PulseFocusPlatform/ppdet/modeling/architectures/blazeface.py

92 lines
2.9 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__ = ['BlazeFace']
@register
class BlazeFace(BaseArch):
"""
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,
see https://arxiv.org/abs/1907.05047
Args:
backbone (nn.Layer): backbone instance
neck (nn.Layer): neck instance
blaze_head (nn.Layer): `blazeHead` instance
post_process (object): `BBoxPostProcess` instance
"""
__category__ = 'architecture'
__inject__ = ['post_process']
def __init__(self, backbone, blaze_head, neck, post_process):
super(BlazeFace, self).__init__()
self.backbone = backbone
self.neck = neck
self.blaze_head = blaze_head
self.post_process = post_process
@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}
blaze_head = create(cfg['blaze_head'], **kwargs)
return {
'backbone': backbone,
'neck': neck,
'blaze_head': blaze_head,
}
def _forward(self):
# Backbone
body_feats = self.backbone(self.inputs)
# neck
neck_feats = self.neck(body_feats)
# blaze Head
if self.training:
return self.blaze_head(neck_feats, self.inputs['image'],
self.inputs['gt_bbox'],
self.inputs['gt_class'])
else:
preds, anchors = self.blaze_head(neck_feats, self.inputs['image'])
bbox, bbox_num = self.post_process(preds, anchors,
self.inputs['im_shape'],
self.inputs['scale_factor'])
return bbox, bbox_num
def get_loss(self, ):
return {"loss": self._forward()}
def get_pred(self):
bbox_pred, bbox_num = self._forward()
output = {
"bbox": bbox_pred,
"bbox_num": bbox_num,
}
return output