qat_export.py

# Copyright (c) 2021 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 argparse
import os
import sys

import paddle
import yaml

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

from paddleseg.cvlibs import Config
from paddleseg.utils import logger, utils
from qat_config import quant_config
from qat_train import skip_quant
from export import SavedSegmentationNet

from paddleslim import QAT

def parse_args():
    parser = argparse.ArgumentParser(description='Model export.')
    parser.add_argument(
        "--config",
        dest="cfg",
        help="The config file.",
        default=None,
        type=str,
        required=True)
    parser.add_argument(
        '--save_dir',
        dest='save_dir',
        help='The directory for saving the exported model',
        type=str,
        default='./output')
    parser.add_argument(
        '--model_path',
        dest='model_path',
        help='The path of model for export',
        type=str,
        default=None)
    parser.add_argument(
        '--without_argmax',
        dest='without_argmax',
        help='Do not add the argmax operation at the end of the network',
        action='store_true')
    parser.add_argument(
        '--with_softmax',
        dest='with_softmax',
        help='Add the softmax operation at the end of the network',
        action='store_true')

    return parser.parse_args()


def main(args):
    os.environ['PADDLESEG_EXPORT_STAGE'] = 'True'
    cfg = Config(args.cfg)
    net = cfg.model

    skip_quant(net)
    quantizer = QAT(config=quant_config)
    quant_net = quantizer.quantize(net)
    logger.info('Quantize the model successfully')

    if args.model_path:
        utils.load_entire_model(quant_net, args.model_path)
        logger.info('Loaded trained params of model successfully')

    if not args.without_argmax or args.with_softmax:
        new_net = SavedSegmentationNet(quant_net, args.without_argmax,
                                       args.with_softmax)
    else:
        new_net = net

    new_net.eval()
    save_path = os.path.join(args.save_dir, 'model')
    input_spec = [
        paddle.static.InputSpec(shape=[None, 3, None, None], dtype='float32')
    ]
    quantizer.save_quantized_model(new_net, save_path, input_spec=input_spec)

    yml_file = os.path.join(args.save_dir, 'deploy.yaml')
    with open(yml_file, 'w') as file:
        transforms = cfg.export_config.get('transforms', [{
            'type': 'Normalize'
        }])
        data = {
            'Deploy': {
                'transforms': transforms,
                'model': 'model.pdmodel',
                'params': 'model.pdiparams'
            }
        }
        yaml.dump(data, file)

    logger.info(f'Model is saved in {args.save_dir}.')


if __name__ == '__main__':
    args = parse_args()
    main(args)