488 lines
22 KiB
Python
488 lines
22 KiB
Python
import argparse
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import torch.distributed as dist
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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 test # import test.py to get mAP after each epoch
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from models import *
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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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mixed_precision = False # not installed
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wdir = 'weights' + os.sep # weights dir
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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 (k-series, 57.7 mAP yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
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hyp = {'giou': 3.31, # giou loss gain
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'cls': 42.4, # cls loss gain
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'cls_pw': 1.0, # cls BCELoss positive_weight
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'obj': 52.0, # 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': 0.213, # iou training threshold
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'lr0': 0.00261, # initial learning rate (SGD=1E-3, Adam=9E-5)
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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'momentum': 0.949, # SGD momentum
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'weight_decay': 0.000489, # optimizer weight decay
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'fl_gamma': 0.5, # focal loss gamma
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'hsv_h': 0.0103, # image HSV-Hue augmentation (fraction)
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'hsv_s': 0.691, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.433, # image HSV-Value augmentation (fraction)
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'degrees': 1.43, # image rotation (+/- deg)
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'translate': 0.0663, # image translation (+/- fraction)
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'scale': 0.11, # image scale (+/- gain)
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'shear': 0.384} # image shear (+/- deg)
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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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for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
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hyp[k] = v
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def train():
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cfg = opt.cfg
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data = opt.data
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img_size = opt.img_size
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epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
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batch_size = opt.batch_size
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accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
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weights = opt.weights # initial training weights
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if 'pw' not in opt.arc: # remove BCELoss positive weights
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hyp['cls_pw'] = 1.
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hyp['obj_pw'] = 1.
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# Initialize
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init_seeds()
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multi_scale = opt.multi_scale
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if multi_scale:
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img_sz_min = round(img_size / 32 / 1.5)
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img_sz_max = round(img_size / 32 * 1.5)
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img_size = img_sz_max * 32 # initiate with maximum multi_scale size
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print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
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# Configure run
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data_dict = parse_data_cfg(data)
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train_path = data_dict['train']
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nc = int(data_dict['classes']) # 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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# Initialize model
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model = Darknet(cfg, arc=opt.arc).to(device)
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# Optimizer
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pg0, pg1 = [], [] # optimizer parameter groups
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for k, v in dict(model.named_parameters()).items():
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if 'Conv2d.weight' in k:
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pg1 += [v] # parameter group 1 (apply weight_decay)
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else:
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pg0 += [v] # parameter group 0
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if opt.adam:
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optimizer = optim.Adam(pg0, lr=hyp['lr0'])
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# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
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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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del pg0, pg1
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_fitness = float('inf')
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attempt_download(weights)
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if weights.endswith('.pt'): # pytorch format
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# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
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chkpt = torch.load(weights, map_location=device)
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# load model
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# if opt.transfer:
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chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
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model.load_state_dict(chkpt['model'], strict=False)
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# else:
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# model.load_state_dict(chkpt['model'])
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# load optimizer
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if chkpt['optimizer'] is not None:
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optimizer.load_state_dict(chkpt['optimizer'])
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best_fitness = chkpt['best_fitness']
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# load results
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if chkpt.get('training_results') is not None:
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with open(results_file, 'w') as file:
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file.write(chkpt['training_results']) # write results.txt
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start_epoch = chkpt['epoch'] + 1
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del chkpt
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elif len(weights) > 0: # darknet format
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# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
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cutoff = load_darknet_weights(model, weights)
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if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
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nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
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if opt.prebias:
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for p in optimizer.param_groups:
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# lower param count allows more aggressive training settings: i.e. SGD ~0.1 lr0, ~0.9 momentum
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p['lr'] *= 100 # lr gain
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if p.get('momentum') is not None: # for SGD but not Adam
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p['momentum'] *= 0.9
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for p in model.parameters():
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if opt.prebias and p.numel() == nf: # train (yolo biases)
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p.requires_grad = True
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elif opt.transfer and p.shape[0] == nf: # train (yolo biases+weights)
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p.requires_grad = True
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else: # freeze layer
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p.requires_grad = False
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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# lf = lambda x: 1 - x / epochs # linear ramp to zero
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# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
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# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
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# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=range(59, 70, 1), gamma=0.8) # gradual fall to 0.1*lr0
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scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in [0.8, 0.9]], gamma=0.1)
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scheduler.last_epoch = start_epoch - 1
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# # Plot lr schedule
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# y = []
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# for _ in range(epochs):
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# scheduler.step()
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# y.append(optimizer.param_groups[0]['lr'])
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# plt.plot(y, label='LambdaLR')
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# plt.xlabel('epoch')
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# plt.ylabel('LR')
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# plt.tight_layout()
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# plt.savefig('LR.png', dpi=300)
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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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# Initialize distributed training
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if device.type != 'cpu' and torch.cuda.device_count() > 1:
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dist.init_process_group(backend='nccl', # 'distributed backend'
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init_method='tcp://127.0.0.1:9999', # distributed training init method
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world_size=1, # number of nodes for distributed training
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rank=0) # distributed training node rank
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model = torch.nn.parallel.DistributedDataParallel(model)
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model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
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# Dataset
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dataset = LoadImagesAndLabels(train_path,
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img_size,
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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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image_weights=opt.img_weights,
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cache_labels=True if epochs > 10 else False,
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cache_images=False if opt.prebias else opt.cache_images)
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# Dataloader
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dataloader = torch.utils.data.DataLoader(dataset,
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batch_size=batch_size,
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num_workers=min([os.cpu_count(), batch_size, 16]),
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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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# Start training
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model.nc = nc # attach number of classes to model
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model.arc = opt.arc # attach yolo architecture
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model.hyp = hyp # attach hyperparameters to model
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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torch_utils.model_info(model, report='summary') # 'full' or 'summary'
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nb = len(dataloader)
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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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t0 = time.time()
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print('Starting %s for %g epochs...' % ('prebias' if opt.prebias else 'training', epochs))
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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model.train()
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print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
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# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
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freeze_backbone = False
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if freeze_backbone and epoch < 2:
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for name, p in model.named_parameters():
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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p.requires_grad = False if epoch == 0 else True
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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).to(device) # mean losses
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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)
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targets = targets.to(device)
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# Multi-Scale training
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if multi_scale:
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if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
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sf = img_size / max(imgs.shape[2:]) # scale factor
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if sf != 1:
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ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
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imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
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# Plot images with bounding boxes
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if ni == 0:
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fname = 'train_batch%g.jpg' % i
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
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if tb_writer:
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tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
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# Hyperparameter burn-in
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# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
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# if ni <= n_burn:
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# for m in model.named_modules():
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# if m[0].endswith('BatchNorm2d'):
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# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
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# g = (i / n_burn) ** 4 # gain rises from 0 - 1
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# for x in optimizer.param_groups:
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# x['lr'] = hyp['lr0'] * g
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# x['weight_decay'] = hyp['weight_decay'] * g
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# Run model
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pred = model(imgs)
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# Compute loss
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loss, loss_items = compute_loss(pred, targets, 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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# Scale loss by nominal batch_size of 64
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loss *= batch_size / 64
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# Compute gradient
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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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# Accumulate gradient for x batches before optimizing
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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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# Print batch results
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
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s = ('%10s' * 2 + '%10.3g' * 6) % (
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'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
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pbar.set_description(s)
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# end batch ------------------------------------------------------------------------------------------------
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# Update scheduler
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scheduler.step()
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# Process epoch results
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final_epoch = epoch + 1 == epochs
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if opt.prebias:
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print_model_biases(model)
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else:
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# Calculate mAP (always test final epoch, skip first 10 if opt.nosave)
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if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
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with torch.no_grad():
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results, maps = test.test(cfg,
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data,
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batch_size=batch_size,
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img_size=opt.img_size,
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model=model,
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conf_thres=0.001 if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
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save_json=final_epoch and epoch > 0 and 'coco.data' in data)
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# Write epoch results
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with open(results_file, 'a') as f:
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f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
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if len(opt.name) and opt.bucket and not opt.prebias:
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os.system('gsutil cp results.txt gs://%s/results%s.txt' % (opt.bucket, opt.name))
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# Write Tensorboard results
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if tb_writer:
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x = list(mloss) + list(results)
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titles = ['GIoU', 'Objectness', 'Classification', 'Train loss',
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'Precision', 'Recall', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
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for xi, title in zip(x, titles):
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tb_writer.add_scalar(title, xi, epoch)
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# Update best mAP
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fitness = sum(results[4:]) # total loss
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if fitness < best_fitness:
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best_fitness = fitness
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# Save training results
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save = (not opt.nosave) or (final_epoch and not opt.evolve) or opt.prebias
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if save:
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with open(results_file, 'r') as f:
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# Create checkpoint
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chkpt = {'epoch': epoch,
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'best_fitness': best_fitness,
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'training_results': f.read(),
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'model': model.module.state_dict() if type(
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model) is nn.parallel.DistributedDataParallel else model.state_dict(),
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'optimizer': None if final_epoch else optimizer.state_dict()}
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# Save last checkpoint
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torch.save(chkpt, last)
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# Save best checkpoint
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if best_fitness == fitness:
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torch.save(chkpt, best)
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# Save backup every 10 epochs (optional)
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if epoch > 0 and epoch % 10 == 0:
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torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
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# Delete checkpoint
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del chkpt
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# end epoch ----------------------------------------------------------------------------------------------------
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# end training
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if len(opt.name) and not opt.prebias:
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fresults, flast, fbest = 'results%s.txt' % opt.name, 'last%s.pt' % opt.name, 'best%s.pt' % opt.name
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os.rename('results.txt', fresults)
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os.rename(wdir + 'last.pt', wdir + flast) if os.path.exists(wdir + 'last.pt') else None
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os.rename(wdir + 'best.pt', wdir + fbest) if os.path.exists(wdir + 'best.pt') else None
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# save to cloud
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if opt.bucket:
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os.system('gsutil cp %s %s gs://%s' % (fresults, wdir + flast, opt.bucket))
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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 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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def prebias():
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# trains output bias layers for 1 epoch and creates new backbone
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if opt.prebias:
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a = opt.img_weights # save settings
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opt.img_weights = False # disable settings
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train() # transfer-learn yolo biases for 1 epoch
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create_backbone(last) # saved results as backbone.pt
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opt.weights = wdir + 'backbone.pt' # assign backbone
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opt.prebias = False # disable prebias
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opt.img_weights = a # reset settings
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=273) # 500200 batches at bs 16, 117263 images = 273 epochs
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parser.add_argument('--batch-size', type=int, default=32) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--accumulate', type=int, default=2, help='batches to accumulate before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data', type=str, default='data/coco.data', help='*.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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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('--transfer', action='store_true', help='transfer learning')
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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('--img-weights', action='store_true', help='select training images by weight')
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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')
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parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # defaultpw, uCE, uBCE
|
||
parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training')
|
||
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
|
||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
|
||
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
|
||
parser.add_argument('--var', type=float, help='debug variable')
|
||
opt = parser.parse_args()
|
||
opt.weights = last if opt.resume else opt.weights
|
||
print(opt)
|
||
device = torch_utils.select_device(opt.device, apex=mixed_precision)
|
||
if device.type == 'cpu':
|
||
mixed_precision = False
|
||
|
||
# scale hyp['obj'] by img_size (evolved at 416)
|
||
hyp['obj'] *= opt.img_size / 416.
|
||
|
||
tb_writer = None
|
||
if not opt.evolve: # Train normally
|
||
try:
|
||
# Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
|
||
from torch.utils.tensorboard import SummaryWriter
|
||
|
||
tb_writer = SummaryWriter()
|
||
except:
|
||
pass
|
||
|
||
prebias() # optional
|
||
train() # train normally
|
||
|
||
else: # Evolve hyperparameters (optional)
|
||
opt.notest = True # only test final epoch
|
||
opt.nosave = True # only save final checkpoint
|
||
if opt.bucket:
|
||
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||
|
||
for _ in range(1): # generations to evolve
|
||
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
|
||
# Select parent(s)
|
||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||
parent = 'weighted' # parent selection method: 'single' or 'weighted'
|
||
if parent == 'single' or len(x) == 1:
|
||
x = x[fitness(x).argmax()]
|
||
elif parent == 'weighted': # weighted combination
|
||
n = min(10, x.shape[0]) # number to merge
|
||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||
w = fitness(x) - fitness(x).min() # weights
|
||
x = (x[:n] * w.reshape(n, 1)).sum(0) / w.sum() # new parent
|
||
for i, k in enumerate(hyp.keys()):
|
||
hyp[k] = x[i + 7]
|
||
|
||
# Mutate
|
||
np.random.seed(int(time.time()))
|
||
s = [.2, .2, .2, .2, .2, .2, .2, .0, .02, .2, .2, .2, .2, .2, .2, .2, .2, .2] # sigmas
|
||
for i, k in enumerate(hyp.keys()):
|
||
x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
|
||
hyp[k] *= float(x) # vary by sigmas
|
||
|
||
# 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
|
||
prebias()
|
||
results = train()
|
||
|
||
# Write mutation results
|
||
print_mutation(hyp, results, opt.bucket)
|
||
|
||
# Plot results
|
||
# plot_evolution_results(hyp)
|