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Commitbcee361

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Alexey Sidnevfacebook-github-bot
Alexey Sidnev
authored andcommitted
Replacetorch.det() with manual implementation for 3x3 matrix
Summary:# BackgroundThere is an unstable error during training (it can happen after several minutes or after several hours).The error is connected to `torch.det()` function in `_check_valid_rotation_matrix()`.if I remove the function `torch.det()` in `_check_valid_rotation_matrix()` or remove the whole functions `_check_valid_rotation_matrix()` the error is disappeared (D29555876).# SolutionReplace `torch.det()` with manual implementation for 3x3 matrix.Reviewed By: patricklabatutDifferential Revision: D29655924fbshipit-source-id: 41bde1119274a705ab849751ece28873d2c45155
1 parent2f668ec commitbcee361

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3 files changed

+89
-1
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3 files changed

+89
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‎pytorch3d/common/workaround.py‎

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# Copyright (c) Facebook, Inc. and its 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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importtorch
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def_safe_det_3x3(t:torch.Tensor):
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"""
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Fast determinant calculation for a batch of 3x3 matrices.
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Note, result of this function might not be the same as `torch.det()`.
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The differences might be in the last significant digit.
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Args:
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t: Tensor of shape (N, 3, 3).
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Returns:
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Tensor of shape (N) with determinants.
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"""
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det= (
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t[...,0,0]* (t[...,1,1]*t[...,2,2]-t[...,1,2]*t[...,2,1])
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-t[...,0,1]* (t[...,1,0]*t[...,2,2]-t[...,2,0]*t[...,1,2])
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+t[...,0,2]* (t[...,1,0]*t[...,2,1]-t[...,2,0]*t[...,1,1])
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)
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returndet

‎pytorch3d/transforms/transform3d.py‎

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importtorch
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from ..common.typesimportDevice,get_device,make_device
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from ..common.workaroundimport_safe_det_3x3
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from .rotation_conversionsimport_axis_angle_rotation
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@@ -774,7 +775,7 @@ def _check_valid_rotation_matrix(R, tol: float = 1e-7):
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eye=torch.eye(3,dtype=R.dtype,device=R.device)
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eye=eye.view(1,3,3).expand(N,-1,-1)
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orthogonal=torch.allclose(R.bmm(R.transpose(1,2)),eye,atol=tol)
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det_R=torch.det(R)
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det_R=_safe_det_3x3(R)
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no_distortion=torch.allclose(det_R,torch.ones_like(det_R))
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ifnot (orthogonalandno_distortion):
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msg="R is not a valid rotation matrix"

‎tests/test_common_workaround.py‎

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# Copyright (c) Facebook, Inc. and its 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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importunittest
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importnumpyasnp
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importtorch
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fromcommon_testingimportTestCaseMixin
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frompytorch3d.common.workaroundimport_safe_det_3x3
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classTestSafeDet3x3(TestCaseMixin,unittest.TestCase):
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defsetUp(self)->None:
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super().setUp()
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torch.manual_seed(42)
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np.random.seed(42)
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def_test_det_3x3(self,batch_size,device):
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t=torch.rand((batch_size,3,3),dtype=torch.float32,device=device)
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actual_det=_safe_det_3x3(t)
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expected_det=t.det()
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self.assertClose(actual_det,expected_det,atol=1e-7)
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deftest_empty_batch(self):
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self._test_det_3x3(0,torch.device("cpu"))
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self._test_det_3x3(0,torch.device("cuda:0"))
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deftest_manual(self):
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t=torch.Tensor(
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[
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[[1,0,0], [0,1,0], [0,0,1]],
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[[2,-5,3], [0,7,-2], [-1,4,1]],
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[[6,1,1], [4,-2,5], [2,8,7]],
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]
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).to(dtype=torch.float32)
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expected_det=torch.Tensor([1,41,-306]).to(dtype=torch.float32)
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self.assertClose(_safe_det_3x3(t),expected_det)
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device_cuda=torch.device("cuda:0")
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self.assertClose(
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_safe_det_3x3(t.to(device=device_cuda)),expected_det.to(device=device_cuda)
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)
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deftest_regression(self):
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tries=32
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device_cpu=torch.device("cpu")
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device_cuda=torch.device("cuda:0")
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batch_sizes=np.random.randint(low=1,high=128,size=tries)
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forbatch_sizeinbatch_sizes:
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self._test_det_3x3(batch_size,device_cpu)
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self._test_det_3x3(batch_size,device_cuda)

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