export_prune_model.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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..', '..', '..'))
sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))

import program
import paddle
from paddle import fluid
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.utils.save_load import init_model
from paddleslim.prune import load_model


def main():
    # Run code with static graph mode.
    try:
        paddle.enable_static()
    except:
        pass

    startup_prog, eval_program, place, config, _ = program.preprocess()

    feeded_var_names, target_vars, fetches_var_name = program.build_export(
        config, eval_program, startup_prog)
    eval_program = eval_program.clone(for_test=True)
    exe = fluid.Executor(place)
    exe.run(startup_prog)

    if config['Global']['checkpoints'] is not None:
        path = config['Global']['checkpoints']
    else:
        path = config['Global']['pretrain_weights']

    load_model(exe, eval_program, path)

    save_inference_dir = config['Global']['save_inference_dir']
    if not os.path.exists(save_inference_dir):
        os.makedirs(save_inference_dir)
    fluid.io.save_inference_model(
        dirname=save_inference_dir,
        feeded_var_names=feeded_var_names,
        main_program=eval_program,
        target_vars=target_vars,
        executor=exe,
        model_filename='model',
        params_filename='params')
    print("inference model saved in {}/model and {}/params".format(
        save_inference_dir, save_inference_dir))
    print("save success, output_name_list:", fetches_var_name)


if __name__ == '__main__':
    main()