person_monitor_fy/dependence/PaddleDetection/static/slim/prune/eval.py

# Copyright (c) 2019 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

# 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)

import paddle.fluid as fluid
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__)

try:
    from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results
    import ppdet.utils.checkpoint as checkpoint
    from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode

    from ppdet.data.reader import create_reader

    from ppdet.core.workspace import load_config, merge_config, create
    from ppdet.utils.cli import ArgsParser
except ImportError as e:
    if sys.argv[0].find('static') >= 0:
        logger.error("Importing ppdet failed when running static model "
                     "with error: {}\n"
                     "please try:\n"
                     "\t1. run static model under PaddleDetection/static "
                     "directory\n"
                     "\t2. run 'pip uninstall ppdet' to uninstall ppdet "
                     "dynamic version firstly.".format(e))
        sys.exit(-1)
    else:
        raise e


def main():
    """
    Main evaluate function
    """
    cfg = load_config(FLAGS.config)
    merge_config(FLAGS.opt)
    check_config(cfg)
    # check if set use_gpu=True in paddlepaddle cpu version
    check_gpu(cfg.use_gpu)
    # check if paddlepaddle version is satisfied
    check_version()

    main_arch = cfg.architecture

    multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)

    # define executor
    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)

    # build program
    model = create(main_arch)
    startup_prog = fluid.Program()
    eval_prog = fluid.Program()
    with fluid.program_guard(eval_prog, startup_prog):
        with fluid.unique_name.guard():
            inputs_def = cfg['EvalReader']['inputs_def']
            feed_vars, loader = model.build_inputs(**inputs_def)
            if multi_scale_test is None:
                fetches = model.eval(feed_vars)
            else:
                fetches = model.eval(feed_vars, multi_scale_test)
    eval_prog = eval_prog.clone(True)

    exe.run(startup_prog)
    reader = create_reader(cfg.EvalReader)
    # When iterable mode, set set_sample_list_generator(reader, place)
    loader.set_sample_list_generator(reader)

    dataset = cfg['EvalReader']['dataset']

    # eval already exists json file
    if FLAGS.json_eval:
        logger.info(
            "In json_eval mode, PaddleDetection will evaluate json files in "
            "output_eval directly. And proposal.json, bbox.json and mask.json "
            "will be detected by default.")
        json_eval_results(
            cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset)
        return

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

    compile_program = fluid.CompiledProgram(eval_prog).with_data_parallel()

    assert cfg.metric != 'OID', "eval process of OID dataset \
                          is not supported."

    if cfg.metric == "WIDERFACE":
        raise ValueError("metric type {} does not support in tools/eval.py, "
                         "please use tools/face_eval.py".format(cfg.metric))
    assert cfg.metric in ['COCO', 'VOC'], \
            "unknown metric type {}".format(cfg.metric)
    extra_keys = []

    if cfg.metric == 'COCO':
        extra_keys = ['im_info', 'im_id', 'im_shape']
    if cfg.metric == 'VOC':
        extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']

    keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)

    # whether output bbox is normalized in model output layer
    is_bbox_normalized = False
    if hasattr(model, 'is_bbox_normalized') and \
            callable(model.is_bbox_normalized):
        is_bbox_normalized = model.is_bbox_normalized()

    sub_eval_prog = None
    sub_keys = None
    sub_values = None
    # build sub-program
    if 'Mask' in main_arch and multi_scale_test:
        sub_eval_prog = fluid.Program()
        with fluid.program_guard(sub_eval_prog, startup_prog):
            with fluid.unique_name.guard():
                inputs_def = cfg['EvalReader']['inputs_def']
                inputs_def['mask_branch'] = True
                feed_vars, eval_loader = model.build_inputs(**inputs_def)
                sub_fetches = model.eval(
                    feed_vars, multi_scale_test, mask_branch=True)
                assert cfg.metric == 'COCO'
                extra_keys = ['im_id', 'im_shape']
        sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
                                                extra_keys)
        sub_eval_prog = sub_eval_prog.clone(True)

    # load model
    if 'weights' in cfg:
        checkpoint.load_checkpoint(exe, eval_prog, cfg.weights)

    resolution = None
    if 'Mask' in cfg.architecture:
        resolution = model.mask_head.resolution

    results = eval_run(
        exe,
        compile_program,
        loader,
        keys,
        values,
        cls,
        cfg,
        sub_eval_prog,
        sub_keys,
        sub_values,
        resolution=resolution)

    # if map_type not set, use default 11point, only use in VOC eval
    map_type = cfg.map_type if 'map_type' in cfg else '11point'
    eval_results(
        results,
        cfg.metric,
        cfg.num_classes,
        resolution,
        is_bbox_normalized,
        FLAGS.output_eval,
        map_type,
        dataset=dataset)


if __name__ == '__main__':
    enable_static_mode()
    parser = ArgsParser()
    parser.add_argument(
        "--json_eval",
        action='store_true',
        default=False,
        help="Whether to re eval with already exists bbox.json or mask.json")
    parser.add_argument(
        "-f",
        "--output_eval",
        default=None,
        type=str,
        help="Evaluation file directory, default is current directory.")

    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()