From 53fec8bb62b85882c58689a9655348b6037ab54b Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Thu, 30 May 2024 17:05:04 -0500 Subject: [PATCH 1/6] Initial test for vnncomp2024 --- .../LSNC_VNNCOMP2024/load_networks.m | 18 +++++++++++++++++ .../vnncomp2024/LSNC_VNNCOMP2024/readme.md | 1 + .../LinearizeNN_benchmark2024/load_networks.m | 7 +++++++ .../LinearizeNN_benchmark2024/readme.md | 1 + .../load_networks.m | 14 +++++++++++++ .../cora-vnncomp2024-benchmark/readme.md | 1 + .../vnncomp2024/ml4acopf_benchmark/README.md | 1 + .../ml4acopf_benchmark/load_networks.m | 14 +++++++++++++ .../vnncomp2024/safeNLP/README.md | 1 + .../vnncomp2024/safeNLP/load_networks.m | 14 +++++++++++++ .../README.md | 1 + .../load_networks.m | 14 +++++++++++++ .../vnncomp2024/vnncomp2023_collins/README.md | 1 + .../vnncomp2023_collins/load_networks.m | 20 +++++++++++++++++++ .../vnncomp2024_cifar100_benchmark/README.md | 1 + .../load_networks.m | 14 +++++++++++++ .../README.md | 1 + .../load_networks.m | 14 +++++++++++++ 18 files changed, 138 insertions(+) create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/readme.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/readme.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/readme.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/load_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/load_networks.m diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/load_networks.m new file mode 100644 index 0000000000..cf2c4d3ce0 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/load_networks.m @@ -0,0 +1,18 @@ +% Load LSNC benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BC", "OutputDataFormats","BC"); + end +end + +% We get these warnings... Not great, probably customized part of the +% network function during training +% Warning: The ONNX file uses IR version 8, while the highest fully-supported IR is version 7. +% Warning: The ONNX file uses Opset version 16, while the fully-supported version is 6~14. The imported network may differ from the ONNX +% network. \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/readme.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/readme.md new file mode 100644 index 0000000000..379e48a578 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LSNC_VNNCOMP2024/readme.md @@ -0,0 +1 @@ +https://github.com/shizhouxing/LSNC_VNNCOMP2024 \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/load_networks.m new file mode 100644 index 0000000000..809e30e56a --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/load_networks.m @@ -0,0 +1,7 @@ +% Load LinearizeNN benchmarks + +netPath = "onnx/AllInOne.onnx"; + +onnxNet = importNetworkFromONNX(netPath, "InputDataFormats","BC", "OutputDataFormats","BC"); + +% All good as well, only one network here \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/readme.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/readme.md new file mode 100644 index 0000000000..f4027567d7 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/LinearizeNN_benchmark2024/readme.md @@ -0,0 +1 @@ +https://github.com/aliabigdeli/LinearizeNN_benchmark2024 \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/load_networks.m new file mode 100644 index 0000000000..0d602e1b01 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/load_networks.m @@ -0,0 +1,14 @@ +% Load CORA benchmarks + +netPath = "nns/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BC", "OutputDataFormats","BC"); + end +end + +% All good, simple as expected \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/readme.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/readme.md new file mode 100644 index 0000000000..190dc253c5 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/cora-vnncomp2024-benchmark/readme.md @@ -0,0 +1 @@ +https://github.com/kollerlukas/cora-vnncomp2024-benchmark \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/README.md new file mode 100644 index 0000000000..89fb41f63a --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/README.md @@ -0,0 +1 @@ +https://github.com/AI4OPT/ml4acopf_benchmark \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/load_networks.m new file mode 100644 index 0000000000..6dd3180302 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/ml4acopf_benchmark/load_networks.m @@ -0,0 +1,14 @@ +% Load ml4acopf benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BC", "OutputDataFormats","BC"); + end +end + +% All networks are loaded, no problem here \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/README.md new file mode 100644 index 0000000000..7e848784d1 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/README.md @@ -0,0 +1 @@ +https://github.com/ANTONIONLP/safeNLP \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/load_networks.m new file mode 100644 index 0000000000..2aff97026e --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/safeNLP/load_networks.m @@ -0,0 +1,14 @@ +% Load safeNLP benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BC", "OutputDataFormats","BC"); + end +end + +% Networks seem very simple... Are we missing something? \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/README.md new file mode 100644 index 0000000000..dff9c17034 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/README.md @@ -0,0 +1 @@ +https://github.com/dynaroars/vnncomp-benchmark-generation \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/load_networks.m new file mode 100644 index 0000000000..a8d0d05a2b --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp-benchmark-generation-dynaroars/load_networks.m @@ -0,0 +1,14 @@ +% Load dynaroars benchmarks + +netPath = "networks/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + end +end + +% All networks are loaded, no problem here \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/README.md new file mode 100644 index 0000000000..84bad156e8 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/README.md @@ -0,0 +1 @@ +https://github.com/loonwerks/vnncomp2023 \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/load_networks.m new file mode 100644 index 0000000000..9cb707eb0a --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2023_collins/load_networks.m @@ -0,0 +1,20 @@ +% Load Collins benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BCSS", "OutputDataFormats","BCT"); + end +end + +% 1) OutputDataFormats (empty, or BCT) +% Probably not, we can skip this one. 3 reshape to reshape layers, not +% standard, lots of parameters in these custom layers... +% Could be accomplished with some manual conversion for these... +% 2) OutputDataFormats (TBC) +% Only generated 1 custom layer (reshape_to_concat), this may be doable +% But still looks like a very challenging benchmark \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/README.md new file mode 100644 index 0000000000..6461082c19 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/README.md @@ -0,0 +1 @@ +https://github.com/huanzhang12/vnncomp2024_cifar100_benchmark \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/load_networks.m new file mode 100644 index 0000000000..7a3ed37377 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_cifar100_benchmark/load_networks.m @@ -0,0 +1,14 @@ +% Load cifar100 benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + end +end + +% Both networks seem fine \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/README.md b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/README.md new file mode 100644 index 0000000000..0fb643cee8 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/README.md @@ -0,0 +1 @@ +https://github.com/huanzhang12/vnncomp2024_tinyimagenet_benchmark \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/load_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/load_networks.m new file mode 100644 index 0000000000..ab0b818455 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/vnncomp2024/vnncomp2024_tinyimagenet_benchmark/load_networks.m @@ -0,0 +1,14 @@ +% Load tinyImagenet benchmarks + +netPath = "onnx/"; + +nns = dir(netPath); + +for i=1:length(nns) + if endsWith(nns(i).name, ".onnx") + disp(nns(i).name); + onnxNet = importNetworkFromONNX(netPath + nns(i).name, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + end +end + +% One network only, it also seems fine \ No newline at end of file From 09834f60176dbb66e24a79eed16c0b66d3b17fd5 Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Fri, 31 May 2024 16:46:15 -0500 Subject: [PATCH 2/6] Initial run for new archcomp benchmarks --- .../engine/nn/layers/ElementwiseAffineLayer.m | 12 ++- code/nnv/engine/set/Star.m | 5 ++ .../benchmarks/CartPole/README.md | 11 +++ .../benchmarks/CartPole/dynamics.m | 12 +++ .../benchmarks/CartPole/model.onnx | Bin 0 -> 19185 bytes .../ARCH-COMP2024/benchmarks/CartPole/reach.m | 66 +++++++++++++++ .../benchmarks/CartPole/specifications.txt | 14 ++++ .../ARCH-COMP2024/benchmarks/NAV/README.md | 39 +++++++++ .../ARCH-COMP2024/benchmarks/NAV/dynamics.m | 11 +++ .../benchmarks/NAV/networks/nn-nav-point.mat | Bin 0 -> 11876 bytes .../benchmarks/NAV/networks/nn-nav-point.onnx | Bin 0 -> 10601 bytes .../benchmarks/NAV/networks/nn-nav-set.mat | Bin 0 -> 11872 bytes .../benchmarks/NAV/networks/nn-nav-set.onnx | Bin 0 -> 10601 bytes .../benchmarks/NAV/reach_point.m | 75 ++++++++++++++++++ .../ARCH-COMP2024/benchmarks/NAV/reach_set.m | 75 ++++++++++++++++++ 15 files changed, 318 insertions(+), 2 deletions(-) create mode 100755 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/README.md create mode 100755 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/dynamics.m create mode 100755 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/model.onnx create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/reach.m create mode 100755 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/specifications.txt create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/README.md create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/dynamics.m create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-point.mat create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-point.onnx create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-set.mat create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-set.onnx create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m diff --git a/code/nnv/engine/nn/layers/ElementwiseAffineLayer.m b/code/nnv/engine/nn/layers/ElementwiseAffineLayer.m index 4e83b8c829..ff0f43853c 100644 --- a/code/nnv/engine/nn/layers/ElementwiseAffineLayer.m +++ b/code/nnv/engine/nn/layers/ElementwiseAffineLayer.m @@ -137,7 +137,7 @@ end % Offset (bias) if obj.DoOffset - image = image.affineMap(diag(ones(1,a.dim)), obj.Offset); % x + b + image = image.affineMap(diag(ones(1,image.dim)), obj.Offset); % x + b end end @@ -150,7 +150,15 @@ % @S: output ImageStar n = length(inputs); - S(n) = ImageStar; + + % Initialize output variables + if isa(inputs, "ImageStar") + S(n) = ImageStar; + else + S(n) = Star; + end + + % Begin computing reachability one set at a time if strcmp(option, 'parallel') parfor i=1:n S(i) = obj.reach_star_single_input(inputs(i)); diff --git a/code/nnv/engine/set/Star.m b/code/nnv/engine/set/Star.m index 1cf2d300ba..19e09bf9cd 100644 --- a/code/nnv/engine/set/Star.m +++ b/code/nnv/engine/set/Star.m @@ -535,8 +535,13 @@ if ~isempty(obj.state_lb) && ~isempty(obj.state_ub) B = Box(obj.state_lb, obj.state_ub); + else + if isa(obj.V, 'single') || isa(obj.C, 'single') || isa(obj.d, 'single') + obj = obj.changeVarsPrecision('double'); + end + lb = zeros(obj.dim, 1); ub = zeros(obj.dim, 1); diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/README.md b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/README.md new file mode 100755 index 0000000000..6e877a56be --- /dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/README.md @@ -0,0 +1,11 @@ +# Proposed ARCH Benchmark - CartPole + +This benchmark is proposed for the ARCH Friendly Competition 2024. + +## Benchmark + +We consider a pendulum (pole) mounted on a movable cart (= CartPole). The cart can be moved by a controller. The carts postition x1, its velocity x2, the angle of the pole x3 (with 0, 2*pi being the upright postion) and its angular velocity x4 define the state vector of the 4-dimensional system. The system starts in a middle postition of the cart, with the pendulum in the upright position. The controllers goal is to counteract slight deviations in the starting values and balance the pendulum in the middle of the track. + +## Specifications and Dynamics + +The system dynamics can be found in ```dynamics.m```. The safe states are defined as a stable upright position, which has to be reached after 8 seconds and has to be hold for at least 2 seconds. The controllers step size is 0.02 seconds. diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/dynamics.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/dynamics.m new file mode 100755 index 0000000000..192a78fabe --- /dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/dynamics.m @@ -0,0 +1,12 @@ +function dx = dynamics(x, f) + +% Cartpole Swingup, 4 states x and the input f (action of the controller) +% The controller takes (x(1), x(2), x(3), x(4)) as input, its output can +% be used directly. + +dx(1,1) = x(2); +dx(2,1) = 2*f; +dx(3,1) = x(4); +dx(4,1) = (0.08*0.41*(9.8*sin(x(3))-2*f*cos(x(3)))-0.0021*x(4))/0.0105; + +end \ No newline at end of file diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/model.onnx b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/CartPole/model.onnx new file mode 100755 index 0000000000000000000000000000000000000000..c837ab82a96714ae4187103ccd9f37cc61d1e77a GIT binary patch literal 19185 zcmb@tc{o;4+%{^SQiezap#dRNk=X0EDN}sf2xYwh23-%CnLQ8H|6RA_{M;6!l~ z17iadi@W*4G9|(?vY{a%TP!T>*9HfB8_CFR^xe8PBGSOfVDs9bb%9YbqyP6CenGyG zijxih$`YHDGzf8y2aJg?tp!fgdbpE4#ir9a9O6G*{ zfBFCa)c=2n!{k51VPf!q3x~=70*?P^|Njk+|1{ixfn(PH0*?P^{~vJF{8u>sQ-2vn zU;n6}&=BvywZ3cqCnECy=X?Jhl!=D_r(6FC5QYEJ_Wu!#nKJ(`!zhXvn@kk{?=Sx! zFQ+II>>Cu~y)MExEKs*tO3F$~OkQ~2f8Oj1=f-T|3L*;`$_*ij} zrNC+Dq2oCx+$NL7%YQpYBZpGKe9BW2 zBbN*x>+Vy}`cv?*Uxn?x9SeJ%L@oD<9l{{h+{nJTc8NSpsl(cKE8bUb0^gohMv}AdV%gG=@$q81*lmPetv7wII8@0hdV z5W4=T#3093Dq5z-i>%DTxw3^A@FN?KHAD*EJDQ2Kx=#LwZ9_BAC`HjAdwE$M^ zN8zq*e8Pb$G`rG(k8P7N&(f8zNM1)&O1kl9{aLI!VaW$*2pF5WyD+@mhF*GI#M>3i z@HUqo(;4Y@V6?G}oxf%pc$nwo$DPyh>_Qjp|DJ#sT$;H6`)+tuaE4KEdQ9q%%d^3S z7f8sLa<=OEYH)r21cN5Xf!R9@A1(MIMuNYr z?tvHMV&Rm%GAkw44rTC1qMu>-p}nwSV=V0WzMQR9{7dKm2*W3*7UB47DZFL; zX4LMQgPno_oLPL7@B2K5x@l50_~=KDs_x*Iq#ec*xfay=SB!z8HGEb6GE5Pfja}1o zP}D7n=LL4C?>iBttlm?{^f<5HKaD;F)6$Y*Oh1)rhZ>e@Rkg<4f#w~xa+QQ3;wM4-#%c-!KoBQCIpfz(JL*1S- z;>)kig(?F^C$xYQxg&mlY0R)?W=t*T+7>M6o!W01=_SM2Ufm^t><~`k(+dj|FChyH z)pG(p*@cX`?9v9mlqY{*K0cf#J`EaaxbQSoF5Y3If%nEDyRUo}Vyj4!0$<+hXs<6^#CcM!^|zdG2V02({m~ zU*J-jEYQ$;E68S+3i|7Boj(-WcfMl6Xy#`6L4iW-UJLPiJDCX)Qv`C`#xYj$CX8-L zobZU*{BPL&PqX&Fbz zG-#d@Oxo^L;t!b%P;!jLLf;nh)wL2r;9Js9Fv#TSj)jJGi{OER5oqi5lhU

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/dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/README.md @@ -0,0 +1,39 @@ + +# NAV Benchmark + +## Property: +The control goal is to navigate a robot to a goal region while avoiding an obstacle. +Time horizon: `t = 6s`. Control period: `0.2s`. + +Initial states: + + x1 = [2.9, 3.1] + x2 = [2.9, 3.1] + x3 = [0, 0] + x4 = [0, 0] + +Dynamic system: [dynamics.m](./dynamics.m) + +Goal region ( t=6 ): + + x1 = [-0.5, 0.5] + x2 = [-0.5, 0.5] + x3 = [-Inf, Inf] + x4 = [-Inf, Inf] + +Obstacle ( always ): + + x1 = [1, 2] + x2 = [1, 2] + x3 = [-Inf, Inf] + x4 = [-Inf, Inf] + +## Networks: + +We provide two networks: +- The first network is trained with standard (point-based) reinforcement learning: `nn-nav-point.onnx` +- The second network is trained set-based to improve its verifiable robustness by integrating reachability analysis into the training process: `nn-nav-set.onnx` + +Reference set-based training: https://arxiv.org/abs/2401.14961 + + diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/dynamics.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/dynamics.m new file mode 100644 index 0000000000..632c28d9ff --- /dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/dynamics.m @@ -0,0 +1,11 @@ +function dx = dynamics(x,u) + +dx = [ + x(3)*cos(x(4)); + x(3)*sin(x(4)); + u(1); + u(2) +]; + +end + diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-point.mat b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/networks/nn-nav-point.mat new file mode 100644 index 0000000000000000000000000000000000000000..04e1915a0912401bf650dbf6b9c88564c64a840e GIT binary patch literal 11876 zcma)?X*3j$|NTQq_U!wXgzS|t$R34gkuZ}avM<@kj8a)5q)4`qkUa?*#=bMzw=rYi z8D=wP{e8X<{?GsSoO{lFbf3M?x#xA>wkG%Qn3&35Q`eNWHMy_${Dq(A71_If?k}GQ zhWP8t-ZgWww!41yimX|Pr~6CKXR?6-`m*=E!(?sUqhw{ZWwkW*wRH8hG-b6lwXe$l zf9lNt6O*+q?|<1q^~{;byqC@#kL-6&plYQlqKo!$xx;}hHRq?rt~&Fxe%3>cZ>!`o zN>wuye<|$=$=}YnR$O_DBg00%=9g_^j;1qZ2X_o$Y(znH>=An?iu#t}R_NGi9~dnLZz0l!81L?RZ^$=j|6I+m=`v zbhnk#3T^gV=A)*`=e+AinwLdF;Fx!qh2sxRM}~V_=2a)Z?fc1>(qyyO_cLGNY$q^J 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a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m new file mode 100644 index 0000000000..f25709cd0f --- /dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m @@ -0,0 +1,75 @@ +function t = reach_point() + + %% Reachability analysis of NAV Benchmark + + %% Load Components + + % Load the controller + net = importNetworkFromONNX('networks/nn-nav-point.onnx', "InputDataFormats", "BC"); + net = matlab2nnv(net); + % Load plant + reachStep = 0.002; + controlPeriod = 0.02; + plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); + plant.set_tensorOrder(2); + + + %% Reachability analysis + + % Initial set + lb = [2.9; 2.9; 0; 0]; + ub = [3.1; 3.1; 0; 0]; + init_set = Star(lb,ub); + + % Store all reachable sets + reachAll = init_set; + + % Reachability options + num_steps = 30; + reachOptions.reachMethod = 'approx-star'; + + % Begin computation + t = tic; + for i=1:num_steps + disp(i); + + % Compute controller output set + input_set = net.reach(init_set,reachOptions); + + % Compute plant reachable set + init_set = plant.stepReachStar(init_set, input_set,'lin'); + reachAll = [reachAll init_set]; + toc(t); + + end + + t = toc(t); + + + %% Visualize results + plant.get_interval_sets; + + f2 = figure; + % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + hold on; + Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,1,2,'b'); + grid; + xlabel('x_1'); + ylabel('x_2'); + + f5 = figure; + % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + hold on; + Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); + grid; + xlabel('x_3'); + ylabel('x_4'); + + % Save figure + % if is_codeocean + % + % else + % + % end + + end \ No newline at end of file diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m new file mode 100644 index 0000000000..3b85b4601d --- /dev/null +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m @@ -0,0 +1,75 @@ +function t = reach_set() + + %% Reachability analysis of NAV Benchmark + + %% Load Components + + % Load the controller + net = importNetworkFromONNX('networks/nn-nav-set.onnx', "InputDataFormats", "BC"); + net = matlab2nnv(net); + % Load plant + reachStep = 0.002; + controlPeriod = 0.02; + plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); + plant.set_tensorOrder(2); + + + %% Reachability analysis + + % Initial set + lb = [2.9; 2.9; 0; 0]; + ub = [3.1; 3.1; 0; 0]; + init_set = Star(lb,ub); + + % Store all reachable sets + reachAll = init_set; + + % Reachability options + num_steps = 30; + reachOptions.reachMethod = 'approx-star'; + + % Begin computation + t = tic; + for i=1:num_steps + disp(i); + + % Compute controller output set + input_set = net.reach(init_set,reachOptions); + + % Compute plant reachable set + init_set = plant.stepReachStar(init_set, input_set,'lin'); + reachAll = [reachAll init_set]; + toc(t); + + end + + t = toc(t); + + + %% Visualize results + plant.get_interval_sets; + + f2 = figure; + % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + hold on; + Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,1,2,'b'); + grid; + xlabel('x_1'); + ylabel('x_2'); + + f5 = figure; + % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + hold on; + Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); + grid; + xlabel('x_3'); + ylabel('x_4'); + + % Save figure + % if is_codeocean + % + % else + % + % end + +end \ No newline at end of file From 2e826ead6a0be32a519cb5c6255ea9360c740439 Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Mon, 3 Jun 2024 17:28:17 -0500 Subject: [PATCH 3/6] Initialize files for vnncomp2024 --- .../Submission/VNN_COMP2024/README.md | 35 + .../Submission/VNN_COMP2024/config.yaml | 9 + .../VNN_COMP2024/convert2022benchmarks.m | 272 ++++++++ .../VNN_COMP2024/convert_scoring_networks.m | 294 ++++++++ .../Submission/VNN_COMP2024/execute.py | 119 ++++ .../Submission/VNN_COMP2024/install_tool.sh | 36 + .../Submission/VNN_COMP2024/post_install.sh | 31 + .../VNN_COMP2024/prepare_instance.sh | 38 ++ .../Submission/VNN_COMP2024/prepare_run.m | 32 + .../Submission/VNN_COMP2024/run_instance.sh | 37 ++ .../VNN_COMP2024/run_vnncomp2024_instance.m | 625 ++++++++++++++++++ 11 files changed, 1528 insertions(+) create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/README.md create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/config.yaml create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/execute.py create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/install_tool.sh create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/post_install.sh create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/prepare_instance.sh create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/prepare_run.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/run_instance.sh create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m diff --git a/code/nnv/examples/Submission/VNN_COMP2024/README.md b/code/nnv/examples/Submission/VNN_COMP2024/README.md new file mode 100644 index 0000000000..c2ba5ffef5 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/README.md @@ -0,0 +1,35 @@ +# Local Testing for VNN-COMP Scripts + +## File Summaries +`prepare_instance.sh` -- a script for starting a local matlab engine within which `nnv` can be ran on an instance. +`run_instance.sh` -- a script for running `nnv` on an instance. + +## `prepare_instance.sh` +Provided for the competition. Handles error-handling and set up of MATLAB engine prior to running `nnv` on the test instance. + +## `run_instance.sh` +Provided for the competition. Executes `nnv` on the test instance. + + +# Preparing for 2024 competition + +TODO: + +- Choose optimal reachability options for each benchmark. + +- Change to the new ONNX import function (will this work from command line so no need to convert models prior to competition?) + +- There were some possible errors with counterexamples based on last year report, take a look at this (some already fixed). + +- Test the python integration with newest MATLAB version. Can we simplify the code and reduce computation time using the matlab engine for python? (This did not work properly on 2023a) + + +# Instructions to test locally (2023 benchmarks) + +1) Ensure NNV is installed and the corresponding toolboxes + +2) Go to https://github.com/ChristopherBrix/vnncomp2023_benchmarks, clone the repo and run (in your desired folder) ./setup.sh + +3) Change line 4 in `test_some_instances.m` to the folder where you downloaded the benchmarks. + +4) Run `test_some_instances.m`. Change the instances tested as desired from the vnncomp_benchmarks repo. diff --git a/code/nnv/examples/Submission/VNN_COMP2024/config.yaml b/code/nnv/examples/Submission/VNN_COMP2024/config.yaml new file mode 100644 index 0000000000..1ed3d08d9e --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/config.yaml @@ -0,0 +1,9 @@ +name: nnv +ami: ami-080fce32dc8c15ced +scripts_dir: code/nnv/examples/Submission/VNN_COMP2023/ +manual_installation_step: True +run_installation_script_as_root: False +run_post_installation_script_as_root: False +run_toolkit_as_root: False +description: > + Matlab Toolbox for Neural Network Verification. This toolbox implements reachability methods for analyzing deep learning models including but not limited to convolutional, recurrent and binary neural networks. NNV is also capable of analyzing neural networks on closed-loop controllers in autonomous cyber-physical systems (CPS). diff --git a/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m b/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m new file mode 100644 index 0000000000..30a3bccd1e --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m @@ -0,0 +1,272 @@ +%% Run some verification instances from the competition + +% path to benchmarks +% vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2022_benchmarks/benchmarks/"; +vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2022_benchmarks/benchmarks/"; + +% Go through some of the instances for every benchmark +% It looks like unless the networks are fully fully supported, we get errors... +% Same problem we ra into when running with no GUI ... +% +% Error using matlab.internal.indentcodeLegacy +%This feature is not supported because: +%Swing is not currently available. +% +%Error in indentcode (line 42) +% indentedText = matlab.internal.indentcodeLegacy(text, language); +% +%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation/genOutputFcn (line 100) +% code = indentcode(code); % Indent the code +% +%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation (line 39) +% this.ModelFunctionCode = genOutputFcn(this, outputFcnName, this.ToplevelGraphTranslation, isGeneratingCustomLayerOPTIONAL); +% +%Error in nnet.internal.cnn.onnx.CustomLayerManager/genCustomLayerFunction (line 499) +% modelTranslation = nnet.internal.cnn.onnx.fcn.ModelTranslation(modelProto, modelFcnPath, modelFcnName, maxNameLength, true); +% +%Error in nnet.internal.cnn.onnx.CustomLayerManager (line 77) +% this.ModelTranslations(i) = genCustomLayerFunction(this, this.ModelProtos(i), nameStem); +% +%Error in nnet.internal.cnn.onnx.ModelTranslationIntoLayers (line 228) +% this.CustomLayerManager = %nnet.internal.cnn.onnx.CustomLayerManager(this.GraphProtoManager, ... +% +%Error in nnet.internal.cnn.onnx.importONNXNetwork (line 32) +%modelTranslationIntoLayers = nnet.internal.cnn.onnx.ModelTranslationIntoLayers(modelProto, ... +% +%Error in importONNXNetwork (line 113) +%Network = nnet.internal.cnn.onnx.importONNXNetwork(modelfile, varargin{:}); +% +%Error in run_vnncomp2023_instance>load_vnncomp_network (line 127) +% net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + + +%% cifar_biasfield + +disp("Running cifar_biasfield...") + +biasfield_path = vnncomp_path + "cifar_biasfield/onnx/"; + +onnx = dir(biasfield_path); + +for i=3:length(onnx) + onnxFile = biasfield_path + onnx(i).name; + net = load_vnncomp_network('cifar_biasfield', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% rl_benchmarks + +disp("Running rl_benchmarks...") + +rl_path = vnncomp_path + "rl_benchmarks/onnx/"; + +onnx = dir(rl_path); + +for i=3:length(onnx) + onnxFile = rl_path + onnx(i).name; + net = load_vnncomp_network('rl_benchmarks', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% reach_prob_density + +disp("Running reach_prob_density...") + +reach_prob_path = vnncomp_path + "reach_prob_density/onnx/"; + +onnx = dir(reach_prob_path); + +for i=3:length(onnx) + onnxFile = reach_prob_path + onnx(i).name; + net = load_vnncomp_network('reach_prob_density', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% mnist_fc + +disp("Running mnist_fc...") + +mnist_path = vnncomp_path + "mnist_fc/onnx/"; + +onnx = dir(mnist_path); + +for i=3:length(onnx) + onnxFile = mnist_path + onnx(i).name; + net = load_vnncomp_network('mnist_fc', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% sri_resnet_a + +disp("Running sri_resnets..") + +sri_path = vnncomp_path + "sri_resnet_a/onnx/"; + +onnx = dir(sri_path); + +for i=3:length(onnx) + onnxFile = sri_path + onnx(i).name; + net = load_vnncomp_network('sri_resnet_a', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name, '.mat'], "net", "-v7.3"); +end + +%% sri_resnet_b + +sri_path = vnncomp_path + "sri_resnet_b/onnx/"; + +onnx = dir(sri_path); + +for i=3:length(onnx) + onnxFile = sri_path + onnx(i).name; + net = load_vnncomp_network('sri_resnet_b', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name, '.mat'], "net", "-v7.3"); +end + +%% cifar2020 + +cifar2020_path = vnncomp_path + "cifar2020/onnx/"; + +onnx = dir(cifar2020_path); + +for i=3:length(onnx) + onnxFile = cifar2020_path + onnx(i).name; + if ~contains(onnxFile, "PGD") + net = load_vnncomp_network('cifar2020', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); + end +end + +%% oval21 + +oval_path = vnncomp_path + "oval21/onnx/"; + +onnx = dir(oval_path); + +for i=3:length(onnx) + onnxFile = oval_path + onnx(i).name; + net = load_vnncomp_network('oval21', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% cifar100_tinyimagenet_resnet + +cifar_path = vnncomp_path + "cifar100_tinyimagenet_resnet/onnx/"; + +onnx = dir(cifar_path); + +for i=3:length(onnx) + onnxFile = cifar_path + onnx(i).name; + net = load_vnncomp_network('cifar100_tinyimagenet_resnet', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + + +%% nn4sys + +disp("Running nn4sys...") + +nn4sys_path = vnncomp_path + "nn4sys/onnx/"; + +onnx = dir(nn4sys_path); + +for i=3:length(onnx) + onnxFile = nn4sys_path + onnx(i).name; + if contains(onnxFile, "lindex") + net = load_vnncomp_network('nn4sys', onnxFile); + name = onnx(i).name; + name = name(1:end-5); +% save(['networks2023/', name], "net", "-v7.3"); + end +end + + +%% tllverify + +disp("Running tllverify..") + +tll_path = vnncomp_path + "tllverifybench/onnx/"; + +onnx = dir(tll_path); + +for i=3:length(onnx) + onnxFile = tll_path + onnx(i).name; + net = load_vnncomp_network('tllverifybench', onnxFile); + name = onnx(i).name; + name = name(1:end-5); +% save(['networks2023/', name], "net", "-v7.3"); +end + + +%% Loading function +function net = load_vnncomp_network(category, onnx) +% load vnncomp 2022 benchmark NNs (subset support) + + if contains(category, "nn4sys") + % nn4sys: onnx to matlab: + net = importONNXNetwork(onnx, "OutputDataFormats", "BC"); % lindex (no reshape) + + elseif contains(category, "tllverify") + % tllverify: onnx to matlab + net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); % no reshape + + elseif contains(category, 'biasfield') + % onnx to nnv + if contains(onnx, "base") + net = importONNXNetwork(onnx); % needs reshape + else + net = importONNXNetwork(onnx, "InputDataFormats", "BC"); % does not need reshape (feature input) + end + + elseif contains(category, 'rl_bench') + % rl: onnx to nnv + net = importONNXNetwork(onnx, "InputDataFormats", "BC", "OutputDataFormats", "BC"); % no need to reshape (feature input) + + elseif contains(category, 'reach_prob') + % reach_prob: onnx to nnv + net = importONNXNetwork(onnx, "InputDataFormats", "BC"); % no reshape + + elseif contains(category, 'mnist_fc') + % mnist: onnx to nnv + net = importONNXNetwork(onnx, 'InputDataFormats',"SSC", 'OutputDataFormats',"BC"); % no reshape + + elseif contains(category, 'sri_resnet') + net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); % reshape + + elseif contains(category, 'cifar100') + net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape + + elseif contains(category, 'oval') + net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape + + elseif contains(category, 'cifar2020') + % onnx to nnv + if ~contains(onnxFile, "PGD") + net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape + else + net = importONNXNetwork(onnx, "InputDataFormats", "BCSS", "OutputDataFormats", "BC"); % reshape? + end + + else % all other benchmarks + % traffic: onnx to matlab: opset15 issues + error("ONNX model not supported") + end + +end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m new file mode 100644 index 0000000000..165159d751 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m @@ -0,0 +1,294 @@ +%% Run some verification instances from the competition + +% path to benchmarks +vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; +% vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; + +% Go through some of the instances for every benchmark +% It looks like unless the networks are fully fully supported, we get errors... +% Same problem we ran into when running with no GUI ... +% +% Error using matlab.internal.indentcodeLegacy +%This feature is not supported because: +%Swing is not currently available. +% +%Error in indentcode (line 42) +% indentedText = matlab.internal.indentcodeLegacy(text, language); +% +%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation/genOutputFcn (line 100) +% code = indentcode(code); % Indent the code +% +%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation (line 39) +% this.ModelFunctionCode = genOutputFcn(this, outputFcnName, this.ToplevelGraphTranslation, isGeneratingCustomLayerOPTIONAL); +% +%Error in nnet.internal.cnn.onnx.CustomLayerManager/genCustomLayerFunction (line 499) +% modelTranslation = nnet.internal.cnn.onnx.fcn.ModelTranslation(modelProto, modelFcnPath, modelFcnName, maxNameLength, true); +% +%Error in nnet.internal.cnn.onnx.CustomLayerManager (line 77) +% this.ModelTranslations(i) = genCustomLayerFunction(this, this.ModelProtos(i), nameStem); +% +%Error in nnet.internal.cnn.onnx.ModelTranslationIntoLayers (line 228) +% this.CustomLayerManager = %nnet.internal.cnn.onnx.CustomLayerManager(this.GraphProtoManager, ... +% +%Error in nnet.internal.cnn.onnx.importONNXNetwork (line 32) +%modelTranslationIntoLayers = nnet.internal.cnn.onnx.ModelTranslationIntoLayers(modelProto, ... +% +%Error in importONNXNetwork (line 113) +%Network = nnet.internal.cnn.onnx.importONNXNetwork(modelfile, varargin{:}); +% +%Error in run_vnncomp2023_instance>load_vnncomp_network (line 127) +% net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + +if ~isfolder("networks2023") + mkdir("networks2023") +end + + +%% acasxu + +disp("Running acas xu...") + +acas_path = vnncomp_path + "acasxu/onnx/"; + +onnx = dir(acas_path); + +for i=3:length(onnx) + onnxFile = acas_path + onnx(i).name; + net = load_vnncomp_network('acasxu', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% ViT + +disp("Running ViT...") + +vit_path = vnncomp_path + "vit/onnx/"; + +onnx = dir(vit_path); + +for i=3:length(onnx) + onnxFile = vit_path + onnx(i).name; + net = load_vnncomp_network('vit', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + + +%% nn4sys + +disp("Running nn4sys...") + +nn4sys_path = vnncomp_path + "nn4sys/onnx/"; + +onnx = dir(nn4sys_path); + +for i=3:length(onnx) + onnxFile = nn4sys_path + onnx(i).name; + if contains(onnxFile, "lindex") + net = load_vnncomp_network('nn4sys', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); + end +end + + +%% dist_shift + +disp("Running dist_shift...") + +dist_path = vnncomp_path + "dist_shift/onnx/"; + +onnxFile = dist_path + "mnist_concat.onnx"; +net = load_vnncomp_network('dist_shift', onnxFile); +name = 'mnist_concat.onnx'; +name = name(1:end-5); +save(['networks2023/', name], "net", "-v7.3"); + + +%% traffic_sign +% we should be able to support it, but matlab does not, so we would have to create the models manually... +% can load most info with Keras importer + +%% collins_rul + +disp("Running collins_rul..") + +rul_path = vnncomp_path + "collins_rul_cnn/onnx/"; + +onnx = dir(rul_path); + +for i=3:length(onnx) + onnxFile = rul_path + onnx(i).name; + net = load_vnncomp_network('collins_rul_cnn', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% cgan + +disp("Running cgan..") + +cgan_path = vnncomp_path + "cgan/onnx/"; + +onnx = dir(cgan_path); + +for i=3:length(onnx) + onnxFile = cgan_path + onnx(i).name; + if ~contains(onnxFile, 'transformer') + net = load_vnncomp_network('cgan', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); + end +end + +%% vggnet16 + +disp("Running vggnet16...") + +vgg_path = vnncomp_path + "vggnet16/onnx/"; + +vgg_instances = ["onnx/vgg16-7.onnx","vnnlib/spec0_screw.vnnlib"]; + +onnxFile = vgg_path + "vgg16-7.onnx"; +net = load_vnncomp_network('vggnet16', onnxFile); +name = 'vgg16-7.onnx'; +name = name(1:end-5); +save(['networks2023/', name], "net", "-v7.3"); + + +%% ml4acopf + +disp("Running ml4acopf..") + +ml4_path = vnncomp_path + "ml4acopf/onnx/"; + +onnx = dir(ml4_path); + +for i=3:length(onnx) + onnxFile = ml4_path + onnx(i).name; + net = load_vnncomp_network('ml4acopf', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + + +%% tllverify + +disp("Running tllverify..") + +tll_path = vnncomp_path + "tllverifybench/onnx/"; + +onnx = dir(tll_path); + +for i=3:length(onnx) + onnxFile = tll_path + onnx(i).name; + net = load_vnncomp_network('tllverifybench', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + +%% Other 2023 benchmarks + +%% test + +% disp("Running test examples..."); +% +% test_path = vnncomp_path + "test/"; +% +% onnxmodels = ["test_nano.onnx", "test_sat.onnx", "test_small.onnx", "test_tiny.onnx", "test_unsat.onnx"]; +% +% for i = 1:length(onnxmodels) +% if contains(onnxmodels(i), "sat") +% onnxFile = test_path + onnxmodels(i); +% net = importONNXNetwork(onnxFile, "InputDataFormats", "BCSS"); +% name = char(onnxmodels(i)); +% name = name(1:end-5); +% save(['networks2023/', name], "net", "-v7.3"); +% end +% end + + +%% yolo + +disp("Running yolo..."); + +yolo_path = vnncomp_path + "yolo/onnx/"; + +onnx = dir(yolo_path); + +for i = 3:length(onnx) + onnxFile = yolo_path + onnx(i).name; + net = load_vnncomp_network('yolo', onnxFile); + name = onnx(i).name; + name = name(1:end-5); + save(['networks2023/', name], "net", "-v7.3"); +end + + +%% Loading function +function net = load_vnncomp_network(category, onnx) +% load vnncomp 2023 benchmark NNs (subset support) + + % collins_rul: onnx to nnvnet + % collins_nets = load_collins_NNs; + if contains(category, 'collins_rul') + net = importNetworkFromONNX(onnx); + + elseif contains(category, "nn4sys") + % nn4sys: onnx to matlab: + net = importNetworkFromONNX(onnx, "OutputDataFormats", "BC"); % lindex + + elseif contains(category, "dist_shift") + % dist_shift: onnx to matlab: + net = importNetworkFromONNX(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + + elseif contains(category, "cgan") + % cgan + net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + + elseif contains(category, "vgg") + % vgg16: onnx to matlab + net = importNetworkFromONNX(onnx); % flattenlayer + + elseif contains(category, "tllverify") + % tllverify: onnx to matlab + net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); + + elseif contains(category, "vit") + % vit: onnx to matlab + net = importNetworkFromONNX(onnx ); + + elseif contains(category, "cctsdb_yolo") + % cctsdb_yolo: onnx to matnet + net = importNetworkFromONNX(onnx); + + elseif contains(category, "collins_yolo") + % collins_yolo: onnx to matlab: + net = importNetworkFromONNX(onnx); + + elseif contains(category, "yolo") + % yolo: onnx to matlab + net = importNetworkFromONNX(onnx); % padlayer + + elseif contains(category, "acasxu") + % acasxu: onnx to nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS"); + + elseif contains(category, "ml4acopf") + % ml4acopf: ? + net = importNetworkFromONNX(onnx, "InputDataFormats", "BC"); + + else % all other benchmarks + % traffic: onnx to matlab: opset15 issues + error("ONNX model not supported") + end + +end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/execute.py b/code/nnv/examples/Submission/VNN_COMP2024/execute.py new file mode 100644 index 0000000000..2fc2371275 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/execute.py @@ -0,0 +1,119 @@ +"""Launch MATLAB Engines with Python + +This module was created for local testing of reachability analysis (https://github.com/verivital/nnv.git) ahead of VNN-COMP 2023. +To perform testing, the following functionality is enabled. + +- Start a MATLAB engine. +- Run an instance of reachability analysis given some arguments through the open MATLAB engine. + +Arguments can be passed to the call to the script and will be parsed accordingly. + +Created by: Samuel Sasaki (VeriVITAL) +Date: June 28, 2023 +""" + + +import sys +import matlab.engine +import time +import os + + +def prepare_instance(category: str, onnx: str, vnnlib: str) -> None: + """Set up the MATLAB engine for running an instance. + + Parameters: + onnx (str): the path to the .onnx file + vnnlib (str): the path to the .vnnlib file + """ + # start matlab engine as a shared engine +# eng = matlab.engine.start_matlab(background=True, option='-r "matlab.engine.shareEngine"') +# print('Engine starting...') + + # keep MATLAB engine open until manually killed +# while True: +# time.sleep(0.5) + print("We aren't actually doing anything here...") + +def run_instance(category, onnx, vnnlib, timeout, outputlocation) -> None: + """Run an instance based on parameters defined in .csv file. + + Parameters: + onnx (str): the path to the .onnx file + vnnlib (str): the path to the .vnnlib file + timeout (int): the time (in ms) to wait before proceeding to the next instance + """ + + eng = matlab.engine.start_matlab() +# eng_name = matlab.engine.find_matlab()[0] +# eng = matlab.engine.connect_matlab(name=eng_name) +# +# print(f'Successfully connected to engine: {eng_name}.') + + eng.addpath(os.getcwd()) + eng.addpath(eng.genpath('/home/ubuntu/toolkit/code/nnv/')) + + status = 2 #initialize with an 'Unknown' status + #toc = time.perf_counter() + #print('timestep :',toc) + future = eng.run_vnncomp2023_instance(category, onnx, vnnlib, outputlocation, nargout = 2, background=True) + + try: + [status, total_time] = future.result(timeout=float(timeout)) + #print('extra time = ',int(toc-tic)) + except matlab.engine.TimeoutError: + print("timeout") + #print('extra time = ',int(toc-tic)) + total_time = timeout + status = 3 + + future.cancel() + eng.quit() + + if status == 3: + resultfile = outputlocation + with open(resultfile, 'w') as f: + f.write('timeout') + # All the other results are written from matlab + + +def _get_args() -> None: + """Get the arguments passed to the script from the command line. + + Expected usage is : [ACTION, PATH_TO_ONNX, PATH_TO_VNNLIB, TIMEOUT, OUTPUTLOCATION] + """ + args = sys.argv[1:] + ACTION = args[0] + + # prepare_instance expects: benchmark_category, onnx, vnnlib + if (ACTION == 'prepare_instance'): + if len(args) != 4: + raise ValueError(f'Incorrect number of arguments, expected 4 got {len(args)}.') + args.append(None) # timeout + args.append(None) # outputlocation + + # run_instance expects: benchmark_category, onnx, vnnlib, timeout, outputlocation + if (ACTION == 'run_instance'): + if len(args) != 6: + raise ValueError(f'Incorrect number of arguments, expected 6 got {len(args)}.') + + print(args) + + return args + + +if __name__=="__main__": + # parse the arguments. + ACTION, CATEGORY, PATH_TO_ONNX, PATH_TO_VNNLIB, TIMEOUT, OUTPUTLOCATION = _get_args() + + # implement logic for each action we might want to take. + switcher = { + 'prepare_instance': lambda: prepare_instance(CATEGORY, PATH_TO_ONNX, PATH_TO_VNNLIB), # prepare_instance(PATH_TO_ONNX, PATH_TO_VNNLIB), + 'run_instance': lambda: run_instance(CATEGORY, PATH_TO_ONNX, PATH_TO_VNNLIB, TIMEOUT, OUTPUTLOCATION) # run_instance(PATH_TO_ONNX, PATH_TO_VNNLIB, TIMEOUT, OUTPUTLOCATION), + } + + # retrieve the correct function call based on the input action. + func = switcher.get(ACTION, 'Invalid')() + + if func == 'Invalid': + raise ValueError(f'Incorrect ACTION. Expected one of {list(switcher.keys())}; instead got {ACTION}.') \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/install_tool.sh b/code/nnv/examples/Submission/VNN_COMP2024/install_tool.sh new file mode 100644 index 0000000000..a8e0be54d9 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/install_tool.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +# Installation script used for VNN-COMP. Ubuntu, MATLAB R2024a (https://github.com/verivital/nnv) + +TOOL_NAME="nnv" +VERSION_STRING="v1" + +# check arguments +if [ "$1" != ${VERSION_STRING} ]; then + echo "Expected first argument (version string) '$VERSION_STRING', got '$1'" + exit 1 +fi + +echo "Installing $TOOL_NAME dependencies" + +# Install support packages (ONNX importer) +curl --retry 100 --retry-connrefused -L -O https://www.dropbox.com/s/4p17xm4tlm8r9gs/sppFile.zip # need to do this step for R2024a +sleep 60 +sudo unzip sppFile.zip -d /home/ubuntu/toolkit/code/nnv +sudo rm sppFile.zip + +# This is called networks2023, but it contains the MATLAB networks for most of 2022 and 2023 (Do we need this step for 2024?) +cd /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/ +curl --retry 100 --retry-connrefused -L -O https://www.dropbox.com/scl/fi/chb0fotern4r5sycz57r9/networks2023.zip?rlkey=1cwvh73b5zgtnb67xdrvlg8am&dl=0 + +sleep 60 + +unzip *.zip* + +ip link show # get mac address (for licensing) + +echo $USER # get usernme (for licensing) + +mkdir ~/.matlab/R2024a_licenses + +# ADD STEPS TO INSTALL GUROBI \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/post_install.sh b/code/nnv/examples/Submission/VNN_COMP2024/post_install.sh new file mode 100644 index 0000000000..f6aa911271 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/post_install.sh @@ -0,0 +1,31 @@ +cd ~/.matlab/R2024a_licenses + +curl --retry 100 --retry-connrefused -L -O +sleep 60 +ls -al + +unzip *.zip* + +ls -al + +cp -f license.lic /usr/local/matlab/licenses/ + +rm /usr/local/matlab/licenses/license_info.xml + +matlab -nodisplay -r "cd /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/; prepare_run; quit" + +sudo apt install -y python3-pip +pip install numpy + +cd /usr/local/matlab/extern/engines/python +python3 -m pip install . + +# ADD STEPS TO INSTALL GUROBI + +# TEST IF MATLAB ENGINE IS INSTALLED PROPERLY +# START_ENGINE ='import matlab.engine\nimport time\neng = matlab.engine.start_matlab() \nprint(eng) \neng.prepare_run() \nexit()' +# python3 -c "exec('import matlab.engine\nimport time\neng = matlab.engine.start_matlab() \nprint(eng) \neng.prepare_run(nargout = 0, background=True) \nexit()')" + +# TEST IF WE CAN FIND MATLAB +# cd /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/ +# ./prepare_instance.sh v1 acasxu acas xu diff --git a/code/nnv/examples/Submission/VNN_COMP2024/prepare_instance.sh b/code/nnv/examples/Submission/VNN_COMP2024/prepare_instance.sh new file mode 100644 index 0000000000..04452300ce --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/prepare_instance.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +# example prepare_instance.sh script for VNNCOMP 2024 for nnv (https://github.com/verivital/nnv.git) +# four arguments, first is "v1", second is a benchmark category identifier string such as "acasxu", third is a path to the .onnx file and fourth is a path to .vnnlib file + +TOOL_NAME="nnv" +VERSION_STRING="v1" + +# check arguments +if [ "$1" != ${VERSION_STRING} ]; then + echo "Expected first argument (version string) '$VERSION_STRING', got '$1'" + exit 1 +fi + +CATEGORY=$2 +ONNX_FILE=$3 +VNNLIB_FILE=$4 + +echo "Preparing $TOOL_NAME for benchmark instance in category '$CATEGORY' with onnx file '$ONNX_FILE' and vnnlib file '$VNNLIB_FILE'" + +# kill any zombie processes +killall -q python3 +# killall -q python +killall -q matlab + +#WAIT_FOR_CONNECTION_TO_CLOSE='import matlab.engine\nimport time\nwhile matlab.engine.find_matlab(): time.sleep(1)' +# python3 -c "exec('$WAIT_FOR_CONNECTION_TO_CLOSE')" +# python -c "exec('$WAIT_FOR_CONNECTION_TO_CLOSE')" + +# start the matlab engine in background and keep the connection open +# python3 /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/execute.py 'prepare_instance' $CATEGORY $ONNX_FILE $VNNLIB_FILE & +# python execute.py 'prepare_instance' $CATEGORY $ONNX_FILE $VNNLIB_FILE & + +WAIT_FOR_CONNECTION_TO_OPEN='import matlab.engine\nimport time\nwhile not matlab.engine.find_matlab(): time.sleep(1) \nprint(eng)' +# python3 -c "exec('$WAIT_FOR_CONNECTION_TO_OPEN')" +# python -c "exec('$WAIT_FOR_CONNECTION_TO_OPEN')" + +exit 0 diff --git a/code/nnv/examples/Submission/VNN_COMP2024/prepare_run.m b/code/nnv/examples/Submission/VNN_COMP2024/prepare_run.m new file mode 100644 index 0000000000..a0b21a7b31 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/prepare_run.m @@ -0,0 +1,32 @@ +function prepare_run() + +%% Print out some info to prepare for the comp + +% remove paths from any prior installation (if any) +% rmpath(genpath('/home/ubuntu/toolkit/code/nnv/')); savepath; + +% installing nnv +cd /home/ubuntu/toolkit/code/nnv/; +install; +% save path to NNV +addpath(genpath('/home/ubuntu/toolkit/code/nnv/')); savepath; + +%disp(""); +%disp("Support package path"); +%disp(matlabshared.supportpkg.getSupportPackageRoot); + +%disp(" ") +%disp("MATLAB root"); +%disp(matlabroot); + +% set support package root to import onnx location +%matlabshared.supportpkg.setSupportPackageRoot('/usr/local/MATLAB/R2022b'); +%addpath(genpath('/usr/local/MATLAB')); + +disp("Support package path"); +disp(matlabshared.supportpkg.getSupportPackageRoot); + +savepath; +% quit; + +end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/run_instance.sh b/code/nnv/examples/Submission/VNN_COMP2024/run_instance.sh new file mode 100644 index 0000000000..d7016a5e8a --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/run_instance.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +# example run_instance.sh script for VNNCOMP 2024 for nnv (https://github.com/verivital/nnv.git) +# four arguments, first is "v1", second is a benchmark category identifier string such as "acasxu", third is a path to the .onnx file and fourth is a path to .vnnlib file +# modified: Samuel Sasaki, June 28th 2023 + +TOOL_NAME="nnv" +VERSION_STRING="v1" + +# check arguments +if [ "$1" != ${VERSION_STRING} ]; then + echo "Expected first argument (version string) '$VERSION_STRING', got '$1'" + exit 1 + +fi + +CATEGORY=$2 +ONNX_FILE=$3 +VNNLIB_FILE=$4 +RESULTS_FILE=$5 +TIMEOUT=$6 + +echo "Running $TOOL_NAME on benchmark instance in category '$CATEGORY' with onnx file '$ONNX_FILE', vnnlib file '$VNNLIB_FILE', results file $RESULTS_FILE, and timeout $TIMEOUT" + +# setup environment variable for tool (doing it earlier won't be persistent with docker)" +#DIR=$(dirname $(dirname $(realpath $0))) +#export PYTHONPATH="$PYTHONPATH:$DIR/src" + +echo $PYTHONPATH +# TIMEOUT=$5 + +# echo "Running $TOOL_NAME on benchmark instance in category '$CATEGORY' with onnx file '$ONNX_FILE', vnnlib file '$VNNLIB_FILE', and timeout $TIMEOUT" + +python3 /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/execute.py 'run_instance' "$CATEGORY" "$ONNX_FILE" "$VNNLIB_FILE" "$TIMEOUT" "$RESULTS_FILE" +# sudo python3 execute.py 'run_instance' "$CATEGORY" "$ONNX_FILE" "$VNNLIB_FILE" "$TIMEOUT" "$RESULTS_FILE" + +echo "" diff --git a/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m new file mode 100644 index 0000000000..e22e3b659e --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m @@ -0,0 +1,625 @@ +function [status, vTime] = run_vnncomp2024_instance(category, onnx, vnnlib, outputfile) +% single script to run all instances (supported) from the vnncomp2023 + +t = tic; % start timer +status = 2; % unknown (to start with) +locally = true; % change to false for submission + +% Process: +% 1) Load components +% 2) SAT? - Randomly evaluate network to search for counterexample +% 3) UNSAT? +% a) Compute reachability +% b) Verify (compute intersection between reach set and output property halfspace) + +%% 1) Load components + +% Load networks +if locally + [net, nnvnet, needReshape] = load_vnncomp_network_local(category, onnx); +else + % have to go to this path for the networks to load properly... lovely + % old_path = pwd; + % cd /home/dieman95/Documents/MATLAB/nnv/code/nnv/examples/Submission/VNN_COMP2023/networks2023/; + % cd /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/networks2023/; + [net, nnvnet, needReshape] = load_vnncomp_network(category, onnx); + % cd(old_path); % go back to where we ran the functions from +end +inputSize = net.Layers(1, 1).InputSize; + + +% Load property to verify +property = load_vnnlib(vnnlib); +lb = property.lb; % input lower bounds +ub = property.ub; % input upper bounds +prop = property.prop; % output spec to verify + + +%% 2) SAT? + +nRand = 100; % number of random inputs (this can be changed) +% We got some penalties last year, why? +% Wrong vnnlib parsing? Wrong counterrexample writing? Do we need to reshape it? +% Let's test last years properties and make sure those errors/bugs are +% fixed before this year's submission + +% Choose how to falsify based on vnnlib file +if ~isa(lb, "cell") && length(prop) == 1 % one input, one output + counterEx = falsify_single(net, lb, ub, inputSize, nRand, prop{1}.Hg, needReshape); +elseif isa(lb, "cell") && length(lb) == length(prop) % multiple inputs, multiple outputs + for spc = 1:length(lb) % try parfeval, parfor does not work for early return + counterEx = falsify_single(net, lb{spc}, ub{spc}, inputSize, nRand, prop{spc}.Hg, needReshape); + if iscell(counterEx) + break + end + end +elseif isa(lb, "cell") && length(prop) == 1 % can violate the output property from any of the input sets + for arr = 1:length(lb) % try parfeval, parfor does not work for early return + counterEx = falsify_single(net, lb{arr}, ub{arr}, inputSize, nRand, prop{1}.Hg, needReshape); + if iscell(counterEx) + break + end + end +else + warning("Working on adding support to other vnnlib properties"); +end + +toc(t); + +%% 3) UNSAT? + +% Define reachability options +% Let's try to choose this better from the get-go + +% Option 1 +% reachOptions_relax100 = struct; +% reachOptions_relax100.reachMethod = 'relax-star-range'; +% reachOptions_relax100.relaxFactor = 1; +% +% % Option 2 +% reachOptions_relax50 = struct; +% reachOptions_relax50.reachMethod = 'relax-star-range'; +% reachOptions_relax50.relaxFactor = 0.5; +% +% % Option 3 +% reachOptions_exact = struct; +% reachOptions_exact.reachMethod = 'exact-star'; +% reachOptions_exact.reachOption = 'parallel'; +% reachOptions_exact.numCores = feature('numcores'); +% +% % Option 4 +% reachOptions_approx.reachMethod = 'approx-star'; +% +% % Choosing reachOptions (based on size, but not sure how to decice really...) +% if prod(inputSize) > 3000 % [32 32 3] +% reachOptions = {reachOptions_relax100; reachOptions_relax50; reachOptions_approx}; +% else +% reachOptions = {reachOptions_relax50; reachOptions_approx; reachOptions_exact}; +% end + + +% Check if property was violated earlier +if iscell(counterEx) + status = 0; +end + +if status == 2 && isa(nnvnet, "NN") % no counterexample found and supported for reachability (otherwise, skip step 3 and write results) + +% Choose how to verify based on vnnlib file + if ~isa(lb, "cell") && length(prop) == 1 % one input, one output + % Get input set + if ~isscalar(inputSize) + lb = reshape(lb, inputSize); + ub = reshape(ub, inputSize); + end + if needReshape + lb = permute(lb, [2 1 3]); + ub = permute(ub, [2 1 3]); + end + IS = ImageStar(lb, ub); + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{1}); + % Verify property + status = verify_specification(ySet, prop); + if status == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{2}); + % Verify property + status = verify_specification(ySet, prop); + if status == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{3}); + % Verify property + status = verify_specification(ySet, prop); + end + end + + elseif isa(lb, "cell") && length(lb) == length(prop) % multiple inputs, multiple outputs + local_status = ones(length(lb),1); + parfor spc = 1:length(lb) + % Get input set + if ~isscalar(inputSize) + lbS = reshape(lb{spc}, inputSize); + ubS = reshape(ub{spc}, inputSize); + else + lbS = lb{spc}; + ubS = ub{spc}; + end + if needReshape + lbS = permute(lbS, [2 1 3]); + ubS = permute(ubS, [2 1 3]); + end + IS = ImageStar(lbS, ubS); + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{1}); + % Verify property + if isempty(ySet.C) + dd = ySet.V; DD = ySet.V; + ySet = Star(dd,DD); + end + local_status(spc) = verify_specification(ySet, prop(spc)); + if local_status(spc) == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{2}); + % Verify property + local_status(spc) = verify_specification(ySet, prop(spc)); + if local_status(spc) == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{3}); + % Verify property + local_status(spc) = verify_specification(ySet, prop(spc)); + end + end + end + if all(local_status == 1) + status = 1; + else + status = 2; + end + elseif isa(lb, "cell") && length(prop) == 1 + local_status = ones(length(lb),1); + parfor spc = 1:length(lb) + % Get input set + if ~isscalar(inputSize) + lbS = reshape(lb{spc}, inputSize); + ubS = reshape(ub{spc}, inputSize); + else + lbS = lb{spc}; + ubS = ub{spc}; + end + if needReshape + lbS = permute(lbS, [2 1 3]); + ubS = permute(ubS, [2 1 3]); + end + IS = ImageStar(lbS, ubS); + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{1}); + % Verify property + local_status(spc) = verify_specification(ySet, prop); + if local_status(spc) == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{2}); + % Verify property + local_status(spc) = verify_specification(ySet, prop); + if local_status(spc) == 2 % refine if unknown + % Compute reachability + ySet = nnvnet.reach(IS, reachOptions{3}); + % Verify property + local_status(spc) = verify_specification(ySet, prop); + end + end + end + if all(local_status == 1) + status = 1; + else + status = 2; + end + else + warning("Working on adding support to other vnnlib properties") + end + +end + +%% 4) Process results + +vTime = toc(t); % save total computation time + +disp(status); +disp(vTime); +disp( " "); + +% Write results to output file +if status == 0 + fid = fopen(outputfile, 'w'); + fprintf(fid, 'sat \n'); + fclose(fid); + write_counterexample(outputfile, counterEx) +elseif status == 1 + fid = fopen(outputfile, 'w'); + fprintf(fid, 'unsat \n'); + fclose(fid); +elseif status == 2 + fid = fopen(outputfile, 'w'); + fprintf(fid, 'unknown \n'); + fclose(fid); +end + +% quit; % does this work when running matlab.engine from python in background? + + +end + +function [net,nnvnet, needReshape] = load_vnncomp_network(category, onnxFile) +% load vnncomp 2024 benchmark NNs (subset support) - scoring and not scoring + + needReshape = 0; % default is to use MATLAB reshape, otherwise use the python reshape + + onnx = split(onnxFile,'/'); + if iscell(onnx) + onnx = onnx{end}; % cell + else + onnx = char(onnx(end)); % string + end + onnx = [onnx(1:end-5), '.mat']; + + % collins_rul: onnx to nnvnet + % collins_nets = load_collins_NNs; + if contains(category, 'collins_rul') + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + if contains(onnx, 'full_window_40') + needReshape = 2; + else + needReshape = 1; + end + + elseif contains(category, "ml4acopf") + net = load(onnx); + net = net.net; + nnvnet = ""; + + elseif contains(category, "nn4sys") + % nn4sys: onnx to matlab + if contains(onnxFile, "lindex") + net = load(onnx); + net = assembleNetwork(net.net); + nnvnet = matlab2nnv(net); + else + error("We don't have those"); + end + + elseif contains(category, "dist_shift") + % dist_shift: onnx to matlab: + net = load(onnx); + net = net.net; + try + nnvnet = matlab2nnv(net); + catch + nnvnet = ""; + end + + elseif contains(category, "cgan") + % cgan + if ~contains(onnxFile, 'transformer') + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + else + error("Loading this one is not supported...") + end + + elseif contains(category, "vgg") + % vgg16: onnx to matlab + net = load(onnx); + net = net.net; + try + nnvnet = matlab2nnv(net); + catch + nnvnet = ""; + end + needReshape = 1; + + elseif contains(category, "tllverify") + % tllverify: onnx to matlab + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, "vit") + % vit: onnx to matlab + net = load(onnx); + net = net.net; + nnvnet = ""; + needReshape= 1; + + elseif contains(category, "cctsdb_yolo") + % cctsdb_yolo: onnx to matnet + net = load(onnx); + net = net.net; + nnvnet = ""; + + elseif contains(category, "collins_yolo") + % collins_yolo: onnx to matlab: + net = load(onnx); + net = net.net; + nnvnet = ""; + + elseif contains(category, "yolo") + % yolo: onnx to matlab + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, "acasxu") + % acasxu: onnx to nnv + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, 'test') + % test: onnx to nnv + if contains(onnx, "sat") + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + else + error("Loading this one is not supported...") + end + + elseif contains(category, 'sri_resnet') + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + needReshape = 1; + + elseif contains(category, 'biasfield') + % onnx to nnv + if contains(onnx, "base") + needReshape = 1; + end + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, 'rl_bench') + % rl: onnx to nnv + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, 'reach_prob') + % reach_prob: onnx to nnv + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, 'mnist_fc') + % mnist: onnx to nnv (input is different here, need to check our + % code still works for it) + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + + elseif contains(category, 'cifar100') + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + needReshape = 1; + + elseif contains(category, 'oval') + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + needReshape = 1; + + elseif contains(category, 'cifar2020') + % onnx to nnv + net = load(onnx); + net = net.net; + nnvnet = matlab2nnv(net); + needReshape = 1; + + else % all other benchmarks + % traffic: onnx to matlab: opset15 issues + error("ONNX model not supported") + end + +end + +% Had to change this because importer fails using the command line... +function [net,nnvnet,needReshape] = load_vnncomp_network_local(category, onnx) +% load vnncomp 2023 benchmark NNs (subset support) + + needReshape = 0; % default is to use MATLAB reshape, otherwise use the python reshape + % collins_rul: onnx to nnvnet + % collins_nets = load_collins_NNs; + if contains(category, 'collins_rul') + net = importONNXNetwork(onnx); + nnvnet = matlab2nnv(net); + if contains(onnx, 'full_window_40') + needReshape = 2; + else + needReshape = 1; + end + + elseif contains(category, "nn4sys") + % nn4sys: onnx to matlab: + if contains(onnx, "lindex") + net = importONNXLayers(onnx, "OutputDataFormats", "BC"); % lindex + nnvnet = matlab2nnv(net); + % net = assembleNetwork(net.net); + % nnvnet = matlab2nnv(net); + else + error("We don't have those"); + end + + elseif contains(category, "dist_shift") + % dist_shift: onnx to matlab: + net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + nnvnet = ""; + + elseif contains(category, "cgan") + % cgan + net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + nnvnet = matlab2nnv(net); + + elseif contains(category, "vgg16") + % vgg16: onnx to matlab + net = importONNXNetwork('vgg16-7.onnx'); % flattenlayer + %reshapedInput = python_reshape(input,net_vgg.Layers(1,1).InputSize); % what is the input? assume it's all the same? + %nnvnet = matlab2nnv(net); + nnvnet = ""; + needReshape = 1; + + elseif contains(category, "tllverify") + % tllverify: onnx to matlab + net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); + nnvnet = matlab2nnv(net); + + elseif contains(category, "vit") + % vit: onnx to matlab + net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + nnvnet = ""; + needReshape= 1; + + elseif contains(category, "cctsdb_yolo") + % cctsdb_yolo: onnx to matnet + net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + nnvnet = ""; + + elseif contains(category, "collins_yolo") + % collins_yolo: onnx to matlab: + net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + nnvnet = ""; + + elseif contains(category, "yolo") + % yolo: onnx to matlab + net = importONNXNetwork(onnx); % padlayer + nnvnet = matlab2nnv(net); + + elseif contains(category, "acasxu") + % acasxu: onnx to nnv + net = importONNXNetwork(onnx, "InputDataFormats","BCSS"); + nnvnet = matlab2nnv(net); + + else % all other benchmarks + % traffic: onnx to matlab: opset15 issues + error("ONNX model not supported") + end + +end + +% Create an array of random examples from input set and reshape if necessary +% We use dlnetwork for simulation (MATLAB data structure) +function xRand = create_random_examples(net, lb, ub, nR, inputSize, needReshape) + xB = Box(lb, ub); % lb, ub must be vectors + xRand = xB.sample(nR-2); + xRand = [lb, ub, xRand]; + if needReshape + if needReshape ==2 % for collins only (full_window_40) + newSize = [inputSize(2) inputSize(1) inputSize(3:end)]; + xRand = reshape(xRand, [newSize nR]); + xRand = permute(xRand, [2 1 3 4]); + else + xRand = reshape(xRand, [inputSize nR]); + xRand = permute(xRand, [2 1 3 4]); + end +% xRand = python_reshape(xRand, [inputSize nR]); + else + xRand = reshape(xRand,[inputSize nR]); % reshape vectors into net input size + end + if isa(net, 'dlnetwork') % need to convert to dlarray + if isa(net.Layers(1, 1), 'nnet.cnn.layer.ImageInputLayer') + xRand = dlarray(xRand, "SSCB"); + elseif isa(net.Layers(1, 1), 'nnet.cnn.layer.FeatureInputLayer') || isa(net.Layers(1, 1), 'nnet.onnx.layer.FeatureInputLayer') + xRand = dlarray(xRand, "CB"); + else + disp(net.Layers(1,1)); + error("Unknown input format"); + end + end +end + +% Write counterexample to output file +function write_counterexample(outputfile, counterEx) + % First line - > sat + % after that, write the variables for each input dimension of the counterexample + % + % Example: + % ( (X_0 0.12132) + % (X_1 3.45454) + % ( .... ) + % (Y_0 2.32342) + % (Y_1 3.24355) + % ( ... ) + % (Y_N 0.02456)) + % + + precision = '%.16g'; % set the precision for all variables written to txt file + % open file and start writing counterexamples + fid = fopen(outputfile, 'a+'); + x = counterEx{1}; + x = reshape(x, [], 1); + % begin specifying value for input example + fprintf(fid,'('); + for i = 1:length(x) + fprintf(fid, "(X_" + string(i-1) + " " + num2str(x(i), precision)+ ")\n"); + end + y = counterEx{2}; + y = reshape(y, [], 1); + % specify values for output example + for j =1:length(y) + fprintf(fid, "(Y_" + string(j-1) + " " + num2str(y(j), precision)+ ")\n"); + end + fprintf(fid, ')'); + % close and save file + fclose(fid); + +end + +% Falsification function (random simulation looking for counterexamples) +function counterEx = falsify_single(net, lb, ub, inputSize, nRand, Hs, needReshape) + counterEx = nan; + xRand = create_random_examples(net, lb, ub, nRand, inputSize, needReshape); + s = size(xRand); + n = length(s); + % look for counterexamples + for i=1:s(n) + x = get_example(xRand, i); + yPred = predict(net, x); + if isa(x, 'dlarray') % if net is a dlnetwork + x = extractdata(x); + yPred = extractdata(yPred); + end + % check if property violated + yPred = reshape(yPred, [], 1); % convert to column vector (if needed) + for h=1:length(Hs) + if Hs(h).contains(double(yPred)) % property violated + counterEx = {x; yPred}; % save input/output of countex-example + break; + end + end + end + +end + +% Get random example from input set +function x = get_example(xRand,i) + s = size(xRand); + n = length(s); + if n == 4 + x = xRand(:,:,:,i); + elseif n == 3 + x = xRand(:,:,i); + elseif n == 2 + x = xRand(:,i); + xsize = size(x); + if xsize(1) ~= 1 && ~isa(x,"dlarray") + x = x'; + end + else + error("InputSize = "+string(s)); + end +end + From 539bec2475add5aa2c91c5fd4c0abb0994b3692c Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Tue, 4 Jun 2024 15:49:02 -0500 Subject: [PATCH 4/6] Working on competitions --- .../ARCH-COMP2024/benchmarks/ACC/acc.mat | Bin 0 -> 1254843 bytes .../ARCH-COMP2024/benchmarks/ACC/acc.pdf | Bin 0 -> 8006 bytes .../ARCH-COMP2024/benchmarks/ACC/reach.m | 3 +- .../benchmarks/NAV/reach_point.m | 2 +- .../ARCH-COMP2024/benchmarks/NAV/reach_set.m | 8 +- .../VNN_COMP2024/convert2022benchmarks.m | 272 ------------ .../VNN_COMP2024/run_vnncomp2024_instance.m | 387 ++++++------------ .../test_extendedTrack_instances.m | 64 +++ .../VNN_COMP2024/test_mainTrack_instances.m | 167 ++++++++ 9 files changed, 352 insertions(+), 551 deletions(-) create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/acc.mat create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/acc.pdf delete mode 100644 code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m create mode 100644 code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/acc.mat 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z=$93jn?M|63)VRO!k=8FVib`ST>mMzftS;gML_*I|8IJ|UxNbnL{;_OA)Qdo|#D z9>C?2nPPQ2u+ww~2dho%))f8IU$hE+N-8rlxRDX5HQLEmhC%Cr?EUn=`CnwO+*S5W zI^32;{9bCd!c(R)>?xgMkp4jP+4HEmV6jvuo?;7ZZPQMUi(i?9-6^Xw`P~g$NADM# z4Kf(m@<|wtxxCi9@relwJa-OqqpTo1vCqPdHAS=Ym}BAI1*J^0QwPXN0k56KaijpP zK_*nwLEz$JnQWo67K)uKEXh9vCk<_<`mOfaBO^ND;Are{%5^v-11DnOuuM`~3iW4j zCUGb;2PbHT7JtO3LoqPXgF@kk7@Vu82O5KeN`k9Ek+H8E1`5}=xq|_}>0s;}ZIoPn zpymi*fw-h3R7^(90@NsbxVrsUg_1J{WA|S*#vYC?|5c%H;{gl`p;CW*PXxTXtWTj}sq88{o`ga3EWd^|ArbO4Pya(EF806o zO2`02{Id@4VdLnG@c_ru0JMg#u6QU2{{fFRvG%S|aDM&!n+nuiP7NU?p{Oh^C9W*3 zs-z?)jZ{{XR8&DKs;K}IXcR(C0s4P0IpDsLqaOyu@nGC+Z#HF EKN@EoZvX%Q literal 0 HcmV?d00001 diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/reach.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/reach.m index 31bdaefb42..f7026b549f 100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/reach.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/ACC/reach.m @@ -4,7 +4,7 @@ % Load components net = load_NN_from_mat('controller_5_20.mat'); -reachStep = 0.02; +reachStep = 0.05; controlPeriod = 0.1; output_mat = [0 0 0 0 1 0;1 0 0 -1 0 0; 0 1 0 0 -1 0]; % feedback: relative distance, relative velocity and ego-car velocity plant = NonLinearODE(6,1,@dynamicsACC, reachStep, controlPeriod, output_mat); @@ -34,7 +34,6 @@ reachPRM.numCores = 1; reachPRM.reachMethod = 'approx-star'; [R,rT] = nncs.reach(reachPRM); -% disp("Time to compute ACC reach sets: " +string(rT)); % Save results if is_codeocean diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m index f25709cd0f..c79f5dce8e 100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m @@ -8,7 +8,7 @@ net = importNetworkFromONNX('networks/nn-nav-point.onnx', "InputDataFormats", "BC"); net = matlab2nnv(net); % Load plant - reachStep = 0.002; + reachStep = 0.01; controlPeriod = 0.02; plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); plant.set_tensorOrder(2); diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m index 3b85b4601d..ea16bbe2ae 100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m @@ -8,7 +8,7 @@ net = importNetworkFromONNX('networks/nn-nav-set.onnx', "InputDataFormats", "BC"); net = matlab2nnv(net); % Load plant - reachStep = 0.002; + reachStep = 0.01; controlPeriod = 0.02; plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); plant.set_tensorOrder(2); @@ -31,7 +31,6 @@ % Begin computation t = tic; for i=1:num_steps - disp(i); % Compute controller output set input_set = net.reach(init_set,reachOptions); @@ -39,7 +38,6 @@ % Compute plant reachable set init_set = plant.stepReachStar(init_set, input_set,'lin'); reachAll = [reachAll init_set]; - toc(t); end @@ -49,7 +47,7 @@ %% Visualize results plant.get_interval_sets; - f2 = figure; + f1 = figure; % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) hold on; Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,1,2,'b'); @@ -57,7 +55,7 @@ xlabel('x_1'); ylabel('x_2'); - f5 = figure; + f2 = figure; % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) hold on; Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); diff --git a/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m b/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m deleted file mode 100644 index 30a3bccd1e..0000000000 --- a/code/nnv/examples/Submission/VNN_COMP2024/convert2022benchmarks.m +++ /dev/null @@ -1,272 +0,0 @@ -%% Run some verification instances from the competition - -% path to benchmarks -% vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2022_benchmarks/benchmarks/"; -vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2022_benchmarks/benchmarks/"; - -% Go through some of the instances for every benchmark -% It looks like unless the networks are fully fully supported, we get errors... -% Same problem we ra into when running with no GUI ... -% -% Error using matlab.internal.indentcodeLegacy -%This feature is not supported because: -%Swing is not currently available. -% -%Error in indentcode (line 42) -% indentedText = matlab.internal.indentcodeLegacy(text, language); -% -%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation/genOutputFcn (line 100) -% code = indentcode(code); % Indent the code -% -%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation (line 39) -% this.ModelFunctionCode = genOutputFcn(this, outputFcnName, this.ToplevelGraphTranslation, isGeneratingCustomLayerOPTIONAL); -% -%Error in nnet.internal.cnn.onnx.CustomLayerManager/genCustomLayerFunction (line 499) -% modelTranslation = nnet.internal.cnn.onnx.fcn.ModelTranslation(modelProto, modelFcnPath, modelFcnName, maxNameLength, true); -% -%Error in nnet.internal.cnn.onnx.CustomLayerManager (line 77) -% this.ModelTranslations(i) = genCustomLayerFunction(this, this.ModelProtos(i), nameStem); -% -%Error in nnet.internal.cnn.onnx.ModelTranslationIntoLayers (line 228) -% this.CustomLayerManager = %nnet.internal.cnn.onnx.CustomLayerManager(this.GraphProtoManager, ... -% -%Error in nnet.internal.cnn.onnx.importONNXNetwork (line 32) -%modelTranslationIntoLayers = nnet.internal.cnn.onnx.ModelTranslationIntoLayers(modelProto, ... -% -%Error in importONNXNetwork (line 113) -%Network = nnet.internal.cnn.onnx.importONNXNetwork(modelfile, varargin{:}); -% -%Error in run_vnncomp2023_instance>load_vnncomp_network (line 127) -% net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape - - -%% cifar_biasfield - -disp("Running cifar_biasfield...") - -biasfield_path = vnncomp_path + "cifar_biasfield/onnx/"; - -onnx = dir(biasfield_path); - -for i=3:length(onnx) - onnxFile = biasfield_path + onnx(i).name; - net = load_vnncomp_network('cifar_biasfield', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% rl_benchmarks - -disp("Running rl_benchmarks...") - -rl_path = vnncomp_path + "rl_benchmarks/onnx/"; - -onnx = dir(rl_path); - -for i=3:length(onnx) - onnxFile = rl_path + onnx(i).name; - net = load_vnncomp_network('rl_benchmarks', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% reach_prob_density - -disp("Running reach_prob_density...") - -reach_prob_path = vnncomp_path + "reach_prob_density/onnx/"; - -onnx = dir(reach_prob_path); - -for i=3:length(onnx) - onnxFile = reach_prob_path + onnx(i).name; - net = load_vnncomp_network('reach_prob_density', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% mnist_fc - -disp("Running mnist_fc...") - -mnist_path = vnncomp_path + "mnist_fc/onnx/"; - -onnx = dir(mnist_path); - -for i=3:length(onnx) - onnxFile = mnist_path + onnx(i).name; - net = load_vnncomp_network('mnist_fc', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% sri_resnet_a - -disp("Running sri_resnets..") - -sri_path = vnncomp_path + "sri_resnet_a/onnx/"; - -onnx = dir(sri_path); - -for i=3:length(onnx) - onnxFile = sri_path + onnx(i).name; - net = load_vnncomp_network('sri_resnet_a', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name, '.mat'], "net", "-v7.3"); -end - -%% sri_resnet_b - -sri_path = vnncomp_path + "sri_resnet_b/onnx/"; - -onnx = dir(sri_path); - -for i=3:length(onnx) - onnxFile = sri_path + onnx(i).name; - net = load_vnncomp_network('sri_resnet_b', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name, '.mat'], "net", "-v7.3"); -end - -%% cifar2020 - -cifar2020_path = vnncomp_path + "cifar2020/onnx/"; - -onnx = dir(cifar2020_path); - -for i=3:length(onnx) - onnxFile = cifar2020_path + onnx(i).name; - if ~contains(onnxFile, "PGD") - net = load_vnncomp_network('cifar2020', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); - end -end - -%% oval21 - -oval_path = vnncomp_path + "oval21/onnx/"; - -onnx = dir(oval_path); - -for i=3:length(onnx) - onnxFile = oval_path + onnx(i).name; - net = load_vnncomp_network('oval21', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% cifar100_tinyimagenet_resnet - -cifar_path = vnncomp_path + "cifar100_tinyimagenet_resnet/onnx/"; - -onnx = dir(cifar_path); - -for i=3:length(onnx) - onnxFile = cifar_path + onnx(i).name; - net = load_vnncomp_network('cifar100_tinyimagenet_resnet', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - - -%% nn4sys - -disp("Running nn4sys...") - -nn4sys_path = vnncomp_path + "nn4sys/onnx/"; - -onnx = dir(nn4sys_path); - -for i=3:length(onnx) - onnxFile = nn4sys_path + onnx(i).name; - if contains(onnxFile, "lindex") - net = load_vnncomp_network('nn4sys', onnxFile); - name = onnx(i).name; - name = name(1:end-5); -% save(['networks2023/', name], "net", "-v7.3"); - end -end - - -%% tllverify - -disp("Running tllverify..") - -tll_path = vnncomp_path + "tllverifybench/onnx/"; - -onnx = dir(tll_path); - -for i=3:length(onnx) - onnxFile = tll_path + onnx(i).name; - net = load_vnncomp_network('tllverifybench', onnxFile); - name = onnx(i).name; - name = name(1:end-5); -% save(['networks2023/', name], "net", "-v7.3"); -end - - -%% Loading function -function net = load_vnncomp_network(category, onnx) -% load vnncomp 2022 benchmark NNs (subset support) - - if contains(category, "nn4sys") - % nn4sys: onnx to matlab: - net = importONNXNetwork(onnx, "OutputDataFormats", "BC"); % lindex (no reshape) - - elseif contains(category, "tllverify") - % tllverify: onnx to matlab - net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); % no reshape - - elseif contains(category, 'biasfield') - % onnx to nnv - if contains(onnx, "base") - net = importONNXNetwork(onnx); % needs reshape - else - net = importONNXNetwork(onnx, "InputDataFormats", "BC"); % does not need reshape (feature input) - end - - elseif contains(category, 'rl_bench') - % rl: onnx to nnv - net = importONNXNetwork(onnx, "InputDataFormats", "BC", "OutputDataFormats", "BC"); % no need to reshape (feature input) - - elseif contains(category, 'reach_prob') - % reach_prob: onnx to nnv - net = importONNXNetwork(onnx, "InputDataFormats", "BC"); % no reshape - - elseif contains(category, 'mnist_fc') - % mnist: onnx to nnv - net = importONNXNetwork(onnx, 'InputDataFormats',"SSC", 'OutputDataFormats',"BC"); % no reshape - - elseif contains(category, 'sri_resnet') - net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); % reshape - - elseif contains(category, 'cifar100') - net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape - - elseif contains(category, 'oval') - net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape - - elseif contains(category, 'cifar2020') - % onnx to nnv - if ~contains(onnxFile, "PGD") - net = importONNXNetwork(onnx, 'OutputDataFormats',"BC"); %reshape - else - net = importONNXNetwork(onnx, "InputDataFormats", "BCSS", "OutputDataFormats", "BC"); % reshape? - end - - else % all other benchmarks - % traffic: onnx to matlab: opset15 issues - error("ONNX model not supported") - end - -end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m index e22e3b659e..c879027828 100644 --- a/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m +++ b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m @@ -1,9 +1,9 @@ function [status, vTime] = run_vnncomp2024_instance(category, onnx, vnnlib, outputfile) -% single script to run all instances (supported) from the vnncomp2023 + +% single script to run all instances (supported) from the vnncomp2024 t = tic; % start timer status = 2; % unknown (to start with) -locally = true; % change to false for submission % Process: % 1) Load components @@ -15,16 +15,9 @@ %% 1) Load components % Load networks -if locally - [net, nnvnet, needReshape] = load_vnncomp_network_local(category, onnx); -else - % have to go to this path for the networks to load properly... lovely - % old_path = pwd; - % cd /home/dieman95/Documents/MATLAB/nnv/code/nnv/examples/Submission/VNN_COMP2023/networks2023/; - % cd /home/ubuntu/toolkit/code/nnv/examples/Submission/VNN_COMP2023/networks2023/; - [net, nnvnet, needReshape] = load_vnncomp_network(category, onnx); - % cd(old_path); % go back to where we ran the functions from -end + +[net, nnvnet, needReshape] = load_vnncomp_network(category, onnx); + inputSize = net.Layers(1, 1).InputSize; @@ -97,6 +90,9 @@ % reachOptions = {reachOptions_relax50; reachOptions_approx; reachOptions_exact}; % end +reachOptions = struct; +reachOptions.reachMethod = 'approx-star'; + % Check if property was violated earlier if iscell(counterEx) @@ -107,113 +103,106 @@ % Choose how to verify based on vnnlib file if ~isa(lb, "cell") && length(prop) == 1 % one input, one output - % Get input set + + % Get input bounds if ~isscalar(inputSize) lb = reshape(lb, inputSize); ub = reshape(ub, inputSize); end + + % Format bounds into correct dimensions if needReshape lb = permute(lb, [2 1 3]); ub = permute(ub, [2 1 3]); end + + % Create input set IS = ImageStar(lb, ub); + % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{1}); + ySet = nnvnet.reach(IS, reachOptions); + % Verify property status = verify_specification(ySet, prop); - if status == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{2}); - % Verify property - status = verify_specification(ySet, prop); - if status == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{3}); - % Verify property - status = verify_specification(ySet, prop); - end - end elseif isa(lb, "cell") && length(lb) == length(prop) % multiple inputs, multiple outputs - local_status = ones(length(lb),1); - parfor spc = 1:length(lb) - % Get input set + + local_status = ones(length(lb),1); % track status for each specification in the vnnlib + + parfor spc = 1:length(lb) % We can compute these in parallel for faster computation + + % Get input bounds if ~isscalar(inputSize) - lbS = reshape(lb{spc}, inputSize); - ubS = reshape(ub{spc}, inputSize); - else - lbS = lb{spc}; - ubS = ub{spc}; + lb_spc = reshape(lb{spc}, inputSize); + ub_spc = reshape(ub{spc}, inputSize); end + + % Format bounds into correct dimensions if needReshape - lbS = permute(lbS, [2 1 3]); - ubS = permute(ubS, [2 1 3]); + lb_spc = permute(lb_spc, [2 1 3]); + ub_spc = permute(ub_spc, [2 1 3]); end - IS = ImageStar(lbS, ubS); + + % Create input set + IS = ImageStar(lb_spc, ub_spc); + % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{1}); + ySet = nnvnet.reach(IS, reachOptions); + % Verify property if isempty(ySet.C) dd = ySet.V; DD = ySet.V; ySet = Star(dd,DD); end + + % Add verification status local_status(spc) = verify_specification(ySet, prop(spc)); - if local_status(spc) == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{2}); - % Verify property - local_status(spc) = verify_specification(ySet, prop(spc)); - if local_status(spc) == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{3}); - % Verify property - local_status(spc) = verify_specification(ySet, prop(spc)); - end - end + end + + % Check for the global verification result if all(local_status == 1) status = 1; else status = 2; end - elseif isa(lb, "cell") && length(prop) == 1 - local_status = ones(length(lb),1); - parfor spc = 1:length(lb) - % Get input set + + elseif isa(lb, "cell") && length(prop) == 1 % one specification, multiple input definitions + + local_status = ones(length(lb),1); % track status for each specification in the vnnlib + + parfor spc = 1:length(lb) % We can compute these in parallel for faster computation + + % Get input bounds if ~isscalar(inputSize) - lbS = reshape(lb{spc}, inputSize); - ubS = reshape(ub{spc}, inputSize); - else - lbS = lb{spc}; - ubS = ub{spc}; + lb_spc = reshape(lb{spc}, inputSize); + ub_spc = reshape(ub{spc}, inputSize); end + + % Format bounds into correct dimensions if needReshape - lbS = permute(lbS, [2 1 3]); - ubS = permute(ubS, [2 1 3]); + lb_spc = permute(lb_spc, [2 1 3]); + ub_spc = permute(ub_spc, [2 1 3]); end - IS = ImageStar(lbS, ubS); + + % Create input set + IS = ImageStar(lb_spc, ub_spc); + % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{1}); + ySet = nnvnet.reach(IS, reachOptions); + % Verify property local_status(spc) = verify_specification(ySet, prop); - if local_status(spc) == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{2}); - % Verify property - local_status(spc) = verify_specification(ySet, prop); - if local_status(spc) == 2 % refine if unknown - % Compute reachability - ySet = nnvnet.reach(IS, reachOptions{3}); - % Verify property - local_status(spc) = verify_specification(ySet, prop); - end - end + end + + % Check for the global verification result if all(local_status == 1) status = 1; else status = 2; end + else warning("Working on adding support to other vnnlib properties") end @@ -249,193 +238,47 @@ end -function [net,nnvnet, needReshape] = load_vnncomp_network(category, onnxFile) -% load vnncomp 2024 benchmark NNs (subset support) - scoring and not scoring - needReshape = 0; % default is to use MATLAB reshape, otherwise use the python reshape - onnx = split(onnxFile,'/'); - if iscell(onnx) - onnx = onnx{end}; % cell - else - onnx = char(onnx(end)); % string - end - onnx = [onnx(1:end-5), '.mat']; - - % collins_rul: onnx to nnvnet - % collins_nets = load_collins_NNs; - if contains(category, 'collins_rul') - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - if contains(onnx, 'full_window_40') - needReshape = 2; - else - needReshape = 1; - end - - elseif contains(category, "ml4acopf") - net = load(onnx); - net = net.net; - nnvnet = ""; - - elseif contains(category, "nn4sys") - % nn4sys: onnx to matlab - if contains(onnxFile, "lindex") - net = load(onnx); - net = assembleNetwork(net.net); - nnvnet = matlab2nnv(net); - else - error("We don't have those"); - end - - elseif contains(category, "dist_shift") - % dist_shift: onnx to matlab: - net = load(onnx); - net = net.net; - try - nnvnet = matlab2nnv(net); - catch - nnvnet = ""; - end - - elseif contains(category, "cgan") - % cgan - if ~contains(onnxFile, 'transformer') - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - else - error("Loading this one is not supported...") - end - - elseif contains(category, "vgg") - % vgg16: onnx to matlab - net = load(onnx); - net = net.net; - try - nnvnet = matlab2nnv(net); - catch - nnvnet = ""; - end - needReshape = 1; - - elseif contains(category, "tllverify") - % tllverify: onnx to matlab - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, "vit") - % vit: onnx to matlab - net = load(onnx); - net = net.net; - nnvnet = ""; - needReshape= 1; - - elseif contains(category, "cctsdb_yolo") - % cctsdb_yolo: onnx to matnet - net = load(onnx); - net = net.net; - nnvnet = ""; - - elseif contains(category, "collins_yolo") - % collins_yolo: onnx to matlab: - net = load(onnx); - net = net.net; - nnvnet = ""; - - elseif contains(category, "yolo") - % yolo: onnx to matlab - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, "acasxu") - % acasxu: onnx to nnv - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, 'test') - % test: onnx to nnv - if contains(onnx, "sat") - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - else - error("Loading this one is not supported...") - end - - elseif contains(category, 'sri_resnet') - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - needReshape = 1; - - elseif contains(category, 'biasfield') - % onnx to nnv - if contains(onnx, "base") - needReshape = 1; - end - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, 'rl_bench') - % rl: onnx to nnv - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, 'reach_prob') - % reach_prob: onnx to nnv - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, 'mnist_fc') - % mnist: onnx to nnv (input is different here, need to check our - % code still works for it) - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - - elseif contains(category, 'cifar100') - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - needReshape = 1; - - elseif contains(category, 'oval') - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - needReshape = 1; - - elseif contains(category, 'cifar2020') - % onnx to nnv - net = load(onnx); - net = net.net; - nnvnet = matlab2nnv(net); - needReshape = 1; - - else % all other benchmarks - % traffic: onnx to matlab: opset15 issues - error("ONNX model not supported") - end - -end - -% Had to change this because importer fails using the command line... -function [net,nnvnet,needReshape] = load_vnncomp_network_local(category, onnx) -% load vnncomp 2023 benchmark NNs (subset support) +function [net,nnvnet,needReshape] = load_vnncomp_network(category, onnx) +% load vnncomp 2023 benchmark NNs +% +% Regular Track Benchmarks +% - 2024 +% - safeNLP +% - cora +% - linearizeNN +% - cifar100 +% - tinyimagenet +% - 2023 +% - nn4sys +% - dist-shift +% - acasxu +% - cgan +% - collins_rul_cnn +% - metaroom +% - tllverifybench +% +% Extended Track Benchmarks +% - 2024 +% - ml4acopf +% - dynaroars/benchmark-generation (name may change for competition) +% - collins_yolo +% - LSNC (not supported) +% - 2023 +% - yolo +% - cctsdb_yolo +% - collins_yolo_robustness (same as collins_yolo I believe) +% - ml4acopf (different than this year's?) +% - traffic_sign_recognition +% - vggnet16 +% - vit +% needReshape = 0; % default is to use MATLAB reshape, otherwise use the python reshape - % collins_rul: onnx to nnvnet - % collins_nets = load_collins_NNs; + if contains(category, 'collins_rul') - net = importONNXNetwork(onnx); + net = importNetworkFromONNX(onnx); nnvnet = matlab2nnv(net); if contains(onnx, 'full_window_40') needReshape = 2; @@ -446,61 +289,63 @@ elseif contains(category, "nn4sys") % nn4sys: onnx to matlab: if contains(onnx, "lindex") - net = importONNXLayers(onnx, "OutputDataFormats", "BC"); % lindex + % nn4sys: onnx to matlab: + net = importNetworkFromONNX(onnx, "OutputDataFormats", "BC"); % lindex nnvnet = matlab2nnv(net); - % net = assembleNetwork(net.net); - % nnvnet = matlab2nnv(net); else error("We don't have those"); end + elseif contains(category, "ml4acopf") + % ml4acopf: ? + net = importNetworkFromONNX(onnx, "InputDataFormats", "BC"); + nnvnet = ""; + elseif contains(category, "dist_shift") % dist_shift: onnx to matlab: - net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + net = importNetworkFromONNX(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape nnvnet = ""; elseif contains(category, "cgan") % cgan - net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape + net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape nnvnet = matlab2nnv(net); elseif contains(category, "vgg16") % vgg16: onnx to matlab - net = importONNXNetwork('vgg16-7.onnx'); % flattenlayer - %reshapedInput = python_reshape(input,net_vgg.Layers(1,1).InputSize); % what is the input? assume it's all the same? - %nnvnet = matlab2nnv(net); + net = importNetworkFromONNX(onnx); % flattenlayer nnvnet = ""; needReshape = 1; elseif contains(category, "tllverify") % tllverify: onnx to matlab - net = importONNXNetwork(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); + net = importNetworkFromONNX((onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); nnvnet = matlab2nnv(net); elseif contains(category, "vit") % vit: onnx to matlab - net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + net = importNetworkFromONNX(onnx); nnvnet = ""; needReshape= 1; elseif contains(category, "cctsdb_yolo") % cctsdb_yolo: onnx to matnet - net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + net = importNetworkFromONNX(onnx); nnvnet = ""; elseif contains(category, "collins_yolo") % collins_yolo: onnx to matlab: - net = importONNXNetwork(onnx, "TargetNetwork","dlnetwork" ); + net = importNetworkFromONNX(onnx, "TargetNetwork","dlnetwork" ); nnvnet = ""; elseif contains(category, "yolo") % yolo: onnx to matlab - net = importONNXNetwork(onnx); % padlayer + net = importNetworkFromONNX(onnx); % padlayer nnvnet = matlab2nnv(net); elseif contains(category, "acasxu") % acasxu: onnx to nnv - net = importONNXNetwork(onnx, "InputDataFormats","BCSS"); + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS"); nnvnet = matlab2nnv(net); else % all other benchmarks diff --git a/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m b/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m new file mode 100644 index 0000000000..d656b15f00 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m @@ -0,0 +1,64 @@ +%% Run some verification instances from the competition + +% path to benchmarks +vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % change to whatever local path vnncomp benchmarks are stored +% vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; + + +%% ViT + +disp("Running ViT...") + +vit_path = vnncomp_path + "vit/"; + +vit_instances = [... + "onnx/ibp_3_3_8.onnx" , "vnnlib/ibp_3_3_8_3850.vnnlib";... + "onnx/pgd_2_3_16.onnx","vnnlib/pgd_2_3_16_1744.vnnlib";... + ]; + +% Run verification for vit +for i=1:length(vit_instances) + onnx = vit_path + vit_instances(i,1); + vnnlib = vit_path + vit_instances(i,2); + run_vnncomp2024_instance("vit",onnx,vnnlib,"vit_results_" + string(i)+".txt"); +end + + + +%% traffic_sign +% we should be able to support it, but matlab does not, so we would have to create the models manually... +% can load most info with Keras importer + + +%% vggnet16 + +disp("Running vggnet16...") + +vgg_path = vnncomp_path + "vggnet16/"; + +vgg_instances = ["onnx/vgg16-7.onnx","vnnlib/spec0_screw.vnnlib"]; + +% Run verification for vgg16 (error when creating random examples, array size too large) +% I can imagine that similar errors will be encountered when running verification +onnx = vgg_path + vgg_instances(1,1); +vnnlib = vgg_path + vgg_instances(1,2); +run_vnncomp2024_instance("vggnet16",onnx,vnnlib,"vgg_results.txt"); + + +%% ml4acopf + +disp("Running ml4acopf..") + +ml4_path = vnncomp_path + "ml4acopf/"; + +ml4_instances = ["onnx/118_ieee_ml4acopf.onnx","vnnlib/118_ieee_prop2.vnnlib";... + "onnx/14_ieee_ml4acopf.onnx","vnnlib/14_ieee_prop9.vnnlib";... + "onnx/300_ieee_ml4acopf.onnx","vnnlib/300_ieee_prop3.vnnlib"]; + +% Run verification for nn4sys +for i=1:length(ml4_instances) + onnx = ml4_path + ml4_instances(i,1); + vnnlib = ml4_path + ml4_instances(i,2); + run_vnncomp2024_instance("ml4acopf",onnx,vnnlib,"ml4_results_" + string(i)+".txt"); +end + diff --git a/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m b/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m new file mode 100644 index 0000000000..4a2ec443ce --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m @@ -0,0 +1,167 @@ +%% Run some verification instances from the competition + +% path to benchmarks +vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % change to whatever local path vnncomp benchmarks are stored +% vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; + + +%% acasxu + +disp("Running acas xu...") + +acas_path = vnncomp_path + "acasxu/"; + +acas_instances = [... + "onnx/ACASXU_run2a_1_1_batch_2000.onnx" , "vnnlib/prop_1.vnnlib";... + "onnx/ACASXU_run2a_2_3_batch_2000.onnx","vnnlib/prop_2.vnnlib";... + "onnx/ACASXU_run2a_3_4_batch_2000.onnx","vnnlib/prop_3.vnnlib";... + "onnx/ACASXU_run2a_2_5_batch_2000.onnx","vnnlib/prop_4.vnnlib";... + "onnx/ACASXU_run2a_1_1_batch_2000.onnx","vnnlib/prop_5.vnnlib";... + "onnx/ACASXU_run2a_1_1_batch_2000.onnx","vnnlib/prop_6.vnnlib";... + "onnx/ACASXU_run2a_1_9_batch_2000.onnx","vnnlib/prop_7.vnnlib";... + "onnx/ACASXU_run2a_2_9_batch_2000.onnx","vnnlib/prop_8.vnnlib";... + "onnx/ACASXU_run2a_3_3_batch_2000.onnx","vnnlib/prop_9.vnnlib";... + "onnx/ACASXU_run2a_4_5_batch_2000.onnx","vnnlib/prop_10.vnnlib";... + ]; + +% Run verification for acas +for i=1:length(acas_instances) + disp(i); + onnx = acas_path + acas_instances(i,1); + vnnlib = acas_path + acas_instances(i,2); + run_vnncomp2024_instance("acasxu",onnx,vnnlib,"acas_results_" + string(i)+".txt"); +end + +%% test + +disp("Running test benchmark...") + +test_path = vnncomp_path + "test/"; + +test_instances = [... + "test_sat.onnx" , "test_prop.vnnlib";... + "test_unsat.onnx" , "test_prop.vnnlib"]; + +% Run verification for test +for i=1:length(test_instances) + onnx = test_path + test_instances(i,1); + vnnlib = test_path + test_instances(i,2); + run_vnncomp2024_instance("test",onnx,vnnlib,"test_results_" + string(i)+".txt"); +end + + +%% nn4sys + +disp("Running nn4sys...") + +nn4sys_path = vnncomp_path + "nn4sys/"; + +nn4sys_instances = [... % all other networks are not supported... + "onnx/lindex.onnx","vnnlib/lindex_1.vnnlib";... + "onnx/lindex_deep.onnx", "vnnlib/lindex_200.vnnlib";... + ]; + +% Run verification for nn4sys +for i=1:length(nn4sys_instances) + onnx = nn4sys_path + nn4sys_instances(i,1); + vnnlib = nn4sys_path + nn4sys_instances(i,2); + run_vnncomp2024_instance("nn4sys",onnx,vnnlib,"nn4sys_results_" + string(i)+".txt"); +end + +% need to update the support for these vnnlib properties, should be pretty quick + +%% dist_shift + +disp("Running dist_shift...") + +dist_path = vnncomp_path + "dist_shift/"; + +dist_instances = ["onnx/mnist_concat.onnx" ,"vnnlib/index7165_delta0.13.vnnlib"]; + +% Run verification for nn4sys +onnx = dist_path + dist_instances(1,1); +vnnlib = dist_path + dist_instances(1,2); +run_vnncomp2024_instance("dist_shift",onnx,vnnlib,"dist_results_" + string(i)+".txt"); + + +%% collins_rul + +disp("Running collins_rul..") + +rul_path = vnncomp_path + "collins_rul_cnn/"; + +rul_instances = ["onnx/NN_rul_small_window_20.onnx" ,"vnnlib/robustness_2perturbations_delta5_epsilon10_w20.vnnlib";... + "onnx/NN_rul_small_window_20.onnx" ,"vnnlib/monotonicity_CI_shift5_w20.vnnlib";... + "onnx/NN_rul_small_window_20.onnx" ,"vnnlib/if_then_5levels_w20.vnnlib";... + "onnx/NN_rul_full_window_20.onnx", "vnnlib/robustness_2perturbations_delta5_epsilon10_w20.vnnlib";... + "onnx/NN_rul_full_window_40.onnx" ,"vnnlib/robustness_2perturbations_delta5_epsilon10_w40.vnnlib"]; + +% Run verification for collins_rul +for i=1:length(rul_instances) + onnx = rul_path + rul_instances(i,1); + vnnlib = rul_path + rul_instances(i,2); + run_vnncomp2024_instance("collins_rul",onnx,vnnlib,"collins_rul_results_" + string(i)+".txt"); +end + +%% cgan + +disp("Running cgan..") + +cgan_path = vnncomp_path + "cgan/"; + +cgan_instances = ["onnx/cGAN_imgSz64_nCh_3.onnx" ,"vnnlib/cGAN_imgSz64_nCh_3_prop_3_input_eps_0.010_output_eps_0.015.vnnlib";... + "onnx/cGAN_imgSz32_nCh_3_nonlinear_activations.onnx","vnnlib/cGAN_imgSz32_nCh_3_nonlinear_activations_prop_0_input_eps_0.015_output_eps_0.020.vnnlib";... + "onnx/cGAN_imgSz32_nCh_1_transposedConvPadding_1.onnx" ,"vnnlib/cGAN_imgSz32_nCh_1_transposedConvPadding_1_prop_0_input_eps_0.015_output_eps_0.020.vnnlib";... + "onnx/cGAN_imgSz32_nCh_3_upsample.onnx","vnnlib/cGAN_imgSz32_nCh_3_upsample_prop_0_input_eps_0.015_output_eps_0.020.vnnlib"]; + +% Run verification for nn4sys +for i=1:length(cgan_instances) + onnx = cgan_path + cgan_instances(i,1); + vnnlib = cgan_path + cgan_instances(i,2); + run_vnncomp2024_instance("cgan",onnx,vnnlib,"cgan_results_" + string(i)+".txt"); +end + + +%% ml4acopf + +disp("Running ml4acopf..") + +ml4_path = vnncomp_path + "ml4acopf/"; + +ml4_instances = ["onnx/118_ieee_ml4acopf.onnx","vnnlib/118_ieee_prop2.vnnlib";... + "onnx/14_ieee_ml4acopf.onnx","vnnlib/14_ieee_prop9.vnnlib";... + "onnx/300_ieee_ml4acopf.onnx","vnnlib/300_ieee_prop3.vnnlib"]; + +% Run verification for nn4sys +for i=1:length(ml4_instances) + onnx = ml4_path + ml4_instances(i,1); + vnnlib = ml4_path + ml4_instances(i,2); + run_vnncomp2024_instance("ml4acopf",onnx,vnnlib,"ml4_results_" + string(i)+".txt"); +end + + +%% tllverify + +disp("Running tllverify..") + +tll_path = vnncomp_path + "tllverifybench/"; + +tll_instances = ["onnx/tllBench_n=2_N=M=8_m=1_instance_0_0.onnx","vnnlib/property_N=8_0.vnnlib";... + "onnx/tllBench_n=2_N=M=16_m=1_instance_1_0.onnx","vnnlib/property_N=16_0.vnnlib";... + "onnx/tllBench_n=2_N=M=24_m=1_instance_2_2.onnx","vnnlib/property_N=24_2.vnnlib";... + "onnx/tllBench_n=2_N=M=32_m=1_instance_3_0.onnx","vnnlib/property_N=32_0.vnnlib";... + "onnx/tllBench_n=2_N=M=48_m=1_instance_5_3.onnx","vnnlib/property_N=48_3.vnnlib";... + "onnx/tllBench_n=2_N=M=56_m=1_instance_6_0.onnx","vnnlib/property_N=56_0.vnnlib";... + "onnx/tllBench_n=2_N=M=64_m=1_instance_7_0.onnx","vnnlib/property_N=64_0.vnnlib"]; + +% Run verification for nn4sys +for i=1:length(tll_instances) + onnx = tll_path + tll_instances(i,1); + vnnlib = tll_path + tll_instances(i,2); + run_vnncomp2024_instance("tllverifybench",onnx,vnnlib,"tllverify_results_" + string(i)+".txt"); +end + + +%% TODO +% Add benchmarks from 2024 +% update nn4sys and dist_shift as they have been updated \ No newline at end of file From a324a26750a79b7205e1e7b0561a43f713260624 Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Fri, 7 Jun 2024 08:52:31 -0500 Subject: [PATCH 5/6] Checking speed improvements on last year's benchmarks --- .../ARCH-COMP2024/benchmarks/ACC/acc.mat | Bin 1254843 -> 1254843 bytes .../ARCH-COMP2024/benchmarks/ACC/acc.pdf | Bin 8006 -> 8137 bytes .../benchmarks/NAV/nav-point.pdf | Bin 0 -> 13755 bytes .../ARCH-COMP2024/benchmarks/NAV/nav-set.pdf | Bin 0 -> 38825 bytes .../benchmarks/NAV/nav_point.mat | Bin 0 -> 1119768 bytes .../ARCH-COMP2024/benchmarks/NAV/nav_set.mat | Bin 0 -> 443488 bytes .../benchmarks/NAV/reach_point.m | 96 +++- .../ARCH-COMP2024/benchmarks/NAV/reach_set.m | 72 ++- .../Reshape_To_ReshapeLayer1015.m | 460 ++++++++++++++++++ .../VNN_COMP2024/convert_scoring_networks.m | 294 ----------- .../VNN_COMP2024/run_all_instances_from_csv.m | 37 ++ .../VNN_COMP2024/run_vnncomp2024_instance.m | 193 +++++--- .../test_extendedTrack_instances.m | 42 +- .../VNN_COMP2024/test_mainTrack_instances.m | 202 ++++++-- 14 files changed, 928 insertions(+), 468 deletions(-) create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/nav-point.pdf create mode 100644 code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/nav-set.pdf create mode 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100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_point.m @@ -1,4 +1,4 @@ -function t = reach_point() +% function rT = reach_point() %% Reachability analysis of NAV Benchmark @@ -7,69 +7,115 @@ % Load the controller net = importNetworkFromONNX('networks/nn-nav-point.onnx', "InputDataFormats", "BC"); net = matlab2nnv(net); + % net.InputSize = 4; + % Load plant - reachStep = 0.01; - controlPeriod = 0.02; + reachStep = 0.02; + controlPeriod = 0.2; plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); plant.set_tensorOrder(2); + % Create NNCS + % nncs = NonlinearNNCS(net,plant); %% Reachability analysis % Initial set - lb = [2.9; 2.9; 0; 0]; - ub = [3.1; 3.1; 0; 0]; + lb = [2.999; 2.999; 0; 0]; + ub = [3.0; 3.0; 0; 0]; init_set = Star(lb,ub); % Store all reachable sets reachAll = init_set; % Reachability options - num_steps = 30; + num_steps = 25; reachOptions.reachMethod = 'approx-star'; + % Reachabilty analysis + % reachPRM.ref_input = input_ref; + % reachPRM.numSteps = 30; + % reachPRM.init_set = init_set; + % reachPRM.numCores = 1; + % reachPRM.reachMethod = 'approx-star'; + % [R,rT] = nncs.reach(reachPRM); + % Got an error from mpt when using Gurobi... + % Checking GUROBI license ... Did not find an environment variable GRB_LICENSE_FILE with GUROBI license. + % + % Error using optimset (line 177) + % Invalid default value for property 'modules' in class 'mptopt': + % No default options available for the function 'linprog'. + % + % Error in mpt_solvers_options (line 601) + % options.linprog = optimset('linprog'); + % + % Error in mpt_subModules (line 28) + % options.(modules_list(i).name) = feval(f); + % + % Error in Polyhedron (line 264) + % MPTOPTIONS = mptopt; + % + % Error in NonlinearNNCS/nextInputSetStar (line 287) + % I1 = Polyhedron('lb', lb, 'ub', ub); + % + % Error in NonlinearNNCS/reach (line 183) + % input_set = obj.nextInputSetStar(fb_I{1}); + % + % Error in reach_point (line 41) + % [R,rT] = nncs.reach(reachPRM); + % Begin computation t = tic; for i=1:num_steps - disp(i); + % disp(i); % Compute controller output set input_set = net.reach(init_set,reachOptions); % Compute plant reachable set - init_set = plant.stepReachStar(init_set, input_set,'lin'); + init_set = plant.stepReachStar(init_set, input_set, 'poly'); reachAll = [reachAll init_set]; - toc(t); + % toc(t); end - t = toc(t); + rT = toc(t); + + R = reachAll; + + % Save results + if is_codeocean + save('/results/logs/nav_point.mat', 'R','rT','-v7.3'); + else + save('nav_point.mat', 'R','rT','-v7.3'); + end %% Visualize results plant.get_interval_sets; - f2 = figure; - % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + f = figure; + rectangle('Position',[-0.5,-0.5,1,1],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1); % goal region hold on; + rectangle('Position',[1,1,1,1],'FaceColor',[0.7 0 0 0.8], 'EdgeColor','r', 'LineWidth', 0.1); % obstacle Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,1,2,'b'); grid; xlabel('x_1'); ylabel('x_2'); - f5 = figure; - % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) - hold on; - Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); - grid; - xlabel('x_3'); - ylabel('x_4'); + % f5 = figure; + % % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + % hold on; + % Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); + % grid; + % xlabel('x_3'); + % ylabel('x_4'); % Save figure - % if is_codeocean - % - % else - % - % end + if is_codeocean + exportgraphics(f,'/results/logs/nav-point.pdf', 'ContentType', 'vector'); + else + exportgraphics(f,'nav-point.pdf','ContentType', 'vector'); + end - end \ No newline at end of file + % end \ No newline at end of file diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m index ea16bbe2ae..20de7d8755 100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/NAV/reach_set.m @@ -1,4 +1,4 @@ -function t = reach_set() +% function rT = reach_set() %% Reachability analysis of NAV Benchmark @@ -8,24 +8,27 @@ net = importNetworkFromONNX('networks/nn-nav-set.onnx', "InputDataFormats", "BC"); net = matlab2nnv(net); % Load plant - reachStep = 0.01; - controlPeriod = 0.02; + reachStep = 0.02; + controlPeriod = 0.2; plant = NonLinearODE(4, 2, @dynamics, reachStep, controlPeriod, eye(4)); - plant.set_tensorOrder(2); + plant.options.zonotopeOrder = 160; + plant.options.intermediateOrder = 200; + plant.options.taylorTerms = 3; + plant.set_tensorOrder(3); %% Reachability analysis % Initial set - lb = [2.9; 2.9; 0; 0]; - ub = [3.1; 3.1; 0; 0]; + lb = [2.999; 2.999; 0; 0]; + ub = [3.0; 3.0; 0; 0]; init_set = Star(lb,ub); % Store all reachable sets reachAll = init_set; % Reachability options - num_steps = 30; + num_steps = 28; reachOptions.reachMethod = 'approx-star'; % Begin computation @@ -34,40 +37,59 @@ % Compute controller output set input_set = net.reach(init_set,reachOptions); + + % input_set = input_set.getBox; + % input_set = Star.get_hypercube_hull(input_set); + % input_set = input_set.toStar; % Compute plant reachable set - init_set = plant.stepReachStar(init_set, input_set,'lin'); + init_set = plant.stepReachStar(init_set, input_set,'lin-adaptive'); + % init_set = Star.get_hypercube_hull(init_set); + % init_set = init_set.toStar; + reachAll = [reachAll init_set]; end - t = toc(t); + rT = toc(t); + + R = reachAll; + + % Save results + if is_codeocean + save('/results/logs/nav_set.mat', 'R','rT','-v7.3'); + else + save('nav_set.mat', 'R','rT','-v7.3'); + end %% Visualize results - plant.get_interval_sets; + plant.get_point_sets; - f1 = figure; - % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + f = figure; + rectangle('Position',[-0.5,-0.5,1,1],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth', 0.1); % goal region hold on; - Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,1,2,'b'); + rectangle('Position',[1,1,1,1],'FaceColor',[0.7 0 0 0.8], 'EdgeColor','r', 'LineWidth', 0.1); % obstacle + Star.plotBoxes_2D_noFill(plant.intermediate_pointSet,1,2,'b'); grid; xlabel('x_1'); ylabel('x_2'); - f2 = figure; - % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) - hold on; - Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); - grid; - xlabel('x_3'); - ylabel('x_4'); + % f5 = figure; + % % rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) + % hold on; + % Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,3,4,'b'); + % grid; + % xlabel('x_3'); + % ylabel('x_4'); % Save figure - % if is_codeocean - % - % else - % + if is_codeocean + exportgraphics(f,'/results/logs/nav-set.pdf', 'ContentType', 'vector'); + else + exportgraphics(f,'nav-set.pdf','ContentType', 'vector'); + end + % end -end \ No newline at end of file +% end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m b/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m new file mode 100644 index 0000000000..819caf98c8 --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m @@ -0,0 +1,460 @@ +classdef Reshape_To_ReshapeLayer1015 < nnet.layer.Layer & nnet.layer.Formattable + % A custom layer auto-generated while importing an ONNX network. + + %#codegen + %#ok<*PROPLC> + %#ok<*NBRAK> + %#ok<*INUSL> + %#ok<*VARARG> + + properties (Learnable) + end + + properties + ONNXParams % An ONNXParameters object containing parameters used by this layer. + end + + methods + function this = Reshape_To_ReshapeLayer1015(name, onnxParams) + this.Name = name; + this.OutputNames = {'cls_onnx__Gemm_8'}; + this.ONNXParams = onnxParams; + end + + function [cls_onnx__Gemm_8] = predict(this, onnx__Reshape_12) + if isdlarray(onnx__Reshape_12) + onnx__Reshape_12 = stripdims(onnx__Reshape_12); + end + onnx__Reshape_12NumDims = 2; + onnxParams = this.ONNXParams; + [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, onnxParams, 'Training', false, ... + 'InputDataPermutation', {[2 1], ['as-is']}, ... + 'OutputDataPermutation', {[2 1], ['as-is']}); + if any(cellfun(@(A)~isnumeric(A) && ~islogical(A), {cls_onnx__Gemm_8})) + fprintf('Runtime error in network. At least one output of custom layer ''%s'' is a non-numeric, non-logical value.\n', 'Reshape_To_ReshapeLayer1015'); + error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ReshapeLayer1015')); + end + cls_onnx__Gemm_8 = dlarray(single(cls_onnx__Gemm_8), 'CB'); + if ~coder.target('MATLAB') + cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); + end + end + + function [cls_onnx__Gemm_8] = forward(this, onnx__Reshape_12) + if isdlarray(onnx__Reshape_12) + onnx__Reshape_12 = stripdims(onnx__Reshape_12); + end + onnx__Reshape_12NumDims = 2; + onnxParams = this.ONNXParams; + [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, onnxParams, 'Training', true, ... + 'InputDataPermutation', {[2 1], ['as-is']}, ... + 'OutputDataPermutation', {[2 1], ['as-is']}); + if any(cellfun(@(A)~isnumeric(A) && ~islogical(A), {cls_onnx__Gemm_8})) + fprintf('Runtime error in network. At least one output of custom layer ''%s'' is a non-numeric, non-logical value.\n', 'Reshape_To_ReshapeLayer1015'); + error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ReshapeLayer1015')); + end + cls_onnx__Gemm_8 = dlarray(single(cls_onnx__Gemm_8), 'CB'); + if ~coder.target('MATLAB') + cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); + end + end + end +end + +function [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims, state] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, params, varargin) +%RESHAPE_TO_RESHAPEFCN Function implementing an imported ONNX network. +% +% THIS FILE WAS AUTO-GENERATED BY importONNXFunction. +% ONNX Operator Set Version: 11 +% +% Variable names in this function are taken from the original ONNX file. +% +% [CLS_ONNX__GEMM_8] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS) +% - Evaluates the imported ONNX network RESHAPE_TO_RESHAPEFCN with input(s) +% ONNX__RESHAPE_12 and the imported network parameters in PARAMS. Returns +% network output(s) in CLS_ONNX__GEMM_8. +% +% [CLS_ONNX__GEMM_8, STATE] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS) +% - Additionally returns state variables in STATE. When training, +% use this form and set TRAINING to true. +% +% [__] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS, 'NAME1', VAL1, 'NAME2', VAL2, ...) +% - Specifies additional name-value pairs described below: +% +% 'Training' +% Boolean indicating whether the network is being evaluated for +% prediction or training. If TRAINING is true, state variables +% will be updated. +% +% 'InputDataPermutation' +% 'auto' - Automatically attempt to determine the permutation +% between the dimensions of the input data and the dimensions of +% the ONNX model input. For example, the permutation from HWCN +% (MATLAB standard) to NCHW (ONNX standard) uses the vector +% [4 3 1 2]. See the documentation for IMPORTONNXFUNCTION for +% more information about automatic permutation. +% +% 'none' - Input(s) are passed in the ONNX model format. See 'Inputs'. +% +% numeric vector - The permutation vector describing the +% transformation between input data dimensions and the expected +% ONNX input dimensions.% +% cell array - If the network has multiple inputs, each cell +% contains 'auto', 'none', or a numeric vector. +% +% 'OutputDataPermutation' +% 'auto' - Automatically attempt to determine the permutation +% between the dimensions of the output and a conventional MATLAB +% dimension ordering. For example, the permutation from NC (ONNX +% standard) to CN (MATLAB standard) uses the vector [2 1]. See +% the documentation for IMPORTONNXFUNCTION for more information +% about automatic permutation. +% +% 'none' - Return output(s) as given by the ONNX model. See 'Outputs'. +% +% numeric vector - The permutation vector describing the +% transformation between the ONNX output dimensions and the +% desired output dimensions.% +% cell array - If the network has multiple outputs, each cell +% contains 'auto', 'none' or a numeric vector. +% +% Inputs: +% ------- +% ONNX__RESHAPE_12 +% - Input(s) to the ONNX network. +% The input size(s) expected by the ONNX file are: +% ONNX__RESHAPE_12: [Unknown, Unknown] Type: FLOAT +% By default, the function will try to permute the input(s) +% into this dimension ordering. If the default is incorrect, +% use the 'InputDataPermutation' argument to control the +% permutation. +% +% +% PARAMS - Network parameters returned by 'importONNXFunction'. +% +% +% Outputs: +% -------- +% CLS_ONNX__GEMM_8 +% - Output(s) of the ONNX network. +% Without permutation, the size(s) of the outputs are: +% CLS_ONNX__GEMM_8: [Unknown, Unknown] Type: FLOAT +% By default, the function will try to permute the output(s) +% from this dimension ordering into a conventional MATLAB +% ordering. If the default is incorrect, use the +% 'OutputDataPermutation' argument to control the permutation. +% +% STATE - (Optional) State variables. When TRAINING is true, these will +% have been updated from the original values in PARAMS.State. +% +% +% See also importONNXFunction + +% Preprocess the input data and arguments: +[onnx__Reshape_12, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_12, params, varargin{:}); +% Put all variables into a single struct to implement dynamic scoping: +[Vars, NumDims] = packageVariables(params, {'onnx__Reshape_12'}, {onnx__Reshape_12}, [onnx__Reshape_12NumDims]); +% Call the top-level graph function: +[cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims, state] = Reshape_To_ReshapeGraph1012(onnx__Reshape_12, NumDims.onnx__Reshape_12, Vars, NumDims, Training, params.State); +% Postprocess the output data +[cls_onnx__Gemm_8] = postprocessOutput(cls_onnx__Gemm_8, outputDataPerms, anyDlarrayInputs, Training, varargin{:}); +end + +function [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims1014, state] = Reshape_To_ReshapeGraph1012(onnx__Reshape_12, onnx__Reshape_12NumDims1013, Vars, NumDims, Training, state) +% Function implementing the graph 'Reshape_To_ReshapeGraph1012' +% Update Vars and NumDims from the graph's formal input parameters. Note that state variables are already in Vars. +Vars.onnx__Reshape_12 = onnx__Reshape_12; +NumDims.onnx__Reshape_12 = onnx__Reshape_12NumDims1013; + +% Execute the operators: +% Reshape: +[shape, NumDims.output] = prepareReshapeArgs(Vars.onnx__Reshape_12, Vars.onnx__Reshape_13, NumDims.onnx__Reshape_12, 0); +Vars.output = reshape(Vars.onnx__Reshape_12, shape{:}); + +% Reshape: +[shape, NumDims.cls_onnx__Gemm_8] = prepareReshapeArgs(Vars.output, Vars.cls_onnx__Reshape_7, NumDims.output, 0); +Vars.cls_onnx__Gemm_8 = reshape(Vars.output, shape{:}); + +% Set graph output arguments from Vars and NumDims: +cls_onnx__Gemm_8 = Vars.cls_onnx__Gemm_8; +cls_onnx__Gemm_8NumDims1014 = NumDims.cls_onnx__Gemm_8; +% Set output state from Vars: +state = updateStruct(state, Vars); +end + +function [inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_12, numDataOutputs, params, varargin) +% Function to validate inputs to Reshape_To_ReshapeFcn: +p = inputParser; +isValidArrayInput = @(x)isnumeric(x) || isstring(x); +isValidONNXParameters = @(x)isa(x, 'ONNXParameters'); +addRequired(p, 'onnx__Reshape_12', isValidArrayInput); +addRequired(p, 'params', isValidONNXParameters); +addParameter(p, 'InputDataPermutation', 'auto'); +addParameter(p, 'OutputDataPermutation', 'auto'); +addParameter(p, 'Training', false); +parse(p, onnx__Reshape_12, params, varargin{:}); +inputDataPerms = p.Results.InputDataPermutation; +outputDataPerms = p.Results.OutputDataPermutation; +Training = p.Results.Training; +if isnumeric(inputDataPerms) + inputDataPerms = {inputDataPerms}; +end +if isstring(inputDataPerms) && isscalar(inputDataPerms) || ischar(inputDataPerms) + inputDataPerms = repmat({inputDataPerms},1,1); +end +if isnumeric(outputDataPerms) + outputDataPerms = {outputDataPerms}; +end +if isstring(outputDataPerms) && isscalar(outputDataPerms) || ischar(outputDataPerms) + outputDataPerms = repmat({outputDataPerms},1,numDataOutputs); +end +end + +function [onnx__Reshape_12, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_12, params, varargin) +% Parse input arguments +[inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_12, 1, params, varargin{:}); +anyDlarrayInputs = any(cellfun(@(x)isa(x, 'dlarray'), {onnx__Reshape_12})); +% Make the input variables into unlabelled dlarrays: +onnx__Reshape_12 = makeUnlabeledDlarray(onnx__Reshape_12); +% Permute inputs if requested: +onnx__Reshape_12 = permuteInputVar(onnx__Reshape_12, inputDataPerms{1}, 2); +end + +function [cls_onnx__Gemm_8] = postprocessOutput(cls_onnx__Gemm_8, outputDataPerms, anyDlarrayInputs, Training, varargin) +% Set output type: +if ~anyDlarrayInputs && ~Training + if isdlarray(cls_onnx__Gemm_8) + cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); + end +end +% Permute outputs if requested: +cls_onnx__Gemm_8 = permuteOutputVar(cls_onnx__Gemm_8, outputDataPerms{1}, 2); +end + + +%% dlarray functions implementing ONNX operators: + +function [DLTShape, numDimsY] = prepareReshapeArgs(X, ONNXShape, numDimsX, allowzero) +% Prepares arguments for implementing the ONNX Reshape operator +ONNXShape = flip(extractdata(ONNXShape)); % First flip the shape to make it correspond to the dimensions of X. +% In ONNX, 0 means "unchanged" if allowzero is false, and -1 means "infer". In DLT, there is no +% "unchanged", and [] means "infer". +DLTShape = num2cell(ONNXShape); % Make a cell array so we can include []. +% Replace zeros with the actual size if allowzero is true +if any(ONNXShape==0) && allowzero==0 + i0 = find(ONNXShape==0); + DLTShape(i0) = num2cell(size(X, numDimsX - numel(ONNXShape) + i0)); % right-align the shape vector and dims +end +if any(ONNXShape == -1) + % Replace -1 with [] + i = ONNXShape == -1; + DLTShape{i} = []; +end +if numel(DLTShape)==1 + DLTShape = [DLTShape 1]; +end +numDimsY = numel(ONNXShape); +end + +%% Utility functions: + +function s = appendStructs(varargin) +% s = appendStructs(s1, s2,...). Assign all fields in s1, s2,... into s. +if isempty(varargin) + s = struct; +else + s = varargin{1}; + for i = 2:numel(varargin) + fromstr = varargin{i}; + fs = fieldnames(fromstr); + for j = 1:numel(fs) + s.(fs{j}) = fromstr.(fs{j}); + end + end +end +end + +function checkInputSize(inputShape, expectedShape, inputName) + +if numel(expectedShape)==0 + % The input is a scalar + if ~isequal(inputShape, [1 1]) + inputSizeStr = makeSizeString(inputShape); + error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, "[1,1]", inputSizeStr)); + end +elseif numel(expectedShape)==1 + % The input is a vector + if ~shapeIsColumnVector(inputShape) || ~iSizesMatch({inputShape(1)}, expectedShape) + expectedShape{2} = 1; + expectedSizeStr = makeSizeString(expectedShape); + inputSizeStr = makeSizeString(inputShape); + error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr)); + end +else + % The input has 2 dimensions or more + + % The input dimensions have been reversed; flip them back to compare to the + % expected ONNX shape. + inputShape = fliplr(inputShape); + + % If the expected shape has fewer dims than the input shape, error. + if numel(expectedShape) < numel(inputShape) + expectedSizeStr = strjoin(["[", strjoin(string(expectedShape), ","), "]"], ""); + error(message('nnet_cnn_onnx:onnx:InputHasGreaterNDims', inputName, expectedSizeStr)); + end + + % Prepad the input shape with trailing ones up to the number of elements in + % expectedShape + inputShape = num2cell([ones(1, numel(expectedShape) - length(inputShape)) inputShape]); + + % Find the number of variable size dimensions in the expected shape + numVariableInputs = sum(cellfun(@(x) isa(x, 'char') || isa(x, 'string'), expectedShape)); + + % Find the number of input dimensions that are not in the expected shape + % and cannot be represented by a variable dimension + nonMatchingInputDims = setdiff(string(inputShape), string(expectedShape)); + numNonMatchingInputDims = numel(nonMatchingInputDims) - numVariableInputs; + + expectedSizeStr = makeSizeString(expectedShape); + inputSizeStr = makeSizeString(inputShape); + if numNonMatchingInputDims == 0 && ~iSizesMatch(inputShape, expectedShape) + % The actual and expected input dimensions match, but in + % a different order. The input needs to be permuted. + error(message('nnet_cnn_onnx:onnx:InputNeedsPermute',inputName, expectedSizeStr, inputSizeStr)); + elseif numNonMatchingInputDims > 0 + % The actual and expected input sizes do not match. + error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr)); + end +end +end + +function doesMatch = iSizesMatch(inputShape, expectedShape) +% Check whether the input and expected shapes match, in order. +% Size elements match if (1) the elements are equal, or (2) the expected +% size element is a variable (represented by a character vector or string) +doesMatch = true; +for i=1:numel(inputShape) + if ~(isequal(inputShape{i},expectedShape{i}) || ischar(expectedShape{i}) || isstring(expectedShape{i})) + doesMatch = false; + return + end +end +end + +function sizeStr = makeSizeString(shape) +sizeStr = strjoin(["[", strjoin(string(shape), ","), "]"], ""); +end + +function isVec = shapeIsColumnVector(shape) +if numel(shape) == 2 && shape(2) == 1 + isVec = true; +else + isVec = false; +end +end +function X = makeUnlabeledDlarray(X) +% Make numeric X into an unlabelled dlarray +if isa(X, 'dlarray') + X = stripdims(X); +elseif isnumeric(X) + if isinteger(X) + % Make ints double so they can combine with anything without + % reducing precision + X = double(X); + end + X = dlarray(X); +end +end + +function [Vars, NumDims] = packageVariables(params, inputNames, inputValues, inputNumDims) +% inputNames, inputValues are cell arrays. inputRanks is a numeric vector. +Vars = appendStructs(params.Learnables, params.Nonlearnables, params.State); +NumDims = params.NumDimensions; +% Add graph inputs +for i = 1:numel(inputNames) + Vars.(inputNames{i}) = inputValues{i}; + NumDims.(inputNames{i}) = inputNumDims(i); +end +end + +function X = permuteInputVar(X, userDataPerm, onnxNDims) +% Returns reverse-ONNX ordering +if onnxNDims == 0 + return; +elseif onnxNDims == 1 && isvector(X) + X = X(:); + return; +elseif isnumeric(userDataPerm) + % Permute into reverse ONNX ordering + if numel(userDataPerm) ~= onnxNDims + error(message('nnet_cnn_onnx:onnx:InputPermutationSize', numel(userDataPerm), onnxNDims)); + end + perm = fliplr(userDataPerm); +elseif isequal(userDataPerm, 'auto') && onnxNDims == 4 + % Permute MATLAB HWCN to reverse onnx (WHCN) + perm = [2 1 3 4]; +elseif isequal(userDataPerm, 'as-is') + % Do not permute the input + perm = 1:ndims(X); +else + % userDataPerm is either 'none' or 'auto' with no default, which means + % it's already in onnx ordering, so just make it reverse onnx + perm = max(2,onnxNDims):-1:1; +end +X = permute(X, perm); +end + +function Y = permuteOutputVar(Y, userDataPerm, onnxNDims) +switch onnxNDims + case 0 + perm = []; + case 1 + if isnumeric(userDataPerm) + % Use the user's permutation because Y is a column vector which + % already matches ONNX. + perm = userDataPerm; + elseif isequal(userDataPerm, 'auto') + % Treat the 1D onnx vector as a 2D column and transpose it + perm = [2 1]; + else + % userDataPerm is 'none'. Leave Y alone because it already + % matches onnx. + perm = []; + end + otherwise + % ndims >= 2 + if isnumeric(userDataPerm) + % Use the inverse of the user's permutation. This is not just the + % flip of the permutation vector. + perm = onnxNDims + 1 - userDataPerm; + elseif isequal(userDataPerm, 'auto') + if onnxNDims == 2 + % Permute reverse ONNX CN to DLT CN (do nothing) + perm = []; + elseif onnxNDims == 4 + % Permute reverse onnx (WHCN) to MATLAB HWCN + perm = [2 1 3 4]; + else + % User wants the output in ONNX ordering, so just reverse it from + % reverse onnx + perm = onnxNDims:-1:1; + end + elseif isequal(userDataPerm, 'as-is') + % Do not permute the input + perm = 1:ndims(Y); + else + % userDataPerm is 'none', so just make it reverse onnx + perm = onnxNDims:-1:1; + end +end +if ~isempty(perm) + Y = permute(Y, perm); +end +end + +function s = updateStruct(s, t) +% Set all existing fields in s from fields in t, ignoring extra fields in t. +for name = transpose(fieldnames(s)) + s.(name{1}) = t.(name{1}); +end +end diff --git a/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m b/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m deleted file mode 100644 index 165159d751..0000000000 --- a/code/nnv/examples/Submission/VNN_COMP2024/convert_scoring_networks.m +++ /dev/null @@ -1,294 +0,0 @@ -%% Run some verification instances from the competition - -% path to benchmarks -vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; -% vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; - -% Go through some of the instances for every benchmark -% It looks like unless the networks are fully fully supported, we get errors... -% Same problem we ran into when running with no GUI ... -% -% Error using matlab.internal.indentcodeLegacy -%This feature is not supported because: -%Swing is not currently available. -% -%Error in indentcode (line 42) -% indentedText = matlab.internal.indentcodeLegacy(text, language); -% -%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation/genOutputFcn (line 100) -% code = indentcode(code); % Indent the code -% -%Error in nnet.internal.cnn.onnx.fcn.ModelTranslation (line 39) -% this.ModelFunctionCode = genOutputFcn(this, outputFcnName, this.ToplevelGraphTranslation, isGeneratingCustomLayerOPTIONAL); -% -%Error in nnet.internal.cnn.onnx.CustomLayerManager/genCustomLayerFunction (line 499) -% modelTranslation = nnet.internal.cnn.onnx.fcn.ModelTranslation(modelProto, modelFcnPath, modelFcnName, maxNameLength, true); -% -%Error in nnet.internal.cnn.onnx.CustomLayerManager (line 77) -% this.ModelTranslations(i) = genCustomLayerFunction(this, this.ModelProtos(i), nameStem); -% -%Error in nnet.internal.cnn.onnx.ModelTranslationIntoLayers (line 228) -% this.CustomLayerManager = %nnet.internal.cnn.onnx.CustomLayerManager(this.GraphProtoManager, ... -% -%Error in nnet.internal.cnn.onnx.importONNXNetwork (line 32) -%modelTranslationIntoLayers = nnet.internal.cnn.onnx.ModelTranslationIntoLayers(modelProto, ... -% -%Error in importONNXNetwork (line 113) -%Network = nnet.internal.cnn.onnx.importONNXNetwork(modelfile, varargin{:}); -% -%Error in run_vnncomp2023_instance>load_vnncomp_network (line 127) -% net = importONNXNetwork(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape - -if ~isfolder("networks2023") - mkdir("networks2023") -end - - -%% acasxu - -disp("Running acas xu...") - -acas_path = vnncomp_path + "acasxu/onnx/"; - -onnx = dir(acas_path); - -for i=3:length(onnx) - onnxFile = acas_path + onnx(i).name; - net = load_vnncomp_network('acasxu', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% ViT - -disp("Running ViT...") - -vit_path = vnncomp_path + "vit/onnx/"; - -onnx = dir(vit_path); - -for i=3:length(onnx) - onnxFile = vit_path + onnx(i).name; - net = load_vnncomp_network('vit', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - - -%% nn4sys - -disp("Running nn4sys...") - -nn4sys_path = vnncomp_path + "nn4sys/onnx/"; - -onnx = dir(nn4sys_path); - -for i=3:length(onnx) - onnxFile = nn4sys_path + onnx(i).name; - if contains(onnxFile, "lindex") - net = load_vnncomp_network('nn4sys', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); - end -end - - -%% dist_shift - -disp("Running dist_shift...") - -dist_path = vnncomp_path + "dist_shift/onnx/"; - -onnxFile = dist_path + "mnist_concat.onnx"; -net = load_vnncomp_network('dist_shift', onnxFile); -name = 'mnist_concat.onnx'; -name = name(1:end-5); -save(['networks2023/', name], "net", "-v7.3"); - - -%% traffic_sign -% we should be able to support it, but matlab does not, so we would have to create the models manually... -% can load most info with Keras importer - -%% collins_rul - -disp("Running collins_rul..") - -rul_path = vnncomp_path + "collins_rul_cnn/onnx/"; - -onnx = dir(rul_path); - -for i=3:length(onnx) - onnxFile = rul_path + onnx(i).name; - net = load_vnncomp_network('collins_rul_cnn', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% cgan - -disp("Running cgan..") - -cgan_path = vnncomp_path + "cgan/onnx/"; - -onnx = dir(cgan_path); - -for i=3:length(onnx) - onnxFile = cgan_path + onnx(i).name; - if ~contains(onnxFile, 'transformer') - net = load_vnncomp_network('cgan', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); - end -end - -%% vggnet16 - -disp("Running vggnet16...") - -vgg_path = vnncomp_path + "vggnet16/onnx/"; - -vgg_instances = ["onnx/vgg16-7.onnx","vnnlib/spec0_screw.vnnlib"]; - -onnxFile = vgg_path + "vgg16-7.onnx"; -net = load_vnncomp_network('vggnet16', onnxFile); -name = 'vgg16-7.onnx'; -name = name(1:end-5); -save(['networks2023/', name], "net", "-v7.3"); - - -%% ml4acopf - -disp("Running ml4acopf..") - -ml4_path = vnncomp_path + "ml4acopf/onnx/"; - -onnx = dir(ml4_path); - -for i=3:length(onnx) - onnxFile = ml4_path + onnx(i).name; - net = load_vnncomp_network('ml4acopf', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - - -%% tllverify - -disp("Running tllverify..") - -tll_path = vnncomp_path + "tllverifybench/onnx/"; - -onnx = dir(tll_path); - -for i=3:length(onnx) - onnxFile = tll_path + onnx(i).name; - net = load_vnncomp_network('tllverifybench', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - -%% Other 2023 benchmarks - -%% test - -% disp("Running test examples..."); -% -% test_path = vnncomp_path + "test/"; -% -% onnxmodels = ["test_nano.onnx", "test_sat.onnx", "test_small.onnx", "test_tiny.onnx", "test_unsat.onnx"]; -% -% for i = 1:length(onnxmodels) -% if contains(onnxmodels(i), "sat") -% onnxFile = test_path + onnxmodels(i); -% net = importONNXNetwork(onnxFile, "InputDataFormats", "BCSS"); -% name = char(onnxmodels(i)); -% name = name(1:end-5); -% save(['networks2023/', name], "net", "-v7.3"); -% end -% end - - -%% yolo - -disp("Running yolo..."); - -yolo_path = vnncomp_path + "yolo/onnx/"; - -onnx = dir(yolo_path); - -for i = 3:length(onnx) - onnxFile = yolo_path + onnx(i).name; - net = load_vnncomp_network('yolo', onnxFile); - name = onnx(i).name; - name = name(1:end-5); - save(['networks2023/', name], "net", "-v7.3"); -end - - -%% Loading function -function net = load_vnncomp_network(category, onnx) -% load vnncomp 2023 benchmark NNs (subset support) - - % collins_rul: onnx to nnvnet - % collins_nets = load_collins_NNs; - if contains(category, 'collins_rul') - net = importNetworkFromONNX(onnx); - - elseif contains(category, "nn4sys") - % nn4sys: onnx to matlab: - net = importNetworkFromONNX(onnx, "OutputDataFormats", "BC"); % lindex - - elseif contains(category, "dist_shift") - % dist_shift: onnx to matlab: - net = importNetworkFromONNX(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape - - elseif contains(category, "cgan") - % cgan - net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape - - elseif contains(category, "vgg") - % vgg16: onnx to matlab - net = importNetworkFromONNX(onnx); % flattenlayer - - elseif contains(category, "tllverify") - % tllverify: onnx to matlab - net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); - - elseif contains(category, "vit") - % vit: onnx to matlab - net = importNetworkFromONNX(onnx ); - - elseif contains(category, "cctsdb_yolo") - % cctsdb_yolo: onnx to matnet - net = importNetworkFromONNX(onnx); - - elseif contains(category, "collins_yolo") - % collins_yolo: onnx to matlab: - net = importNetworkFromONNX(onnx); - - elseif contains(category, "yolo") - % yolo: onnx to matlab - net = importNetworkFromONNX(onnx); % padlayer - - elseif contains(category, "acasxu") - % acasxu: onnx to nnv - net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS"); - - elseif contains(category, "ml4acopf") - % ml4acopf: ? - net = importNetworkFromONNX(onnx, "InputDataFormats", "BC"); - - else % all other benchmarks - % traffic: onnx to matlab: opset15 issues - error("ONNX model not supported") - end - -end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/run_all_instances_from_csv.m b/code/nnv/examples/Submission/VNN_COMP2024/run_all_instances_from_csv.m new file mode 100644 index 0000000000..d21a17660f --- /dev/null +++ b/code/nnv/examples/Submission/VNN_COMP2024/run_all_instances_from_csv.m @@ -0,0 +1,37 @@ +%% Run as many benchmarks as possible from 2023 + +vnncomp_path = "C:\Users\diego\Documents\Research\vnncomp2023_benchmarks\benchmarks\"; + +benchmarks = dir(vnncomp_path); + +notSupported = {'test'; 'traffic_signs_recognition'; 'cctsdb_yolo'}; % skip for now + +for i=4:length(benchmarks) + + if ~contains(notSupported, benchmarks(i).name) % skip evaluation of benchmarks not supported + + benchpath = vnncomp_path + benchmarks(i).name; + + instances = readmatrix(benchpath + "/instances.csv", "OutputType","string", "Delimiter",","); + + results_dir = "results_approx_" + benchmarks(i).name; + mkdir(results_dir); + + for k=1:size(instances,1) + + onnx = benchpath + filesep + instances(k,1); + vnnlib = benchpath + filesep + instances(k,2); + + try + run_vnncomp2024_instance(benchmarks(i).name,onnx,vnnlib, results_dir + "/instance_" + string(k)+".txt"); + catch ME + fid = fopen(results_dir + "/instance_" + string(k)+".txt", 'w'); + fprintf(fid, [ME.message '\n']); + fclose(fid); + end + + end + + end + +end \ No newline at end of file diff --git a/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m index c879027828..1903521ac4 100644 --- a/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m +++ b/code/nnv/examples/Submission/VNN_COMP2024/run_vnncomp2024_instance.m @@ -1,4 +1,4 @@ -function [status, vTime] = run_vnncomp2024_instance(category, onnx, vnnlib, outputfile) +function [status, tTime] = run_vnncomp2024_instance(category, onnx, vnnlib, outputfile) % single script to run all instances (supported) from the vnncomp2024 @@ -57,7 +57,7 @@ warning("Working on adding support to other vnnlib properties"); end -toc(t); +cEX_time = toc(t); %% 3) UNSAT? @@ -92,6 +92,7 @@ reachOptions = struct; reachOptions.reachMethod = 'approx-star'; +% reachOptions.reachMethod = 'exact-star'; % Check if property was violated earlier @@ -99,26 +100,15 @@ status = 0; end +vT = tic; + if status == 2 && isa(nnvnet, "NN") % no counterexample found and supported for reachability (otherwise, skip step 3 and write results) % Choose how to verify based on vnnlib file if ~isa(lb, "cell") && length(prop) == 1 % one input, one output - - % Get input bounds - if ~isscalar(inputSize) - lb = reshape(lb, inputSize); - ub = reshape(ub, inputSize); - end - - % Format bounds into correct dimensions - if needReshape - lb = permute(lb, [2 1 3]); - ub = permute(ub, [2 1 3]); - end - - % Create input set - IS = ImageStar(lb, ub); + IS = create_input_set(lb, ub, inputSize, needReshape); + % Compute reachability ySet = nnvnet.reach(IS, reachOptions); @@ -130,21 +120,11 @@ local_status = ones(length(lb),1); % track status for each specification in the vnnlib parfor spc = 1:length(lb) % We can compute these in parallel for faster computation + + lb_spc = lb{spc}; + ub_spc = ub{spc}; - % Get input bounds - if ~isscalar(inputSize) - lb_spc = reshape(lb{spc}, inputSize); - ub_spc = reshape(ub{spc}, inputSize); - end - - % Format bounds into correct dimensions - if needReshape - lb_spc = permute(lb_spc, [2 1 3]); - ub_spc = permute(ub_spc, [2 1 3]); - end - - % Create input set - IS = ImageStar(lb_spc, ub_spc); + IS = create_input_set(lb_spc, ub_spc, inputSize, needReshape); % Compute reachability ySet = nnvnet.reach(IS, reachOptions); @@ -173,20 +153,10 @@ parfor spc = 1:length(lb) % We can compute these in parallel for faster computation - % Get input bounds - if ~isscalar(inputSize) - lb_spc = reshape(lb{spc}, inputSize); - ub_spc = reshape(ub{spc}, inputSize); - end - - % Format bounds into correct dimensions - if needReshape - lb_spc = permute(lb_spc, [2 1 3]); - ub_spc = permute(ub_spc, [2 1 3]); - end - - % Create input set - IS = ImageStar(lb_spc, ub_spc); + lb_spc = lb{spc}; + ub_spc = ub{spc}; + + IS = create_input_set(lb_spc, ub_spc, inputSize, needReshape); % Compute reachability ySet = nnvnet.reach(IS, reachOptions); @@ -209,27 +179,34 @@ end +vT = toc(vT); + %% 4) Process results -vTime = toc(t); % save total computation time +tTime = toc(t); % save total computation time -disp(status); -disp(vTime); +disp("Verification result: " + string(status)); +disp("Counterexample search time: " + string(cEX_time)); +disp("Reachability time: " + string(vT)); +disp("Total Time: "+ string(tTime)); disp( " "); % Write results to output file if status == 0 fid = fopen(outputfile, 'w'); fprintf(fid, 'sat \n'); + fprintf(fid, '%f \n', tTime); % remove this line when running on submission site fclose(fid); write_counterexample(outputfile, counterEx) elseif status == 1 fid = fopen(outputfile, 'w'); fprintf(fid, 'unsat \n'); + fprintf(fid, '%f \n', tTime); % remove this line when running on submission site fclose(fid); elseif status == 2 fid = fopen(outputfile, 'w'); fprintf(fid, 'unknown \n'); + fprintf(fid, '%f \n', tTime); % remove this line when running on submission site fclose(fid); end @@ -238,10 +215,41 @@ end +%% Helper functions + +function IS = create_input_set(lb, ub, inputSize, needReshape) + % Get input bounds + if ~isscalar(inputSize) + lb = reshape(lb, inputSize); + ub = reshape(ub, inputSize); + end + + % Format bounds into correct dimensions + if needReshape == 1 + lb = permute(lb, [2 1 3]); + ub = permute(ub, [2 1 3]); + elseif needReshape == 2 + newSize = [inputSize(2) inputSize(1) inputSize(3:end)]; + lb = reshape(lb, newSize); + lb = permute(lb, [2 1 3 4]); + ub = reshape(ub, newSize); + ub = permute(ub, [2 1 3 4]); + end + + % Create input set + % IS = ImageStar(lb, ub); % too many constraints, slower + i_diff = ub - lb; + V(:,:,:,1) = lb; % assume lb is center of set (instead of img) + V(:,:,:,2) = i_diff ; % basis vectors + C = [1; -1]; % constraints + d = [1; -1]; + IS = ImageStar(V, C, d, 0, 1); % input set + +end function [net,nnvnet,needReshape] = load_vnncomp_network(category, onnx) -% load vnncomp 2023 benchmark NNs +% load participating vnncomp 2024 benchmark NNs % % Regular Track Benchmarks % - 2024 @@ -289,7 +297,7 @@ elseif contains(category, "nn4sys") % nn4sys: onnx to matlab: if contains(onnx, "lindex") - % nn4sys: onnx to matlab: + % nn4sys: onnx to nnv net = importNetworkFromONNX(onnx, "OutputDataFormats", "BC"); % lindex nnvnet = matlab2nnv(net); else @@ -297,49 +305,56 @@ end elseif contains(category, "ml4acopf") - % ml4acopf: ? + % ml4acopf: onnx to matlab net = importNetworkFromONNX(onnx, "InputDataFormats", "BC"); nnvnet = ""; elseif contains(category, "dist_shift") - % dist_shift: onnx to matlab: + % dist_shift: onnx to matlab, , matlab to nnv? net = importNetworkFromONNX(onnx, "InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape - nnvnet = ""; + try + nnvnet = matlab2nnv(net); + catch + nnvnet = ""; + end elseif contains(category, "cgan") - % cgan + % cgan: onnx to nnv net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); %reshape nnvnet = matlab2nnv(net); - elseif contains(category, "vgg16") + elseif contains(category, "vggnet16") % vgg16: onnx to matlab net = importNetworkFromONNX(onnx); % flattenlayer nnvnet = ""; needReshape = 1; elseif contains(category, "tllverify") - % tllverify: onnx to matlab - net = importNetworkFromONNX((onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); + % tllverify: onnx to nnv + net = importNetworkFromONNX(onnx,"InputDataFormats", "BC", 'OutputDataFormats',"BC"); nnvnet = matlab2nnv(net); elseif contains(category, "vit") % vit: onnx to matlab - net = importNetworkFromONNX(onnx); + net = importNetworkFromONNX(onnx, "InputDataFormats", "BCSS", 'OutputDataFormats',"BC"); nnvnet = ""; needReshape= 1; elseif contains(category, "cctsdb_yolo") - % cctsdb_yolo: onnx to matnet - net = importNetworkFromONNX(onnx); - nnvnet = ""; + % cctsdb_yolo: onnx to matlab + error("We do not support this one"); + % net = importNetworkFromONNX(onnx); + % nnvnet = ""; + % needReshape = ?; + % We cannot support this one elseif contains(category, "collins_yolo") - % collins_yolo: onnx to matlab: - net = importNetworkFromONNX(onnx, "TargetNetwork","dlnetwork" ); + % collins_yolo: onnx to matlab + net = importNetworkFromONNX(onnx); nnvnet = ""; elseif contains(category, "yolo") - % yolo: onnx to matlab + % yolo: onnx to nnv net = importNetworkFromONNX(onnx); % padlayer nnvnet = matlab2nnv(net); @@ -348,6 +363,54 @@ net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS"); nnvnet = matlab2nnv(net); + elseif contains(category, "cifar100") + % cifar100: onnx to nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + needReshape = 1; + + elseif contains(category, "tinyimagenet") + % tinyimagenet: onnx to nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + % needReshpae = ? + + elseif contains(category, "LinearizeNN") + % LinerizeNN: onnx to nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BC", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + % needReshape = ? + + elseif contains(category, "safeNLP") + % safeNLP: onnx to nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BC", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + % needReshape = ? + + elseif contains(category, "cora") + % cora benchmark: onnx 2 nnv + net = importNetworkFromONNX(onnx, "InputDataFormats","BC", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + % needReshape = ? + + elseif contains(category, "LSNC") + % lyapunov benchmark: onnx to nnv (barely, some IR and opset version differences) + net = importNetworkFromONNX(onnx, "InputDataFormats","BC", "OutputDataFormats","BC"); + nnvnet = ""; + % needReshape = ? + + elseif contains(category, "generation") + % dynaroars/vnncomp-benchmark-generation + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + % needReshape = ? + + elseif contains(category, "metaroom") + % metaroom: onnx to matlab + net = importNetworkFromONNX(onnx, "InputDataFormats","BCSS", "OutputDataFormats","BC"); + nnvnet = matlab2nnv(net); + needReshape = 2; + else % all other benchmarks % traffic: onnx to matlab: opset15 issues error("ONNX model not supported") @@ -362,7 +425,7 @@ xRand = xB.sample(nR-2); xRand = [lb, ub, xRand]; if needReshape - if needReshape ==2 % for collins only (full_window_40) + if needReshape ==2 % for collins only (full_window_40) and metaroom newSize = [inputSize(2) inputSize(1) inputSize(3:end)]; xRand = reshape(xRand, [newSize nR]); xRand = permute(xRand, [2 1 3 4]); diff --git a/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m b/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m index d656b15f00..58c65071a7 100644 --- a/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m +++ b/code/nnv/examples/Submission/VNN_COMP2024/test_extendedTrack_instances.m @@ -1,8 +1,9 @@ %% Run some verification instances from the competition % path to benchmarks -vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % change to whatever local path vnncomp benchmarks are stored +% vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % change to whatever local path vnncomp benchmarks are stored % vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; +vnncomp_path = "C:\Users\diego\Documents\Research\vnncomp2023_benchmarks\benchmarks\"; %% ViT @@ -12,8 +13,8 @@ vit_path = vnncomp_path + "vit/"; vit_instances = [... - "onnx/ibp_3_3_8.onnx" , "vnnlib/ibp_3_3_8_3850.vnnlib";... - "onnx/pgd_2_3_16.onnx","vnnlib/pgd_2_3_16_1744.vnnlib";... + "onnx/ibp_3_3_8.onnx" , "vnnlib/ibp_3_3_8_9119.vnnlib";... + "onnx/pgd_2_3_16.onnx","vnnlib/pgd_2_3_16_4021.vnnlib";... ]; % Run verification for vit @@ -36,13 +37,21 @@ vgg_path = vnncomp_path + "vggnet16/"; -vgg_instances = ["onnx/vgg16-7.onnx","vnnlib/spec0_screw.vnnlib"]; +vgg_instances = ["onnx/vgg16-7.onnx","vnnlib/spec0_pretzel.vnnlib";... + "onnx/vgg16-7.onnx","vnnlib/spec6_groom.vnnlib";... + "onnx/vgg16-7.onnx","vnnlib/spec16_restaurant.vnnlib";... +]; % Run verification for vgg16 (error when creating random examples, array size too large) % I can imagine that similar errors will be encountered when running verification -onnx = vgg_path + vgg_instances(1,1); -vnnlib = vgg_path + vgg_instances(1,2); -run_vnncomp2024_instance("vggnet16",onnx,vnnlib,"vgg_results.txt"); +% It seems okay on my home computer (MATLAB R2024a) + +% Run verification for vgg16 +for i=1:length(vgg_instances) + onnx = vgg_path + vgg_instances(i,1); + vnnlib = vgg_path + vgg_instances(i,2); + run_vnncomp2024_instance("vggnet16",onnx,vnnlib,"vgg_results_" + string(i)+".txt"); +end %% ml4acopf @@ -55,10 +64,27 @@ "onnx/14_ieee_ml4acopf.onnx","vnnlib/14_ieee_prop9.vnnlib";... "onnx/300_ieee_ml4acopf.onnx","vnnlib/300_ieee_prop3.vnnlib"]; -% Run verification for nn4sys +% Run verification for ml4acopf for i=1:length(ml4_instances) onnx = ml4_path + ml4_instances(i,1); vnnlib = ml4_path + ml4_instances(i,2); run_vnncomp2024_instance("ml4acopf",onnx,vnnlib,"ml4_results_" + string(i)+".txt"); end +%% cctsdb_yolo + +disp("Running cctsdb_yolo..") + +cctyolo_path = vnncomp_path + "cctsdb_yolo/"; + +cctyolo_instances = ["onnx/patch-1.onnx", "spec_onnx_patch-1_idx_00087_1.vnnlib"; ... + "onnx/patch-3.onnx", "spec_onnx_patch-1_idx_00055_0.vnnlib"; ... +]; + +% Run verification for cctsdb_yolo +for i=1:length(cctyolo_instances) + onnx = cctyolo_path + cctyolo_instances(i,1); + vnnlib = cctyolo_path + cctyolo_instances(i,2); + run_vnncomp2024_instance("cctsdb_yolo",onnx,vnnlib,"cctsdb_yolo_results_" + string(i)+".txt"); +end + diff --git a/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m b/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m index 4a2ec443ce..81764a3a9e 100644 --- a/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m +++ b/code/nnv/examples/Submission/VNN_COMP2024/test_mainTrack_instances.m @@ -1,8 +1,13 @@ %% Run some verification instances from the competition -% path to benchmarks -vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % change to whatever local path vnncomp benchmarks are stored +% path to benchmarks ( change to whatever local path vnncomp benchmarks are) +% vnncomp_path = "/home/manzand/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; % vnncomp_path = "/home/dieman95/Documents/MATLAB/vnncomp2023_benchmarks/benchmarks/"; +vnncomp_path = "C:\Users\diego\Documents\Research\vnncomp2023_benchmarks\benchmarks\"; + +% Things look better than last year, let's make sure we have no penalties this time +% Can we support any other benchmarks? what are the errors we are getting +% in some of them? %% acasxu @@ -26,34 +31,19 @@ % Run verification for acas for i=1:length(acas_instances) - disp(i); onnx = acas_path + acas_instances(i,1); vnnlib = acas_path + acas_instances(i,2); run_vnncomp2024_instance("acasxu",onnx,vnnlib,"acas_results_" + string(i)+".txt"); end -%% test - -disp("Running test benchmark...") - -test_path = vnncomp_path + "test/"; - -test_instances = [... - "test_sat.onnx" , "test_prop.vnnlib";... - "test_unsat.onnx" , "test_prop.vnnlib"]; - -% Run verification for test -for i=1:length(test_instances) - onnx = test_path + test_instances(i,1); - vnnlib = test_path + test_instances(i,2); - run_vnncomp2024_instance("test",onnx,vnnlib,"test_results_" + string(i)+".txt"); -end - %% nn4sys disp("Running nn4sys...") +% We got some penalties here as well... +% I already looked into this after competition 2023 and it seems fixed + nn4sys_path = vnncomp_path + "nn4sys/"; nn4sys_instances = [... % all other networks are not supported... @@ -76,16 +66,25 @@ dist_path = vnncomp_path + "dist_shift/"; -dist_instances = ["onnx/mnist_concat.onnx" ,"vnnlib/index7165_delta0.13.vnnlib"]; +dist_instances = ["onnx/mnist_concat.onnx" ,"vnnlib/index6731_delta0.13.vnnlib";... + "onnx/mnist_concat.onnx" ,"vnnlib/index7_delta0.13.vnnlib";... + "onnx/mnist_concat.onnx" ,"vnnlib/index9830_delta0.13.vnnlib";... + ]; % something is failing with these ones, let's take a look at that -% Run verification for nn4sys -onnx = dist_path + dist_instances(1,1); -vnnlib = dist_path + dist_instances(1,2); -run_vnncomp2024_instance("dist_shift",onnx,vnnlib,"dist_results_" + string(i)+".txt"); +% Run verification for dist_shift +for i=1:length(dist_instances) + onnx = dist_path + dist_instances(i,1); + vnnlib = dist_path + dist_instances(i,2); + run_vnncomp2024_instance("dist_shift",onnx,vnnlib,"dist_results_" + string(i)+".txt"); +end %% collins_rul +% We got penaltis here last year? Let's check that out +% Results (SAT/UNSAT seem to match those of nnenum which had no penalties) +% let's check the counterexamples generated... + disp("Running collins_rul..") rul_path = vnncomp_path + "collins_rul_cnn/"; @@ -109,12 +108,17 @@ cgan_path = vnncomp_path + "cgan/"; -cgan_instances = ["onnx/cGAN_imgSz64_nCh_3.onnx" ,"vnnlib/cGAN_imgSz64_nCh_3_prop_3_input_eps_0.010_output_eps_0.015.vnnlib";... - "onnx/cGAN_imgSz32_nCh_3_nonlinear_activations.onnx","vnnlib/cGAN_imgSz32_nCh_3_nonlinear_activations_prop_0_input_eps_0.015_output_eps_0.020.vnnlib";... - "onnx/cGAN_imgSz32_nCh_1_transposedConvPadding_1.onnx" ,"vnnlib/cGAN_imgSz32_nCh_1_transposedConvPadding_1_prop_0_input_eps_0.015_output_eps_0.020.vnnlib";... - "onnx/cGAN_imgSz32_nCh_3_upsample.onnx","vnnlib/cGAN_imgSz32_nCh_3_upsample_prop_0_input_eps_0.015_output_eps_0.020.vnnlib"]; - -% Run verification for nn4sys +cgan_instances = ["onnx/cGAN_imgSz32_nCh_1.onnx", "vnnlib/cGAN_imgSz32_nCh_1_prop_0_input_eps_0.010_output_eps_0.015.vnnlib";... + "onnx/cGAN_imgSz32_nCh_3.onnx", "vnnlib/cGAN_imgSz32_nCh_3_prop_0_input_eps_0.015_output_eps_0.020.vnnlib";... + "onnx/cGAN_imgSz64_nCh_1.onnx", "vnnlib/cGAN_imgSz64_nCh_1_prop_1_input_eps_0.005_output_eps_0.010.vnnlib";... + "onnx/cGAN_imgSz64_nCh_3.onnx", "vnnlib/cGAN_imgSz64_nCh_3_prop_2_input_eps_0.005_output_eps_0.010.vnnlib";... + % "onnx/cGAN_imgSz32_nCh_3_nonlinear_activations.onnx", "vnnlib/cGAN_imgSz32_nCh_3_nonlinear_activations_prop_0_input_eps_0.010_output_eps_0.015.vnnlib";... % conv2D error (data and filter have different data types) + "onnx/cGAN_imgSz32_nCh_1_transposedConvPadding_1.onnx", "vnnlib/cGAN_imgSz32_nCh_1_transposedConvPadding_1_prop_0_input_eps_0.010_output_eps_0.015.vnnlib";... + % "onnx/cGAN_imgSz32_nCh_3_upsample.onnx", "vnnlib/cGAN_imgSz32_nCh_3_upsample_prop_0_input_eps_0.010_output_eps_0.015.vnnlib";... % reshape error reachability + % "onnx/cGAN_imgSz32_nCh_3_small_transformer.onnx", "vnnlib/cGAN_imgSz32_nCh_3_small_transformer_prop_0_input_eps_0.005_output_eps_0.010.vnnlib";... % onnx loading error +]; + +% Run verification for cgan for i=1:length(cgan_instances) onnx = cgan_path + cgan_instances(i,1); vnnlib = cgan_path + cgan_instances(i,2); @@ -122,24 +126,6 @@ end -%% ml4acopf - -disp("Running ml4acopf..") - -ml4_path = vnncomp_path + "ml4acopf/"; - -ml4_instances = ["onnx/118_ieee_ml4acopf.onnx","vnnlib/118_ieee_prop2.vnnlib";... - "onnx/14_ieee_ml4acopf.onnx","vnnlib/14_ieee_prop9.vnnlib";... - "onnx/300_ieee_ml4acopf.onnx","vnnlib/300_ieee_prop3.vnnlib"]; - -% Run verification for nn4sys -for i=1:length(ml4_instances) - onnx = ml4_path + ml4_instances(i,1); - vnnlib = ml4_path + ml4_instances(i,2); - run_vnncomp2024_instance("ml4acopf",onnx,vnnlib,"ml4_results_" + string(i)+".txt"); -end - - %% tllverify disp("Running tllverify..") @@ -152,15 +138,129 @@ "onnx/tllBench_n=2_N=M=32_m=1_instance_3_0.onnx","vnnlib/property_N=32_0.vnnlib";... "onnx/tllBench_n=2_N=M=48_m=1_instance_5_3.onnx","vnnlib/property_N=48_3.vnnlib";... "onnx/tllBench_n=2_N=M=56_m=1_instance_6_0.onnx","vnnlib/property_N=56_0.vnnlib";... - "onnx/tllBench_n=2_N=M=64_m=1_instance_7_0.onnx","vnnlib/property_N=64_0.vnnlib"]; + % "onnx/tllBench_n=2_N=M=64_m=1_instance_7_0.onnx","vnnlib/property_N=64_0.vnnlib";... + ]; % last one not finishing after a loooong time -% Run verification for nn4sys +% Run verification for tllverify for i=1:length(tll_instances) onnx = tll_path + tll_instances(i,1); vnnlib = tll_path + tll_instances(i,2); run_vnncomp2024_instance("tllverifybench",onnx,vnnlib,"tllverify_results_" + string(i)+".txt"); end +%% metaroom + +disp("Running metaroom..") + +metaroom_path = vnncomp_path + "metaroom/"; + +% We can probably do exact analysis from the beginning on all instances here +metaroom_instances = ["onnx/6cnn_ry_20_3_no_custom_OP.onnx", "vnnlib/spec_idx_109_eps_0.00000436.vnnlib";... + "onnx/4cnn_ry_117_19_no_custom_OP.onnx", "vnnlib/spec_idx_3_eps_0.00000436.vnnlib";... + "onnx/6cnn_ry_65_10_no_custom_OP.onnx", "vnnlib/spec_idx_133_eps_0.00000436.vnnlib";... + "onnx/6cnn_ry_104_17_no_custom_OP.onnx", "vnnlib/spec_idx_96_eps_0.00000436.vnnlib";... + "onnx/6cnn_ry_94_15_no_custom_OP.onnx", "vnnlib/spec_idx_149_eps_0.00000436.vnnlib";... + "onnx/4cnn_ry_19_3_no_custom_OP.onnx", "vnnlib/spec_idx_5_eps_0.00000436.vnnlib";... + ]; + +% Run verification for metaroom +for i=1:length(metaroom_instances) + onnx = metaroom_path + metaroom_instances(i,1); + vnnlib = metaroom_path + metaroom_instances(i,2); + run_vnncomp2024_instance("metaroom",onnx,vnnlib,"metaroom_results_" + string(i)+".txt"); +end + + +%% safeNLP + +disp("Running safeNLP..") + +safeNLP_path = "C:/Users/diego/Documents/Research/vnncomp2024/safeNLP/"; + +% All are unsat in about a second, we can prob do exact on all as well + +% We can probably do exact analysis from the beginning on all instances here +safeNLP_instances = ["onnx/ruarobot.onnx", "vnnlib/ruarobot/prop_8_perturbations.vnnlib";... + "onnx/ruarobot.onnx", "vnnlib/ruarobot/prop_1239_perturbations.vnnlib";... + "onnx/ruarobot.onnx", "vnnlib/ruarobot/prop_6012_perturbations.vnnlib";... + "onnx/medical.onnx", "vnnlib/medical/prop_0_perturbations.vnnlib";... + "onnx/medical.onnx", "vnnlib/medical/prop_5_perturbations.vnnlib";... + "onnx/medical.onnx", "vnnlib/medical/prop_53_perturbations.vnnlib";... + ]; + +% Run verification for safeNLP +for i=1:length(safeNLP_instances) + onnx = safeNLP_path + safeNLP_instances(i,1); + vnnlib = safeNLP_path + safeNLP_instances(i,2); + run_vnncomp2024_instance("safeNLP",onnx,vnnlib,"safeNLP_results_" + string(i)+".txt"); +end + + +%% Cora + +disp("Running Cora benchmark..") + +cora_path = "C:/Users/diego/Documents/Research/vnncomp2024/cora-vnncomp2024-benchmark/"; + +% Some sat, most unsat, fairly fast using exact, can go with that as well +% Some unknowns... How is this possible with exact analysis? + +% We can probably do exact analysis from the beginning on all instances here +cora_instances = ["nns/mnist-point.onnx", "benchmark-files/mnist-img0.vnnlib";... + "nns/mnist-trades.onnx", "benchmark-files/mnist-img1.vnnlib";... + "nns/mnist-set.onnx", "benchmark-files/mnist-img20.vnnlib";... + "nns/svhn-point.onnx", "benchmark-files/svhn-img140.vnnlib";... + "nns/svhn-set.onnx", "benchmark-files/svhn-img323.vnnlib";... + "nns/svhn-trades.onnx", "benchmark-files/svhn-img472.vnnlib";... + "nns/cifar10-set.onnx", "benchmark-files/cifar10-img347.vnnlib";... + "nns/cifar10-point.onnx", "benchmark-files/cifar10-img353.vnnlib";... + "nns/cifar10-trades.onnx", "benchmark-files/cifar10-img266.vnnlib"... + ]; + +% Run verification for safeNLP +for i=1:length(cora_instances) + onnx = cora_path + cora_instances(i,1); + vnnlib = cora_path + cora_instances(i,2); + run_vnncomp2024_instance("cora",onnx,vnnlib,"cora_results_" + string(i)+".txt"); +end + + +%% Cifar100 + +disp("Running Cifar-100..") + +cifar100_path = "C:/Users/diego/Documents/Research/vnncomp2024/vnncomp2024-cifar100_benchmark/"; + +% no vnnlib properties provided in this year's repo +cifar100_instances = ["onnx/CIFAR100_resnet_medium.onnx", ""; ... + "onnx/CIFAR100_resnet_large.onnx", "";... + ]; + +% Run verification for cifar100 +for i=1:length(cifar100_instances) + onnx = cifar100_path + cifar100_instances(i,1); + vnnlib = cifar100_path + cifar100_instances(i,2); + run_vnncomp2024_instance("cifar100",onnx,vnnlib,"cifar100_results_" + string(i)+".txt"); +end + + +%% tinyimagenet + +disp("Running tinyimagenet..") + +tinyimagenet_path = "C:/Users/diego/Documents/Research/vnncomp2024/vnncomp2024-cifar100_benchmark/"; + +% no vnnlib properties provided in this year's repo +% (Some may be available in 2022 repo) +tinyimagenet_instances = ["onnx/TinyImageNet_resnet_medium.onnx", ""; ... + ]; + +% Run verification for tinyimagenet +for i=1:length(tinyimagenet_instances) + onnx = tinyimagenet_path + tinyimagenet_instances(i,1); + vnnlib = tinyimagenet_path + tinyimagenet_instances(i,2); + run_vnncomp2024_instance("tinyimagenet",onnx,vnnlib,"tinyimagenet_results_" + string(i)+".txt"); +end %% TODO % Add benchmarks from 2024 From c8726656948cb73a76346df5d5d09c511853303e Mon Sep 17 00:00:00 2001 From: Diego Manzanas Date: Fri, 7 Jun 2024 15:37:35 -0500 Subject: [PATCH 6/6] Delete files --- .../ARCH-COMP2024/benchmarks/Airplane/reach.m | 16 +- .../Reshape_To_ReshapeLayer1015.m | 460 ------------------ 2 files changed, 8 insertions(+), 468 deletions(-) delete mode 100644 code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m diff --git a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/Airplane/reach.m b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/Airplane/reach.m index 89f057b610..44be76ad5e 100644 --- a/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/Airplane/reach.m +++ b/code/nnv/examples/Submission/ARCH-COMP2024/benchmarks/Airplane/reach.m @@ -14,19 +14,19 @@ % define the plant as specified by nnv plant = NonLinearODE(12,6,@dynamics, reachStep, controlPeriod, eye(12)); % Set plant reachability options - plant.set_taylorTerms(20) + plant.set_taylorTerms(2) plant.set_zonotopeOrder(10); %% Reachability analysis % Initial set (smaller partition to falsify property) - lb = [0; 0; 0; 0; 0.8; 0; 0; 0; 0; 0; 0; 0]; - ub = [0; 0; 0; 0; 0.81; 0; 0; 0; 0; 0; 0; 0]; + lb = [0; 0; 0; 1; 1; 1; 0.99; 0; 0; 0; 0; 0]; + ub = [0; 0; 0; 1; 1; 1; 1; 0; 0; 0; 0; 0]; init_set = Star(lb,ub); % Store all reachable sets reachAll = init_set; % Execute reachabilty analysis - num_steps = 8; + num_steps = 20; reachOptions.reachMethod = 'approx-star'; t = tic; for i=1:num_steps @@ -51,10 +51,10 @@ f2 = figure; rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) hold on; - Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,2,5,'b'); + Star.plotBoxes_2D_noFill(plant.intermediate_reachSet,2,7,'b'); grid; xlabel('x_2'); - ylabel('x_5'); + ylabel('x_7'); f5 = figure; rectangle('Position',[-1,-1,2,2],'FaceColor',[0 0.5 0 0.5],'EdgeColor','y', 'LineWidth',0.1) @@ -66,10 +66,10 @@ % Save figure if is_codeocean - exportgraphics(f2,'/results/logs/airplane_2v5.pdf', 'ContentType', 'vector'); + exportgraphics(f2,'/results/logs/airplane_2v7.pdf', 'ContentType', 'vector'); exportgraphics(f5,'/results/logs/airplane_8v9.pdf', 'ContentType', 'vector'); else - exportgraphics(f2,'airplane_3v4.pdf','ContentType', 'vector'); + exportgraphics(f2,'airplane_2v7.pdf','ContentType', 'vector'); exportgraphics(f5,'airplane_8v9.pdf', 'ContentType', 'vector'); end diff --git a/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m b/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m deleted file mode 100644 index 819caf98c8..0000000000 --- a/code/nnv/examples/Submission/VNN_COMP2024/+mnist_concat/Reshape_To_ReshapeLayer1015.m +++ /dev/null @@ -1,460 +0,0 @@ -classdef Reshape_To_ReshapeLayer1015 < nnet.layer.Layer & nnet.layer.Formattable - % A custom layer auto-generated while importing an ONNX network. - - %#codegen - %#ok<*PROPLC> - %#ok<*NBRAK> - %#ok<*INUSL> - %#ok<*VARARG> - - properties (Learnable) - end - - properties - ONNXParams % An ONNXParameters object containing parameters used by this layer. - end - - methods - function this = Reshape_To_ReshapeLayer1015(name, onnxParams) - this.Name = name; - this.OutputNames = {'cls_onnx__Gemm_8'}; - this.ONNXParams = onnxParams; - end - - function [cls_onnx__Gemm_8] = predict(this, onnx__Reshape_12) - if isdlarray(onnx__Reshape_12) - onnx__Reshape_12 = stripdims(onnx__Reshape_12); - end - onnx__Reshape_12NumDims = 2; - onnxParams = this.ONNXParams; - [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, onnxParams, 'Training', false, ... - 'InputDataPermutation', {[2 1], ['as-is']}, ... - 'OutputDataPermutation', {[2 1], ['as-is']}); - if any(cellfun(@(A)~isnumeric(A) && ~islogical(A), {cls_onnx__Gemm_8})) - fprintf('Runtime error in network. At least one output of custom layer ''%s'' is a non-numeric, non-logical value.\n', 'Reshape_To_ReshapeLayer1015'); - error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ReshapeLayer1015')); - end - cls_onnx__Gemm_8 = dlarray(single(cls_onnx__Gemm_8), 'CB'); - if ~coder.target('MATLAB') - cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); - end - end - - function [cls_onnx__Gemm_8] = forward(this, onnx__Reshape_12) - if isdlarray(onnx__Reshape_12) - onnx__Reshape_12 = stripdims(onnx__Reshape_12); - end - onnx__Reshape_12NumDims = 2; - onnxParams = this.ONNXParams; - [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, onnxParams, 'Training', true, ... - 'InputDataPermutation', {[2 1], ['as-is']}, ... - 'OutputDataPermutation', {[2 1], ['as-is']}); - if any(cellfun(@(A)~isnumeric(A) && ~islogical(A), {cls_onnx__Gemm_8})) - fprintf('Runtime error in network. At least one output of custom layer ''%s'' is a non-numeric, non-logical value.\n', 'Reshape_To_ReshapeLayer1015'); - error(message('nnet_cnn_onnx:onnx:BadCustomLayerRuntimeOutput', 'Reshape_To_ReshapeLayer1015')); - end - cls_onnx__Gemm_8 = dlarray(single(cls_onnx__Gemm_8), 'CB'); - if ~coder.target('MATLAB') - cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); - end - end - end -end - -function [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims, state] = Reshape_To_ReshapeFcn(onnx__Reshape_12, onnx__Reshape_12NumDims, params, varargin) -%RESHAPE_TO_RESHAPEFCN Function implementing an imported ONNX network. -% -% THIS FILE WAS AUTO-GENERATED BY importONNXFunction. -% ONNX Operator Set Version: 11 -% -% Variable names in this function are taken from the original ONNX file. -% -% [CLS_ONNX__GEMM_8] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS) -% - Evaluates the imported ONNX network RESHAPE_TO_RESHAPEFCN with input(s) -% ONNX__RESHAPE_12 and the imported network parameters in PARAMS. Returns -% network output(s) in CLS_ONNX__GEMM_8. -% -% [CLS_ONNX__GEMM_8, STATE] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS) -% - Additionally returns state variables in STATE. When training, -% use this form and set TRAINING to true. -% -% [__] = Reshape_To_ReshapeFcn(ONNX__RESHAPE_12, PARAMS, 'NAME1', VAL1, 'NAME2', VAL2, ...) -% - Specifies additional name-value pairs described below: -% -% 'Training' -% Boolean indicating whether the network is being evaluated for -% prediction or training. If TRAINING is true, state variables -% will be updated. -% -% 'InputDataPermutation' -% 'auto' - Automatically attempt to determine the permutation -% between the dimensions of the input data and the dimensions of -% the ONNX model input. For example, the permutation from HWCN -% (MATLAB standard) to NCHW (ONNX standard) uses the vector -% [4 3 1 2]. See the documentation for IMPORTONNXFUNCTION for -% more information about automatic permutation. -% -% 'none' - Input(s) are passed in the ONNX model format. See 'Inputs'. -% -% numeric vector - The permutation vector describing the -% transformation between input data dimensions and the expected -% ONNX input dimensions.% -% cell array - If the network has multiple inputs, each cell -% contains 'auto', 'none', or a numeric vector. -% -% 'OutputDataPermutation' -% 'auto' - Automatically attempt to determine the permutation -% between the dimensions of the output and a conventional MATLAB -% dimension ordering. For example, the permutation from NC (ONNX -% standard) to CN (MATLAB standard) uses the vector [2 1]. See -% the documentation for IMPORTONNXFUNCTION for more information -% about automatic permutation. -% -% 'none' - Return output(s) as given by the ONNX model. See 'Outputs'. -% -% numeric vector - The permutation vector describing the -% transformation between the ONNX output dimensions and the -% desired output dimensions.% -% cell array - If the network has multiple outputs, each cell -% contains 'auto', 'none' or a numeric vector. -% -% Inputs: -% ------- -% ONNX__RESHAPE_12 -% - Input(s) to the ONNX network. -% The input size(s) expected by the ONNX file are: -% ONNX__RESHAPE_12: [Unknown, Unknown] Type: FLOAT -% By default, the function will try to permute the input(s) -% into this dimension ordering. If the default is incorrect, -% use the 'InputDataPermutation' argument to control the -% permutation. -% -% -% PARAMS - Network parameters returned by 'importONNXFunction'. -% -% -% Outputs: -% -------- -% CLS_ONNX__GEMM_8 -% - Output(s) of the ONNX network. -% Without permutation, the size(s) of the outputs are: -% CLS_ONNX__GEMM_8: [Unknown, Unknown] Type: FLOAT -% By default, the function will try to permute the output(s) -% from this dimension ordering into a conventional MATLAB -% ordering. If the default is incorrect, use the -% 'OutputDataPermutation' argument to control the permutation. -% -% STATE - (Optional) State variables. When TRAINING is true, these will -% have been updated from the original values in PARAMS.State. -% -% -% See also importONNXFunction - -% Preprocess the input data and arguments: -[onnx__Reshape_12, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_12, params, varargin{:}); -% Put all variables into a single struct to implement dynamic scoping: -[Vars, NumDims] = packageVariables(params, {'onnx__Reshape_12'}, {onnx__Reshape_12}, [onnx__Reshape_12NumDims]); -% Call the top-level graph function: -[cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims, state] = Reshape_To_ReshapeGraph1012(onnx__Reshape_12, NumDims.onnx__Reshape_12, Vars, NumDims, Training, params.State); -% Postprocess the output data -[cls_onnx__Gemm_8] = postprocessOutput(cls_onnx__Gemm_8, outputDataPerms, anyDlarrayInputs, Training, varargin{:}); -end - -function [cls_onnx__Gemm_8, cls_onnx__Gemm_8NumDims1014, state] = Reshape_To_ReshapeGraph1012(onnx__Reshape_12, onnx__Reshape_12NumDims1013, Vars, NumDims, Training, state) -% Function implementing the graph 'Reshape_To_ReshapeGraph1012' -% Update Vars and NumDims from the graph's formal input parameters. Note that state variables are already in Vars. -Vars.onnx__Reshape_12 = onnx__Reshape_12; -NumDims.onnx__Reshape_12 = onnx__Reshape_12NumDims1013; - -% Execute the operators: -% Reshape: -[shape, NumDims.output] = prepareReshapeArgs(Vars.onnx__Reshape_12, Vars.onnx__Reshape_13, NumDims.onnx__Reshape_12, 0); -Vars.output = reshape(Vars.onnx__Reshape_12, shape{:}); - -% Reshape: -[shape, NumDims.cls_onnx__Gemm_8] = prepareReshapeArgs(Vars.output, Vars.cls_onnx__Reshape_7, NumDims.output, 0); -Vars.cls_onnx__Gemm_8 = reshape(Vars.output, shape{:}); - -% Set graph output arguments from Vars and NumDims: -cls_onnx__Gemm_8 = Vars.cls_onnx__Gemm_8; -cls_onnx__Gemm_8NumDims1014 = NumDims.cls_onnx__Gemm_8; -% Set output state from Vars: -state = updateStruct(state, Vars); -end - -function [inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_12, numDataOutputs, params, varargin) -% Function to validate inputs to Reshape_To_ReshapeFcn: -p = inputParser; -isValidArrayInput = @(x)isnumeric(x) || isstring(x); -isValidONNXParameters = @(x)isa(x, 'ONNXParameters'); -addRequired(p, 'onnx__Reshape_12', isValidArrayInput); -addRequired(p, 'params', isValidONNXParameters); -addParameter(p, 'InputDataPermutation', 'auto'); -addParameter(p, 'OutputDataPermutation', 'auto'); -addParameter(p, 'Training', false); -parse(p, onnx__Reshape_12, params, varargin{:}); -inputDataPerms = p.Results.InputDataPermutation; -outputDataPerms = p.Results.OutputDataPermutation; -Training = p.Results.Training; -if isnumeric(inputDataPerms) - inputDataPerms = {inputDataPerms}; -end -if isstring(inputDataPerms) && isscalar(inputDataPerms) || ischar(inputDataPerms) - inputDataPerms = repmat({inputDataPerms},1,1); -end -if isnumeric(outputDataPerms) - outputDataPerms = {outputDataPerms}; -end -if isstring(outputDataPerms) && isscalar(outputDataPerms) || ischar(outputDataPerms) - outputDataPerms = repmat({outputDataPerms},1,numDataOutputs); -end -end - -function [onnx__Reshape_12, Training, outputDataPerms, anyDlarrayInputs] = preprocessInput(onnx__Reshape_12, params, varargin) -% Parse input arguments -[inputDataPerms, outputDataPerms, Training] = parseInputs(onnx__Reshape_12, 1, params, varargin{:}); -anyDlarrayInputs = any(cellfun(@(x)isa(x, 'dlarray'), {onnx__Reshape_12})); -% Make the input variables into unlabelled dlarrays: -onnx__Reshape_12 = makeUnlabeledDlarray(onnx__Reshape_12); -% Permute inputs if requested: -onnx__Reshape_12 = permuteInputVar(onnx__Reshape_12, inputDataPerms{1}, 2); -end - -function [cls_onnx__Gemm_8] = postprocessOutput(cls_onnx__Gemm_8, outputDataPerms, anyDlarrayInputs, Training, varargin) -% Set output type: -if ~anyDlarrayInputs && ~Training - if isdlarray(cls_onnx__Gemm_8) - cls_onnx__Gemm_8 = extractdata(cls_onnx__Gemm_8); - end -end -% Permute outputs if requested: -cls_onnx__Gemm_8 = permuteOutputVar(cls_onnx__Gemm_8, outputDataPerms{1}, 2); -end - - -%% dlarray functions implementing ONNX operators: - -function [DLTShape, numDimsY] = prepareReshapeArgs(X, ONNXShape, numDimsX, allowzero) -% Prepares arguments for implementing the ONNX Reshape operator -ONNXShape = flip(extractdata(ONNXShape)); % First flip the shape to make it correspond to the dimensions of X. -% In ONNX, 0 means "unchanged" if allowzero is false, and -1 means "infer". In DLT, there is no -% "unchanged", and [] means "infer". -DLTShape = num2cell(ONNXShape); % Make a cell array so we can include []. -% Replace zeros with the actual size if allowzero is true -if any(ONNXShape==0) && allowzero==0 - i0 = find(ONNXShape==0); - DLTShape(i0) = num2cell(size(X, numDimsX - numel(ONNXShape) + i0)); % right-align the shape vector and dims -end -if any(ONNXShape == -1) - % Replace -1 with [] - i = ONNXShape == -1; - DLTShape{i} = []; -end -if numel(DLTShape)==1 - DLTShape = [DLTShape 1]; -end -numDimsY = numel(ONNXShape); -end - -%% Utility functions: - -function s = appendStructs(varargin) -% s = appendStructs(s1, s2,...). Assign all fields in s1, s2,... into s. -if isempty(varargin) - s = struct; -else - s = varargin{1}; - for i = 2:numel(varargin) - fromstr = varargin{i}; - fs = fieldnames(fromstr); - for j = 1:numel(fs) - s.(fs{j}) = fromstr.(fs{j}); - end - end -end -end - -function checkInputSize(inputShape, expectedShape, inputName) - -if numel(expectedShape)==0 - % The input is a scalar - if ~isequal(inputShape, [1 1]) - inputSizeStr = makeSizeString(inputShape); - error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, "[1,1]", inputSizeStr)); - end -elseif numel(expectedShape)==1 - % The input is a vector - if ~shapeIsColumnVector(inputShape) || ~iSizesMatch({inputShape(1)}, expectedShape) - expectedShape{2} = 1; - expectedSizeStr = makeSizeString(expectedShape); - inputSizeStr = makeSizeString(inputShape); - error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr)); - end -else - % The input has 2 dimensions or more - - % The input dimensions have been reversed; flip them back to compare to the - % expected ONNX shape. - inputShape = fliplr(inputShape); - - % If the expected shape has fewer dims than the input shape, error. - if numel(expectedShape) < numel(inputShape) - expectedSizeStr = strjoin(["[", strjoin(string(expectedShape), ","), "]"], ""); - error(message('nnet_cnn_onnx:onnx:InputHasGreaterNDims', inputName, expectedSizeStr)); - end - - % Prepad the input shape with trailing ones up to the number of elements in - % expectedShape - inputShape = num2cell([ones(1, numel(expectedShape) - length(inputShape)) inputShape]); - - % Find the number of variable size dimensions in the expected shape - numVariableInputs = sum(cellfun(@(x) isa(x, 'char') || isa(x, 'string'), expectedShape)); - - % Find the number of input dimensions that are not in the expected shape - % and cannot be represented by a variable dimension - nonMatchingInputDims = setdiff(string(inputShape), string(expectedShape)); - numNonMatchingInputDims = numel(nonMatchingInputDims) - numVariableInputs; - - expectedSizeStr = makeSizeString(expectedShape); - inputSizeStr = makeSizeString(inputShape); - if numNonMatchingInputDims == 0 && ~iSizesMatch(inputShape, expectedShape) - % The actual and expected input dimensions match, but in - % a different order. The input needs to be permuted. - error(message('nnet_cnn_onnx:onnx:InputNeedsPermute',inputName, expectedSizeStr, inputSizeStr)); - elseif numNonMatchingInputDims > 0 - % The actual and expected input sizes do not match. - error(message('nnet_cnn_onnx:onnx:InputNeedsResize',inputName, expectedSizeStr, inputSizeStr)); - end -end -end - -function doesMatch = iSizesMatch(inputShape, expectedShape) -% Check whether the input and expected shapes match, in order. -% Size elements match if (1) the elements are equal, or (2) the expected -% size element is a variable (represented by a character vector or string) -doesMatch = true; -for i=1:numel(inputShape) - if ~(isequal(inputShape{i},expectedShape{i}) || ischar(expectedShape{i}) || isstring(expectedShape{i})) - doesMatch = false; - return - end -end -end - -function sizeStr = makeSizeString(shape) -sizeStr = strjoin(["[", strjoin(string(shape), ","), "]"], ""); -end - -function isVec = shapeIsColumnVector(shape) -if numel(shape) == 2 && shape(2) == 1 - isVec = true; -else - isVec = false; -end -end -function X = makeUnlabeledDlarray(X) -% Make numeric X into an unlabelled dlarray -if isa(X, 'dlarray') - X = stripdims(X); -elseif isnumeric(X) - if isinteger(X) - % Make ints double so they can combine with anything without - % reducing precision - X = double(X); - end - X = dlarray(X); -end -end - -function [Vars, NumDims] = packageVariables(params, inputNames, inputValues, inputNumDims) -% inputNames, inputValues are cell arrays. inputRanks is a numeric vector. -Vars = appendStructs(params.Learnables, params.Nonlearnables, params.State); -NumDims = params.NumDimensions; -% Add graph inputs -for i = 1:numel(inputNames) - Vars.(inputNames{i}) = inputValues{i}; - NumDims.(inputNames{i}) = inputNumDims(i); -end -end - -function X = permuteInputVar(X, userDataPerm, onnxNDims) -% Returns reverse-ONNX ordering -if onnxNDims == 0 - return; -elseif onnxNDims == 1 && isvector(X) - X = X(:); - return; -elseif isnumeric(userDataPerm) - % Permute into reverse ONNX ordering - if numel(userDataPerm) ~= onnxNDims - error(message('nnet_cnn_onnx:onnx:InputPermutationSize', numel(userDataPerm), onnxNDims)); - end - perm = fliplr(userDataPerm); -elseif isequal(userDataPerm, 'auto') && onnxNDims == 4 - % Permute MATLAB HWCN to reverse onnx (WHCN) - perm = [2 1 3 4]; -elseif isequal(userDataPerm, 'as-is') - % Do not permute the input - perm = 1:ndims(X); -else - % userDataPerm is either 'none' or 'auto' with no default, which means - % it's already in onnx ordering, so just make it reverse onnx - perm = max(2,onnxNDims):-1:1; -end -X = permute(X, perm); -end - -function Y = permuteOutputVar(Y, userDataPerm, onnxNDims) -switch onnxNDims - case 0 - perm = []; - case 1 - if isnumeric(userDataPerm) - % Use the user's permutation because Y is a column vector which - % already matches ONNX. - perm = userDataPerm; - elseif isequal(userDataPerm, 'auto') - % Treat the 1D onnx vector as a 2D column and transpose it - perm = [2 1]; - else - % userDataPerm is 'none'. Leave Y alone because it already - % matches onnx. - perm = []; - end - otherwise - % ndims >= 2 - if isnumeric(userDataPerm) - % Use the inverse of the user's permutation. This is not just the - % flip of the permutation vector. - perm = onnxNDims + 1 - userDataPerm; - elseif isequal(userDataPerm, 'auto') - if onnxNDims == 2 - % Permute reverse ONNX CN to DLT CN (do nothing) - perm = []; - elseif onnxNDims == 4 - % Permute reverse onnx (WHCN) to MATLAB HWCN - perm = [2 1 3 4]; - else - % User wants the output in ONNX ordering, so just reverse it from - % reverse onnx - perm = onnxNDims:-1:1; - end - elseif isequal(userDataPerm, 'as-is') - % Do not permute the input - perm = 1:ndims(Y); - else - % userDataPerm is 'none', so just make it reverse onnx - perm = onnxNDims:-1:1; - end -end -if ~isempty(perm) - Y = permute(Y, perm); -end -end - -function s = updateStruct(s, t) -% Set all existing fields in s from fields in t, ignoring extra fields in t. -for name = transpose(fieldnames(s)) - s.(name{1}) = t.(name{1}); -end -end

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