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127 lines
4.4 KiB
127 lines
4.4 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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from paddlers_slim.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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from ..post_process import JDEBBoxPostProcess |
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__all__ = ['YOLOv3'] |
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@register |
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class YOLOv3(BaseArch): |
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__category__ = 'architecture' |
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__shared__ = ['data_format'] |
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__inject__ = ['post_process'] |
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def __init__(self, |
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backbone='DarkNet', |
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neck='YOLOv3FPN', |
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yolo_head='YOLOv3Head', |
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post_process='BBoxPostProcess', |
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data_format='NCHW', |
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for_mot=False): |
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""" |
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YOLOv3 network, see https://arxiv.org/abs/1804.02767 |
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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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yolo_head (nn.Layer): anchor_head instance |
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bbox_post_process (object): `BBoxPostProcess` instance |
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data_format (str): data format, NCHW or NHWC |
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for_mot (bool): whether return other features for multi-object tracking |
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models, default False in pure object detection models. |
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""" |
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super(YOLOv3, self).__init__(data_format=data_format) |
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self.backbone = backbone |
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self.neck = neck |
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self.yolo_head = yolo_head |
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self.post_process = post_process |
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self.for_mot = for_mot |
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self.return_idx = isinstance(post_process, JDEBBoxPostProcess) |
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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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yolo_head = create(cfg['yolo_head'], **kwargs) |
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return { |
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'backbone': backbone, |
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'neck': neck, |
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"yolo_head": yolo_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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neck_feats = self.neck(body_feats, self.for_mot) |
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if isinstance(neck_feats, dict): |
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assert self.for_mot == True |
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emb_feats = neck_feats['emb_feats'] |
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neck_feats = neck_feats['yolo_feats'] |
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if self.training: |
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yolo_losses = self.yolo_head(neck_feats, self.inputs) |
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if self.for_mot: |
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return {'det_losses': yolo_losses, 'emb_feats': emb_feats} |
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else: |
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return yolo_losses |
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else: |
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yolo_head_outs = self.yolo_head(neck_feats) |
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if self.for_mot: |
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boxes_idx, bbox, bbox_num, nms_keep_idx = self.post_process( |
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yolo_head_outs, self.yolo_head.mask_anchors) |
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output = { |
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'bbox': bbox, |
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'bbox_num': bbox_num, |
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'boxes_idx': boxes_idx, |
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'nms_keep_idx': nms_keep_idx, |
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'emb_feats': emb_feats, |
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} |
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else: |
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if self.return_idx: |
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_, bbox, bbox_num, _ = self.post_process( |
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yolo_head_outs, self.yolo_head.mask_anchors) |
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elif self.post_process is not None: |
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bbox, bbox_num = self.post_process( |
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yolo_head_outs, self.yolo_head.mask_anchors, |
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self.inputs['im_shape'], self.inputs['scale_factor']) |
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else: |
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bbox, bbox_num = self.yolo_head.post_process( |
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yolo_head_outs, self.inputs['scale_factor']) |
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output = {'bbox': bbox, 'bbox_num': bbox_num} |
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return output |
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def get_loss(self): |
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return self._forward() |
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def get_pred(self): |
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return self._forward()
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