Fix tensor type mismatch: ensure int types for CUDA operations

- Cast tile_bounds components to int() to avoid Float/Int type mismatch
- Cast img_h, img_w to int() in all gsplat function calls
- Cast dimensions in tensor.view() operations to int()
- This fixes 'expected scalar type Int but found Float' error in Docker environment

gradio_models.py

@@ -154,9 +154,9 @@ class GradioGaussianSplatting2D(nn.Module):
         )
         self.img_h, self.img_w = self.gt_images.shape[1:]
         self.tile_bounds = (
-            (self.img_w + self.block_w - 1) // self.block_w,
-            (self.img_h + self.block_h - 1) // self.block_h,
-            1,
+            int((self.img_w + self.block_w - 1) // self.block_w),
+            int((self.img_h + self.block_h - 1) // self.block_h),
+            int(1),
         )
 
     def _init_bit_precision(self, args):
@@ -388,7 +388,7 @@ class GradioGaussianSplatting2D(nn.Module):
             )
 
         begin = perf_counter()
-        tmp = project_gaussians_2d_scale_rot(xy, scale, rot, img_h, img_w, tile_bounds)
+        tmp = project_gaussians_2d_scale_rot(xy, scale, rot, int(img_h), int(img_w), tile_bounds)
         xy, radii, conics, num_tiles_hit = tmp
 
         if not self.disable_tiles:
@@ -399,15 +399,15 @@ class GradioGaussianSplatting2D(nn.Module):
                 conics,
                 num_tiles_hit,
                 feat,
-                img_h,
-                img_w,
+                int(img_h),
+                int(img_w),
                 self.block_h,
                 self.block_w,
                 enable_topk_norm,
             )
             out_image = rasterize_gaussians_sum(*tmp)
         else:
-            tmp = xy, conics, feat, img_h, img_w
+            tmp = xy, conics, feat, int(img_h), int(img_w)
             out_image = rasterize_gaussians_no_tiles(*tmp)
 
         render_time = perf_counter() - begin
@@ -416,7 +416,7 @@ class GradioGaussianSplatting2D(nn.Module):
             return render_time
 
         out_image = (
-            out_image.view(-1, img_h, img_w, self.feat_dim)
+            out_image.view(-1, int(img_h), int(img_w), self.feat_dim)
             .permute(0, 3, 1, 2)
             .contiguous()
         )
@@ -469,7 +469,7 @@ class GradioGaussianSplatting2D(nn.Module):
                 )
 
             tmp = project_gaussians_2d_scale_rot(
-                xy, scale, rot, self.img_h, self.img_w, self.tile_bounds
+                xy, scale, rot, int(self.img_h), int(self.img_w), self.tile_bounds
             )
             xy, radii, conics, num_tiles_hit = tmp
 
@@ -481,19 +481,19 @@ class GradioGaussianSplatting2D(nn.Module):
                     conics,
                     num_tiles_hit,
                     feat,
-                    self.img_h,
-                    self.img_w,
+                    int(self.img_h),
+                    int(self.img_w),
                     self.block_h,
                     self.block_w,
                     enable_topk_norm,
                 )
                 out_image = rasterize_gaussians_sum(*tmp)
             else:
-                tmp = xy, conics, feat, self.img_h, self.img_w
+                tmp = xy, conics, feat, int(self.img_h), int(self.img_w)
                 out_image = rasterize_gaussians_no_tiles(*tmp)
 
             out_image = (
-                out_image.view(-1, self.img_h, self.img_w, self.feat_dim)
+                out_image.view(-1, int(self.img_h), int(self.img_w), self.feat_dim)
                 .permute(0, 3, 1, 2)
                 .contiguous()
             ).squeeze(dim=0)