parameters: (fpn_rois,
                               min_level,
                               max_level,
                               refer_level,
                               refer_scale,
                               rois_num=None,
                               name=None)
return_statement: return multi_rois, restore_ind
docstring: r"""
  	
      **This op only takes LoDTensor as input.** In Feature Pyramid Networks 
      (FPN) models, it is needed to distribute all proposals into different FPN 
      level, with respect to scale of the proposals, the referring scale and the 
      referring level. Besides, to restore the order of proposals, we return an 
      array which indicates the original index of rois in current proposals. 
      To compute FPN level for each roi, the formula is given as follows:
      
      .. math::
  
          roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
  
          level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
  
      where BBoxArea is a function to compute the area of each roi.
  
      Args:
  
          fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is 
              float32 or float64. The input fpn_rois.
          min_level(int32): The lowest level of FPN layer where the proposals come 
              from.
          max_level(int32): The highest level of FPN layer where the proposals
              come from.
          refer_level(int32): The referring level of FPN layer with specified scale.
          refer_scale(int32): The referring scale of FPN layer with specified level.
          rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. 
              The shape is [B] and data type is int32. B is the number of images.
              If it is not None then return a list of 1-D Tensor. Each element 
              is the output RoIs' number of each image on the corresponding level
              and the shape is [B]. None by default.
          name(str, optional): For detailed information, please refer 
              to :ref:`api_guide_Name`. Usually name is no need to set and 
              None by default. 
  
      Returns:
          Tuple:
  
          multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] 
          and data type of float32 and float64. The length is 
          max_level-min_level+1. The proposals in each FPN level.
  
          restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is 
          the number of total rois. The data type is int32. It is
          used to restore the order of fpn_rois.
  
          rois_num_per_level(List): A list of 1-D Tensor and each Tensor is 
          the RoIs' number in each image on the corresponding level. The shape 
          is [B] and data type of int32. B is the number of images
  
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
              import paddle
              paddle.enable_static()
              fpn_rois = fluid.data(
                  name='data', shape=[None, 4], dtype='float32', lod_level=1)
              multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
                  fpn_rois=fpn_rois,
                  min_level=2,
                  max_level=5,
                  refer_level=4,
                  refer_scale=224)
docstring_summary: r"""
  	
      **This op only takes LoDTensor as input.** In Feature Pyramid Networks 
      (FPN) models, it is needed to distribute all proposals into different FPN 
      level, with respect to scale of the proposals, the referring scale and the 
      referring level. Besides, to restore the order of proposals, we return an 
      array which indicates the original index of rois in current proposals. 
      To compute FPN level for each roi, the formula is given as follows:
      
      .. math::
docstring_tokens: r ** This op only takes LoDTensor as input . ** In Feature Pyramid Networks ( FPN ) models it is needed to distribute all proposals into different FPN level with respect to scale of the proposals the referring scale and the referring level . Besides to restore the order of proposals we return an array which indicates the original index of rois in current proposals . To compute FPN level for each roi the formula is given as follows : .. math ::
function: def distribute_fpn_proposals(fpn_rois,
                               min_level,
                               max_level,
                               refer_level,
                               refer_scale,
                               rois_num=None,
                               name=None):
      r"""
  	
      **This op only takes LoDTensor as input.** In Feature Pyramid Networks 
      (FPN) models, it is needed to distribute all proposals into different FPN 
      level, with respect to scale of the proposals, the referring scale and the 
      referring level. Besides, to restore the order of proposals, we return an 
      array which indicates the original index of rois in current proposals. 
      To compute FPN level for each roi, the formula is given as follows:
      
      .. math::
  
          roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}
  
          level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)
  
      where BBoxArea is a function to compute the area of each roi.
  
      Args:
  
          fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is 
              float32 or float64. The input fpn_rois.
          min_level(int32): The lowest level of FPN layer where the proposals come 
              from.
          max_level(int32): The highest level of FPN layer where the proposals
              come from.
          refer_level(int32): The referring level of FPN layer with specified scale.
          refer_scale(int32): The referring scale of FPN layer with specified level.
          rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. 
              The shape is [B] and data type is int32. B is the number of images.
              If it is not None then return a list of 1-D Tensor. Each element 
              is the output RoIs' number of each image on the corresponding level
              and the shape is [B]. None by default.
          name(str, optional): For detailed information, please refer 
              to :ref:`api_guide_Name`. Usually name is no need to set and 
              None by default. 
  
      Returns:
          Tuple:
  
          multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] 
          and data type of float32 and float64. The length is 
          max_level-min_level+1. The proposals in each FPN level.
  
          restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is 
          the number of total rois. The data type is int32. It is
          used to restore the order of fpn_rois.
  
          rois_num_per_level(List): A list of 1-D Tensor and each Tensor is 
          the RoIs' number in each image on the corresponding level. The shape 
          is [B] and data type of int32. B is the number of images
  
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
              import paddle
              paddle.enable_static()
              fpn_rois = fluid.data(
                  name='data', shape=[None, 4], dtype='float32', lod_level=1)
              multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
                  fpn_rois=fpn_rois,
                  min_level=2,
                  max_level=5,
                  refer_level=4,
                  refer_scale=224)
      """
      num_lvl = max_level - min_level + 1
  
      if in_dygraph_mode():
          assert rois_num is not None, "rois_num should not be None in dygraph mode."
          attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
                   refer_level, 'refer_scale', refer_scale)
          multi_rois, restore_ind, rois_num_per_level = _C_ops.distribute_fpn_proposals(
              fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
          return multi_rois, restore_ind, rois_num_per_level
  
      check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
                               'distribute_fpn_proposals')
      helper = LayerHelper('distribute_fpn_proposals', **locals())
      dtype = helper.input_dtype('fpn_rois')
      multi_rois = [
          helper.create_variable_for_type_inference(dtype) for i in range(num_lvl)
      ]
  
      restore_ind = helper.create_variable_for_type_inference(dtype='int32')
  
      inputs = {'FpnRois': fpn_rois}
      outputs = {
          'MultiFpnRois': multi_rois,
          'RestoreIndex': restore_ind,
      }
  
      if rois_num is not None:
          inputs['RoisNum'] = rois_num
          rois_num_per_level = [
              helper.create_variable_for_type_inference(dtype='int32')
              for i in range(num_lvl)
          ]
          outputs['MultiLevelRoIsNum'] = rois_num_per_level
  
      helper.append_op(
          type='distribute_fpn_proposals',
          inputs=inputs,
          outputs=outputs,
          attrs={
              'min_level': min_level,
              'max_level': max_level,
              'refer_level': refer_level,
              'refer_scale': refer_scale
          })
      if rois_num is not None:
          return multi_rois, restore_ind, rois_num_per_level
      return multi_rois, restore_ind
function_tokens: def distribute_fpn_proposals ( fpn_rois , min_level , max_level , refer_level , refer_scale , rois_num = None , name = None ) : num_lvl = max_level - min_level + 1 if in_dygraph_mode ( ) : assert rois_num is not None , "rois_num should not be None in dygraph mode." attrs = ( 'min_level' , min_level , 'max_level' , max_level , 'refer_level' , refer_level , 'refer_scale' , refer_scale ) multi_rois , restore_ind , rois_num_per_level = _C_ops . distribute_fpn_proposals ( fpn_rois , rois_num , num_lvl , num_lvl , * attrs ) return multi_rois , restore_ind , rois_num_per_level check_variable_and_dtype ( fpn_rois , 'fpn_rois' , [ 'float32' , 'float64' ] , 'distribute_fpn_proposals' ) helper = LayerHelper ( 'distribute_fpn_proposals' , * * locals ( ) ) dtype = helper . input_dtype ( 'fpn_rois' ) multi_rois = [ helper . create_variable_for_type_inference ( dtype ) for i in range ( num_lvl ) ] restore_ind = helper . create_variable_for_type_inference ( dtype = 'int32' ) inputs = { 'FpnRois' : fpn_rois } outputs = { 'MultiFpnRois' : multi_rois , 'RestoreIndex' : restore_ind , } if rois_num is not None : inputs [ 'RoisNum' ] = rois_num rois_num_per_level = [ helper . create_variable_for_type_inference ( dtype = 'int32' ) for i in range ( num_lvl ) ] outputs [ 'MultiLevelRoIsNum' ] = rois_num_per_level helper . append_op ( type = 'distribute_fpn_proposals' , inputs = inputs , outputs = outputs , attrs = { 'min_level' : min_level , 'max_level' : max_level , 'refer_level' : refer_level , 'refer_scale' : refer_scale } ) if rois_num is not None : return multi_rois , restore_ind , rois_num_per_level return multi_rois , restore_ind