import argparse import sys import time from models import * from utils.datasets import * from utils.utils import * 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('-data_config_path', type=str, default='cfg/coco.data', help='data config file path') parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('-multi_scale', default=False, help='random image sizes per batch 320 - 608') parser.add_argument('-img_size', type=int, default=32 * 13, help='pixels') parser.add_argument('-resume', default=False, help='resume training flag') parser.add_argument('-batch_report', default=False, help='report TP, FP, FN, P and R per batch (slower)') parser.add_argument('-freeze_darknet53', default=False, help='freeze darknet53.conv.74 layers for first epoch') parser.add_argument('-var', type=float, default=0, help='optional test variable') opt = parser.parse_args() if opt.multi_scale: # pass maximum multi_scale size opt.img_size = 608 print(opt) # Import test.py to get mAP after each epoch sys.argv[1:] = [] # delete any train.py command-line arguments before they reach test.py import test # must follow sys.argv[1:] = [] cuda = torch.cuda.is_available() device = torch.device('cuda:0' if cuda else 'cpu') random.seed(0) np.random.seed(0) torch.manual_seed(0) if cuda: torch.cuda.manual_seed(0) torch.cuda.manual_seed_all(0) torch.backends.cudnn.benchmark = True def main(opt): os.makedirs('weights', exist_ok=True) # Configure run data_config = parse_data_config(opt.data_config_path) num_classes = int(data_config['classes']) train_path = '../coco/trainvalno5k.txt' # Initialize model model = Darknet(opt.cfg, opt.img_size) # Get dataloader dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, multi_scale=opt.multi_scale, augment=True) lr0 = 0.001 if opt.resume: checkpoint = torch.load('weights/latest.pt', map_location='cpu') model.load_state_dict(checkpoint['model']) if torch.cuda.device_count() > 1: raise Exception('Multi-GPU not currently supported: https://github.com/ultralytics/yolov3/issues/21') # print('Using ', torch.cuda.device_count(), ' GPUs') # model = nn.DataParallel(model) model.to(device).train() # # Transfer learning (train only YOLO layers) # for i, (name, p) in enumerate(model.named_parameters()): # if p.shape[0] != 650: # not YOLO layer # p.requires_grad = False # Set optimizer optimizer = torch.optim.SGD(filter(lambda p: p.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: start_epoch = 0 best_loss = float('inf') # Initialize model with darknet53 weights (optional) if not os.path.isfile('weights/darknet53.conv.74'): os.system('wget https://pjreddie.com/media/files/darknet53.conv.74 -P weights') load_weights(model, 'weights/darknet53.conv.74') if torch.cuda.device_count() > 1: raise Exception('Multi-GPU not currently supported: https://github.com/ultralytics/yolov3/issues/21') # print('Using ', torch.cuda.device_count(), ' GPUs') # model = nn.DataParallel(model) model.to(device).train() # Set optimizer optimizer = torch.optim.SGD(filter(lambda p: p.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, t1 = time.time(), time.time() mean_recall, mean_precision = 0, 0 print('%11s' * 16 % ( 'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time')) for epoch in range(opt.epochs): epoch += start_epoch # 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 layers for first epoch if opt.freeze_darknet53: if epoch == 0: for i, (name, p) in enumerate(model.named_parameters()): if int(name.split('.')[1]) < 75: # if layer < 75 p.requires_grad = False elif epoch == 1: for i, (name, p) in enumerate(model.named_parameters()): if int(name.split('.')[1]) < 75: # if layer < 75 p.requires_grad = True ui = -1 rloss = defaultdict(float) # running loss metrics = torch.zeros(3, num_classes) 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, batch_report=opt.batch_report, var=opt.var) loss.backward() # accumulated_batches = 1 # accumulate gradient for 4 batches before stepping optimizer # 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) if opt.batch_report: TP, FP, FN = metrics metrics += model.losses['metrics'] # Precision precision = TP / (TP + FP) k = (TP + FP) > 0 if k.sum() > 0: mean_precision = precision[k].mean() # Recall recall = TP / (TP + FN) k = (TP + FN) > 0 if k.sum() > 0: mean_recall = recall[k].mean() s = ('%11s%11s' + '%11.3g' * 14) % ( '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'], rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'], rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'], model.losses['FP'], model.losses['FN'], time.time() - t1) t1 = 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, 'weights/latest.pt') # Save best checkpoint if best_loss == loss_per_target: os.system('cp weights/latest.pt weights/best.pt') # Save backup weights every 5 epochs if (epoch > 0) & (epoch % 5 == 0): os.system('cp weights/latest.pt weights/backup' + str(epoch) + '.pt') # Calculate mAP test.opt.weights_path = 'weights/latest.pt' mAP, R, P = test.main(test.opt) # Write epoch results with open('results.txt', 'a') as file: file.write(s + '%11.3g' * 3 % (mAP, P, R) + '\n') # Save final model dt = time.time() - t0 print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1))) if __name__ == '__main__': torch.cuda.empty_cache() main(opt)