wehub-resource-sync
124 lines
3.1 KiB
Python
124 lines
3.1 KiB
Python
#!/usr/bin/env python
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# --------------------------------------------------------------------------------
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# MPViT: Multi-Path Vision Transformer for Dense Prediction
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# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
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# All Rights Reserved.
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# Written by Youngwan Lee
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# --------------------------------------------------------------------------------
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"""
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Detection Training Script for MPViT.
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"""
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import os
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import itertools
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import torch
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from typing import Any, Dict, List, Set
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from detectron2.data import build_detection_train_loader
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from detectron2.checkpoint import DetectionCheckpointer
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from detectron2.config import get_cfg
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from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
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from detectron2.evaluation import COCOEvaluator
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from detectron2.solver.build import maybe_add_gradient_clipping
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from ditod import add_vit_config
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from ditod import DetrDatasetMapper
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from detectron2.data.datasets import register_coco_instances
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import logging
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from detectron2.utils.logger import setup_logger
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from detectron2.utils import comm
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from detectron2.engine.defaults import create_ddp_model
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import weakref
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from detectron2.engine.train_loop import AMPTrainer, SimpleTrainer
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from ditod import MyDetectionCheckpointer, ICDAREvaluator
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from ditod import MyTrainer
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def setup(args):
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"""
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Create configs and perform basic setups.
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"""
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cfg = get_cfg()
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# add_coat_config(cfg)
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add_vit_config(cfg)
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cfg.merge_from_file(args.config_file)
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cfg.merge_from_list(args.opts)
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cfg.freeze()
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default_setup(cfg, args)
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return cfg
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def main(args):
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"""
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register publaynet first
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"""
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register_coco_instances(
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"publaynet_train",
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{},
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"./publaynet_data/train.json",
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"./publaynet_data/train"
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)
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register_coco_instances(
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"publaynet_val",
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{},
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"./publaynet_data/val.json",
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"./publaynet_data/val"
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)
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register_coco_instances(
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"icdar2019_train",
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{},
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"data/train.json",
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"data/train"
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)
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register_coco_instances(
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"icdar2019_test",
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{},
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"data/test.json",
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"data/test"
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)
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cfg = setup(args)
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if args.eval_only:
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model = MyTrainer.build_model(cfg)
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DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
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cfg.MODEL.WEIGHTS, resume=args.resume
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)
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res = MyTrainer.test(cfg, model)
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return res
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trainer = MyTrainer(cfg)
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trainer.resume_or_load(resume=args.resume)
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return trainer.train()
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if __name__ == "__main__":
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parser = default_argument_parser()
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parser.add_argument("--debug", action="store_true", help="enable debug mode")
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args = parser.parse_args()
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print("Command Line Args:", args)
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if args.debug:
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import debugpy
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print("Enabling attach starts.")
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debugpy.listen(address=('0.0.0.0', 9310))
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debugpy.wait_for_client()
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print("Enabling attach ends.")
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launch(
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main,
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args.num_gpus,
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num_machines=args.num_machines,
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machine_rank=args.machine_rank,
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dist_url=args.dist_url,
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args=(args,),
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)
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