mirror of https://github.com/JDAI-CV/fast-reid.git
126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
# 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.engine import default_argument_parser, default_setup, launch
|
|
from fastreid.utils.checkpoint import Checkpointer
|
|
from fastreid.data.datasets import DATASET_REGISTRY
|
|
from fastreid.data.build import _root, build_reid_train_loader, build_reid_test_loader
|
|
from fastreid.data.transforms import build_transforms
|
|
from fastreid.utils import comm
|
|
|
|
from fastattr import *
|
|
|
|
|
|
class AttrTrainer(DefaultTrainer):
|
|
sample_weights = None
|
|
|
|
@classmethod
|
|
def build_model(cls, cfg):
|
|
"""
|
|
Returns:
|
|
torch.nn.Module:
|
|
It now calls :func:`fastreid.modeling.build_model`.
|
|
Overwrite it if you'd like a different model.
|
|
"""
|
|
model = DefaultTrainer.build_model(cfg)
|
|
if cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED and \
|
|
AttrTrainer.sample_weights is not None:
|
|
setattr(model, "sample_weights", AttrTrainer.sample_weights.to(model.device))
|
|
else:
|
|
setattr(model, "sample_weights", None)
|
|
return model
|
|
|
|
@classmethod
|
|
def build_train_loader(cls, cfg):
|
|
|
|
logger = logging.getLogger("fastreid.attr_dataset")
|
|
train_items = list()
|
|
attr_dict = None
|
|
for d in cfg.DATASETS.NAMES:
|
|
dataset = DATASET_REGISTRY.get(d)(root=_root, combineall=cfg.DATASETS.COMBINEALL)
|
|
if comm.is_main_process():
|
|
dataset.show_train()
|
|
if attr_dict is not None:
|
|
assert attr_dict == dataset.attr_dict, f"attr_dict in {d} does not match with previous ones"
|
|
else:
|
|
attr_dict = dataset.attr_dict
|
|
train_items.extend(dataset.train)
|
|
|
|
train_transforms = build_transforms(cfg, is_train=True)
|
|
train_set = AttrDataset(train_items, train_transforms, attr_dict)
|
|
|
|
data_loader = build_reid_train_loader(cfg, train_set=train_set)
|
|
AttrTrainer.sample_weights = data_loader.dataset.sample_weights
|
|
return data_loader
|
|
|
|
@classmethod
|
|
def build_test_loader(cls, cfg, dataset_name):
|
|
dataset = DATASET_REGISTRY.get(dataset_name)(root=_root)
|
|
attr_dict = dataset.attr_dict
|
|
if comm.is_main_process():
|
|
dataset.show_test()
|
|
test_items = dataset.test
|
|
|
|
test_transforms = build_transforms(cfg, is_train=False)
|
|
test_set = AttrDataset(test_items, test_transforms, attr_dict)
|
|
data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)
|
|
return data_loader
|
|
|
|
@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 = AttrTrainer.build_model(cfg)
|
|
|
|
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
|
|
|
|
res = AttrTrainer.test(cfg, model)
|
|
return res
|
|
|
|
trainer = AttrTrainer(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,),
|
|
)
|