text: import math
  import numpy as np
  import sys
  import time
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from utils import bbox_iou, multi_bbox_ious, convert2cpu
  
  class YoloLayer(nn.Module):
      def __init__(self, anchor_mask=[], num_classes=0, anchors=[1.0], num_anchors=1, use_cuda=None):
          super(YoloLayer, self).__init__()
          use_cuda = torch.cuda.is_available() and (True if use_cuda is None else use_cuda)
          self.device = torch.device("cuda" if use_cuda else "cpu")
  
          self.anchor_mask = anchor_mask
          self.num_classes = num_classes
          self.anchors = anchors
          self.num_anchors = num_anchors
          self.anchor_step = len(anchors)//num_anchors
          self.rescore = 0
          self.ignore_thresh = 0.5
          self.truth_thresh = 1.
          self.nth_layer = 0
          self.seen = 0
          self.net_width = 0
          self.net_height = 0
  
      def get_mask_boxes(self, output):
          masked_anchors = []
          for m in self.anchor_mask:
              masked_anchors += self.anchors[m*self.anchor_step:(m+1)*self.anchor_step]
  
          masked_anchors = torch.FloatTensor(masked_anchors).to(self.device)
          num_anchors = torch.IntTensor([len(self.anchor_mask)]).to(self.device)
          return {'x':output, 'a':masked_anchors, 'n':num_anchors}
  
      def build_targets(self, pred_boxes, target, anchors, nA, nH, nW):
          nB = target.size(0)
          anchor_step = anchors.size(1) # anchors[nA][anchor_step]
          noobj_mask = torch.ones (nB, nA, nH, nW)
          obj_mask   = torch.zeros(nB, nA, nH, nW)
          tcoord     = torch.zeros( 4, nB, nA, nH, nW)
          tconf      = torch.zeros(nB, nA, nH, nW)
          tcls       = torch.zeros(nB, nA, nH, nW)
  
          nAnchors = nA*nH*nW
          nPixels  = nH*nW
          nGT = 0
          nRecall = 0
          nRecall75 = 0
  
          # it works faster on CPU than on GPU.
          anchors = anchors.to("cpu")
  
          for b in range(nB):
              cur_pred_boxes = pred_boxes[b*nAnchors:(b+1)*nAnchors].t()
              cur_ious = torch.zeros(nAnchors)
              tbox = target[b].view(-1,5).to("cpu")
  
              for t in range(50):
                  if tbox[t][1] == 0:
                      break
                  gx, gy = tbox[t][1] * nW, tbox[t][2] * nH
                  gw, gh = tbox[t][3] * self.net_width, tbox[t][4] * self.net_height
                  cur_gt_boxes = torch.FloatTensor([gx, gy, gw, gh]).repeat(nAnchors,1).t()
                  cur_ious = torch.max(cur_ious, multi_bbox_ious(cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False))
              ignore_ix = (cur_ious>self.ignore_thresh).view(nA,nH,nW)
              noobj_mask[b][ignore_ix] = 0
  
              for t in range(50):
                  if tbox[t][1] == 0:
                      break
                  nGT += 1
                  gx, gy = tbox[t][1] * nW, tbox[t][2] * nH
                  gw, gh = tbox[t][3] * self.net_width, tbox[t][4] * self.net_height
                  gw, gh = gw.float(), gh.float()
                  gi, gj = int(gx), int(gy)
  
                  tmp_gt_boxes = torch.FloatTensor([0, 0, gw, gh]).repeat(nA,1).t()
                  anchor_boxes = torch.cat((torch.zeros(nA, anchor_step), anchors),1).t()
                  _, best_n = torch.max(multi_bbox_ious(anchor_boxes, tmp_gt_boxes, x1y1x2y2=False), 0)
  
                  gt_box = torch.FloatTensor([gx, gy, gw, gh])
                  pred_box = pred_boxes[b*nAnchors+best_n*nPixels+gj*nW+gi]
                  iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False)
  
                  obj_mask  [b][best_n][gj][gi] = 1
                  noobj_mask[b][best_n][gj][gi] = 0
                  tcoord [0][b][best_n][gj][gi] = gx - gi
                  tcoord [1][b][best_n][gj][gi] = gy - gj
                  tcoord [2][b][best_n][gj][gi] = math.log(gw/anchors[best_n][0])
                  tcoord [3][b][best_n][gj][gi] = math.log(gh/anchors[best_n][1])
                  tcls      [b][best_n][gj][gi] = tbox[t][0]
                  tconf     [b][best_n][gj][gi] = iou if self.rescore else 1.
  
                  if iou > 0.5:
                      nRecall += 1
                      if iou > 0.75:
                          nRecall75 += 1
  
          return nGT, nRecall, nRecall75, obj_mask, noobj_mask, tcoord, tconf, tcls
  
      def forward(self, output, target):
          #output : BxAs*(4+1+num_classes)*H*W
          mask_tuple = self.get_mask_boxes(output)
          t0 = time.time()
          nB = output.data.size(0)    # batch size
          nA = mask_tuple['n'].item() # num_anchors
          nC = self.num_classes
          nH = output.data.size(2)
          nW = output.data.size(3)
          anchor_step = mask_tuple['a'].size(0)//nA
          anchors = mask_tuple['a'].view(nA, anchor_step).to(self.device)
          cls_anchor_dim = nB*nA*nH*nW
  
          output  = output.view(nB, nA, (5+nC), nH, nW)
          cls_grid = torch.linspace(5,5+nC-1,nC).long().to(self.device)
          ix = torch.LongTensor(range(0,5)).to(self.device)
          pred_boxes = torch.FloatTensor(4, cls_anchor_dim).to(self.device)
  
          coord = output.index_select(2, ix[0:4]).view(nB*nA, -1, nH*nW).transpose(0,1).contiguous().view(-1,cls_anchor_dim)  # x, y, w, h
          coord[0:2] = coord[0:2].sigmoid()
          conf = output.index_select(2, ix[4]).view(cls_anchor_dim).sigmoid()
  
          cls  = output.index_select(2, cls_grid)
          cls  = cls.view(nB*nA, nC, nH*nW).transpose(1,2).contiguous().view(cls_anchor_dim, nC)
  
          t1 = time.time()
          grid_x = torch.linspace(0, nW-1, nW).repeat(nB*nA, nH, 1).view(cls_anchor_dim).to(self.device)
          grid_y = torch.linspace(0, nH-1, nH).repeat(nW,1).t().repeat(nB*nA, 1, 1).view(cls_anchor_dim).to(self.device)
          anchor_w = anchors.index_select(1, ix[0]).repeat(nB, nH*nW).view(cls_anchor_dim)
          anchor_h = anchors.index_select(1, ix[1]).repeat(nB, nH*nW).view(cls_anchor_dim)
  
          pred_boxes[0] = coord[0] + grid_x
          pred_boxes[1] = coord[1] + grid_y
          pred_boxes[2] = coord[2].exp() * anchor_w
          pred_boxes[3] = coord[3].exp() * anchor_h
          # for build_targets. it works faster on CPU than on GPU
          pred_boxes = convert2cpu(pred_boxes.transpose(0,1).contiguous().view(-1,4)).detach()
  
          t2 = time.time()
          nGT, nRecall, nRecall75, obj_mask, noobj_mask, tcoord, tconf, tcls = \
              self.build_targets(pred_boxes, target.detach(), anchors.detach(), nA, nH, nW)
  
          cls_mask = (obj_mask == 1)
          tcls = tcls[cls_mask].long().view(-1).to(self.device)
          cls_mask = cls_mask.view(-1, 1).repeat(1,nC).to(self.device)
          cls = cls[cls_mask].view(-1, nC)
  
          nProposals = int((conf > 0.25).sum())
          
          tcoord = tcoord.view(4, cls_anchor_dim).to(self.device)
          tconf = tconf.view(cls_anchor_dim).to(self.device)        
  
          conf_mask = (obj_mask + noobj_mask).view(cls_anchor_dim).to(self.device)
          obj_mask = obj_mask.view(cls_anchor_dim).to(self.device)
  
          t3 = time.time()
          loss_coord = nn.MSELoss(reduction='sum')(coord*obj_mask, tcoord*obj_mask)/nB
          loss_conf = nn.MSELoss(reduction='sum')(conf*conf_mask, tconf*conf_mask)/nB
          loss_cls = nn.CrossEntropyLoss(reduction='sum')(cls, tcls)/nB if cls.size(0) > 0 else 0 
          loss = loss_coord + loss_conf + loss_cls
  
          t4 = time.time()
          if False:
              print('-'*30)
              print('        activation : %f' % (t1 - t0))
              print(' create pred_boxes : %f' % (t2 - t1))
              print('     build targets : %f' % (t3 - t2))
              print('       create loss : %f' % (t4 - t3))
              print('             total : %f' % (t4 - t0))
          print('%d: Layer(%03d) nGT %3d, nRC %3d, nRC75 %3d, nPP %3d, loss: box %6.3f, conf %6.3f, class %6.3f, total %7.3f' 
                  % (self.seen, self.nth_layer, nGT, nRecall, nRecall75, nProposals, loss_coord, loss_conf, loss_cls, loss))
          if math.isnan(loss.item()):
              print(conf, tconf)
              sys.exit(0)
          return loss
  