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"""Test gradient implementations."""
import logging
import unittest
import torch
from torch.func import jacfwd, vmap
from siclib.geometry.camera import camera_models
from siclib.geometry.gravity import Gravity
from siclib.geometry.jacobians import J_up_projection
from siclib.geometry.manifolds import SphericalManifold
from siclib.geometry.perspective_fields import J_perspective_field, get_perspective_field
from siclib.models.optimization.lm_optimizer import LMOptimizer
from siclib.utils.conversions import deg2rad, fov2focal
# flake8: noqa E731
# mypy: ignore-errors
H, W = 320, 320
K1 = -0.1
# CAMERA_MODEL = "pinhole"
CAMERA_MODEL = "simple_radial"
# CAMERA_MODEL = "simple_divisional"
Camera = camera_models[CAMERA_MODEL]
# detect anomaly
torch.autograd.set_detect_anomaly(True)
logger = logging.getLogger("geocalib.models.base_model")
logger.setLevel("ERROR")
def get_toy_rpf(roll=None, pitch=None, vfov=None) -> torch.Tensor:
"""Return a random roll, pitch, focal length if not specified."""
if roll is None:
roll = deg2rad((torch.rand(1) - 0.5) * 90) # -45 ~ 45
elif not isinstance(roll, torch.Tensor):
roll = torch.tensor(deg2rad(roll)).unsqueeze(0)
if pitch is None:
pitch = deg2rad((torch.rand(1) - 0.5) * 90) # -45 ~ 45
elif not isinstance(pitch, torch.Tensor):
pitch = torch.tensor(deg2rad(pitch)).unsqueeze(0)
if vfov is None:
vfov = deg2rad(5 + torch.rand(1) * 75) # 5 ~ 80
elif not isinstance(vfov, torch.Tensor):
vfov = torch.tensor(deg2rad(vfov)).unsqueeze(0)
return torch.stack([roll, pitch, fov2focal(vfov, H)], dim=-1).float()
class TestJacobianFunctions(unittest.TestCase):
"""Test the jacobian functions."""
eps = 5e-3
def validate(self, J: torch.Tensor, J_auto: torch.Tensor):
"""Check if the jacobians are close and finite."""
self.assertTrue(torch.all(torch.isfinite(J)), "found nan in numerical")
self.assertTrue(torch.all(torch.isfinite(J_auto)), "found nan in auto")
text_j = f" > {self.eps}\nJ:\n{J[0, 0].numpy()}\nJ_auto:\n{J_auto[0, 0].numpy()}"
max_diff = torch.max(torch.abs(J - J_auto))
text = f"Overall - max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J, J_auto, atol=self.eps), text)
def test_spherical_plus(self):
"""Test the spherical plus operator."""
rpf = get_toy_rpf()
gravity = Gravity.from_rp(rpf[..., 0], rpf[..., 1])
J = gravity.J_update(spherical=True)
# auto jacobian
delta = gravity.vec3d.new_zeros(gravity.vec3d.shape)[..., :-1]
def spherical_plus(delta: torch.Tensor) -> torch.Tensor:
"""Plus operator."""
return SphericalManifold.plus(gravity.vec3d, delta)
J_auto = vmap(jacfwd(spherical_plus))(delta).squeeze(0)
self.validate(J, J_auto)
def test_up_projection_uv(self):
"""Test the up projection jacobians."""
rpf = get_toy_rpf()
r, p, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
gravity = Gravity.from_rp(r, p)
uv = camera.normalize(camera.pixel_coordinates())
J = J_up_projection(uv, gravity.vec3d, "uv")
# auto jacobian
def projection_uv(uv: torch.Tensor) -> torch.Tensor:
"""Projection."""
abc = gravity.vec3d
projected_up2d = abc[..., None, :2] - abc[..., 2, None, None] * uv
return projected_up2d[0, 0]
J_auto = vmap(jacfwd(projection_uv))(uv[0])[None]
self.validate(J, J_auto)
def test_up_projection_abc(self):
"""Test the up projection jacobians."""
rpf = get_toy_rpf()
r, p, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
gravity = Gravity.from_rp(r, p)
uv = camera.normalize(camera.pixel_coordinates())
J = J_up_projection(uv, gravity.vec3d, "abc")
# auto jacobian
def projection_abc(abc: torch.Tensor) -> torch.Tensor:
"""Projection."""
return abc[..., None, :2] - abc[..., 2, None, None] * uv
J_auto = vmap(jacfwd(projection_abc))(gravity.vec3d)[0]
self.validate(J, J_auto)
def test_undistort_pts(self):
"""Test the undistortion jacobians."""
if CAMERA_MODEL == "pinhole":
return
rpf = get_toy_rpf()
_, _, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
uv = camera.normalize(camera.pixel_coordinates())
J = camera.J_undistort(uv, "pts")
# auto jacobian
def func_pts(pts):
return camera.undistort(pts)[0][0]
J_auto = vmap(jacfwd(func_pts))(uv[0])[None].squeeze(-3)
self.validate(J, J_auto)
def test_undistort_k1(self):
"""Test the undistortion jacobians."""
if CAMERA_MODEL == "pinhole":
return
rpf = get_toy_rpf()
_, _, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
uv = camera.normalize(camera.pixel_coordinates())
J = camera.J_undistort(uv, "dist")
# auto jacobian
def func_k1(k1):
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
return camera.undistort(uv)[0][0]
J_auto = vmap(jacfwd(func_k1))(camera.dist[..., :1]).squeeze(-1)
self.validate(J, J_auto)
def test_up_projection_offset(self):
"""Test the up projection offset jacobians."""
if CAMERA_MODEL == "pinhole":
return
rpf = get_toy_rpf()
# J = up_projection_offset(rpf)
_, _, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
uv = camera.normalize(camera.pixel_coordinates())
J = camera.up_projection_offset(uv)
# auto jacobian
def projection_uv(uv: torch.Tensor) -> torch.Tensor:
"""Projection."""
s, _ = camera.distort(uv, return_scale=True)
return s[0, 0, 0]
J_auto = vmap(jacfwd(projection_uv))(uv[0])[None].squeeze(-2)
self.validate(J, J_auto)
def test_J_up_projection_offset_uv(self):
"""Test the up projection offset jacobians."""
if CAMERA_MODEL == "pinhole":
return
rpf = get_toy_rpf()
_, _, f = rpf.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": [K1]})
uv = camera.normalize(camera.pixel_coordinates())
J = camera.J_up_projection_offset(uv, "uv")
# auto jacobian
def projection_uv(uv: torch.Tensor) -> torch.Tensor:
"""Projection."""
return camera.up_projection_offset(uv)[0, 0]
J_auto = vmap(jacfwd(projection_uv))(uv[0])[None]
# print(J.shape, J_auto.shape)
self.validate(J, J_auto)
class TestEuclidean(unittest.TestCase):
"""Test the Euclidean manifold jacobians."""
eps = 5e-3
def validate(self, J: torch.Tensor, J_auto: torch.Tensor):
"""Check if the jacobians are close and finite."""
self.assertTrue(torch.all(torch.isfinite(J)), "found nan in numerical")
self.assertTrue(torch.all(torch.isfinite(J_auto)), "found nan in auto")
# print(f"analytical:\n{J[0, 0, 0].numpy()}\nauto:\n{J_auto[0, 0, 0].numpy()}")
text_j = f" > {self.eps}\nJ:\n{J[0, 0, 0].numpy()}\nJ_auto:\n{J_auto[0, 0, 0].numpy()}"
J_up2grav = J[..., :2, :2]
J_up2grav_auto = J_auto[..., :2, :2]
max_diff = torch.max(torch.abs(J_up2grav - J_up2grav_auto))
text = f"UP - GRAV max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2grav, J_up2grav_auto, atol=self.eps), text)
J_up2focal = J[..., :2, 2]
J_up2focal_auto = J_auto[..., :2, 2]
max_diff = torch.max(torch.abs(J_up2focal - J_up2focal_auto))
text = f"UP - FOCAL max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2focal, J_up2focal_auto, atol=self.eps), text)
if CAMERA_MODEL != "pinhole":
J_up2k1 = J[..., :2, 3]
J_up2k1_auto = J_auto[..., :2, 3]
max_diff = torch.max(torch.abs(J_up2k1 - J_up2k1_auto))
text = f"UP - K1 max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2k1, J_up2k1_auto, atol=self.eps), text)
J_lat2grav = J[..., 2:, :2]
J_lat2grav_auto = J_auto[..., 2:, :2]
max_diff = torch.max(torch.abs(J_lat2grav - J_lat2grav_auto))
text = f"LAT - GRAV max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2grav, J_lat2grav_auto, atol=self.eps), text)
J_lat2focal = J[..., 2:, 2]
J_lat2focal_auto = J_auto[..., 2:, 2]
max_diff = torch.max(torch.abs(J_lat2focal - J_lat2focal_auto))
text = f"LAT - FOCAL max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2focal, J_lat2focal_auto, atol=self.eps), text)
if CAMERA_MODEL != "pinhole":
J_lat2k1 = J[..., 2:, 3]
J_lat2k1_auto = J_auto[..., 2:, 3]
max_diff = torch.max(torch.abs(J_lat2k1 - J_lat2k1_auto))
text = f"LAT - K1 max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2k1, J_lat2k1_auto, atol=self.eps), text)
max_diff = torch.max(torch.abs(J - J_auto[..., : J.shape[-1]]))
text = f"Overall - max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J, J_auto[..., : J.shape[-1]], atol=self.eps), text)
def local_pf_calc(self, rpfk: torch.Tensor):
"""Calculate the perspective field."""
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
up, lat = get_perspective_field(camera, gravity)
persp = torch.cat([up, torch.sin(lat)], dim=-3)
return persp.permute(0, 2, 3, 1).reshape(1, -1, 3)
def test_random(self):
"""Random rpf."""
rpf = get_toy_rpf()
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=False), -2)
J_auto = jacfwd(self.local_pf_calc)(rpfk).squeeze(-2, -3).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_zero_roll(self):
"""Roll = 0."""
rpf = get_toy_rpf(roll=0)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=False), -2)
J_auto = jacfwd(self.local_pf_calc)(rpfk).squeeze(-2, -3).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_zero_pitch(self):
"""Pitch = 0."""
rpf = get_toy_rpf(pitch=0)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=False), -2)
J_auto = jacfwd(self.local_pf_calc)(rpfk).squeeze(-2, -3).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_max_roll(self):
"""Roll = -45, 45."""
for roll in [-45, 45]:
rpf = get_toy_rpf(roll=roll)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=False), -2)
J_auto = jacfwd(self.local_pf_calc)(rpfk).squeeze(-2, -3).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_max_pitch(self):
"""Pitch = -45, 45."""
for pitch in [-45, 45]:
rpf = get_toy_rpf(pitch=pitch)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=False), -2)
J_auto = jacfwd(self.local_pf_calc)(rpfk).squeeze(-2, -3).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
class TestSpherical(unittest.TestCase):
"""Test the spherical manifold jacobians."""
eps = 5e-3
def validate(self, J: torch.Tensor, J_auto: torch.Tensor):
"""Check if the jacobians are close and finite."""
self.assertTrue(torch.all(torch.isfinite(J)), "found nan in numerical")
self.assertTrue(torch.all(torch.isfinite(J_auto)), "found nan in auto")
text_j = f" > {self.eps}\nJ:\n{J[0, 0, 0].numpy()}\nJ_auto:\n{J_auto[0, 0, 0].numpy()}"
J_up2grav = J[..., :2, :2]
J_up2grav_auto = J_auto[..., :2, :2]
max_diff = torch.max(torch.abs(J_up2grav - J_up2grav_auto))
text = f"UP - GRAV max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2grav, J_up2grav_auto, atol=self.eps), text)
J_up2focal = J[..., :2, 2]
J_up2focal_auto = J_auto[..., :2, 2]
max_diff = torch.max(torch.abs(J_up2focal - J_up2focal_auto))
text = f"UP - FOCAL max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2focal, J_up2focal_auto, atol=self.eps), text)
if CAMERA_MODEL != "pinhole":
J_up2k1 = J[..., :2, 3]
J_up2k1_auto = J_auto[..., :2, 3]
max_diff = torch.max(torch.abs(J_up2k1 - J_up2k1_auto))
text = f"UP - K1 max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_up2k1, J_up2k1_auto, atol=self.eps), text)
J_lat2grav = J[..., 2:, :2]
J_lat2grav_auto = J_auto[..., 2:, :2]
max_diff = torch.max(torch.abs(J_lat2grav - J_lat2grav_auto))
text = f"LAT - GRAV max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2grav, J_lat2grav_auto, atol=self.eps), text)
J_lat2focal = J[..., 2:, 2]
J_lat2focal_auto = J_auto[..., 2:, 2]
max_diff = torch.max(torch.abs(J_lat2focal - J_lat2focal_auto))
text = f"LAT - FOCAL max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2focal, J_lat2focal_auto, atol=self.eps), text)
if CAMERA_MODEL != "pinhole":
J_lat2k1 = J[..., 2:, 3]
J_lat2k1_auto = J_auto[..., 2:, 3]
max_diff = torch.max(torch.abs(J_lat2k1 - J_lat2k1_auto))
text = f"LAT - K1 max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J_lat2k1, J_lat2k1_auto, atol=self.eps), text)
max_diff = torch.max(torch.abs(J - J_auto[..., : J.shape[-1]]))
text = f"Overall - max diff is {max_diff:.4f}" + text_j
self.assertTrue(torch.allclose(J, J_auto[..., : J.shape[-1]], atol=self.eps), text)
def local_pf_calc(self, uvfk: torch.Tensor, gravity: Gravity):
"""Calculate the perspective field."""
delta, f, k1 = uvfk[..., :2], uvfk[..., 2], uvfk[..., 3]
cam = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
up, lat = get_perspective_field(cam, gravity.update(delta, spherical=True))
persp = torch.cat([up, torch.sin(lat)], dim=-3)
return persp.permute(0, 2, 3, 1).reshape(1, -1, 3)
def test_random(self):
"""Test random rpf."""
rpf = get_toy_rpf()
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=True), -2)
uvfk = torch.zeros_like(rpfk)
uvfk[..., 2] = f
uvfk[..., 3] = k1
func = lambda uvfk: self.local_pf_calc(uvfk, gravity)
J_auto = jacfwd(func)(uvfk).squeeze(-2).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_zero_roll(self):
"""Test roll = 0."""
rpf = get_toy_rpf(roll=0)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=True), -2)
uvfk = torch.zeros_like(rpfk)
uvfk[..., 2] = f
uvfk[..., 3] = k1
func = lambda uvfk: self.local_pf_calc(uvfk, gravity)
J_auto = jacfwd(func)(uvfk).squeeze(-2).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_zero_pitch(self):
"""Test pitch = 0."""
rpf = get_toy_rpf(pitch=0)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=True), -2)
uvfk = torch.zeros_like(rpfk)
uvfk[..., 2] = f
uvfk[..., 3] = k1
func = lambda uvfk: self.local_pf_calc(uvfk, gravity)
J_auto = jacfwd(func)(uvfk).squeeze(-2).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_max_roll(self):
"""Test roll = -45, 45."""
for roll in [-45, 45]:
rpf = get_toy_rpf(roll=roll)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=True), -2)
uvfk = torch.zeros_like(rpfk)
uvfk[..., 2] = f
uvfk[..., 3] = k1
func = lambda uvfk: self.local_pf_calc(uvfk, gravity)
J_auto = jacfwd(func)(uvfk).squeeze(-2).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
def test_max_pitch(self):
"""Test pitch = -45, 45."""
for pitch in [-45, 45]:
rpf = get_toy_rpf(pitch=pitch)
rpfk = torch.cat([rpf, torch.tensor([[K1]])], dim=-1)
r, p, f, k1 = rpfk.unbind(dim=-1)
camera = Camera.from_dict({"height": [H], "width": [W], "f": f, "k1": k1})
gravity = Gravity.from_rp(r, p)
J = torch.cat(J_perspective_field(camera, gravity, spherical=True), -2)
uvfk = torch.zeros_like(rpfk)
uvfk[..., 2] = f
uvfk[..., 3] = k1
func = lambda uvfk: self.local_pf_calc(uvfk, gravity)
J_auto = jacfwd(func)(uvfk).squeeze(-2).reshape(1, H, W, 3, 4)
self.validate(J, J_auto)
class TestLM(unittest.TestCase):
"""Test the LM optimizer."""
eps = 1e-3
def test_random_spherical(self):
"""Test random rpf."""
rpf = get_toy_rpf()
gravity = Gravity.from_rp(rpf[..., 0], rpf[..., 1])
camera = Camera.from_dict({"height": [H], "width": [W], "f": rpf[..., 2], "k1": [K1]})
up, lat = get_perspective_field(camera, gravity)
lm = LMOptimizer({"use_spherical_manifold": True, "camera_model": CAMERA_MODEL})
out = lm({"up_field": up, "latitude_field": lat})
cam_opt = out["camera"]
gravity_opt = out["gravity"]
if hasattr(cam_opt, "k1"):
text = f"cam_opt: {cam_opt.k1.numpy()} | rpf: {[K1]}"
self.assertTrue(
torch.allclose(cam_opt.k1, torch.tensor([K1]).float(), atol=self.eps), text
)
text = f"cam_opt: {cam_opt.f[..., 1].numpy()} | rpf: {rpf[..., 2].numpy()}"
self.assertTrue(torch.allclose(cam_opt.f[..., 1], rpf[..., 2], atol=self.eps), text)
text = f"gravity_opt.roll: {gravity_opt.roll.numpy()} | rpf: {rpf[..., 0].numpy()}"
self.assertTrue(torch.allclose(gravity_opt.roll, rpf[..., 0], atol=self.eps), text)
text = f"gravity_opt.pitch: {gravity_opt.pitch.numpy()} | rpf: {rpf[..., 1].numpy()}"
self.assertTrue(torch.allclose(gravity_opt.pitch, rpf[..., 1], atol=self.eps), text)
def test_random(self):
"""Test random rpf."""
rpf = get_toy_rpf()
gravity = Gravity.from_rp(rpf[..., 0], rpf[..., 1])
camera = Camera.from_dict({"height": [H], "width": [W], "f": rpf[..., 2], "k1": [K1]})
up, lat = get_perspective_field(camera, gravity)
lm = LMOptimizer({"use_spherical_manifold": False, "camera_model": CAMERA_MODEL})
out = lm({"up_field": up, "latitude_field": lat})
cam_opt = out["camera"]
gravity_opt = out["gravity"]
if hasattr(cam_opt, "k1"):
text = f"cam_opt: {cam_opt.k1.numpy()} | rpf: {[K1]}"
self.assertTrue(
torch.allclose(cam_opt.k1, torch.tensor([K1]).float(), atol=self.eps), text
)
text = f"cam_opt: {cam_opt.f[..., 1].numpy()} | rpf: {rpf[..., 2].numpy()}"
self.assertTrue(torch.allclose(cam_opt.f[..., 1], rpf[..., 2], atol=self.eps), text)
text = f"gravity_opt.roll: {gravity_opt.roll.numpy()} | rpf: {rpf[..., 0].numpy()}"
self.assertTrue(torch.allclose(gravity_opt.roll, rpf[..., 0], atol=self.eps), text)
text = f"gravity_opt.pitch: {gravity_opt.pitch.numpy()} | rpf: {rpf[..., 1].numpy()}"
self.assertTrue(torch.allclose(gravity_opt.pitch, rpf[..., 1], atol=self.eps), text)
if __name__ == "__main__":
unittest.main()
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