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Replaced all np.int and np.float with the respective 32-bit variants #26

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Replaced all np.int and np.float with the respective 32-bit variants #26

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2 changes: 1 addition & 1 deletion eval/mot/motmetrics/distances.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,7 @@ def boxiou(a, b):
a_vol = np.prod(a_size, axis=-1)
b_vol = np.prod(b_size, axis=-1)
u_vol = a_vol + b_vol - i_vol
return np.where(i_vol == 0, np.zeros_like(i_vol, dtype=np.float),
return np.where(i_vol == 0, np.zeros_like(i_vol, dtype=np.float32),
math_util.quiet_divide(i_vol, u_vol))


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4 changes: 2 additions & 2 deletions eval/posetrack21/posetrack21/trackeval/datasets/posetrack.py
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Expand Up @@ -332,7 +332,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['original_gt_ids'][t] = data['gt_ids'][t].copy()
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int32)

gt_dets = data['gt_dets'][t]
num_gt_joints += count_valid_joints(gt_dets)
Expand All @@ -344,7 +344,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['original_tracker_ids'][t] = data['tracker_ids'][t].copy()
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int32)
tracker_dets = data['tracker_dets'][t]
num_tracker_joints += count_valid_joints(tracker_dets)

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10 changes: 5 additions & 5 deletions eval/posetrack21/posetrack21/trackeval/datasets/posetrack_mot.py
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Expand Up @@ -202,7 +202,7 @@ def _load_raw_file(self, tracker, seq, is_gt):
time_key = str(t+1)
if time_key in read_data.keys():
try:
time_data = np.asarray(read_data[time_key], dtype=np.float)
time_data = np.asarray(read_data[time_key], dtype=np.float32)
except ValueError:
if is_gt:
raise TrackEvalException(
Expand Down Expand Up @@ -330,7 +330,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):

# Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
# which are labeled as belonging to a distractor class.
to_remove_tracker = np.array([], np.int)
to_remove_tracker = np.array([], np.int32)
if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:

# Check all classes are valid:
Expand Down Expand Up @@ -420,7 +420,7 @@ def get_preprocessed_seq_data(self, raw_data, cls):
det_idx = unmatched_det_idxs[remove_idx]
dets_to_remove.append(det_idx)

to_remove_tracker = np.array(dets_to_remove, dtype=np.int)
to_remove_tracker = np.array(dets_to_remove, dtype=np.int32)

# Apply preprocessing to remove all unwanted tracker dets.
data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
Expand All @@ -444,14 +444,14 @@ def get_preprocessed_seq_data(self, raw_data, cls):
gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
for t in range(raw_data['num_timesteps']):
if len(data['gt_ids'][t]) > 0:
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int32)
if len(unique_tracker_ids) > 0:
unique_tracker_ids = np.unique(unique_tracker_ids)
tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
for t in range(raw_data['num_timesteps']):
if len(data['tracker_ids'][t]) > 0:
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int32)

# Record overview statistics.
data['num_tracker_dets'] = num_tracker_dets
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14 changes: 7 additions & 7 deletions eval/posetrack21/posetrack21/trackeval/metrics/hota.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,20 +31,20 @@ def eval_sequence(self, data):
# Initialise results
res = {}
for field in self.float_array_fields + self.integer_array_fields:
res[field] = np.zeros((len(self.array_labels)), dtype=np.float)
res[field] = np.zeros((len(self.array_labels)), dtype=np.float32)
for field in self.float_fields:
res[field] = 0

# Return result quickly if tracker or gt sequence is empty
if data['num_tracker_dets'] == 0:
res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float)
res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)
res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float32)
res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float32)
res['LocA(0)'] = 1.0
res = self._compute_final_fields(res)
return res
if data['num_gt_dets'] == 0:
res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float)
res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)
res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float32)
res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float32)
res['LocA(0)'] = 1.0
res = self._compute_final_fields(res)
return res
Expand Down Expand Up @@ -343,9 +343,9 @@ def _summary_result(self, metric, result):
vals.append("{0:1.5g}".format(100 * np.mean(result)))
# we have an array for each joint
elif metric in self.integer_array_fields:
vals.append("{0:d}".format(np.mean(result).astype(np.int)))
vals.append("{0:d}".format(np.mean(result).astype(np.int32)))
elif metric in self.integer_fields:
vals.append("{0:d}".format(result.astype(np.int)))
vals.append("{0:d}".format(result.astype(np.int32)))
else:
raise TrackEvalException(f"Unknown metric {metric}")

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16 changes: 8 additions & 8 deletions eval/posetrack21/posetrack21/trackeval/metrics/hota_pose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,17 +54,17 @@ def eval_sequence(self, data):
assert res['HOTA_TP'].shape[1] == 15
# Return result quickly if tracker or gt sequence is empty
if data['num_tracker_dets'] == 0:
res['HOTA_FN'] = data['num_gt_joints'][None, :] * np.ones((len(self.array_labels), self.n_joints), dtype=np.float)
res['LocA'] = np.ones((len(self.array_labels), self.n_joints), dtype=np.float)
res['LocA(0)'] = np.ones((self.n_joints), dtype=np.float)
res['HOTA_FN'] = data['num_gt_joints'][None, :] * np.ones((len(self.array_labels), self.n_joints), dtype=np.float32)
res['LocA'] = np.ones((len(self.array_labels), self.n_joints), dtype=np.float32)
res['LocA(0)'] = np.ones((self.n_joints), dtype=np.float32)
res = self._compute_final_fields(res, compute_avg=True)

return res

if data['num_gt_dets'] == 0:
res['HOTA_FP'] = data['num_tracker_joints'][None, :] * np.ones((len(self.array_labels), self.n_joints), dtype=np.float)
res['LocA'] = np.ones((len(self.array_labels), self.n_joints), dtype=np.float)
res['LocA(0)'] = np.ones((self.n_joints), dtype=np.float)
res['HOTA_FP'] = data['num_tracker_joints'][None, :] * np.ones((len(self.array_labels), self.n_joints), dtype=np.float32)
res['LocA'] = np.ones((len(self.array_labels), self.n_joints), dtype=np.float32)
res['LocA(0)'] = np.ones((self.n_joints), dtype=np.float32)
res = self._compute_final_fields(res, compute_avg=True)
return res

Expand Down Expand Up @@ -330,10 +330,10 @@ def _summary_result(self, metric, result):
# we have an array for each joint
elif metric in self.integer_array_fields:
for j in range(result.shape[1]):
vals.append("{0:d}".format(np.mean(result[:, j]).astype(np.int)))
vals.append("{0:d}".format(np.mean(result[:, j]).astype(np.int32)))
elif metric in self.integer_fields:
for j in range(result.shape[0]):
vals.append("{0:d}".format(result[j].astype(np.int)))
vals.append("{0:d}".format(result[j].astype(np.int32)))
else:
raise TrackEvalException(f"Unknown metric {metric}")

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