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

if not evolve:

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)

# Loggers

data_dict = None

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 = 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=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 (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

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)

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

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.optimizer == 'Adam':

optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum

elif opt.optimizer == 'AdamW':

optimizer = AdamW(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:

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=opt.cache, rect=opt.rect, rank=LOCAL_RANK,

workers=workers, image_weights=opt.image_weights, quad=opt.quad,

prefix=colorstr('train: '), shuffle=True)

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 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()

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 * 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 ------------------------------------------------------------------

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, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # 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 # 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,

'date': datetime.now().isoformat()}

# 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()

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('--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='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', 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('--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', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')

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()

# 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

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"

# 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)