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Evaluation Fixes #51

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Merged
merged 3 commits into from
Mar 4, 2019
Merged

Evaluation Fixes #51

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edee31e
correct recall edge case
Alex-nutonomy Mar 4, 2019
a715cfd
standardize dist to xy only
Alex-nutonomy Mar 4, 2019
d8f99a2
return max error if recall is too low
Alex-nutonomy Mar 4, 2019
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2 changes: 1 addition & 1 deletion python-sdk/nuscenes/eval/eval_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@ def filter_boxes(sample_boxes: List[Dict], pose_record: Dict, cs_record: Dict, e
result = []
ego_dists = []
for box_sensor, box_global in zip(sample_boxes_sensor, sample_boxes):
dist = np.sqrt(np.sum(box_sensor.center ** 2))
dist = np.sqrt(np.sum(box_sensor.center[:2] ** 2))
if dist <= eval_range:
result.append(box_global) # Add the sample_box, not the box.
ego_dists.append(dist)
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24 changes: 19 additions & 5 deletions python-sdk/nuscenes/eval/nuscenes_eval.py
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Expand Up @@ -421,10 +421,6 @@ def average_precision(self, all_annotations: dict, all_results: dict, class_name
else:
rec = 0 * tp

# Store original values.
metrics['rec'] = rec
metrics['prec'] = prec

# IF there are no data points, add a point at (rec, prec) of (0.01, 0) such that the AP equals 0.
if len(prec) == 0:
rec = np.array([0.01])
Expand All @@ -434,14 +430,32 @@ def average_precision(self, all_annotations: dict, all_results: dict, class_name
if rec[0] != 0:
rec = np.append(0.0, rec)
prec = np.append(prec[0], prec)
metrics['trans_err'].insert(0, np.nan)
metrics['vel_err'].insert(0, np.nan)
metrics['scale_err'].insert(0, np.nan)
metrics['orient_err'].insert(0, np.nan)
metrics['attr_err'].insert(0, np.nan)
metrics['conf'].insert(0, 1)

# For debugging only.
metrics['ego_dist'].insert(0, np.nan)
metrics['vel_magn'].insert(0, np.nan)

# Store modified rec and prec values.
metrics['rec'] = rec
metrics['prec'] = prec

# If the max recall is below the minimum recall range, return the maximum error
if max(rec) < min(score_range):
return np.nan, dict()

# Find indices of rec that are close to the interpolated recall thresholds.
assert all(rec == sorted(rec)) # np.searchsorted requires sorted inputs.
thresh_count = int((score_range[1] - score_range[0]) * 100 + 1)
rec_interp = np.linspace(score_range[0], score_range[1], thresh_count)
threshold_inds = np.searchsorted(rec, rec_interp, side='left').astype(np.float32)
threshold_inds[threshold_inds == len(rec)] = np.nan # Mark unachieved recall values as such.
assert np.nanmax(threshold_inds) < len(sortind) # Check that threshold indices are not out of bounds.
assert np.nanmax(threshold_inds) < len(rec) # Check that threshold indices are not out of bounds.
metrics['threshold_inds'] = threshold_inds

# Interpolation of precisions to the nearest lower recall threshold.
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