SA-ConvLSTM-Pytorch
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Pytorch implementation of Self-Attention ConvLSTM
Overview
- Pytorch implementation of Self-Attention ConvLSTM for Spatiotemporal Prediction.
- Pytorch implementation of ConvLSTM.
(testing on MovingMNIST)
Examples
ConvLSTM
python -m examples.moving_mnist_convlstm
Self-Attention ConvLSTM
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.
self_attention_convlstm/
Self Attention ConvLSTM implementation based on Self-Attention ConvLSTM for Spatiotemporal Prediction.
self_attention_memory_convlstm/
Self-Attention ConvLSTM (with memory module) implementation based on Self-Attention ConvLSTM for Spatiotemporal Prediction.
Visualized Attention Maps

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.pyself_attention_memory_convlstm/model.pyself_attention_memory_convlstm/self_attention_memory.py