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 * # 320 --epochs 1 # 0.109 0.297 0.15 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 # 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 # Training hyperparameters d hyp = {'giou': 1.153, # giou loss gain 'xy': 4.062, # xy loss gain 'wh': 0.1845, # wh loss gain 'cls': 24.28, # cls loss gain 'cls_pw': 3.05, # cls BCELoss positive_weight 'obj': 20.93, # obj loss gain 'obj_pw': 2.842, # obj BCELoss positive_weight 'iou_t': 0.2759, # iou training threshold 'lr0': 0.001357, # initial learning rate 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'momentum': 0.916, # SGD momentum 'weight_decay': 0.000572, # optimizer weight decay 'hsv_s': 0.5, # image HSV-Saturation augmentation (fraction) 'hsv_v': 0.5, # image HSV-Value augmentation (fraction) 'degrees': 5, # image rotation (+/- deg) 'translate': 0.1, # image translation (+/- fraction) 'scale': 0.1, # image scale (+/- gain) 'shear': 2} # image shear (+/- deg) # # Training hyperparameters e # hyp = {'giou': 1.607, # giou loss gain # 'xy': 4.062, # xy loss gain # 'wh': 0.1845, # wh loss gain # 'cls': 39.27, # cls loss gain # 'cls_pw': 3.726, # cls BCELoss positive_weight # 'obj': 31.26, # obj loss gain # 'obj_pw': 2.634, # obj BCELoss positive_weight # 'iou_t': 0.273, # iou target-anchor training threshold # 'lr0': 0.001542, # initial learning rate # 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) # 'momentum': 0.8364, # SGD momentum # 'weight_decay': 0.0008393} # optimizer weight decay 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 = 8 * 8 = 64 # Initialize init_seeds() weights = 'weights' + os.sep last = weights + 'last.pt' best = weights + 'best.pt' device = torch_utils.select_device() multi_scale = opt.multi_scale if 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 # 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']) cutoff = -1 # backbone reaches to cutoff layer start_epoch = 0 best_fitness = 0.0 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['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) # Dataset dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, hyp=hyp, # augmentation hyperparameters rect=opt.rect) # rectangular training # 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) # sampler = torch.utils.data.distributed.DistributedSampler(dataset) # Dataloader dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=opt.num_workers, shuffle=not opt.rect, # Shuffle=True unless rectangular training is used pin_memory=True, collate_fn=dataset.collate_fn) # Mixed precision training https://github.com/NVIDIA/apex mixed_precision = True if mixed_precision: try: from apex import amp model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0) except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259 mixed_precision = False # 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, report='summary') # 'full' or 'summary' 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 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 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) # 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 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 TODO: short-side to 32-multiple https://github.com/ultralytics/yolov3/issues/358 if multi_scale: if (i + nb * epoch) / 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 imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Plot images with bounding boxes if epoch == 0 and i == 0: plot_images(imgs=imgs, targets=targets, paths=paths, 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=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) # 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, batch_size=batch_size, img_size=opt.img_size, 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 map fitness = results[2] if fitness > best_fitness: best_fitness = fitness # Save training results save = (not opt.nosave) or ((not opt.evolve) and (epoch == epochs - 1)) 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)) return results def print_mutation(hyp, results): # Write mutation results a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys b = '%11.3g' * 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)) if opt.bucket: os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt with open('evolve.txt', 'a') as f: # append result f.write(c + b + '\n') x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows np.savetxt('evolve2.txt', x[np.argsort(-fitness(x))] , '%11.3g') # save sort by fitness os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt else: with open('evolve.txt', 'a') as f: f.write(c + b + '\n') def fitness(x): # returns fitness of hyp evolution vectors return x[:, 2] * 0.5 + x[:, 3] * 0.5 # fitness = weighted combination of mAP and F1 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='batch size') parser.add_argument('--accumulate', type=int, default=4, 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_64img.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('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers') 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('--var', default=0, type=int, help='debug variable') opt = parser.parse_args() print(opt) if opt.evolve: opt.notest = True # only test final epoch opt.nosave = True # only save final checkpoint # Train results = train(opt.cfg, opt.data, img_size=opt.img_size, epochs=opt.epochs, batch_size=opt.batch_size, accumulate=opt.accumulate) # Evolve hyperparameters (optional) if opt.evolve: print_mutation(hyp, results) # Write mutation results for _ in range(1000): # generations to evolve # 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 + 5] # Mutate init_seeds(seed=int(time.time())) s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .05, .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, 0.95), (0, 0.001), (0, .8), (0, .8), (0, .8), (0, .8)] 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) # # 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)