251 lines
9.5 KiB
Python
251 lines
9.5 KiB
Python
import argparse
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import time
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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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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def train(
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cfg,
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data_cfg,
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img_size=416,
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resume=False,
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epochs=270,
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batch_size=16,
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accumulate=1,
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multi_scale=False,
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freeze_backbone=False,
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num_workers=4,
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transfer=False # Transfer learning (train only YOLO layers)
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):
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weights = 'weights' + os.sep
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latest = weights + 'latest.pt'
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best = weights + 'best.pt'
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device = torch_utils.select_device()
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if multi_scale:
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img_size = 608 # initiate with maximum multi_scale size
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num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
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else:
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torch.backends.cudnn.benchmark = True # unsuitable for multiscale
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# Configure run
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train_path = parse_data_cfg(data_cfg)['train']
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# Initialize model
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model = Darknet(cfg, img_size).to(device)
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# Optimizer
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lr0 = 0.001 # initial learning rate
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optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=0.9, weight_decay=0.0005)
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_loss = float('inf')
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yl = get_yolo_layers(model) # yolo layers
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nf = int(model.module_defs[yl[0] - 1]['filters']) # yolo layer size (i.e. 255)
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if resume: # Load previously saved model
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if transfer: # Transfer learning
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chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
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model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
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strict=False)
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for p in model.parameters():
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p.requires_grad = True if p.shape[0] == nf else False
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else: # resume from latest.pt
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chkpt = torch.load(latest, map_location=device) # load checkpoint
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model.load_state_dict(chkpt['model'])
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start_epoch = chkpt['epoch'] + 1
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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_loss = chkpt['best_loss']
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del chkpt
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else: # Initialize model with backbone (optional)
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if '-tiny.cfg' in cfg:
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cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
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else:
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cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
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# Set scheduler (reduce lr at epoch 250)
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scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[250], gamma=0.1, last_epoch=start_epoch - 1)
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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# Initialize distributed training
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if torch.cuda.device_count() > 1:
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dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
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model = torch.nn.parallel.DistributedDataParallel(model)
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sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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else:
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sampler = None
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# Dataloader
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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num_workers=num_workers,
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shuffle=False,
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pin_memory=False,
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collate_fn=dataset.collate_fn,
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sampler=sampler)
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# Start training
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t = time.time()
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model_info(model)
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nB = len(dataloader)
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n_burnin = min(round(nB / 5 + 1), 1000) # burn-in batches
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for epoch in range(start_epoch, epochs):
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model.train()
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print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
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# Update scheduler
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scheduler.step()
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# Freeze backbone at epoch 0, unfreeze at epoch 1
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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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mloss = defaultdict(float) # mean loss
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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imgs = imgs.to(device)
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targets = targets.to(device)
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nt = len(targets)
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if nt == 0: # if no targets continue
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continue
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# Plot images with bounding boxes
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plot_images = False
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if plot_images:
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fig = plt.figure(figsize=(10, 10))
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for ip in range(len(imgs)):
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boxes = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy().T * img_size
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plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
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plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
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plt.axis('off')
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fig.tight_layout()
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fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi)
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# SGD burn-in
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if epoch == 0 and i <= n_burnin:
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lr = lr0 * (i / n_burnin) ** 4
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for x in optimizer.param_groups:
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x['lr'] = lr
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# Run model
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pred = model(imgs)
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# Build targets
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target_list = build_targets(model, targets)
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# Compute loss
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loss, loss_dict = compute_loss(pred, target_list)
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# Compute gradient
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loss.backward()
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# Accumulate gradient for x batches before optimizing
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if (i + 1) % accumulate == 0 or (i + 1) == nB:
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optimizer.step()
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optimizer.zero_grad()
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# Running epoch-means of tracked metrics
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for key, val in loss_dict.items():
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mloss[key] = (mloss[key] * i + val) / (i + 1)
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nB - 1),
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mloss['xy'], mloss['wh'], mloss['conf'], mloss['cls'],
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mloss['total'], nt, time.time() - t)
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t = time.time()
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print(s)
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# Multi-Scale training (320 - 608 pixels) every 10 batches
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if multi_scale and (i + 1) % 10 == 0:
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dataset.img_size = random.choice(range(10, 20)) * 32
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print('multi_scale img_size = %g' % dataset.img_size)
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# Calculate mAP
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with torch.no_grad():
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results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model)
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# Write epoch results
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with open('results.txt', 'a') as file:
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file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
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# Update best loss
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test_loss = results[4]
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if test_loss < best_loss:
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best_loss = results[0]
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# Save training results
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save = True and not opt.nosave
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if save:
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# Create checkpoint
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chkpt = {'epoch': epoch,
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'best_loss': best_loss,
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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': optimizer.state_dict()}
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# Save latest checkpoint
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torch.save(chkpt, latest)
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# Save best checkpoint
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if best_loss == test_loss:
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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, weights + 'backup%g.pt' % epoch)
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# Delete checkpoint
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del chkpt
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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=270, help='number of epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches 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-cfg', type=str, default='data/coco.data', help='coco.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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parser.add_argument('--img-size', type=int, default=416, help='pixels')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
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parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
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parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
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parser.add_argument('--backend', default='nccl', type=str, help='distributed backend')
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parser.add_argument('--nosave', action='store_true', help='do not save training results')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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init_seeds()
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train(
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opt.cfg,
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opt.data_cfg,
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img_size=opt.img_size,
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resume=opt.resume or opt.transfer,
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transfer=opt.transfer,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulate=opt.accumulate,
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multi_scale=opt.multi_scale,
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num_workers=opt.num_workers
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)
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