261 lines
9.5 KiB
Python
261 lines
9.5 KiB
Python
# Copyright (c) 2022 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 copy
|
|
import paddle
|
|
from ppdet.core.workspace import register, create
|
|
from .meta_arch import BaseArch
|
|
|
|
__all__ = ['PPYOLOE', 'PPYOLOEWithAuxHead']
|
|
# PP-YOLOE and PP-YOLOE+ are recommended to use this architecture, especially when use distillation or aux head
|
|
# PP-YOLOE and PP-YOLOE+ can also use the same architecture of YOLOv3 in yolo.py when not use distillation or aux head
|
|
|
|
|
|
@register
|
|
class PPYOLOE(BaseArch):
|
|
"""
|
|
PPYOLOE network, see https://arxiv.org/abs/2203.16250
|
|
|
|
Args:
|
|
backbone (nn.Layer): backbone instance
|
|
neck (nn.Layer): neck instance
|
|
yolo_head (nn.Layer): anchor_head instance
|
|
post_process (object): `BBoxPostProcess` instance
|
|
ssod_loss (object): 'SSODPPYOLOELoss' instance, only used for semi-det(ssod)
|
|
for_distill (bool): whether for distillation
|
|
feat_distill_place (str): distill which feature for distillation
|
|
for_mot (bool): whether return other features for multi-object tracking
|
|
models, default False in pure object detection models.
|
|
"""
|
|
|
|
__category__ = 'architecture'
|
|
__shared__ = ['for_distill']
|
|
__inject__ = ['post_process', 'ssod_loss']
|
|
|
|
def __init__(self,
|
|
backbone='CSPResNet',
|
|
neck='CustomCSPPAN',
|
|
yolo_head='PPYOLOEHead',
|
|
post_process='BBoxPostProcess',
|
|
ssod_loss='SSODPPYOLOELoss',
|
|
for_distill=False,
|
|
feat_distill_place='neck_feats',
|
|
for_mot=False):
|
|
super(PPYOLOE, self).__init__()
|
|
self.backbone = backbone
|
|
self.neck = neck
|
|
self.yolo_head = yolo_head
|
|
self.post_process = post_process
|
|
self.for_mot = for_mot
|
|
|
|
# for ssod, semi-det
|
|
self.is_teacher = False
|
|
self.ssod_loss = ssod_loss
|
|
|
|
# distill
|
|
self.for_distill = for_distill
|
|
self.feat_distill_place = feat_distill_place
|
|
if for_distill:
|
|
assert feat_distill_place in ['backbone_feats', 'neck_feats']
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, *args, **kwargs):
|
|
backbone = create(cfg['backbone'])
|
|
|
|
kwargs = {'input_shape': backbone.out_shape}
|
|
neck = create(cfg['neck'], **kwargs)
|
|
|
|
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)
|
|
neck_feats = self.neck(body_feats, self.for_mot)
|
|
|
|
self.is_teacher = self.inputs.get('is_teacher', False) # for semi-det
|
|
if self.training or self.is_teacher:
|
|
yolo_losses = self.yolo_head(neck_feats, self.inputs)
|
|
|
|
if self.for_distill:
|
|
if self.feat_distill_place == 'backbone_feats':
|
|
self.yolo_head.distill_pairs['backbone_feats'] = body_feats
|
|
elif self.feat_distill_place == 'neck_feats':
|
|
self.yolo_head.distill_pairs['neck_feats'] = neck_feats
|
|
else:
|
|
raise ValueError
|
|
return yolo_losses
|
|
else:
|
|
|
|
yolo_head_outs = self.yolo_head(neck_feats)
|
|
|
|
if self.post_process is not None:
|
|
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:
|
|
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)
|
|
extra_data['nms_keep_idx'] = nms_keep_idx
|
|
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()
|
|
|
|
def get_loss_keys(self):
|
|
return ['loss_cls', 'loss_iou', 'loss_dfl', 'loss_contrast']
|
|
|
|
def get_ssod_loss(self, student_head_outs, teacher_head_outs, train_cfg):
|
|
ssod_losses = self.ssod_loss(student_head_outs, teacher_head_outs,
|
|
train_cfg)
|
|
return ssod_losses
|
|
|
|
|
|
@register
|
|
class PPYOLOEWithAuxHead(BaseArch):
|
|
__category__ = 'architecture'
|
|
__inject__ = ['post_process']
|
|
|
|
def __init__(self,
|
|
backbone='CSPResNet',
|
|
neck='CustomCSPPAN',
|
|
yolo_head='PPYOLOEHead',
|
|
aux_head='SimpleConvHead',
|
|
post_process='BBoxPostProcess',
|
|
for_mot=False,
|
|
detach_epoch=5):
|
|
"""
|
|
PPYOLOE network, see https://arxiv.org/abs/2203.16250
|
|
|
|
Args:
|
|
backbone (nn.Layer): backbone instance
|
|
neck (nn.Layer): neck instance
|
|
yolo_head (nn.Layer): anchor_head instance
|
|
post_process (object): `BBoxPostProcess` instance
|
|
for_mot (bool): whether return other features for multi-object tracking
|
|
models, default False in pure object detection models.
|
|
"""
|
|
super(PPYOLOEWithAuxHead, self).__init__()
|
|
self.backbone = backbone
|
|
self.neck = neck
|
|
self.aux_neck = copy.deepcopy(self.neck)
|
|
|
|
self.yolo_head = yolo_head
|
|
self.aux_head = aux_head
|
|
self.post_process = post_process
|
|
self.for_mot = for_mot
|
|
self.detach_epoch = detach_epoch
|
|
|
|
@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)
|
|
aux_neck = copy.deepcopy(neck)
|
|
|
|
# head
|
|
kwargs = {'input_shape': neck.out_shape}
|
|
yolo_head = create(cfg['yolo_head'], **kwargs)
|
|
aux_head = create(cfg['aux_head'], **kwargs)
|
|
|
|
return {
|
|
'backbone': backbone,
|
|
'neck': neck,
|
|
"yolo_head": yolo_head,
|
|
'aux_head': aux_head,
|
|
}
|
|
|
|
def _forward(self):
|
|
body_feats = self.backbone(self.inputs)
|
|
neck_feats = self.neck(body_feats, self.for_mot)
|
|
|
|
if self.training:
|
|
if self.inputs['epoch_id'] >= self.detach_epoch:
|
|
aux_neck_feats = self.aux_neck([f.detach() for f in body_feats])
|
|
dual_neck_feats = (paddle.concat(
|
|
[f.detach(), aux_f], axis=1) for f, aux_f in
|
|
zip(neck_feats, aux_neck_feats))
|
|
else:
|
|
aux_neck_feats = self.aux_neck(body_feats)
|
|
dual_neck_feats = (paddle.concat(
|
|
[f, aux_f], axis=1) for f, aux_f in
|
|
zip(neck_feats, aux_neck_feats))
|
|
aux_cls_scores, aux_bbox_preds = self.aux_head(dual_neck_feats)
|
|
loss = self.yolo_head(
|
|
neck_feats,
|
|
self.inputs,
|
|
aux_pred=[aux_cls_scores, aux_bbox_preds])
|
|
return loss
|
|
else:
|
|
yolo_head_outs = self.yolo_head(neck_feats)
|
|
|
|
if self.post_process is not None:
|
|
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:
|
|
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
|
|
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()
|