118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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import paddle
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import paddle.nn.functional as F
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__all__ = ['BlazeFace']
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@register
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class BlazeFace(BaseArch):
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"""
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BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,
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see https://arxiv.org/abs/1907.05047
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Args:
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backbone (nn.Layer): backbone instance
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neck (nn.Layer): neck instance
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blaze_head (nn.Layer): `blazeHead` instance
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post_process (object): `BBoxPostProcess` instance
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"""
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__category__ = 'architecture'
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__inject__ = ['post_process']
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def __init__(self, backbone, blaze_head, neck, post_process):
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super(BlazeFace, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.blaze_head = blaze_head
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self.post_process = post_process
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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# fpn
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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# head
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kwargs = {'input_shape': neck.out_shape}
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blaze_head = create(cfg['blaze_head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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'blaze_head': blaze_head,
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}
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def _forward(self):
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# Backbone
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body_feats = self.backbone(self.inputs)
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# neck
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neck_feats = self.neck(body_feats)
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# blaze Head
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if self.training:
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return self.blaze_head(neck_feats, self.inputs['image'],
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self.inputs['gt_bbox'],
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self.inputs['gt_class'])
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else:
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preds, anchors = self.blaze_head(neck_feats, self.inputs['image'])
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bbox, bbox_num, nms_keep_idx = self.post_process(
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preds, anchors, self.inputs['im_shape'],
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self.inputs['scale_factor'])
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if self.use_extra_data:
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extra_data = {} # record the bbox output before nms, such like scores and nms_keep_idx
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"""extra_data:{
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'scores': predict scores,
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'nms_keep_idx': bbox index before nms,
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}
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"""
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preds_logits = preds[1] # [[1xNumBBoxNumClass]]
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extra_data['scores'] = F.softmax(paddle.concat(
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preds_logits, axis=1)).transpose([0, 2, 1])
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extra_data['logits'] = paddle.concat(
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preds_logits, axis=1).transpose([0, 2, 1])
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extra_data['nms_keep_idx'] = nms_keep_idx # bbox index before nms
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return bbox, bbox_num, extra_data
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else:
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return bbox, bbox_num
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def get_loss(self, ):
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return {"loss": self._forward()}
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def get_pred(self):
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if self.use_extra_data:
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bbox_pred, bbox_num, extra_data = self._forward()
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output = {
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"bbox": bbox_pred,
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"bbox_num": bbox_num,
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"extra_data": extra_data
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}
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else:
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bbox_pred, bbox_num = self._forward()
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output = {
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"bbox": bbox_pred,
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"bbox_num": bbox_num,
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}
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return output
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