def train(cfg, saver):
"""
Train a new dataset with distillation
e.g.: train: dukemtmc with market module
"""
source_tr = TrainComponent(cfg, 0)
to_load = {'module': source_tr.model}
saver.to_save = to_load
saver.load_checkpoint(is_best=True)
dataset_name = [cfg.DATASET.NAME, cfg.CONTINUATION.DATASET_NAME]
train_loader, num_classes = make_train_data_loader(cfg, dataset_name[1])
current_tr = TrainComponent(cfg, num_classes)
to_load = {'module': current_tr.model}
saver.to_save = to_load
saver.load_checkpoint(is_best=True)
valid = make_multi_valid_data_loader(cfg, dataset_name)
# inference(cfg, current_tr.module, valid)
do_continuous_train(cfg, train_loader, valid, source_tr, current_tr, saver)
if __name__ == '__main__':
cfg, saver = main(
["CONTINUATION.IF_ON", True, "TEST.IF_RE_RANKING", False])
train(cfg, saver)
source_name = cfg.DATASET.NAME
target_name = cfg.FEAT.DATASET_NAME
source_loader, _ = make_train_data_loader_for_extract(cfg, source_name)
tr = TrainComponent(cfg, 702)
to_load = {'model': tr.model}
saver.checkpoint_params = to_load
saver.load_checkpoint(is_best=True)
feat, _ = do_extract(cfg, source_loader, tr)
feat = feat.cpu()
if cfg.MODEL.DEVICE == 'cuda':
torch.cuda.empty_cache()
logger.info(f"Extracting feat is done. {feat.size()}")
train_loader, num_classes = make_data_with_loader_with_feat_label(
cfg, source_name, target_name, feat)
valid = make_multi_valid_data_loader(cfg, [source_name])
# inference(cfg, train_component.module, valid)
do_train_with_feat(cfg, train_loader, valid, tr, saver)
if __name__ == '__main__':
cfg, saver = main(["FEAT.IF_ON", True])
train(cfg, saver)
sys.path.append('.')
sys.path.append('..')
from data import make_train_data_loader, make_train_data_loader_with_expand, make_multi_valid_data_loader
from tools.component import main, TrainComponent
from engine.trainer import do_train
def train(cfg, saver):
dataset_name = [cfg.DATASET.NAME]
if cfg.JOINT.IF_ON:
for name in cfg.JOINT.DATASET_NAME:
dataset_name.append(name)
train_loader, num_classes = make_train_data_loader_with_expand(
cfg, dataset_name)
else:
train_loader, num_classes = make_train_data_loader(
cfg, dataset_name[0])
valid = make_multi_valid_data_loader(cfg, dataset_name)
train_component = TrainComponent(cfg, num_classes)
do_train(cfg, train_loader, valid, train_component, saver)
if __name__ == '__main__':
cfg, saver = main()
train(cfg, saver)
import sys
sys.path.append('.')
sys.path.append('..')
from data import make_multi_valid_data_loader
from engine.inference import inference
from tools.component import main, TrainComponent
def test(cfg, saver):
dataset_name = [cfg.DATASET.NAME]
valid = make_multi_valid_data_loader(cfg, dataset_name, verbose=True)
tr = TrainComponent(cfg, 0)
to_load = {'module': tr.model}
saver.to_save = to_load
saver.load_checkpoint(is_best=True)
inference(cfg, tr.model, valid)
if __name__ == '__main__':
cfg, saver = main(["TEST.IF_ON", True])
test(cfg, saver)
for k in k_s:
logger.info("")
logger.info('*' * 60)
logger.info(f"Start ebll training using {0.001 * k}")
logger.info('*' * 60)
logger.info("")
copy_cfg = copy.deepcopy(cfg)
copy_cfg["CONTINUATION"]["IF_ON"] = True
copy_cfg["EBLL"]["AE_LOSS_WEIGHT"] = 0.001 * k
ebll_tr = TrainComponent(copy_cfg, num_classes)
ebll_tr.model = copy.deepcopy(current_tr.model)
saver.checkpoint_params['model'] = ebll_tr.model
ebll_valid = make_multi_valid_data_loader(cfg, dataset_name)
ebll_train(cfg,
train_loader,
ebll_valid,
source_tr,
ebll_tr,
autoencoder_tr,
saver)
if "ae_dist" not in cfg.CONTINUATION.LOSS_TYPE:
break
if __name__ == '__main__':
cfg, saver = main(["EBLL.IF_ON", True, "TEST.IF_RE_RANKING", False])
train(cfg, saver)