DeepLearningExamples/PaddlePaddle/Classification/RN50v1.5/utils/save_load.py

# Copyright (c) 2022 NVIDIA Corporation.  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 errno
import os
import re
import shutil
import tempfile
import logging
import paddle

_PDOPT_SUFFIX = '.pdopt'
_PDPARAMS_SUFFIX = '.pdparams'


def _mkdir_if_not_exist(path):
    """
    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):
                logging.warning(
                    'be happy if some process has already created %s', path)
            else:
                raise OSError(f'Failed to mkdir {path}')


def _load_state(path):
    """
    Load model parameters from .pdparams file.
    Args:
        path(str): Path to .pdparams file.
    Returns:
        state(dict): Dict of parameters loaded from file.
    """
    if os.path.exists(path + _PDOPT_SUFFIX):
        tmp = tempfile.mkdtemp()
        dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
        shutil.copy(path + _PDPARAMS_SUFFIX, dst + _PDPARAMS_SUFFIX)
        state = paddle.static.load_program_state(dst)
        shutil.rmtree(tmp)
    else:
        state = paddle.static.load_program_state(path)
    return state


def load_params(prog, path, ignore_params=None):
    """
    Load model from the given path.
    Args:
        prog (paddle.static.Program): Load weight to which Program object.
        path (string): Model path.
        ignore_params (list): Ignore variable to load when finetuning.
    """
    if not (os.path.isdir(path) or os.path.exists(path + _PDPARAMS_SUFFIX)):
        raise ValueError(f"Model pretrain path {path} does not exists.")

    logging.info("Loading parameters from %s...", path)

    ignore_set = set()
    state = _load_state(path)

    # ignore the parameter which mismatch the shape
    # between the model and pretrain weight.
    all_var_shape = {}
    for block in prog.blocks:
        for param in block.all_parameters():
            all_var_shape[param.name] = param.shape
    ignore_set.update([
        name for name, shape in all_var_shape.items()
        if name in state and shape != state[name].shape
    ])

    if ignore_params:
        all_var_names = [var.name for var in prog.list_vars()]
        ignore_list = filter(
            lambda var: any([re.match(name, var) for name in ignore_params]),
            all_var_names)
        ignore_set.update(list(ignore_list))

    if len(ignore_set) > 0:
        for k in ignore_set:
            if k in state:
                logging.warning(
                    'variable %s is already excluded automatically', k)
                del state[k]

    paddle.static.set_program_state(prog, state)


def init_ckpt(path_to_ckpt, program, exe):
    """
    Init from checkpoints or pretrained model in given path.
    Args:
        path_to_ckpt(str): The path to files of checkpoints,
                           including '.pdparams' and '.pdopt'.
        program(paddle.static.Program): The program to init model.
        exe(paddle.static.Executor): The executor to run program.
    """
    paddle.static.load(program, path_to_ckpt, exe)
    logging.info("Finish initalizing the checkpoint from %s", path_to_ckpt)


def init_pretrained(path_to_pretrained, program):
    """
    Init from checkpoints or pretrained model in given path.
    Args:
        path_to_pretrained(str): The path to file of pretrained model.
        program(paddle.static.Program): The program to init model.
    """
    if not isinstance(path_to_pretrained, list):
        pretrained_model = [path_to_pretrained]
    for pretrain in pretrained_model:
        load_params(program, pretrain)
    logging.info("Finish initalizing pretrained parameters from %s",
                 pretrained_model)


def init_program(args, program, exe):
    """
    Init from given checkpoint or pretrained parameters .
    Args:
        args(Namespace): Arguments obtained from ArgumentParser.
        program(paddle.static.Program): The program to init model.
        exe(paddle.static.Executor): The executor to run program.
    """
    if args.from_checkpoint is not None:
        init_ckpt(args.from_checkpoint, program, exe)
        logging.info("Training will start at the %d-th epoch",
                     args.start_epoch)
    elif args.from_pretrained_params is not None:
        init_pretrained(args.from_pretrained_params, program)


def save_model(program, model_path, epoch_id, prefix):
    """
    Save a model to given path.
    Args:
        program(paddle.static.Program): The program to be saved.
        model_path(str): The path to save model.
        epoch_id(int): The current epoch id.
    """
    if paddle.distributed.get_rank() != 0:
        return
    model_path = os.path.join(model_path, str(epoch_id))
    _mkdir_if_not_exist(model_path)
    model_prefix = os.path.join(model_path, prefix)
    paddle.static.save(program, model_prefix)
    logging.info("Already save model in %s", model_path)