Add new Utils: pack & unpack data; cdf sampling; query grid (#57)

* new utils

* proper test for pack

* add test por cdf and query occ

* add deprecated warning

* bump version

* fix list return to tuple

tests/test_ray_marching.py

 import pytest
 import torch
 
-from nerfacc import OccupancyGrid, ray_marching, unpack_to_ray_indices
+from nerfacc import OccupancyGrid, ray_marching, unpack_info
 
 device = "cuda:0"
 batch_size = 128
@@ -39,7 +39,7 @@ def test_marching_with_grid():
         far_plane=1.0,
         render_step_size=1e-2,
     )
-    ray_indices = unpack_to_ray_indices(packed_info).long()
+    ray_indices = unpack_info(packed_info).long()
     samples = (
         rays_o[ray_indices] + rays_d[ray_indices] * (t_starts + t_ends) / 2.0
     )

tests/test_resampling.py

+import pytest
+import torch
+
+from nerfacc import ray_marching, ray_resampling
+
+device = "cuda:0"
+batch_size = 128
+
+
+@pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
+def test_resampling():
+    rays_o = torch.rand((batch_size, 3), device=device)
+    rays_d = torch.randn((batch_size, 3), device=device)
+    rays_d = rays_d / rays_d.norm(dim=-1, keepdim=True)
+
+    packed_info, t_starts, t_ends = ray_marching(
+        rays_o,
+        rays_d,
+        near_plane=0.1,
+        far_plane=1.0,
+        render_step_size=1e-3,
+    )
+    weights = torch.rand((t_starts.shape[0],), device=device)
+    packed_info, t_starts, t_ends = ray_resampling(
+        packed_info, t_starts, t_ends, weights, n_samples=32
+    )
+    assert t_starts.shape == t_ends.shape == (batch_size * 32, 1)
+
+
+if __name__ == "__main__":
+    test_resampling()