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fixed the rotation and translation order in pgo datasets #560

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Jul 6, 2023
Merged

fixed the rotation and translation order in pgo datasets #560

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b152c08
fixed the rotation and translation order in pgo datasets
fantaosha Jun 27, 2023
978ddef
add minor revisions
fantaosha Jun 28, 2023
690c590
minor
mhmukadam Jun 28, 2023
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fixed the rotation and translation order in pgo datasets
  • Loading branch information
@fantaosha
fantaosha committed Jun 27, 2023
commit b152c08a6c9c3f258c452f5a9e99055de549b474
4 changes: 2 additions & 2 deletions examples/configs/pose_graph/pose_graph_synthetic.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,8 @@ profile: True
savemat: True

num_poses: 256
rotation_noise: 0.05
translation_noise: 0.02
rotation_noise: 0.02
translation_noise: 0.05
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loop_closure_ratio: 0.2
loop_closure_outlier_ratio: 0.25
dataset_size: 256
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2 changes: 1 addition & 1 deletion examples/pose_graph/pose_graph_synthetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,8 +121,8 @@ def run(cfg: omegaconf.OmegaConf):
dtype = torch.float64
pg, _ = theg.PoseGraphDataset.generate_synthetic_3D(
num_poses=cfg.num_poses,
rotation_noise=cfg.rotation_noise,
translation_noise=cfg.translation_noise,
rotation_noise=cfg.rotation_noise,
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loop_closure_ratio=cfg.loop_closure_ratio,
loop_closure_outlier_ratio=cfg.loop_closure_outlier_ratio,
batch_size=cfg.batch_size,
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24 changes: 12 additions & 12 deletions tests/theseus_tests/test_pgo_benchmark.py
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Expand Up @@ -32,10 +32,10 @@ def default_cfg():
def test_pgo_losses(default_cfg, linear_solver_cls):
# for everything except cholmod (need to turn off adaptive damping for that one)
expected_losses = [
-0.052539531380404174,
-0.0692269852973897,
-0.036454724975527056,
-0.0611037305508654,
-0.29886279606812166,
-0.3054215856589109,
-0.27485602196709225,
-0.3005231105990632,
]

default_cfg.inner_optim.linear_solver_cls = linear_solver_cls
Expand All @@ -47,10 +47,10 @@ def test_pgo_losses(default_cfg, linear_solver_cls):
if linear_solver_cls == "CholmodSparseSolver":
default_cfg.inner_optim.optimizer_kwargs.adaptive_damping = False
expected_losses = [
-0.05253953280865485,
-0.0692269853141562,
-0.036454725860367604,
-0.06110373100778682,
-0.2988627961673474,
-0.30542158576120654,
-0.27485602213117594,
-0.3005231108739672,
]
default_cfg.device = "cpu"
losses = pgo.run(default_cfg)
Expand All @@ -64,10 +64,10 @@ def test_pgo_losses(default_cfg, linear_solver_cls):
def test_pgo_losses_baspacho(default_cfg):
# for everything except cholmod (need to turn off adaptive damping for that one)
expected_losses = [
-0.05253953137899042,
-0.06922698529800682,
-0.03645472497536786,
-0.061103730548485655,
-0.2988627960682926,
-0.30542158565900696,
-0.27485602196705955,
-0.3005231105991407,
]

default_cfg.inner_optim.linear_solver_cls = "BaspachoSparseSolver"
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14 changes: 7 additions & 7 deletions theseus/utils/examples/pose_graph/dataset.py
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Expand Up @@ -237,8 +237,8 @@ def histogram(self) -> str:
@staticmethod
def generate_synthetic_3D(
num_poses: int,
rotation_noise: float = 0.05,
translation_noise: float = 0.1,
translation_noise: float = 0.05,
rotation_noise: float = 0.1,
loop_closure_ratio: float = 0.2,
loop_closure_outlier_ratio: float = 0.05,
max_num_loop_closures: int = 10,
Expand Down Expand Up @@ -282,10 +282,10 @@ def generate_synthetic_3D(
noise_relative_pose = th.SE3.exp_map(
torch.cat(
[
rotation_noise
* (2 * torch.rand(dataset_size, 3, dtype=dtype) - 1),
translation_noise
* (2.0 * torch.rand(dataset_size, 3, dtype=dtype) - 1),
rotation_noise
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* (2.0 * torch.rand(dataset_size, 3, dtype=dtype) - 1),
],
dim=1,
)
Expand Down Expand Up @@ -322,10 +322,10 @@ def generate_synthetic_3D(
noise_relative_pose = th.SE3.exp_map(
torch.cat(
[
rotation_noise
* (2 * torch.rand(1, 3, dtype=dtype) - 1),
translation_noise
* (2.0 * torch.rand(1, 3, dtype=dtype) - 1),
rotation_noise
* (2.0 * torch.rand(1, 3, dtype=dtype) - 1),
],
dim=1,
)
Expand Down Expand Up @@ -354,8 +354,8 @@ def generate_synthetic_3D(
noise_pose = th.SE3.exp_map(
torch.cat(
[
rotation_noise * (2 * torch.rand(1, 3, dtype=dtype) - 1),
translation_noise * (2.0 * torch.rand(1, 3, dtype=dtype) - 1),
rotation_noise * (2.0 * torch.rand(1, 3, dtype=dtype) - 1),
],
dim=1,
)
Expand Down