Glenn Jocher
120d40c06a
This updates the PR to a one-liner to minimize additions. Perhaps we can include opt in the future but let's start with this for now.
454 lines
22 KiB
Python
454 lines
22 KiB
Python
import argparse
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import torch.distributed as dist
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.optim.lr_scheduler as lr_scheduler
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import torch.utils.data
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from torch.utils.tensorboard import SummaryWriter
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import test # import test.py to get mAP after each epoch
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from models.yolo import Model
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from utils import google_utils
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from utils.datasets import *
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from utils.utils import *
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mixed_precision = True
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try: # Mixed precision training https://github.com/NVIDIA/apex
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from apex import amp
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except:
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print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
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mixed_precision = False # not installed
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# Hyperparameters
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hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
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'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
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'momentum': 0.937, # SGD momentum/Adam beta1
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'weight_decay': 5e-4, # optimizer weight decay
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'giou': 0.05, # giou loss gain
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'cls': 0.58, # cls loss gain
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'cls_pw': 1.0, # cls BCELoss positive_weight
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'obj': 1.0, # obj loss gain (*=img_size/320 if img_size != 320)
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'obj_pw': 1.0, # obj BCELoss positive_weight
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'iou_t': 0.20, # iou training threshold
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'anchor_t': 4.0, # anchor-multiple threshold
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'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
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'hsv_h': 0.014, # image HSV-Hue augmentation (fraction)
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'hsv_s': 0.68, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
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'degrees': 0.0, # image rotation (+/- deg)
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'translate': 0.0, # image translation (+/- fraction)
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'scale': 0.5, # image scale (+/- gain)
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'shear': 0.0} # image shear (+/- deg)
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def train(hyp):
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print(f'Hyperparameters {hyp}')
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log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution' # run directory
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wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory
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os.makedirs(wdir, exist_ok=True)
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last = wdir + 'last.pt'
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best = wdir + 'best.pt'
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results_file = log_dir + os.sep + 'results.txt'
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# Save run settings
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with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
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yaml.dump(hyp, f, sort_keys=False)
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with open(Path(log_dir) / 'opt.yaml', 'w') as f:
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yaml.dump(vars(opt), f, sort_keys=False)
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epochs = opt.epochs # 300
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batch_size = opt.batch_size # 64
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weights = opt.weights # initial training weights
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# Configure
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init_seeds(1)
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with open(opt.data) as f:
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data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
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train_path = data_dict['train']
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test_path = data_dict['val']
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nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names
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assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
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# Remove previous results
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for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
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os.remove(f)
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# Create model
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model = Model(opt.cfg, nc=nc).to(device)
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# Image sizes
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gs = int(max(model.stride)) # grid size (max stride)
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imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
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# Optimizer
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nbs = 64 # nominal batch size
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accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
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hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
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pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
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for k, v in model.named_parameters():
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if v.requires_grad:
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if '.bias' in k:
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pg2.append(v) # biases
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elif '.weight' in k and '.bn' not in k:
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pg1.append(v) # apply weight decay
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else:
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pg0.append(v) # all else
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if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
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optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
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else:
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optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
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optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
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optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
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print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
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del pg0, pg1, pg2
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# Scheduler https://arxiv.org/pdf/1812.01187.pdf
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lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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# plot_lr_scheduler(optimizer, scheduler, epochs, save_dir=log_dir)
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# Load Model
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google_utils.attempt_download(weights)
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start_epoch, best_fitness = 0, 0.0
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if weights.endswith('.pt'): # pytorch format
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ckpt = torch.load(weights, map_location=device) # load checkpoint
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# load model
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try:
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ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items()
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if model.state_dict()[k].shape == v.shape} # to FP32, filter
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model.load_state_dict(ckpt['model'], strict=False)
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except KeyError as e:
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s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \
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"Please delete or update %s and try again, or use --weights '' to train from scratch." \
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% (opt.weights, opt.cfg, opt.weights, opt.weights)
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raise KeyError(s) from e
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# load optimizer
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if ckpt['optimizer'] is not None:
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optimizer.load_state_dict(ckpt['optimizer'])
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best_fitness = ckpt['best_fitness']
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# load results
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if ckpt.get('training_results') is not None:
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with open(results_file, 'w') as file:
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file.write(ckpt['training_results']) # write results.txt
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# epochs
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start_epoch = ckpt['epoch'] + 1
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if epochs < start_epoch:
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print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
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(opt.weights, ckpt['epoch'], epochs))
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epochs += ckpt['epoch'] # finetune additional epochs
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del ckpt
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# Mixed precision training https://github.com/NVIDIA/apex
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if mixed_precision:
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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# Distributed training
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if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
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dist.init_process_group(backend='nccl', # distributed backend
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init_method='tcp://127.0.0.1:9999', # init method
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world_size=1, # number of nodes
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rank=0) # node rank
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model = torch.nn.parallel.DistributedDataParallel(model)
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# pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
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# Trainloader
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dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
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hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect)
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mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
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nb = len(dataloader) # number of batches
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assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg)
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# Testloader
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testloader = create_dataloader(test_path, imgsz_test, batch_size, gs, opt,
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hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0]
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# Model parameters
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hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
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model.nc = nc # attach number of classes to model
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model.hyp = hyp # attach hyperparameters to model
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model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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model.names = names
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# Class frequency
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labels = np.concatenate(dataset.labels, 0)
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c = torch.tensor(labels[:, 0]) # classes
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# cf = torch.bincount(c.long(), minlength=nc) + 1.
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# model._initialize_biases(cf.to(device))
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plot_labels(labels, save_dir=log_dir)
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if tb_writer:
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tb_writer.add_hparams(hyp, {})
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tb_writer.add_histogram('classes', c, 0)
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# Check anchors
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if not opt.noautoanchor:
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check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
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# Exponential moving average
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ema = torch_utils.ModelEMA(model, updates=start_epoch * nb / accumulate)
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# Start training
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t0 = time.time()
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nw = max(3 * nb, 1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
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scheduler.last_epoch = start_epoch - 1 # do not move
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print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
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print('Using %g dataloader workers' % dataloader.num_workers)
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print('Starting training for %g epochs...' % epochs)
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# torch.autograd.set_detect_anomaly(True)
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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model.train()
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# Update image weights (optional)
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if dataset.image_weights:
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w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
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image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
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dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
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# Update mosaic border
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# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
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# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
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mloss = torch.zeros(4, device=device) # mean losses
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print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
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pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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ni = i + nb * epoch # number integrated batches (since train start)
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imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
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# Warmup
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if ni <= nw:
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xi = [0, nw] # x interp
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# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
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accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
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for j, x in enumerate(optimizer.param_groups):
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# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
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x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
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if 'momentum' in x:
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x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
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# Multi-scale
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if opt.multi_scale:
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sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
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sf = sz / max(imgs.shape[2:]) # scale factor
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if sf != 1:
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ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
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imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
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# Forward
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pred = model(imgs)
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# Loss
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loss, loss_items = compute_loss(pred, targets.to(device), model)
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if not torch.isfinite(loss):
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print('WARNING: non-finite loss, ending training ', loss_items)
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return results
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# Backward
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if mixed_precision:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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# Optimize
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if ni % accumulate == 0:
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optimizer.step()
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optimizer.zero_grad()
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ema.update(model)
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# Print
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
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s = ('%10s' * 2 + '%10.4g' * 6) % (
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'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
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pbar.set_description(s)
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# Plot
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if ni < 3:
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f = str(Path(log_dir) / ('train_batch%g.jpg' % ni)) # filename
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result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
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if tb_writer and result is not None:
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tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
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# tb_writer.add_graph(model, imgs) # add model to tensorboard
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# end batch ------------------------------------------------------------------------------------------------
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# Scheduler
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scheduler.step()
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# mAP
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ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
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final_epoch = epoch + 1 == epochs
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if not opt.notest or final_epoch: # Calculate mAP
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results, maps, times = test.test(opt.data,
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batch_size=batch_size,
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imgsz=imgsz_test,
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save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
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model=ema.ema,
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single_cls=opt.single_cls,
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dataloader=testloader,
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save_dir=log_dir)
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# Write
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with open(results_file, 'a') as f:
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f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
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if len(opt.name) and opt.bucket:
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os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
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# Tensorboard
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if tb_writer:
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tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
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'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
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'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
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for x, tag in zip(list(mloss[:-1]) + list(results), tags):
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tb_writer.add_scalar(tag, x, epoch)
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# Update best mAP
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fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
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if fi > best_fitness:
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best_fitness = fi
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# Save model
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save = (not opt.nosave) or (final_epoch and not opt.evolve)
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if save:
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with open(results_file, 'r') as f: # create checkpoint
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ckpt = {'epoch': epoch,
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'best_fitness': best_fitness,
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'training_results': f.read(),
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'model': ema.ema,
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'optimizer': None if final_epoch else optimizer.state_dict()}
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# Save last, best and delete
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torch.save(ckpt, last)
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if (best_fitness == fi) and not final_epoch:
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torch.save(ckpt, best)
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del ckpt
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# end epoch ----------------------------------------------------------------------------------------------------
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# end training
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# Strip optimizers
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n = ('_' if len(opt.name) and not opt.name.isnumeric() else '') + opt.name
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fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
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for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
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if os.path.exists(f1):
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os.rename(f1, f2) # rename
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ispt = f2.endswith('.pt') # is *.pt
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strip_optimizer(f2) if ispt else None # strip optimizer
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os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload
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# Finish
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if not opt.evolve:
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plot_results(save_dir=log_dir) # save as results.png
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print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
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dist.destroy_process_group() if device.type != 'cpu' and torch.cuda.device_count() > 1 else None
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torch.cuda.empty_cache()
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return results
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if __name__ == '__main__':
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check_git_status()
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
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parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
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parser.add_argument('--hyp', type=str, default='', help='hyp.yaml path (optional)')
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parser.add_argument('--epochs', type=int, default=300)
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parser.add_argument('--batch-size', type=int, default=16)
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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parser.add_argument('--resume', nargs='?', const='get_last', default=False,
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help='resume from given path/to/last.pt, or most recent run if blank.')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
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parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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parser.add_argument('--weights', type=str, default='', help='initial weights path')
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parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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opt = parser.parse_args()
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last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run
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if last and not opt.weights:
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print(f'Resuming training from {last}')
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opt.weights = last if opt.resume and not opt.weights else opt.weights
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opt.cfg = check_file(opt.cfg) # check file
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opt.data = check_file(opt.data) # check file
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if opt.hyp: # update hyps
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opt.hyp = check_file(opt.hyp) # check file
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with open(opt.hyp) as f:
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hyp.update(yaml.load(f, Loader=yaml.FullLoader)) # update hyps
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print(opt)
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opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
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device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
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if device.type == 'cpu':
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mixed_precision = False
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# Train
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if not opt.evolve:
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tb_writer = SummaryWriter(log_dir=increment_dir('runs/exp', opt.name))
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print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
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train(hyp)
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# Evolve hyperparameters (optional)
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else:
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tb_writer = None
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opt.notest, opt.nosave = True, True # only test/save final epoch
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if opt.bucket:
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os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
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for _ in range(10): # generations to evolve
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if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
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# Select parent(s)
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parent = 'single' # parent selection method: 'single' or 'weighted'
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x = np.loadtxt('evolve.txt', ndmin=2)
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n = min(5, len(x)) # number of previous results to consider
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x = x[np.argsort(-fitness(x))][:n] # top n mutations
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w = fitness(x) - fitness(x).min() # weights
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if parent == 'single' or len(x) == 1:
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# x = x[random.randint(0, n - 1)] # random selection
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x = x[random.choices(range(n), weights=w)[0]] # weighted selection
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elif parent == 'weighted':
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x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
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|
|
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# Mutate
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mp, s = 0.9, 0.2 # mutation probability, sigma
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npr = np.random
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npr.seed(int(time.time()))
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g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
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ng = len(g)
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v = np.ones(ng)
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while all(v == 1): # mutate until a change occurs (prevent duplicates)
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v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
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for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
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hyp[k] = x[i + 7] * v[i] # mutate
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|
|
|
# Clip to limits
|
|
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
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|
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)]
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|
for k, v in zip(keys, limits):
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|
hyp[k] = np.clip(hyp[k], v[0], v[1])
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|
|
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# Train mutation
|
|
results = train(hyp.copy())
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|
|
|
# Write mutation results
|
|
print_mutation(hyp, results, opt.bucket)
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|
|
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# Plot results
|
|
# plot_evolution_results(hyp)
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