import argparse import time import torch.distributed as dist from torch.utils.data import DataLoader import test # Import test.py to get mAP after each epoch from models import * from utils.datasets import * from utils.utils import * import torch.distributed as dist def train( cfg, data_cfg, img_size=416, resume=False, epochs=270, batch_size=16, accumulate=1, multi_scale=False, freeze_backbone=False, num_workers=4 ): weights = 'weights' + os.sep latest = weights + 'latest.pt' best = weights + 'best.pt' device = torch_utils.select_device() if multi_scale: img_size = 608 # initiate with maximum multi_scale size else: torch.backends.cudnn.benchmark = True # unsuitable for multiscale # Configure run train_path = parse_data_cfg(data_cfg)['train'] # Initialize model model = Darknet(cfg, img_size).to(device) # Optimizer lr0 = 0.001 # initial learning rate optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9, weight_decay=0.0005) cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_loss = float('inf') if resume: # Load previously saved PyTorch model checkpoint = torch.load(latest, map_location=device) # load checkpoint model.load_state_dict(checkpoint['model']) start_epoch = checkpoint['epoch'] + 1 if checkpoint['optimizer'] is not None: optimizer.load_state_dict(checkpoint['optimizer']) best_loss = checkpoint['best_loss'] del checkpoint # current, saved else: # Initialize model with backbone (optional) if cfg.endswith('yolov3.cfg'): cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74') elif cfg.endswith('yolov3-tiny.cfg'): cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15') # Transfer learning (train only YOLO layers) # for i, (name, p) in enumerate(model.named_parameters()): # p.requires_grad = True if (p.shape[0] == 255) else False # Set scheduler (reduce lr at epoch 250) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[250], gamma=0.1, last_epoch=start_epoch - 1) # Dataset dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True) # Initialize distributed training if torch.cuda.device_count() > 1: dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank) model = torch.nn.parallel.DistributedDataParallel(model) sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: sampler = None # Dataloader dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True, pin_memory=False, collate_fn=dataset.collate_fn, sampler=sampler) # Start training nB = len(dataloader) t = time.time() model_info(model) n_burnin = min(round(nB / 5 + 1), 1000) # burn-in batches for epoch in range(start_epoch, epochs): model.train() print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time')) # Update scheduler scheduler.step() # Freeze backbone at epoch 0, unfreeze at epoch 1 if freeze_backbone and epoch < 2: for name, p in model.named_parameters(): if int(name.split('.')[1]) < cutoff: # if layer < 75 p.requires_grad = False if epoch == 0 else True mloss = defaultdict(float) # mean loss for i, (imgs, targets, _, _) in enumerate(dataloader): imgs = imgs.to(device) targets = targets.to(device) nT = len(targets) if nT == 0: # if no targets continue continue # Plot images with bounding boxes plot_images = False if plot_images: fig = plt.figure(figsize=(10, 10)) for ip in range(batch_size): labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0)) plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-') plt.axis('off') fig.tight_layout() fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi) # SGD burn-in if epoch == 0 and i <= n_burnin: lr = lr0 * (i / n_burnin) ** 4 for x in optimizer.param_groups: x['lr'] = lr # Run model pred = model(imgs) # Build targets target_list = build_targets(model, targets) # Compute loss loss, loss_dict = compute_loss(pred, target_list) # Compute gradient loss.backward() # Accumulate gradient for x batches before optimizing if (i + 1) % accumulate == 0 or (i + 1) == nB: optimizer.step() optimizer.zero_grad() # Running epoch-means of tracked metrics for key, val in loss_dict.items(): mloss[key] = (mloss[key] * i + val) / (i + 1) s = ('%8s%12s' + '%10.3g' * 7) % ( '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nB - 1), mloss['xy'], mloss['wh'], mloss['conf'], mloss['cls'], mloss['total'], nT, time.time() - t) t = time.time() print(s) # Multi-Scale training (320 - 608 pixels) every 10 batches if multi_scale and (i + 1) % 10 == 0: dataset.img_size = random.choice(range(10, 20)) * 32 print('multi_scale img_size = %g' % dataset.img_size) # Update best loss if mloss['total'] < best_loss: best_loss = mloss['total'] # Save training results save = True if save: # Save latest checkpoint checkpoint = {'epoch': epoch, 'best_loss': best_loss, 'model': model.module.state_dict() if type( model) is nn.parallel.DistributedDataParallel else model.state_dict(), 'optimizer': optimizer.state_dict()} torch.save(checkpoint, latest) # Save best checkpoint if best_loss == mloss['total']: os.system('cp ' + latest + ' ' + best) # Save backup weights every 5 epochs (optional) if epoch > 0 and epoch % 5 == 0: os.system('cp ' + latest + ' ' + weights + 'backup%g.pt' % epoch) # Calculate mAP if type(model) is nn.parallel.DistributedDataParallel: model = model.module with torch.no_grad(): P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size) # Write epoch results with open('results.txt', 'a') as file: file.write(s + '%11.3g' * 3 % (P, R, mAP) + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--epochs', type=int, default=270, help='number of epochs') parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch') parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing') parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path') parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608') parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels') parser.add_argument('--resume', action='store_true', help='resume training flag') parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers') parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method') parser.add_argument('--rank', default=0, type=int, help='distributed training node rank') parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training') parser.add_argument('--backend', default='nccl', type=str, help='distributed backend') opt = parser.parse_args() print(opt, end='\n\n') init_seeds() train( opt.cfg, opt.data_cfg, img_size=opt.img_size, resume=opt.resume, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate, multi_scale=opt.multi_scale, num_workers=opt.num_workers )