train.py //只須要看if __name__ == '__main__'之後的代碼就能夠了node
import argparse 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(混合精度訓練) https://github.com/NVIDIA/apex from apex import amp except: mixed_precision = False # not installed wdir = 'weights' + os.sep # weights dir權重路徑 last = wdir + 'last.pt' best = wdir + 'best.pt' results_file = 'results.txt' # Hyperparameters超參數 (results68: 59.2 mAP@0.5 yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310 hyp = {'giou': 3.54, # giou loss gain 'cls': 37.4, # cls loss gain 'cls_pw': 1.0, # cls BCELoss positive_weight 'obj': 64.3, # obj loss gain (*=img_size/416 if img_size != 416) 'obj_pw': 1.0, # obj BCELoss positive_weight 'iou_t': 0.225, # iou training threshold 'lr0': 0.00579, # initial learning rate (SGD=1E-3, Adam=9E-5) 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'momentum': 0.937, # SGD momentum 'weight_decay': 0.000484, # optimizer weight decay 'fl_gamma': 0.5, # focal loss gamma 'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction) 'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction) 'hsv_v': 0.36, # image HSV-Value augmentation (fraction) 'degrees': 1.98, # image rotation (+/- deg) 'translate': 0.05, # image translation (+/- fraction) 'scale': 0.05, # image scale (+/- gain) 'shear': 0.641} # image shear (+/- deg) # Overwrite hyp with hyp*.txt (optional) f = glob.glob('hyp*.txt') if f: print('Using %s' % f[0]) for k, v in zip(hyp.keys(), np.loadtxt(f[0])): hyp[k] = v def train(): cfg = opt.cfg data = opt.data img_size = opt.img_size epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs batch_size = opt.batch_size accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64 weights = opt.weights # initial training weights if 'pw' not in opt.arc: # remove BCELoss positive weights hyp['cls_pw'] = 1. hyp['obj_pw'] = 1. # Initialize init_seeds() if opt.multi_scale: img_sz_min = round(img_size / 32 / 1.5) img_sz_max = round(img_size / 32 * 1.5) 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'] test_path = data_dict['valid'] nc = int(data_dict['classes']) # number of classes # Remove previous results for f in glob.glob('*_batch*.jpg') + glob.glob(results_file): os.remove(f) # Initialize model model = Darknet(cfg, arc=opt.arc).to(device) # Optimizer pg0, pg1 = [], [] # optimizer parameter groups for k, v in dict(model.named_parameters()).items(): if 'Conv2d.weight' in k: pg1 += [v] # parameter group 1 (apply weight_decay) else: pg0 += [v] # parameter group 0 if opt.adam: optimizer = optim.Adam(pg0, lr=hyp['lr0']) # optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1) else: optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay del pg0, pg1 # https://github.com/alphadl/lookahead.pytorch # optimizer = torch_utils.Lookahead(optimizer, k=5, alpha=0.5) cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_fitness = float('inf') attempt_download(weights) if weights.endswith('.pt'): # pytorch format # possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc. chkpt = torch.load(weights, map_location=device) # load model try: chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()} model.load_state_dict(chkpt['model'], strict=False) # model.load_state_dict(chkpt['model']) except KeyError as e: s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \ "See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights) raise KeyError(s) from e # load optimizer if chkpt['optimizer'] is not None: optimizer.load_state_dict(chkpt['optimizer']) best_fitness = chkpt['best_fitness'] # load results if chkpt.get('training_results') is not None: with open(results_file, 'w') as file: file.write(chkpt['training_results']) # write results.txt start_epoch = chkpt['epoch'] + 1 del chkpt elif len(weights) > 0: # darknet format # possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc. cutoff = load_darknet_weights(model, weights) if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255) if opt.prebias: for p in optimizer.param_groups: # lower param count allows more aggressive training settings: i.e. SGD ~0.1 lr0, ~0.9 momentum p['lr'] *= 100 # lr gain if p.get('momentum') is not None: # for SGD but not Adam p['momentum'] *= 0.9 for p in model.parameters(): if opt.prebias and p.numel() == nf: # train (yolo biases) p.requires_grad = True elif opt.transfer and p.shape[0] == nf: # train (yolo biases+weights) p.requires_grad = True else: # freeze layer p.requires_grad = False # 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=range(59, 70, 1), gamma=0.8) # gradual fall to 0.1*lr0 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 device.type != 'cpu' and 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, find_unused_parameters=True) 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_labels=epochs > 10, cache_images=opt.cache_images and not opt.prebias) # Dataloader batch_size = min(batch_size, len(dataset)) nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers print('Using %g dataloader workers' % nw) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=nw, shuffle=not opt.rect, # Shuffle=True unless rectangular training is used pin_memory=True, collate_fn=dataset.collate_fn) # Test Dataloader if not opt.prebias: testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size, batch_size, hyp=hyp, cache_labels=True, cache_images=opt.cache_images), batch_size=batch_size, num_workers=nw, pin_memory=True, collate_fn=dataset.collate_fn) # Start training nb = len(dataloader) model.nc = nc # attach number of classes to model model.arc = opt.arc # attach yolo architecture model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights torch_utils.model_info(model, report='summary') # 'full' or 'summary' maps = np.zeros(nc) # mAP per class # torch.autograd.set_detect_anomaly(True) results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification' t0 = time.time() print('Starting %s for %g epochs...' % ('prebias' if opt.prebias else 'training', epochs)) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ model.train() print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size')) # 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(4).to(device) # mean losses pbar = tqdm(enumerate(dataloader), total=nb) # progress bar for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device) targets = targets.to(device) # Multi-Scale training if opt.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 ni == 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) if not torch.isfinite(loss): print('WARNING: non-finite loss, ending training ', loss_items) return results # Scale loss by nominal batch_size of 64 loss *= batch_size / 64 # 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 ni % accumulate == 0: 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' * 6) % ( '%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size) pbar.set_description(s) # end batch ------------------------------------------------------------------------------------------------ # Update scheduler scheduler.step() # Process epoch results final_epoch = epoch + 1 == epochs if opt.prebias: print_model_biases(model) else: # Calculate mAP if not opt.notest 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 and epoch > 0 else 0.1, # 0.1 for speed save_json=final_epoch and epoch > 0 and 'coco.data' in data, dataloader=testloader) # Write epoch results with open(results_file, 'a') as f: f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls) if len(opt.name) and opt.bucket and not opt.prebias: os.system('gsutil cp results.txt gs://%s/results%s.txt' % (opt.bucket, opt.name)) # Write Tensorboard results if tb_writer: x = list(mloss) + list(results) titles = ['GIoU', '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 = sum(results[4:]) # total loss if fitness < best_fitness: best_fitness = fitness # Save training results save = (not opt.nosave) or (final_epoch and not opt.evolve) or opt.prebias if save: with open(results_file, 'r') as f: # Create checkpoint chkpt = {'epoch': epoch, 'best_fitness': best_fitness, 'training_results': f.read(), 'model': model.module.state_dict() if type( model) is nn.parallel.DistributedDataParallel else model.state_dict(), 'optimizer': None if final_epoch else optimizer.state_dict()} # Save last checkpoint torch.save(chkpt, last) # 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, wdir + 'backup%g.pt' % epoch) # Delete checkpoint del chkpt # end epoch ---------------------------------------------------------------------------------------------------- # end training if len(opt.name) and not opt.prebias: fresults, flast, fbest = 'results%s.txt' % opt.name, 'last%s.pt' % opt.name, 'best%s.pt' % opt.name os.rename('results.txt', fresults) os.rename(wdir + 'last.pt', wdir + flast) if os.path.exists(wdir + 'last.pt') else None os.rename(wdir + 'best.pt', wdir + fbest) if os.path.exists(wdir + 'best.pt') else None # save to cloud if opt.bucket: os.system('gsutil cp %s %s gs://%s' % (fresults, wdir + flast, opt.bucket)) plot_results() # save as results.png print('%g epochs completed in %.3f hours.\n' % (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 def prebias(): # trains output bias layers for 1 epoch and creates new backbone if opt.prebias: a = opt.img_weights # save settings opt.img_weights = False # disable settings train() # transfer-learn yolo biases for 1 epoch create_backbone(last) # saved results as backbone.pt opt.weights = wdir + 'backbone.pt' # assign backbone opt.prebias = False # disable prebias opt.img_weights = a # reset settings if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--epochs', type=int, default=273) # 500200 batches at bs 16, 117263 images = 273 epochs parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64 parser.add_argument('--accumulate', type=int, default=4, help='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='*.data file path') parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches') 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 from last.pt') parser.add_argument('--transfer', action='store_true', help='transfer learning') 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('--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') parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='initial weights') parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # defaultpw, uCE, uBCE parser.add_argument('--prebias', action='store_true', help='transfer-learn yolo biases prior to training') parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)') parser.add_argument('--adam', action='store_true', help='use adam optimizer') parser.add_argument('--var', type=float, help='debug variable') opt = parser.parse_args() opt.weights = last if opt.resume else opt.weights print(opt) device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size) if device.type == 'cpu': mixed_precision = False # scale hyp['obj'] by img_size (evolved at 416) hyp['obj'] *= opt.img_size / 416. 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 prebias() # optional train() # train normally 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(1): # generations to evolve if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate # Select parent(s) x = np.loadtxt('evolve.txt', ndmin=2) parent = 'weighted' # parent selection method: 'single' or 'weighted' if parent == 'single' or len(x) == 1: x = x[fitness(x).argmax()] elif parent == 'weighted': # weighted combination n = min(10, x.shape[0]) # number to merge x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() # weights x = (x[:n] * w.reshape(n, 1)).sum(0) / w.sum() # new parent for i, k in enumerate(hyp.keys()): hyp[k] = x[i + 7] # Mutate np.random.seed(int(time.time())) s = [.2, .2, .2, .2, .2, .2, .2, .0, .02, .2, .2, .2, .2, .2, .2, .2, .2, .2] # 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', 'fl_gamma'] limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)] for k, v in zip(keys, limits): hyp[k] = np.clip(hyp[k], v[0], v[1]) # Train mutation prebias() results = train() # Write mutation results print_mutation(hyp, results, opt.bucket) # Plot results # plot_evolution_results(hyp)