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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from pytorch3d.ops import perspective_n_points
from pytorch3d.transforms import rotation_conversions
from .common_testing import TestCaseMixin
def reproj_error(x_world, y, R, T, weight=None):
# applies the affine transform, projects, and computes the reprojection error
y_hat = torch.matmul(x_world, R) + T[:, None, :]
y_hat = y_hat / y_hat[..., 2:]
if weight is None:
weight = y.new_ones((1, 1))
return (((weight[:, :, None] * (y - y_hat[..., :2])) ** 2).sum(dim=-1) ** 0.5).mean(
dim=-1
)
class TestPerspectiveNPoints(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(42)
@classmethod
def _generate_epnp_test_from_2d(cls, y):
"""
Instantiate random x_world, x_cam, R, T given a set of input
2D projections y.
"""
batch_size = y.shape[0]
x_cam = torch.cat((y, torch.rand_like(y[:, :, :1]) * 2.0 + 3.5), dim=2)
x_cam[:, :, :2] *= x_cam[:, :, 2:] # unproject
R = rotation_conversions.random_rotations(batch_size).to(y)
T = torch.randn_like(R[:, :1, :])
T[:, :, 2] = (T[:, :, 2] + 3.0).clamp(2.0)
x_world = torch.matmul(x_cam - T, R.transpose(1, 2))
return x_cam, x_world, R, T
def _run_and_print(self, x_world, y, R, T, print_stats, skip_q, check_output=False):
sol = perspective_n_points.efficient_pnp(
x_world, y.expand_as(x_world[:, :, :2]), skip_quadratic_eq=skip_q
)
err_2d = reproj_error(x_world, y, sol.R, sol.T)
R_est_quat = rotation_conversions.matrix_to_quaternion(sol.R)
R_quat = rotation_conversions.matrix_to_quaternion(R)
num_pts = x_world.shape[-2]
if check_output:
assert_msg = (
f"test_perspective_n_points assertion failure for "
f"n_points={num_pts}, "
f"skip_quadratic={skip_q}, "
f"no noise."
)
self.assertClose(err_2d, sol.err_2d, msg=assert_msg)
self.assertTrue((err_2d < 1e-3).all(), msg=assert_msg)
def norm_fn(t):
return t.norm(dim=-1)
self.assertNormsClose(
T, sol.T[:, None, :], rtol=4e-3, norm_fn=norm_fn, msg=assert_msg
)
self.assertNormsClose(
R_quat, R_est_quat, rtol=3e-3, norm_fn=norm_fn, msg=assert_msg
)
if print_stats:
torch.set_printoptions(precision=5, sci_mode=False)
for err_2d, err_3d, R_gt, T_gt in zip(
sol.err_2d,
sol.err_3d,
torch.cat((sol.R, R), dim=-1),
torch.stack((sol.T, T[:, 0, :]), dim=-1),
):
print("2D Error: %1.4f" % err_2d.item())
print("3D Error: %1.4f" % err_3d.item())
print("R_hat | R_gt\n", R_gt)
print("T_hat | T_gt\n", T_gt)
def _testcase_from_2d(
self, y, print_stats, benchmark, skip_q=False, skip_check_thresh=5
):
"""
In case num_pts < 6, EPnP gets unstable, so we check it doesn't crash
"""
x_cam, x_world, R, T = TestPerspectiveNPoints._generate_epnp_test_from_2d(
y[None].repeat(16, 1, 1)
)
if print_stats:
print("Run without noise")
if benchmark: # return curried call
torch.cuda.synchronize()
def result():
self._run_and_print(x_world, y, R, T, False, skip_q)
torch.cuda.synchronize()
return result
self._run_and_print(
x_world,
y,
R,
T,
print_stats,
skip_q,
check_output=True if y.shape[1] > skip_check_thresh else False,
)
# in the noisy case, there are no guarantees, so we check it doesn't crash
if print_stats:
print("Run with noise")
x_world += torch.randn_like(x_world) * 0.1
self._run_and_print(x_world, y, R, T, print_stats, skip_q)
def case_with_gaussian_points(
self, batch_size=10, num_pts=20, print_stats=False, benchmark=True, skip_q=False
):
return self._testcase_from_2d(
torch.randn((num_pts, 2)).cuda() / 3.0,
print_stats=print_stats,
benchmark=benchmark,
skip_q=skip_q,
)
def test_perspective_n_points(self, print_stats=False):
if print_stats:
print("RUN ON A DENSE GRID")
u = torch.linspace(-1.0, 1.0, 20)
v = torch.linspace(-1.0, 1.0, 15)
for skip_q in [False, True]:
self._testcase_from_2d(
torch.cartesian_prod(u, v).cuda(), print_stats, False, skip_q
)
for num_pts in range(6, 3, -1):
for skip_q in [False, True]:
if print_stats:
print(f"RUN ON {num_pts} points; skip_quadratic: {skip_q}")
self.case_with_gaussian_points(
num_pts=num_pts,
print_stats=print_stats,
benchmark=False,
skip_q=skip_q,
)
def test_weighted_perspective_n_points(self, batch_size=16, num_pts=200):
# instantiate random x_world and y
y = torch.randn((batch_size, num_pts, 2)).cuda() / 3.0
x_cam, x_world, R, T = TestPerspectiveNPoints._generate_epnp_test_from_2d(y)
# randomly drop 50% of the rows
weights = (torch.rand_like(x_world[:, :, 0]) > 0.5).float()
# make sure we retain at least 6 points for each case
weights[:, :6] = 1.0
# fill ignored y with trash to ensure that we get different
# solution in case the weighting is wrong
y = y + (1 - weights[:, :, None]) * 100.0
def norm_fn(t):
return t.norm(dim=-1)
for skip_quadratic_eq in (True, False):
# get the solution for the 0/1 weighted case
sol = perspective_n_points.efficient_pnp(
x_world, y, skip_quadratic_eq=skip_quadratic_eq, weights=weights
)
sol_R_quat = rotation_conversions.matrix_to_quaternion(sol.R)
sol_T = sol.T
# check that running only on points with non-zero weights ends in the
# same place as running the 0/1 weighted version
for i in range(batch_size):
ok = weights[i] > 0
x_world_ok = x_world[i, ok][None]
y_ok = y[i, ok][None]
sol_ok = perspective_n_points.efficient_pnp(
x_world_ok, y_ok, skip_quadratic_eq=False
)
R_est_quat_ok = rotation_conversions.matrix_to_quaternion(sol_ok.R)
self.assertNormsClose(sol_T[i], sol_ok.T[0], rtol=3e-3, norm_fn=norm_fn)
self.assertNormsClose(
sol_R_quat[i], R_est_quat_ok[0], rtol=3e-4, norm_fn=norm_fn
)
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