import argparse import time from models import * from utils.datasets import * from utils.utils import * from utils import torch_utils # Import test.py to get mAP after each epoch import test DARKNET_WEIGHTS_FILENAME = 'darknet53.conv.74' DARKNET_WEIGHTS_URL = 'https://pjreddie.com/media/files/{}'.format(DARKNET_WEIGHTS_FILENAME) def train( net_config_path, data_config_path, img_size=416, resume=False, epochs=100, batch_size=16, accumulated_batches=1, weights_path='weights', report=False, multi_scale=False, freeze_backbone=True, var=0, ): device = torch_utils.select_device() print("Using device: \"{}\"".format(device)) if multi_scale: # pass maximum multi_scale size img_size = 608 else: torch.backends.cudnn.benchmark = True os.makedirs(weights_path, exist_ok=True) latest_weights_file = os.path.join(weights_path, 'latest.pt') best_weights_file = os.path.join(weights_path, 'best.pt') # Configure run data_config = parse_data_config(data_config_path) num_classes = int(data_config['classes']) train_path = data_config['train'] # Initialize model model = Darknet(net_config_path, img_size) # Get dataloader dataloader = load_images_and_labels(train_path, batch_size=batch_size, img_size=img_size, multi_scale=multi_scale, augment=True) lr0 = 0.001 if resume: checkpoint = torch.load(latest_weights_file, 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) def_weight_file = os.path.join(weights_path, DARKNET_WEIGHTS_FILENAME) if not os.path.isfile(def_weight_file): os.system('wget {} -P {}'.format( DARKNET_WEIGHTS_URL, weights_path)) assert os.path.isfile(def_weight_file) load_weights(model, def_weight_file) 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 = time.time() mean_recall, mean_precision = 0, 0 for epoch in range(epochs): epoch += start_epoch print(('%8s%12s' + '%10s' * 14) % ('Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', '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 layers for first epoch if freeze_backbone: 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=report, 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) if 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 = ('%8s%12s' + '%10.3g' * 14) % ( '%g/%g' % (epoch, 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() - 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_weights_file) # Save best checkpoint if best_loss == loss_per_target: os.system('cp {} {}'.format( latest_weights_file, best_weights_file, )) # Save backup weights every 5 epochs if (epoch > 0) & (epoch % 5 == 0): backup_file_name = 'backup{}.pt'.format(epoch) backup_file_path = os.path.join(weights_path, backup_file_name) os.system('cp {} {}'.format( latest_weights_file, backup_file_path, )) # Calculate mAP mAP, R, P = test.test( net_config_path, data_config_path, latest_weights_file, 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('--data-config', type=str, default='cfg/coco.data', help='path to data config file') parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg 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('--weights-path', type=str, default='weights', help='path to store weights') parser.add_argument('--resume', action='store_true', help='resume training flag') parser.add_argument('--report', action='store_true', help='report TP, FP, FN, P and R per batch (slower)') parser.add_argument('--freeze', action='store_true', 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() print(opt, end='\n\n') init_seeds() torch.cuda.empty_cache() train( opt.cfg, opt.data_config, img_size=opt.img_size, resume=opt.resume, epochs=opt.epochs, batch_size=opt.batch_size, accumulated_batches=opt.accumulated_batches, weights_path=opt.weights_path, report=opt.report, multi_scale=opt.multi_scale, freeze_backbone=opt.freeze, var=opt.var, )