test

app.py

@@ -13,9 +13,9 @@ def pil_to_torch(image):
 
     ref_size = 256
     if image.shape[2] > image.shape[3]:
-        size = (ref_size, ref_size * image.shape[2]//image.shape[3])
+        size = (ref_size, ref_size * image.shape[3]//image.shape[2])
     else:
-        size = (ref_size * image.shape[3]//image.shape[2], ref_size)
+        size = (ref_size * image.shape[2]//image.shape[3], ref_size)
 
     image = torch.nn.functional.interpolate(image, size=size, mode='bilinear')
     return image
@@ -55,7 +55,7 @@ output_images = gr.Image(label='Denoised Image')
 noise_image = gr.Image(label='Noisy Image')
 input_image_output = gr.Image(label='Input Image')
 
-noise_levels = gr.Dropdown(choices=[0.1, 0.2, 0.3, 0.4, 0.5], value=0.1, label='Noise Level')
+noise_levels = gr.Dropdown(choices=[0.1, 0.2, 0.3, 0.5, 1], value=0.1, label='Noise Level')
 
 denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'BM3D', 'MedianFilter', 'TV', 'TGV'], value='DnCNN', label='Denoiser')
 
@@ -65,7 +65,7 @@ demo = gr.Interface(
     examples=[['https://deepinv.github.io/deepinv/_static/deepinv_logolarge.png', 0.1, 'DnCNN']],
     outputs=[noise_image, output_images],
     title="Image Denoising with DeepInverse",
-    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU. We also automatically resize the input image to 256 vertical pixels to reduce the computation time. For more advanced models, please run the code locally.",
+    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU. We also automatically resize the input image to 256 pixels to reduce the computation time. For more advanced models, please run the code locally.",
 )
 
 demo.launch()