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np.float -> np.float64 #360

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np.float -> np.float64 #360

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2 changes: 1 addition & 1 deletion yolox/tracker/byte_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ class STrack(BaseTrack):
def __init__(self, tlwh, score):

# wait activate
self._tlwh = np.asarray(tlwh, dtype=np.float)
self._tlwh = np.asarray(tlwh, dtype=np.float64)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
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12 changes: 6 additions & 6 deletions yolox/tracker/matching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,13 +58,13 @@ def ious(atlbrs, btlbrs):

:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float64)
if ious.size == 0:
return ious

ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float)
np.ascontiguousarray(atlbrs, dtype=np.float64),
np.ascontiguousarray(btlbrs, dtype=np.float64)
)

return ious
Expand Down Expand Up @@ -118,13 +118,13 @@ def embedding_distance(tracks, detections, metric='cosine'):
:return: cost_matrix np.ndarray
"""

cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float64)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float64)
#for i, track in enumerate(tracks):
#cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float64)
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
return cost_matrix

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