78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
#!/usr/bin/env python
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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import os
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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, PascalVOCDetectionEvaluator
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from detectron2.layers import get_norm
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from detectron2.modeling.roi_heads import ROI_HEADS_REGISTRY, Res5ROIHeads
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@ROI_HEADS_REGISTRY.register()
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class Res5ROIHeadsExtraNorm(Res5ROIHeads):
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"""
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As described in the MOCO paper, there is an extra BN layer
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following the res5 stage.
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"""
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def _build_res5_block(self, cfg):
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seq, out_channels = super()._build_res5_block(cfg)
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norm = cfg.MODEL.RESNETS.NORM
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norm = get_norm(norm, out_channels)
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seq.add_module("norm", norm)
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return seq, out_channels
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class Trainer(DefaultTrainer):
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@classmethod
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def build_evaluator(cls, cfg, dataset_name, output_folder=None):
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if output_folder is None:
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output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
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if "coco" in dataset_name:
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return COCOEvaluator(dataset_name, cfg, True, output_folder)
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else:
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assert "voc" in dataset_name
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return PascalVOCDetectionEvaluator(dataset_name)
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def setup(args):
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cfg = get_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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cfg = setup(args)
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if args.eval_only:
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model = Trainer.build_model(cfg)
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DetectionCheckpointer(
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model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
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cfg.MODEL.WEIGHTS, resume=args.resume)
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res = Trainer.test(cfg, model)
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return res
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trainer = Trainer(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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args = default_argument_parser().parse_args()
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print("Command Line Args:", args)
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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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