str(writer.count()) + ' images in the queue...')
writer.stop()
det_loader.stop()
except KeyboardInterrupt:
print_finish_info()
# Thread won't be killed when press Ctrl+C
if args.sp:
det_loader.terminate()
while (writer.running()):
time.sleep(1)
print('===========================> Rendering remaining ' +
str(writer.count()) + ' images in the queue...')
writer.stop()
else:
# subprocesses are killed, manually clear queues
writer.commit()
writer.clear_queues()
# det_loader.clear_queues()
final_result = writer.results()
kp_data_file_name = os.path.splitext(
os.path.basename(input_source))[0] + '.json'
write_json(final_result,
output_file_path,
form=args.format,
for_eval=args.eval,
lbl_name=lbl_name,
input_file_name=input_source,
output_file_name=kp_data_file_name)
print(f"Results have been written to json for {input_source}")
class AlphaPose:
"""docstring for ClassName"""
def __init__(self, args):
self.cfg = update_config(args.cfg)
args.gpus = [int(i) for i in args.gpus.split(',')
] if torch.cuda.device_count() >= 1 else [-1]
args.device = torch.device(
"cuda:" + str(args.gpus[0]) if args.gpus[0] >= 0 else "cpu")
args.detbatch = args.detbatch * len(args.gpus)
args.posebatch = args.posebatch * len(args.gpus)
args.tracking = (args.detector == 'tracker')
self.mode, self.input_source = self.check_input(args)
# Load pose model
self.pose_model = builder.build_sppe(self.cfg.MODEL,
preset_cfg=self.cfg.DATA_PRESET)
print(f'Loading pose model from {args.checkpoint}...')
self.pose_model.load_state_dict(
torch.load(args.checkpoint, map_location=args.device))
if len(args.gpus) > 1:
self.pose_model = torch.nn.DataParallel(self.pose_model,
device_ids=args.gpus).to(
args.device)
else:
self.pose_model.to(args.device)
self.pose_model.eval()
self.args = args
def check_input(self, args):
# for images
if len(args.inputpath) or len(args.inputlist) or len(args.inputimg):
inputpath = args.inputpath
inputlist = args.inputlist
inputimg = args.inputimg
if len(inputlist):
im_names = open(inputlist, 'r').readlines()
elif len(inputpath) and inputpath != '/':
for root, dirs, files in os.walk(inputpath):
im_names = files
elif len(inputimg):
im_names = [inputimg]
return 'image', im_names
else:
raise NotImplementedError
def loop(self):
n = 0
while True:
yield n
n += 1
def predict(self, image, img_name):
args = self.args
# Load detection loader
det_loader = DetectionLoader(self.input_source, [img_name], [image],
get_detector(args),
self.cfg,
args,
batchSize=args.detbatch,
mode=self.mode).start()
# Init data writer
queueSize = args.qsize
self.writer = DataWriter(self.cfg,
args,
save_video=False,
queueSize=queueSize).start()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
data_len = det_loader.length
im_names_desc = tqdm(range(data_len), dynamic_ncols=True)
batchSize = args.posebatch
if args.flip:
batchSize = int(batchSize / 2)
try:
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inps, orig_img, im_name, boxes, scores, ids,
cropped_boxes) = det_loader.read()
if orig_img is None:
break
if boxes is None or boxes.nelement() == 0:
self.writer.save(None, None, None, None, None,
orig_img, os.path.basename(im_name))
continue
if args.profile:
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
inps = inps.to(args.device)
datalen = inps.size(0)
leftover = 0
if (datalen) % batchSize:
leftover = 1
num_batches = datalen // batchSize + leftover
hm = []
for j in range(num_batches):
inps_j = inps[j * batchSize:min((j + 1) *
batchSize, datalen)]
if args.flip:
inps_j = torch.cat((inps_j, flip(inps_j)))
hm_j = self.pose_model(inps_j)
if args.flip:
hm_j_flip = flip_heatmap(hm_j[int(len(hm_j) / 2):],
det_loader.joint_pairs,
shift=True)
hm_j = (hm_j[0:int(len(hm_j) / 2)] + hm_j_flip) / 2
hm.append(hm_j)
hm = torch.cat(hm)
if args.profile:
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
hm = hm.cpu()
self.writer.save(boxes, scores, ids, hm, cropped_boxes,
orig_img, os.path.basename(im_name))
if args.profile:
ckpt_time, post_time = getTime(ckpt_time)
runtime_profile['pn'].append(post_time)
if args.profile:
# TQDM
im_names_desc.set_description(
'det time: {dt:.4f} | pose time: {pt:.4f} | post processing: {pn:.4f}'
.format(dt=np.mean(runtime_profile['dt']),
pt=np.mean(runtime_profile['pt']),
pn=np.mean(runtime_profile['pn'])))
while (self.writer.running()):
time.sleep(1)
print('===========================> Rendering remaining ' +
str(self.writer.count()) + ' images in the queue...')
self.writer.stop()
det_loader.stop()
except KeyboardInterrupt:
self.print_finish_info(args)
# Thread won't be killed when press Ctrl+C
if args.sp:
det_loader.terminate()
while (self.writer.running()):
time.sleep(1)
print('===========================> Rendering remaining ' +
str(self.writer.count()) + ' images in the queue...')
self.writer.stop()
else:
# subprocesses are killed, manually clear queues
self.writer.commit()
self.writer.clear_queues()
# det_loader.clear_queues()
final_result = self.writer.results()
return write_json(final_result,
args.outputpath,
form=args.format,
for_eval=args.eval)