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train_net.py
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train_net.py
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#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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, PascalVOCDetectionEvaluator
from detectron2.layers import get_norm
from detectron2.modeling.roi_heads import ROI_HEADS_REGISTRY, Res5ROIHeads
@ROI_HEADS_REGISTRY.register()
class Res5ROIHeadsExtraNorm(Res5ROIHeads):
"""
As described in the MOCO paper, there is an extra BN layer
following the res5 stage.
"""
def _build_res5_block(self, cfg):
seq, out_channels = super()._build_res5_block(cfg)
norm = cfg.MODEL.RESNETS.NORM
norm = get_norm(norm, out_channels)
seq.add_module("norm", norm)
return seq, out_channels
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")
if "coco" in dataset_name:
return COCOEvaluator(dataset_name, cfg, True, output_folder)
else:
assert "voc" in dataset_name
return PascalVOCDetectionEvaluator(dataset_name)
def setup(args):
cfg = get_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()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)