# encoding: utf-8 """ @author: xingyu liao @contact: sherlockliao01@gmail.com """ import logging import sys sys.path.append('.') from fastreid.config import get_cfg from fastreid.engine import DefaultTrainer from fastreid.modeling import build_model from fastreid.engine import default_argument_parser, default_setup, launch from fastreid.utils.checkpoint import Checkpointer from fastattr import * class Trainer(DefaultTrainer): def build_model(self, cfg): """ Returns: torch.nn.Module: It now calls :func:`fastreid.modeling.build_model`. Overwrite it if you'd like a different model. """ model = build_model(cfg, sample_weights=self.sample_weights) logger = logging.getLogger("fastreid.attr_model") logger.info("Model:\n{}".format(model)) return model def build_train_loader(self, cfg): data_loader = build_attr_train_loader(cfg) self.sample_weights = data_loader.dataset.sample_weights return data_loader @classmethod def build_test_loader(cls, cfg, dataset_name): return build_attr_test_loader(cfg, dataset_name) @classmethod def build_evaluator(cls, cfg, dataset_name, output_folder=None): data_loader = cls.build_test_loader(cfg, dataset_name) return data_loader, AttrEvaluator(cfg, output_folder) def setup(args): """ Create configs and perform basic setups. """ cfg = get_cfg() add_attr_config(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,), )