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Backbone JS AJAX同步技术与前端SPA示例

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### 知识点详细说明: #### 1. Backbone JS 简述 Backbone.js 是一个轻量级的 MVC(模型-视图-控制器)JavaScript 库,它为复杂网页应用提供了结构。通过使用 Backbone.js,开发者能够将界面与数据逻辑分离,使得应用程序的每个部分都可以独立开发和测试。Backbone.js 主要提供了一种模式,用于组织代码并确保数据与视图的同步。 #### 2. AJAX 同步的概念 AJAX(Asynchronous JavaScript and XML)是一种在无需重新加载整个页面的情况下,能够更新部分网页的技术。同步是 AJAX 请求的两种模式之一,与异步相对。在同步模式下,当 AJAX 请求发送时,浏览器会停止响应用户的操作直到请求完成,这可能导致用户体验的延迟。Backbone JS 在处理与服务器的数据同步时,可以使用 AJAX 实现模型和集合的数据同步。 #### 3. 单页应用程序(SPA)的特点 单页应用程序(SPA)是一种网页应用程序,它通过动态重写当前页面与用户交互,而不是传统的多页面应用程序,在每次用户操作后都会加载新页面。SPA 可以提供流畅的用户体验,因为它们不需要重新加载整个页面。Backbone.js 是构建 SPA 的流行框架之一。 #### 4. 外部数据库同步 在前端应用中,通常需要与服务器上的外部数据库进行数据交互。Backbone.js 提供了机制(如 Backbone.sync 方法)来管理模型和集合与服务器的数据同步。开发者可以自定义同步方法以支持 RESTful 架构或其他后端服务。 #### 5. 防止僵尸视图 在单页应用程序中,视图可能会因为数据的变化而变得过时。僵尸视图是指用户看到的是旧数据的状态,而没有反映出最新的数据。Backbone.js 允许开发者监听模型和集合的变化,并在数据更新时更新视图,从而防止出现僵尸视图的情况。 #### 6. 维护浏览器历史状态 良好的用户界面设计应当包括后退按钮的工作和其他浏览器历史功能。Backbone.js 框架内置了对浏览器历史状态管理的支持,通过使用 Router 类可以实现客户端路由,从而记录用户在应用程序内的导航历史。 #### 7. 获取数据的承诺(Promises) 在应用程序中,获取数据通常是一个异步操作。在 JavaScript 中,Promise 对象代表了一个最终可能完成或者失败的异步操作,并且其结果可以被传递。Backbone.js 的模型和集合通常与 promise 相关,以确保在数据被成功获取并处理之前,不会进行视图的渲染。 #### 8. 文件结构分析 - **backbone-sync-example**: 此部分指出了我们所讨论的示例应用的名字。 - **backbone-sync-example-master**: 由于文件名称后缀了"-master",这通常表明这是一个源代码仓库中的主分支,意味着它应该是最完整和稳定的代码版本。 #### 9. JavaScript 语言的使用 该应用程序是使用 JavaScript 编写的,这表明开发者需要具备 JavaScript 编程基础和对浏览器端脚本操作的熟悉度。理解 JavaScript 事件循环、原型链、闭包、异步编程等概念对于开发此类应用程序至关重要。 #### 10. 实践中的最佳实践 在应用程序开发中,有许多最佳实践需要遵循。例如,代码的组织、注释的书写、可读性和可维护性的保证。在本例中,为了避免僵尸视图和维护历史状态,开发者需要采取特定的编码策略,如使用事件监听器来监听模型的变化,并更新视图状态。 ### 结论 上述内容介绍了在构建一个使用 Backbone.js 和 AJAX 技术的单页应用程序时所涉及的关键概念和技术点。了解这些知识点有助于开发人员高效地设计和实现能够与外部数据库同步的前端应用,并确保良好的用户体验。

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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release. Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch Usage - Multi-GPU DDP training: $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 Models: https://github.com/ultralytics/yolov5/tree/master/models Datasets: https://github.com/ultralytics/yolov5/tree/master/data Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data """ import argparse import math import os os.environ["GIT_PYTHON_REFRESH"] = "quiet" # add there import random import sys import time from copy import deepcopy from datetime import datetime from pathlib import Path import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm # import numpy # import torch.serialization # torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct]) FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative import val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) from utils.loggers import Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first) LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv('RANK', -1)) WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze callbacks.run('on_pretrain_routine_start') # Directories w = save_dir / 'weights' # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / 'last.pt', w / 'best.pt' # Hyperparameters if isinstance(hyp, str): with open(hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / 'hyp.yaml', hyp) yaml_save(save_dir / 'opt.yaml', vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance # Register actions for k in methods(loggers): callbacks.register_action(k, callback=getattr(loggers, k)) # Process custom dataset artifact link data_dict = loggers.remote_dataset if resume: # If resuming runs from remote artifact weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size # Config plots = not evolve and not opt.noplots # create plots cuda = device.type != 'cpu' init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict['train'], data_dict['val'] nc = 1 if single_cls else int(data_dict['nc']) # number of classes names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset # Model check_suffix(weights, '.pt') # check weights pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location='cpu', weights_only=False) # load checkpoint to CPU to avoid CUDA memory leak model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f'freezing {k}') v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] else: lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear 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 best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') 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 train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == 'val' else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '), shuffle=True) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, prefix=colorstr('val: '))[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision callbacks.run('on_pretrain_routine_end', labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) 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.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class callbacks.run('on_train_start') LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' f"Logging results to {colorstr('bold', save_dir)}\n" f'Starting training for {epochs} epochs...') for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ callbacks.run('on_train_epoch_start') model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: 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 # Update mosaic border (optional) # 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(3, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- callbacks.run('on_train_batch_start') ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.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 / 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 == 0 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 = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Forward # with torch.cuda.amp.autocast(amp): with torch.amp.autocast(device_type='cuda'): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss *= 4. # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x['lr'] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP callbacks.run('on_train_epoch_end', epoch=epoch) ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run(data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { 'epoch': epoch, 'best_fitness': best_fitness, 'model': deepcopy(de_parallel(model)).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'opt': vars(opt), 'git': GIT_INFO, # {remote, branch, commit} if a git repo 'date': datetime.now().isoformat()} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f'epoch{epoch}.pt') del ckpt callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f'\nValidating {f}...') results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss) # val best model with plots if is_coco: callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) callbacks.run('on_train_end', last, best, epoch, results) torch.cuda.empty_cache() return results def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default=r'C:data/AAAA.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=100, help='total training epochs') parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') 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('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noplots', action='store_true', help='save no plot files') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', 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('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') parser.add_argument('--name', default='welding_defect_yolov5s_20241101_300', 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('--cos-lr', action='store_true', help='cosine LR scheduler') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') parser.add_argument('--save-period', type=int, default=5, help='Save checkpoint every x epochs (disabled if < 1)') parser.add_argument('--seed', type=int, default=0, help='Global training seed') parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') # Logger arguments parser.add_argument('--entity', default=None, help='Entity') parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): # Checks if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements() # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors='ignore') as f: d = yaml.safe_load(f) else: d = torch.load(last, map_location='cpu')['opt'] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' if opt.evolve: if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve opt.project = str(ROOT / 'runs/evolve') opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == 'cfg': opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' assert not opt.image_weights, f'--image-weights {msg}' assert not opt.evolve, f'--evolve {msg}' assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' torch.cuda.set_device(LOCAL_RANK) device = torch.device('cuda', LOCAL_RANK) dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # 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) 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) with open(opt.hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict if 'anchors' not in hyp: # anchors commented in hyp.yaml hyp['anchors'] = 3 if opt.noautoanchor: del hyp['anchors'], meta['anchors'] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' if opt.bucket: os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) parent = 'single' # parent selection method: 'single' or 'weighted' x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) 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() + 1E-6 # weights (sum > 0) 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([meta[k][0] for k in hyp.keys()]) # 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, callbacks) callbacks = Callbacks() # Write mutation results keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss') print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) # Plot results plot_evolve(evolve_csv) LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' f"Results saved to {colorstr('bold', save_dir)}\n" f'Usage example: $ python train.py --hyp {evolve_yaml}') def run(**kwargs): # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) 为什么训练之后,他的runs里面并没有显示best.pt跟last.pt 请查找原因

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import argparse import math import os os.environ["GIT_PYTHON_REFRESH"] = "quiet" import random import subprocess import sys import time from copy import deepcopy from datetime import datetime, timedelta from pathlib import Path try: import comet_ml # must be imported before torch (if installed) except ImportError: comet_ml = None import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch.optim import lr_scheduler from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative import val as validate # for end-of-epoch mAP from models.experimental import attempt_load from models.yolo import Model from utils.autoanchor import check_anchors from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url from utils.general import ( LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save, ) from utils.loggers import LOGGERS, Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve from utils.torch_utils import ( EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, smart_resume, torch_distributed_zero_first, ) LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = ( Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, ) callbacks.run("on_pretrain_routine_start") # Directories w = save_dir / "weights" # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir last, best = w / "last.pt", w / "best.pt" # Hyperparameters if isinstance(hyp, str): with open(hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: yaml_save(save_dir / "hyp.yaml", hyp) yaml_save(save_dir / "opt.yaml", vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: include_loggers = list(LOGGERS) if getattr(opt, "ndjson_console", False): include_loggers.append("ndjson_console") if getattr(opt, "ndjson_file", False): include_loggers.append("ndjson_file") loggers = Loggers( save_dir=save_dir, weights=weights, opt=opt, hyp=hyp, logger=LOGGER, include=tuple(include_loggers), ) # Register actions for k in methods(loggers): callbacks.register_action(k, callback=getattr(loggers, k)) # Process custom dataset artifact link data_dict = loggers.remote_dataset if resume: # If resuming runs from remote artifact weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size # Config plots = not evolve and not opt.noplots # create plots cuda = device.type != "cpu" init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None train_path, val_path = data_dict["train"], data_dict["val"] nc = 1 if single_cls else int(data_dict["nc"]) # number of classes names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset # Model check_suffix(weights, ".pt") # check weights pretrained = weights.endswith(".pt") if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create amp = check_amp(model) # check AMP # Freeze freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): LOGGER.info(f"freezing {k}") v.requires_grad = False # Image size gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) # Scheduler if opt.cos_lr: lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] else: def lf(x): """Linear learning rate scheduler function with decay calculated by epoch proportion.""" return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear 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 best_fitness, start_epoch = 0.0, 0 if pretrained: if resume: best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) del ckpt, csd # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning( "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." ) 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 train_loader, dataset = create_dataloader( train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, hyp=hyp, augment=True, cache=None if opt.cache == "val" else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr("train: "), shuffle=True, seed=opt.seed, ) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" # Process 0 if RANK in {-1, 0}: val_loader = create_dataloader( val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, workers=workers * 2, pad=0.5, prefix=colorstr("val: "), )[0] if not resume: if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision callbacks.run("on_pretrain_routine_end", labels, names) # DDP mode if cuda and RANK != -1: model = smart_DDP(model) # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) 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.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nb = len(train_loader) # number of batches nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class callbacks.run("on_train_start") LOGGER.info( f"Image sizes {imgsz} train, {imgsz} val\n" f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n" f"Logging results to {colorstr('bold', save_dir)}\n" f"Starting training for {epochs} epochs..." ) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ callbacks.run("on_train_epoch_start") model.train() # Update image weights (optional, single-GPU only) if opt.image_weights: 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 # Update mosaic border (optional) # 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(3, device=device) # mean losses if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size")) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- callbacks.run("on_train_batch_start") ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 # Warmup if ni <= nw: xi = [0, nw] # x interp # compute_loss.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 / 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 == 0 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(int(imgsz * 0.5), int(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 = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward with torch.cuda.amp.autocast(amp): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: loss *= 4.0 # Backward scaler.scale(loss).backward() # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html if ni - last_opt_step >= accumulate: scaler.unscale_(optimizer) # unscale gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) last_opt_step = ni # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB) pbar.set_description( ("%11s" * 2 + "%11.4g" * 5) % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) ) callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler lr = [x["lr"] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP callbacks.run("on_train_epoch_end", epoch=epoch) ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP results, maps, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, half=amp, model=ema.ema, single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, plots=False, callbacks=callbacks, compute_loss=compute_loss, ) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]] stop = stopper(epoch=epoch, fitness=fi) # early stop check if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { "epoch": epoch, "best_fitness": best_fitness, "model": deepcopy(de_parallel(model)).half(), "ema": deepcopy(ema.ema).half(), "updates": ema.updates, "optimizer": optimizer.state_dict(), "opt": vars(opt), "git": GIT_INFO, # {remote, branch, commit} if a git repo "date": datetime.now().isoformat(), } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: torch.save(ckpt, w / f"epoch{epoch}.pt") del ckpt callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training broadcast_list = [stop if RANK == 0 else None] dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks if RANK != 0: stop = broadcast_list[0] if stop: break # must break all DDP ranks # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: LOGGER.info(f"\nValidating {f}...") results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, imgsz=imgsz, model=attempt_load(f, device).half(), iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 single_cls=single_cls, dataloader=val_loader, save_dir=save_dir, save_json=is_coco, verbose=True, plots=plots, callbacks=callbacks, compute_loss=compute_loss, ) # val best model with plots if is_coco: callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi) callbacks.run("on_train_end", last, best, epoch, results) torch.cuda.empty_cache() return results def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="A_dataset/yolov5s.yaml", help="model.yaml path") parser.add_argument("--data", type=str, default=ROOT / "A_dataset/dataset.yaml", help="dataset.yaml path") parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") parser.add_argument("--epochs", type=int, default=100, help="total training epochs") parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") 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("--noval", action="store_true", help="only validate final epoch") parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") parser.add_argument("--noplots", action="store_true", help="save no plot files") parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") parser.add_argument( "--evolve_population", type=str, default=ROOT / "data/hyps", help="location for loading population" ) parser.add_argument("--resume_evolve", type=str, default=None, help="resume evolve from last generation") parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") parser.add_argument("--device", default="", 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("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") parser.add_argument("--workers", type=int, default=0, help="max dataloader workers (per RANK in DDP mode)") parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") 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("--cos-lr", action="store_true", help="cosine LR scheduler") parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") parser.add_argument("--seed", type=int, default=0, help="Global training seed") parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Logger arguments parser.add_argument("--entity", default=None, help="Entity") parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option') parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval") parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use") # NDJSON logging parser.add_argument("--ndjson-console", action="store_true", help="Log ndjson to console") parser.add_argument("--ndjson-file", action="store_true", help="Log ndjson to file") return parser.parse_known_args()[0] if known else parser.parse_args() def main(opt, callbacks=Callbacks()): if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() check_requirements(ROOT / "requirements.txt") # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) opt_yaml = last.parent.parent / "opt.yaml" # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): with open(opt_yaml, errors="ignore") as f: d = yaml.safe_load(f) else: d = torch.load(last, map_location="cpu")["opt"] opt = argparse.Namespace(**d) # replace opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project), ) # checks assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" if opt.evolve: if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve opt.project = str(ROOT / "runs/evolve") opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume if opt.name == "cfg": opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: msg = "is not compatible with YOLOv5 Multi-GPU DDP training" assert not opt.image_weights, f"--image-weights {msg}" assert not opt.evolve, f"--evolve {msg}" assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) device = torch.device("cuda", LOCAL_RANK) dist.init_process_group( backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=10800) ) # Train if not opt.evolve: train(opt.hyp, opt, device, callbacks) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit) meta = { "lr0": (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) "lrf": (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) "momentum": (False, 0.6, 0.98), # SGD momentum/Adam beta1 "weight_decay": (False, 0.0, 0.001), # optimizer weight decay "warmup_epochs": (False, 0.0, 5.0), # warmup epochs (fractions ok) "warmup_momentum": (False, 0.0, 0.95), # warmup initial momentum "warmup_bias_lr": (False, 0.0, 0.2), # warmup initial bias lr "box": (False, 0.02, 0.2), # box loss gain "cls": (False, 0.2, 4.0), # cls loss gain "cls_pw": (False, 0.5, 2.0), # cls BCELoss positive_weight "obj": (False, 0.2, 4.0), # obj loss gain (scale with pixels) "obj_pw": (False, 0.5, 2.0), # obj BCELoss positive_weight "iou_t": (False, 0.1, 0.7), # IoU training threshold "anchor_t": (False, 2.0, 8.0), # anchor-multiple threshold "anchors": (False, 2.0, 10.0), # anchors per output grid (0 to ignore) "fl_gamma": (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) "hsv_h": (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction) "hsv_s": (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) "hsv_v": (True, 0.0, 0.9), # image HSV-Value augmentation (fraction) "degrees": (True, 0.0, 45.0), # image rotation (+/- deg) "translate": (True, 0.0, 0.9), # image translation (+/- fraction) "scale": (True, 0.0, 0.9), # image scale (+/- gain) "shear": (True, 0.0, 10.0), # image shear (+/- deg) "perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 "flipud": (True, 0.0, 1.0), # image flip up-down (probability) "fliplr": (True, 0.0, 1.0), # image flip left-right (probability) "mosaic": (True, 0.0, 1.0), # image mosaic (probability) "mixup": (True, 0.0, 1.0), # image mixup (probability) "copy_paste": (True, 0.0, 1.0), # segment copy-paste (probability) } # GA configs pop_size = 50 mutation_rate_min = 0.01 mutation_rate_max = 0.5 crossover_rate_min = 0.5 crossover_rate_max = 1 min_elite_size = 2 max_elite_size = 5 tournament_size_min = 2 tournament_size_max = 10 with open(opt.hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict if "anchors" not in hyp: # anchors commented in hyp.yaml hyp["anchors"] = 3 if opt.noautoanchor: del hyp["anchors"], meta["anchors"] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" if opt.bucket: # download evolve.csv if exists subprocess.run( [ "gsutil", "cp", f"gs://{opt.bucket}/evolve.csv", str(evolve_csv), ] ) # Delete the items in meta dictionary whose first value is False del_ = [item for item, value_ in meta.items() if value_[0] is False] hyp_GA = hyp.copy() # Make a copy of hyp dictionary for item in del_: del meta[item] # Remove the item from meta dictionary del hyp_GA[item] # Remove the item from hyp_GA dictionary # Set lower_limit and upper_limit arrays to hold the search space boundaries lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()]) upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()]) # Create gene_ranges list to hold the range of values for each gene in the population gene_ranges = [(lower_limit[i], upper_limit[i]) for i in range(len(upper_limit))] # Initialize the population with initial_values or random values initial_values = [] # If resuming evolution from a previous checkpoint if opt.resume_evolve is not None: assert os.path.isfile(ROOT / opt.resume_evolve), "evolve population path is wrong!" with open(ROOT / opt.resume_evolve, errors="ignore") as f: evolve_population = yaml.safe_load(f) for value in evolve_population.values(): value = np.array([value[k] for k in hyp_GA.keys()]) initial_values.append(list(value)) # If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population else: yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith(".yaml")] for file_name in yaml_files: with open(os.path.join(opt.evolve_population, file_name)) as yaml_file: value = yaml.safe_load(yaml_file) value = np.array([value[k] for k in hyp_GA.keys()]) initial_values.append(list(value)) # Generate random values within the search space for the rest of the population if initial_values is None: population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size)] elif pop_size > 1: population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size - len(initial_values))] for initial_value in initial_values: population = [initial_value] + population # Run the genetic algorithm for a fixed number of generations list_keys = list(hyp_GA.keys()) for generation in range(opt.evolve): if generation >= 1: save_dict = {} for i in range(len(population)): little_dict = {list_keys[j]: float(population[i][j]) for j in range(len(population[i]))} save_dict[f"gen{str(generation)}number{str(i)}"] = little_dict with open(save_dir / "evolve_population.yaml", "w") as outfile: yaml.dump(save_dict, outfile, default_flow_style=False) # Adaptive elite size elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve)) # Evaluate the fitness of each individual in the population fitness_scores = [] for individual in population: for key, value in zip(hyp_GA.keys(), individual): hyp_GA[key] = value hyp.update(hyp_GA) results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results keys = ( "metrics/precision", "metrics/recall", "metrics/mAP_0.5", "metrics/mAP_0.5:0.95", "val/box_loss", "val/obj_loss", "val/cls_loss", ) print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) fitness_scores.append(results[2]) # Select the fittest individuals for reproduction using adaptive tournament selection selected_indices = [] for _ in range(pop_size - elite_size): # Adaptive tournament size tournament_size = max( max(2, tournament_size_min), int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))), ) # Perform tournament selection to choose the best individual tournament_indices = random.sample(range(pop_size), tournament_size) tournament_fitness = [fitness_scores[j] for j in tournament_indices] winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))] selected_indices.append(winner_index) # Add the elite individuals to the selected indices elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]] selected_indices.extend(elite_indices) # Create the next generation through crossover and mutation next_generation = [] for _ in range(pop_size): parent1_index = selected_indices[random.randint(0, pop_size - 1)] parent2_index = selected_indices[random.randint(0, pop_size - 1)] # Adaptive crossover rate crossover_rate = max( crossover_rate_min, min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve)) ) if random.uniform(0, 1) < crossover_rate: crossover_point = random.randint(1, len(hyp_GA) - 1) child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:] else: child = population[parent1_index] # Adaptive mutation rate mutation_rate = max( mutation_rate_min, min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve)) ) for j in range(len(hyp_GA)): if random.uniform(0, 1) < mutation_rate: child[j] += random.uniform(-0.1, 0.1) child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1]) next_generation.append(child) # Replace the old population with the new generation population = next_generation # Print the best solution found best_index = fitness_scores.index(max(fitness_scores)) best_individual = population[best_index] print("Best solution found:", best_individual) # Plot results plot_evolve(evolve_csv) LOGGER.info( f"Hyperparameter evolution finished {opt.evolve} generations\n" f"Results saved to {colorstr('bold', save_dir)}\n" f"Usage example: $ python train.py --hyp {evolve_yaml}" ) def generate_individual(input_ranges, individual_length): individual = [] for i in range(individual_length): lower_bound, upper_bound = input_ranges[i] individual.append(random.uniform(lower_bound, upper_bound)) return individual def run(**kwargs): opt = parse_opt(True) for k, v in kwargs.items(): setattr(opt, k, v) main(opt) return opt if __name__ == "__main__": opt = parse_opt() main(opt) 这是yolov5自带的train训练代码,我现在想要修改它,让该代码简洁一些,并且仍然能完成训练

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E:\study\anaconda\envs\myenv\python.exe D:\yolov5-6.0\train.py train: weights=yolov5s.pt, cfg=models/yolov5s_SE.yaml, data=data\coco128.yaml, hyp=data\hyps\hyp.scratch.yaml, epochs=100, batch_size=1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 Torch 2.6.0+cpu CPU hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/ Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 runs (RECOMMENDED) from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2]

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