckpt = torch.load(ckpt, map_location='cpu', weights_only=False) # 添加 weights_only=False assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' apriori = opt.global_rank, opt.local_rank with open(Path(ckpt).parent.parent / 'opt.yaml') as f: opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate logger.info('Resuming training from %s' % ckpt) else: # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) opt.name = 'evolve' if opt.evolve else opt.name opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run # DDP mode opt.total_batch_size = opt.batch_size device = select_device(opt.device, batch_size=opt.batch_size) if opt.local_rank != -1: assert torch.cuda.device_count() > opt.local_rank torch.cuda.set_device(opt.local_rank) device = torch.device('cuda', opt.local_rank) dist.init_process_group(backend='nccl', init_method='env://') # distributed backend assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' opt.batch_size = opt.total_batch_size // opt.world_size # Hyperparameters with open(opt.hyp) as f: hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps # Train logger.info(opt) if not opt.evolve: tb_writer = None # init loggers if opt.global_rank in [-1, 0]: prefix = colorstr('tensorboard: ') logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") tb_writer = SummaryWriter(opt.save_dir) # Tensorboard train(hyp, opt, device, tb_writer) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr 'box': (1, 0.02, 0.2), # box loss gain 'cls': (1, 0.2, 4.0), # cls loss gain 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight 'iou_t': (0, 0.1, 0.7), # IoU training threshold 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) 'scale': (1, 0.0, 0.9), # image scale (+/- gain) 'shear': (1, 0.0, 10.0), # image shear (+/- deg) 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) 'mosaic': (1, 0.0, 1.0), # image mixup (probability) 'mixup': (1, 0.0, 1.0)} # image mixup (probability) assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' opt.notest, opt.nosave = True, True # only test/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here if opt.bucket: os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists for _ in range(300): # generations to evolve if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate # Select parent(s) parent = 'single' # parent selection method: 'single' or 'weighted' x = np.loadtxt('evolve.txt', ndmin=2) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() # weights if parent == 'single' or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == 'weighted': x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([x[0] for x in meta.values()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 7] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device) # Write mutation results print_mutation(hyp.copy(), results, yaml_file, opt.bucket) # Plot results plot_evolution(yaml_file) print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') 上述文件运行时显示”Traceback (most recent call last): File "D:\YOLOv5-Lite-1.4\YOLOv5-Lite-master\train.py", line 11, in <module> import ckpt File "D:\YOLOv5-Lite-1.4\YOLOv5-Lite-master\.venv\Lib\site-packages\ckpt\__init__.py", line 5, in <module> from .config import get_ckpt_dir, set_ckpt_dir File "D:\YOLOv5-Lite-1.4\YOLOv5-Lite-master\.venv\Lib\site-packages\ckpt\config.py", line 81, in <module> set_ckpt_dir() ~~~~~~~~~~~~^^ File "D:\YOLOv5-Lite-1.4\YOLOv5-Lite-master\.venv\Lib\site-packages\ckpt\config.py", line 53, in set_ckpt_dir ckpt_dir = resolve_ckpt_dir(ckpt_dir) File "D:\YOLOv5-Lite-1.4\YOLOv5-Lite-master\.venv\Lib\site-packages\ckpt\config.py", line 40, in resolve_ckpt_dir raise Exception("Could not find ckpt-directory") Exception: Could not find ckpt-directory“给出完整详细的解决方案,给出修改后的完整的可运行的代码,给出可运行的代码构成,给出具体修改的代码位置行数
05-12
标题SpringBoot智能在线预约挂号系统研究AI更换标题第1章引言介绍智能在线预约挂号系统的研究背景、意义、国内外研究现状及论文创新点。1.1研究背景与意义阐述智能在线预约挂号系统对提升医疗服务效率的重要性。1.2国内外研究现状分析国内外智能在线预约挂号系统的研究与应用情况。1.3研究方法及创新点概述本文采用的技术路线、研究方法及主要创新点。第2章相关理论总结智能在线预约挂号系统相关理论,包括系统架构、开发技术等。2.1系统架构设计理论介绍系统架构设计的基本原则和常用方法。2.2SpringBoot开发框架理论阐述SpringBoot框架的特点、优势及其在系统开发中的应用。2.3数据库设计与管理理论介绍数据库设计原则、数据模型及数据库管理系统。2.4网络安全与数据保护理论讨论网络安全威胁、数据保护技术及其在系统中的应用。第3章SpringBoot智能在线预约挂号系统设计详细介绍系统的设计方案,包括功能模块划分、数据库设计等。3.1系统功能模块设计划分系统功能模块,如用户管理、挂号管理、医生排班等。3.2数据库设计与实现设计数据库表结构,确定字段类型、主键及外键关系。3.3用户界面设计设计用户友好的界面,提升用户体验。3.4系统安全设计阐述系统安全策略,包括用户认证、数据加密等。第4章系统实现与测试介绍系统的实现过程,包括编码、测试及优化等。4.1系统编码实现采用SpringBoot框架进行系统编码实现。4.2系统测试方法介绍系统测试的方法、步骤及测试用例设计。4.3系统性能测试与分析对系统进行性能测试,分析测试结果并提出优化建议。4.4系统优化与改进根据测试结果对系统进行优化和改进,提升系统性能。第5章研究结果呈现系统实现后的效果,包括功能实现、性能提升等。5.1系统功能实现效果展示系统各功能模块的实现效果,如挂号成功界面等。5.2系统性能提升效果对比优化前后的系统性能
在金融行业中,对信用风险的判断是核心环节之一,其结果对机构的信贷政策和风险控制策略有直接影响。本文将围绕如何借助机器学习方法,尤其是Sklearn工具包,建立用于判断信用状况的预测系统。文中将涵盖逻辑回归、支持向量机等常见方法,并通过实际操作流程进行说明。 一、机器学习基本概念 机器学习属于人工智能的子领域,其基本理念是通过数据自动学习规律,而非依赖人工设定规则。在信贷分析中,该技术可用于挖掘历史数据中的潜在规律,进而对未来的信用表现进行预测。 二、Sklearn工具包概述 Sklearn(Scikit-learn)是Python语言中广泛使用的机器学习模块,提供多种数据处理和建模功能。它简化了数据清洗、特征提取、模型构建、验证与优化等流程,是数据科学项目中的常用工具。 三、逻辑回归模型 逻辑回归是一种常用于分类任务的线性模型,特别适用于二类问题。在信用评估中,该模型可用于判断借款人是否可能违约。其通过逻辑函数将输出映射为0到1之间的概率值,从而表示违约的可能性。 四、支持向量机模型 支持向量机是一种用于监督学习的算法,适用于数据维度高、样本量小的情况。在信用分析中,该方法能够通过寻找最佳分割面,区分违约与非违约客户。通过选用不同核函数,可应对复杂的非线性关系,提升预测精度。 五、数据预处理步骤 在建模前,需对原始数据进行清理与转换,包括处理缺失值、识别异常点、标准化数值、筛选有效特征等。对于信用评分,常见的输入变量包括收入水平、负债比例、信用历史记录、职业稳定性等。预处理有助于减少噪声干扰,增强模型的适应性。 六、模型构建与验证 借助Sklearn,可以将数据集划分为训练集和测试集,并通过交叉验证调整参数以提升模型性能。常用评估指标包括准确率、召回率、F1值以及AUC-ROC曲线。在处理不平衡数据时,更应关注模型的召回率与特异性。 七、集成学习方法 为提升模型预测能力,可采用集成策略,如结合多个模型的预测结果。这有助于降低单一模型的偏差与方差,增强整体预测的稳定性与准确性。 综上,基于机器学习的信用评估系统可通过Sklearn中的多种算法,结合合理的数据处理与模型优化,实现对借款人信用状况的精准判断。在实际应用中,需持续调整模型以适应市场变化,保障预测结果的长期有效性。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
------------------------------------------------ [video] created imageWriter from file:///home/songzhiyi/jetson-inference/python/training/classification/cat.jpg ------------------------------------------------ imageWriter video options: ------------------------------------------------ -- URI: file:///home/songzhiyi/jetson-inference/python/training/classification/cat.jpg - protocol: file - location: cat.jpg - extension: jpg -- deviceType: file -- ioType: output -- codec: unknown -- codecType: omx -- frameRate: 0 -- bitRate: 0 -- numBuffers: 4 -- zeroCopy: true ------------------------------------------------ [OpenGL] glDisplay -- X screen 0 resolution: 1707x1067 [OpenGL] glDisplay -- X window resolution: 1707x1067 libGL error: MESA-LOADER: failed to open swrast (search paths /usr/lib/aarch64-linux-gnu/dri:\$${ORIGIN}/dri:/usr/lib/dri) libGL error: failed to load driver: swrast [OpenGL] glDisplay -- display device initialized (1707x1067) [video] created glDisplay from display://0 ------------------------------------------------ glDisplay video options: ------------------------------------------------ -- URI: display://0 - protocol: display - location: 0 -- deviceType: display -- ioType: output -- width: 1707 -- height: 1067 -- frameRate: 0 -- numBuffers: 4 -- zeroCopy: true ------------------------------------------------ [image] loaded 'data/cat_dog/test/cat/01.jpg' (500x334, 3 channels) imagenet: 94.80% class #0 (cat) [OpenGL] glDisplay -- set the window size to 500x334 [OpenGL] creating 500x334 texture (GL_RGB8 format, 501000 bytes) Segmentation fault (core dumped)
07-01
<think>我们正在处理一个关于程序运行出现Segmentationfault (core dumped)错误的问题。用户提供的日志显示,程序在加载图像并进行分类后,在创建OpenGL纹理时发生了段错误。根据日志,程序做了以下事情:1.创建了一个imageWriter对象,用于输出到文件(cat.jpg)。2.创建了一个glDisplay对象,用于在屏幕上显示(display://0)。3.加载了一张图片(data/cat_dog/test/cat/01.jpg),并进行了分类(输出结果为94.80%的概率是猫)。4.然后尝试创建一个500x334的纹理,此时发生了段错误(Segmentationfault)。可能的原因:1.内存问题:可能是由于分配纹理内存时超出了可用内存(尤其是在嵌入式设备如Jetson上,内存可能有限)。2.驱动问题:日志中已经出现了两个libGL错误,表明OpenGL驱动可能有问题(特别是swrast驱动加载失败)。3.资源限制:Jetson设备可能由于资源不足(如GPU内存不足)导致段错误。4.代码缺陷:在创建纹理时可能存在bug。建议的解决步骤:1.检查OpenGL驱动和环境:确保Jetson上的OpenGL驱动安装正确。可以尝试更新驱动或使用其他OpenGL程序测试。2.检查内存使用:在运行程序前,查看可用内存(使用free -m命令)和GPU内存(使用tegrastats工具)。如果内存不足,尝试减少同时运行的程序或优化程序。3.简化程序:尝试在不显示的情况下运行(即不使用glDisplay),看是否还会崩溃。如果只是显示时崩溃,则问题可能出在OpenGL部分。4.检查图像尺寸:虽然500x334的图片不大,但可能是由于图片通道数或其他属性导致的问题。尝试加载其他图片或调整图片尺寸。5.使用调试工具:如gdb,运行程序并捕获coredump文件,分析崩溃时的堆栈跟踪。由于用户没有提供具体的代码,我们只能根据日志推测。但是,从日志中可以看出,程序使用了jetson-inference库,这是一个用于Jetson的深度学习推理库。如果问题持续,可能需要检查该库的版本和兼容性。根据以上分析,我们可以这样回答:</think>根据日志分析,程序在运行过程中出现了`Segmentation fault (core dumped)`错误。核心问题发生在OpenGL纹理创建阶段: ```text [OpenGL] creating 500x334 texture (GL_RGB8 format, 501000 bytes) Segmentation fault (core dumped) ``` ### 可能原因及解决方案: 1. **OpenGL驱动问题** - 日志显示`failed to load driver: swrast`,表明OpenGL软渲染驱动加载失败 - ✅ 检查驱动安装:`sudo apt install mesa-utils libgl1-mesa-glx` - ✅ 验证驱动:运行`glxinfo | grep "OpenGL renderer"` 2. **GPU内存不足** - Jetson设备内存有限,创建500x334 RGB纹理需约1.5MB显存 - ✅ 监控显存:`tegrastats --interval 1000` - ✅ 降低分辨率或使用`cudaMalloc`替代OpenGL纹理 3. **零拷贝配置冲突** - 日志显示`zeroCopy: true`可能引发内存访问冲突 - ✅ 尝试禁用零拷贝:在代码中设置`zeroCopy=false` 4. **图像通道格式不匹配** - 加载图像为3通道(RGB),但纹理创建可能需RGBA格式 - ✅ 转换图像格式:使用`cv2.cvtColor(img, cv2.COLOR_RGB2RGBA)` 5. **多线程资源竞争** - 同时操作文件写入(`imageWriter`)和显示(`glDisplay`)可能冲突 - ✅ 添加互斥锁或顺序执行资源操作
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值