Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,079 Bytes
61c2d32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Point supervision Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_train_loader, MetadataCatalog
from detectron2.engine import (
default_argument_parser,
default_setup,
DefaultTrainer,
launch,
)
from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results
from detectron2.projects.point_rend import add_pointrend_config
from detectron2.utils.logger import setup_logger
from point_sup import add_point_sup_config, PointSupDatasetMapper
class Trainer(DefaultTrainer):
"""
We use the "DefaultTrainer" which contains pre-defined default logic for
standard training workflow. They may not work for you, especially if you
are working on a new research project. In that case you can write your
own training loop. You can use "tools/plain_train_net.py" as an example.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
if cfg.INPUT.POINT_SUP:
mapper = PointSupDatasetMapper(cfg, is_train=True)
else:
mapper = None
return build_detection_train_loader(cfg, mapper=mapper)
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_pointrend_config(cfg)
add_point_sup_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "point_sup" module
setup_logger(
output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="point_sup"
)
return cfg
def main(args):
cfg = setup(args)
if args.eval_only:
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
res = Trainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
"""
If you'd like to do anything fancier than the standard training logic,
consider writing your own training loop (see plain_train_net.py) or
subclassing the trainer.
"""
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
def invoke_main() -> None:
args = default_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,),
)
if __name__ == "__main__":
invoke_main() # pragma: no cover
|