475 lines
22 KiB
Python
475 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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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 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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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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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 https://github.com/ultralytics/yolov3/issues/310
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hyp = {'giou': 3.54, # giou loss gain
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'cls': 37.4, # cls loss gain
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'cls_pw': 1.0, # cls BCELoss positive_weight
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'obj': 64.3, # 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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'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
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'lrf': 0.0005, # final learning rate (with cos scheduler)
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'momentum': 0.937, # SGD momentum
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'weight_decay': 0.000484, # optimizer weight decay
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'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
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'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
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'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
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'degrees': 1.98 * 0, # image rotation (+/- deg)
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'translate': 0.05 * 0, # image translation (+/- fraction)
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'scale': 0.05 * 0, # image scale (+/- gain)
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'shear': 0.641 * 0} # 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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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():
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cfg = opt.cfg
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data = opt.data
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epochs = 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 = max(round(64 / batch_size), 1) # accumulate n times before optimizer update (bs 64)
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weights = opt.weights # initial training weights
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imgsz_min, imgsz_max, imgsz_test = opt.img_size # img sizes (min, max, test)
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# Image Sizes
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gs = 64 # (pixels) grid size
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assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
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opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
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if opt.multi_scale:
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if imgsz_min == imgsz_max:
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imgsz_min //= 1.5
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imgsz_max //= 0.667
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grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
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imgsz_min, imgsz_max = grid_min * gs, grid_max * gs
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img_size = imgsz_max # initialize with max size
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# Configure run
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init_seeds()
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data_dict = parse_data_cfg(data)
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train_path = data_dict['train']
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test_path = data_dict['valid']
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nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
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hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
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# Remove previous results
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for f in glob.glob('*_batch*.png') + glob.glob(results_file):
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os.remove(f)
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# Initialize model
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model = Darknet(cfg).to(device)
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# Optimizer
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pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
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for k, v in dict(model.named_parameters()).items():
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if '.bias' in k:
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pg2 += [v] # biases
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elif 'Conv2d.weight' in k:
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pg1 += [v] # apply weight_decay
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else:
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pg0 += [v] # all else
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if opt.adam:
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# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
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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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optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
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del pg0, pg1, pg2
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start_epoch = 0
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best_fitness = 0.0
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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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try:
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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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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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"See https://github.com/ultralytics/yolov3/issues/657" % (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 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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load_darknet_weights(model, weights)
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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) * 0.95 + 0.05 # cosine
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler.last_epoch=start_epoch - 1 # see link below
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# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
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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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# 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', # 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, find_unused_parameters=True)
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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, img_size, 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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# 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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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 EMA
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ema = torch_utils.ModelEMA(model)
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# Start training
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nb = len(dataloader) # number of batches
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n_burn = max(3 * nb, 500) # burn-in iterations, max(3 epochs, 500 iterations)
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maps = np.zeros(nc) # mAP per class
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# torch.autograd.set_detect_anomaly(True)
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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('Image sizes %g - %g train, %g test' % (imgsz_min, imgsz_max, 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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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).to(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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targets = targets.to(device)
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# Burn-in
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if ni <= n_burn * 2:
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model.gr = np.interp(ni, [0, n_burn * 2], [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
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if ni == n_burn: # burnin complete
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print_model_biases(model)
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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, [0, n_burn], [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, [0, n_burn], [0.9, hyp['momentum']])
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# Multi-Scale training
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if opt.multi_scale:
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if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
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img_size = random.randrange(grid_min, grid_max + 1) * gs
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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 / gs) * gs 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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# 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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# Optimize accumulated gradient
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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 batch results
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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 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
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pbar.set_description(s)
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# Plot images with bounding boxes
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if ni < 1:
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f = 'train_batch%g.png' % i # filename
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
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if tb_writer:
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tb_writer.add_image(f, cv2.imread(f)[:, :, ::-1], dataformats='HWC')
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# tb_writer.add_graph(model, imgs) # add model to tensorboard
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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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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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is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
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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=imgsz_test,
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model=ema.ema,
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save_json=final_epoch and is_coco,
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single_cls=opt.single_cls,
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dataloader=testloader)
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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:
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os.system('gsutil cp results.txt gs://%s/results/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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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 training results
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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:
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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': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.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 == fi) and not final_epoch:
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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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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 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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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=300) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
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parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data 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', nargs='+', type=int, default=[320, 640], help='[min_train, max-train, test] img 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='weights/yolov3-spp-ultralytics.pt', help='initial weights path')
|
||
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
|
||
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
|
||
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
|
||
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
|
||
opt = parser.parse_args()
|
||
opt.weights = last if opt.resume else opt.weights
|
||
check_git_status()
|
||
print(opt)
|
||
opt.img_size.extend([opt.img_size[-1]] * (3 - len(opt.img_size))) # extend to 3 sizes (min, max, test)
|
||
device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
|
||
if device.type == 'cpu':
|
||
mixed_precision = False
|
||
|
||
# scale hyp['obj'] by img_size (evolved at 320)
|
||
# hyp['obj'] *= opt.img_size[0] / 320.
|
||
|
||
tb_writer = None
|
||
if not opt.evolve: # Train normally
|
||
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
|
||
tb_writer = SummaryWriter(comment=opt.name)
|
||
train() # train normally
|
||
|
||
else: # Evolve hyperparameters (optional)
|
||
opt.notest, opt.nosave = True, True # only test/save final epoch
|
||
if opt.bucket:
|
||
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||
|
||
for _ in range(1): # generations to evolve
|
||
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
|
||
# Select parent(s)
|
||
parent = 'single' # parent selection method: 'single' or 'weighted'
|
||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||
n = min(5, len(x)) # number of previous results to consider
|
||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||
w = fitness(x) - fitness(x).min() # weights
|
||
if parent == 'single' or len(x) == 1:
|
||
# x = x[random.randint(0, n - 1)] # random selection
|
||
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
|
||
elif parent == 'weighted':
|
||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||
|
||
# Mutate
|
||
method, mp, s = 3, 0.9, 0.2 # method, mutation probability, sigma
|
||
npr = np.random
|
||
npr.seed(int(time.time()))
|
||
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
|
||
ng = len(g)
|
||
if method == 1:
|
||
v = (npr.randn(ng) * npr.random() * g * s + 1) ** 2.0
|
||
elif method == 2:
|
||
v = (npr.randn(ng) * npr.random(ng) * g * s + 1) ** 2.0
|
||
elif method == 3:
|
||
v = np.ones(ng)
|
||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||
# v = (g * (npr.random(ng) < mp) * npr.randn(ng) * s + 1) ** 2.0
|
||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||
hyp[k] = x[i + 7] * v[i] # mutate
|
||
|
||
# Clip to limits
|
||
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
|
||
limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)]
|
||
for k, v in zip(keys, limits):
|
||
hyp[k] = np.clip(hyp[k], v[0], v[1])
|
||
|
||
# Train mutation
|
||
results = train()
|
||
|
||
# Write mutation results
|
||
print_mutation(hyp, results, opt.bucket)
|
||
|
||
# Plot results
|
||
# plot_evolution_results(hyp)
|