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
263 lines
9.9 KiB
Python
263 lines
9.9 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.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(
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p1, p2, lengths1, lengths2, K: int, norm: int = 2
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) -> 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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if norm == 2:
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diff = (diff * diff).sum(2)
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elif norm == 1:
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diff = diff.abs().sum(2)
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else:
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raise ValueError("No support for norm %d" % (norm))
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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, return_sorted):
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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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norms = [1, 2]
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versions = [0, 1, 2, 3]
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factors = [Ns, Ds, P1s, P2s, Ks, norms]
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for N, D, P1, P2, K, norm 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(
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x, y, lengths1=None, lengths2=None, K=K, norm=norm
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)
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out2 = knn_points(
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x_cuda,
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y_cuda,
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K=K,
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norm=norm,
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version=version,
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return_sorted=return_sorted,
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)
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if K > 1 and not return_sorted:
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# check out2 is not sorted
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self.assertFalse(torch.allclose(out1[0], out2[0]))
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self.assertFalse(torch.allclose(out1[1], out2[1]))
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# now sort out2
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dists, idx, _ = out2
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if P2 < K:
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dists[..., P2:] = float("inf")
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dists, sort_idx = dists.sort(dim=2)
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dists[..., P2:] = 0
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else:
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dists, sort_idx = dists.sort(dim=2)
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idx = idx.gather(2, sort_idx)
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out2 = _KNN(dists, idx, None)
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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, return_sorted=True)
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def test_knn_vs_python_square_cuda(self):
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device = get_random_cuda_device()
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# Check both cases where the output is sorted and unsorted
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self._knn_vs_python_square_helper(device, return_sorted=True)
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self._knn_vs_python_square_helper(device, return_sorted=False)
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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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norms = [1, 2]
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factors = [Ns, Ds, P1s, P2s, Ks, norms]
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for N, D, P1, P2, K, norm 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, norm=norm
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)
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out2 = knn_points(
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x_csrc, y_csrc, lengths1=lengths1, lengths2=lengths2, K=K, norm=norm
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)
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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 = get_random_cuda_device()
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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 = get_random_cuda_device()
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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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def test_knn_check_version(self):
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try:
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from pytorch3d._C import knn_check_version
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except ImportError:
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# knn_check_version will only be defined if we compiled with CUDA support
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return
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for D in range(-10, 10):
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for K in range(-10, 20):
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v0 = True
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v1 = 1 <= D <= 32
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v2 = 1 <= D <= 8 and 1 <= K <= 32
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v3 = 1 <= D <= 8 and 1 <= K <= 4
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all_expected = [v0, v1, v2, v3]
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for version in range(-10, 10):
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actual = knn_check_version(version, D, K)
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expected = False
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if 0 <= version < len(all_expected):
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expected = all_expected[version]
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self.assertEqual(actual, expected)
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def test_invalid_norm(self):
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device = get_random_cuda_device()
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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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with self.assertRaisesRegex(ValueError, "Support for 1 or 2 norm."):
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knn_points(x, y, K=K, norm=3)
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with self.assertRaisesRegex(ValueError, "Support for 1 or 2 norm."):
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knn_points(x, y, K=K, norm=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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