pass
else:
keys = [keys[args.data_idx]]
logger.info('validation %s set size=%d' % (coco_json_file, len(keys)))
write_json = '../etcs/%s_%s_%0.1f.json' % (args.model, args.resize,
args.resize_out_ratio)
logger.debug('initialization %s : %s' %
(args.model, get_graph_path(args.model)))
w, h = model_wh(args.resize)
if w == 0 or h == 0:
e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368))
else:
e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
print('FLOPs: ', e.get_flops())
result = []
tqdm_keys = tqdm(keys)
for i, k in enumerate(tqdm_keys):
img_meta = cocoGt.loadImgs(k)[0]
img_idx = img_meta['id']
img_name = os.path.join(image_dir, img_meta['file_name'])
image = read_imgfile(img_name, None, None)
if image is None:
logger.error('image not found, path=%s' % img_name)
sys.exit(-1)
# inference the image with the specified network
t = time.time()
logger.info("validation %s set size=%d" % (coco_json_file, len(keys)))
write_json = "../etcs/%s_%s_%0.1f.json" % (
args.model,
args.resize,
args.resize_out_ratio,
)
logger.debug("initialization %s : %s" %
(args.model, get_graph_path(args.model)))
w, h = model_wh(args.resize)
if w == 0 or h == 0:
e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368))
else:
e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
print("FLOPs: ", e.get_flops())
result = []
tqdm_keys = tqdm(keys)
for i, k in enumerate(tqdm_keys):
img_meta = cocoGt.loadImgs(k)[0]
img_idx = img_meta["id"]
img_name = os.path.join(image_dir, img_meta["file_name"])
image = read_imgfile(img_name, None, None)
if image is None:
logger.error("image not found, path=%s" % img_name)
sys.exit(-1)
# inference the image with the specified network
t = time.time()
