146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import errno
|
|
import os
|
|
import pickle
|
|
import six
|
|
|
|
import paddle
|
|
|
|
__all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
|
|
|
|
|
|
def _mkdir_if_not_exist(path, logger):
|
|
"""
|
|
mkdir if not exists, ignore the exception when multiprocess mkdir together
|
|
"""
|
|
if not os.path.exists(path):
|
|
try:
|
|
os.makedirs(path)
|
|
except OSError as e:
|
|
if e.errno == errno.EEXIST and os.path.isdir(path):
|
|
logger.warning(
|
|
'be happy if some process has already created {}'.format(
|
|
path))
|
|
else:
|
|
raise OSError('Failed to mkdir {}'.format(path))
|
|
|
|
|
|
def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False):
|
|
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
|
|
raise ValueError("Model pretrain path {} does not "
|
|
"exists.".format(path))
|
|
if load_static_weights:
|
|
pre_state_dict = paddle.static.load_program_state(path)
|
|
param_state_dict = {}
|
|
model_dict = model.state_dict()
|
|
for key in model_dict.keys():
|
|
weight_name = model_dict[key].name
|
|
weight_name = weight_name.replace('binarize', '').replace(
|
|
'thresh', '') # for DB
|
|
if weight_name in pre_state_dict.keys():
|
|
# logger.info('Load weight: {}, shape: {}'.format(
|
|
# weight_name, pre_state_dict[weight_name].shape))
|
|
if 'encoder_rnn' in key:
|
|
# delete axis which is 1
|
|
pre_state_dict[weight_name] = pre_state_dict[
|
|
weight_name].squeeze()
|
|
# change axis
|
|
if len(pre_state_dict[weight_name].shape) > 1:
|
|
pre_state_dict[weight_name] = pre_state_dict[
|
|
weight_name].transpose((1, 0))
|
|
param_state_dict[key] = pre_state_dict[weight_name]
|
|
else:
|
|
param_state_dict[key] = model_dict[key]
|
|
model.set_state_dict(param_state_dict)
|
|
return
|
|
|
|
param_state_dict = paddle.load(path + '.pdparams')
|
|
model.set_state_dict(param_state_dict)
|
|
return
|
|
|
|
|
|
def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
|
|
"""
|
|
load model from checkpoint or pretrained_model
|
|
"""
|
|
gloabl_config = config['Global']
|
|
checkpoints = gloabl_config.get('checkpoints')
|
|
pretrained_model = gloabl_config.get('pretrained_model')
|
|
best_model_dict = {}
|
|
if checkpoints:
|
|
assert os.path.exists(checkpoints + ".pdparams"), \
|
|
"Given dir {}.pdparams not exist.".format(checkpoints)
|
|
assert os.path.exists(checkpoints + ".pdopt"), \
|
|
"Given dir {}.pdopt not exist.".format(checkpoints)
|
|
para_dict = paddle.load(checkpoints + '.pdparams')
|
|
opti_dict = paddle.load(checkpoints + '.pdopt')
|
|
model.set_state_dict(para_dict)
|
|
if optimizer is not None:
|
|
optimizer.set_state_dict(opti_dict)
|
|
|
|
if os.path.exists(checkpoints + '.states'):
|
|
with open(checkpoints + '.states', 'rb') as f:
|
|
states_dict = pickle.load(f) if six.PY2 else pickle.load(
|
|
f, encoding='latin1')
|
|
best_model_dict = states_dict.get('best_model_dict', {})
|
|
if 'epoch' in states_dict:
|
|
best_model_dict['start_epoch'] = states_dict['epoch'] + 1
|
|
|
|
logger.info("resume from {}".format(checkpoints))
|
|
elif pretrained_model:
|
|
load_static_weights = gloabl_config.get('load_static_weights', False)
|
|
if not isinstance(pretrained_model, list):
|
|
pretrained_model = [pretrained_model]
|
|
if not isinstance(load_static_weights, list):
|
|
load_static_weights = [load_static_weights] * len(pretrained_model)
|
|
for idx, pretrained in enumerate(pretrained_model):
|
|
load_static = load_static_weights[idx]
|
|
load_dygraph_pretrain(
|
|
model, logger, path=pretrained, load_static_weights=load_static)
|
|
logger.info("load pretrained model from {}".format(
|
|
pretrained_model))
|
|
else:
|
|
logger.info('train from scratch')
|
|
return best_model_dict
|
|
|
|
|
|
def save_model(net,
|
|
optimizer,
|
|
model_path,
|
|
logger,
|
|
is_best=False,
|
|
prefix='ppocr',
|
|
**kwargs):
|
|
"""
|
|
save model to the target path
|
|
"""
|
|
_mkdir_if_not_exist(model_path, logger)
|
|
model_prefix = os.path.join(model_path, prefix)
|
|
paddle.save(net.state_dict(), model_prefix + '.pdparams')
|
|
paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
|
|
|
|
# save metric and config
|
|
with open(model_prefix + '.states', 'wb') as f:
|
|
pickle.dump(kwargs, f, protocol=2)
|
|
if is_best:
|
|
logger.info('save best model is to {}'.format(model_prefix))
|
|
else:
|
|
logger.info("save model in {}".format(model_prefix))
|