def main(args):
cfg = setup(args)
logger = logging.getLogger('fastreid.' + __name__)
if args.eval_only:
cfg.defrost()
cfg.MODEL.BACKBONE.PRETRAIN = False
model = Trainer.build_model(cfg)
model = nn.DataParallel(model)
model = model.cuda()
Checkpointer(model, save_dir=cfg.OUTPUT_DIR).load(
cfg.MODEL.WEIGHTS) # load trained model
if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(model):
prebn_cfg = cfg.clone()
prebn_cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
prebn_cfg.DATASETS.NAMES = tuple([
cfg.TEST.PRECISE_BN.DATASET
]) # set dataset name for PreciseBN
logger.info("prepare precise BN dataset")
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
model,
# Build a new data loader to not affect training
Trainer.build_train_loader(prebn_cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
).update_stats()
res = Trainer.test(cfg, model)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
logger = logging.getLogger(__name__)
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
cfg.DATASETS.NAMES = tuple([cfg.TEST.PRECISE_BN.DATASET
]) # set dataset name for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
]
if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(self.model):
logger.info("Prepare precise BN dataset")
ret.append(
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
))
if len(cfg.MODEL.FREEZE_LAYERS) > 0 and cfg.SOLVER.FREEZE_ITERS > 0:
ret.append(
hooks.LayerFreeze(
self.model,
cfg.MODEL.FREEZE_LAYERS,
cfg.SOLVER.FREEZE_ITERS,
))
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation before checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD))
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), 200))
return ret
