static void Main(string[] args)
{
string CifarDataBatch = null;
int split_size = 0;
var p = new OptionSet();
p.Add <string>("dataset=", "CIFAR dataset file name", (x => CifarDataBatch = x));
p.Add <int>("split-size=", "Number of images per split", (x => split_size = x));
Cmd.RunOptionSet(p, args);
if (CifarDataBatch == null || split_size <= 0)
{
Console.WriteLine("Invalid arguments, use --help");
Environment.Exit(1);
}
/* Initialize parameters */
Options.InitializeNNAnalysis();
// Plain old CIFAR binary format
ImageDataset data = CIFAR.ReadData(CifarDataBatch, CIFAR.ALL_IMAGES, 0);
// Split
var splits = data.ShuffleSplitMany(split_size);
int count = 0;
foreach (var s in splits)
{
CIFAR.WriteData(CifarDataBatch + ".split_" + count.ToString(), s);
count++;
}
}
static void Main(string[] args)
{
string CifarDataBatch = null;
string[] split_files = null;
var p = new OptionSet();
p.Add <string>("dataset=", "CIFAR dataset file name to store result", (x => CifarDataBatch = x));
p.Add <string>("files=", "CIFAR dataset files to join", (x => split_files = x.Split()));
Cmd.RunOptionSet(p, args);
if (CifarDataBatch == null || split_files == null)
{
Console.WriteLine("Invalid arguments, use --help");
Environment.Exit(1);
}
List <ImageDataset> dss = new List <ImageDataset>();
Console.WriteLine("Joining files ...");
for (int i = 0; i < split_files.Length; i++)
{
Console.WriteLine(split_files[i]);
dss.Add(CIFAR.ReadData(split_files[i], CIFAR.ALL_IMAGES, 0));
}
var data = Data.UnionMany(dss);
var sd = data.Dataset.CreateShuffle(new Random());
data.Dataset = sd;
Console.WriteLine("Output file ...");
Console.WriteLine(CifarDataBatch);
CIFAR.WriteData(CifarDataBatch, data);
}
static void Main(string[] args)
{
string CifarDataBatch = null;
int how_many = 1;
RANDTYPE randomness = RANDTYPE.UNIFORM;
var p = new OptionSet();
p.Add <string>("dataset=", "CIFAR dataset file name", (x => CifarDataBatch = x));
p.Add <int>("how-many=", "Number of new images per image", (x => how_many = x));
p.Add <string>("randomness=", "Gaussian|Uniform", (x => randomness = (x.Equals("Gaussian") ? RANDTYPE.GAUSSIAN : RANDTYPE.UNIFORM)));
int xoffset = 0;
int yoffset = 0;
bool geometric = false;
p.Add("geometric", "Use geometric transform", (x => geometric = (x != null)));
p.Add <int>("xoffset=", "x-offset for geometric transform", (x => xoffset = x));
p.Add <int>("yoffset=", "y-offset for geometric transform", (x => yoffset = x));
bool random = false;
double epsilon = 0.0;
p.Add("random", "Use random perturbation", (x => random = (x != null)));
p.Add <double>("epsilon=", "Distance (for uniform) or standard deviation (for gaussian) random perturbation", (x => epsilon = x));
bool brightness = false;
double brightness_offset = 0.0;
p.Add("brightness", "Use brightness perturbation", (x => brightness = (x != null)));
p.Add <double>("brightness-offset=", "Brightness offset (<= RobustnessOptions.MaxValue - RobustnessOptions.MinValue)", (x => brightness_offset = x));
bool contrast = false;
double contrast_min_factor = 1.0;
double contrast_max_factor = 1.0;
p.Add("contrast", "Use contrast perturbation", (x => contrast = (x != null)));
p.Add <double>("contrast-min-factor=", "Contrast min factor (0.0-1.0)", (x => contrast_min_factor = x));
p.Add <double>("contrast-max-factor=", "Contrast max factor (0.0-1.0)", (x => contrast_max_factor = x));
bool lossy_jpeg = false;
int photoquality = 90;
p.Add("lossy-jpeg", "Use lossy jpeg perturbation (default photoquality = 90)", (x => lossy_jpeg = (x != null)));
p.Add <int>("jpeg-photoquality=", "Lossy jpeg photoquality", (x => photoquality = x));
bool rotate = false;
float angle = 45.0F;
p.Add("rotation", "Rotation transformation (default angle = 45.0)", (x => rotate = (x != null)));
p.Add <double>("rotation-angle=", (x => angle = (float)x));
bool perturbe_only = false;
p.Add("perturbe-only", "Only perturbe (not augment)", (x => perturbe_only = (x != null)));
Cmd.RunOptionSet(p, args);
if (CifarDataBatch == null)
{
Console.WriteLine("Invalid arguments, use --help");
Environment.Exit(1);
}
/* Initialize parameters */
Options.InitializeNNAnalysis();
// Plain old CIFAR binary format
ImageDataset data = CIFAR.ReadData(CifarDataBatch, CIFAR.ALL_IMAGES, 0);
IAugmentor augmentor = null; // TODO
if (geometric)
{
augmentor = new AugmentGeometric(CIFAR.InputCoordinates, randomness, how_many, xoffset, yoffset);
goto KONT;
}
if (random)
{
augmentor = new AugmentRandom(CIFAR.InputCoordinates, randomness, how_many, epsilon);
goto KONT;
}
if (brightness)
{
augmentor = new AugmentBrightness(CIFAR.InputCoordinates, randomness, how_many, brightness_offset);
goto KONT;
}
if (contrast)
{
augmentor = new AugmentContrast(CIFAR.InputCoordinates, how_many, contrast_min_factor, contrast_max_factor);
goto KONT;
}
if (lossy_jpeg)
{
augmentor = new AugmentLossyJpeg(CIFAR.InputCoordinates, how_many, photoquality);
goto KONT;
}
if (rotate)
{
augmentor = new AugmentRotation(CIFAR.InputCoordinates, how_many, angle);
goto KONT;
}
KONT:
int count = data.Dataset.Count();
ImageDataset initial = null;
if (perturbe_only)
{
initial = new ImageDataset(new Dataset(10), CIFAR.InputCoordinates.ChannelCount,
CIFAR.InputCoordinates.RowCount,
CIFAR.InputCoordinates.ColumnCount, true);
}
else
{
initial = data;
}
for (int i = 0; i < count; i++)
{
double[] datum = data.Dataset.GetDatum(i);
int label = data.Dataset.GetLabel(i);
var augmented = augmentor.Augment(datum);
initial.Update(augmented, label);
}
if (perturbe_only)
{
CIFAR.WriteData(CifarDataBatch + ".perturbed", initial);
}
else
{
CIFAR.WriteData(CifarDataBatch + ".augmented", initial);
}
}
static void Main(string[] args)
{
string CifarNNFile = null;
string CifarDataBatch = null;
bool just_accuracy = false;
bool just_loss = false;
bool raw_directory = false;
var p = new OptionSet();
p.Add <string>("nnet=", "CIFAR neural network file name", (x => CifarNNFile = x));
p.Add <string>("dataset=", "CIFAR dataset file name", (x => CifarDataBatch = x));
p.Add <string>("rawdir", "If set then --dataset value should be a directory in raw directory format", (x => raw_directory = (x != null)));
p.Add <bool> ("optimization=", "Do optimization (Default: true)", (x => RobustnessOptions.DoOptimization = x));
p.Add <double>("sub=", "Subsample from 'live' constraints (0.0-1.0)", (x => RobustnessOptions.LiveConstraintSamplingRatio = x));
p.Add <string>("registry=", "Unique name to store output examples and statistics", (x => RobustnessOptions.Registry = x));
p.Add <bool> ("cegar=", "Do CEGAR (default: true)", (x => RobustnessOptions.CEGAR = x));
p.Add <string>("only-accuracy", "Only evaluate accuracy", (x => just_accuracy = (x != null)));
p.Add <string>("only-loss", "Only evaluate loss", (x => just_loss = (x != null)));
p.Add <double>("bound=", "Linfinity-ball to search", (x => RobustnessOptions.Epsilon = x));
p.Add <double>("minval=", "Minimum value of each entry", (x => RobustnessOptions.MinValue = x));
p.Add <double>("maxval=", "Maximum value of each entry", (x => RobustnessOptions.MaxValue = x));
p.Add <string>("no-quant-safety", "Quantization integrality safety off", (x => RobustnessOptions.QuantizationSafety = (x == null)));
p.Add <double>("scale-preprocessed=", "If image data is preprocessed, scale before dumping to registry", (x => RobustnessOptions.ScalePreProcessed = x));
p.Add <double>("offset-preprocessed=", "If image data is preprocessed, offset scaled before dumping to registry", (x => RobustnessOptions.OffsetPreProcessed = x));
p.Add <string>("max-conf", "Use max-conf objective", (x =>
{
if (x != null)
{
RobustnessOptions.ObjectiveKind = LPSObjectiveKind.MaxConf;
}
}));
p.Add <double>("winner-diff=", "Winning label should be that much different than second best", (x => RobustnessOptions.LabelConfidenceDiff = x));
p.Add <string>("log-png", "Log png files", (x => RobustnessOptions.SavePNGCounterexamples = (x != null)));
bool only_filter = false;
double filter_conf = 0.98;
p.Add("only-filter", "Only filter by confidence", (x => only_filter = (x != null)));
p.Add <double>("filter-conf=", "Filter confidence", (x => filter_conf = x));
Cmd.RunOptionSet(p, args);
if (CifarNNFile == null || CifarDataBatch == null)
{
Console.WriteLine("Invalid arguments, use --help");
Environment.Exit(1);
}
/* Initialize parameters */
Options.InitializeNNAnalysis();
NeuralNet nn = CIFAR.GetNN(CifarNNFile);
ImageDataset data;
if (raw_directory)
{
// our raw data format (see lmdb2raw.py)
data = CIFAR.ReadDirectoryData(CifarDataBatch);
}
else
{
// Plain old CIFAR binary format
data = CIFAR.ReadData(CifarDataBatch, CIFAR.ALL_IMAGES, 0);
}
if (just_accuracy)
{
NNAccuracy.GetAccuracy(nn, data.Dataset);
return;
}
if (just_loss)
{
NNAccuracy.GetLoss(nn, data.Dataset);
return;
}
if (only_filter)
{
string filtered = RobustnessOptions.Registry + "-filtered-" + filter_conf.ToString();
Console.WriteLine("Orig {0} data", data.Dataset.Count());
var ds = NNAccuracy.KeepAboveConfidenceThreshold(nn, data.Dataset, filter_conf);
Console.WriteLine("Kept {0} data", ds.Count());
ImageDataset ret = new ImageDataset(ds,
CIFAR.InputCoordinates.ChannelCount,
CIFAR.InputCoordinates.RowCount,
CIFAR.InputCoordinates.ColumnCount, true);
CIFAR.WriteData(filtered, ret);
return;
}
RobustnessOptions.Dump();
string synthImagesName = RobustnessOptions.Registry + "-synth";
int labelcount = data.Dataset.LabelCount();
ImageDataset acc = new ImageDataset(new Dataset(labelcount),
CIFAR.InputCoordinates.ChannelCount,
CIFAR.InputCoordinates.RowCount,
CIFAR.InputCoordinates.ColumnCount, true);
int state = 0;
Action <LabelWithConfidence> snapshot = x =>
{
acc.Dataset.Data.Add(new MemAccessor <double[]>(x.datum));
acc.Dataset.Labels.Add(new MemAccessor <int>(x.actualLabel));
state++;
if (state >= 4)
{
CIFAR.WriteData(synthImagesName, acc);
state = 0;
}
};
ImageDataset synth = Robustness.SynthesizeCounterexamplesAndStore(nn, data, snapshot);
if (synth.Dataset.Count() == 0)
{
Console.WriteLine("Did not synthesize any counterexamples, nothing to dump ...");
return;
}
if (raw_directory)
{
throw new NotImplementedException("Output to raw directory format not yet implemented!");
}
else
{
CIFAR.WriteData(RobustnessOptions.Registry + "-synth", synth);
}
}