change pytorch verision to avoid OOM error

app.py

@@ -14,7 +14,6 @@ if os.getenv('SYSTEM') == 'spaces':
         'pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html'.split())
     subprocess.run(
         'pip install git+https://github.com/YuliangXiu/kaolin.git'.split())
-    # subprocess.run('pip install https://download.is.tue.mpg.de/icon/kaolin-0.11.0-cp38-cp38-linux_x86_64.whl'.split())
     subprocess.run('pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1110/download.html'.split())
     subprocess.run(
         'pip install git+https://github.com/Project-Splinter/human_det.git'.split())
@@ -73,7 +72,7 @@ def generate_image(seed, psi):
 
 random.seed(1993)
 model_types = ['icon-filter', 'pifu', 'pamir']
-examples = [[item, random.choice(model_types)] for item in sorted(glob.glob('examples/*.png'))]
+examples = [[item, random.choice(model_types)] for item in random.sample(sorted(glob.glob('examples/*.png')), 4)]
 
 with gr.Blocks() as demo:
     gr.Markdown(description)
@@ -123,7 +122,7 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
 
     # demo.launch(debug=False, enable_queue=False,
-    #             auth=("icon@tue.mpg.de", "icon_2022"),
+    #             auth=(os.environ['USER'], os.environ['PASSWORD']),
     #             auth_message="Register at icon.is.tue.mpg.de to get HuggingFace username and password.")
 
     demo.launch(debug=True, enable_queue=True)

apps/infer.py

@@ -454,6 +454,7 @@ def generate_model(in_path, model_type):
     overlap_path = os.path.join(config_dict['out_dir'], cfg.name, f"png/{data['name']}_overlap.png")
     
     torch.cuda.empty_cache()
+    
     del model
     del dataset
     del local_affine_model

lib/common/seg3d_lossless.py

@@ -584,7 +584,7 @@ class Seg3dLossless(nn.Module):
 
         final = occupancys[1:, 1:, 1:].contiguous()
 
-        if final.shape[0] > 256:
+        if final.shape[0] >= 256:
             # for voxelgrid larger than 256^3, the required GPU memory will be > 9GB
             # thus we use CPU marching_cube to avoid "CUDA out of memory"
             occu_arr = final.detach().cpu().numpy()                 # non-smooth surface