PulseFocusPlatform/ppdet/modeling/architectures/centernet.py

103 lines
3.3 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
from ppdet.core.workspace import register, create
from .meta_arch import BaseArch
__all__ = ['CenterNet']
@register
class CenterNet(BaseArch):
"""
CenterNet network, see http://arxiv.org/abs/1904.07850
Args:
backbone (object): backbone instance
neck (object): 'CenterDLAFPN' instance
head (object): 'CenterHead' instance
post_process (object): 'CenterNetPostProcess' instance
for_mot (bool): whether return other features used in tracking model
"""
__category__ = 'architecture'
__inject__ = ['post_process']
def __init__(self,
backbone='DLA',
neck='CenterDLAFPN',
head='CenterHead',
post_process='CenterNetPostProcess',
for_mot=False):
super(CenterNet, self).__init__()
self.backbone = backbone
self.neck = neck
self.head = head
self.post_process = post_process
self.for_mot = for_mot
@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}
head = create(cfg['head'], **kwargs)
return {'backbone': backbone, 'neck': neck, "head": head}
def _forward(self):
body_feats = self.backbone(self.inputs)
neck_feat = self.neck(body_feats)
head_out = self.head(neck_feat, self.inputs)
if self.for_mot:
head_out.update({'neck_feat': neck_feat})
return head_out
def get_pred(self):
head_out = self._forward()
if self.for_mot:
bbox, bbox_inds = self.post_process(
head_out['heatmap'],
head_out['size'],
head_out['offset'],
im_shape=self.inputs['im_shape'],
scale_factor=self.inputs['scale_factor'])
output = {
"bbox": bbox,
"bbox_inds": bbox_inds,
"neck_feat": head_out['neck_feat']
}
else:
bbox, bbox_num = self.post_process(
head_out['heatmap'],
head_out['size'],
head_out['offset'],
im_shape=self.inputs['im_shape'],
scale_factor=self.inputs['scale_factor'])
output = {
"bbox": bbox,
"bbox_num": bbox_num,
}
return output
def get_loss(self):
return self._forward()