张板“未找到标量数据”。

Question

这是我第一次使用张板。 我遵循了许多教程，但是回到我身上的结果与下面的图像相同。

这是我使用张量板编写的代码。

self.writer = SummaryWriter('SimCLR_Code/SimCLR/runs') 

...

def train(self, train_loader): 
        scaler = GradScaler(enabled=self.args.fp16_precision) 

        sess = tf.Session() 
        self.writer = tf.summary.FileWriter('./runs/graphs', sess.graph)
        #saver = tf.train.Saver(tf.global_variables()) 
        #ckpt = tf.train.get

        # save config file
        save_config_file(self.writer.log_dir, self.args)

        n_iter = 0  #global optimization step
        logging.info(f"Start SimCLR training for {self.args.epochs} epochs.")
        logging.info(f"Training with gpu: {self.args.disable_cuda}.")

        for epoch_counter in range(self.args.epochs): 
            for images, _ in tqdm(train_loader):
                images = torch.cat(images, dim=0)

                images = images.to(self.args.device)

                with autocast(enabled=self.args.fp16_precision):
                    features = self.model(images) 
                    logits, labels = self.info_nce_loss(features) 
                    loss = self.criterion(logits, labels) 

                self.optimizer.zero_grad() 
                scaler.scale(loss).backward() 

                scaler.step(self.optimizer) 
                scaler.update()

                if n_iter % self.args.log_every_n_steps == 0: 
                    top1, top5 = accuracy(logits, labels, topk=(1, 5))
                    self.writer.add_scalar('loss', loss, global_step=n_iter)
                    self.writer.add_scalar('acc/top1', top1[0], global_step=n_iter)
                    self.writer.add_scalar('acc/top5', top5[0], global_step=n_iter)
                    self.writer.add_scalar('learning_rate', self.scheduler.get_lr()[0], global_step=n_iter)

                n_iter += 1 

                # warmup for the first 10 epochs
                if epoch_counter >= 10:
                    try: 
                        self.scheduler.step()  
                    except ZeroDivisionError:
                        print("ZeroDivision")
                logging.debug(f"Epoch: {epoch_counter}\tLoss: {loss}\tTop1 accuracy: {top1[0]}")  #훈련과정 출력

        logging.info("Training has finished.")
        # save model checkpoints
        checkpoint_name = 'checkpoint_{:04d}.pth.tar'.format(self.args.epochs)
        save_checkpoint({
            'epoch': self.args.epochs,
            'arch': self.args.arch,
            'state_dict': self.model.state_dict(),
            'optimizer': self.optimizer.state_dict(),
        }, is_best=False, filename=os.path.join(self.writer.log_dir, checkpoint_name))
        logging.info(f"Model checkpoint and metadata has been saved at {self.writer.log_dir}.")

        # merged = tf.summary.merge_all() 
        # writer = tf.summary.FileWriter
        
        self.writer.flush()
        self.writer.close()

这是我的文件路由

我已经考虑了几天，但是没有进展。 请给我很多建议。