nargs='+')
parser.add_argument(
'--benchmark',
action='store_true',
help="Benchmark the speed of the model + postprocessing")
parser.add_argument(
'--config',
help="A list of KEY=VALUE to overwrite those defined in config.py",
nargs='+')
parser.add_argument('--compact', help='Save a model to .pb')
parser.add_argument('--serving', help='Save a model to serving file')
args = parser.parse_args()
if args.config:
cfg.update_args(args.config)
register_mot(cfg.DATA.BASEDIR) # add the mot datasets to the registry
MODEL = ResNetFPNModel() if cfg.MODE_FPN else ResNetC4Model()
if not tf.test.is_gpu_available():
from tensorflow.python.framework import test_util
assert get_tf_version_tuple() >= (1, 7) and test_util.IsMklEnabled(), \
"Inference requires either GPU support or MKL support!"
assert args.load
finalize_configs(is_training=False)
if args.predict or args.visualize:
cfg.TEST.RESULT_SCORE_THRESH = cfg.TEST.RESULT_SCORE_THRESH_VIS
if args.visualize:
do_visualize(MODEL, args.load)
img_per_shard = num_imgs // num_shards
img_range = (shard * img_per_shard, (shard + 1) *
img_per_shard if shard + 1 < num_shards else num_imgs)
# no filter for training
ds = DataFromListOfDict(roidbs[img_range[0]:img_range[1]],
["file_name", "image_id"])
def f(fname):
im = cv2.imread(fname, cv2.IMREAD_COLOR)
assert im is not None, fname
return im
ds = MapDataComponent(ds, f, 0)
# Evaluation itself may be multi-threaded, therefore don't add prefetch here.
return ds
if __name__ == "__main__":
import os
from tensorpack.dataflow import PrintData
from mot.object_detection.config import finalize_configs
register_mot(cfg.DATA.BASEDIR)
finalize_configs(is_training=True)
ds = get_train_dataflow()
ds = PrintData(ds, 10)
TestDataSpeed(ds, 50000).start()
for k in ds:
pass