如何使用Tensorboard一张图显示多条曲线

2022-10-14 17:28| 来源: 网络整理| 查看: 265

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, [email protected], [email protected]] 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

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