mirror of
https://github.com/facebookresearch/pytorch3d.git
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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
168 lines
6.2 KiB
Python
168 lines
6.2 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 math import radians
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.renderer.camera_utils import camera_to_eye_at_up, rotate_on_spot
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from pytorch3d.renderer.cameras import (
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get_world_to_view_transform,
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look_at_view_transform,
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PerspectiveCameras,
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)
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from pytorch3d.transforms import axis_angle_to_matrix
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from torch.nn.functional import normalize
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def _batched_dotprod(x: torch.Tensor, y: torch.Tensor):
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"""
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Takes two tensors of shape (N,3) and returns their batched
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dot product along the last dimension as a tensor of shape
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(N,).
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"""
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return torch.einsum("ij,ij->i", x, y)
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class TestCameraUtils(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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torch.manual_seed(42)
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def test_invert_eye_at_up(self):
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# Generate random cameras and check we can reconstruct their eye, at,
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# and up vectors.
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N = 13
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eye = torch.rand(N, 3)
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at = torch.rand(N, 3)
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up = torch.rand(N, 3)
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R, T = look_at_view_transform(eye=eye, at=at, up=up)
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cameras = PerspectiveCameras(R=R, T=T)
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eye2, at2, up2 = camera_to_eye_at_up(cameras.get_world_to_view_transform())
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# The retrieved eye matches
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self.assertClose(eye, eye2, atol=1e-5)
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self.assertClose(cameras.get_camera_center(), eye)
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# at-eye as retrieved must be a vector in the same direction as
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# the original.
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self.assertClose(normalize(at - eye), normalize(at2 - eye2))
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# The up vector as retrieved should be rotated the same amount
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# around at-eye as the original. The component in the at-eye
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# direction is unimportant, as is the length.
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# So check that (up x (at-eye)) as retrieved is in the same
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# direction as its original value.
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up_check = torch.cross(up, at - eye, dim=-1)
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up_check2 = torch.cross(up2, at - eye, dim=-1)
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self.assertClose(normalize(up_check), normalize(up_check2))
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# Master check that we get the same camera if we reinitialise.
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R2, T2 = look_at_view_transform(eye=eye2, at=at2, up=up2)
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cameras2 = PerspectiveCameras(R=R2, T=T2)
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cam_trans = cameras.get_world_to_view_transform()
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cam_trans2 = cameras2.get_world_to_view_transform()
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self.assertClose(cam_trans.get_matrix(), cam_trans2.get_matrix(), atol=1e-5)
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def test_rotate_on_spot_yaw(self):
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N = 14
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eye = torch.rand(N, 3)
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at = torch.rand(N, 3)
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up = torch.rand(N, 3)
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R, T = look_at_view_transform(eye=eye, at=at, up=up)
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# Moving around the y axis looks left.
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angles = torch.FloatTensor([0, -radians(10), 0])
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rotation = axis_angle_to_matrix(angles)
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R_rot, T_rot = rotate_on_spot(R, T, rotation)
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eye_rot, at_rot, up_rot = camera_to_eye_at_up(
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get_world_to_view_transform(R=R_rot, T=T_rot)
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)
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self.assertClose(eye, eye_rot, atol=1e-5)
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# Make vectors pointing exactly left and up
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left = torch.cross(up, at - eye, dim=-1)
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left_rot = torch.cross(up_rot, at_rot - eye_rot, dim=-1)
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fully_up = torch.cross(at - eye, left, dim=-1)
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fully_up_rot = torch.cross(at_rot - eye_rot, left_rot, dim=-1)
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# The up direction is unchanged
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self.assertClose(normalize(fully_up), normalize(fully_up_rot), atol=1e-5)
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# The camera has moved left
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agree = _batched_dotprod(torch.cross(left, left_rot, dim=1), fully_up)
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self.assertGreater(agree.min(), 0)
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# Batch dimension for rotation
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R_rot2, T_rot2 = rotate_on_spot(R, T, rotation.expand(N, 3, 3))
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self.assertClose(R_rot, R_rot2)
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self.assertClose(T_rot, T_rot2)
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# No batch dimension for either
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R_rot3, T_rot3 = rotate_on_spot(R[0], T[0], rotation)
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self.assertClose(R_rot[:1], R_rot3)
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self.assertClose(T_rot[:1], T_rot3)
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# No batch dimension for R, T
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R_rot4, T_rot4 = rotate_on_spot(R[0], T[0], rotation.expand(N, 3, 3))
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self.assertClose(R_rot[:1].expand(N, 3, 3), R_rot4)
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self.assertClose(T_rot[:1].expand(N, 3), T_rot4)
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def test_rotate_on_spot_pitch(self):
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N = 14
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eye = torch.rand(N, 3)
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at = torch.rand(N, 3)
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up = torch.rand(N, 3)
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R, T = look_at_view_transform(eye=eye, at=at, up=up)
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# Moving around the x axis looks down.
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angles = torch.FloatTensor([-radians(10), 0, 0])
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rotation = axis_angle_to_matrix(angles)
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R_rot, T_rot = rotate_on_spot(R, T, rotation)
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eye_rot, at_rot, up_rot = camera_to_eye_at_up(
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get_world_to_view_transform(R=R_rot, T=T_rot)
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)
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self.assertClose(eye, eye_rot, atol=1e-5)
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# A vector pointing left is unchanged
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left = torch.cross(up, at - eye, dim=-1)
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left_rot = torch.cross(up_rot, at_rot - eye_rot, dim=-1)
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self.assertClose(normalize(left), normalize(left_rot), atol=1e-5)
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# The camera has moved down
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fully_up = torch.cross(at - eye, left, dim=-1)
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fully_up_rot = torch.cross(at_rot - eye_rot, left_rot, dim=-1)
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agree = _batched_dotprod(torch.cross(fully_up, fully_up_rot, dim=1), left)
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self.assertGreater(agree.min(), 0)
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def test_rotate_on_spot_roll(self):
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N = 14
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eye = torch.rand(N, 3)
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at = torch.rand(N, 3)
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up = torch.rand(N, 3)
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R, T = look_at_view_transform(eye=eye, at=at, up=up)
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# Moving around the z axis rotates the image.
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angles = torch.FloatTensor([0, 0, -radians(10)])
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rotation = axis_angle_to_matrix(angles)
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R_rot, T_rot = rotate_on_spot(R, T, rotation)
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eye_rot, at_rot, up_rot = camera_to_eye_at_up(
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get_world_to_view_transform(R=R_rot, T=T_rot)
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)
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self.assertClose(eye, eye_rot, atol=1e-5)
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self.assertClose(normalize(at - eye), normalize(at_rot - eye), atol=1e-5)
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# The camera has moved clockwise
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agree = _batched_dotprod(torch.cross(up, up_rot, dim=1), at - eye)
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self.assertGreater(agree.min(), 0)
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