ConvLSTM Pytorch Implementation
Goal
The ConvLSTM model is mainly used as skeleton to design a BCI (Brain Computer Interface) decoder for our project (Decode the kinematic signal from neural signal). This repo is implementation of ConvLSTM in Pytorch. The implemenation is inherited from the paper: Convolutional LSTM Network-A Machine LearningApproach for Precipitation Nowcasting
BCI decoder is a part in BCI system, which is clearly shown in the above figure.
Example of using ConvLSTM
convlstm_decoder.py contains an example of defining a ConvLSTM decoder.
Here is an example of defining 1 layer bidirectional ConvLSTM:
convlstm_layer = []
img_size_list=[(10, 10)]
num_layers = 1 # number of layer
input_channel = 96 # the number of electrodes in Utah array
hidden_channels = [256] # the output channels for each layer
kernel_size = [(7, 7)] # the kernel size of cnn for each layer
stride = [(1, 1)] # the stride size of cnn for each layer
padding = [(0, 0)] # padding size of cnn for each layer
for i in range(num_layers):
layer = convlstm.ConvLSTM(img_size=img_size_list[i],
input_dim=input_channel,
hidden_dim=hidden_channels[i],
kernel_size=kernel_size[i],
stride=stride[i],
padding=padding[i],
cnn_dropout=0.2,
rnn_dropout=0.,
batch_first=True,
bias=True,
peephole=False,
layer_norm=False,
return_sequence=True,
bidirectional=True)
convlstm_layer.append(layer)
input_channel = hidden_channels[i]Explaination
The imlementation firstly was inherited from the repo.
However, I changed the source to have more exactly to the original paper [1].
1. ConvLSTM definition
Which are following in the paper definition:
The ConvLSTM Cell is defined as following figure:
2. Bidirectional ConvLSTM decoder
Our BCI decoder is a 5 timesteps bidirectional ConvLSTM, which contains two ConvLSTM layer: a forward layer to learn direction from left to right input, a backward layer to learn direction from right to left input. Detail in following figure:
3. Input, output for decoder
The input of our decoder is spike count or LMP, and output is velocity.
Environment
This repository is tested on Python 3.7.0, Pytorch 1.6.0
References
[1] Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems (pp. 802-810).