This repository provides an implementation of Temporal Convolutional Networks (TCN) [1] in PyTorch, with focus on flexibility and fine-grained control over architecture parameters.
Additionally, it incorporates separable convolutions and pooling layers, contributing to the creation of more streamlined and computationally efficient networks.
To install both the package and dependencies needed to run the examples using pip, run:
pip install deep-tcn
pip install deep-tcn[examples]Alternatively, having first cloned the repository, you can do theh same using poetry:
poetry install
poetry install --all extras-
Causal Convolutions: Causal convolutions are employed, making the architecture suitable for sequential data.
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Separable Convolutions: The implementation includes support for separable convolutions, aiming to reduce the overall number of network parameters.
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(Channel) Pooling Layers: Channel pooling layers are integrated to further enhance the efficiency of the network by reducing dimensionality.
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Flexible Depth Configuration: Optionally, network depth can be increased by adding nondilated convolutions after dilated convolutional layers.
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Residual Blocks with Full Preactivation: Residual blocks are designed following the "full preactivation" design, according to [2]
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Supported Normalization Layers:
- Group Normalization
- Weight Normalization
- Batch Normalization
Please refer to the scripts under examples/ as a starting point.
[1] He et al.: Identity Mappings in Deep Residual Networks. ArXiv, 2016. Link
[2] Lea et al.: Temporal Convolutional Networks: A Unified Approach to Action Segmentation. ArXiv, 2016. Link