export_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, sys
# add python path of PadleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
if parent_path not in sys.path:
    sys.path.append(parent_path)

# ignore numba warning
import warnings
warnings.filterwarnings('ignore')
import glob
import numpy as np
from PIL import Image

import paddle
import paddle.nn as nn
from paddle.static import InputSpec

from ppdet.core.workspace import load_config, merge_config, create
from ppdet.utils.check import check_gpu, check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.utils.checkpoint import load_weight
from export_utils import dump_infer_config
from paddle.jit import to_static

from ppdet.utils.logger import setup_logger
logger = setup_logger('eval')


def parse_args():
    parser = ArgsParser()
    parser.add_argument(
        "--output_dir",
        type=str,
        default="output_inference",
        help="Directory for storing the output model files.")
    args = parser.parse_args()
    return args


def dygraph_to_static(model, save_dir, cfg):
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    image_shape = None
    if 'inputs_def' in cfg['TestReader']:
        inputs_def = cfg['TestReader']['inputs_def']
        image_shape = inputs_def.get('image_shape', None)
    if image_shape is None:
        image_shape = [3, None, None]
    # Save infer cfg
    dump_infer_config(cfg,
                      os.path.join(save_dir, 'infer_cfg.yml'), image_shape,
                      model)

    input_spec = [{
        "image": InputSpec(
            shape=[None] + image_shape, name='image'),
        "im_shape": InputSpec(
            shape=[None, 2], name='im_shape'),
        "scale_factor": InputSpec(
            shape=[None, 2], name='scale_factor')
    }]

    export_model = to_static(model, input_spec=input_spec)
    # save Model
    paddle.jit.save(export_model, os.path.join(save_dir, 'model'))


def run(FLAGS, cfg):

    # Model
    main_arch = cfg.architecture
    model = create(cfg.architecture)
    cfg_name = os.path.basename(FLAGS.config).split('.')[0]
    save_dir = os.path.join(FLAGS.output_dir, cfg_name)

    # Init Model
    load_weight(model, cfg.weights)

    # export config and model
    dygraph_to_static(model, save_dir, cfg)
    logger.info('Export model to {}'.format(save_dir))


def main():
    paddle.set_device("cpu")
    FLAGS = parse_args()

    cfg = load_config(FLAGS.config)
    # TODO: to be refined in the future
    if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn':
        FLAGS.opt['norm_type'] = 'bn'
    merge_config(FLAGS.opt)
    check_config(cfg)
    check_gpu(cfg.use_gpu)
    check_version()

    run(FLAGS, cfg)


if __name__ == '__main__':
    main()