added device to image_demo. added scale to name of experiment. fix bug srnet again.

src/common/validation.py

@@ -20,7 +20,7 @@ def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cu
         pred_lr_image = pred_lr_image.cpu().numpy()  
         run_time_ns = time.perf_counter_ns() - start_time
         torch.cuda.empty_cache()
-        
+
         if not output_image_path is None:
             Image.fromarray(pred_lr_image).save(output_image_path)
 

src/image_demo.py

@@ -14,9 +14,10 @@ class ImageDemoOptions():
         self.parser.add_argument('--model_paths', '-n', nargs='+', type=str, default=["../models/last_transfered_net.pth","../models/last_transfered_lut.pth"], help="Model paths for comparison")
         self.parser.add_argument('--hr_image_path', '-a', type=str, default="../data/Set14/HR/monarch.png", help="HR image path")
         self.parser.add_argument('--lr_image_path', '-b', type=str, default="../data/Set14/LR/X4/monarch.png", help="LR image path")
-        self.parser.add_argument('--output_path', type=str, default="../models/", help="Output path.")  
-        self.parser.add_argument('--batch_size', type=int, default=2**10, help="Size of the batch for the input domain values.")  
-        self.parser.add_argument('--mirror', action='store_true', default=False) 
+        self.parser.add_argument('--output_path', type=str, default="../models/", help="Output path.")
+        self.parser.add_argument('--batch_size', type=int, default=2**10, help="Size of the batch for the input domain values.")
+        self.parser.add_argument('--mirror', action='store_true', default=False)
+        self.parser.add_argument('--device', default='cuda', help='Device of the model')
 
     def parse_args(self): 
         args = self.parser.parse_args()       
@@ -44,7 +45,7 @@ config = config_inst.parse_args()
 
 start_script_time = datetime.now()
 print(config_inst)
-models = [LoadCheckpoint(x).cuda() for x in config.model_paths]
+models = [LoadCheckpoint(x).to(config.device) for x in config.model_paths]
 for m in models:
     print(m)
 
@@ -56,7 +57,7 @@ if config.mirror:
 lr_image = lr_image.copy()
 image_gt = image_gt.copy()
 
-input_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1)[None,...].cuda()
+input_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1)[None,...].to(config.device)
 
 predictions = []
 for model in models:

src/models/srnet.py

@@ -102,7 +102,7 @@ class SRNetR90(nn.Module):
         x = x.permute(0,1,2,4,3,5)
         x = x.reshape(b, c, h*scale, w*scale)
         return x
-            
+
     def forward(self, x):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)

src/train.py

@@ -76,7 +76,7 @@ def prepare_experiment_folder(config):
     assert all([name in os.listdir(config.datasets_dir) for name in config.train_datasets]), f"On of the {config.train_datasets} was not found in {config.datasets_dir}."
     assert all([name in os.listdir(config.datasets_dir) for name in config.val_datasets]), f"On of the {config.val_datasets} was not found in {config.datasets_dir}."
     
-    config.exp_dir = (config.models_dir / f"{config.model}_{'_'.join(config.train_datasets)}").resolve()
+    config.exp_dir = (config.models_dir / f"{config.model}_{'_'.join(config.train_datasets)}_x{config.scale}").resolve()
 
     if not config.exp_dir.exists():
         config.exp_dir.mkdir()
@@ -180,7 +180,9 @@ if __name__ == "__main__":
         loss.backward()
         optimizer.step()
         optimizer.zero_grad()
-
+        del hr_patch
+        del lr_patch
+        torch.cuda.empty_cache()
         forward_backward_time += time.time() - start_time
 
         # For monitoring