mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
b2b0c5a442
Summary: Adds knn backward to return `grad_pts1` and `grad_pts2`. Adds `knn_gather` to return the nearest neighbors in pts2. The BM tests include backward pass and are ran on an M40. ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- KNN_SQUARE_32_256_128_3_24_cpu 39558 43485 13 KNN_SQUARE_32_256_128_3_24_cuda:0 1080 1404 463 KNN_SQUARE_32_256_512_3_24_cpu 81950 85781 7 KNN_SQUARE_32_256_512_3_24_cuda:0 1519 1641 330 -------------------------------------------------------------------------------- Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- KNN_RAGGED_32_256_128_3_24_cpu 13798 14650 37 KNN_RAGGED_32_256_128_3_24_cuda:0 1576 1713 318 KNN_RAGGED_32_256_512_3_24_cpu 31255 32210 16 KNN_RAGGED_32_256_512_3_24_cuda:0 2024 2162 248 -------------------------------------------------------------------------------- ``` Reviewed By: jcjohnson Differential Revision: D20945556 fbshipit-source-id: a16f616029c6b5f8c2afceb5f2bc12c5c20d2f3c
191 lines
7.0 KiB
Python
191 lines
7.0 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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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 TestCaseMixin
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from pytorch3d.ops.knn import _KNN, knn_gather, knn_points
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class TestKNN(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 _knn_points_naive(p1, p2, lengths1, lengths2, K: int) -> torch.Tensor:
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"""
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Naive PyTorch implementation of K-Nearest Neighbors.
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Returns always sorted results
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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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dists = torch.zeros((N, P1, K), dtype=torch.float32, device=p1.device)
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idx = torch.zeros((N, P1, K), dtype=torch.int64, device=p1.device)
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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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pp1 = p1[n, :num1].view(num1, 1, D)
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pp2 = p2[n, :num2].view(1, num2, D)
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diff = pp1 - pp2
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diff = (diff * diff).sum(2)
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num2 = min(num2, K)
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for i in range(num1):
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dd = diff[i]
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srt_dd, srt_idx = dd.sort()
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dists[n, i, :num2] = srt_dd[:num2]
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idx[n, i, :num2] = srt_idx[:num2]
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return _KNN(dists=dists, idx=idx, knn=None)
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def _knn_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, 3, 10]
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versions = [0, 1, 2, 3]
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factors = [Ns, Ds, P1s, P2s, Ks]
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for N, D, P1, P2, K in product(*factors):
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for version in versions:
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if version == 3 and K > 4:
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continue
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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._knn_points_naive(x, y, lengths1=None, lengths2=None, K=K)
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out2 = knn_points(x_cuda, y_cuda, K=K, version=version)
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self.assertClose(out1[0], out2[0])
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self.assertTrue(torch.all(out1[1] == out2[1]))
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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_knn_vs_python_square_cpu(self):
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device = torch.device("cpu")
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self._knn_vs_python_square_helper(device)
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def test_knn_vs_python_square_cuda(self):
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device = torch.device("cuda:0")
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self._knn_vs_python_square_helper(device)
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def _knn_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 = [1, 3, 10]
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factors = [Ns, Ds, P1s, P2s, Ks]
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for N, D, P1, P2, K 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._knn_points_naive(
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x, y, lengths1=lengths1, lengths2=lengths2, K=K
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)
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out2 = knn_points(x_csrc, y_csrc, lengths1=lengths1, lengths2=lengths2, K=K)
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self.assertClose(out1[0], out2[0])
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self.assertTrue(torch.all(out1[1] == out2[1]))
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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_knn_vs_python_ragged_cpu(self):
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device = torch.device("cpu")
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self._knn_vs_python_ragged_helper(device)
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def test_knn_vs_python_ragged_cuda(self):
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device = torch.device("cuda:0")
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self._knn_vs_python_ragged_helper(device)
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def test_knn_gather(self):
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device = torch.device("cuda:0")
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N, P1, P2, K, D = 4, 16, 12, 8, 3
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x = torch.rand((N, P1, D), device=device)
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y = torch.rand((N, P2, D), device=device)
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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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out = knn_points(x, y, lengths1=lengths1, lengths2=lengths2, K=K)
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y_nn = knn_gather(y, out.idx, lengths2)
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for n in range(N):
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for p1 in range(P1):
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for k in range(K):
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if k < lengths2[n]:
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self.assertClose(y_nn[n, p1, k], y[n, out.idx[n, p1, k]])
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else:
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self.assertTrue(torch.all(y_nn[n, p1, k] == 0.0))
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@staticmethod
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def knn_square(N: int, P1: int, P2: int, D: int, K: int, device: str):
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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 = knn_points(pts1, pts2, K=K)
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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 knn_ragged(N: int, P1: int, P2: int, D: int, K: int, device: str):
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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 = knn_points(pts1, pts2, lengths1=lengths1, lengths2=lengths2, K=K)
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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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