# 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 math

import os

import random

import sys

import time

from copy import deepcopy

from datetime import datetime

from pathlib import Path

import numpy as np

import torch

import torch.distributed as dist

import torch.nn as nn

import yaml

from torch.cuda import amp

from torch.nn.parallel import DistributedDataParallel as DDP

from torch.optim import SGD, Adam, 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.callbacks import Callbacks

from utils.datasets import create_dataloader

from utils.downloads import attempt_download

from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,

                           check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,

                           intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,

                           print_args, print_mutation, strip_optimizer)

from utils.loggers import Loggers

from utils.loggers.wandb.wandb_utils import check_wandb_resume

from utils.loss import ComputeLoss

from utils.metrics import fitness

from utils.plots import plot_evolve, plot_labels

from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first

LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html

RANK = int(os.getenv('RANK', -1))

WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))

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

    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 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:

        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()

    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='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', 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', 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()

    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

        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)