text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
  #   
  # Licensed under the Apache License, Version 2.0 (the "License");   
  # you may not use this file except in compliance with the License.  
  # You may obtain a copy of the License at   
  #   
  #     http://www.apache.org/licenses/LICENSE-2.0    
  #   
  # Unless required by applicable law or agreed to in writing, software   
  # distributed under the License is distributed on an "AS IS" BASIS, 
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
  # See the License for the specific language governing permissions and   
  # limitations under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import paddle
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
  
  __all__ = ['PicoDet']
  
  
  @register
  class PicoDet(BaseArch):
      """
      Generalized Focal Loss network, see https://arxiv.org/abs/2006.04388
  
      Args:
          backbone (object): backbone instance
          neck (object): 'FPN' instance
          head (object): 'PicoHead' instance
      """
  
      __category__ = 'architecture'
  
      def __init__(self, backbone, neck, head='PicoHead'):
          super(PicoDet, self).__init__()
          self.backbone = backbone
          self.neck = neck
          self.head = head
          self.lite_deploy = False
  
      @classmethod
      def from_config(cls, cfg, *args, **kwargs):
          backbone = create(cfg['backbone'])
  
          kwargs = {'input_shape': backbone.out_shape}
          neck = create(cfg['neck'], **kwargs)
  
          kwargs = {'input_shape': neck.out_shape}
          head = create(cfg['head'], **kwargs)
  
          return {
              'backbone': backbone,
              'neck': neck,
              "head": head,
          }
  
      def _forward(self):
          body_feats = self.backbone(self.inputs)
          fpn_feats = self.neck(body_feats)
          head_outs = self.head(fpn_feats)
          if self.training or self.lite_deploy:
              return head_outs
          else:
              im_shape = self.inputs['im_shape']
              scale_factor = self.inputs['scale_factor']
              bboxes, bbox_num = self.head.post_process(head_outs, im_shape,
                                                        scale_factor)
              return bboxes, bbox_num
  
      def get_loss(self, ):
          loss = {}
  
          head_outs = self._forward()
          loss_gfl = self.head.get_loss(head_outs, self.inputs)
          loss.update(loss_gfl)
          total_loss = paddle.add_n(list(loss.values()))
          loss.update({'loss': total_loss})
          return loss
  
      def get_pred(self):
          if self.lite_deploy:
              return {'picodet': self._forward()[0]}
          else:
              bbox_pred, bbox_num = self._forward()
              output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
              return output
  