import argparse import time import test # Import test.py to get mAP after each epoch from models import * from utils.datasets import * from utils.utils import * def train( cfg, data_cfg, img_size=416, resume=False, epochs=270, batch_size=16, accumulate=1, multi_scale=False, freeze_backbone=False, ): 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) # Get dataloader dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, augment=True) # from torch.utils.data import DataLoader # dataloader = DataLoader(dataloader, batch_size=batch_size, num_workers=1) 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 checkpoin 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') if torch.cuda.device_count() > 1: model = nn.DataParallel(model) # 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 # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1) # Start training t0 = time.time() model_info(model) n_burnin = min(round(len(dataloader) / 5 + 1), 1000) # burn-in batches for epoch in range(epochs): model.train() epoch += start_epoch print(('\n%8s%12s' + '%10s' * 7) % ( 'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time')) # Update scheduler (automatic) # scheduler.step() # Update scheduler (manual) if epoch > 250: lr = lr0 / 10 else: lr = lr0 for x in optimizer.param_groups: x['lr'] = lr # Freeze backbone at epoch 0, unfreeze at epoch 1 if freeze_backbone and epoch < 2: for i, (name, p) in enumerate(model.named_parameters()): if int(name.split('.')[1]) < cutoff: # if layer < 75 p.requires_grad = False if (epoch == 0) else True ui = -1 rloss = defaultdict(float) for i, (imgs, targets, _, _) in enumerate(dataloader): if targets.shape[1] == 100: # multithreaded 100-size block targets = targets.view((-1, 6)) targets = targets[targets[:, 5].nonzero().squeeze()] nT = targets.shape[0] if nT == 0: # if no targets continue continue # 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.to(device)) # Build targets target_list = build_targets(model, targets.to(device), pred) # 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) == len(dataloader): optimizer.step() optimizer.zero_grad() # Running epoch-means of tracked metrics ui += 1 for key, val in loss_dict.items(): rloss[key] = (rloss[key] * ui + val) / (ui + 1) s = ('%8s%12s' + '%10.3g' * 7) % ( '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['xy'], rloss['wh'], rloss['conf'], rloss['cls'], rloss['total'], nT, time.time() - t0) t0 = time.time() print(s) # Multi-Scale training (320 - 608 pixels) every 10 batches if multi_scale and (i + 1) % 10 == 0: dataloader.img_size = random.choice(range(10, 20)) * 32 print('multi_scale img_size = %g' % dataloader.img_size) # Update best loss if rloss['total'] < best_loss: best_loss = rloss['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.DataParallel else model.state_dict(), 'optimizer': optimizer.state_dict()} torch.save(checkpoint, latest) # Save best checkpoint if best_loss == rloss['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{}.pt'.format(epoch)) # Calculate mAP with torch.no_grad(): P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, model=model) # 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') 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, )