Shield:
Baronig, Ferrand, Sabathiel & Legenstein 2024: Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation
Install dependencies
conda env create -f environment.yml
Start the corresponding experiment with
python run.py experiment=<experiment name> ++logdir=path/to/my/logdir ++datadir=path/to/my/datadir
The datadir is mandatory, it should contain the datasets. For SHD and SSC, the data will be downloaded if it does not exist at the defined location (datadir/SHDWrapper). For BSD, the dataset is created on the fly, so the datadir can point to an empty directory.
The resultdir is optional. By default, results will be placed in a local 'results' folder in the root direcory of this repo.
<experiment name> corresponds to one of the experiments in the ./config/experiment folder.
For configuration, we use Hydra. To override any parameter, use the ++ syntax. Example to override number of training epochs:
python run.py experiment=SHD_SE_adLIF_small ++logdir=path/to/my/logdir ++datadir=path/to/my/datadir ++n_epochs=10
To run the BSD task with a different number of classes (Figure 6b), run
python run.py experiment=BSD_SE_adLIF ++logdir=path/to/my/logdir ++datadir=path/to/my/datadir ++dataset.num_classes=10
In config/main.yaml, global parameters can be set, for example a device (e.g. 'cpu', 'cuda:0') that will be used by the SingleDeviceStrategy from Pytorch Lightning.
In some tasks (e.g. SHD and SSC) we have to deal with different-length sequences within the same minibatch. We handle this case by a custom masking procedure, using a block index array (block_idx) for each data sample, which acts similar to a mask. We append zeros to samples of shorter sequence length to ensure uniform sequence length within a batch, but mask the padded timesteps with zeros in the block_idx array. The value in the block_idx array then gives the corresponding target class in the target vector. Example
data vector: |1011010100101001010000000000000|
|-----data---------|--padding---|
---> time
block_idx: |1111111111111111111100000000000|
target: [-1, 3]
The data vector contains a block of data, concatenated with zeros to match the length of the longest sequence item in the minibatch. The block_idx contains ones at the time steps where data is and zeros at padded time steps. The target is a vector such that target[block_idx] gives the target of the block. In this example, block 0 has target -1 which is ignored, and block 1 (which is the valid data) has target of class 3.
We use this structure to also support per-timestep labels as for example in the ECG task. Example:
data vector: |1 0 1 1 0 0 1 0 0 0 0 0 0|
|-----data---|--padding---|
---> time
block_idx: |1 2 3 4 5 6 7 0 0 0 0 0 0|
target: [-1, 4, 3, 1, 3, 4, 6, 3]
In this example, the block_idx contains multiple blocks (1 to 7) and the target vector contains a target for each block (e.g. target for block 4 is given by target[4] = 3).
With this method, we can efficiently collect the per-block predictions with torch.scatter_reduce and thereby ignore the padded time steps.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
