fix reported errors

README.md

@@ -33,6 +33,7 @@ We provide scripts for reproducing all the results from our paper. You can train
 * **imageio**
 * matplotlib
 * tqdm
+* cv2 >= 3.xx (Only if you use video input/output)
 
 **Recent updates**
 

src/data/srdata.py

@@ -77,6 +77,9 @@ class SRData(data.Dataset):
         if train:
             n_patches = args.batch_size * args.test_every
             n_images = len(args.data_train) * len(self.images_hr)
+            if n_images == 0:
+                self.repeat = 0
+            else:
                 self.repeat = max(n_patches // n_images, 1)
 
     # Below functions as used to prepare images

src/main.py

@@ -6,12 +6,12 @@ import model
 import loss
 from option import args
 from trainer import Trainer
-from videotester import VideoTester
 
 torch.manual_seed(args.seed)
 checkpoint = utility.checkpoint(args)
 
 if args.data_test == 'video':
+    from videotester import VideoTester
     model = model.Model(args, checkpoint)
     t = VideoTester(args, model, checkpoint)
     t.test()

src/option.py

@@ -114,7 +114,7 @@ parser.add_argument('--optimizer', default='ADAM',
                     help='optimizer to use (SGD | ADAM | RMSprop)')
 parser.add_argument('--momentum', type=float, default=0.9,
                     help='SGD momentum')
-parser.add_argument('--beta', type=tuple, default=(0.9, 0.999),
+parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
                     help='ADAM beta')
 parser.add_argument('--epsilon', type=float, default=1e-8,
                     help='ADAM epsilon for numerical stability')

src/utility.py

@@ -192,7 +192,7 @@ def make_optimizer(args, my_model):
     elif args.optimizer == 'ADAM':
         optimizer_function = optim.Adam
         kwargs = {
-            'betas': args.beta,
+            'betas': args.betas,
             'eps': args.epsilon
         }
     elif args.optimizer == 'RMSprop':