Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Related tags

Deep Learning Non-AR-Spatial-Temporal-Transformer
Overview

Non-AR Spatial-Temporal Transformer

Introduction

Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting (submitted to ICML 2021).

We propose a Non-Autoregressive Transformer architecture for time series forecasting, aiming at overcoming the time delay and accumulative error issues in the canonical Transformer. Moreover, we present a novel spatial-temporal attention mechanism, building a bridge by a learned temporal influence map to fill the gaps between the spatial and temporal attention, so that spatial and temporal dependencies can be processed integrally.

overview

overview

Requirements

  • Python >= 3.6
  • CUDA >= 10.2
  • PyTorch >= 1.7.0

Usage

  • Install the requirements.

    pip install -r requirements.txt

  • Preparing the NuScenes dataset.

    1. Donload the NuScenes dataset from here.
    2. Run the NuScenes preprocessing script. ($NUSCENES_VERSION is selected from v1.0-mini, v1.0-trainval or v1.0-test) 
      python scripts/nuscenes_preprocess.py $YOUR_NUSCENES_DATA_ROOT $SAVE_ROOT -v $NUSCENES_VERSION

  • Preparing the SMARTS-EGO dataset.

    1. This dataset is generated by a modified SMARTS simulator.
    2. We will publish the samples used in our experiments, once this paper is accepted.

  • Train the model with NuScenes dataset.

    python run.py ./configs/non_ar_transformer_nuscenes.py

  • Train the model with SMARTS-EGO dataset.

    python run.py ./configs/non_ar_transformer_smarts.py
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Comments
  • The file missing

    The file missing

    Hello，I am interested in your work, but I meet a question when I run the code, I wonder if there is a missing file named scenes_list_file.txt? Looking forward to your reply.

    Best Wishes

    opened by chenchl19941118 1
  • how to train nuscenes dataset

    how to train nuscenes dataset

    I have downloaded the nuscenes mini dataset, and prepare it according to the README.md. I get the follow exception, when I run python run.py ./configs/non_ar_transformer_nuscenes.py. Should I provide the file path to data to run.py? And how?

    File "run.py", line 73, in main trainer.fit(model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/states.py", line 48, in wrapped_fn result = fn(self, *args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 1073, in fit results = self.accelerator_backend.train(model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/accelerators/gpu_backend.py", line 51, in train results = self.trainer.run_pretrain_routine(model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 1224, in run_pretrain_routine self._run_sanity_check(ref_model, model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 1249, in _run_sanity_check self.reset_val_dataloader(ref_model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py", line 337, in reset_val_dataloader self.num_val_batches, self.val_dataloaders = self._reset_eval_dataloader(model, 'val') File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py", line 299, in _reset_eval_dataloader num_batches = len(dataloader) if _has_len(dataloader) else float('inf') File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py", line 70, in _has_len raise ValueError('Dataloader returned 0 length.' ValueError: Dataloader returned 0 length. Please make sure that your Dataloader at least returns 1 batch

    opened by k-lam 3
Owner
Chen Kai
Chen Kai
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