PulseFocusPlatform/ppdet/modeling/architectures/yolo.py

105 lines
3.2 KiB
Python

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__ = ['YOLOv3']
@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
@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)
neck_feats = self.neck(body_feats, self.for_mot)
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:
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:
bbox, bbox_num = self.post_process(
yolo_head_outs, self.yolo_head.mask_anchors,
self.inputs['im_shape'], self.inputs['scale_factor'])
output = {'bbox': bbox, 'bbox_num': bbox_num}
return output
def get_loss(self):
return self._forward()
def get_pred(self):
return self._forward()