def main(args):
cfg = setup(args)
show = True
register_openlogo(cfg.DATASETS.TRAIN[0], "datasets/data/openlogo",
"trainval", "supervised_imageset")
register_openlogo(cfg.DATASETS.TEST[0], "datasets/data/openlogo", "test",
"supervised_imageset")
trainer = DefaultTrainer(cfg)
evaluator = OpenLogoDetectionEvaluator(cfg.DATASETS.TEST[0])
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)
if show:
visualize(cfg, amount=20)
res = trainer.test(cfg, model, evaluators=[evaluator])
if comm.is_main_process():
verify_results(cfg, res)
if cfg.TEST.AUG.ENABLED:
res.update(trainer.test_with_TTA(cfg, model))
return res
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
if cfg.TEST.AUG.ENABLED:
trainer.register_hooks([
hooks.EvalHook(0,
lambda: trainer.test_with_TTA(cfg, trainer.model))
])
return trainer.train()
def main(args):
# if args.unitest:
# return unitest()
cfg = setup(args)
for d in ["train", 'val']:
# train for 6998images , val for 1199 images
DatasetCatalog.register("chefCap_" + d,
lambda d=d: get_chefcap_image_dicts())
MetadataCatalog.get("chefCap_" + d).set(
thing_classes=list(things_class_dict.keys()))
if d == 'val':
MetadataCatalog.get("chefCap_val").evaluator_type = "pascal_voc"
MetadataCatalog.get("chefCap_val").year = 2012
MetadataCatalog.get(
"chefCap_val"
).dirname = "/opt/work/chefCap/detectron2_fasterrcnn/data"
# for d in ["/opt/work/chefCap/data/ziped/Making-PascalVOC-export/"]:
# DatasetCatalog.register("chefCap_val",
# lambda d=d: get_chefcap_image_dicts(d))
# MetadataCatalog.get("chefCap_val").set(
# thing_classes=['face-head', 'mask-head', 'face-cap', 'mask-cap'])
# MetadataCatalog.get("chefCap_val").evaluator_type = "pascal_voc"
# MetadataCatalog.get("chefCap_val").dirname = "/opt/work/chefCap/data/ziped/Making-PascalVOC-export/"
# MetadataCatalog.get("chefCap_val").year = 2012
if args.eval_only:
model = DefaultTrainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume)
res = DefaultTrainer.test(cfg, model)
if cfg.TEST.AUG.ENABLED:
res.update(DefaultTrainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
# if cfg.TEST.AUG.ENABLED:
# trainer.register_hooks(
# [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
# )
return trainer.train()
