fix: use of cpu in demos

README.md

@@ -57,7 +57,10 @@ from vggt.utils.load_fn import load_and_preprocess_images
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 # bfloat16 is supported on Ampere GPUs (Compute Capability 8.0+) 
-dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+if device == "cuda":
+    dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+else:
+    dtype = torch.float32
 
 # Initialize the model and load the pretrained weights.
 # This will automatically download the model weights the first time it's run, which may take a while.

demo_gradio.py

@@ -49,8 +49,6 @@ def run_model(target_dir, model) -> dict:
 
     # Device check
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    if not torch.cuda.is_available():
-        raise ValueError("CUDA is not available. Check your environment.")
 
     # Move model to device
     model = model.to(device)
@@ -68,10 +66,16 @@ def run_model(target_dir, model) -> dict:
 
     # Run inference
     print("Running inference...")
-    dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+    if device == "cuda":
+        dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+    else:
+        dtype = torch.float32
 
     with torch.no_grad():
-        with torch.cuda.amp.autocast(dtype=dtype):
+        if device == "cuda":
+            with torch.cuda.amp.autocast(dtype=dtype):
+                predictions = model(images)
+        else:
             predictions = model(images)
 
     # Convert pose encoding to extrinsic and intrinsic matrices

demo_viser.py

@@ -374,7 +374,10 @@ def main():
     print(f"Preprocessed images shape: {images.shape}")
 
     print("Running inference...")    
-    dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+    if torch.cuda.is_available():
+        dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+    else:
+        dtype = torch.float32
 
     with torch.no_grad():
         with torch.cuda.amp.autocast(dtype=dtype):