diff --git a/test.py b/test.py index a39ac9e..1638a5e 100644 --- a/test.py +++ b/test.py @@ -17,7 +17,9 @@ def test(data, verbose=False, model=None, dataloader=None, + save_dir='', merge=False): + # Initialize/load model and set device training = model is not None if training: # called by train.py @@ -28,7 +30,7 @@ def test(data, merge = opt.merge # use Merge NMS # Remove previous - for f in glob.glob('test_batch*.jpg'): + for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')): os.remove(f) # Load model @@ -157,10 +159,10 @@ def test(data, # Plot images if batch_i < 1: - f = 'test_batch%g_gt.jpg' % batch_i # filename - plot_images(img, targets, paths, f, names) # ground truth - f = 'test_batch%g_pred.jpg' % batch_i - plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions + f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename + plot_images(img, targets, paths, str(f), names) # ground truth + f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i) + plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions # Compute statistics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy diff --git a/train.py b/train.py index c97e96b..c4d4db0 100644 --- a/train.py +++ b/train.py @@ -20,15 +20,11 @@ except: print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex') mixed_precision = False # not installed -wdir = 'weights' + os.sep # weights dir -os.makedirs(wdir, exist_ok=True) -last = wdir + 'last.pt' -best = wdir + 'best.pt' -results_file = 'results.txt' # Hyperparameters -hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) - 'momentum': 0.937, # SGD momentum +hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD + 'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) + 'momentum': 0.937, # SGD momentum/Adam beta1 'weight_decay': 5e-4, # optimizer weight decay 'giou': 0.05, # giou loss gain 'cls': 0.58, # cls loss gain @@ -45,21 +41,17 @@ hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) 'translate': 0.0, # image translation (+/- fraction) 'scale': 0.5, # image scale (+/- gain) 'shear': 0.0} # image shear (+/- deg) -print(hyp) -# 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 -# Print focal loss if gamma > 0 -if hyp['fl_gamma']: - print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma']) +def train(hyp): + log_dir = tb_writer.log_dir # run directory + wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory + os.makedirs(wdir, exist_ok=True) + last = wdir + 'last.pt' + best = wdir + 'best.pt' + results_file = log_dir + os.sep + 'results.txt' -def train(hyp): epochs = opt.epochs # 300 batch_size = opt.batch_size # 64 weights = opt.weights # initial training weights @@ -97,8 +89,11 @@ def train(hyp): else: pg0.append(v) # all else - optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \ - optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + 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 optimizer.add_param_group({'params': pg2}) # add pg2 (biases) print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) @@ -107,7 +102,7 @@ def train(hyp): # Scheduler https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) - # plot_lr_scheduler(optimizer, scheduler, epochs) + plot_lr_scheduler(optimizer, scheduler, epochs, save_dir=log_dir) # Load Model google_utils.attempt_download(weights) @@ -176,13 +171,19 @@ def train(hyp): model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights model.names = data_dict['names'] + # Save run settings + with open(Path(log_dir) / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(Path(log_dir) / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + # Class frequency labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. # model._initialize_biases(cf.to(device)) + plot_labels(labels, save_dir=log_dir) if tb_writer: - plot_labels(labels) tb_writer.add_histogram('classes', c, 0) # Check anchors @@ -273,7 +274,7 @@ def train(hyp): # Plot if ni < 3: - f = 'train_batch%g.jpg' % ni # filename + f = str(Path(log_dir) / ('train_batch%g.jpg' % ni)) # filename result = plot_images(images=imgs, targets=targets, paths=paths, fname=f) if tb_writer and result is not None: tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) @@ -294,7 +295,8 @@ def train(hyp): save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'), model=ema.ema, single_cls=opt.single_cls, - dataloader=testloader) + dataloader=testloader, + save_dir=log_dir) # Write with open(results_file, 'a') as f: @@ -346,7 +348,7 @@ def train(hyp): # Finish if not opt.evolve: - plot_results() # save as results.png + plot_results(save_dir=log_dir) # 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 device.type != 'cpu' and torch.cuda.device_count() > 1 else None torch.cuda.empty_cache() @@ -356,13 +358,14 @@ def train(hyp): if __name__ == '__main__': check_git_status() parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='', help='hyp.yaml path (optional)') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=16) - parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='*.cfg path') - parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes') 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('--resume', nargs='?', const = 'get_last', default=False, help='resume from given path/to/last.pt, or most recent run if blank.') 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('--noautoanchor', action='store_true', help='disable autoanchor check') @@ -372,13 +375,17 @@ if __name__ == '__main__': parser.add_argument('--weights', type=str, default='', help='initial weights path') parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--adam', action='store_true', help='use adam optimizer') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') opt = parser.parse_args() + + last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run + if last and not opt.weights: + print(f'Resuming training from {last}') opt.weights = last if opt.resume and not opt.weights else opt.weights opt.cfg = check_file(opt.cfg) # check file opt.data = check_file(opt.data) # check file + opt.hyp = check_file(opt.hyp) if opt.hyp else '' # check file print(opt) opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size) @@ -388,7 +395,13 @@ if __name__ == '__main__': # Train if not opt.evolve: tb_writer = SummaryWriter(comment=opt.name) + if opt.hyp: # update hyps + with open(opt.hyp) as f: + hyp.update(yaml.load(f, Loader=yaml.FullLoader)) + + print(f'Beginning training with {hyp}\n\n') print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/') + train(hyp) # Evolve hyperparameters (optional) diff --git a/utils/utils.py b/utils/utils.py index c77d940..4776794 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -37,6 +37,12 @@ def init_seeds(seed=0): torch_utils.init_seeds(seed=seed) +def get_latest_run(search_dir = './runs'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key = os.path.getctime) + + def check_git_status(): # Suggest 'git pull' if repo is out of date if platform in ['linux', 'darwin']: @@ -1028,7 +1034,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max return mosaic -def plot_lr_scheduler(optimizer, scheduler, epochs=300): +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): # Plot LR simulating training for full epochs optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] @@ -1042,7 +1048,7 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300): plt.xlim(0, epochs) plt.ylim(0) plt.tight_layout() - plt.savefig('LR.png', dpi=200) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) def plot_test_txt(): # from utils.utils import *; plot_test() @@ -1107,7 +1113,7 @@ def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_st plt.savefig(f.replace('.txt', '.png'), dpi=200) -def plot_labels(labels): +def plot_labels(labels, save_dir= ''): # plot dataset labels c, b = labels[:, 0], labels[:, 1:].transpose() # classees, boxes @@ -1128,7 +1134,7 @@ def plot_labels(labels): ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet') ax[2].set_xlabel('width') ax[2].set_ylabel('height') - plt.savefig('labels.png', dpi=200) + plt.savefig(Path(save_dir) / 'labels.png', dpi=200) plt.close() @@ -1174,7 +1180,7 @@ def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_re fig.savefig(f.replace('.txt', '.png'), dpi=200) -def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.utils import *; plot_results() +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir= ''): # from utils.utils import *; plot_results() # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training fig, ax = plt.subplots(2, 5, figsize=(12, 6)) ax = ax.ravel() @@ -1184,7 +1190,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.ut os.system('rm -rf storage.googleapis.com') files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] else: - files = glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt') + files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt') for fi, f in enumerate(files): try: results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T @@ -1205,4 +1211,4 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.ut fig.tight_layout() ax[1].legend() - fig.savefig('results.png', dpi=200) + fig.savefig(Path(save_dir) / 'results.png', dpi=200)