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https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
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
This commit is contained in:
Jeremy Reizenstein
Facebook GitHub Bot
parent
9ad98c87c3
commit
ee2b2feb98
@ -192,6 +192,7 @@ def add_pointclouds_to_volumes(
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initial_volumes: "Volumes",
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mode: str = "trilinear",
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min_weight: float = 1e-4,
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_python: bool = False,
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) -> "Volumes":
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"""
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Add a batch of point clouds represented with a `Pointclouds` structure
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@ -249,6 +250,8 @@ def add_pointclouds_to_volumes(
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min_weight: A scalar controlling the lowest possible total per-voxel
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weight used to normalize the features accumulated in a voxel.
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Only active for `mode==trilinear`.
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_python: Set to True to use a pure Python implementation, e.g. for test
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purposes, which requires more memory and may be slower.
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Returns:
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updated_volumes: Output `Volumes` structure containing the conversion result.
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@ -283,6 +286,7 @@ def add_pointclouds_to_volumes(
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grid_sizes=initial_volumes.get_grid_sizes(),
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mask=mask,
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mode=mode,
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_python=_python,
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)
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return initial_volumes.update_padded(
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@ -299,6 +303,7 @@ def add_points_features_to_volume_densities_features(
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min_weight: float = 1e-4,
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mask: Optional[torch.Tensor] = None,
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grid_sizes: Optional[torch.LongTensor] = None,
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_python: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Convert a batch of point clouds represented with tensors of per-point
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@ -340,6 +345,7 @@ def add_points_features_to_volume_densities_features(
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grid_sizes: `LongTensor` of shape (minibatch, 3) representing the
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spatial resolutions of each of the the non-flattened `volumes` tensors,
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or None to indicate the whole volume is used for every batch element.
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_python: Set to True to use a pure Python implementation.
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Returns:
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volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)`
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volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)`
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@ -362,6 +368,66 @@ def add_points_features_to_volume_densities_features(
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.expand(volume_densities.shape[0], 3)
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)
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if _python:
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return _add_points_features_to_volume_densities_features_python(
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points_3d=points_3d,
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points_features=points_features,
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volume_densities=volume_densities,
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volume_features=volume_features,
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mode=mode,
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min_weight=min_weight,
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mask=mask,
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grid_sizes=grid_sizes,
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)
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if mode == "trilinear":
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splat = True
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elif mode == "nearest":
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splat = False
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else:
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raise ValueError('No such interpolation mode "%s"' % mode)
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volume_densities, volume_features = _points_to_volumes(
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points_3d,
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points_features,
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volume_densities,
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volume_features,
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grid_sizes,
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1.0, # point_weight
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mask,
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True, # align_corners
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splat,
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)
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if splat:
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# divide each feature by the total weight of the votes
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volume_features = volume_features / volume_densities.clamp(min_weight)
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else:
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# divide each feature by the total weight of the votes
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volume_features = volume_features / volume_densities.clamp(1.0)
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return volume_features, volume_densities
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def _add_points_features_to_volume_densities_features_python(
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*,
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points_3d: torch.Tensor,
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points_features: torch.Tensor,
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volume_densities: torch.Tensor,
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volume_features: Optional[torch.Tensor],
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mode: str,
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min_weight: float,
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mask: Optional[torch.Tensor],
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grid_sizes: torch.LongTensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Python implementation for add_points_features_to_volume_densities_features.
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Returns:
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volume_features: Output volume of shape `(minibatch, feature_dim, D, H, W)`
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volume_densities: Occupancy volume of shape `(minibatch, 1, D, H, W)`
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containing the total amount of votes cast to each of the voxels.
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"""
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ba, n_points, feature_dim = points_features.shape
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# flatten densities and features
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v_shape = volume_densities.shape[2:]
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volume_densities_flatten = volume_densities.view(ba, -1, 1)
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@ -376,7 +442,7 @@ def add_points_features_to_volume_densities_features(
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volume_features_flatten = volume_features.view(ba, feature_dim, n_voxels)
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if mode == "trilinear": # do the splatting (trilinear interp)
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volume_features, volume_densities = splat_points_to_volumes(
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volume_features, volume_densities = _splat_points_to_volumes(
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points_3d,
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points_features,
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volume_densities_flatten,
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@ -386,7 +452,7 @@ def add_points_features_to_volume_densities_features(
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min_weight=min_weight,
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)
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elif mode == "nearest": # nearest neighbor interp
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volume_features, volume_densities = round_points_to_volumes(
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volume_features, volume_densities = _round_points_to_volumes(
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points_3d,
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points_features,
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volume_densities_flatten,
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@ -400,7 +466,6 @@ def add_points_features_to_volume_densities_features(
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# reshape into the volume shape
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volume_features = volume_features.view(ba, feature_dim, *v_shape)
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volume_densities = volume_densities.view(ba, 1, *v_shape)
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return volume_features, volume_densities
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@ -441,7 +506,7 @@ def _check_points_to_volumes_inputs(
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)
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def splat_points_to_volumes(
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def _splat_points_to_volumes(
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points_3d: torch.Tensor,
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points_features: torch.Tensor,
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volume_densities: torch.Tensor,
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@ -574,7 +639,7 @@ def splat_points_to_volumes(
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return volume_features, volume_densities
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def round_points_to_volumes(
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def _round_points_to_volumes(
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points_3d: torch.Tensor,
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points_features: torch.Tensor,
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volume_densities: torch.Tensor,
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@ -6,6 +6,7 @@
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import unittest
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from functools import partial
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from itertools import product
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from typing import Tuple
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import numpy as np
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@ -254,7 +255,7 @@ class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
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for volume_size in ([25, 25, 25], [30, 25, 15]):
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for interp_mode in ("trilinear", "nearest"):
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for python, interp_mode in product([True, False], ["trilinear", "nearest"]):
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(pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
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volume_size=volume_size,
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@ -266,7 +267,10 @@ class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
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)
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volumes = add_pointclouds_to_volumes(
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pointclouds, initial_volumes, mode=interp_mode
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pointclouds,
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initial_volumes,
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mode=interp_mode,
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_python=python,
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)
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V_color, V_density = volumes.features(), volumes.densities()
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