def main():
"register data"
register_dataset()
"checkout raw dataset annotation"
# checkout_dataset_annotation()
"train"
trainer=train(train_flag=True,resume_load=True)
# single_predict()
"check pre annotation"
# checkout_pre_annotation(visual_num=10)
"predict and merge_nms"
# # single_predict()
# for i in ["model_0004093.pth"]:
# predict_my(visual=True)
# # "merge"
# merge()
# "-------------"
"checkout merge predict result annotation "
# checkout_pre_annotation()
# checkout_dataset_annotation((save_path=os.path.join(cfg.OUTPUT_DIR,"my_merge_visual"),subdata_name="pv_raw_test")
"data analysis ,coco format save,output visual,megrgeresult visual--"
# result_analysis()
"""Evaluate object proposal, instance detection/segmentation, keypoint detectionoutputs using COCO's metrics and APIs."""
def main():
"register data"
register_dataset()
"checkout raw dataset annotation"
# checkout_dataset_annotation()
"train"
train(train_flag=False,resume_load=True)
# single_predict()
"check pre annotation"
# checkout_pre_annotation(visual_num=10)
"predict and merge_nms"
single_predict()
def main():
"register data"
register_dataset()
"checkout raw dataset annotation"
# checkout_dataset_annotation()
"train"
# trainer=train(train_flag=False,resume_load=False)
# single_predict()
"check pre annotation"
# checkout_pre_annotation(visual_num=10)
"predict and merge_nms"
# single_predict()
# checkout_pre_annotation(visual_num=5)
for i in ["model_0032717.pth"]:
predict_my(model_weight=i,visual=True)