0a7220ed201eee2ad463264a72d5ce42c33dbcc1,trainval_net.py,,,#,113

Before Change

  elif args.optimizer == "sgd":
    optimizer = torch.optim.SGD([
      {"params": fasterRCNN.RCNN_base.RCNN_base_model[0].parameters(), "lr": lr * 0.0},
      {"params": fasterRCNN.RCNN_base.RCNN_base_model[1].parameters(), "lr": lr * 0.1},
      {"params": fasterRCNN.RCNN_base.RCNN_base_model[2].parameters()},
      {"params": fasterRCNN.RCNN_base.RCNN_rpn.parameters()},

After Change

          print("\t\t\tfg/bg=(%d/%d)" % (0, 0))
          print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box %.4f" % (0, 0, 0, 0))
          if args.use_tfboard:
            info = {
              "loss": loss_temp / args.disp_interval
            }
            for tag, value in info.items():
              logger.scalar_summary(tag, value, step)

        else:
          print("[session %d][epoch %2d][iter %4d] loss: %.4f, lr4ft: %.2e, lr4tr: %.2e" \
            % (args.session, epoch, step, loss_temp / args.disp_interval, lr * 0.1, lr))
          print("\t\t\tfg/bg=(%d/%d)" % (fasterRCNN.fg_cnt, fasterRCNN.bg_cnt))
          print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box: %.4f" %
            (fasterRCNN.RCNN_base.RCNN_rpn.rpn_loss_cls.data[0], \
             fasterRCNN.RCNN_base.RCNN_rpn.rpn_loss_box.data[0], \
             fasterRCNN.RCNN_loss_cls.data[0], \
             fasterRCNN.RCNN_loss_bbox.data[0]))
          if args.use_tfboard:
            info = {
              "loss": loss_temp / args.disp_interval,
              "loss_rpn_cls": fasterRCNN.RCNN_base.RCNN_rpn.rpn_loss_cls.data[0],
              "loss_rpn_box": fasterRCNN.RCNN_base.RCNN_rpn.rpn_loss_box.data[0],
              "loss_rcnn_cls": fasterRCNN.RCNN_loss_cls.data[0],
              "loss_rcnn_box": fasterRCNN.RCNN_loss_bbox.data[0]
            }
            for tag, value in info.items():
              logger.scalar_summary(tag, value, step)

        loss_temp = 0

Instances