workaround for torch.jit

demo/mesh_recon.py

@@ -4,10 +4,14 @@ import numpy as np
 import torch
 import trimesh
 
+import spaces
 
-device = torch.device('cpu')
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # use torch hub
+# zeroGPU hack from https://huggingface.co/spaces/zero-gpu-explorers/README/discussions/9
+torch.jit.script = lambda f: f
 model = torch.hub.load("isl-org/ZoeDepth", "ZoeD_NK", pretrained=True).to(device).eval()
 
 
@@ -97,6 +101,7 @@ def depth_edges_mask(depth):
     return mask
 
 
+@spaces.GPU
 def mesh_reconstruction(
         masked_image: np.ndarray,
         mask: np.ndarray,