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|
- 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.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
- wdir = 'weights' + os.sep # weights dir
- os.makedirs(wdir, exist_ok=True)
- last = wdir + 'last.pt'
- best = wdir + 'best.pt'
- results_file = 'results.txt'
- # Hyperparameters
- hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
- 'momentum': 0.937, # SGD momentum
- 'weight_decay': 5e-4, # optimizer weight decay
- 'giou': 0.05, # giou loss gain
- 'cls': 0.58, # 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': 0.014, # image HSV-Hue augmentation (fraction)
- 'hsv_s': 0.68, # image HSV-Saturation augmentation (fraction)
- 'hsv_v': 0.36, # 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)
- print(hyp)
- # Overwrite hyp with hyp*.txt (optional)
- f = glob.glob('hyp*.txt')
- if f:
- print('Using %s' % f[0])
- for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
- hyp[k] = v
- # Print focal loss if gamma > 0
- if hyp['fl_gamma']:
- print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
- def train(hyp):
- epochs = opt.epochs # 300
- batch_size = opt.batch_size # 64
- weights = opt.weights # initial training weights
- # Configure
- init_seeds(1)
- 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 = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
- # Remove previous results
- for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
- os.remove(f)
- # Create model
- model = Model(opt.cfg).to(device)
- assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
- model.names = data_dict['names']
- # 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
- accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
- hyp['weight_decay'] *= 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
- optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \
- 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
- 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:
- ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items()
- if model.state_dict()[k].shape == v.shape} # to FP32, filter
- model.load_state_dict(ckpt['model'], strict=False)
- except KeyError as e:
- s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s." \
- % (opt.weights, opt.cfg, opt.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
- start_epoch = ckpt['epoch'] + 1
- 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) * 0.9 + 0.1 # cosine
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
- scheduler.last_epoch = start_epoch - 1 # do not move
- # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
- # plot_lr_scheduler(optimizer, scheduler, epochs)
- # Initialize distributed training
- if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
- dist.init_process_group(backend='nccl', # distributed backend
- init_method='tcp://127.0.0.1:9999', # init method
- world_size=1, # number of nodes
- rank=0) # node rank
- model = torch.nn.parallel.DistributedDataParallel(model)
- # pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
- # Dataset
- dataset = LoadImagesAndLabels(train_path, imgsz, batch_size,
- augment=True,
- hyp=hyp, # augmentation hyperparameters
- rect=opt.rect, # rectangular training
- cache_images=opt.cache_images,
- single_cls=opt.single_cls,
- stride=gs)
- mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
- assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg)
- # Dataloader
- batch_size = min(batch_size, len(dataset))
- nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
- dataloader = torch.utils.data.DataLoader(dataset,
- batch_size=batch_size,
- num_workers=nw,
- shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
- pin_memory=True,
- collate_fn=dataset.collate_fn)
- # Testloader
- testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, imgsz_test, batch_size,
- hyp=hyp,
- rect=True,
- cache_images=opt.cache_images,
- single_cls=opt.single_cls,
- stride=gs),
- batch_size=batch_size,
- num_workers=nw,
- pin_memory=True,
- collate_fn=dataset.collate_fn)
- # 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
- # Class frequency
- 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))
- if tb_writer:
- plot_labels(labels)
- tb_writer.add_histogram('classes', c, 0)
- # Check anchors
- if not opt.noautoanchor:
- check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
- # Exponential moving average
- ema = torch_utils.ModelEMA(model)
- # Start training
- t0 = time.time()
- nb = len(dataloader) # number of batches
- n_burn = max(3 * nb, 1e3) # burn-in 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'
- print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
- print('Using %g dataloader workers' % nw)
- 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)
- if dataset.image_weights:
- 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
- mloss = torch.zeros(4, device=device) # mean losses
- print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
- pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
- ni = i + nb * epoch # number integrated batches (since train start)
- imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
- # Burn-in
- if ni <= n_burn:
- xi = [0, n_burn] # 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 / 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)
- 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()
- ema.update(model)
- # Print
- 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 = 'train_batch%g.jpg' % i # filename
- res = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
- if tb_writer:
- tb_writer.add_image(f, res, dataformats='HWC', global_step=epoch)
- # tb_writer.add_graph(model, imgs) # add model to tensorboard
- # end batch ------------------------------------------------------------------------------------------------
- # Scheduler
- scheduler.step()
- # mAP
- ema.update_attr(model)
- final_epoch = epoch + 1 == epochs
- if not opt.notest or final_epoch: # Calculate mAP
- results, maps, times = test.test(opt.data,
- batch_size=batch_size,
- imgsz=imgsz_test,
- save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
- model=ema.ema,
- single_cls=opt.single_cls,
- dataloader=testloader)
- # 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 results.txt gs://%s/results/results%s.txt' % (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/F1',
- '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(model, '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
- n = opt.name
- if len(n):
- n = '_' + n if not n.isnumeric() else n
- 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
- if not opt.evolve:
- plot_results() # 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 device.type != 'cpu' and torch.cuda.device_count() > 1 else None
- torch.cuda.empty_cache()
- return results
- if __name__ == '__main__':
- check_git_status()
- parser = argparse.ArgumentParser()
- parser.add_argument('--epochs', type=int, default=300)
- parser.add_argument('--batch-size', type=int, default=16)
- parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='*.cfg path')
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
- 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', action='store_true', help='resume training from last.pt')
- 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='', 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('--adam', action='store_true', help='use adam optimizer')
- 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')
- opt = parser.parse_args()
- opt.weights = last if opt.resume else opt.weights
- opt.cfg = check_file(opt.cfg) # check file
- opt.data = check_file(opt.data) # check file
- print(opt)
- 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)
- if device.type == 'cpu':
- mixed_precision = False
- # Train
- if not opt.evolve:
- tb_writer = SummaryWriter(comment=opt.name)
- print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
- train(hyp)
- # Evolve hyperparameters (optional)
- else:
- 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())
- # Write mutation results
- print_mutation(hyp, results, opt.bucket)
- # Plot results
- # plot_evolution_results(hyp)
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