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|
- # 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__).absolute()
- sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
- 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.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_file, check_git_status, check_img_size, \
- check_requirements, print_mutation, set_logging, one_cycle, colorstr, methods
- 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
- LOGGER = logging.getLogger(__name__)
- 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=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.mkdir(parents=True, exist_ok=True) # make dir
- last, best = w / 'last.pt', w / 'best.pt'
- # Hyperparameters
- if isinstance(hyp, str):
- with open(hyp) 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(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
- pretrained = weights.endswith('.pt')
- if pretrained:
- with torch_distributed_zero_first(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):
- print(f'freezing {k}')
- v.requires_grad = False
- # 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
- # 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 = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
- # 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=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.on_pretrain_routine_end()
- # DDP mode
- if cuda and RANK != -1:
- model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
- # 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.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.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.on_train_epoch_end(epoch=epoch)
- ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
- final_epoch = epoch + 1 == epochs
- 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,
- save_json=is_coco and final_epoch,
- verbose=nc < 50 and final_epoch,
- plots=plots and final_epoch,
- 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.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)
- del ckpt
- callbacks.on_model_save(last, epoch, final_epoch, best_fitness, fi)
- # Stop Single-GPU
- if 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.')
- if not evolve:
- if is_coco: # COCO dataset
- for m in [last, best] if best.exists() else [last]: # speed, mAP tests
- results, _, _ = val.run(data_dict,
- batch_size=batch_size // WORLD_SIZE * 2,
- imgsz=imgsz,
- model=attempt_load(m, device).half(),
- iou_thres=0.7, # NMS IoU threshold for best pycocotools results
- single_cls=single_cls,
- dataloader=val_loader,
- save_dir=save_dir,
- save_json=True,
- plots=False)
- # Strip optimizers
- for f in last, best:
- if f.exists():
- strip_optimizer(f) # strip optimizers
- callbacks.on_train_end(last, best, plots, epoch)
- 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='yolov5s.pt', help='initial weights path')
- parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--hyp', type=str, default='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')
- 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='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')
- parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
- parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
- parser.add_argument('--patience', type=int, default=30, help='EarlyStopping patience (epochs)')
- opt = parser.parse_known_args()[0] if known else parser.parse_args()
- return opt
- def main(opt):
- # Checks
- set_logging(RANK)
- if RANK in [-1, 0]:
- print(colorstr('train: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
- check_git_status()
- check_requirements(requirements=FILE.parent / 'requirements.txt', exclude=['thop'])
- # Resume
- if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run
- 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') 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 = 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'
- if opt.evolve:
- opt.project = 'runs/evolve'
- opt.exist_ok = opt.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:
- from datetime import timedelta
- 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)
- if WORLD_SIZE > 1 and RANK == 0:
- _ = [print('Destroying process group... ', end=''), dist.destroy_process_group(), print('Done.')]
- # 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) 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([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(results, hyp.copy(), save_dir, opt.bucket)
- # Plot results
- plot_evolve(evolve_csv)
- print(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)
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