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train_inseg.py
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train_inseg.py
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import os
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import COCOEvaluator
from detectron2.data import (
MetadataCatalog,
build_detection_train_loader,
DatasetCatalog,
)
from detectron2.modeling import build_model
from detectron2.utils import comm
from yolov7.config import add_yolo_config
from yolov7.data.dataset_mapper import MyDatasetMapper2, MyDatasetMapper
from yolov7.utils.allreduce_norm import all_reduce_norm
"""
This using for train instance segmentation!
"""
class Trainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
return COCOEvaluator(dataset_name, output_dir=output_folder)
@classmethod
def build_train_loader(cls, cfg):
# return build_detection_train_loader(cfg,
# mapper=MyDatasetMapper(cfg, True))
cls.custom_mapper = MyDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=cls.custom_mapper)
@classmethod
def build_model(cls, cfg):
model = build_model(cfg)
return model
def run_step(self):
self._trainer.iter = self.iter
self._trainer.run_step()
if comm.get_world_size() == 1:
self.model.update_iter(self.iter)
else:
self.model.module.update_iter(self.iter)
def setup(args):
cfg = get_cfg()
add_yolo_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
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)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
args = default_argument_parser().parse_args()
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)