added output values clamp for y srnet/srlut

src/common/layers.py

@@ -101,7 +101,7 @@ class ConvUpscaleBlock(nn.Module):
         x = round_func(x)
         return x   
 
-
+# https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L105
 class RgbToYcbcr(nn.Module):
     r"""Convert an image from RGB to YCbCr.
 
@@ -131,7 +131,6 @@ class RgbToYcbcr(nn.Module):
         cr = (r - y) * 0.713 + delta
         return torch.stack([y, cb, cr], -3)
 
-
 class YcbcrToRgb(nn.Module):
     r"""Convert an image from YCbCr to Rgb.
 

src/common/validation.py

@@ -87,8 +87,8 @@ def valid_steps(model, datasets, config, log_prefix=""):
             'Dataset', 
             'AVG PSNR', 
             'AVG SSIM', 
-            f'AVG {config.device} Time, s', 
+            f'AVG {config.device} time, s', 
-            f'P95 {config.device} Time, s',
+            f'P95 {config.device} time, s',
             'Image count',
             'AVG image area',
             'Total area',

src/image_demo.py

@@ -17,6 +17,7 @@ class ImageDemoOptions():
         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('--project_path', type=str, default="../", help="Project 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) 
 
     def parse_args(self): 
         args = self.parser.parse_args()       
@@ -51,8 +52,13 @@ lut_model = LoadCheckpoint(config.lut_model_path).cuda()
 print(net_model)
 print(lut_model)
 
-lr_image = cv2.imread(str(config.lr_image_path))[:,:,::-1].copy()
+lr_image = cv2.imread(str(config.lr_image_path))[:,:,::-1]
-image_gt = cv2.imread(str(config.hr_image_path))[:,:,::-1].copy()
+image_gt = cv2.imread(str(config.hr_image_path))[:,:,::-1]
+if config.mirror:
+    lr_image = lr_image[:,::-1,:]
+    image_gt = image_gt[:,::-1,:]
+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()
 

src/models/srlut.py

@@ -99,14 +99,14 @@ class SRLutRot90Y(nn.Module):
         lut_model = SRLutRot90Y(quantization_interval=quantization_interval, scale=scale)
         lut_model.stage_lut = nn.Parameter(torch.tensor(stage_lut).type(torch.float32))
         return lut_model
-
+    
     def forward(self, x):
         b,c,h,w = x.shape
         x = self.rgb_to_ycbcr(x)
         y = x[:,0:1,:,:]
         cbcr = x[:,1:,:,:]
         cbcr_scaled = F.interpolate(cbcr, size=[h*self.scale, w*self.scale], mode='bilinear')
-
+        
         output = torch.zeros([b, 1, h*self.scale, w*self.scale], dtype=torch.float32, device=x.device)
         for rotations_count in range(4):
             rotated = torch.rot90(y, k=rotations_count, dims=[2, 3])
@@ -115,7 +115,7 @@ class SRLutRot90Y(nn.Module):
             output += unrotated_prediction
         output /= 4
         output = torch.cat([output, cbcr_scaled], dim=1)  
-        output = self.ycbcr_to_rgb(output)
+        output = self.ycbcr_to_rgb(output).clamp(0, 255)        
         return output
 
     def __repr__(self):

src/models/srnet.py

@@ -106,7 +106,7 @@ class SRNetDenseRot90Y(nn.Module):
             output += torch.rot90(rx, k=-rotations_count, dims=[2, 3])
         output /= 4
         output = torch.cat([output, cbcr_scaled], dim=1)  
-        output = self.ycbcr_to_rgb(output)      
+        output = self.ycbcr_to_rgb(output).clamp(0, 255)       
         return output
 
     def get_lut_model(self, quantization_interval=16, batch_size=2**10):

src/validate.py

@@ -84,10 +84,10 @@ if __name__ == "__main__":
             lr_dir_path = Path(config.datasets_dir) / test_dataset_name / "LR" / f"X{model.scale}",
         )
 
-    results = valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Model {model.__class__.__name__}")
+    results = valid_steps(model=model, datasets=test_datasets, config=config, log_prefix=f"Model {config.model_name}")
 
     results.to_csv(config.results_path)
-    print()
+    print(config.model_name)
     print(results)
     print()
     print(f"Results saved to {config.results_path}")