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forecast_baseline_perf.py
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forecast_baseline_perf.py
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import argparse, json, pickle
import os, sys
from os.path import join, isfile, basename
from glob import glob
from time import perf_counter
import multiprocessing as mp
import traceback
from tqdm import tqdm
# setting environment variables - we have noticed that
# numpy creates extra threads which slows down computation,
# these environment variables prevent that
os.environ["MKL_NUM_THREADS"]="1"
os.environ["NUMEXPR_NUM_THREADS"]="1"
os.environ["OMP_NUM_THREADS"]="1"
import numpy as np
import torch
import os
from pycocotools.coco import COCO
# utility functions for mmdetection
from utils import print_stats
from utils.mmdet import init_detector, inference_detector, parse_det_result
# utility functions to forecast
from utils.forecast import ltrb2ltwh_, ltwh2ltrb_, iou_assoc, extrap_clean_up, \
bbox2z, bbox2x, x2bbox, make_F, make_Q, \
batch_kf_predict_only, batch_kf_predict, \
batch_kf_update
# multiprocessing
import multiprocessing as mp
# benchmark toolkit API
from sap_toolkit.client import EvalClient
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--annot-path', type=str, required=True)
# 30 is the fps of the stream received from evaluation server, don't change this
parser.add_argument('--fps', type=float, default=30)
parser.add_argument('--eta', type=float, default=0, help='eta >= -1') # frame
parser.add_argument('--eval-config', type=str, required=True)
parser.add_argument('--config', type=str, required=True)
parser.add_argument('--weights', type=str, required=True)
parser.add_argument('--in-scale', type=float, default=None)
parser.add_argument('--no-mask', action='store_true', default=False)
parser.add_argument('--cpu-pre', action='store_true', default=False)
parser.add_argument('--dynamic-schedule', action='store_true', default=False)
parser.add_argument('--match-iou-th', type=float, default=0.3)
parser.add_argument('--forecast-rt-ub', type=float, default=0.003) # seconds
parser.add_argument('--overwrite', action='store_true', default=False)
parser.add_argument('--perf-factor', type=float, default=1)
opts = parser.parse_args()
return opts
def det_process(opts, frame_recv, det_res_send, w_img, h_img, config, client_state):
# initialize evaluation client using state of old client
os.nice(45)
try:
model = init_detector(opts)
# warm up the GPU
_ = inference_detector(model, np.zeros((h_img, w_img, 3), np.uint8))
torch.cuda.synchronize()
eval_client = EvalClient(config, state=client_state, verbose=False)
while 1:
fidx = frame_recv.recv()
if fidx == 'wait_for_ready':
det_res_send.send('ready')
continue
if fidx is None:
# exit flag
break
fidx , t1 = fidx
_, img = eval_client.get_frame(fid=fidx)
t2 = perf_counter()
t_send_frame = t2 - t1
result = inference_detector(model, img, gpu_pre=not opts.cpu_pre)
torch.cuda.synchronize()
t3 = perf_counter()
det_res_send.send([result, t_send_frame, t3, t3-t2])
except Exception:
# report all errors from the child process to the parent
# forward traceback info as well
det_res_send.send(Exception("".join(traceback.format_exception(*sys.exc_info()))))
def main():
assert torch.cuda.device_count() == 1 # mmdet only supports single GPU testing
opts = parse_args()
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']
# initialize model and mapping
config = json.load(open(opts.eval_config, 'r'))
img = db.imgs[0]
w_img, h_img = img['width'], img['height']
# initialize evaluation client
eval_client = EvalClient(config, verbose=False)
# launch detector process
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, config, eval_client.get_state()))
det_proc.start()
# dynamic scheduling
if opts.dynamic_schedule:
# initialize runtime mean to 0.0
mean_rtf = 0
# initialize arrays to store detection, sending, receiving, association and forecasting times
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_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 seq in tqdm(seqs):
# Request stream for current sequence from evaluation server
eval_client.request_stream(seq)
fidx = 0
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 get ready to process sequence
# it is possible that unfetched results remain in the pipe, this asks the detector to flush those
frame_send.send('wait_for_ready')
while 1:
msg = det_res_recv.recv() # wait till the detector is ready
if msg == 'ready':
break
elif isinstance(msg, Exception):
raise msg
t_unit = 1/opts.fps
# get the time when stream's first frame was received
t_start = eval_client.get_stream_start_time()
count_detections = 0
while fidx is not None:
t1 = perf_counter()
t_elapsed = t1 - t_start
# identify latest available frame
fidx_continous = t_elapsed*opts.fps*opts.perf_factor
fidx, _ = eval_client.get_frame()
if fidx is None:
break
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:
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, t_det = result
if opts.dynamic_schedule:
sum_rtf = mean_rtf*count_detections + t_det*opts.fps*opts.perf_factor
count_detections += 1
mean_rtf = sum_rtf/count_detections
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)
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 len(bboxes_t3):
ltwh2ltrb_(bboxes_t3)
# send result to benchmark toolkit
if fidx_t2 is not None:
eval_client.send_result_to_server(bboxes_t3, scores_t3, labels_t3)
print("detection time: ", 1e3*np.array(t_det_all).mean())
print("association time: ", 1e3*np.array(t_assoc_all).mean())
print("sending time: ", 1e3*np.array(t_send_frame_all).mean())
print("receiving time: ", 1e3*np.array(t_recv_res_all).mean())
print("forecasting time: ", 1e3*np.array(t_forecast_all).mean())
# stop current stream
print("Stopping stream ", seq)
eval_client.stop_stream()
# shut down evaluation client
eval_client.close()
# terminates the child process
frame_send.send(None)
# 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)
if __name__ == '__main__':
main()