import argparse import time import torch.distributed as dist import torch.optim as optim import torch.optim.lr_scheduler as lr_scheduler import test # import test.py to get mAP after each epoch from models import * from utils.datasets import * from utils.utils import * mixed_precision = True try: # Mixed precision training https://github.com/NVIDIA/apex from apex import amp except: mixed_precision = False # not installed # 320 --epochs 1 # 0.109 0.297 0.150 0.126 7.04 1.666 4.062 0.1845 42.6 3.34 12.61 8.338 0.2705 0.001 -4 0.9 0.0005 a 320 giou + best_anchor False # 0.223 0.218 0.138 0.189 9.28 1.153 4.376 0.08263 24.28 3.05 20.93 2.842 0.2759 0.001357 -5.036 0.9158 0.0005722 b mAP/F1 - 50/50 weighting # 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874 c # 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973 d # 0.187 0.237 0.144 0.186 14.6 1.607 4.202 0.09439 39.27 3.726 31.26 2.634 0.273 0.001542 -5.036 0.8364 0.0008393 e # 0.250 0.217 0.136 0.195 3.3 1.2 2 0.604 15.7 3.67 20 1.36 0.194 0.00128 -4 0.95 0.000201 0.8 0.388 1.2 0.119 0.0589 0.401 f # 0.269 0.225 0.149 0.218 6.71 1.13 5.25 0.246 22.4 3.64 17.8 1.31 0.256 0.00146 -4 0.936 0.00042 0.123 0.18 1.81 0.0987 0.0788 0.441 g # 0.179 0.274 0.165 0.187 7.95 1.22 7.62 0.224 17 5.71 17.7 3.28 0.295 0.00136 -4 0.875 0.000319 0.131 0.208 2.14 0.14 0.0773 0.228 h # 0.296 0.228 0.152 0.220 5.18 1.43 4.27 0.265 11.7 4.81 11.5 1.56 0.281 0.0013 -4 0.944 0.000427 0.0599 0.142 1.03 0.0552 0.0555 0.434 i # 320 --epochs 2 # 0.242 0.296 0.196 0.231 5.67 0.8541 4.286 0.1539 21.61 1.957 22.9 2.894 0.3689 0.001844 -4 0.913 0.000467 # ha 0.417 mAP @ epoch 100 # 0.298 0.244 0.167 0.247 4.99 0.8896 4.067 0.1694 21.41 2.033 25.61 1.783 0.4115 0.00128 -4 0.950 0.000377 # hb # 0.268 0.268 0.178 0.240 4.36 1.104 5.596 0.2087 14.47 2.599 16.27 2.406 0.4114 0.001585 -4 0.950 0.000524 # hc # 0.161 0.327 0.190 0.193 7.82 1.153 4.062 0.1845 24.28 3.05 20.93 2.842 0.2759 0.001357 -4 0.916 0.000572 # hd 0.438 mAP @ epoch 100 # Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310 hyp = {'giou': 1.582, # giou loss gain 'xy': 4.688, # xy loss gain 'wh': 0.1857, # wh loss gain 'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20, uBCE=~200,~30) 'cls_pw': 1.446, # cls BCELoss positive_weight 'obj': 21.35, # obj loss gain 'obj_pw': 3.941, # obj BCELoss positive_weight 'iou_t': 0.2635, # iou training threshold 'lr0': 0.002324, # initial learning rate 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'momentum': 0.97, # SGD momentum 'weight_decay': 0.0004569, # optimizer weight decay 'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction) 'hsv_v': 0.3174, # image HSV-Value augmentation (fraction) 'degrees': 1.113, # image rotation (+/- deg) 'translate': 0.06797, # image translation (+/- fraction) 'scale': 0.1059, # image scale (+/- gain) 'shear': 0.5768} # image shear (+/- deg) # # Hyperparameters (i-series) # hyp = {'giou': 1.43, # giou loss gain # 'xy': 4.688, # xy loss gain # 'wh': 0.1857, # wh loss gain # 'cls': 11.7, # cls loss gain # 'cls_pw': 4.81, # cls BCELoss positive_weight # 'obj': 11.5, # obj loss gain # 'obj_pw': 1.56, # obj BCELoss positive_weight # 'iou_t': 0.281, # iou training threshold # 'lr0': 0.0013, # initial learning rate # 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) # 'momentum': 0.944, # SGD momentum # 'weight_decay': 0.000427, # optimizer weight decay # 'hsv_s': 0.0599, # image HSV-Saturation augmentation (fraction) # 'hsv_v': 0.142, # image HSV-Value augmentation (fraction) # 'degrees': 1.03, # image rotation (+/- deg) # 'translate': 0.0552, # image translation (+/- fraction) # 'scale': 0.0555, # image scale (+/- gain) # 'shear': 0.434} # image shear (+/- deg) def train(cfg, data, img_size=416, epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs batch_size=16, accumulate=4): # effective bs = batch_size * accumulate = 16 * 4 = 64 # Initialize init_seeds() weights = 'weights' + os.sep last = weights + 'last.pt' best = weights + 'best.pt' device = torch_utils.select_device(apex=mixed_precision) multi_scale = opt.multi_scale if multi_scale: img_sz_min = round(img_size / 32 / 1.5) + 1 img_sz_max = round(img_size / 32 * 1.5) - 1 img_size = img_sz_max * 32 # initiate with maximum multi_scale size print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size)) # Configure run data_dict = parse_data_cfg(data) train_path = data_dict['train'] nc = int(data_dict['classes']) # number of classes # Initialize model model = Darknet(cfg).to(device) # Optimizer optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'], nesterov=True) # optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1) cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_fitness = 0. if opt.resume or opt.transfer: # Load previously saved model if opt.transfer: # Transfer learning nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255) chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device) model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255}, strict=False) for p in model.parameters(): p.requires_grad = True if p.shape[0] == nf else False else: # resume from last.pt if opt.bucket: os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket chkpt = torch.load(last, map_location=device) # load checkpoint model.load_state_dict(chkpt['model']) if chkpt['optimizer'] is not None: optimizer.load_state_dict(chkpt['optimizer']) best_fitness = chkpt['best_fitness'] if chkpt.get('training_results') is not None: with open('results.txt', 'w') as file: file.write(chkpt['training_results']) # write results.txt start_epoch = chkpt['epoch'] + 1 del chkpt else: # Initialize model with backbone (optional) if '-tiny.cfg' in cfg: cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15') else: cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74') # Remove old results for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'): os.remove(f) # Scheduler https://github.com/ultralytics/yolov3/issues/238 # lf = lambda x: 1 - x / epochs # linear ramp to zero # lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp # lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp # scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in [0.8, 0.9]], gamma=0.1) scheduler.last_epoch = start_epoch - 1 # # Plot lr schedule # y = [] # for _ in range(epochs): # scheduler.step() # y.append(optimizer.param_groups[0]['lr']) # plt.plot(y, label='LambdaLR') # plt.xlabel('epoch') # plt.ylabel('LR') # plt.tight_layout() # plt.savefig('LR.png', dpi=300) # Mixed precision training https://github.com/NVIDIA/apex if mixed_precision: model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0) # Initialize distributed training if torch.cuda.device_count() > 1: dist.init_process_group(backend='nccl', # 'distributed backend' init_method='tcp://127.0.0.1:9999', # distributed training init method world_size=1, # number of nodes for distributed training rank=0) # distributed training node rank model = torch.nn.parallel.DistributedDataParallel(model) model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level # Dataset dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, hyp=hyp, # augmentation hyperparameters rect=opt.rect, # rectangular training image_weights=opt.img_weights, cache_images=opt.cache_images) # Dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=min(os.cpu_count(), batch_size), shuffle=not opt.rect, # Shuffle=True unless rectangular training is used pin_memory=True, collate_fn=dataset.collate_fn) # Start training model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights model_info(model, report='summary') # 'full' or 'summary' nb = len(dataloader) maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss t0 = time.time() for epoch in range(start_epoch, epochs): model.train() print(('\n' + '%10s' * 9) % ('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size')) # Update scheduler if epoch > 0: scheduler.step() # Freeze backbone at epoch 0, unfreeze at epoch 1 (optional) freeze_backbone = False 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 # Update image weights (optional) if dataset.image_weights: w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w) dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx mloss = torch.zeros(5).to(device) # mean losses pbar = tqdm(enumerate(dataloader), total=nb) # progress bar for i, (imgs, targets, paths, _) in pbar: imgs = imgs.to(device) targets = targets.to(device) # Multi-Scale training ni = (i + nb * epoch) # number integrated batches (since train start) if multi_scale: if ni / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32 sf = img_size / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 32-multiple) imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Plot images with bounding boxes if epoch == 0 and i == 0: fname = 'train_batch%g.jpg' % i plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname) if tb_writer: tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC') # Hyperparameter burn-in # n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches # if ni <= n_burn: # for m in model.named_modules(): # if m[0].endswith('BatchNorm2d'): # m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01 # g = (i / n_burn) ** 4 # gain rises from 0 - 1 # for x in optimizer.param_groups: # x['lr'] = hyp['lr0'] * g # x['weight_decay'] = hyp['weight_decay'] * g # Run model pred = model(imgs) # Compute loss loss, loss_items = compute_loss(pred, targets, model, giou_loss=not opt.xywh) if torch.isnan(loss): print('WARNING: nan loss detected, ending training') return results # Compute gradient if mixed_precision: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() # Accumulate gradient for x batches before optimizing if (i + 1) % accumulate == 0 or (i + 1) == nb: optimizer.step() optimizer.zero_grad() # Print batch results mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB) s = ('%10s' * 2 + '%10.3g' * 7) % ( '%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size) pbar.set_description(s) # Calculate mAP (always test final epoch, skip first 5 if opt.nosave) final_epoch = epoch + 1 == epochs if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch: with torch.no_grad(): results, maps = test.test(cfg, data, batch_size=batch_size, img_size=opt.img_size, model=model, conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed save_json=final_epoch and 'coco.data' in data) # Write epoch results with open('results.txt', 'a') as file: file.write(s + '%11.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls) # Write Tensorboard results if tb_writer: x = list(mloss[:5]) + list(results[:7]) titles = ['GIoU/XY', 'Width/Height', 'Objectness', 'Classification', 'Train loss', 'Precision', 'Recall', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'] for xi, title in zip(x, titles): tb_writer.add_scalar(title, xi, epoch) # Update best map fitness = results[2] # mAP if fitness > best_fitness: best_fitness = fitness # Save training results save = (not opt.nosave) or ((not opt.evolve) and final_epoch) if save: with open('results.txt', 'r') as file: # Create checkpoint chkpt = {'epoch': epoch, 'best_fitness': best_fitness, 'training_results': file.read(), 'model': model.module.state_dict() if type( model) is nn.parallel.DistributedDataParallel else model.state_dict(), 'optimizer': optimizer.state_dict()} # Save last checkpoint torch.save(chkpt, last) if opt.bucket: os.system('gsutil cp %s gs://%s' % (last, opt.bucket)) # upload to bucket # Save best checkpoint if best_fitness == fitness: torch.save(chkpt, best) # Save backup every 10 epochs (optional) if epoch > 0 and epoch % 10 == 0: torch.save(chkpt, weights + 'backup%g.pt' % epoch) # Delete checkpoint del chkpt # Report time print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) dist.destroy_process_group() if torch.cuda.device_count() > 1 else None torch.cuda.empty_cache() return results if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--epochs', type=int, default=273, help='number of epochs') parser.add_argument('--batch-size', type=int, default=32, help='batch size') parser.add_argument('--accumulate', type=int, default=2, help='number of batches to accumulate before optimizing') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path') parser.add_argument('--data', type=str, default='data/coco.data', help='coco.data file path') parser.add_argument('--multi-scale', action='store_true', help='train at (1/1.5)x - 1.5x sizes') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', action='store_true', help='resume training flag') parser.add_argument('--transfer', action='store_true', help='transfer learning flag') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--xywh', action='store_true', help='use xywh loss instead of GIoU loss') parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--img-weights', action='store_true', help='select training images by weight') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') opt = parser.parse_args() print(opt) tb_writer = None if not opt.evolve: # Train normally try: # Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/ from torch.utils.tensorboard import SummaryWriter tb_writer = SummaryWriter() except: pass results = train(opt.cfg, opt.data, img_size=opt.img_size, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate) else: # Evolve hyperparameters (optional) opt.notest = True # only test final epoch opt.nosave = True # only save final checkpoint if opt.bucket: os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists for _ in range(100): # generations to evolve if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate # Get best hyperparameters x = np.loadtxt('evolve.txt', ndmin=2) x = x[fitness(x).argmax()] # select best fitness hyps for i, k in enumerate(hyp.keys()): hyp[k] = x[i + 7] # Mutate init_seeds(seed=int(time.time())) s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .20, .20, .20, .20, .20, .20] # sigmas for i, k in enumerate(hyp.keys()): x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300) hyp[k] *= float(x) # vary by sigmas # Clip to limits keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale'] limits = [(1e-4, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9)] for k, v in zip(keys, limits): hyp[k] = np.clip(hyp[k], v[0], v[1]) # Train mutation results = train(opt.cfg, opt.data, img_size=opt.img_size, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate) # Write mutation results print_mutation(hyp, results, opt.bucket) # Plot results # plot_evolution_results(hyp)