forked from PulseFocusPlatform/PulseFocusPlatform
105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
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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__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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@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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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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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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