"__author__": "MOCO" if k.startswith("encoder_q.") else "CLS",
"matching_heuristics": True
}
with open(save_path, "wb") as f:
pkl.dump(res, f)
if __name__ == "__main__":
args = train_argument_parser().parse_args()
if args.num_gpus is None:
args.num_gpus = torch.cuda.device_count()
extra_sys_path = ".." if args.dir is None else args.dir
sys.path.append(extra_sys_path)
from config import config
from net import build_model
config.link_log()
logger.info("Create soft link to {}".format(config.OUTPUT_DIR))
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args, config, build_model),
)
valid_files = get_valid_files(args, cfg, logger)
# * means all if need specific format then *.csv
for current_file in valid_files:
cfg.MODEL.WEIGHTS = current_file
model = build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
if cfg.TEST.AUG.ENABLED:
res = Trainer.test_with_TTA(cfg, model)
else:
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
# return res
if __name__ == "__main__":
args = test_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args, ),
)
logger.info("{} iters in {} seconds.".format(max_iter, timer.seconds()))
if __name__ == "__main__":
parser = default_argument_parser()
parser.add_argument("--task",
choices=["train", "eval", "data"],
required=True)
args = parser.parse_args()
assert not args.eval_only
if args.task == "data":
f = benchmark_data
elif args.task == "train":
"""
Note: training speed may not be representative.
The training cost of a R-CNN model varies with the content of the data
and the quality of the model.
"""
f = benchmark_train
elif args.task == "eval":
f = benchmark_eval
# only benchmark single-GPU inference.
assert args.num_gpus == 1 and args.num_machines == 1
launch(f,
args.num_gpus,
args.num_machines,
args.machine_rank,
args.dist_url,
args=(args, ))
