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105 lines
3.4 KiB
105 lines
3.4 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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import paddle |
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from paddlers.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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__all__ = ['FCOS'] |
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@register |
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class FCOS(BaseArch): |
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""" |
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FCOS network, see https://arxiv.org/abs/1904.01355 |
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Args: |
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backbone (object): backbone instance |
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neck (object): 'FPN' instance |
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fcos_head (object): 'FCOSHead' instance |
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post_process (object): 'FCOSPostProcess' instance |
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""" |
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__category__ = 'architecture' |
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__inject__ = ['fcos_post_process'] |
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def __init__(self, |
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backbone, |
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neck, |
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fcos_head='FCOSHead', |
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fcos_post_process='FCOSPostProcess'): |
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super(FCOS, self).__init__() |
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self.backbone = backbone |
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self.neck = neck |
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self.fcos_head = fcos_head |
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self.fcos_post_process = fcos_post_process |
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@classmethod |
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def from_config(cls, cfg, *args, **kwargs): |
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backbone = create(cfg['backbone']) |
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kwargs = {'input_shape': backbone.out_shape} |
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neck = create(cfg['neck'], **kwargs) |
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kwargs = {'input_shape': neck.out_shape} |
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fcos_head = create(cfg['fcos_head'], **kwargs) |
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return { |
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'backbone': backbone, |
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'neck': neck, |
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"fcos_head": fcos_head, |
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} |
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def _forward(self): |
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body_feats = self.backbone(self.inputs) |
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fpn_feats = self.neck(body_feats) |
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fcos_head_outs = self.fcos_head(fpn_feats, self.training) |
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if not self.training: |
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scale_factor = self.inputs['scale_factor'] |
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bboxes = self.fcos_post_process(fcos_head_outs, scale_factor) |
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return bboxes |
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else: |
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return fcos_head_outs |
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def get_loss(self, ): |
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loss = {} |
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tag_labels, tag_bboxes, tag_centerness = [], [], [] |
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for i in range(len(self.fcos_head.fpn_stride)): |
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# labels, reg_target, centerness |
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k_lbl = 'labels{}'.format(i) |
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if k_lbl in self.inputs: |
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tag_labels.append(self.inputs[k_lbl]) |
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k_box = 'reg_target{}'.format(i) |
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if k_box in self.inputs: |
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tag_bboxes.append(self.inputs[k_box]) |
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k_ctn = 'centerness{}'.format(i) |
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if k_ctn in self.inputs: |
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tag_centerness.append(self.inputs[k_ctn]) |
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fcos_head_outs = self._forward() |
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loss_fcos = self.fcos_head.get_loss(fcos_head_outs, tag_labels, |
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tag_bboxes, tag_centerness) |
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loss.update(loss_fcos) |
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total_loss = paddle.add_n(list(loss.values())) |
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loss.update({'loss': total_loss}) |
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return loss |
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def get_pred(self): |
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bbox_pred, bbox_num = self._forward() |
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output = {'bbox': bbox_pred, 'bbox_num': bbox_num} |
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return output
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