def default_setup(cfg, args):
"""
Perform some basic common setups at the beginning of a job, including:
1. Set up the Fs3c logger
2. Log basic information about environment, cmdline arguments, and config
3. Backup the config to the output directory
Args:
cfg (CfgNode): the full config to be used
args (argparse.NameSpace): the command line arguments to be logged
"""
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
PathManager.mkdirs(output_dir)
rank = comm.get_rank()
setup_logger(output_dir, distributed_rank=rank, name="fvcore")
logger = setup_logger(output_dir, distributed_rank=rank)
logger.info("Rank of current process: {}. World size: {}".format(
rank, comm.get_world_size()))
if not cfg.MUTE_HEADER:
logger.info("Environment info:\n" + collect_env_info())
logger.info("Command line arguments: " + str(args))
if hasattr(args, "config_file"):
logger.info("Contents of args.config_file={}:\n{}".format(
args.config_file,
PathManager.open(args.config_file, "r").read()))
if not cfg.MUTE_HEADER:
logger.info("Running with full config:\n{}".format(cfg))
if comm.is_main_process() and output_dir:
# Note: some of our scripts may expect the existence of
# config.yaml in output directory
path = os.path.join(output_dir, "config.yaml")
with PathManager.open(path, "w") as f:
f.write(cfg.dump())
logger.info("Full config saved to {}".format(os.path.abspath(path)))
# make sure each worker has a different, yet deterministic seed if specified
seed_all_rng(None if cfg.SEED < 0 else cfg.SEED + rank)
# cudnn benchmark has large overhead. It shouldn't be used considering the small size of
# typical validation set.
if not (hasattr(args, "eval_only") and args.eval_only):
torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
return dataset_dicts
if __name__ == "__main__":
"""
Test the LVIS json dataset loader.
Usage:
python -m fs3c.data.datasets.lvis \
path/to/json path/to/image_root dataset_name vis_limit
"""
import sys
import numpy as np
from fs3c.utils.logger import setup_logger
from PIL import Image
from fs3c.utils.visualizer import Visualizer
logger = setup_logger(name=__name__)
meta = MetadataCatalog.get(sys.argv[3])
dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])
logger.info("Done loading {} samples.".format(len(dicts)))
dirname = "lvis-data-vis"
os.makedirs(dirname, exist_ok=True)
for d in dicts[:int(sys.argv[4])]:
img = np.array(Image.open(d["file_name"]))
visualizer = Visualizer(img, metadata=meta)
vis = visualizer.draw_dataset_dict(d)
fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
vis.save(fpath)
except KeyError:
pass
return ret
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="A script that visualizes the json predictions from COCO or LVIS dataset."
)
parser.add_argument("--input", required=True, help="JSON file produced by the model")
parser.add_argument("--output", required=True, help="output directory")
parser.add_argument("--dataset", help="name of the dataset", default="coco_2017_val")
parser.add_argument("--conf-threshold", default=0.5, type=float, help="confidence threshold")
args = parser.parse_args()
logger = setup_logger()
with PathManager.open(args.input, "r") as f:
predictions = json.load(f)
pred_by_image = defaultdict(list)
for p in predictions:
pred_by_image[p["image_id"]].append(p)
dicts = list(DatasetCatalog.get(args.dataset))
metadata = MetadataCatalog.get(args.dataset)
if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
def dataset_id_map(ds_id):
return metadata.thing_dataset_id_to_contiguous_id[ds_id]