person_monitor/dependence/PaddleDetection/static/ppdet/utils/eval_utils.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 logging
import numpy as np
import os
import time

import paddle.fluid as fluid

from .voc_eval import bbox_eval as voc_bbox_eval
from .post_process import mstest_box_post_process, mstest_mask_post_process

__all__ = ['parse_fetches', 'eval_run', 'eval_results', 'json_eval_results']

logger = logging.getLogger(__name__)


def parse_fetches(fetches, prog=None, extra_keys=None):
    """
    Parse fetch variable infos from model fetches,
    values for fetch_list and keys for stat
    """
    keys, values = [], []
    cls = []
    for k, v in fetches.items():
        if hasattr(v, 'name'):
            keys.append(k)
            #v.persistable = True
            values.append(v.name)
        else:
            cls.append(v)

    if prog is not None and extra_keys is not None:
        for k in extra_keys:
            try:
                v = fluid.framework._get_var(k, prog)
                keys.append(k)
                values.append(v.name)
            except Exception:
                pass

    return keys, values, cls


def length2lod(length_lod):
    offset_lod = [0]
    for i in length_lod:
        offset_lod.append(offset_lod[-1] + i)
    return [offset_lod]


def get_sub_feed(input, place):
    new_dict = {}
    res_feed = {}
    key_name = ['bbox', 'im_info', 'im_id', 'im_shape', 'bbox_flip']
    for k in key_name:
        if k in input.keys():
            new_dict[k] = input[k]
    for k in input.keys():
        if 'image' in k:
            new_dict[k] = input[k]
    for k, v in new_dict.items():
        data_t = fluid.LoDTensor()
        data_t.set(v[0], place)
        if 'bbox' in k:
            lod = length2lod(v[1][0])
            data_t.set_lod(lod)
        res_feed[k] = data_t
    return res_feed


def clean_res(result, keep_name_list):
    clean_result = {}
    for k in result.keys():
        if k in keep_name_list:
            clean_result[k] = result[k]
    result.clear()
    return clean_result


def get_masks(result):
    import pycocotools.mask as mask_util
    if result is None:
        return {}
    seg_pred = result['segm'][0].astype(np.uint8)
    cate_label = result['cate_label'][0].astype(np.int)
    cate_score = result['cate_score'][0].astype(np.float)
    num_ins = seg_pred.shape[0]
    masks = []
    for idx in range(num_ins - 1):
        cur_mask = seg_pred[idx, ...]
        rle = mask_util.encode(
            np.array(
                cur_mask[:, :, np.newaxis], order='F'))[0]
        rst = (rle, cate_score[idx])
        masks.append([cate_label[idx], rst])
    return masks


def eval_run(exe,
             compile_program,
             loader,
             keys,
             values,
             cls,
             cfg=None,
             sub_prog=None,
             sub_keys=None,
             sub_values=None,
             resolution=None):
    """
    Run evaluation program, return program outputs.
    """
    iter_id = 0
    results = []
    if len(cls) != 0:
        values = []
        for i in range(len(cls)):
            _, accum_map = cls[i].get_map_var()
            cls[i].reset(exe)
            values.append(accum_map)

    images_num = 0
    start_time = time.time()
    has_bbox = 'bbox' in keys

    try:
        loader.start()
        while True:
            outs = exe.run(compile_program,
                           fetch_list=values,
                           return_numpy=False)
            res = {
                k: (np.array(v), v.recursive_sequence_lengths())
                for k, v in zip(keys, outs)
            }
            multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
            mask_multi_scale_test = multi_scale_test and 'Mask' in cfg.architecture

            if multi_scale_test:
                post_res = mstest_box_post_process(res, multi_scale_test,
                                                   cfg.num_classes)
                res.update(post_res)
            if mask_multi_scale_test:
                place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
                sub_feed = get_sub_feed(res, place)
                sub_prog_outs = exe.run(sub_prog,
                                        feed=sub_feed,
                                        fetch_list=sub_values,
                                        return_numpy=False)
                sub_prog_res = {
                    k: (np.array(v), v.recursive_sequence_lengths())
                    for k, v in zip(sub_keys, sub_prog_outs)
                }
                post_res = mstest_mask_post_process(sub_prog_res, cfg)
                res.update(post_res)
            if multi_scale_test:
                res = clean_res(
                    res, ['im_info', 'bbox', 'im_id', 'im_shape', 'mask'])
            if 'mask' in res:
                from ppdet.utils.post_process import mask_encode
                res['mask'] = mask_encode(res, resolution)
            post_config = getattr(cfg, 'PostProcess', None)
            if 'Corner' in cfg.architecture and post_config is not None:
                from ppdet.utils.post_process import corner_post_process
                corner_post_process(res, post_config, cfg.num_classes)
            if 'TTFNet' in cfg.architecture:
                res['bbox'][1].append([len(res['bbox'][0])])
            if 'segm' in res:
                res['segm'] = get_masks(res)
            results.append(res)
            if iter_id % 100 == 0:
                logger.info('Test iter {}'.format(iter_id))
            iter_id += 1
            if 'bbox' not in res or len(res['bbox'][1]) == 0:
                has_bbox = False
            images_num += len(res['bbox'][1][0]) if has_bbox else 1
    except (StopIteration, fluid.core.EOFException):
        loader.reset()
    logger.info('Test finish iter {}'.format(iter_id))

    end_time = time.time()
    fps = images_num / (end_time - start_time)
    if has_bbox:
        logger.info('Total number of images: {}, inference time: {} fps.'.
                    format(images_num, fps))
    else:
        logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
            images_num, fps))

    return results


def eval_results(results,
                 metric,
                 num_classes,
                 resolution=None,
                 is_bbox_normalized=False,
                 output_directory=None,
                 map_type='11point',
                 dataset=None,
                 save_only=False):
    """Evaluation for evaluation program results"""
    box_ap_stats = []
    if metric == 'COCO':
        from ppdet.utils.coco_eval import proposal_eval, bbox_eval, mask_eval, segm_eval
        anno_file = dataset.get_anno()
        with_background = dataset.with_background
        if 'proposal' in results[0]:
            output = 'proposal.json'
            if output_directory:
                output = os.path.join(output_directory, 'proposal.json')
            proposal_eval(results, anno_file, output)
        if 'bbox' in results[0]:
            output = 'bbox.json'
            if output_directory:
                output = os.path.join(output_directory, 'bbox.json')

            box_ap_stats = bbox_eval(
                results,
                anno_file,
                output,
                with_background,
                is_bbox_normalized=is_bbox_normalized,
                save_only=save_only)

        if 'mask' in results[0]:
            output = 'mask.json'
            if output_directory:
                output = os.path.join(output_directory, 'mask.json')
            mask_eval(
                results, anno_file, output, resolution, save_only=save_only)
        if 'segm' in results[0]:
            output = 'segm.json'
            if output_directory:
                output = os.path.join(output_directory, output)
            mask_ap_stats = segm_eval(
                results, anno_file, output, save_only=save_only)
            if len(box_ap_stats) == 0:
                box_ap_stats = mask_ap_stats
    else:
        if 'accum_map' in results[-1]:
            res = np.mean(results[-1]['accum_map'][0])
            logger.info('mAP: {:.2f}'.format(res * 100.))
            box_ap_stats.append(res * 100.)
        elif 'bbox' in results[0]:
            box_ap = voc_bbox_eval(
                results,
                num_classes,
                is_bbox_normalized=is_bbox_normalized,
                map_type=map_type)
            box_ap_stats.append(box_ap)
    return box_ap_stats


def json_eval_results(metric, json_directory=None, dataset=None):
    """
    cocoapi eval with already exists proposal.json, bbox.json or mask.json
    """
    assert metric == 'COCO'
    from ppdet.utils.coco_eval import cocoapi_eval
    anno_file = dataset.get_anno()
    json_file_list = ['proposal.json', 'bbox.json', 'mask.json']
    if json_directory:
        assert os.path.exists(
            json_directory), "The json directory:{} does not exist".format(
                json_directory)
        for k, v in enumerate(json_file_list):
            json_file_list[k] = os.path.join(str(json_directory), v)

    coco_eval_style = ['proposal', 'bbox', 'segm']
    for i, v_json in enumerate(json_file_list):
        if os.path.exists(v_json):
            cocoapi_eval(v_json, coco_eval_style[i], anno_file=anno_file)
        else:
            logger.info("{} not exists!".format(v_json))