add

App_main.py

@@ -94,7 +94,7 @@ with gr.Blocks() as demo:
     image_output = gr.outputs.Image(label='SR Result', type='numpy')
     with gr.Row():
         checkpoint = gr.inputs.Radio(['UC', 'AID'], label='Checkpoint')
-        scale = gr.Slider(1, 12, value=4.0, step=0.1, label='scale')
+        scale = gr.Slider(1, 10, value=4.0, step=0.1, label='scale')
 
 io = gr.Interface(fn=sr_func,
                   inputs=[image_input,

examples/resize.py

@@ -7,14 +7,14 @@ from torchvision.transforms import InterpolationMode
 
 patch_size = 48
 
-for file in glob.glob("*.tif"):
+for file in glob.glob("*.jpg"):
     img = transforms.ToTensor()(Image.open(file).convert('RGB')) * 255
     img_lr = transforms.Resize(patch_size, InterpolationMode.BICUBIC)(
-        transforms.CenterCrop(4 * patch_size)(img))
+        transforms.CenterCrop(8 * patch_size)(img))
 
-    img_hr = transforms.CenterCrop(4 * patch_size)(img)
+    img_hr = transforms.CenterCrop(8 * patch_size)(img)
 
-    cv2.imwrite(f'UC_{file.split(".")[0]}_LR.png', img_lr.permute((1, 2, 0)).numpy())
-    print(f'UC_{file.split(".")[0]}_LR.png')
-    cv2.imwrite(f'UC_{file.split(".")[0]}_HR.png', img_hr.permute((1, 2, 0)).numpy())
+    cv2.imwrite(f'AID_{file.split(".")[0]}_LR.png', img_lr.permute((1, 2, 0)).numpy())
+    print(f'AID_{file.split(".")[0]}_LR.png')
+    cv2.imwrite(f'AID_{file.split(".")[0]}_HR.png', img_hr.permute((1, 2, 0)).numpy())