84 lines
2.7 KiB
Python
84 lines
2.7 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.core.workspace import register, create
|
|
from .meta_arch import BaseArch
|
|
|
|
__all__ = ['S2ANet']
|
|
|
|
|
|
@register
|
|
class S2ANet(BaseArch):
|
|
__category__ = 'architecture'
|
|
__inject__ = ['head']
|
|
|
|
def __init__(self, backbone, neck, head):
|
|
"""
|
|
S2ANet, see https://arxiv.org/pdf/2008.09397.pdf
|
|
|
|
Args:
|
|
backbone (object): backbone instance
|
|
neck (object): `FPN` instance
|
|
head (object): `Head` instance
|
|
"""
|
|
super(S2ANet, self).__init__()
|
|
self.backbone = backbone
|
|
self.neck = neck
|
|
self.s2anet_head = head
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, *args, **kwargs):
|
|
backbone = create(cfg['backbone'])
|
|
kwargs = {'input_shape': backbone.out_shape}
|
|
neck = cfg['neck'] and create(cfg['neck'], **kwargs)
|
|
|
|
out_shape = neck and neck.out_shape or backbone.out_shape
|
|
kwargs = {'input_shape': out_shape}
|
|
head = create(cfg['head'], **kwargs)
|
|
|
|
return {'backbone': backbone, 'neck': neck, "head": head}
|
|
|
|
def _forward(self):
|
|
body_feats = self.backbone(self.inputs)
|
|
if self.neck is not None:
|
|
body_feats = self.neck(body_feats)
|
|
if self.training:
|
|
loss = self.s2anet_head(body_feats, self.inputs)
|
|
return loss
|
|
else:
|
|
head_outs = self.s2anet_head(body_feats)
|
|
# post_process
|
|
bboxes, bbox_num = self.s2anet_head.get_bboxes(head_outs)
|
|
# rescale the prediction back to origin image
|
|
im_shape = self.inputs['im_shape']
|
|
scale_factor = self.inputs['scale_factor']
|
|
bboxes = self.s2anet_head.get_pred(bboxes, bbox_num, im_shape,
|
|
scale_factor)
|
|
# output
|
|
output = {'bbox': bboxes, 'bbox_num': bbox_num}
|
|
return output
|
|
|
|
def get_loss(self, ):
|
|
loss = self._forward()
|
|
return loss
|
|
|
|
def get_pred(self):
|
|
output = self._forward()
|
|
return output
|