3D Detection Eval docstrings + typing fixes.

.github/workflows/ci.yml

@@ -12,7 +12,7 @@ jobs:
         python_version:
           ["3.8", "3.9", "3.10"]
         venv_backend:
-          ["virtualenv", "mamba"]  # Testing two different resolvers (pip, mamba).
+          ["virtualenv"]
     defaults:
       run:
         shell: bash -l {0}

src/av2/evaluation/detection/eval.py

@@ -52,11 +52,11 @@
     e.g. AP, ATE, ASE, AOE, CDS by default.
 """
 import logging
+from multiprocessing import get_context
 from typing import Dict, Final, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
-from joblib import Parallel, delayed
 
 from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames
 from av2.evaluation.detection.utils import (
@@ -95,7 +95,7 @@ def evaluate(
     Each sweep is processed independently, computing assignment between detections and ground truth annotations.
 
     Args:
-        dts: (N,15) Table of detections.
+        dts: (N,14) Table of detections.
         gts: (M,15) Table of ground truth annotations.
         cfg: Detection configuration.
         n_jobs: Number of jobs running concurrently during evaluation.
@@ -106,23 +106,30 @@ def evaluate(
 
     Raises:
         RuntimeError: If accumulation fails.
+        ValueError: If ROI pruning is enabled but a dataset directory is not specified.
     """
+    if cfg.eval_only_roi_instances and cfg.dataset_dir is None:
+        raise ValueError(
+            "ROI pruning has been enabled, but the dataset directory has not be specified. "
+            "Please set `dataset_directory` to the split root, e.g. av2/sensor/val."
+        )
+
     # Sort both the detections and annotations by lexicographic order for grouping.
     dts = dts.sort_values(list(UUID_COLUMN_NAMES))
     gts = gts.sort_values(list(UUID_COLUMN_NAMES))
 
-    dts_npy: NDArrayFloat = dts.loc[:, DTS_COLUMN_NAMES].to_numpy()
-    gts_npy: NDArrayFloat = gts.loc[:, GTS_COLUMN_NAMES].to_numpy()
+    dts_npy: NDArrayFloat = dts[list(DTS_COLUMN_NAMES)].to_numpy()
+    gts_npy: NDArrayFloat = gts[list(GTS_COLUMN_NAMES)].to_numpy()
 
-    dts_uuids: List[str] = dts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist()
-    gts_uuids: List[str] = gts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist()
+    dts_uuids: List[str] = dts[list(UUID_COLUMN_NAMES)].to_numpy().tolist()
+    gts_uuids: List[str] = gts[list(UUID_COLUMN_NAMES)].to_numpy().tolist()
 
     # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category")
     # into a single string to optimize the subsequent grouping operation.
     # `groupby_mapping` produces a mapping from the uuid to the group of detections / annotations
     # which fall into that group.
-    uuid_to_dts = groupby([":".join(x) for x in dts_uuids], dts_npy)
-    uuid_to_gts = groupby([":".join(x) for x in gts_uuids], gts_npy)
+    uuid_to_dts = groupby([":".join(map(str, x)) for x in dts_uuids], dts_npy)
+    uuid_to_gts = groupby([":".join(map(str, x)) for x in gts_uuids], gts_npy)
 
     log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None
     log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None
@@ -154,9 +161,9 @@ def evaluate(
         args_list.append(args)
 
     logger.info("Starting evaluation ...")
-    outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=n_jobs, verbose=1)(
-        delayed(accumulate)(*args) for args in args_list
-    )
+    with get_context("spawn").Pool(processes=n_jobs) as p:
+        outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = p.starmap(accumulate, args_list)
+
     if outputs is None:
         raise RuntimeError("Accumulation has failed! Please check the integrity of your detections and annotations.")
     dts_list, gts_list = zip(*outputs)
@@ -182,7 +189,7 @@ def summarize_metrics(
     """Calculate and print the 3D object detection metrics.
 
     Args:
-        dts: (N,15) Table of detections.
+        dts: (N,14) Table of detections.
         gts: (M,15) Table of ground truth annotations.
         cfg: Detection configuration.
 

src/av2/evaluation/detection/utils.py

@@ -57,7 +57,6 @@ class DetectionCfg:
         max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be
             considered for evaluation.
         num_recall_samples: Number of recall points to sample uniformly in [0, 1].
-        splits: Tuple of split names to evaluate.
         tp_threshold_m: Center distance threshold for the true positive metrics (in meters).
     """
 
@@ -70,7 +69,6 @@ class DetectionCfg:
     max_num_dts_per_category: int = 100
     max_range_m: float = 200.0
     num_recall_samples: int = 100
-    split: str = "val"
     tp_threshold_m: float = 2.0
 
     @property

src/av2/geometry/utm.py

@@ -7,7 +7,7 @@
 WGS84: https://en.wikipedia.org/wiki/World_Geodetic_System
 """
 
-from enum import unique, Enum
+from enum import Enum, unique
 from typing import Dict, Final, Tuple, Union
 
 import numpy as np
@@ -83,7 +83,7 @@ def convert_city_coords_to_utm(points_city: Union[NDArrayFloat, NDArrayInt], cit
     latitude, longitude = CITY_ORIGIN_LATLONG_DICT[city_name]
     # get (easting, northing) of origin
     origin_utm = convert_gps_to_utm(latitude=latitude, longitude=longitude, city_name=city_name)
-    
+
     points_utm: NDArrayFloat = points_city.astype(float) + np.array(origin_utm, dtype=float)
     return points_utm
 

tests/geometry/test_utm.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 import av2.geometry.utm as geo_utils
-from av2.geometry.utm import CityName, CITY_ORIGIN_LATLONG_DICT
+from av2.geometry.utm import CityName
 from av2.utils.typing import NDArrayFloat