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README
MIT

ConvLSTM-Pytorch

ConvRNN cell

Implement ConvLSTM/ConvGRU cell with Pytorch. This idea has been proposed in this paper: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Experiments with ConvLSTM on MovingMNIST

Encoder-decoder structure. Takes in a sequence of 10 movingMNIST fames and attempts to output the remaining frames.

Instructions

Requires Pytorch v1.1 or later (and GPUs)

Clone repository

git clone https://github.com/jhhuang96/ConvLSTM-PyTorch.git

To run endoder-decoder network for prediction moving-mnist:

python main.py

Moving Mnist Generator

The script data/mm.py is the script to generate customized Moving Mnist based on MNIST.

MovingMNIST(is_train=True,
            root='data/',
            n_frames_input=args.frames_input,
            n_frames_output=args.frames_output,
            num_objects=[3])
  • is_train: If True, use script to generate data. If False, directly use Moving Mnist data downloaded from http://www.cs.toronto.edu/~nitish/unsupervised_video/
  • root: The path of MNIST data
  • n_frames_input: Number of input frames (int)
  • n_frames_output: Number of output frames (int)
  • num_objects: Number of digits in a frame (List) . [3] means there are 3 digits in each frame

Result

Result

  • The first line is the real data for the first 10 frames
  • The second line is prediction of the model for the last 10 frames

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}
}
@inproceedings{xingjian2017deep,
    title={Deep learning for precipitation nowcasting: a benchmark and a new model},
    author={Shi, Xingjian and Gao, Zhihan and Lausen, Leonard and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-kin and Woo, Wang-chun},
    booktitle={Advances in Neural Information Processing Systems},
    year={2017}
}
MIT License Copyright (c) 2020 jhhuang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST 展开 收起
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