forked from PulseFocusPlatform/PulseFocusPlatform
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
# 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
|
|
|
|
import paddle
|
|
from ppdet.modeling.mot.utils import scale_coords
|
|
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_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 == 'ReID':
|
|
emb_feats = det_outs['emb_feats']
|
|
embs_and_gts = self.reid(emb_feats, self.inputs, test_emb=True)
|
|
return embs_and_gts
|
|
|
|
elif self.metric == 'MOT':
|
|
emb_feats = det_outs['emb_feats']
|
|
emb_outs = self.reid(emb_feats, self.inputs)
|
|
|
|
boxes_idx = det_outs['boxes_idx']
|
|
bbox = det_outs['bbox']
|
|
|
|
input_shape = self.inputs['image'].shape[2:]
|
|
im_shape = self.inputs['im_shape']
|
|
scale_factor = self.inputs['scale_factor']
|
|
|
|
bbox[:, 2:] = scale_coords(bbox[:, 2:], input_shape, im_shape,
|
|
scale_factor)
|
|
|
|
nms_keep_idx = det_outs['nms_keep_idx']
|
|
|
|
pred_dets = paddle.concat((bbox[:, 2:], bbox[:, 1:2]), axis=1)
|
|
|
|
emb_valid = paddle.gather_nd(emb_outs, boxes_idx)
|
|
pred_embs = paddle.gather_nd(emb_valid, nms_keep_idx)
|
|
|
|
online_targets = self.tracker.update(pred_dets, pred_embs)
|
|
return online_targets
|
|
|
|
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()
|