Apr 26, 2018 updates

    Now compatible with PyTorch 0.4.0
    Minor bug fixes

README.md

@@ -25,19 +25,21 @@ We provide scripts for reproducing all the results from our paper. You can train
 * Training and evaluation requires less memory.
 * Python-based.
 
-**Recent updates**
-* Apr 9, 2018
-  * VGG and Adversarial loss is implemented based on [SRGAN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf). [WGAN](https://arxiv.org/abs/1701.07875) and [gradient penalty](https://arxiv.org/abs/1704.00028) are also implemented, but they are not tested yet.
-  * Many codes are refactored. If there exists a bug, please report it.
-  * [D-DBPN](https://arxiv.org/abs/1803.02735) is implemented. Default setting is D-DBPN-L.
 ## Dependencies
 * Python (Tested with 3.6)
-* PyTorch >= 0.3.1
+* PyTorch >= 0.4.0
 * numpy
 * scipy
 * matplotlib
 * tqdm
 
+**Recent updates**
+
+* Apr 26, 2018
+  * Compatible with PyTorch 0.4.0
+  * Please use the legacy/0.3.1 branch if you are using the old version of PyTorch.
+  * Minor bug fixes
+
 ## Code
 Clone this repository into any place you want.
 ```bash
@@ -154,3 +156,9 @@ sh demo.sh
   * We also provide ``MDSR_baseline_jpeg`` model that suppresses JPEG artifacts in original low-resolution image. Please use it if you have any trouble.
   * ``MyImage`` dataset is changed to ``Demo`` dataset. Also, it works more efficient than before.
   * Some codes and script are re-written.
+
+* Apr 9, 2018
+  * VGG and Adversarial loss is implemented based on [SRGAN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf). [WGAN](https://arxiv.org/abs/1701.07875) and [gradient penalty](https://arxiv.org/abs/1704.00028) are also implemented, but they are not tested yet.
+  * Many codes are refactored. If there exists a bug, please report it.
+  * [D-DBPN](https://arxiv.org/abs/1803.02735) is implemented. Default setting is D-DBPN-L.
+

code/data/demo.py

@@ -15,6 +15,7 @@ class Demo(data.Dataset):
         self.scale = args.scale
         self.idx_scale = 0
         self.train = False
+        self.benchmark = False
 
         self.filelist = []
         for f in os.listdir(args.dir_demo):

code/trainer.py

@@ -87,7 +87,7 @@ class Trainer():
                 tqdm_test = tqdm(self.loader_test, ncols=80)
                 for idx_img, (lr, hr, filename, _) in enumerate(tqdm_test):
                     filename = filename[0]
-                    no_eval = isinstance(hr[0], int)
+                    no_eval = (hr.item() == -1)
                     if not no_eval:
                         lr, hr = self.prepare([lr, hr])
                     else: