Use C++/CUDA in points2vols

Summary: Move the core of add_points_to_volumes to the new C++/CUDA implementation. Add new flag to let the user stop this happening. Avoids copies. About a 30% speedup on the larger cases, up to 50% on the smaller cases. New timings ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_1000 4575 12591 110 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_10000 25468 29186 20 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_100000 202085 209897 3 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_1000 46059 48188 11 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_10000 83759 95669 7 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_100000 326056 339393 2 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_1000 2379 4738 211 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_10000 12100 63099 42 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_100000 63323 63737 8 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_1000 45216 45479 12 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_10000 57205 58524 9 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_100000 139499 139926 4 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_1000 40129 40431 13 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_10000 204949 239293 3 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_100000 1664541 1664541 1 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_1000 391573 395108 2 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_10000 674869 674869 1 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_100000 2713632 2713632 1 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_1000 12726 13506 40 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_10000 73103 73299 7 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_100000 598634 598634 1 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_1000 398742 399256 2 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_10000 543129 543129 1 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_100000 1242956 1242956 1 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_1000 1814 8884 276 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_10000 1996 8851 251 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_100000 4608 11529 109 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_1000 5183 12508 97 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_10000 7106 14077 71 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_100000 25914 31818 20 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_1000 1778 8823 282 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_10000 1825 8613 274 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_100000 3154 10161 159 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_1000 4888 9404 103 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_10000 5194 9963 97 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_100000 8109 14933 62 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_1000 3320 10306 151 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_10000 7003 8595 72 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_100000 49140 52957 11 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_1000 35890 36918 14 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_10000 58890 59337 9 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_100000 286878 287600 2 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_1000 2484 8805 202 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_10000 3967 9090 127 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_100000 19423 19799 26 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_1000 33228 33329 16 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_10000 37292 37370 14 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_100000 73550 74017 7 -------------------------------------------------------------------------------- ``` Previous timings ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_1000 10100 46422 50 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_10000 28442 32100 18 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_100000 241127 254269 3 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_1000 54149 79480 10 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_10000 125459 212734 4 ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_100000 512739 512739 1 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_1000 2866 13365 175 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_10000 7026 12604 72 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_100000 48822 55607 11 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_1000 38098 38576 14 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_10000 48006 54120 11 ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_100000 131563 138536 4 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_1000 64615 91735 8 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_10000 228815 246095 3 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_100000 3086615 3086615 1 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_1000 464298 465292 2 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_10000 1053440 1053440 1 ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_100000 6736236 6736236 1 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_1000 11940 12440 42 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_10000 56641 58051 9 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_100000 711492 711492 1 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_1000 326437 329846 2 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_10000 418514 427911 2 ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_100000 1524285 1524285 1 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_1000 5949 13602 85 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_10000 5817 13001 86 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_100000 23833 25971 21 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_1000 9029 16178 56 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_10000 11595 18601 44 ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_100000 46986 47344 11 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_1000 2554 9747 196 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_10000 2676 9537 187 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_100000 6567 14179 77 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_1000 5840 12811 86 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_10000 6102 13128 82 ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_100000 11945 11995 42 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_1000 7642 13671 66 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_10000 25190 25260 20 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_100000 212018 212134 3 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_1000 40421 45692 13 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_10000 92078 92132 6 ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_100000 457211 457229 2 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_1000 3574 10377 140 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_10000 7222 13023 70 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_100000 48127 48165 11 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_1000 34732 35295 15 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_10000 43050 51064 12 ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_100000 106028 106058 5 -------------------------------------------------------------------------------- ``` Reviewed By: nikhilaravi Differential Revision: D29548609 fbshipit-source-id: 7026e832ea299145c3f6b55687f3c1601294f5c0
facebookresearch · Oct 1, 2021 · ee2b2fe · ee2b2fe
1 parent 9ad98c8
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diff --git a/pytorch3d/ops/points_to_volumes.py b/pytorch3d/ops/points_to_volumes.py
@@ -192,6 +192,7 @@ def add_pointclouds_to_volumes(
     initial_volumes: "Volumes",
     mode: str = "trilinear",
     min_weight: float = 1e-4,
+    _python: bool = False,
 ) -> "Volumes":
     """
     Add a batch of point clouds represented with a `Pointclouds` structure
@@ -249,6 +250,8 @@ def add_pointclouds_to_volumes(
         min_weight: A scalar controlling the lowest possible total per-voxel
             weight used to normalize the features accumulated in a voxel.
             Only active for `mode==trilinear`.
+        _python: Set to True to use a pure Python implementation, e.g. for test
+            purposes, which requires more memory and may be slower.
 
     Returns:
         updated_volumes: Output `Volumes` structure containing the conversion result.
@@ -283,6 +286,7 @@ def add_pointclouds_to_volumes(
         grid_sizes=initial_volumes.get_grid_sizes(),
         mask=mask,
         mode=mode,
+        _python=_python,
     )
 
     return initial_volumes.update_padded(
@@ -299,6 +303,7 @@ def add_points_features_to_volume_densities_features(
     min_weight: float = 1e-4,
     mask: Optional[torch.Tensor] = None,
     grid_sizes: Optional[torch.LongTensor] = None,
+    _python: bool = False,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Convert a batch of point clouds represented with tensors of per-point
@@ -340,6 +345,7 @@ def add_points_features_to_volume_densities_features(
         grid_sizes: `LongTensor` of shape (minibatch, 3) representing the
             spatial resolutions of each of the the non-flattened `volumes` tensors,
             or None to indicate the whole volume is used for every batch element.
+        _python: Set to True to use a pure Python implementation.
     Returns:
         volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)`
         volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)`
@@ -362,6 +368,66 @@ def add_points_features_to_volume_densities_features(
             .expand(volume_densities.shape[0], 3)
         )
 
+    if _python:
+        return _add_points_features_to_volume_densities_features_python(
+            points_3d=points_3d,
+            points_features=points_features,
+            volume_densities=volume_densities,
+            volume_features=volume_features,
+            mode=mode,
+            min_weight=min_weight,
+            mask=mask,
+            grid_sizes=grid_sizes,
+        )
+
+    if mode == "trilinear":
+        splat = True
+    elif mode == "nearest":
+        splat = False
+    else:
+        raise ValueError('No such interpolation mode "%s"' % mode)
+    volume_densities, volume_features = _points_to_volumes(
+        points_3d,
+        points_features,
+        volume_densities,
+        volume_features,
+        grid_sizes,
+        1.0,  # point_weight
+        mask,
+        True,  # align_corners
+        splat,
+    )
+    if splat:
+        # divide each feature by the total weight of the votes
+        volume_features = volume_features / volume_densities.clamp(min_weight)
+    else:
+        # divide each feature by the total weight of the votes
+        volume_features = volume_features / volume_densities.clamp(1.0)
+
+    return volume_features, volume_densities
+
+
+def _add_points_features_to_volume_densities_features_python(
+    *,
+    points_3d: torch.Tensor,
+    points_features: torch.Tensor,
+    volume_densities: torch.Tensor,
+    volume_features: Optional[torch.Tensor],
+    mode: str,
+    min_weight: float,
+    mask: Optional[torch.Tensor],
+    grid_sizes: torch.LongTensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Python implementation for add_points_features_to_volume_densities_features.
+
+    Returns:
+        volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)`
+        volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)`
+            containing the total amount of votes cast to each of the voxels.
+    """
+    ba, n_points, feature_dim = points_features.shape
+
     # flatten densities and features
     v_shape = volume_densities.shape[2:]
     volume_densities_flatten = volume_densities.view(ba, -1, 1)
@@ -376,7 +442,7 @@ def add_points_features_to_volume_densities_features(
         volume_features_flatten = volume_features.view(ba, feature_dim, n_voxels)
 
     if mode == "trilinear":  # do the splatting (trilinear interp)
-        volume_features, volume_densities = splat_points_to_volumes(
+        volume_features, volume_densities = _splat_points_to_volumes(
             points_3d,
             points_features,
             volume_densities_flatten,
@@ -386,7 +452,7 @@ def add_points_features_to_volume_densities_features(
             min_weight=min_weight,
         )
     elif mode == "nearest":  # nearest neighbor interp
-        volume_features, volume_densities = round_points_to_volumes(
+        volume_features, volume_densities = _round_points_to_volumes(
             points_3d,
             points_features,
             volume_densities_flatten,
@@ -400,7 +466,6 @@ def add_points_features_to_volume_densities_features(
     # reshape into the volume shape
     volume_features = volume_features.view(ba, feature_dim, *v_shape)
     volume_densities = volume_densities.view(ba, 1, *v_shape)
-
     return volume_features, volume_densities
 
 
@@ -441,7 +506,7 @@ def _check_points_to_volumes_inputs(
         )
 
 
-def splat_points_to_volumes(
+def _splat_points_to_volumes(
     points_3d: torch.Tensor,
     points_features: torch.Tensor,
     volume_densities: torch.Tensor,
@@ -574,7 +639,7 @@ def splat_points_to_volumes(
     return volume_features, volume_densities
 
 
-def round_points_to_volumes(
+def _round_points_to_volumes(
     points_3d: torch.Tensor,
     points_features: torch.Tensor,
     volume_densities: torch.Tensor,

diff --git a/tests/test_points_to_volumes.py b/tests/test_points_to_volumes.py
@@ -6,6 +6,7 @@
 
 import unittest
 from functools import partial
+from itertools import product
 from typing import Tuple
 
 import numpy as np
@@ -254,7 +255,7 @@ def test_from_point_cloud(self, interp_mode="trilinear"):
 
         for volume_size in ([25, 25, 25], [30, 25, 15]):
 
-            for interp_mode in ("trilinear", "nearest"):
+            for python, interp_mode in product([True, False], ["trilinear", "nearest"]):
 
                 (pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
                     volume_size=volume_size,
@@ -266,7 +267,10 @@ def test_from_point_cloud(self, interp_mode="trilinear"):
                 )
 
                 volumes = add_pointclouds_to_volumes(
-                    pointclouds, initial_volumes, mode=interp_mode
+                    pointclouds,
+                    initial_volumes,
+                    mode=interp_mode,
+                    _python=python,
                 )
 
                 V_color, V_density = volumes.features(), volumes.densities()