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	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
	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_img_size, \
	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.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, \
	val_scales, val_flips = \
	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, opt.val_scales, opt.val_flips

	val_flips = [None if f == -1 else f for f in val_flips]

	# 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

	labels_dir = data_dict.get('labels', 'labels')
	kp_flip = data_dict.get('kp_flip')
	kp_bbox = data_dict.get('kp_bbox')
	num_coords = data_dict.get('num_coords', 0)

	# 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'), num_coords=num_coords).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'), num_coords=num_coords).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)
	# if opt.autobalance:
	# optimizer.add_param_group({'params': model.loss_coeffs}) # for autobalancing if used

	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, labels_dir, 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: '), kp_flip=kp_flip, kp_bbox=kp_bbox)
	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, labels_dir, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
	hyp=hyp, cache=None if noval else opt.cache, rect=False, rank=-1,
	workers=workers, pad=0.5,
	prefix=colorstr('val: '), kp_flip=kp_flip, kp_bbox=kp_bbox)[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['kp'] *= 3. / nl
	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, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls, kp)
	scheduler.last_epoch = start_epoch - 1 # do not move
	scaler = amp.GradScaler(enabled=cuda)
	stopper = EarlyStopping(patience=opt.patience)
	compute_loss = ComputeLoss(model, autobalance=False, num_coords=num_coords) # 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(4, device=device) # mean losses
	if RANK != -1:
	train_loader.sampler.set_epoch(epoch)
	pbar = enumerate(train_loader)
	LOGGER.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'kps', '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' * 6) % (
	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[:3]] # 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) or stopper.possible_stop
	if not noval or final_epoch: # Calculate mAP
	results, maps, _ = val.run(data_dict,
	batch_size=batch_size // WORLD_SIZE,
	imgsz=imgsz,
	conf_thres=0.01,
	model=ema.ema,
	dataloader=val_loader,
	compute_loss=compute_loss,
	scales=val_scales,
	flips=val_flips)

	# 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 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.')
	if not evolve:
	# 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='yolov5s6.pt', help='initial weights path')
	parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
	parser.add_argument('--data', type=str, default='data/coco-kp.yaml', help='dataset.yaml path')
	parser.add_argument('--hyp', type=str, default='data/hyps/hyp.kp-p6.yaml', help='hyperparameters path')
	parser.add_argument('--epochs', type=int, default=300)
	parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')
	parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=1280, 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=100, help='EarlyStopping patience (epochs without improvement)')
	parser.add_argument('--val-scales', type=float, nargs='+', default=[1])
	parser.add_argument('--val-flips', type=int, nargs='+', default=[-1])
	parser.add_argument('--autobalance', action='store_true', help='Learn keypoint and object loss scaling')
	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 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([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)

	# 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)