Merge pull request #104 from alexstoken/advanced_logging

Log command line options, hyperparameters, and weights per run in `runs/`

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

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)

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)