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91 lines
2.9 KiB
91 lines
2.9 KiB
# Copyright (c) 2022 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 paddlers_slim.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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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 = self.post_process(preds, anchors, |
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self.inputs['im_shape'], |
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self.inputs['scale_factor']) |
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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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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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