Merge pull request #118 from agimus-project/py38

fix type hints for python 3.8 compatibility

experiments/generate_dataset.py

@@ -2,6 +2,7 @@
 import time
 import typing as tp
 from dataclasses import dataclass
+from typing import List
 
 import hydra
 import numpy as np
@@ -15,7 +16,7 @@
 @dataclass
 class DatasetGenerationConfig:
     dataset_id: str
-    chunk_ids: tp.Optional[list[int]]
+    chunk_ids: tp.Optional[List[int]]
     debug: bool = False
     verbose: bool = True
     overwrite: bool = False

experiments/job-runner/job_runner/configs.py

@@ -1,5 +1,6 @@
 import typing as tp
 from dataclasses import dataclass
+from typing import Dict, List
 
 from hydra.core.config_store import ConfigStore
 
@@ -31,21 +32,21 @@ class SlurmJobConfig(JobConfig):
     account: str
     qos: str
     time: str
-    additional_parameters: tp.Optional[dict[str, tp.Any]]
+    additional_parameters: tp.Optional[Dict[str, tp.Any]]
 
 
 @dataclass
 class CodeSnapshotConfig:
     snapshot_dir: tp.Optional[str]
     exclude_path: tp.Optional[str]
-    python_packages_dir: tp.Optional[list[str]] = None
+    python_packages_dir: tp.Optional[List[str]] = None
 
 
 @dataclass
 class JobEnvironmentConfig:
     conda_env: str
     code_snapshot: tp.Optional[CodeSnapshotConfig] = None
-    env: tp.Optional[dict[str, str]] = None
+    env: tp.Optional[Dict[str, str]] = None
 
 
 @dataclass

experiments/job-runner/job_runner/utils.py

@@ -1,11 +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():
@@ -14,7 +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] = (),
 ):

experiments/make_shapenet_ids.py

@@ -4,14 +4,15 @@
 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
@@ -26,7 +27,7 @@ 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"]
@@ -55,7 +56,7 @@ 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, tp.Union[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

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

@@ -1,6 +1,6 @@
 # Standard Library
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any, Dict, Optional
 
 # Third Party
 import torch
@@ -157,7 +157,7 @@ def get_save_dir(cfg: EvalConfig) -> Path:
 def run_eval(
     cfg: EvalConfig,
     save_dir: Optional[Path] = None,
-) -> dict[str, Any]:
+) -> 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/prediction_runner.py

@@ -17,7 +17,7 @@
 # Standard Library
 import time
 from collections import defaultdict
-from typing import Optional
+from typing import Dict, Optional
 
 # Third Party
 import numpy as np
@@ -84,7 +84,7 @@ def run_inference_pipeline(
         obs_tensor: ObservationTensor,
         gt_detections: DetectionsType,
         initial_estimates: Optional[PoseEstimatesType] = None,
-    ) -> dict[str, PoseEstimatesType]:
+    ) -> Dict[str, PoseEstimatesType]:
         """Runs inference pipeline, extracts the results.
 
         Returns: A dict with keys
@@ -160,7 +160,7 @@ 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/pose_estimator.py

@@ -1,6 +1,6 @@
 import time
 from collections import defaultdict
-from typing import Any, Optional
+from typing import Any, Optional, Tuple
 
 import numpy as np
 import torch
@@ -146,7 +146,7 @@ def run_inference_pipeline(
         coarse_estimates: Optional[PoseEstimatesType] = None,
         detection_th: float = 0.7,
         mask_th: float = 0.8,
-    ) -> tuple[PoseEstimatesType, dict]:
+    ) -> Tuple[PoseEstimatesType, dict]:
         timing_str = ""
         timer = SimpleTimer()
         timer.start()
@@ -248,7 +248,7 @@ 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
@@ -357,7 +357,7 @@ 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

@@ -1,4 +1,5 @@
 from collections import defaultdict
+from typing import Tuple
 
 import torch
 from torch.utils.data import DataLoader, TensorDataset
@@ -133,7 +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,
@@ -148,7 +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/scripts/run_full_cosypose_eval_new.py

@@ -2,6 +2,7 @@
 import copy
 import os
 from pathlib import Path
+from typing import Dict, Tuple
 
 # Third Party
 from omegaconf import OmegaConf
@@ -68,7 +69,7 @@ def create_eval_cfg(
     detection_type: str,
     coarse_estimation_type: str,
     ds_name: str,
-) -> tuple[str, EvalConfig]:
+) -> Tuple[str, EvalConfig]:
     cfg = copy.deepcopy(cfg)
 
     cfg.inference.detection_type = detection_type
@@ -107,7 +108,7 @@ def run_full_eval(cfg: FullEvalConfig) -> None:
     # Iterate over each dataset
     for ds_name in cfg.ds_names:
         # create the EvalConfig objects that we will call `run_eval` on
-        eval_configs: dict[str, EvalConfig] = {}
+        eval_configs: Dict[str, EvalConfig] = {}
         for detection_type, coarse_estimation_type in cfg.detection_coarse_types:
             name, cfg_ = create_eval_cfg(
                 cfg,

happypose/pose_estimators/cosypose/cosypose/scripts/run_inference_on_example.py

@@ -5,7 +5,7 @@
 ########################
 # Add cosypose to my path -> dirty
 from pathlib import Path
-from typing import Union
+from typing import Tuple, Union
 
 # Third Party
 import numpy as np
@@ -47,7 +47,7 @@
 def load_observation(
     example_dir: Path,
     load_depth: bool = False,
-) -> tuple[np.ndarray, Union[None, np.ndarray], CameraData]:
+) -> Tuple[np.ndarray, Union[None, np.ndarray], CameraData]:
     camera_data = CameraData.from_json((example_dir / "camera_data.json").read_text())
 
     rgb = np.array(Image.open(example_dir / "image_rgb.png"), dtype=np.uint8)

happypose/pose_estimators/megapose/evaluation/data_utils.py

@@ -15,7 +15,7 @@
 
 
 # Standard Library
-from typing import Optional
+from typing import List, Optional
 
 # Third Party
 import numpy as np
@@ -30,7 +30,7 @@
 
 def parse_obs_data(
     obs: SceneObservation,
-    object_labels: Optional[list[str]] = None,
+    object_labels: Optional[List[str]] = None,
 ) -> PandasTensorCollection:
     """Parses object data into PandasTensorCollection.
 

happypose/pose_estimators/megapose/evaluation/eval_config.py

@@ -16,7 +16,7 @@
 
 # Standard Library
 from dataclasses import dataclass
-from typing import Optional
+from typing import List, Optional
 
 # MegaPose
 from happypose.pose_estimators.megapose.inference.types import InferenceConfig
@@ -85,8 +85,8 @@ class EvalConfig:
 @dataclass
 class FullEvalConfig(EvalConfig):
     # Full eval
-    detection_coarse_types: Optional[list] = None
-    ds_names: Optional[list[str]] = None
+    detection_coarse_types: Optional[List] = None
+    ds_names: Optional[List[str]] = None
     run_bop_eval: bool = True
     eval_coarse_also: bool = False
     convert_only: bool = False

happypose/pose_estimators/megapose/evaluation/evaluation.py

@@ -16,7 +16,7 @@
 
 # Standard Library
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any, Dict, Optional
 
 # Third Party
 import torch
@@ -80,7 +80,7 @@ def get_save_dir(cfg: EvalConfig) -> Path:
 def run_eval(
     cfg: EvalConfig,
     save_dir: Optional[Path] = None,
-) -> dict[str, Any]:
+) -> 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/megapose/inference/depth_refiner.py

@@ -16,7 +16,7 @@
 
 # Standard Library
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import Optional, Tuple
 
 # Third Party
 import torch
@@ -33,7 +33,7 @@ def refine_poses(
         masks: Optional[torch.tensor] = None,
         depth: Optional[torch.tensor] = None,
         K: Optional[torch.tensor] = None,
-    ) -> tuple[PoseEstimatesType, dict]:
+    ) -> Tuple[PoseEstimatesType, dict]:
         """Run the depth refinement.
 
         Args:

happypose/pose_estimators/megapose/inference/icp_refiner.py

@@ -15,7 +15,7 @@
 
 
 # Standard Library
-from typing import Optional
+from typing import Dict, Optional, Tuple
 
 # Third Party
 import cv2
@@ -236,7 +236,7 @@ def refine_poses(
         masks: Optional[torch.tensor] = None,
         depth: Optional[torch.tensor] = None,
         K: Optional[torch.tensor] = None,
-    ) -> tuple[PoseEstimatesType, dict]:
+    ) -> Tuple[PoseEstimatesType, Dict]:
         """Runs icp refinement. See superclass DepthRefiner for full documentation."""
         assert depth is not None
         assert K is not None