From e9656a0f322551140db55306f1c90481e0c29dba Mon Sep 17 00:00:00 2001 From: p38720654 Date: Wed, 29 Nov 2023 20:42:50 +0800 Subject: [PATCH] ADD file via upload --- train.py | 519 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 519 insertions(+) create mode 100644 train.py diff --git a/train.py b/train.py new file mode 100644 index 0000000..b2e55aa --- /dev/null +++ b/train.py @@ -0,0 +1,519 @@ +import argparse + +import torch.distributed as dist +import torch.nn.functional as F +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +import torch.utils.data +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +import test # import test.py to get mAP after each epoch +from models.yolo import Model +from utils import google_utils +from utils.datasets import * +from utils.utils import * + +mixed_precision = True +try: # Mixed precision training https://github.com/NVIDIA/apex + from apex import amp +except: + print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex') + mixed_precision = False # not installed + +# Hyperparameters +hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD + 'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) + 'momentum': 0.937, # SGD momentum/Adam beta1 + 'weight_decay': 5e-4, # optimizer weight decay + 'giou': 0.05, # giou loss gain + 'cls': 0.5, # cls loss gain + 'cls_pw': 1.0, # cls BCELoss positive_weight + 'obj': 1.0, # obj loss gain (*=img_size/320 if img_size != 320) + 'obj_pw': 1.0, # obj BCELoss positive_weight + 'iou_t': 0.20, # iou training threshold + 'anchor_t': 4.0, # anchor-multiple threshold + 'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5) + 'hsv_h': 0.015, # image HSV-Hue augmentation (fraction) + 'hsv_s': 0.7, # image HSV-Saturation augmentation (fraction) + 'hsv_v': 0.4, # image HSV-Value augmentation (fraction) + 'degrees': 0.0, # image rotation (+/- deg) + 'translate': 0.0, # image translation (+/- fraction) + 'scale': 0.5, # image scale (+/- gain) + 'shear': 0.0} # image shear (+/- deg) + + +def train(hyp, tb_writer, opt, device): + print(f'Hyperparameters {hyp}') + log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution' # run directory + wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory + os.makedirs(wdir, exist_ok=True) + last = wdir + 'last.pt' + best = wdir + 'best.pt' + results_file = log_dir + os.sep + 'results.txt' + epochs, batch_size, total_batch_size, weights, rank = \ + opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.local_rank + # TODO: Init DDP logging. Only the first process is allowed to log. + # Since I see lots of print here, the logging configuration is skipped here. We may see repeated outputs. + + # Save run settings + with open(Path(log_dir) / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(Path(log_dir) / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + init_seeds(2 + rank) + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict + train_path = data_dict['train'] + test_path = data_dict['val'] + nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Remove previous results + if rank in [-1, 0]: + for f in glob.glob('*_batch*.jpg') + glob.glob(results_file): + os.remove(f) + + # Create model + model = Model(opt.cfg, nc=nc).to(device) + + # Image sizes + gs = int(max(model.stride)) # grid size (max stride) + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # Optimizer + nbs = 64 # nominal batch size + # default DDP implementation is slow for accumulation according to: https://pytorch.org/docs/stable/notes/ddp.html + # all-reduce operation is carried out during loss.backward(). + # Thus, there would be redundant all-reduce communications in a accumulation procedure, + # which means, the result is still right but the training speed gets slower. + # TODO: If acceleration is needed, there is an implementation of allreduce_post_accumulation + # in https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/run_pretraining.py + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_parameters(): + if v.requires_grad: + if '.bias' in k: + pg2.append(v) # biases + elif '.weight' in k and '.bn' not in k: + pg1.append(v) # apply weight decay + else: + pg0.append(v) # all else + + if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # Load Model + with torch_distributed_zero_first(rank): + google_utils.attempt_download(weights) + start_epoch, best_fitness = 0, 0.0 + if weights.endswith('.pt'): # pytorch format + ckpt = torch.load(weights, map_location=device) # load checkpoint + + # load model + try: + exclude = ['anchor'] # exclude keys + ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items() + if k in model.state_dict() and not any(x in k for x in exclude) + and model.state_dict()[k].shape == v.shape} + model.load_state_dict(ckpt['model'], strict=False) + print('Transferred %g/%g items from %s' % (len(ckpt['model']), len(model.state_dict()), weights)) + except KeyError as e: + s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \ + "Please delete or update %s and try again, or use --weights '' to train from scratch." \ + % (weights, opt.cfg, weights, weights) + raise KeyError(s) from e + + # load optimizer + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = ckpt['best_fitness'] + + # load results + if ckpt.get('training_results') is not None: + with open(results_file, 'w') as file: + file.write(ckpt['training_results']) # write results.txt + + # epochs + start_epoch = ckpt['epoch'] + 1 + if epochs < start_epoch: + print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % + (weights, ckpt['epoch'], epochs)) + epochs += ckpt['epoch'] # finetune additional epochs + + del ckpt + + # Mixed precision training https://github.com/NVIDIA/apex + if mixed_precision: + model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0) + + # Scheduler https://arxiv.org/pdf/1812.01187.pdf + lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.8 + 0.2 # cosine + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822 + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # DP mode + if device.type != 'cpu' and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and device.type != 'cpu' and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + print('Using SyncBatchNorm()') + + # Exponential moving average + ema = torch_utils.ModelEMA(model) if rank in [-1, 0] else None + + # DDP mode + if device.type != 'cpu' and rank != -1: + model = DDP(model, device_ids=[rank], output_device=rank) + + # Trainloader + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, + cache=opt.cache_images, rect=opt.rect, local_rank=rank, + world_size=opt.world_size) + mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches + assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) + + # Testloader + if rank in [-1, 0]: + # local_rank is set to -1. Because only the first process is expected to do evaluation. + testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, hyp=hyp, augment=False, + cache=opt.cache_images, rect=True, local_rank=-1, world_size=opt.world_size)[0] + + # Model parameters + hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou) + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights + model.names = names + + # Class frequency + if rank in [-1, 0]: + labels = np.concatenate(dataset.labels, 0) + c = torch.tensor(labels[:, 0]) # classes + # cf = torch.bincount(c.long(), minlength=nc) + 1. + # model._initialize_biases(cf.to(device)) + plot_labels(labels, save_dir=log_dir) + if tb_writer: + # tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384 + tb_writer.add_histogram('classes', c, 0) + + # Check anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + + # Start training + t0 = time.time() + nw = max(3 * nb, 1e3) # number of warmup iterations, max(3 epochs, 1k iterations) + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification' + scheduler.last_epoch = start_epoch - 1 # do not move + if rank in [0, -1]: + print('Image sizes %g train, %g test' % (imgsz, imgsz_test)) + print('Using %g dataloader workers' % dataloader.num_workers) + print('Starting training for %g epochs...' % epochs) + # torch.autograd.set_detect_anomaly(True) + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + model.train() + + # Update image weights (optional) + # When in DDP mode, the generated indices will be broadcasted to synchronize dataset. + if dataset.image_weights: + # Generate indices. + if rank in [-1, 0]: + w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights + image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w) + dataset.indices = random.choices(range(dataset.n), weights=image_weights, + k=dataset.n) # rand weighted idx + # Broadcast. + if rank != -1: + indices = torch.zeros([dataset.n], dtype=torch.int) + if rank == 0: + indices[:] = torch.from_tensor(dataset.indices, dtype=torch.int) + dist.broadcast(indices, 0) + if rank != 0: + dataset.indices = indices.cpu().numpy() + + # Update mosaic border + # 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: + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + if rank in [-1, 0]: + print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size')) + 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 + # model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / total_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, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [0.9, 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 = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + pred = model(imgs) + + # Loss + loss, loss_items = compute_loss(pred, targets.to(device), model) # scaled by batch_size + if rank != -1: + loss *= opt.world_size # gradient averaged between devices in DDP mode + if not torch.isfinite(loss): + print('WARNING: non-finite loss, ending training ', loss_items) + return results + + # Backward + if mixed_precision: + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + else: + loss.backward() + + # Optimize + if ni % accumulate == 0: + optimizer.step() + optimizer.zero_grad() + if ema is not None: + ema.update(model) + + # Print + if rank in [-1, 0]: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB) + s = ('%10s' * 2 + '%10.4g' * 6) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + pbar.set_description(s) + + # Plot + if ni < 3: + f = str(Path(log_dir) / ('train_batch%g.jpg' % ni)) # filename + result = plot_images(images=imgs, targets=targets, paths=paths, fname=f) + if tb_writer and result is not None: + tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) + # tb_writer.add_graph(model, imgs) # add model to tensorboard + + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + scheduler.step() + + # Only the first process in DDP mode is allowed to log or save checkpoints. + if rank in [-1, 0]: + # mAP + if ema is not None: + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride']) + final_epoch = epoch + 1 == epochs + if not opt.notest or final_epoch: # Calculate mAP + results, maps, times = test.test(opt.data, + batch_size=total_batch_size, + imgsz=imgsz_test, + save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'), + model=ema.ema.module if hasattr(ema.ema, 'module') else ema.ema, + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=log_dir) + + # Write + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls) + if len(opt.name) and opt.bucket: + os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) + + # Tensorboard + if tb_writer: + tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss', + 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/giou_loss', 'val/obj_loss', 'val/cls_loss'] + for x, tag in zip(list(mloss[:-1]) + list(results), tags): + tb_writer.add_scalar(tag, x, epoch) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1] + if fi > best_fitness: + best_fitness = fi + + # Save model + save = (not opt.nosave) or (final_epoch and not opt.evolve) + if save: + with open(results_file, 'r') as f: # create checkpoint + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': f.read(), + 'model': ema.ema.module if hasattr(ema, 'module') else ema.ema, + 'optimizer': None if final_epoch else optimizer.state_dict()} + + # Save last, best and delete + torch.save(ckpt, last) + if (best_fitness == fi) and not final_epoch: + torch.save(ckpt, best) + del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + + if rank in [-1, 0]: + # Strip optimizers + n = ('_' if len(opt.name) and not opt.name.isnumeric() else '') + opt.name + fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n + for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]): + if os.path.exists(f1): + os.rename(f1, f2) # rename + ispt = f2.endswith('.pt') # is *.pt + strip_optimizer(f2) if ispt else None # strip optimizer + os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload + # Finish + if not opt.evolve: + plot_results(save_dir=log_dir) # save as results.png + print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + + dist.destroy_process_group() if rank not in [-1, 0] else None + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/mydata.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='', help='hyp.yaml path (optional)') + parser.add_argument('--epochs', type=int, default=5) + parser.add_argument('--batch-size', type=int, default=1, help="Total batch size for all gpus.") + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const='get_last', default=False, + help='resume from given path/to/last.pt, or most recent run if blank.') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') + parser.add_argument('--weights', type=str, default='./weights/csgo_for_train.pt', help='initial weights path') + parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') + 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 as single-class dataset') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') + opt = parser.parse_args() + + last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run + if last and not opt.weights: + print(f'Resuming training from {last}') + opt.weights = last if opt.resume and not opt.weights else opt.weights + if opt.local_rank in [-1, 0]: + check_git_status() + opt.cfg = check_file(opt.cfg) # check file + opt.data = check_file(opt.data) # check file + if opt.hyp: # update hyps + opt.hyp = check_file(opt.hyp) # check file + with open(opt.hyp) as f: + hyp.update(yaml.load(f, Loader=yaml.FullLoader)) # update hyps + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size) + opt.total_batch_size = opt.batch_size + opt.world_size = 1 + if device.type == 'cpu': + mixed_precision = False + elif opt.local_rank != -1: + # DDP mode + assert torch.cuda.device_count() > opt.local_rank + torch.cuda.set_device(opt.local_rank) + device = torch.device("cuda", opt.local_rank) + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend + + opt.world_size = dist.get_world_size() + assert opt.batch_size % opt.world_size == 0, "Batch size is not a multiple of the number of devices given!" + opt.batch_size = opt.total_batch_size // opt.world_size + print(opt) + + # Train + if not opt.evolve: + if opt.local_rank in [-1, 0]: + print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/') + tb_writer = SummaryWriter(log_dir=increment_dir('runs/exp', opt.name)) + else: + tb_writer = None + train(hyp, tb_writer, opt, device) + + # Evolve hyperparameters (optional) + else: + assert opt.local_rank == -1, "DDP mode currently not implemented for Evolve!" + + tb_writer = None + opt.notest, opt.nosave = True, True # only test/save final epoch + if opt.bucket: + os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists + + for _ in range(10): # generations to evolve + if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt('evolve.txt', ndmin=2) + 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() # weights + 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 = 0.9, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains + ng = len(g) + 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] = x[i + 7] * v[i] # mutate + + # Clip to limits + keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma'] + limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)] + for k, v in zip(keys, limits): + hyp[k] = np.clip(hyp[k], v[0], v[1]) + + # Train mutation + results = train(hyp.copy(), tb_writer, opt, device) + + # Write mutation results + print_mutation(hyp, results, opt.bucket) + + # Plot results + # plot_evolution_results(hyp)