person_monitor/dependence/PaddleDetection/ppdet/modeling/architectures/jde.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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ppdet.core.workspace import register, create
from .meta_arch import BaseArch

__all__ = ['JDE']


@register
class JDE(BaseArch):
    __category__ = 'architecture'
    __shared__ = ['metric']
    """
    JDE network, see https://arxiv.org/abs/1909.12605v1

    Args:
        detector (object): detector model instance
        reid (object): reid model instance
        tracker (object): tracker instance
        metric (str): 'MOTDet' for training and detection evaluation, 'ReID'
            for ReID embedding evaluation, or 'MOT' for multi object tracking
            evaluation.
    """

    def __init__(self,
                 detector='YOLOv3',
                 reid='JDEEmbeddingHead',
                 tracker='JDETracker',
                 metric='MOT'):
        super(JDE, self).__init__()
        self.detector = detector
        self.reid = reid
        self.tracker = tracker
        self.metric = metric

    @classmethod
    def from_config(cls, cfg, *args, **kwargs):
        detector = create(cfg['detector'])
        kwargs = {'input_shape': detector.neck.out_shape}

        reid = create(cfg['reid'], **kwargs)

        tracker = create(cfg['tracker'])

        return {
            "detector": detector,
            "reid": reid,
            "tracker": tracker,
        }

    def _forward(self):
        det_outs = self.detector(self.inputs)

        if self.training:
            emb_feats = det_outs['emb_feats']
            loss_confs = det_outs['det_losses']['loss_confs']
            loss_boxes = det_outs['det_losses']['loss_boxes']
            jde_losses = self.reid(
                emb_feats,
                self.inputs,
                loss_confs=loss_confs,
                loss_boxes=loss_boxes)
            return jde_losses
        else:
            if self.metric == 'MOTDet':
                det_results = {
                    'bbox': det_outs['bbox'],
                    'bbox_num': det_outs['bbox_num'],
                }
                return det_results

            elif self.metric == 'MOT':
                emb_feats = det_outs['emb_feats']
                bboxes = det_outs['bbox']
                boxes_idx = det_outs['boxes_idx']
                nms_keep_idx = det_outs['nms_keep_idx']

                pred_dets, pred_embs = self.reid(
                    emb_feats,
                    self.inputs,
                    bboxes=bboxes,
                    boxes_idx=boxes_idx,
                    nms_keep_idx=nms_keep_idx)
                return pred_dets, pred_embs

            else:
                raise ValueError("Unknown metric {} for multi object tracking.".
                                 format(self.metric))

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()