import argparse import time import torch.distributed as dist import torch.optim as optim import torch.optim.lr_scheduler as lr_scheduler from torch.utils.data import DataLoader import test # Import test.py to get mAP after each epoch from models import * from utils.datasets import * from utils.utils import * # Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart) hyp = {'giou': .035, # giou loss gain 'xy': 0.20, # xy loss gain 'wh': 0.10, # wh loss gain 'cls': 0.035, # cls loss gain 'conf': 1.61, # conf loss gain 'conf_bpw': 3.53, # conf BCELoss positive_weight 'iou_t': 0.29, # iou target-anchor training threshold 'lr0': 0.001, # initial learning rate 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'momentum': 0.90, # SGD momentum 'weight_decay': 0.0005} # optimizer weight decay # Hyperparameters: Original, Metrics: 0.172 0.304 0.156 0.205 (square) # hyp = {'xy': 0.5, # xy loss gain # 'wh': 0.0625, # wh loss gain # 'cls': 0.0625, # cls loss gain # 'conf': 4, # conf loss gain # 'iou_t': 0.1, # iou target-anchor training threshold # 'lr0': 0.001, # initial learning rate # 'lrf': -5., # final learning rate = lr0 * (10 ** lrf) # 'momentum': 0.9, # SGD momentum # 'weight_decay': 0.0005} # optimizer weight decay # Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.225 0.251 0.145 0.218 (rect) # hyp = {'xy': 0.4499, # xy loss gain # 'wh': 0.05121, # wh loss gain # 'cls': 0.04207, # cls loss gain # 'conf': 2.853, # conf loss gain # 'iou_t': 0.2487, # iou target-anchor training threshold # 'lr0': 0.0005301, # initial learning rate # 'lrf': -5., # final learning rate = lr0 * (10 ** lrf) # 'momentum': 0.8823, # SGD momentum # 'weight_decay': 0.0004149} # optimizer weight decay # Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.178 0.313 0.167 0.212 (square) # hyp = {'xy': 0.4664, # xy loss gain # 'wh': 0.08437, # wh loss gain # 'cls': 0.05145, # cls loss gain # 'conf': 4.244, # conf loss gain # 'iou_t': 0.09121, # iou target-anchor training threshold # 'lr0': 0.0004938, # initial learning rate # 'lrf': -5., # final learning rate = lr0 * (10 ** lrf) # 'momentum': 0.9025, # SGD momentum # 'weight_decay': 0.0005417} # optimizer weight decay def train( cfg, data_cfg, img_size=416, resume=False, epochs=100, # 500200 batches at bs 4, 117263 images = 68 epochs batch_size=16, accumulate=4, # effective bs = 64 = batch_size * accumulate freeze_backbone=False, transfer=False # Transfer learning (train only YOLO layers) ): init_seeds() weights = 'weights' + os.sep latest = weights + 'latest.pt' best = weights + 'best.pt' device = torch_utils.select_device() torch.backends.cudnn.benchmark = True # possibly unsuitable for multiscale img_size_test = img_size # image size for testing multi_scale = not opt.single_scale if multi_scale: img_size_min = round(img_size / 32 / 1.5) img_size_max = round(img_size / 32 * 1.5) img_size = img_size_max * 32 # initiate with maximum multi_scale size # Configure run data_dict = parse_data_cfg(data_cfg) 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']) cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_loss = float('inf') nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255) if resume: # Load previously saved model if transfer: # Transfer learning 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 latest.pt chkpt = torch.load(latest, map_location=device) # load checkpoint model.load_state_dict(chkpt['model']) start_epoch = chkpt['epoch'] + 1 if chkpt['optimizer'] is not None: optimizer.load_state_dict(chkpt['optimizer']) best_loss = chkpt['best_loss'] 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.xlabel('LR') # plt.tight_layout() # plt.savefig('LR.png', dpi=300) # Dataset rectangular_training = False dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, rect=rectangular_training) # Initialize distributed training if torch.cuda.device_count() > 1: dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank) model = torch.nn.parallel.DistributedDataParallel(model) # sampler = torch.utils.data.distributed.DistributedSampler(dataset) # Dataloader dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=opt.num_workers, shuffle=not rectangular_training, # Shuffle=True unless rectangular training is used pin_memory=True, collate_fn=dataset.collate_fn) # Mixed precision training https://github.com/NVIDIA/apex # install help: https://github.com/NVIDIA/apex/issues/259 mixed_precision = False if mixed_precision: from apex import amp model, optimizer = amp.initialize(model, optimizer, opt_level='O1') # Start training 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) nb = len(dataloader) maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches t, t0 = time.time(), time.time() for epoch in range(start_epoch, epochs): model.train() print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time')) # Update scheduler scheduler.step() # Freeze backbone at epoch 0, unfreeze at epoch 1 (optional) 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) # w = model.class_weights.cpu().numpy() * (1 - maps) # 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) # random weighted index mloss = torch.zeros(5).to(device) # mean losses for i, (imgs, targets, _, _) in enumerate(dataloader): imgs = imgs.to(device) targets = targets.to(device) # Multi-Scale training if multi_scale: if (i + 1 + nb * epoch) / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32 print('img_size = %g' % img_size) scale_factor = img_size / max(imgs.shape[-2:]) imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False) # Plot images with bounding boxes if epoch == 0 and i == 0: plot_images(imgs=imgs, targets=targets, fname='train_batch%g.jpg' % i) # SGD burn-in if epoch == 0 and i <= n_burnin: lr = hyp['lr0'] * (i / n_burnin) ** 4 for x in optimizer.param_groups: x['lr'] = lr # Run model pred = model(imgs) # Compute loss loss, loss_items = compute_loss(pred, targets, model, giou_loss=opt.giou) 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 s = ('%8s%12s' + '%10.3g' * 7) % ( '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t) t = time.time() print(s) # Calculate mAP (always test final epoch, skip first 5 if opt.nosave) if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1: with torch.no_grad(): results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size_test, model=model, conf_thres=0.1) # Write epoch results with open('results.txt', 'a') as file: file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss # Update best loss test_loss = results[4] if test_loss < best_loss: best_loss = test_loss # Save training results save = (not opt.nosave) or (epoch == epochs - 1) if save: # Create checkpoint chkpt = {'epoch': epoch, 'best_loss': best_loss, 'model': model.module.state_dict() if type( model) is nn.parallel.DistributedDataParallel else model.state_dict(), 'optimizer': optimizer.state_dict()} # Save latest checkpoint torch.save(chkpt, latest) # Save best checkpoint if best_loss == test_loss: 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 dt = (time.time() - t0) / 3600 print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt)) return results def print_mutation(hyp, results): # Write mutation results a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys b = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss) print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) with open('evolve.txt', 'a') as f: f.write(c + b + '\n') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--epochs', type=int, default=68, help='number of epochs') parser.add_argument('--batch-size', type=int, default=8, help='batch size') parser.add_argument('--accumulate', type=int, default=8, 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-cfg', type=str, default='data/coco_64img.data', help='coco.data file path') parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') 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('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers') parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method') parser.add_argument('--rank', default=0, type=int, help='distributed training node rank') parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training') parser.add_argument('--backend', default='nccl', type=str, help='distributed backend') parser.add_argument('--nosave', action='store_true', help='do not save training results') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss') parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution') parser.add_argument('--var', default=0, type=int, help='debug variable') opt = parser.parse_args() print(opt) if opt.evolve: opt.notest = True # save time by only testing final epoch opt.nosave = True # do not save checkpoints # Train results = train( opt.cfg, opt.data_cfg, img_size=opt.img_size, resume=opt.resume or opt.transfer, transfer=opt.transfer, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate, ) # Evolve hyperparameters (optional) if opt.evolve: best_fitness = results[2] # use mAP for fitness # Write mutation results print_mutation(hyp, results) gen = 1000 # generations to evolve for _ in range(gen): # Mutate hyperparameters old_hyp = hyp.copy() init_seeds(seed=int(time.time())) s = [.4, .4, .4, .4, .4, .4, .4, .4, .4, .04, .4] # fractional sigmas for i, k in enumerate(hyp.keys()): x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100) hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma # Clip to limits keys = ['lr0', 'iou_t', 'momentum', 'weight_decay'] limits = [(1e-4, 1e-2), (0, 0.00), (0.70, 0.99), (0, 0.01)] for k, v in zip(keys, limits): hyp[k] = np.clip(hyp[k], v[0], v[1]) # Determine mutation fitness results = train( opt.cfg, opt.data_cfg, img_size=opt.img_size, resume=opt.resume or opt.transfer, transfer=opt.transfer, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate, ) mutation_fitness = results[2] # Write mutation results print_mutation(hyp, results) # Update hyperparameters if fitness improved if mutation_fitness > best_fitness: # Fitness improved! print('Fitness improved!') best_fitness = mutation_fitness else: hyp = old_hyp.copy() # reset hyp to # # Plot results # import numpy as np # import matplotlib.pyplot as plt # a = np.loadtxt('evolve_1000val.txt') # x = a[:, 2] * a[:, 3] # metric = mAP * F1 # weights = (x - x.min()) ** 2 # fig = plt.figure(figsize=(14, 7)) # for i in range(len(hyp)): # y = a[:, i + 5] # mu = (y * weights).sum() / weights.sum() # plt.subplot(2, 5, i+1) # plt.plot(x.max(), mu, 'o') # plt.plot(x, y, '.') # print(list(hyp.keys())[i],'%.4g' % mu)