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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DensePose Training Script.
This script is similar to the training script in detectron2/tools.
It is an example of how a user might use detectron2 for a new project.
"""
import logging
import os
from collections import OrderedDict
from fvcore.common.file_io import PathManager
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, get_cfg
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results
from detectron2.modeling import DatasetMapperTTA
from detectron2.utils.logger import setup_logger
from densepose import (
DensePoseCOCOEvaluator,
DensePoseGeneralizedRCNNWithTTA,
add_dataset_category_config,
add_densepose_config,
load_from_cfg,
)
from densepose.data import DatasetMapper, build_detection_test_loader, build_detection_train_loader
class Trainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg: CfgNode, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluators = [COCOEvaluator(dataset_name, cfg, True, output_folder)]
if cfg.MODEL.DENSEPOSE_ON:
evaluators.append(DensePoseCOCOEvaluator(dataset_name, True, output_folder))
return DatasetEvaluators(evaluators)
@classmethod
def build_test_loader(cls, cfg: CfgNode, dataset_name):
return build_detection_test_loader(cfg, dataset_name, mapper=DatasetMapper(cfg, False))
@classmethod
def build_train_loader(cls, cfg: CfgNode):
return build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True))
@classmethod
def test_with_TTA(cls, cfg: CfgNode, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA
# Only support some R-CNN models.
logger.info("Running inference with test-time augmentation ...")
transform_data = load_from_cfg(cfg)
model = DensePoseGeneralizedRCNNWithTTA(cfg, model, transform_data, DatasetMapperTTA(cfg))
evaluators = [
cls.build_evaluator(
cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
)
for name in cfg.DATASETS.TEST
]
res = cls.test(cfg, model, evaluators)
res = OrderedDict({k + "_TTA": v for k, v in res.items()})
return res
def setup(args):
cfg = get_cfg()
add_dataset_category_config(cfg)
add_densepose_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "densepose" module
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="densepose")
return cfg
def main(args):
cfg = setup(args)
# disable strict kwargs checking: allow one to specify path handle
# hints through kwargs, like timeout in DP evaluation
PathManager.set_strict_kwargs_checking(False)
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)
if cfg.TEST.AUG.ENABLED:
res.update(Trainer.test_with_TTA(cfg, model))
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = Trainer(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()
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,),
)
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