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DC-research / TEMPO

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.
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UnboundLocalError: local variable 'seq_trend' referenced before assignment AFTER training for 1 epoch by train_TEMPO.py #10

Open Lanxin1011 opened 2 weeks ago

Lanxin1011 commented 2 weeks ago

Dear Authors: Thanks for you brilliant work! I encountered an error when running the train_TEMPO.py script, could you please take a look on how to solve the problem? Thanks a lot!

Here is the error info: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 1757/1757 [08:11<00:00, 3.5100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 1757/1757 [08:11<00:00, 3.58it/s] Epoch: 1 cost time: 491.01318168640137 0it [00:00, ?it/s] Traceback (most recent call last): File "train_TEMPO.py", line 342, in <module> vali_loss = vali(model, vali_data, vali_loader, criterion, args, device, ii) File "/mnt/workspace/TEMPO/utils/tools.py", line 352, in vali seq_trend = seq_trend.float().to(device) UnboundLocalError: local variable 'seq_trend' referenced before assignment

Lanxin1011 commented 2 weeks ago

I modified line 343 as batch_x, batch_y, batch_x_mark, batch_y_mark, seq_trend, seq_seasonal, seq_resid = data[0], data[1], data[2], data[3], data[4], data[5], data[6] , and the error disappeared. Could you please tell me if the modification I've made is correct?

Best regards

idevede commented 1 week ago

Hi Lanxin1011,

Thank you for your interest in our work and for actively engaging with the experiments. You are correct regarding your suggested changes. I intended to add code adaptability for different dataloaders but inadvertently omitted this code. Let me fix it quickly.

Best