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__, *(['..'] * 3)))
if parent_path not in sys.path:
    sys.path.append(parent_path)

from paddle import fluid

from ppdet.core.workspace import load_config, merge_config, create
from ppdet.utils.cli import ArgsParser
import ppdet.utils.checkpoint as checkpoint
from ppdet.utils.check import check_config, check_version
from paddleslim.prune import Pruner
from paddleslim.analysis import flops

import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)


def prune_feed_vars(feeded_var_names, target_vars, prog):
    """
    Filter out feed variables which are not in program,
    pruned feed variables are only used in post processing
    on model output, which are not used in program, such
    as im_id to identify image order, im_shape to clip bbox
    in image.
    """
    exist_var_names = []
    prog = prog.clone()
    prog = prog._prune(targets=target_vars)
    global_block = prog.global_block()
    for name in feeded_var_names:
        try:
            v = global_block.var(name)
            exist_var_names.append(str(v.name))
        except Exception:
            logger.info('save_inference_model pruned unused feed '
                        'variables {}'.format(name))
            pass
    return exist_var_names


def save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog):
    cfg_name = os.path.basename(FLAGS.config).split('.')[0]
    save_dir = os.path.join(FLAGS.output_dir, cfg_name)
    feed_var_names = [var.name for var in feed_vars.values()]
    target_vars = list(test_fetches.values())
    feed_var_names = prune_feed_vars(feed_var_names, target_vars, infer_prog)
    logger.info("Export inference model to {}, input: {}, output: "
                "{}...".format(save_dir, feed_var_names,
                               [str(var.name) for var in target_vars]))
    fluid.io.save_inference_model(
        save_dir,
        feeded_var_names=feed_var_names,
        target_vars=target_vars,
        executor=exe,
        main_program=infer_prog,
        params_filename="__params__")


def main():
    cfg = load_config(FLAGS.config)
    merge_config(FLAGS.opt)
    check_config(cfg)
    check_version()

    main_arch = cfg.architecture

    # Use CPU for exporting inference model instead of GPU
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)

    model = create(main_arch)

    startup_prog = fluid.Program()
    infer_prog = fluid.Program()
    with fluid.program_guard(infer_prog, startup_prog):
        with fluid.unique_name.guard():
            inputs_def = cfg['TestReader']['inputs_def']
            inputs_def['use_dataloader'] = False
            feed_vars, _ = model.build_inputs(**inputs_def)
            test_fetches = model.test(feed_vars)
    infer_prog = infer_prog.clone(True)

    pruned_params = FLAGS.pruned_params
    assert (
        FLAGS.pruned_params is not None
    ), "FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option."
    pruned_params = FLAGS.pruned_params.strip().split(",")
    logger.info("pruned params: {}".format(pruned_params))
    pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(",")]
    logger.info("pruned ratios: {}".format(pruned_ratios))
    assert (len(pruned_params) == len(pruned_ratios)
            ), "The length of pruned params and pruned ratios should be equal."
    assert (pruned_ratios > [0] * len(pruned_ratios) and
            pruned_ratios < [1] * len(pruned_ratios)
            ), "The elements of pruned ratios should be in range (0, 1)."

    base_flops = flops(infer_prog)
    pruner = Pruner()
    infer_prog, _, _ = pruner.prune(
        infer_prog,
        fluid.global_scope(),
        params=pruned_params,
        ratios=pruned_ratios,
        place=place,
        only_graph=True)
    pruned_flops = flops(infer_prog)
    logger.info("pruned FLOPS: {}".format(
        float(base_flops - pruned_flops) / base_flops))

    exe.run(startup_prog)
    checkpoint.load_checkpoint(exe, infer_prog, cfg.weights)

    save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)


if __name__ == '__main__':
    parser = ArgsParser()
    parser.add_argument(
        "--output_dir",
        type=str,
        default="output",
        help="Directory for storing the output model files.")

    parser.add_argument(
        "-p",
        "--pruned_params",
        default=None,
        type=str,
        help="The parameters to be pruned when calculating sensitivities.")
    parser.add_argument(
        "--pruned_ratios",
        default=None,
        type=str,
        help="The ratios pruned iteratively for each parameter when calculating sensitivities."
    )

    FLAGS = parser.parse_args()
    main()