diff --git a/.~c9_invoke_Hc4Qn.py b/.~c9_invoke_Hc4Qn.py deleted file mode 100644 index 95f73c090903..000000000000 --- a/.~c9_invoke_Hc4Qn.py +++ /dev/null @@ -1,626 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Train a YOLOv5 model on a custom dataset - -Usage: - $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 -""" - -import argparse -import logging -import math -import os -import random -import sys -import time -from copy import deepcopy -from pathlib import Path - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -import yaml -from torch.cuda import amp -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.optim import Adam, SGD, 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 # 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.datasets import create_dataloader -from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ - strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \ - check_file, check_yaml, check_suffix, print_args, print_mutation, one_cycle, colorstr, methods, LOGGER -from utils.downloads import attempt_download -from utils.loss import ComputeLoss -from utils.plots import plot_labels, plot_evolve -from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \ - torch_distributed_zero_first -from utils.loggers.wandb.wandb_utils import check_wandb_resume -from utils.metrics import fitness -from utils.loggers import Loggers -from utils.callbacks import Callbacks - -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)) - - -def train(hyp, # path/to/hyp.yaml or hyp dictionary - 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 - - # 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())) - - # Save run settings - with open(save_dir / 'hyp.yaml', 'w') as f: - yaml.safe_dump(hyp, f, sort_keys=False) - with open(save_dir / 'opt.yaml', 'w') as f: - yaml.safe_dump(vars(opt), f, sort_keys=False) - data_dict = None - - # Loggers - if RANK in [-1, 0]: - loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance - if loggers.wandb: - data_dict = loggers.wandb.data_dict - if resume: - weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp - - # Register actions - for k in methods(loggers): - callbacks.register_action(k, callback=getattr(loggers, k)) - - # Config - plots = not evolve # create plots - cuda = device.type != 'cpu' - init_seeds(1 + RANK) - 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 = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names - assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check - is_coco = data.endswith('coco.yaml') and nc == 80 # 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=device) # load checkpoint - 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 - - # Freeze - freeze = [f'model.{x}.' for x in range(freeze)] # layers to freeze - for k, v in model.named_parameters(): - v.requires_grad = True # train all layers - 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) - - # 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 - LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}") - - g0, g1, g2 = [], [], [] # optimizer parameter groups - for v in model.modules(): - if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias - g2.append(v.bias) - if isinstance(v, nn.BatchNorm2d): # weight (no decay) - g0.append(v.weight) - elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay) - g1.append(v.weight) - - if opt.adam: - optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum - else: - optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) - - optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) # add g1 with weight_decay - optimizer.add_param_group({'params': g2}) # add g2 (biases) - LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups " - f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias") - del g0, g1, g2 - - # Scheduler - 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'] - - # Epochs - start_epoch = ckpt['epoch'] + 1 - if resume: - assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.' - if epochs < start_epoch: - LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") - epochs += ckpt['epoch'] # finetune additional epochs - - del ckpt, csd - - # DP mode - if cuda and RANK == -1 and torch.cuda.device_count() > 1: - logging.warning('DP not recommended, instead 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=opt.cache, rect=opt.rect, rank=LOCAL_RANK, - workers=workers, image_weights=opt.image_weights, quad=opt.quad, - prefix=colorstr('train: ')) - mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class - nb = len(train_loader) # number of batches - 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, pad=0.5, - prefix=colorstr('val: '))[0] - - if not 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) - - # Anchors - if not opt.noautoanchor: - check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) - model.half().float() # pre-reduce anchor precision - - callbacks.run('on_pretrain_routine_end') - - # DDP mode - if cuda and RANK != -1: - model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) - - # Model parameters - 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() - 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 - last_opt_step = -1 - 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) - stopper = EarlyStopping(patience=opt.patience) - compute_loss = ComputeLoss(model) # init loss class - LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' - f'Using {train_loader.num_workers} 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 ------------------------------------------------------------------ - 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' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', '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 - # 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 == 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 = nn.functional.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 *= WORLD_SIZE # gradient averaged between devices in DDP mode - if opt.quad: - loss *= 4. - - # Backward - scaler.scale(loss).backward() - - # Optimize - if ni - last_opt_step >= accumulate: - 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(('%10s' * 2 + '%10.4g' * 5) % ( - f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) - callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn) - # 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, _ = val.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz, - 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, mAP@.5, mAP@.5-.95] - 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(), - 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None} - - # Save last, best and delete - torch.save(ckpt, last) - if best_fitness == fi: - torch.save(ckpt, best) - if (epoch > 0) and (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) - - # Stop Single-GPU - if RANK == -1 and stopper(epoch=epoch, fitness=fi): - break - - # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576 - # stop = stopper(epoch=epoch, fitness=fi) - # if RANK == 0: - # dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks - - # Stop DPP - # with torch_distributed_zero_first(RANK): - # 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, _, _ = val.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 results at 0.65 - single_cls=single_cls, - dataloader=val_loader, - save_dir=save_dir, - save_json=is_coco, - verbose=True, - plots=True, - 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, plots, epoch, results) - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") - - 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='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=300) - 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 check') - 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='--cache images in "ram" (default) or "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('--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('--workers', type=int, default=8, help='maximum number of dataloader workers') - 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('--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('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') - - # Weights & Biases arguments - parser.add_argument('--entity', default=None, help='W&B: Entity') - parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table') - parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') - parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') - - opt = parser.parse_known_args()[0] if known else parser.parse_args() - return opt - - -def main(opt, callbacks=Callbacks()): - # Checks - if RANK in [-1, 0]: - print_args(FILE.stem, opt) - check_git_status() - check_requirements(exclude=['thop']) - - # Resume - if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run - opt.resume = True if opt.resume == 'True' else opt.resume - ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path - assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' - with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f: - opt = argparse.Namespace(**yaml.safe_load(f)) # replace - opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate - LOGGER.info(f'Resuming training from {ckpt}') - 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: - opt.project = str(ROOT / 'runs/evolve') - opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume - 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: - assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' - assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count' - assert not opt.image_weights, '--image-weights argument is not compatible with DDP training' - assert not opt.evolve, '--evolve argument is not compatible with DDP training' - 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) - if WORLD_SIZE > 1 and RANK == 0: - LOGGER.info('Destroying process group... ') - dist.destroy_process_group() - - # 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 - 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 {save_dir}') # 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) - - # Write mutation results - print_mutation(results, hyp.copy(), save_dir, opt.bucket) - - # Plot results - plot_evolve(evolve_csv) - LOGGER.info(f'Hyperparameter evolution finished\n' - f"Results saved to {colorstr('bold', save_dir)}\n" - f'Use best hyperparameters 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) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt)