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SA-ConvLSTM-Pytorch

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Pytorch implementation of Self-Attention ConvLSTM

// repository documentation

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# Overview

- Pytorch implementation of [Self-Attention ConvLSTM for Spatiotemporal Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/6819/6673).
- Pytorch implementation of [ConvLSTM](https://arxiv.org/abs/1506.04214v2).

(testing on MovingMNIST)

## Examples

### ConvLSTM

```bash
python -m examples.moving_mnist_convlstm  
```

### Self-Attention ConvLSTM

```bash
python -m examples.moving_mnist_self_attention_memory_convlstm
```
## Directories

### `convlstm/`

ConvLSTM implementation based on [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://paperswithcode.com/paper/convolutional-lstm-network-a-machine-learning).

### `self_attention_convlstm/`

Self Attention ConvLSTM implementation based on [Self-Attention ConvLSTM for Spatiotemporal Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/6819/6673).

### `self_attention_memory_convlstm/`

Self-Attention ConvLSTM (with memory module) implementation based on [Self-Attention ConvLSTM for Spatiotemporal Prediction](https://ojs.aaai.org/index.php/AAAI/article/view/6819/6673).

### Visualized Attention Maps

![sa-convlstm](https://raw.githubusercontent.com/tsugumi-sys/SA-ConvLSTM-Pytorch/main/fig/sa-convlstm.png)

The above figure is SAM-ConvLSTM formulation process. alpha_{h} in the figure is
used for visualizing attention maps in evaluation (`pipeline/evaluator.py`). Also see the
following files for all calculation process.

- `self_attention_memory_convlstm/cell.py`
- `self_attention_memory_convlstm/model.py`
- `self_attention_memory_convlstm/self_attention_memory.py`

SA-ConvLSTM-Pytorch

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