CPU only

gradio_app.py

@@ -5,7 +5,6 @@ import torch
 import torch.nn.functional as F
 import os
 import time
-import spaces
 import subprocess
 
 from dataloader.stereo import transforms
@@ -103,8 +102,8 @@ class MatchStereoDemo:
         if self.model is None:
             return
             
-        dummy_left = torch.randn(1, 3, 512, 512, device=self.device, dtype=self.dtype)
-        dummy_right = torch.randn(1, 3, 512, 512, device=self.device, dtype=self.dtype)
+        dummy_left = torch.randn(1, 3, 256, 256, device=self.device, dtype=self.dtype)
+        dummy_right = torch.randn(1, 3, 256, 256, device=self.device, dtype=self.dtype)
         
         with torch.no_grad():
             _ = self.model(dummy_left, dummy_right, stereo=(self.current_mode == 'stereo'))
@@ -138,12 +137,11 @@ class MatchStereoDemo:
             "720P": (round_to_32(1280), round_to_32(720)),
             "1080P": (round_to_32(1920), round_to_32(1080)),
             "2K": (round_to_32(2048), round_to_32(1080)),
-            "4K UHD": (round_to_32(3840), round_to_32(2160))
+            ## "4K UHD": (round_to_32(3840), round_to_32(2160))
         }
         
         return size_presets.get(size_name, None)
 
-    @spaces.GPU
     def process_images(self, left_image, right_image, mode, variant, 
                       low_res_init=False, inference_size_name="Original", 
                       precision="fp32", mat_impl="pytorch"):
@@ -373,9 +371,9 @@ with gr.Blocks(title="MatchStereo/MatchFlow Demo") as demo:
                     interactive=current_has_cuda
                 )
                 mat_impl = gr.Radio(
-                    choices=["cuda", "pytorch"],
+                    choices=["pytorch", "cuda"],
                     label="MatchAttention Implementation",
-                    value="cuda",
+                    value="pytorch",
                     info="MatchAttention implementations",
                     interactive=current_has_cuda
                 )