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=100, batch_size=16, accumulated_batches=1, multi_scale=False, freeze_backbone=False, var=0, ): weights = 'weights' + os.sep latest = weights + 'latest.pt' best = weights + 'best.pt' device = torch_utils.select_device() if multi_scale: # pass maximum multi_scale size img_size = 608 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) # Get dataloader dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, multi_scale=multi_scale, augment=True) lr0 = 0.001 cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_loss = float('inf') if resume: checkpoint = torch.load(latest, map_location='cpu') # Load weights to resume from model.load_state_dict(checkpoint['model']) # if torch.cuda.device_count() > 1: # model = nn.DataParallel(model) model.to(device).train() # 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 optimizer optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9) 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'): load_darknet_weights(model, weights + 'darknet53.conv.74') cutoff = 75 elif cfg.endswith('yolov3-tiny.cfg'): load_darknet_weights(model, weights + 'yolov3-tiny.conv.15') cutoff = 15 # if torch.cuda.device_count() > 1: # model = nn.DataParallel(model) model.to(device).train() # Set optimizer optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9) # Set scheduler # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1) model_info(model) t0 = time.time() for epoch in range(epochs): epoch += start_epoch print(('%8s%12s' + '%10s' * 7) % ( 'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time')) # Update scheduler (automatic) # scheduler.step() # Update scheduler (manual) at 0, 54, 61 epochs to 1e-3, 1e-4, 1e-5 if epoch > 50: lr = lr0 / 10 else: lr = lr0 for g in optimizer.param_groups: g['lr'] = lr # Freeze darknet53.conv.74 for first epoch 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) # running loss optimizer.zero_grad() for i, (imgs, targets, _, _) in enumerate(dataloader): if sum([len(x) for x in targets]) < 1: # if no targets continue continue # SGD burn-in if (epoch == 0) & (i <= 1000): lr = lr0 * (i / 1000) ** 4 for g in optimizer.param_groups: g['lr'] = lr # Compute loss, compute gradient, update parameters loss = model(imgs.to(device), targets, var=var) loss.backward() # accumulate gradient for x batches before optimizing if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1): optimizer.step() optimizer.zero_grad() # Running epoch-means of tracked metrics ui += 1 for key, val in model.losses.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['loss'], model.losses['nT'], time.time() - t0) t0 = time.time() print(s) # Update best loss loss_per_target = rloss['loss'] / rloss['nT'] if loss_per_target < best_loss: best_loss = loss_per_target # Save latest checkpoint checkpoint = {'epoch': epoch, 'best_loss': best_loss, 'model': model.state_dict(), 'optimizer': optimizer.state_dict()} torch.save(checkpoint, latest) # Save best checkpoint if best_loss == loss_per_target: os.system('cp ' + latest + ' ' + best) # Save backup weights every 5 epochs (optional) # if (epoch > 0) & (epoch % 5 == 0): # os.system('cp ' + latest + ' ' + weights + 'backup{}.pt'.format(epoch))) # Calculate mAP with torch.no_grad(): mAP, R, P = 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 % (mAP, P, R) + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--epochs', type=int, default=100, help='number of epochs') parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch') parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step') 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('--var', type=float, default=0, help='test variable') 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, accumulated_batches=opt.accumulated_batches, multi_scale=opt.multi_scale, var=opt.var, )