mirror of
https://github.com/facebookresearch/pytorch3d.git
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04d8bf6a43
Summary: Efficient PnP algorithm to fit 2D to 3D correspondences under perspective assumption. Benchmarked both variants of nullspace and pick one; SVD takes 7 times longer in the 100K points case. Reviewed By: davnov134, gkioxari Differential Revision: D20095754 fbshipit-source-id: 2b4519729630e6373820880272f674829eaed073
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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from typing import Callable, Optional, Union
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import numpy as np
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import torch
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TensorOrArray = Union[torch.Tensor, np.ndarray]
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class TestCaseMixin(unittest.TestCase):
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def assertSeparate(self, tensor1, tensor2) -> None:
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"""
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Verify that tensor1 and tensor2 have their data in distinct locations.
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"""
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self.assertNotEqual(tensor1.storage().data_ptr(), tensor2.storage().data_ptr())
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def assertNotSeparate(self, tensor1, tensor2) -> None:
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"""
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Verify that tensor1 and tensor2 have their data in the same locations.
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"""
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self.assertEqual(tensor1.storage().data_ptr(), tensor2.storage().data_ptr())
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def assertAllSeparate(self, tensor_list) -> None:
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"""
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Verify that all tensors in tensor_list have their data in
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distinct locations.
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"""
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ptrs = [i.storage().data_ptr() for i in tensor_list]
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self.assertCountEqual(ptrs, set(ptrs))
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def assertNormsClose(
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self,
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input: TensorOrArray,
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other: TensorOrArray,
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norm_fn: Callable[[TensorOrArray], TensorOrArray],
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*,
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rtol: float = 1e-05,
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atol: float = 1e-08,
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equal_nan: bool = False,
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msg: Optional[str] = None,
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) -> None:
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"""
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Verifies that two tensors or arrays have the same shape and are close
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given absolute and relative tolerance; raises AssertionError otherwise.
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A custom norm function is computed before comparison. If no such pre-
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processing needed, pass `torch.abs` or, equivalently, call `assertClose`.
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Args:
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input, other: two tensors or two arrays.
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norm_fn: The function evaluates
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`all(norm_fn(input - other) <= atol + rtol * norm_fn(other))`.
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norm_fn is a tensor -> tensor function; the output has:
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* all entries non-negative,
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* shape defined by the input shape only.
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rtol, atol, equal_nan: as for torch.allclose.
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msg: message in case the assertion is violated.
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Note:
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Optional arguments here are all keyword-only, to avoid confusion
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with msg arguments on other assert functions.
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"""
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self.assertEqual(np.shape(input), np.shape(other))
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diff = norm_fn(input - other)
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other_ = norm_fn(other)
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# We want to generalise allclose(input, output), which is essentially
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# all(diff <= atol + rtol * other)
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# but with a sophisticated handling non-finite values.
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# We work that around by calling allclose() with the following arguments:
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# allclose(diff + other_, other_). This computes what we want because
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# all(|diff + other_ - other_| <= atol + rtol * |other_|) ==
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# all(|norm_fn(input - other)| <= atol + rtol * |norm_fn(other)|) ==
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# all(norm_fn(input - other) <= atol + rtol * norm_fn(other)).
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backend = torch if torch.is_tensor(input) else np
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close = backend.allclose(
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diff + other_, other_, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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self.assertTrue(close, msg)
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def assertClose(
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self,
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input: TensorOrArray,
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other: TensorOrArray,
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*,
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rtol: float = 1e-05,
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atol: float = 1e-08,
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equal_nan: bool = False,
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msg: Optional[str] = None,
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) -> None:
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"""
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Verifies that two tensors or arrays have the same shape and are close
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given absolute and relative tolerance, i.e. checks
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`all(|input - other| <= atol + rtol * |other|)`;
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raises AssertionError otherwise.
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Args:
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input, other: two tensors or two arrays.
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rtol, atol, equal_nan: as for torch.allclose.
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msg: message in case the assertion is violated.
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Note:
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Optional arguments here are all keyword-only, to avoid confusion
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with msg arguments on other assert functions.
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"""
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self.assertEqual(np.shape(input), np.shape(other))
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backend = torch if torch.is_tensor(input) else np
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close = backend.allclose(
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input, other, rtol=rtol, atol=atol, equal_nan=equal_nan
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)
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self.assertTrue(close, msg)
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