def parse_args():
parser = ArgsParser()
parser.add_argument(
"--output_eval",
default=None,
type=str,
help="Evaluation directory, default is current directory.")
parser.add_argument(
'--json_eval',
action='store_true',
default=False,
help='Whether to re eval with already exists bbox.json or mask.json')
parser.add_argument("--slim_config",
default=None,
type=str,
help="Configuration file of slim method.")
# TODO: bias should be unified
parser.add_argument("--bias",
action="store_true",
help="whether add bias or not while getting w and h")
parser.add_argument(
"--classwise",
action="store_true",
help="whether per-category AP and draw P-R Curve or not.")
parser.add_argument('--save_prediction_only',
action='store_true',
default=False,
help='Whether to save the evaluation results only')
args = parser.parse_args()
return args
parser = ArgsParser()
parser.add_argument("--infer_dir",
type=str,
default=None,
help="Directory for images to perform inference on.")
parser.add_argument(
"--infer_img",
type=str,
default=None,
help="Image path, has higher priority over --infer_dir")
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output visualization files.")
parser.add_argument(
"--draw_threshold",
type=float,
default=0.5,
help="Threshold to reserve the result for visualization.")
parser.add_argument("--use_tb",
type=bool,
default=False,
help="whether to record the data to Tensorboard.")
parser.add_argument('--tb_log_dir',
type=str,
default="tb_log_dir/image",
help='Tensorboard logging directory for image.')
FLAGS = parser.parse_args()
main()
help="Whether to perform evaluation in train")
parser.add_argument("--use_vdl",
default=True,
type=bool,
help="whether to record the data to VisualDL.")
# NOTE:args for profiler tools, used for benchmark
parser.add_argument('--is_profiler',
default=0,
type=int,
help='The switch of profiler tools. (used for benchmark)')
parser.add_argument('--profiler_path',
default="save_models/detection.profiler",
type=str,
help='The profiler output file path. (used for benchmark)')
args = parser.parse_args()
def main():
env = os.environ
args.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
if args.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
cfg = load_config(args.config)
merge_config(args.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
def main():
parser = ArgsParser()
parser.add_argument('--n',
'-n',
default=9,
type=int,
help='num of clusters')
parser.add_argument('--iters',
'-i',
default=1000,
type=int,
help='num of iterations for kmeans')
parser.add_argument('--verbose',
'-v',
default=True,
type=bool,
help='whether print result')
parser.add_argument('--size',
'-s',
default=None,
type=str,
help='image size: w,h, using comma as delimiter')
parser.add_argument('--method',
'-m',
default='v2',
type=str,
help='cluster method, v2 is only supported now')
parser.add_argument('--cache_path',
default='cache',
type=str,
help='cache path')
parser.add_argument('--cache',
action='store_true',
help='whether use cache')
FLAGS = parser.parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
# get dataset
dataset = cfg['TrainReader']['dataset']
if FLAGS.size:
if ',' in FLAGS.size:
size = list(map(int, FLAGS.size.split(',')))
assert len(size) == 2, "the format of size is incorrect"
else:
size = int(FLAGS.size)
size = [size, size]
elif 'image_shape' in cfg['TestReader']['inputs_def']:
size = cfg['TestReader']['inputs_def']['image_shape'][1:]
else:
raise ValueError('size is not specified')
if FLAGS.method == 'v2':
cluster = YOLOv2AnchorCluster(FLAGS.n, dataset, size, FLAGS.cache_path,
FLAGS.cache, FLAGS.iters, FLAGS.verbose)
else:
raise ValueError('cluster method: %s is not supported' % FLAGS.method)
anchors = cluster()
