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pair param with key when load trained model params

pull/4748/head
WenmuZhou 2021-11-24 09:40:33 +00:00
parent 91b79f6929
commit 4a6f7ceca6
1 changed files with 10 additions and 9 deletions
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19
ppocr/utils/save_load.py
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@ -57,22 +57,23 @@ def load_model(config, model, optimizer=None):
if checkpoints.endswith('pdparams'):
checkpoints = checkpoints.replace('.pdparams', '')
assert os.path.exists(checkpoints + ".pdparams"), \
f"The {checkpoints}.pdparams does not exists!"
"The {}.pdparams does not exists!".format(checkpoints)
# load params from trained model
params = paddle.load(checkpoints + '.pdparams')
state_dict = model.state_dict()
new_state_dict = {}
for key, value in state_dict.items():
if key not in params:
logger.warning(f"{key} not in loaded params {params.keys()} !")
logger.warning("{} not in loaded params {} !".format(
key, params.keys()))
pre_value = params[key]
if list(value.shape) == list(pre_value.shape):
new_state_dict[key] = pre_value
else:
logger.warning(
f"The shape of model params {key} {value.shape} not matched with loaded params shape {pre_value.shape} !"
)
"The shape of model params {} {} not matched with loaded params shape {} !".
format(key, value.shape, pre_value.shape))
model.set_state_dict(new_state_dict)
optim_dict = paddle.load(checkpoints + '.pdopt')
@ -99,7 +100,7 @@ def load_pretrained_params(model, path):
if path.endswith('pdparams'):
path = path.replace('.pdparams', '')
assert os.path.exists(path + ".pdparams"), \
f"The {path}.pdparams does not exists!"
"The {}.pdparams does not exists!".format(path)
params = paddle.load(path + '.pdparams')
state_dict = model.state_dict()
@ -109,10 +110,10 @@ def load_pretrained_params(model, path):
new_state_dict[k1] = params[k2]
else:
logger.warning(
f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
)
"The shape of model params {} {} not matched with loaded params {} {} !".
format(k1, state_dict[k1].shape, k2, params[k2].shape))
model.set_state_dict(new_state_dict)
logger.info(f"load pretrain successful from {path}")
logger.info("load pretrain successful from {}".format(path))
return model