mirror of https://github.com/JDAI-CV/fast-reid.git
107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
#!/usr/bin/env python
|
|
# encoding: utf-8
|
|
"""
|
|
@author: sherlock
|
|
@contact: sherlockliao01@gmail.com
|
|
"""
|
|
|
|
import json
|
|
import logging
|
|
import sys
|
|
import os
|
|
|
|
sys.path.append('.')
|
|
|
|
from fastreid.config import get_cfg
|
|
from fastreid.engine import default_argument_parser, default_setup, launch
|
|
from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
|
|
from fastreid.evaluation.clas_evaluator import ClasEvaluator
|
|
from fastreid.utils.checkpoint import Checkpointer, PathManager
|
|
from fastreid.utils import comm
|
|
from fastreid.engine import DefaultTrainer
|
|
|
|
from fastclas import *
|
|
|
|
|
|
class Trainer(DefaultTrainer):
|
|
|
|
@classmethod
|
|
def build_train_loader(cls, cfg):
|
|
"""
|
|
Returns:
|
|
iterable
|
|
It now calls :func:`fastreid.data.build_reid_train_loader`.
|
|
Overwrite it if you'd like a different data loader.
|
|
"""
|
|
logger = logging.getLogger("fastreid.clas_dataset")
|
|
logger.info("Prepare training set")
|
|
data_loader = build_reid_train_loader(cfg, Dataset=ClasDataset)
|
|
|
|
# Save index to class dictionary
|
|
output_dir = cfg.OUTPUT_DIR
|
|
if comm.is_main_process() and output_dir:
|
|
path = os.path.join(output_dir, "idx2class.json")
|
|
with PathManager.open(path, "w") as f:
|
|
json.dump(data_loader.dataset.idx_to_class, f)
|
|
|
|
return data_loader
|
|
|
|
@classmethod
|
|
def build_test_loader(cls, cfg, dataset_name):
|
|
"""
|
|
Returns:
|
|
iterable
|
|
It now calls :func:`fastreid.data.build_reid_test_loader`.
|
|
Overwrite it if you'd like a different data loader.
|
|
"""
|
|
return build_reid_test_loader(cfg, dataset_name, Dataset=ClasDataset)
|
|
|
|
@classmethod
|
|
def build_evaluator(cls, cfg, dataset_name, output_dir=None):
|
|
data_loader, _ = cls.build_test_loader(cfg, dataset_name)
|
|
return data_loader, ClasEvaluator(cfg, output_dir)
|
|
|
|
|
|
def setup(args):
|
|
"""
|
|
Create configs and perform basic setups.
|
|
"""
|
|
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:
|
|
cfg.defrost()
|
|
cfg.MODEL.BACKBONE.PRETRAIN = False
|
|
model = Trainer.build_model(cfg)
|
|
|
|
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
|
|
|
|
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,),
|
|
)
|