text: from __future__ import absolute_import
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  
  from model.utils.config import cfg
  from .proposal_layer import _ProposalLayer
  from .anchor_target_layer import _AnchorTargetLayer
  from model.utils.net_utils import _smooth_l1_loss
  
  import numpy as np
  import math
  import pdb
  import time
  
  class _RPN(nn.Module):
      """ region proposal network """
      def __init__(self, din):
          super(_RPN, self).__init__()
          
          # 表示输入的特征图谱的深度
          self.din = din  # get depth of input feature map, e.g., 512
          # anchor的尺寸
          self.anchor_scales = cfg.ANCHOR_SCALES
          self.anchor_ratios = cfg.ANCHOR_RATIOS
          self.feat_stride = cfg.FEAT_STRIDE[0]
  
          # define the convrelu layers processing input feature map
          # kernel size == (3, 3), stride = 1, padding = 1
          self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)
  
          # define bg/fg classifcation score layer
          # nc_score_out = 9 * 2 (ps: anchor_number * bg/fg)
          self.nc_score_out = len(self.anchor_scales) * len(self.anchor_ratios) * 2 # 2(bg/fg) * 9 (anchors)
          # 输出尺寸不变, 深度变为 2 * 9 , 对应9个anchor box 前后景的概率
          self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)
  
          # define anchor box offset prediction layer
          # 定义 anchor_box 的坐标偏移量预测层
          self.nc_bbox_out = len(self.anchor_scales) * len(self.anchor_ratios) * 4 # 4(coords) * 9 (anchors)
          self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)
  
          # define proposal layer
          self.RPN_proposal = _ProposalLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)
  
          # define anchor target layer
          # 定义 anchor 匹配层 (将anchor与gt_box匹配)
          self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride, self.anchor_scales, self.anchor_ratios)
  
          self.rpn_loss_cls = 0
          self.rpn_loss_box = 0
  
      @staticmethod
      def reshape(x, d):
          '''resize the tensor'''
          input_shape = x.size()
          x = x.view(
              input_shape[0],
              int(d),
              int(float(input_shape[1] * input_shape[2]) / float(d)),
              input_shape[3]
          )
          return x
  
      def forward(self, base_feat, im_info, gt_boxes, num_boxes):
  
          batch_size = base_feat.size(0)
  
          # return feature map after convrelu layer
          # 首先得到经过RPN网络第一层卷积的特征图谱
          rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
          # get rpn classification score
          rpn_cls_score = self.RPN_cls_score(rpn_conv1)
  
          rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
          rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
          rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
  
          # get rpn offsets to the anchor boxes
          rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
  
          # proposal layer
          cfg_key = 'TRAIN' if self.training else 'TEST'
  
          rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
                                   im_info, cfg_key))
  
          self.rpn_loss_cls = 0
          self.rpn_loss_box = 0
  
          # generating training labels and build the rpn loss
          if self.training:
              assert gt_boxes is not None
  
              rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
  
              # compute classification loss
              rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
              rpn_label = rpn_data[0].view(batch_size, -1)
  
              rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
              rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
              rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
              rpn_label = Variable(rpn_label.long())
              self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
              fg_cnt = torch.sum(rpn_label.data.ne(0))
  
              rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
  
              # compute bbox regression loss
              rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
              rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
              rpn_bbox_targets = Variable(rpn_bbox_targets)
  
              self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
                                                              rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
  
          return rois, self.rpn_loss_cls, self.rpn_loss_box
  