def load_ckpt(self, model_path):
"""
load training checkpoint for further training or predicting.
Args:
model_path: the path of saved checkpoint/parameters.
"""
assert self._train_init_prog is not None or self._pred_init_prog is not None, "model graph not built. You should at least build_forward or build_predict_forward to load its checkpoint."
if self._train_init_prog is not None:
saver.init_pretraining_params(
self._exe,
model_path,
convert=False,
main_program=self._train_init_prog,
strict=True)
elif self._pred_init_prog is not None:
saver.init_pretraining_params(
self._exe,
model_path,
convert=False,
main_program=self._pred_init_prog,
strict=True)
else:
raise Exception("model not found. You should at least build_forward or build_predict_forward to load its checkpoint.")
def load_pretrain(self, pretrain_model_path=None):
# load pretrain model (or ckpt)
if pretrain_model_path is None:
assert 'pretrain_model_path' in self.main_conf, "pretrain_model_path NOT set."
pretrain_model_path = self.main_conf['pretrain_model_path']
init_pretraining_params(self.exe,
pretrain_model_path,
main_program=fluid.default_startup_program())
def load_pretrain(self, model_path, convert=False):
"""
load pretrain models(backbone) for training.
Args:
model_path: the path of saved pretrained parameters.
"""
assert self._train_init_prog is not None, "training graph not found. You should at least build_forward to load its pretrained parameters."
saver.init_pretraining_params(self._exe,
model_path,
convert=convert,
main_program=self._train_init_prog)
def load_ckpt(self, model_path):
"""
load training checkpoint for further training or predicting.
Args:
model_path: the path of saved checkpoint/parameters.
"""
# load pretrain model (or ckpt)
# assert self._exe is not None, "You need to random_init_params before load checkpoints."
# if phase == 'train' and not self._train_init:
# self._init_exe_prog(for_train=True)
# self._exe.run(self._train_init_prog)
# if phase == 'predict' and not self._predict_init:
# self._init_exe_prog(for_train=False)
# self._exe.run(self._pred_init_prog)
assert self._train_init_prog is not None or self._pred_init_prog is not None, "model graph not built. You should at least build_forward or build_predict_forward to load its checkpoint."
# if phase == 'train':
# assert self._train_init_prog is not None, "train graph not found! You should build_forward first before load checkpoint."
if self._train_init_prog is not None:
saver.init_pretraining_params(self._exe,
model_path,
convert=False,
main_program=self._train_init_prog,
strict=True)
# elif phase == 'predict':
elif self._pred_init_prog is not None:
# assert self._pred_init_prog is not None, "predict graph not found! You should build_predict_head first before load checkpoint."
saver.init_pretraining_params(self._exe,
model_path,
convert=False,
main_program=self._pred_init_prog,
strict=True)
else:
raise Exception(
"model not found. You should at least build_forward or build_predict_forward to load its checkpoint."
)
