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configs\__pycache__ (0, 2021-06-17)
configs\__pycache__\config_3x3_16_3x3_32_3x3_64.cpython-37.pyc (2050, 2021-06-17)
configs\__pycache__\config_3x3_32_3x3_64_3x3_128.cpython-37.pyc (2051, 2021-06-17)
configs\config_3x3_16_3x3_32_3x3_64.py (2481, 2021-06-17)
configs\config_3x3_32_3x3_64_3x3_128.py (2486, 2021-06-17)
datas (0, 2021-06-17)
datas\train-images-idx3-ubyte.gz (9912422, 2021-06-17)
main.py (3200, 2021-06-17)
networks (0, 2021-06-17)
networks\BinaryDiceLoss.py (1334, 2021-06-17)
networks\ConvLSTM.py (6115, 2021-06-17)
networks\CrossEntropyLoss.py (635, 2021-06-17)
networks\__pycache__ (0, 2021-06-17)
networks\__pycache__\BinaryDiceLoss.cpython-37.pyc (1351, 2021-06-17)
networks\__pycache__\ConvLSTM.cpython-37.pyc (5500, 2021-06-17)
networks\__pycache__\CrossEntropyLoss.cpython-37.pyc (1026, 2021-06-17)
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output\cache\099_00010.png (102911, 2021-06-17)
output\cache\099_00030.png (109192, 2021-06-17)
requirements.txt (77, 2021-06-17)
utils (0, 2021-06-17)
utils\__pycache__ (0, 2021-06-17)
utils\__pycache__\dataset.cpython-37.pyc (3091, 2021-06-17)
utils\__pycache__\functions.cpython-37.pyc (2546, 2021-06-17)
utils\__pycache__\utils.cpython-37.pyc (1150, 2021-06-17)
utils\dataset.py (3454, 2021-06-17)
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# ConvLSTM.pytorch This repository is an unofficial pytorch implementation of [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://arxiv.org/abs/1506.04214v1). We reimplement the experiments in the paper based on the MovingMNIST dataset, which is followed by [Github](https://github.com/jhhuang96/ConvLSTM-PyTorch). Detailed understanding is available on my [Blog](https://www.cnblogs.com/CZiFan/p/12630949.html). ## Requirements - Pytorch>=0.4.0 - CPU or GPU - Other packages can be installed with the following instruction: ``` pip install requirements.txt ``` ## Quick start Running the code with the following command, and the '--config' parameter represents different network architectures. ``` python main.py --config 3x3_16_3x3_32_3x3_*** ``` ## Results | Model | Parameters(M) | Flops(G) | DiceLoss | |---|---|---|---| | 3x3_16_3x3_32_3x3_*** | 0.61 | 9.19 | 0.682311 | | 3x3_32_3x3_***_3x3_128 | 2.45 | 36.35 | 0.665905 | * Note: In order to reduce the number of parameters and flops, we did not strictly follow the model architecture in the paper, but modified it into unet style structure.   ## Citation ``` @inproceedings{xingjian2015convolutional, title={Convolutional LSTM network: A machine learning approach for precipitation nowcasting}, author={Xingjian, SHI and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and Woo, Wang-chun}, booktitle={Advances in neural information processing systems}, pages={802--810}, year={2015} } ```

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