dnnf - dnn property falsification
Project description
Normalize DNN Properties for Falsification
Install
The required dependencies are:
- git
- wget
- gcc-7
- g++-7
- virtualenv
- python3.7
- python3.7-dev
- python2.7
Please ensure that these dependencies are installed prior to running the rest of the installation script. For example, on a fresh Ubuntu 20.04 system, the dependencies can be installed using apt as follows::
$ sudo add-apt-repository ppa:deadsnakes/ppa $ sudo apt-get update $ sudo apt-get install python3.7 $ sudo apt-get install python3.7-dev $ sudo apt-get install python2.7 $ sudo apt-get install python3-virtualenv $ sudo apt-get install gcc-7 $ sudo apt-get install g++-7
To install DNNF, and the tools used to run the study to the local directory, run the provided installation script::
$ ./install.sh
This may take several minutes and there may be several prompts during installation. This artifact was tested on machines running Ubuntu 20.04 and CentOS 7. Both machines used gcc version 7.
Execution
To execute DNNF, first activate the virtual environment with::
$ . .env.d/openenv.sh
Then run the tool with::
$ python -m dnnf PROPERTY --network NAME PATH
Where PROPERTY
is the path to the property specification, NAME
is the name of the network used in the property specification (typically N
), and PATH
is the path to a DNN model in the ONNX
_ format.
To see additional options, run::
$ python -m dnnf -h
Running on the Benchmarks ^^^^^^^^^^^^^^^^^^^^^^^^^
To execute DNNF on a problem in one of the benchmarks, first navigate to the desired benchmark directory in artifacts
(i.e., acas_benchmark
, neurifydave_benchmark
, or ghpr_benchmark
). Then run DNNF as specified above. For example, to run DNNF with the Projected Gradient Descent adversarial attack from cleverhans
_ on an ACAS property and network, run::
$ cd artifacts/acas_benchmark $ python -m dnnf properties/property_2.py --network N onnx/N_3_1.onnx --backend cleverhans.ProjectedGradientDescent
Which will produce output similar to::
Falsifying: Forall(x0, (((x0 <= [[ 0.68 0.5 0.5 0.5 -0.45]]) & ([[ 0.6 -0.5 -0.5 0.45 -0.5 ]] <= x0)) ==> (numpy.argmax(N(x0)) != 0)))
dnnf result: sat time: 2.6067
Because we use DNNV to run verifiers, in order to run a verifier on a problem in one of the benchmarks, please read the instructions in the DNNV
_ repository.
As an example, to run the ERAN deepzono verifier on the same ACAS property and network as above, run::
$ cd artifacts/acas_benchmark $ python -m dnnv onnx/N_3_1.onnx properties/property_2.py --eran
Which should produce output similar to::
Verifying Network: Input_0 : Input([1 5], dtype=float32) Gemm_0 : Gemm(Input_0, ndarray(shape=(50, 5)), ndarray(shape=(50,))) Relu_0 : Relu(Gemm_0) Gemm_1 : Gemm(Relu_0, ndarray(shape=(50, 50)), ndarray(shape=(50,))) Relu_1 : Relu(Gemm_1) Gemm_2 : Gemm(Relu_1, ndarray(shape=(50, 50)), ndarray(shape=(50,))) Relu_2 : Relu(Gemm_2) Gemm_3 : Gemm(Relu_2, ndarray(shape=(50, 50)), ndarray(shape=(50,))) Relu_3 : Relu(Gemm_3) Gemm_4 : Gemm(Relu_3, ndarray(shape=(50, 50)), ndarray(shape=(50,))) Relu_4 : Relu(Gemm_4) Gemm_5 : Gemm(Relu_4, ndarray(shape=(50, 50)), ndarray(shape=(50,))) Relu_5 : Relu(Gemm_5) Gemm_6 : Gemm(Relu_5, ndarray(shape=(5, 50)), ndarray(shape=(5,)))
Verifying property: Forall(x0, ((([[ 0.6 -0.5 -0.5 0.45 -0.5 ]] <= x0) & (x0 <= [[ 0.68 0.5 0.5 0.5 -0.45]])) ==> (numpy.argmax(N(x0)) != 0))) ... dnnv.verifiers.eran result: unknown time: 2.5711
Running the Evaluation ^^^^^^^^^^^^^^^^^^^^^^
To run the full evaluation in our paper (WARNING: this may take several hundred hours), run::
$ scripts/run_all.sh
This script will sequentially run all falsifiers and verifiers on all benchmarks.
It will save results in the results/
directory, as comma separated values files.
There will be one file for each method and benchmark variant.
These files can be combined into a single csv by running the following in the root directory::
$ python tools/combine_results.py
Which will generate a file called results.csv
in the current directory.
If you have access to a cluster with slurm, execution may be sped up by running script scripts/run_all_slurm.sh
, which will launch slurm jobs rather than running each technique sequentially.
Troubleshooting ^^^^^^^^^^^^^^^
If any of the tools fail to run, these steps may help to identify and fix the issue.
First ensure that the directory .venv/
was created in the root of this director.
If this directory does not exist then virtualenv was likely not installed or could not be found by the installation script.
Try re-installing virtualenv and ensure it is visible on the execution path, then run ./install.sh
again.
If virtualenv is installed but the .venv/
directory is still missing, then python3.7 may not have been found by the installation script.
Try re-installing python3.7 and ensure it is visible on the execution path, then run ./install.sh
again.
If one of the verifiers fails to run because the executable could not be found, try installing the verifier again with the verifier specific installation script in the scripts/
directory (e.g., ./scripts/install_neurify.sh
to install neurify).
.. _DNNV: https://github.com/dlshriver/DNNV .. _ONNX: https://onnx.ai .. _cleverhans: https://github.com/tensorflow/cleverhans
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