refactor

python-sdk/nuscenes/eval/tracking/configs/tracking_nips_2019.json

@@ -1,4 +1,31 @@
 {
+  "tracking_names": [
+    "bicycle",
+    "bus",
+    "car",
+    "motorcycle",
+    "pedestrian",
+    "trailer",
+    "truck"
+  ],
+  "pretty_tracking_names": {
+    "bicycle": "Bicycle",
+    "bus": "Bus",
+    "car": "Car",
+    "motorcycle": "Motorcycle",
+    "pedestrian": "Pedestrian",
+    "trailer": "Trailer",
+    "truck": "Truck"
+  },
+  "tracking_colors": {
+    "bicycle": "C9",
+    "bus": "C2",
+    "car": "C0",
+    "motorcycle": "C6",
+    "pedestrian": "C5",
+    "trailer": "C3",
+    "truck": "C1"
+  },
   "class_range": {
     "car": 50,
     "truck": 50,

python-sdk/nuscenes/eval/tracking/constants.py

@@ -1,33 +1,12 @@
 # nuScenes dev-kit.
 # Code written by Holger Caesar, Caglayan Dicle and Oscar Beijbom, 2019.
 
-TRACKING_NAMES = ['bicycle', 'bus', 'car', 'motorcycle', 'pedestrian', 'trailer', 'truck']
 
 AMOT_METRICS = ['amota', 'amotp']
 INTERNAL_METRICS = ['recall', 'motar', 'gt']
 LEGACY_METRICS = ['mota', 'motp', 'mt', 'ml', 'faf', 'tp', 'fp', 'fn', 'ids', 'frag', 'tid', 'lgd']
 TRACKING_METRICS = [*AMOT_METRICS, *INTERNAL_METRICS, *LEGACY_METRICS]
 
-PRETTY_TRACKING_NAMES = {
-    'bicycle': 'Bicycle',
-    'bus': 'Bus',
-    'car': 'Car',
-    'motorcycle': 'Motorcycle',
-    'pedestrian': 'Pedestrian',
-    'trailer': 'Trailer',
-    'truck': 'Truck'
-}
-
-TRACKING_COLORS = {
-    'bicycle': 'C9',  # Differs from detection.
-    'bus': 'C2',
-    'car': 'C0',
-    'motorcycle': 'C6',
-    'pedestrian': 'C5',
-    'trailer': 'C3',
-    'truck': 'C1'
-}
-
 # Define mapping for metrics averaged over classes.
 AVG_METRIC_MAP = {  # Mapping from average metric name to individual per-threshold metric name.
     'amota': 'motar',

python-sdk/nuscenes/eval/tracking/data_classes.py

@@ -1,19 +1,22 @@
 # nuScenes dev-kit.
 # Code written by Holger Caesar, Caglayan Dicle and Oscar Beijbom, 2019.
 
-from typing import Dict, Tuple, Any
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 
 from nuscenes.eval.common.data_classes import MetricData, EvalBox
 from nuscenes.eval.common.utils import center_distance
-from nuscenes.eval.tracking.constants import TRACKING_NAMES, TRACKING_METRICS, AMOT_METRICS
+from nuscenes.eval.tracking.constants import TRACKING_METRICS, AMOT_METRICS
 
 
 class TrackingConfig:
     """ Data class that specifies the tracking evaluation settings. """
 
     def __init__(self,
+                 tracking_names: List[str],
+                 pretty_tracking_names: Dict[str, str],
+                 tracking_colors: Dict[str, str],
                  class_range: Dict[str, int],
                  dist_fcn: str,
                  dist_th_tp: float,
@@ -22,8 +25,12 @@ def __init__(self,
                  metric_worst: Dict[str, float],
                  num_thresholds: int):
 
-        assert set(class_range.keys()) == set(TRACKING_NAMES), "Class count mismatch."
-
+        assert set(class_range.keys()) == set(tracking_names), "Class count mismatch."
+        global TRACKING_NAMES
+        TRACKING_NAMES = tracking_names
+        self.tracking_names = tracking_names
+        self.pretty_tracking_names = pretty_tracking_names
+        self.tracking_colors = tracking_colors
         self.class_range = class_range
         self.dist_fcn = dist_fcn
         self.dist_th_tp = dist_th_tp
@@ -45,6 +52,9 @@ def __eq__(self, other):
     def serialize(self) -> dict:
         """ Serialize instance into json-friendly format. """
         return {
+            'tracking_names': self.tracking_names,
+            'pretty_tracking_names': self.pretty_tracking_names,
+            'tracking_colors': self.tracking_colors,
             'class_range': self.class_range,
             'dist_fcn': self.dist_fcn,
             'dist_th_tp': self.dist_th_tp,
@@ -57,7 +67,10 @@ def serialize(self) -> dict:
     @classmethod
     def deserialize(cls, content: dict):
         """ Initialize from serialized dictionary. """
-        return cls(content['class_range'],
+        return cls(content['tracking_names'],
+                   content['pretty_tracking_names'],
+                   content['tracking_colors'],
+                   content['class_range'],
                    content['dist_fcn'],
                    content['dist_th_tp'],
                    content['min_recall'],

python-sdk/nuscenes/eval/tracking/evaluate.py

@@ -188,12 +188,11 @@ def savepath(name):
             return os.path.join(self.plot_dir, name + '.pdf')
 
         # Plot a summary.
-        summary_plot(md_list, min_recall=self.cfg.min_recall, savepath=savepath('summary'))
+        summary_plot(self.cfg, md_list, savepath=savepath('summary'))
 
         # For each metric, plot all the classes in one diagram.
         for metric_name in LEGACY_METRICS:
-            recall_metric_curve(md_list, metric_name,
-                                self.cfg.min_recall, savepath=savepath('%s' % metric_name))
+            recall_metric_curve(self.cfg, md_list, metric_name, savepath=savepath('%s' % metric_name))
 
     def main(self, render_curves: bool = True) -> Dict[str, Any]:
         """
@@ -230,7 +229,7 @@ def main(self, render_curves: bool = True) -> Dict[str, Any]:
     # Settings.
     parser = argparse.ArgumentParser(description='Evaluate nuScenes tracking results.',
                                      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('result_path', type=str, help='The submission as a JSON file.')
+    parser.add_argument('--result_path', type=str, help='The submission as a JSON file.')
     parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics',
                         help='Folder to store result metrics, graphs and example visualizations.')
     parser.add_argument('--eval_set', type=str, default='val',

python-sdk/nuscenes/eval/tracking/render.py

@@ -10,21 +10,21 @@
 from pyquaternion import Quaternion
 
 from nuscenes.eval.common.render import setup_axis
-from nuscenes.eval.tracking.constants import TRACKING_COLORS, PRETTY_TRACKING_NAMES
 from nuscenes.eval.tracking.data_classes import TrackingBox, TrackingMetricDataList
 from nuscenes.utils.data_classes import Box
+from nuscenes.eval.tracking.data_classes import TrackingConfig
 
 Axis = Any
 
 
-def summary_plot(md_list: TrackingMetricDataList,
-                 min_recall: float,
+def summary_plot(cfg: TrackingConfig,
+                 md_list: TrackingMetricDataList,
                  ncols: int = 2,
                  savepath: str = None) -> None:
     """
     Creates a summary plot with which includes all traditional metrics for each class.
+    :param cfg: A TrackingConfig object.
     :param md_list: TrackingMetricDataList instance.
-    :param min_recall: Minimum recall value.
     :param ncols: How many columns the resulting plot should have.
     :param savepath: If given, saves the the rendering here instead of displaying.
     """
@@ -38,7 +38,7 @@ def summary_plot(md_list: TrackingMetricDataList,
     for ind, metric_name in enumerate(rel_metrics):
         row = ind // ncols
         col = np.mod(ind, ncols)
-        recall_metric_curve(md_list, metric_name, min_recall, ax=axes[row, col])
+        recall_metric_curve(cfg, md_list, metric_name, ax=axes[row, col])
 
     # Set layout with little white space and save to disk.
     plt.tight_layout()
@@ -47,19 +47,20 @@ def summary_plot(md_list: TrackingMetricDataList,
         plt.close()
 
 
-def recall_metric_curve(md_list: TrackingMetricDataList,
+def recall_metric_curve(cfg: TrackingConfig,
+                        md_list: TrackingMetricDataList,
                         metric_name: str,
-                        min_recall: float,
                         savepath: str = None,
                         ax: Axis = None) -> None:
     """
     Plot the recall versus metric curve for the given metric.
+    :param cfg: A TrackingConfig object.
     :param md_list: TrackingMetricDataList instance.
     :param metric_name: The name of the metric to plot.
-    :param min_recall: Minimum recall value.
     :param savepath: If given, saves the the rendering here instead of displaying.
     :param ax: Axes onto which to render or None to create a new axis.
     """
+    min_recall = cfg.min_recall  # Minimum recall value from config.
     # Setup plot.
     if ax is None:
         _, ax = plt.subplots(1, 1, figsize=(7.5, 5))
@@ -84,8 +85,8 @@ def recall_metric_curve(md_list: TrackingMetricDataList,
 
         ax.plot(recalls,
                 values,
-                label='%s' % PRETTY_TRACKING_NAMES[tracking_name],
-                color=TRACKING_COLORS[tracking_name])
+                label='%s' % cfg.pretty_tracking_names[tracking_name],
+                color=cfg.tracking_colors[tracking_name])
 
     # Scale count statistics and FAF logarithmically.
     if metric_name in ['mt', 'ml', 'faf', 'tp', 'fp', 'fn', 'ids', 'frag']:

python-sdk/nuscenes/eval/tracking/tests/test_evaluate.py

@@ -14,7 +14,6 @@
 
 from nuscenes import NuScenes
 from nuscenes.eval.common.config import config_factory
-from nuscenes.eval.tracking.constants import TRACKING_NAMES
 from nuscenes.eval.tracking.evaluate import TrackingEval
 from nuscenes.eval.tracking.utils import category_to_tracking_name
 from nuscenes.utils.splits import create_splits_scenes
@@ -41,10 +40,12 @@ def _mock_submission(nusc: NuScenes,
         :param split: Dataset split to use.
         :param add_errors: Whether to use GT or add errors to it.
         """
+        # Get config.
+        cfg = config_factory('tracking_nips_2019')
 
         def random_class(category_name: str, _add_errors: bool = False) -> Optional[str]:
             # Alter 10% of the valid labels.
-            class_names = sorted(TRACKING_NAMES)
+            class_names = sorted(cfg.tracking_names)
             tmp = category_to_tracking_name(category_name)
 
             if tmp is None: