PaddleX/paddlex/paddleseg/utils/utils.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import contextlib
import filelock
import math
import os
import tempfile
from urllib.parse import urlparse, unquote

import paddle

from paddlex.paddleseg.utils import logger, seg_env
from paddlex.paddleseg.utils.download import download_file_and_uncompress


@contextlib.contextmanager
def generate_tempdir(directory: str=None, **kwargs):
    '''Generate a temporary directory'''
    directory = seg_env.TMP_HOME if not directory else directory
    with tempfile.TemporaryDirectory(dir=directory, **kwargs) as _dir:
        yield _dir


def load_entire_model(model, pretrained):
    if pretrained is not None:
        load_pretrained_model(model, pretrained)
    else:
        logger.warning('Not all pretrained params of {} are loaded, ' \
                       'training from scratch or a pretrained backbone.'.format(model.__class__.__name__))


def load_pretrained_model(model, pretrained_model):
    if pretrained_model is not None:
        logger.info('Loading pretrained model from {}'.format(
            pretrained_model))
        # download pretrained model from url
        if urlparse(pretrained_model).netloc:
            pretrained_model = unquote(pretrained_model)
            savename = pretrained_model.split('/')[-1]
            if not savename.endswith(('tgz', 'tar.gz', 'tar', 'zip')):
                savename = pretrained_model.split('/')[-2]
            else:
                savename = savename.split('.')[0]
            with generate_tempdir() as _dir:
                with filelock.FileLock(
                        os.path.join(seg_env.TMP_HOME, savename)):
                    pretrained_model = download_file_and_uncompress(
                        pretrained_model,
                        savepath=_dir,
                        extrapath=seg_env.PRETRAINED_MODEL_HOME,
                        extraname=savename)

                    pretrained_model = os.path.join(pretrained_model,
                                                    'model.pdparams')

        if os.path.exists(pretrained_model):
            para_state_dict = paddle.load(pretrained_model)

            model_state_dict = model.state_dict()
            keys = model_state_dict.keys()
            num_params_loaded = 0
            for k in keys:
                if k not in para_state_dict:
                    logger.warning("{} is not in pretrained model".format(k))
                elif list(para_state_dict[k].shape) != list(model_state_dict[k]
                                                            .shape):
                    logger.warning(
                        "[SKIP] Shape of pretrained params {} doesn't match.(Pretrained: {}, Actual: {})"
                        .format(k, para_state_dict[k].shape, model_state_dict[
                            k].shape))
                else:
                    model_state_dict[k] = para_state_dict[k]
                    num_params_loaded += 1
            model.set_dict(model_state_dict)
            logger.info("There are {}/{} variables loaded into {}.".format(
                num_params_loaded,
                len(model_state_dict), model.__class__.__name__))

        else:
            raise ValueError('The pretrained model directory is not Found: {}'.
                             format(pretrained_model))
    else:
        logger.info(
            'No pretrained model to load, {} will be trained from scratch.'.
            format(model.__class__.__name__))


def resume(model, optimizer, resume_model):
    if resume_model is not None:
        logger.info('Resume model from {}'.format(resume_model))
        if os.path.exists(resume_model):
            resume_model = os.path.normpath(resume_model)
            ckpt_path = os.path.join(resume_model, 'model.pdparams')
            para_state_dict = paddle.load(ckpt_path)
            ckpt_path = os.path.join(resume_model, 'model.pdopt')
            opti_state_dict = paddle.load(ckpt_path)
            model.set_state_dict(para_state_dict)
            optimizer.set_state_dict(opti_state_dict)

            iter = resume_model.split('_')[-1]
            iter = int(iter)
            return iter
        else:
            raise ValueError(
                'Directory of the model needed to resume is not Found: {}'.
                format(resume_model))
    else:
        logger.info('No model needed to resume.')