PaddleOCR/ppocr/utils/save_load.py

# 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

from ppocr.utils.logging import get_logger

__all__ = ['init_model', 'save_model', 'load_dygraph_params']


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 init_model(config, model, optimizer=None, lr_scheduler=None):
    """
    load model from checkpoint or pretrained_model
    """
    logger = get_logger()
    global_config = config['Global']
    checkpoints = global_config.get('checkpoints')
    pretrained_model = global_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:
        if not isinstance(pretrained_model, list):
            pretrained_model = [pretrained_model]
        for pretrained in pretrained_model:
            if not (os.path.isdir(pretrained) or
                    os.path.exists(pretrained + '.pdparams')):
                raise ValueError("Model pretrain path {} does not "
                                 "exists.".format(pretrained))
            param_state_dict = paddle.load(pretrained + '.pdparams')
            model.set_state_dict(param_state_dict)
            logger.info("load pretrained model from {}".format(
                pretrained_model))
    else:
        logger.info('train from scratch')
    return best_model_dict


def load_dygraph_params(config, model, logger, optimizer):
    ckp = config['Global']['checkpoints']
    if ckp and os.path.exists(ckp + ".pdparams"):
        pre_best_model_dict = init_model(config, model, optimizer)
        return pre_best_model_dict
    else:
        pm = config['Global']['pretrained_model']
        if pm is None:
            return {}
        if not os.path.exists(pm) and not os.path.exists(pm + ".pdparams"):
            logger.info(f"The pretrained_model {pm} does not exists!")
            return {}
        pm = pm if pm.endswith('.pdparams') else pm + '.pdparams'
        params = paddle.load(pm)
        state_dict = model.state_dict()
        new_state_dict = {}
        for k1, k2 in zip(state_dict.keys(), params.keys()):
            if list(state_dict[k1].shape) == list(params[k2].shape):
                new_state_dict[k1] = params[k2]
            else:
                logger.info(
                    f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
                )
        model.set_state_dict(new_state_dict)
        logger.info(f"loaded pretrained_model successful from {pm}")
        return {}


def load_pretrained_params(model, path):
    if path is None:
        return False
    if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
        print(f"The pretrained_model {path} does not exists!")
        return False

    path = path if path.endswith('.pdparams') else path + '.pdparams'
    params = paddle.load(path)
    state_dict = model.state_dict()
    new_state_dict = {}
    for k1, k2 in zip(state_dict.keys(), params.keys()):
        if list(state_dict[k1].shape) == list(params[k2].shape):
            new_state_dict[k1] = params[k2]
        else:
            print(
                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
            )
    model.set_state_dict(new_state_dict)
    print(f"load pretrain successful from {path}")
    return model


def save_model(model,
               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(model.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))