mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
231 lines
8.4 KiB
Python
231 lines
8.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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from itertools import product
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import torch
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from common_testing import get_random_cuda_device, TestCaseMixin
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from pytorch3d.ops import sample_points_from_meshes
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from pytorch3d.ops.ball_query import ball_query
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from pytorch3d.ops.knn import _KNN
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from pytorch3d.utils import ico_sphere
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class TestBallQuery(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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torch.manual_seed(1)
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@staticmethod
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def _ball_query_naive(
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p1, p2, lengths1, lengths2, K: int, radius: float
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) -> torch.Tensor:
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"""
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Naive PyTorch implementation of ball query.
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"""
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N, P1, D = p1.shape
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_N, P2, _D = p2.shape
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assert N == _N and D == _D
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if lengths1 is None:
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lengths1 = torch.full((N,), P1, dtype=torch.int64, device=p1.device)
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if lengths2 is None:
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lengths2 = torch.full((N,), P2, dtype=torch.int64, device=p1.device)
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radius2 = radius * radius
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dists = torch.zeros((N, P1, K), dtype=torch.float32, device=p1.device)
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idx = torch.full((N, P1, K), fill_value=-1, dtype=torch.int64, device=p1.device)
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# Iterate through the batches
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for n in range(N):
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num1 = lengths1[n].item()
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num2 = lengths2[n].item()
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# Iterate through the points in the p1
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for i in range(num1):
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# Iterate through the points in the p2
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count = 0
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for j in range(num2):
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dist = p2[n, j] - p1[n, i]
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dist2 = (dist * dist).sum()
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if dist2 < radius2 and count < K:
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dists[n, i, count] = dist2
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idx[n, i, count] = j
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count += 1
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return _KNN(dists=dists, idx=idx, knn=None)
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def _ball_query_vs_python_square_helper(self, device):
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Ns = [1, 4]
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Ds = [3, 5, 8]
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P1s = [8, 24]
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P2s = [8, 16, 32]
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Ks = [1, 5]
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Rs = [3, 5]
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factors = [Ns, Ds, P1s, P2s, Ks, Rs]
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for N, D, P1, P2, K, R in product(*factors):
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x = torch.randn(N, P1, D, device=device, requires_grad=True)
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x_cuda = x.clone().detach()
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x_cuda.requires_grad_(True)
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y = torch.randn(N, P2, D, device=device, requires_grad=True)
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y_cuda = y.clone().detach()
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y_cuda.requires_grad_(True)
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# forward
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out1 = self._ball_query_naive(
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x, y, lengths1=None, lengths2=None, K=K, radius=R
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)
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out2 = ball_query(x_cuda, y_cuda, K=K, radius=R)
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# Check dists
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self.assertClose(out1.dists, out2.dists)
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# Check idx
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self.assertTrue(torch.all(out1.idx == out2.idx))
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# backward
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grad_dist = torch.ones((N, P1, K), dtype=torch.float32, device=device)
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loss1 = (out1.dists * grad_dist).sum()
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loss1.backward()
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loss2 = (out2.dists * grad_dist).sum()
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loss2.backward()
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self.assertClose(x_cuda.grad, x.grad, atol=5e-6)
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self.assertClose(y_cuda.grad, y.grad, atol=5e-6)
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def test_ball_query_vs_python_square_cpu(self):
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device = torch.device("cpu")
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self._ball_query_vs_python_square_helper(device)
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def test_ball_query_vs_python_square_cuda(self):
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device = get_random_cuda_device()
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self._ball_query_vs_python_square_helper(device)
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def _ball_query_vs_python_ragged_helper(self, device):
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Ns = [1, 4]
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Ds = [3, 5, 8]
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P1s = [8, 24]
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P2s = [8, 16, 32]
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Ks = [2, 3, 10]
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Rs = [1.4, 5] # radius
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factors = [Ns, Ds, P1s, P2s, Ks, Rs]
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for N, D, P1, P2, K, R in product(*factors):
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x = torch.rand((N, P1, D), device=device, requires_grad=True)
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y = torch.rand((N, P2, D), device=device, requires_grad=True)
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lengths1 = torch.randint(low=1, high=P1, size=(N,), device=device)
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lengths2 = torch.randint(low=1, high=P2, size=(N,), device=device)
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x_csrc = x.clone().detach()
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x_csrc.requires_grad_(True)
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y_csrc = y.clone().detach()
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y_csrc.requires_grad_(True)
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# forward
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out1 = self._ball_query_naive(
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x, y, lengths1=lengths1, lengths2=lengths2, K=K, radius=R
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)
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out2 = ball_query(
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x_csrc,
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y_csrc,
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lengths1=lengths1,
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lengths2=lengths2,
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K=K,
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radius=R,
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)
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self.assertClose(out1.idx, out2.idx)
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self.assertClose(out1.dists, out2.dists)
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# backward
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grad_dist = torch.ones((N, P1, K), dtype=torch.float32, device=device)
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loss1 = (out1.dists * grad_dist).sum()
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loss1.backward()
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loss2 = (out2.dists * grad_dist).sum()
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loss2.backward()
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self.assertClose(x_csrc.grad, x.grad, atol=5e-6)
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self.assertClose(y_csrc.grad, y.grad, atol=5e-6)
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def test_ball_query_vs_python_ragged_cpu(self):
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device = torch.device("cpu")
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self._ball_query_vs_python_ragged_helper(device)
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def test_ball_query_vs_python_ragged_cuda(self):
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device = get_random_cuda_device()
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self._ball_query_vs_python_ragged_helper(device)
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def test_ball_query_output_simple(self):
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device = get_random_cuda_device()
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N, P1, P2, K = 5, 8, 16, 4
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sphere = ico_sphere(level=2, device=device).extend(N)
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points_1 = sample_points_from_meshes(sphere, P1)
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points_2 = sample_points_from_meshes(sphere, P2) * 5.0
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radius = 6.0
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naive_out = self._ball_query_naive(
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points_1, points_2, lengths1=None, lengths2=None, K=K, radius=radius
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)
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cuda_out = ball_query(points_1, points_2, K=K, radius=radius)
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# All points should have N sample neighbors as radius is large
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# Zero is a valid index but can only be present once (i.e. no zero padding)
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naive_out_zeros = (naive_out.idx == 0).sum(dim=-1).max()
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cuda_out_zeros = (cuda_out.idx == 0).sum(dim=-1).max()
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self.assertTrue(naive_out_zeros == 0 or naive_out_zeros == 1)
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self.assertTrue(cuda_out_zeros == 0 or cuda_out_zeros == 1)
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# All points should now have zero sample neighbors as radius is small
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radius = 0.5
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naive_out = self._ball_query_naive(
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points_1, points_2, lengths1=None, lengths2=None, K=K, radius=radius
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)
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cuda_out = ball_query(points_1, points_2, K=K, radius=radius)
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naive_out_allzeros = (naive_out.idx == -1).all()
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cuda_out_allzeros = (cuda_out.idx == -1).sum()
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self.assertTrue(naive_out_allzeros)
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self.assertTrue(cuda_out_allzeros)
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@staticmethod
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def ball_query_square(
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N: int, P1: int, P2: int, D: int, K: int, radius: float, device: str
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):
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device = torch.device(device)
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pts1 = torch.randn(N, P1, D, device=device, requires_grad=True)
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pts2 = torch.randn(N, P2, D, device=device, requires_grad=True)
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grad_dists = torch.randn(N, P1, K, device=device)
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torch.cuda.synchronize()
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def output():
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out = ball_query(pts1, pts2, K=K, radius=radius)
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loss = (out.dists * grad_dists).sum()
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loss.backward()
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torch.cuda.synchronize()
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return output
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@staticmethod
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def ball_query_ragged(
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N: int, P1: int, P2: int, D: int, K: int, radius: float, device: str
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):
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device = torch.device(device)
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pts1 = torch.rand((N, P1, D), device=device, requires_grad=True)
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pts2 = torch.rand((N, P2, D), device=device, requires_grad=True)
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lengths1 = torch.randint(low=1, high=P1, size=(N,), device=device)
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lengths2 = torch.randint(low=1, high=P2, size=(N,), device=device)
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grad_dists = torch.randn(N, P1, K, device=device)
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torch.cuda.synchronize()
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def output():
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out = ball_query(
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pts1, pts2, lengths1=lengths1, lengths2=lengths2, K=K, radius=radius
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)
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loss = (out.dists * grad_dists).sum()
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loss.backward()
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torch.cuda.synchronize()
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return output
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