151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
# 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
|
|
|
|
from ppdet.core.workspace import register, create
|
|
from .meta_arch import BaseArch
|
|
from ..post_process import JDEBBoxPostProcess
|
|
|
|
__all__ = ['YOLOv3']
|
|
# YOLOv3,PP-YOLO,PP-YOLOv2,PP-YOLOE,PP-YOLOE+ use the same architecture as YOLOv3
|
|
# PP-YOLOE and PP-YOLOE+ are recommended to use PPYOLOE architecture in ppyoloe.py, especially when use distillation or aux head
|
|
|
|
|
|
@register
|
|
class YOLOv3(BaseArch):
|
|
__category__ = 'architecture'
|
|
__shared__ = ['data_format']
|
|
__inject__ = ['post_process']
|
|
|
|
def __init__(self,
|
|
backbone='DarkNet',
|
|
neck='YOLOv3FPN',
|
|
yolo_head='YOLOv3Head',
|
|
post_process='BBoxPostProcess',
|
|
data_format='NCHW',
|
|
for_mot=False):
|
|
"""
|
|
YOLOv3 network, see https://arxiv.org/abs/1804.02767
|
|
|
|
Args:
|
|
backbone (nn.Layer): backbone instance
|
|
neck (nn.Layer): neck instance
|
|
yolo_head (nn.Layer): anchor_head instance
|
|
bbox_post_process (object): `BBoxPostProcess` instance
|
|
data_format (str): data format, NCHW or NHWC
|
|
for_mot (bool): whether return other features for multi-object tracking
|
|
models, default False in pure object detection models.
|
|
"""
|
|
super(YOLOv3, self).__init__(data_format=data_format)
|
|
self.backbone = backbone
|
|
self.neck = neck
|
|
self.yolo_head = yolo_head
|
|
self.post_process = post_process
|
|
self.for_mot = for_mot
|
|
self.return_idx = isinstance(post_process, JDEBBoxPostProcess)
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, *args, **kwargs):
|
|
# backbone
|
|
backbone = create(cfg['backbone'])
|
|
|
|
# fpn
|
|
kwargs = {'input_shape': backbone.out_shape}
|
|
neck = create(cfg['neck'], **kwargs)
|
|
|
|
# head
|
|
kwargs = {'input_shape': neck.out_shape}
|
|
yolo_head = create(cfg['yolo_head'], **kwargs)
|
|
|
|
return {
|
|
'backbone': backbone,
|
|
'neck': neck,
|
|
"yolo_head": yolo_head,
|
|
}
|
|
|
|
def _forward(self):
|
|
body_feats = self.backbone(self.inputs)
|
|
if self.for_mot:
|
|
neck_feats = self.neck(body_feats, self.for_mot)
|
|
else:
|
|
neck_feats = self.neck(body_feats)
|
|
|
|
if isinstance(neck_feats, dict):
|
|
assert self.for_mot == True
|
|
emb_feats = neck_feats['emb_feats']
|
|
neck_feats = neck_feats['yolo_feats']
|
|
|
|
if self.training:
|
|
yolo_losses = self.yolo_head(neck_feats, self.inputs)
|
|
|
|
if self.for_mot:
|
|
return {'det_losses': yolo_losses, 'emb_feats': emb_feats}
|
|
else:
|
|
return yolo_losses
|
|
|
|
else:
|
|
yolo_head_outs = self.yolo_head(neck_feats)
|
|
|
|
if self.for_mot:
|
|
# the detection part of JDE MOT model
|
|
boxes_idx, bbox, bbox_num, nms_keep_idx = self.post_process(
|
|
yolo_head_outs, self.yolo_head.mask_anchors)
|
|
output = {
|
|
'bbox': bbox,
|
|
'bbox_num': bbox_num,
|
|
'boxes_idx': boxes_idx,
|
|
'nms_keep_idx': nms_keep_idx,
|
|
'emb_feats': emb_feats,
|
|
}
|
|
else:
|
|
if self.return_idx:
|
|
# the detection part of JDE MOT model
|
|
_, bbox, bbox_num, nms_keep_idx = self.post_process(
|
|
yolo_head_outs, self.yolo_head.mask_anchors)
|
|
elif self.post_process is not None:
|
|
# anchor based YOLOs: YOLOv3,PP-YOLO,PP-YOLOv2 use mask_anchors
|
|
bbox, bbox_num, nms_keep_idx = self.post_process(
|
|
yolo_head_outs, self.yolo_head.mask_anchors,
|
|
self.inputs['im_shape'], self.inputs['scale_factor'])
|
|
else:
|
|
# anchor free YOLOs: PP-YOLOE, PP-YOLOE+
|
|
bbox, bbox_num, nms_keep_idx = self.yolo_head.post_process(
|
|
yolo_head_outs, self.inputs['scale_factor'])
|
|
|
|
if self.use_extra_data:
|
|
extra_data = {} # record the bbox output before nms, such like scores and nms_keep_idx
|
|
"""extra_data:{
|
|
'scores': predict scores,
|
|
'nms_keep_idx': bbox index before nms,
|
|
}
|
|
"""
|
|
extra_data['scores'] = yolo_head_outs[0] # predict scores (probability)
|
|
# Todo: get logits output
|
|
extra_data['nms_keep_idx'] = nms_keep_idx
|
|
# Todo support for mask_anchors yolo
|
|
output = {'bbox': bbox, 'bbox_num': bbox_num, 'extra_data': extra_data}
|
|
else:
|
|
output = {'bbox': bbox, 'bbox_num': bbox_num}
|
|
|
|
return output
|
|
|
|
def get_loss(self):
|
|
return self._forward()
|
|
|
|
def get_pred(self):
|
|
return self._forward()
|