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