def main():
opts = parse_args()
mkdir2(opts.out_dir)
db = COCO(opts.annot_path)
class_names = [c['name'] for c in db.dataset['categories']]
n_class = len(class_names)
coco_mapping = None if opts.no_class_mapping else db.dataset.get('coco_mapping', None)
if coco_mapping is not None:
coco_mapping = np.asarray(coco_mapping)
seqs = db.dataset['sequences']
seq_dirs = db.dataset['seq_dirs']
if opts.cached_res:
cache_in_ccf = '_ccf' in basename(opts.cached_res)
if cache_in_ccf:
# speed up based on the assumption of sequential storage
cache_end_idx = 0
cached_res = pickle.load(open(opts.cached_res, 'rb'))
else:
assert torch.cuda.device_count() == 1 # mmdet only supports single GPU testing
model = init_detector(opts)
np.random.seed(opts.seed)
runtime = pickle.load(open(opts.runtime, 'rb'))
runtime_dist = dist_from_dict(runtime, opts.perf_factor)
runtime_all = []
n_processed = 0
n_total = 0
for sid, seq in enumerate(tqdm(seqs)):
frame_list = [img for img in db.imgs.values() if img['sid'] == sid]
n_frame = len(frame_list)
n_total += n_frame
if not opts.cached_res:
# load all frames in advance
frames = []
for img in frame_list:
img_path = join(opts.data_root, seq_dirs[sid], img['name'])
frames.append(imread(img_path))
timestamps = []
results_parsed = []
input_fidx = []
runtime = []
last_fidx = None
if opts.cached_res and cache_in_ccf:
results_raw = None
else:
results_raw = []
t_total = n_frame/opts.fps
t_elapsed = 0
if opts.dynamic_schedule:
mean_rtf = runtime_dist.mean()*opts.fps
else:
stride_cnt = 0
while 1:
if t_elapsed >= t_total:
break
# identify latest available frame
fidx_continous = t_elapsed*opts.fps
fidx = int(np.floor(fidx_continous))
if fidx == last_fidx:
# algorithm is fast and has some idle time
fidx += 1
if fidx == n_frame:
break
t_elapsed = fidx/opts.fps
last_fidx = fidx
if opts.dynamic_schedule:
if mean_rtf > 1:
# when runtime <= 1, it should always process every frame
fidx_remainder = fidx_continous - fidx
if mean_rtf < np.floor(fidx_remainder + mean_rtf):
# wait till next frame
continue
else:
if stride_cnt % opts.det_stride == 0:
stride_cnt = 1
else:
stride_cnt += 1
continue
if opts.cached_res:
img = frame_list[fidx]
if cache_in_ccf:
cache_end_idx, bboxes, scores, labels, masks = \
result_from_ccf(cached_res, img['id'], cache_end_idx)
ltwh2ltrb_(bboxes)
else:
result = cached_res[img['id']]
bboxes, scores, labels, masks = \
parse_det_result(result, coco_mapping, n_class)
else:
frame = frames[fidx]
result = inference_detector(model, frame)
bboxes, scores, labels, masks = \
parse_det_result(result, coco_mapping, n_class)
rt_this = runtime_dist.draw()
t_elapsed += rt_this
if t_elapsed >= t_total:
break
timestamps.append(t_elapsed)
if results_raw is not None:
results_raw.append(result)
results_parsed.append((bboxes, scores, labels, masks))
input_fidx.append(fidx)
runtime.append(rt_this)
out_path = join(opts.out_dir, seq + '.pkl')
if opts.overwrite or not isfile(out_path):
out_dict = {
'results_parsed': results_parsed,
'timestamps': timestamps,
'input_fidx': input_fidx,
'runtime': runtime,
}
if results_raw is not None:
out_dict['results_raw'] = results_raw
pickle.dump(out_dict, open(out_path, 'wb'))
runtime_all += runtime
n_processed += len(results_parsed)
runtime_all_np = np.array(runtime_all)
n_small_runtime = (runtime_all_np < 1.0/opts.fps).sum()
out_path = join(opts.out_dir, 'time_info.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump({
'runtime_all': runtime_all,
'n_processed': n_processed,
'n_total': n_total,
'n_small_runtime': n_small_runtime,
}, open(out_path, 'wb'))
# convert to ms for display
s2ms = lambda x: 1e3*x
print(f'{n_processed}/{n_total} frames processed')
print_stats(runtime_all_np, 'Runtime (ms)', cvt=s2ms)
print(f'Runtime smaller than unit time interval: '
f'{n_small_runtime}/{n_processed} '
f'({100.0*n_small_runtime/n_processed:.4g}%)')
def main():
opts = parse_args()
assert opts.forecast_before_assoc, "Not implemented"
mkdir2(opts.out_dir)
vis_out = bool(opts.vis_dir)
if vis_out:
mkdir2(opts.vis_dir)
db = COCO(opts.annot_path)
class_names = [c['name'] for c in db.dataset['categories']]
n_class = len(class_names)
coco_mapping = db.dataset.get('coco_mapping', None)
if coco_mapping is not None:
coco_mapping = np.asarray(coco_mapping)
seqs = db.dataset['sequences']
seq_dirs = db.dataset['seq_dirs']
results_ccf = []
in_time = 0
miss = 0
shifts = 0
given_tracks = opts.assoc == 'given'
assert not given_tracks, "Not implemented"
t_assoc = []
t_forecast = []
# tt = []
with torch.no_grad():
kf_F = torch.eye(8)
# kf_F[3, 7] = 1
kf_Q = torch.eye(8)
kf_R = 10 * torch.eye(4)
kf_P_init = 100 * torch.eye(8).unsqueeze(0)
for sid, seq in enumerate(tqdm(seqs)):
frame_list = [img for img in db.imgs.values() if img['sid'] == sid]
results = pickle.load(open(join(opts.in_dir, seq + '.pkl'), 'rb'))
# use raw results when possible in case we change class subset during evaluation
results_raw = results.get('results_raw', None)
if results_raw is None:
results_parsed = results['results_parsed']
timestamps = results['timestamps']
input_fidx = results['input_fidx']
# t1 -> det1, t2 -> det2, interpolate at t3 (t3 is the current time)
det_latest_p1 = 0 # latest detection index + 1
det_t2 = None # detection index at t2
kf_x = torch.empty((0, 8, 1))
kf_P = torch.empty((0, 8, 8))
n_matched12 = 0
if not given_tracks:
tkidx = 0 # track starting index
for ii, img in enumerate(frame_list):
# pred, gt association by time
t = (ii - opts.eta) / opts.fps
while det_latest_p1 < len(
timestamps) and timestamps[det_latest_p1] <= t:
det_latest_p1 += 1
if det_latest_p1 == 0:
# no detection output
miss += 1
bboxes_t3, scores, labels, tracks = [], [], [], []
else:
det_latest = det_latest_p1 - 1
ifidx = input_fidx[det_latest]
in_time += int(ii == ifidx)
shifts += ii - ifidx
if det_latest != det_t2:
# new detection
# we can now throw away old result (t1)
# the old one is kept for forecasting purpose
if len(kf_x) and opts.forecast_before_assoc:
dt = ifidx - input_fidx[det_t2]
dt = int(
dt
) # convert from numpy to basic python format
w_img, h_img = img['width'], img['height']
kf_F = make_F(kf_F, dt)
kf_Q = make_Q(kf_Q, dt)
kf_x, kf_P = batch_kf_predict(
kf_F, kf_x, kf_P, kf_Q)
bboxes_f = x2bbox(kf_x)
det_t2 = det_latest
if results_raw is None:
result_t2 = results_parsed[det_t2]
bboxes_t2, scores_t2, labels_t2, _ = result_t2[:4]
if not given_tracks and len(result_t2) > 4:
tracks_t2 = np.asarray(result_t2[4])
else:
tracks_t2 = np.empty((0, ), np.uint32)
else:
result_t2 = results_raw[det_t2]
bboxes_t2, scores_t2, labels_t2, _ = \
parse_det_result(result_t2, coco_mapping, n_class)
t1 = perf_counter()
n = len(bboxes_t2)
if n:
if not given_tracks:
# put high scores det first for better iou matching
score_argsort = np.argsort(scores_t2)[::-1]
bboxes_t2 = bboxes_t2[score_argsort]
scores_t2 = scores_t2[score_argsort]
labels_t2 = labels_t2[score_argsort]
ltrb2ltwh_(bboxes_t2)
if given_tracks:
raise NotImplementedError
order1, order2, n_matched12 = track_based_shuffle(
tracks, tracks_t2, no_unmatched1=True)
tracks = tracks[order2]
else:
updated = False
if len(kf_x):
order1, order2, n_matched12, tracks, tkidx = iou_assoc(
bboxes_f,
labels,
tracks,
tkidx,
bboxes_t2,
labels_t2,
opts.match_iou_th,
no_unmatched1=True,
)
if n_matched12:
kf_x = kf_x[order1]
kf_P = kf_P[order1]
kf_x, kf_P = batch_kf_update(
bbox2z(bboxes_t2[
order2[:n_matched12]]),
kf_x,
kf_P,
kf_R,
)
kf_x_new = bbox2x(
bboxes_t2[order2[n_matched12:]])
n_unmatched2 = len(
bboxes_t2) - n_matched12
kf_P_new = kf_P_init.expand(
n_unmatched2, -1, -1)
kf_x = torch.cat((kf_x, kf_x_new))
kf_P = torch.cat((kf_P, kf_P_new))
labels = labels_t2[order2]
scores = scores_t2[order2]
updated = True
if not updated:
# start from scratch
kf_x = bbox2x(bboxes_t2)
kf_P = kf_P_init.expand(
len(bboxes_t2), -1, -1)
labels = labels_t2
scores = scores_t2
if not given_tracks:
tracks = np.arange(tkidx,
tkidx + n,
dtype=np.uint32)
tkidx += n
t2 = perf_counter()
t_assoc.append(t2 - t1)
t3 = perf_counter()
if len(kf_x):
dt = ii - ifidx
w_img, h_img = img['width'], img['height']
# PyTorch small matrix multiplication is slow
# use numpy instead
# kf_F = make_F(kf_F, dt)
# bboxes_t3 = x2bbox(batch_kf_predict_only(kf_F, kf_x))
# t5 = perf_counter()
kf_x_np = kf_x[:, :, 0].numpy()
# t6 = perf_counter()
bboxes_t3 = kf_x_np[:n_matched12, :
4] + dt * kf_x_np[:n_matched12, 4:]
if n_matched12 < len(kf_x):
bboxes_t3 = np.concatenate(
(bboxes_t3, kf_x_np[n_matched12:, :4]))
bboxes_t3, keep = extrap_clean_up(bboxes_t3,
w_img,
h_img,
lt=True)
# torch_keep = torch.from_numpy(keep) # boolean mask
# kf_x = kf_x[torch_keep]
# kf_P = kf_P[torch_keep]
# labels = labels[keep]
# scores = scores[keep]
# tracks = tracks[keep]
labels_t3 = labels[keep]
scores_t3 = scores[keep]
tracks_t3 = tracks[keep]
# tt.append(t6 - t5)
t4 = perf_counter()
t_forecast.append(t4 - t3)
n = len(bboxes_t3)
for i in range(n):
result_dict = {
'image_id': img['id'],
'bbox': bboxes_t3[i],
'score': scores_t3[i],
'category_id': labels_t3[i],
}
results_ccf.append(result_dict)
if vis_out:
img_path = join(opts.data_root, seq_dirs[sid], img['name'])
I = imread(img_path)
vis_path = join(opts.vis_dir, seq,
img['name'][:-3] + 'jpg')
bboxes = bboxes_t3.copy()
if n:
ltwh2ltrb_(bboxes)
if opts.overwrite or not isfile(vis_path):
vis_track(
I,
bboxes,
tracks,
labels,
class_names,
None,
scores,
out_scale=opts.vis_scale,
out_file=vis_path,
)
# pr.disable()
# pr.dump_stats('pps_iou_lin_extrap.prof')
# pickle.dump([t_assoc, t_forecast], open('pps_iou_lin_forecast_time', 'wb'))
s2ms = lambda x: 1e3 * x
if len(t_assoc):
print_stats(t_assoc, "RT association (ms)", cvt=s2ms)
if len(t_forecast):
print_stats(t_forecast, "RT forecasting (ms)", cvt=s2ms)
# if len(tt):
# print_stats(tt, "RT forecasting2 (ms)", cvt=s2ms)
out_path = join(opts.out_dir, 'results_ccf.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump(results_ccf, open(out_path, 'wb'))
if not opts.no_eval:
eval_summary = eval_ccf(db, results_ccf)
out_path = join(opts.out_dir, 'eval_summary.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump(eval_summary, open(out_path, 'wb'))
if vis_out:
print(f'python vis/make_videos.py "{opts.vis_dir}" --fps {opts.fps}')
def main():
assert torch.cuda.device_count() == 1 # mmdet only supports single GPU testing
opts = parse_args()
mkdir2(opts.out_dir)
db = COCO(opts.annot_path)
class_names = [c['name'] for c in db.dataset['categories']]
n_class = len(class_names)
coco_mapping = db.dataset.get('coco_mapping', None)
if coco_mapping is not None:
coco_mapping = np.asarray(coco_mapping)
seqs = db.dataset['sequences']
seq_dirs = db.dataset['seq_dirs']
img = db.imgs[0]
w_img, h_img = img['width'], img['height']
mp.set_start_method('spawn')
frame_recv, frame_send = mp.Pipe(False)
det_res_recv, det_res_send = mp.Pipe(False)
det_proc = mp.Process(target=det_process, args=(opts, frame_recv, det_res_send, w_img, h_img))
det_proc.start()
if opts.dynamic_schedule:
runtime = pickle.load(open(opts.runtime, 'rb'))
runtime_dist = dist_from_dict(runtime, opts.perf_factor)
mean_rtf = runtime_dist.mean()*opts.fps
n_total = 0
t_det_all = []
t_send_frame_all = []
t_recv_res_all = []
t_assoc_all = []
t_forecast_all = []
with torch.no_grad():
kf_F = torch.eye(8)
kf_Q = torch.eye(8)
kf_R = 10*torch.eye(4)
kf_P_init = 100*torch.eye(8).unsqueeze(0)
for sid, seq in enumerate(tqdm(seqs)):
frame_list = [img for img in db.imgs.values() if img['sid'] == sid]
frame_list = [join(opts.data_root, seq_dirs[sid], img['name']) for img in frame_list]
n_frame = len(frame_list)
n_total += n_frame
timestamps = []
results_parsed = []
input_fidx = []
processing = False
fidx_t2 = None # detection input index at t2
fidx_latest = None
tkidx = 0 # track starting index
kf_x = torch.empty((0, 8, 1))
kf_P = torch.empty((0, 8, 8))
n_matched12 = 0
# let detector process to read all the frames
frame_send.send(frame_list)
# it is possible that unfetched results remain in the pipe
while 1:
msg = det_res_recv.recv() # wait till the detector is ready
if msg == 'ready':
break
elif isinstance(msg, Exception):
raise msg
t_total = n_frame/opts.fps
t_unit = 1/opts.fps
t_start = perf_counter()
while 1:
t1 = perf_counter()
t_elapsed = t1 - t_start
if t_elapsed >= t_total:
break
# identify latest available frame
fidx_continous = t_elapsed*opts.fps
fidx = int(np.floor(fidx_continous))
if fidx == fidx_latest:
# algorithm is fast and has some idle time
wait_for_next = True
else:
wait_for_next = False
if opts.dynamic_schedule:
if mean_rtf >= 1:
# when runtime < 1, it should always process every frame
fidx_remainder = fidx_continous - fidx
if mean_rtf < np.floor(fidx_remainder + mean_rtf):
# wait till next frame
wait_for_next = True
if wait_for_next:
# sleep
continue
if not processing:
t_start_frame = perf_counter()
frame_send.send((fidx, t_start_frame))
fidx_latest = fidx
processing = True
# wait till query - forecast-rt-ub
wait_time = t_unit - opts.forecast_rt_ub
if det_res_recv.poll(wait_time): # wait
# new result
result = det_res_recv.recv()
if isinstance(result, Exception):
raise result
result, t_send_frame, t_start_res = result
bboxes_t2, scores_t2, labels_t2, _ = \
parse_det_result(result, coco_mapping, n_class)
processing = False
t_det_end = perf_counter()
t_det_all.append(t_det_end - t_start_frame)
t_send_frame_all.append(t_send_frame)
t_recv_res_all.append(t_det_end - t_start_res)
# associate across frames
t_assoc_start = perf_counter()
if len(kf_x):
dt = fidx_latest - fidx_t2
kf_F = make_F(kf_F, dt)
kf_Q = make_Q(kf_Q, dt)
kf_x, kf_P = batch_kf_predict(kf_F, kf_x, kf_P, kf_Q)
bboxes_f = x2bbox(kf_x)
fidx_t2 = fidx_latest
n = len(bboxes_t2)
if n:
# put high scores det first for better iou matching
score_argsort = np.argsort(scores_t2)[::-1]
bboxes_t2 = bboxes_t2[score_argsort]
scores_t2 = scores_t2[score_argsort]
labels_t2 = labels_t2[score_argsort]
ltrb2ltwh_(bboxes_t2)
updated = False
if len(kf_x):
order1, order2, n_matched12, tracks, tkidx = iou_assoc(
bboxes_f, labels, tracks, tkidx,
bboxes_t2, labels_t2, opts.match_iou_th,
no_unmatched1=True,
)
if n_matched12:
kf_x = kf_x[order1]
kf_P = kf_P[order1]
kf_x, kf_P = batch_kf_update(
bbox2z(bboxes_t2[order2[:n_matched12]]),
kf_x,
kf_P,
kf_R,
)
kf_x_new = bbox2x(bboxes_t2[order2[n_matched12:]])
n_unmatched2 = len(bboxes_t2) - n_matched12
kf_P_new = kf_P_init.expand(n_unmatched2, -1, -1)
kf_x = torch.cat((kf_x, kf_x_new))
kf_P = torch.cat((kf_P, kf_P_new))
labels = labels_t2[order2]
scores = scores_t2[order2]
updated = True
if not updated:
# start from scratch
kf_x = bbox2x(bboxes_t2)
kf_P = kf_P_init.expand(len(bboxes_t2), -1, -1)
labels = labels_t2
scores = scores_t2
tracks = np.arange(tkidx, tkidx + n, dtype=np.uint32)
tkidx += n
t_assoc_end = perf_counter()
t_assoc_all.append(t_assoc_end - t_assoc_start)
# apply forecasting for the current query
t_forecast_start = perf_counter()
query_pointer = fidx + opts.eta + 1
if len(kf_x):
dt = (query_pointer - fidx_t2)
kf_x_np = kf_x[:, :, 0].numpy()
bboxes_t3 = kf_x_np[:n_matched12, :4] + dt*kf_x_np[:n_matched12, 4:]
if n_matched12 < len(kf_x):
bboxes_t3 = np.concatenate((bboxes_t3, kf_x_np[n_matched12:, :4]))
bboxes_t3, keep = extrap_clean_up(bboxes_t3, w_img, h_img, lt=True)
labels_t3 = labels[keep]
scores_t3 = scores[keep]
tracks_t3 = tracks[keep]
else:
bboxes_t3 = np.empty((0, 4), dtype=np.float32)
scores_t3 = np.empty((0,), dtype=np.float32)
labels_t3 = np.empty((0,), dtype=np.int32)
tracks_t3 = np.empty((0,), dtype=np.int32)
t_forecast_end = perf_counter()
t_forecast_all.append(t_forecast_end - t_forecast_start)
t3 = perf_counter()
t_elapsed = t3 - t_start
if t_elapsed >= t_total:
break
if len(bboxes_t3):
ltwh2ltrb_(bboxes_t3)
if fidx_t2 is not None:
timestamps.append(t_elapsed)
results_parsed.append((bboxes_t3, scores_t3, labels_t3, None, tracks_t3))
input_fidx.append(fidx_t2)
out_path = join(opts.out_dir, seq + '.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump({
'results_parsed': results_parsed,
'timestamps': timestamps,
'input_fidx': input_fidx,
}, open(out_path, 'wb'))
# terminates the child process
frame_send.send(None)
out_path = join(opts.out_dir, 'time_info.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump({
'n_total': n_total,
't_det': t_det_all,
't_send_frame': t_send_frame_all,
't_recv_res': t_recv_res_all,
't_assoc': t_assoc_all,
't_forecast': t_forecast_all,
}, open(out_path, 'wb'))
# convert to ms for display
s2ms = lambda x: 1e3*x
print_stats(t_det_all, 'Runtime detection (ms)', cvt=s2ms)
print_stats(t_send_frame_all, 'Runtime sending the frame (ms)', cvt=s2ms)
print_stats(t_recv_res_all, 'Runtime receiving the result (ms)', cvt=s2ms)
print_stats(t_assoc_all, "Runtime association (ms)", cvt=s2ms)
print_stats(t_forecast_all, "Runtime forecasting (ms)", cvt=s2ms)
def main():
opts = parse_args()
mkdir2(opts.out_dir)
db = COCO(opts.annot_path)
class_names = [c['name'] for c in db.dataset['categories']]
n_class = len(class_names)
coco_mapping = db.dataset.get('coco_mapping', None)
if coco_mapping is not None:
coco_mapping = np.asarray(coco_mapping)
seqs = db.dataset['sequences']
seq_dirs = db.dataset['seq_dirs']
if opts.cached_res:
cache_in_ccf = '_ccf' in basename(opts.cached_res)
if cache_in_ccf:
# speed up based on the assumption of sequential storage
cache_end_idx = 0
cached_res = pickle.load(open(opts.cached_res, 'rb'))
else:
assert torch.cuda.device_count(
) == 1 # mmdet only supports single GPU testing
model = init_detector(opts)
np.random.seed(opts.seed)
runtime = pickle.load(open(opts.runtime, 'rb'))
runtime_dist = dist_from_dict(runtime, opts.perf_factor)
runtime_all = []
n_processed = 0
n_total = 0
for sid, seq in enumerate(tqdm(seqs)):
frame_list = [img for img in db.imgs.values() if img['sid'] == sid]
n_frame = len(frame_list)
n_total += n_frame
if not opts.cached_res:
# load all frames in advance
frames = []
for img in frame_list:
img_path = join(opts.data_root, seq_dirs[sid], img['name'])
frames.append(imread(img_path))
timestamps = []
results_raw = []
results_parsed = []
input_fidx = []
runtime = []
if opts.cached_res and cache_in_ccf:
results_raw = None
else:
results_raw = []
for ii in range(n_frame):
t = ii / opts.fps
if opts.cached_res:
img = frame_list[ii]
if cache_in_ccf:
cache_end_idx, bboxes, scores, labels, masks = \
result_from_ccf(cached_res, img['id'], cache_end_idx)
ltwh2ltrb_(bboxes)
else:
result = cached_res[img['id']]
bboxes, scores, labels, masks = \
parse_det_result(result, coco_mapping, n_class)
else:
frame = frames[ii]
result = inference_detector(model, frame)
bboxes, scores, labels, masks = \
parse_det_result(result, coco_mapping, n_class)
rt_this = runtime_dist.draw()
t_finishing = t + rt_this
timestamps.append(t_finishing)
if results_raw is not None:
results_raw.append(result)
results_parsed.append((bboxes, scores, labels, masks))
input_fidx.append(ii)
runtime.append(rt_this)
# since parallel excecution, the order is not guaranteed
idx = np.argsort(timestamps)
timestamps = [timestamps[i] for i in idx]
if results_raw is not None:
results_raw = [results_raw[i] for i in idx]
results_parsed = [results_parsed[i] for i in idx]
input_fidx = [input_fidx[i] for i in idx]
runtime = [runtime[i] for i in idx]
out_path = join(opts.out_dir, seq + '.pkl')
if opts.overwrite or not isfile(out_path):
out_dict = {
'results_parsed': results_parsed,
'timestamps': timestamps,
'input_fidx': input_fidx,
'runtime': runtime,
}
if results_raw is not None:
out_dict['results_raw'] = results_raw
pickle.dump(out_dict, open(out_path, 'wb'))
runtime_all += runtime
n_processed += len(results_parsed)
runtime_all_np = np.array(runtime_all)
n_small_runtime = (runtime_all_np < 1.0 / opts.fps).sum()
out_path = join(opts.out_dir, 'time_info.pkl')
if opts.overwrite or not isfile(out_path):
pickle.dump(
{
'runtime_all': runtime_all,
'n_processed': n_processed,
'n_total': n_total,
'n_small_runtime': n_small_runtime,
}, open(out_path, 'wb'))
# convert to ms for display
s2ms = lambda x: 1e3 * x
print(f'{n_processed}/{n_total} frames processed')
print_stats(runtime_all_np, 'Runtime (ms)', cvt=s2ms)
print(f'Runtime smaller than unit time interval: '
f'{n_small_runtime}/{n_processed} '
f'({100.0*n_small_runtime/n_processed:.4g}%)')