Lornatang
2368603484
In torch==1.5, the import of the API has changed. Although it does not interrupt the operation of the program, it seems to me to be an implicit error and may throw an exception in later versions.
450 lines
21 KiB
Python
450 lines
21 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.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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wdir = 'weights' + os.sep # weights dir
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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 = 'results.txt'
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# Hyperparameters
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hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
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'momentum': 0.937, # SGD momentum
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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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print(hyp)
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# Overwrite hyp with hyp*.txt (optional)
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f = glob.glob('hyp*.txt')
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if f:
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print('Using %s' % f[0])
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for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
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hyp[k] = v
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# Print focal loss if gamma > 0
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if hyp['fl_gamma']:
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print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
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def train(hyp):
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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 = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
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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).to(device)
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assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
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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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optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \
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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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# 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. Specify --weights '' or specify a --cfg compatible with %s." \
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% (opt.weights, opt.cfg, 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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start_epoch = ckpt['epoch'] + 1
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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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# 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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scheduler.last_epoch = start_epoch - 1 # do not move
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# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
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# plot_lr_scheduler(optimizer, scheduler, epochs)
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# Initialize 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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# Dataset
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dataset = LoadImagesAndLabels(train_path, imgsz, batch_size,
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augment=True,
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hyp=hyp, # augmentation hyperparameters
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rect=opt.rect, # rectangular training
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cache_images=opt.cache_images,
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single_cls=opt.single_cls)
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mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
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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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# Dataloader
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batch_size = min(batch_size, len(dataset))
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nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
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dataloader = torch.utils.data.DataLoader(dataset,
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batch_size=batch_size,
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num_workers=nw,
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shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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# Testloader
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testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, imgsz_test, batch_size,
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hyp=hyp,
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rect=True,
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cache_images=opt.cache_images,
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single_cls=opt.single_cls),
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batch_size=batch_size,
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num_workers=nw,
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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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 = data_dict['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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if tb_writer:
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plot_labels(labels)
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tb_writer.add_histogram('classes', c, 0)
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# Check anchors
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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)
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# Start training
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t0 = time.time()
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nb = len(dataloader) # number of batches
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n_burn = max(3 * nb, 1e3) # burn-in 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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print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
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print('Using %g dataloader workers' % nw)
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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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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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# Burn-in
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if ni <= n_burn:
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xi = [0, n_burn] # 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 = 'train_batch%g.jpg' % i # filename
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res = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
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if tb_writer:
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tb_writer.add_image(f, res, 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)
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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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fast=epoch < epochs / 2)
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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 results.txt gs://%s/results/results%s.txt' % (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/F1',
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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.module if hasattr(model, 'module') else 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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n = opt.name
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if len(n):
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n = '_' + n if not n.isnumeric() else n
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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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if not opt.evolve:
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plot_results() # 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('--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('--cfg', type=str, default='models/yolov5s.yaml', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
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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', action='store_true', help='resume training from last.pt')
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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('--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('--adam', action='store_true', help='use adam optimizer')
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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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opt.weights = last if opt.resume 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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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(comment=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)
|
|
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
|
|
|
|
# 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)]
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|
for k, v in zip(keys, limits):
|
|
hyp[k] = np.clip(hyp[k], v[0], v[1])
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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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