TorchCNNBuilder is an open-source framework for the automatic creation of CNN architectures. This framework should first of all help researchers in the applicability of CNN models for a huge range of tasks, taking over most of the writing of the architecture code. This framework is distributed under the 3-Clause BSD license. All the functionality is written only using pytorch (no third-party dependencies)
The simplest way to install framework is using pip:
pip install torchcnnbuilder
The basic structure of the framework is presented below. Each subdirectory has its own example of using the appropriate available functionality. You can check <directory>_examples.ipynb files in order to see the ways to use the proposed toolkit. In short, there is the following functionality:
- the ability to calculate the size of tensors after (transposed) convolutional layers
- preprocessing an n-dimensional time series in
TensorDataset - automatic creation of (transposed) convolutional sequences
- automatic creation of (transposed) convolutional layers and (transposed) blocks from convolutional layers
The structure of the main part of the package:
├── examples
│ ├── builder_examples.ipynb
│ ├── preprocess_examples.ipynb
│ ├── models_examples.ipynb
│ └── tools # additional functions for the examples
└── torchcnnbuilder
├── preprocess
│ └── time_series.py
├── builder.py
└── models.py
Initially, the library was created to help predict n-dimensional time series (geodata), so there is a corresponding functionality and templates of predictive models (like ForecasterBase)