[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,14 +1,14 @@
 repos:
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.12.11
+  rev: v0.16.2
   hooks:
   - id: ruff
     args:
     - --fix
     - --exit-non-zero-on-fix
   - id: ruff-format
 - repo: https://github.com/pappasam/toml-sort
-  rev: v0.24.2
+  rev: v0.24.4
   hooks:
   - id: toml-sort-fix
     exclude: poetry.lock

experiments/generate_dataset.py

@@ -1,8 +1,6 @@
 import copy
 import time
-import typing as tp
 from dataclasses import dataclass
-from typing import List
 
 import hydra
 import numpy as np
@@ -16,7 +14,7 @@
 @dataclass
 class DatasetGenerationConfig:
     dataset_id: str
-    chunk_ids: tp.Optional[List[int]]
+    chunk_ids: list[int] | None
     debug: bool = False
     verbose: bool = True
     overwrite: bool = False

experiments/job-runner/job_runner/configs.py

@@ -1,6 +1,5 @@
 import typing as tp
 from dataclasses import dataclass
-from typing import Dict, List
 
 from hydra.core.config_store import ConfigStore
 
@@ -16,7 +15,7 @@ class NodeConfig:
 @dataclass
 class SlurmQueueConfig(NodeConfig):
     partition: str
-    constraint: tp.Optional[str] = None
+    constraint: str | None = None
 
 
 @dataclass
@@ -32,31 +31,31 @@ class SlurmJobConfig(JobConfig):
     account: str
     qos: str
     time: str
-    additional_parameters: tp.Optional[Dict[str, tp.Any]]
+    additional_parameters: dict[str, tp.Any] | None
 
 
 @dataclass
 class CodeSnapshotConfig:
-    snapshot_dir: tp.Optional[str]
-    exclude_path: tp.Optional[str]
-    python_packages_dir: tp.Optional[List[str]] = None
+    snapshot_dir: str | None
+    exclude_path: str | None
+    python_packages_dir: list[str] | None = None
 
 
 @dataclass
 class JobEnvironmentConfig:
     conda_env: str
-    code_snapshot: tp.Optional[CodeSnapshotConfig] = None
-    env: tp.Optional[Dict[str, str]] = None
+    code_snapshot: CodeSnapshotConfig | None = None
+    env: dict[str, str] | None = None
 
 
 @dataclass
 class RunnerConfig:
     log_dir: str
     job_env: JobEnvironmentConfig
-    local_node: tp.Optional[NodeConfig]
-    local_job: tp.Optional[JobConfig]
-    slurm_queue: tp.Optional[SlurmQueueConfig]
-    slurm_job: tp.Optional[SlurmJobConfig]
+    local_node: NodeConfig | None
+    local_job: JobConfig | None
+    slurm_queue: SlurmQueueConfig | None
+    slurm_job: SlurmJobConfig | None
     use_slurm: bool = False
 
 

experiments/job-runner/job_runner/utils.py

@@ -1,13 +1,12 @@
 import pathlib
 import typing as tp
-from typing import List
 
 import submitit
 
 from job_runner.configs import JobEnvironmentConfig, RunnerConfig
 
 
-def make_setup(cfg: JobEnvironmentConfig) -> List[str]:
+def make_setup(cfg: JobEnvironmentConfig) -> list[str]:
     setup = []
     if cfg.env:
         for k, v in cfg.env.items():
@@ -16,7 +15,7 @@ def make_setup(cfg: JobEnvironmentConfig) -> List[str]:
 
 
 def make_snapshots(
-    code_directories: List[pathlib.Path],
+    code_directories: list[pathlib.Path],
     output_dir: pathlib.Path,
     exclude: tp.Sequence[str] = (),
 ):
@@ -28,7 +27,6 @@ def make_snapshots(
         )
         with snapshot:
             pass
-    return
 
 
 def make_submitit_executor(

experiments/make_shapenet_ids.py

@@ -1,18 +1,16 @@
 import json
 import os
 import pathlib as p
-import typing as tp
 from collections import deque
 from dataclasses import dataclass
-from typing import Dict, List
 
 
 @dataclass
 class ShapeNetSynset:
     id: str
     name: str
-    parents: List[str]
-    children: List[str]
+    parents: list[str]
+    children: list[str]
 
 
 @dataclass
@@ -27,7 +25,7 @@ def read_models(shapenet_dir):
     # TODO: This probably has issues / is poorly implemented and very slow
     taxonomy = json.load(open(shapenet_dir / "taxonomy.json"))
 
-    id_to_synset: Dict[int, ShapeNetSynset] = {}
+    id_to_synset: dict[int, ShapeNetSynset] = {}
 
     for synset in taxonomy:
         synset_id = synset["synsetId"]
@@ -56,7 +54,7 @@ def get_names(synset_id, id_to_synset):
         return names
 
     models_path = shapenet_dir.glob("**/**/models/model_normalized.obj")
-    models: List[Dict[str, tp.Union[int, str]]] = []
+    models: list[dict[str, int | str]] = []
     for n, model_path in enumerate(models_path):
         source_id = model_path.parent.parent.name
         synset_id = model_path.parent.parent.parent.name

experiments/postprocess_dataset.py

@@ -42,7 +42,6 @@ def process_key(key, ds_dir, stoi_obj, out_dir):
             gt_n["obj_id"] = stoi_obj[gt_n["obj_id"]]
         gt = [inout._gt_as_json(d) for d in gt]
         inout.save_json(out_dir / f"{key}.gt.json", gt)
-    return
 
 
 def process_keys(keys, *args):

happypose/pose_estimators/cosypose/cosypose/datasets/bop.py

@@ -53,7 +53,6 @@ def build_index(ds_dir, save_file, split, save_file_annotations):
     )
     frame_index.to_feather(save_file)
     save_file_annotations.write_bytes(pickle.dumps(annotations))
-    return
 
 
 class BOPDataset:

happypose/pose_estimators/cosypose/cosypose/datasets/bop_object_datasets.py

@@ -1,10 +1,9 @@
 import json
 from pathlib import Path
-from typing import Union
 
 
 class BOPObjectDataset:
-    def __init__(self, ds_dir, label_format: Union[None, str] = None):
+    def __init__(self, ds_dir, label_format: None | str = None):
         ds_dir = Path(ds_dir)
         infos_file = ds_dir / "models_info.json"
         infos = json.loads(infos_file.read_text())

happypose/pose_estimators/cosypose/cosypose/evaluation/evaluation.py

@@ -1,6 +1,6 @@
 # Standard Library
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any
 
 # Third Party
 import torch
@@ -140,8 +140,8 @@ def get_save_dir(cfg: EvalConfig) -> Path:
 
 def run_eval(
     cfg: EvalConfig,
-    save_dir: Optional[Path] = None,
-) -> Dict[str, Any]:
+    save_dir: Path | None = None,
+) -> dict[str, Any]:
     """Run eval for a single setting on a single dataset.
 
     A single setting is a (detection_type, coarse_estimation_type) such

happypose/pose_estimators/cosypose/cosypose/evaluation/meters/base.py

@@ -46,4 +46,3 @@ def gather_distributed(self, tmp_dir):
 
         if world_size > 1:
             torch.distributed.barrier()
-        return

happypose/pose_estimators/cosypose/cosypose/evaluation/prediction_runner.py

@@ -16,7 +16,6 @@
 # Standard Library
 import time
 from collections import defaultdict
-from typing import Dict, Optional
 
 # Third Party
 import numpy as np
@@ -82,8 +81,8 @@ def run_inference_pipeline(
         pose_estimator: PoseEstimator,
         obs_tensor: ObservationTensor,
         gt_detections: DetectionsType,
-        initial_estimates: Optional[PoseEstimatesType] = None,
-    ) -> Dict[str, PoseEstimatesType]:
+        initial_estimates: PoseEstimatesType | None = None,
+    ) -> dict[str, PoseEstimatesType]:
         """Runs inference pipeline, extracts the results.
 
         Returns: A dict with keys
@@ -160,7 +159,7 @@ def run_inference_pipeline(
     def get_predictions(
         self,
         pose_estimator: PoseEstimator,
-    ) -> Dict[str, PoseEstimatesType]:
+    ) -> dict[str, PoseEstimatesType]:
         """Runs predictions.
 
         Returns: A dict with keys

happypose/pose_estimators/cosypose/cosypose/integrated/detector.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import numpy as np
 import pandas as pd
 import torch
@@ -31,7 +29,7 @@ def __init__(self, model, ds_name):
     def get_detections(
         self,
         observation: ObservationTensor,
-        detection_th: Optional[float] = None,
+        detection_th: float | None = None,
         output_masks: bool = False,
         mask_th: float = 0.8,
         one_instance_per_class: bool = False,

happypose/pose_estimators/cosypose/cosypose/integrated/pose_estimator.py

@@ -1,6 +1,6 @@
 import time
 from collections import defaultdict
-from typing import Any, List, Optional, Tuple
+from typing import Any
 
 import numpy as np
 import torch
@@ -35,9 +35,9 @@ class PoseEstimator(PoseEstimationModule):
 
     def __init__(
         self,
-        refiner_model: Optional[torch.nn.Module] = None,
-        coarse_model: Optional[torch.nn.Module] = None,
-        detector_model: Optional[torch.nn.Module] = None,
+        refiner_model: torch.nn.Module | None = None,
+        coarse_model: torch.nn.Module | None = None,
+        detector_model: torch.nn.Module | None = None,
         # depth_refiner: Optional[DepthRefiner] = None,
         bsz_objects: int = 8,
         bsz_images: int = 256,
@@ -139,18 +139,18 @@ def make_TCO_init(self, detections, K):
     def run_inference_pipeline(
         self,
         observation: ObservationTensor,
-        detections: Optional[DetectionsType] = None,
-        data_TCO_init: Optional[PandasTensorCollection] = None,
-        run_detector: Optional[bool] = None,
+        detections: DetectionsType | None = None,
+        data_TCO_init: PandasTensorCollection | None = None,
+        run_detector: bool | None = None,
         n_refiner_iterations: int = 1,
         n_coarse_iterations: int = 1,
-        bsz_images: Optional[int] = None,
-        bsz_objects: Optional[int] = None,
-        coarse_estimates: Optional[PoseEstimatesType] = None,
+        bsz_images: int | None = None,
+        bsz_objects: int | None = None,
+        coarse_estimates: PoseEstimatesType | None = None,
         detection_th: float = 0.7,
         mask_th: float = 0.8,
-        labels_to_keep: Optional[List[str]] = None,
-    ) -> Tuple[PoseEstimatesType, dict]:
+        labels_to_keep: list[str] | None = None,
+    ) -> tuple[PoseEstimatesType, dict]:
         timing_str = ""
         timer = SimpleTimer()
         timer.start()
@@ -265,7 +265,7 @@ def forward_coarse_model(
         n_iterations: int = 5,
         keep_all_outputs: bool = False,
         cuda_timer: bool = False,
-    ) -> Tuple[dict, dict]:
+    ) -> tuple[dict, dict]:
         """Runs the refiner model for the specified number of iterations.
 
         Will actually use the batched_model_predictions to stay within
@@ -377,7 +377,7 @@ def forward_refiner(
         n_iterations: int = 5,
         keep_all_outputs: bool = False,
         cuda_timer: bool = False,
-    ) -> Tuple[dict, dict]:
+    ) -> tuple[dict, dict]:
         """Runs the refiner model for the specified number of iterations.
 
         Will actually use the batched_model_predictions to stay within

happypose/pose_estimators/cosypose/cosypose/integrated/pose_predictor.py

@@ -1,5 +1,4 @@
 from collections import defaultdict
-from typing import Tuple
 
 import torch
 from torch.utils.data import DataLoader, TensorDataset
@@ -135,7 +134,7 @@ def forward_coarse_model(
         K,
         data_TCO_init,
         n_coarse_iterations,
-    ) -> Tuple[PoseEstimatesType, dict]:
+    ) -> tuple[PoseEstimatesType, dict]:
         return self.batched_model_predictions(
             self.coarse_model,
             images,
@@ -150,7 +149,7 @@ def forward_refiner(
         K,
         data_TCO,
         n_refiner_iterations,
-    ) -> Tuple[dict, dict]:
+    ) -> tuple[dict, dict]:
         return self.batched_model_predictions(
             self.refiner_model,
             images,

happypose/pose_estimators/cosypose/cosypose/libmesh/urdf_utils.py

@@ -137,4 +137,3 @@ def obj_to_urdf(obj_path, urdf_path):
 
     xmlstr = minidom.parseString(ET.tostring(robot)).toprettyxml(indent="   ")
     Path(urdf_path).write_text(xmlstr)  # Write xml file
-    return

happypose/pose_estimators/cosypose/cosypose/models/flownet.py

@@ -1,6 +1,6 @@
 # See Implementation here https://github.com/ClementPinard/FlowNetPytorch/blob/master/models/FlowNetS.py
 import torch
-import torch.nn as nn
+from torch import nn
 from torch.nn.init import constant_, kaiming_normal_
 
 from happypose.pose_estimators.cosypose.cosypose.config import LOCAL_DATA_DIR

happypose/pose_estimators/cosypose/cosypose/scripts/run_bop_inference.py

@@ -208,7 +208,6 @@ def run_inference(args):
         logger.info(f"Saved predictions in {save_dir}")
 
     torch.distributed.barrier()
-    return
 
 
 def main():

happypose/pose_estimators/cosypose/cosypose/scripts/run_cosypose_eval.py

@@ -1,4 +1,3 @@
-# ruff: noqa: E402
 from happypose.pose_estimators.cosypose.cosypose.utils.tqdm import patch_tqdm
 
 patch_tqdm()

happypose/pose_estimators/cosypose/cosypose/scripts/run_custom_scenario.py

@@ -103,7 +103,6 @@ def save_scene_json(objects, cameras, results_scene_path):
 
     scene = {"objects": list_objects, "cameras": list_cameras}
     results_scene_path.write_text(json.dumps(scene))
-    return
 
 
 def main():