ReluSplitter is a DNN verification(DNNV) benchmark generation tool for ReLU-based network. It takes input as a DNNV instance (network + property) and generates a modified network s.t. the modified network carries the same semantic of the orginal network, but it is harder to verify the property on the modified network. ReluSplitter achieves this by systematically de-stablizing stable neurons in the network.
Currently, our tool accepts input in the standard onnx and vnnlib format, and
supports two common layer types.
- Fully-connected layer with ReLU activation
- Convolutional layer with ReLU activation
Technically, any network with above layers can be splitted. However, you might encounter compatibility issues due to factors like ONNX version differences or complex network structures. If you believe your input should work but doesn't, feel free to open an issue or contact us.
- ✅ Semantic-preserving: The generated network always retains the same semantics as the original. For example, if the original instance is
UNSAT, the modified instance will also beUNSAT. - ⚡ Fast generation: A new DNNV instance can be created within seconds—significantly faster than alternative benchmark generation approaches that require training or distillation.
Coming soon
basic usage
- info
python main.py info --net data/mnist_fc/onnx/mnist-net_256x6.onnx --spec data/mnist_fc/vnnlib/prop_5_0.03.vnnlib - split
python main.py split --net data/mnist_fc/onnx/mnist-net_256x6.onnx --spec data/mnist_fc/vnnlib/prop_5_0.03.vnnlib
ReluSplitter also provide a number of parameters for users to explore. For example: the number of split, the scaling factors, or the type of neuron to split (stably active, stably inactive, unstable).
Summary
Coming soon
Coming soon