如何使用Tensorboard一张图显示多条曲线

问题遇到的现象和发生背景

在对yolov5模型进行稀疏训练时使用tensorboard来监控训练过程，想把基础训练时的损失函数和mAP与稀疏训练进行对比，放在一张图里，但是不知道怎么做。

问题相关代码，请勿粘贴截图

关于损失函数：

# Forward
            # with amp.autocast(enabled=cuda):
            pred = model(imgs)  # forward
            loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
            if RANK != -1:
                loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
            if opt.quad:
                loss *= 4.

            # Backward
            # scaler.scale(loss).backward()
            loss.backward()
            # # ============================= sparsity training ========================== #
            srtmp = opt.sr*(1 - 0.9*epoch/epochs)
            if opt.st:
                ignore_bn_list = []
                for k, m in model.named_modules():
                    if isinstance(m, Bottleneck):
                        if m.add:
                            ignore_bn_list.append(k.rsplit(".", 2)[0] + ".cv1.bn")
                            ignore_bn_list.append(k + '.cv1.bn')
                            ignore_bn_list.append(k + '.cv2.bn')
                    if isinstance(m, nn.BatchNorm2d) and (k not in ignore_bn_list):
                        m.weight.grad.data.add_(srtmp * torch.sign(m.weight.data))  # L1
                        m.bias.grad.data.add_(opt.sr*10 * torch.sign(m.bias.data))  # L1

关于mAP:

if RANK in [-1, 0]:
            # mAP
            callbacks.run('on_train_epoch_end', epoch=epoch)
            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
            if not noval or final_epoch:  # Calculate mAP
                results, maps, _ = val.run(data_dict,
                                           batch_size=batch_size // WORLD_SIZE * 2,
                                           imgsz=imgsz,
                                           model=ema.ema,
                                           single_cls=single_cls,
                                           dataloader=val_loader,
                                           save_dir=save_dir,
                                           plots=False,
                                           callbacks=callbacks,
                                           compute_loss=compute_loss)

            # Update best mAP
            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            if fi > best_fitness:
                best_fitness = fi
            log_vals = list(mloss) + list(results) + lr + [srtmp]
            callbacks.run('on_fit_epoch_end', log_vals, bn_weights.numpy() ,epoch, best_fitness, fi)

我想要达到的结果

https://blog.csdn.net/qq_26558047/article/details/107045144?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522165037541816780255242800%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=165037541816780255242800&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-1-107045144.142^v9^pc_search_result_control_group,157^v4^control&utm_term=%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8Tensorboard%E4%B8%80%E5%BC%A0%E5%9B%BE%E6%98%BE%E7%A4%BA%E5%A4%9A%E6%9D%A1%E6%9B%B2%E7%BA%BF&spm=1018.2226.3001.4187
https://blog.csdn.net/u011606714/article/details/79423528?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522165037541816780255242800%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=165037541816780255242800&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-2-79423528.142^v9^pc_search_result_control_group,157^v4^control&utm_term=%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8Tensorboard%E4%B8%80%E5%BC%A0%E5%9B%BE%E6%98%BE%E7%A4%BA%E5%A4%9A%E6%9D%A1%E6%9B%B2%E7%BA%BF&spm=1018.2226.3001.4187
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