init project

app.py

@@ -39,7 +39,8 @@ import torchvision.transforms as tvf
 
 
 silent = False
-pe3r = Models('cuda' if torch.cuda.is_available() else 'cpu')
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+pe3r = Models(device)
 
 
 def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
@@ -244,7 +245,6 @@ def slerp_multiple(vectors, t_values):
 
 # @torch.no_grad
 # def get_mask_from_img_sam1(mobilesamv2, yolov8, sam1_image, yolov8_image, original_size, input_size, transform):
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 #     sam_mask=[]
 #     img_area = original_size[0] * original_size[1]
@@ -444,8 +444,6 @@ def get_reconstructed_scene(outdir, filelist, schedule, niter, min_conf_thr,
     """
     if len(filelist) < 2:
         raise gradio.Error("Please input at least 2 images.")
-    
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
     images = Images(filelist=filelist, device=device)
     
@@ -499,6 +497,7 @@ def get_reconstructed_scene(outdir, filelist, schedule, niter, min_conf_thr,
 
     outfile = get_3D_model_from_scene(outdir, scene, min_conf_thr, as_pointcloud, mask_sky,
                                       clean_depth, transparent_cams, cam_size)
+    torch.cuda.empty_cache()
     # also return rgb, depth and confidence imgs
     # depth is normalized with the max value for all images
     # we apply the jet colormap on the confidence maps
@@ -524,8 +523,6 @@ def get_reconstructed_scene(outdir, filelist, schedule, niter, min_conf_thr,
 # def get_3D_object_from_scene(outdir, text, threshold, scene, min_conf_thr, as_pointcloud, 
 #                  mask_sky, clean_depth, transparent_cams, cam_size):
     
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    
 #     texts = [text]
 #     inputs = pe3r.siglip_tokenizer(text=texts, padding="max_length", return_tensors="pt")
 #     inputs = {key: value.to(device) for key, value in inputs.items()}

modules/dust3r/cloud_opt/base_opt.py

@@ -55,9 +55,6 @@ class BasePCOptimizer (nn.Module):
                          iterationsCount=None,
                          verbose=True):
         super().__init__()
-        
-        self.device = device
-
         if not isinstance(view1['idx'], list):
             view1['idx'] = view1['idx'].tolist()
         if not isinstance(view2['idx'], list):