private static Layer parse_cost(KeyValuePair[] options, SizeParams parameters)
{
string typeS = OptionList.option_find_str(options, "type", "sse");
CostType type = (CostType)Enum.Parse(typeof(CostType), typeS);
float scale = OptionList.option_find_float_quiet(options, "scale", 1);
Layer layer = Layer.make_cost_layer(parameters.Batch, parameters.Inputs, type, scale);
layer.Ratio = OptionList.option_find_float_quiet(options, "ratio", 0);
return(layer);
}
private static Layer parse_softmax(KeyValuePair[] options, SizeParams parameters)
{
int groups = OptionList.option_find_int_quiet(options, "groups", 1);
Layer layer = Layer.make_softmax_layer(parameters.Batch, parameters.Inputs, groups);
layer.Temperature = OptionList.option_find_float_quiet(options, "temperature", 1);
string treeFile = OptionList.option_find_str(options, "tree", "");
if (!string.IsNullOrEmpty(treeFile))
{
layer.SoftmaxTree = new Tree(treeFile);
}
return(layer);
}
private static Layer parse_convolutional(KeyValuePair[] options, SizeParams parameters)
{
int n = OptionList.option_find_int(options, "filters", 1);
int size = OptionList.option_find_int(options, "size", 1);
int stride = OptionList.option_find_int(options, "stride", 1);
int pad = OptionList.option_find_int_quiet(options, "pad", 0);
int padding = OptionList.option_find_int_quiet(options, "padding", 0);
if (pad != 0)
{
padding = size / 2;
}
string activationS = OptionList.option_find_str(options, "activation", "logistic");
Activation activation = ActivationsHelper.Get_activation(activationS);
int batch, h, w, c;
h = parameters.H;
w = parameters.W;
c = parameters.C;
batch = parameters.Batch;
if (!(h != 0 && w != 0 && c != 0))
{
Utils.Error("Layer before convolutional Layer must output image.");
}
bool batchNormalize = OptionList.option_find_int_quiet(options, "batch_normalize", 0) != 0;
bool binary = OptionList.option_find_int_quiet(options, "binary", 0) != 0;
bool xnor = OptionList.option_find_int_quiet(options, "xnor", 0) != 0;
Layer layer = Layer.make_convolutional_layer(batch, h, w, c, n, size, stride, padding, activation, batchNormalize, binary, xnor, parameters.Net.Adam);
layer.Flipped = OptionList.option_find_int_quiet(options, "flipped", 0);
layer.Dot = OptionList.option_find_float_quiet(options, "dot", 0);
if (parameters.Net.Adam)
{
layer.B1 = parameters.Net.B1;
layer.B2 = parameters.Net.B2;
layer.Eps = parameters.Net.Eps;
}
return(layer);
}
private static void parse_net_options(KeyValuePair[] options, Network net)
{
net.Batch = OptionList.option_find_int(options, "batch", 1);
net.LearningRate = OptionList.option_find_float(options, "learning_rate", .001f);
net.Momentum = OptionList.option_find_float(options, "momentum", .9f);
net.Decay = OptionList.option_find_float(options, "decay", .0001f);
int subdivs = OptionList.option_find_int(options, "subdivisions", 1);
net.TimeSteps = OptionList.option_find_int_quiet(options, "time_steps", 1);
net.Batch /= subdivs;
net.Batch *= net.TimeSteps;
net.Subdivisions = subdivs;
net.Adam = OptionList.option_find_int_quiet(options, "adam", 0) != 0;
if (net.Adam)
{
net.B1 = OptionList.option_find_float(options, "B1", .9f);
net.B2 = OptionList.option_find_float(options, "B2", .999f);
net.Eps = OptionList.option_find_float(options, "eps", .000001f);
}
net.H = OptionList.option_find_int_quiet(options, "height", 0);
net.W = OptionList.option_find_int_quiet(options, "width", 0);
net.C = OptionList.option_find_int_quiet(options, "channels", 0);
net.Inputs = OptionList.option_find_int_quiet(options, "inputs", net.H * net.W * net.C);
net.MaxCrop = OptionList.option_find_int_quiet(options, "max_crop", net.W * 2);
net.MinCrop = OptionList.option_find_int_quiet(options, "min_crop", net.W);
net.Angle = OptionList.option_find_float_quiet(options, "angle", 0);
net.Aspect = OptionList.option_find_float_quiet(options, "aspect", 1);
net.Saturation = OptionList.option_find_float_quiet(options, "saturation", 1);
net.Exposure = OptionList.option_find_float_quiet(options, "exposure", 1);
net.Hue = OptionList.option_find_float_quiet(options, "hue", 0);
if (net.Inputs == 0 && !(net.H != 0 && net.W != 0 && net.C != 0))
{
Utils.Error("No input parameters supplied");
}
string policyS = OptionList.option_find_str(options, "policy", "constant");
net.Policy = get_policy(policyS);
net.BurnIn = OptionList.option_find_int_quiet(options, "burn_in", 0);
if (net.Policy == LearningRatePolicy.Step)
{
net.Step = OptionList.option_find_int(options, "step", 1);
net.Scale = OptionList.option_find_float(options, "scale", 1);
}
else if (net.Policy == LearningRatePolicy.Steps)
{
string l = OptionList.option_find(options, "steps");
string p = OptionList.option_find(options, "scales");
if (string.IsNullOrEmpty(l) || string.IsNullOrEmpty(p))
{
Utils.Error("STEPS policy must have steps and scales in cfg file");
}
var lines = l.Split(',');
int[] steps = new int[lines.Length];
float[] scales = new float[lines.Length];
for (var i = 0; i < lines.Length; ++i)
{
steps[i] = int.Parse(lines[i]);
scales[i] = float.Parse(lines[i]);
}
net.Scales = scales;
net.Steps = steps;
net.NumSteps = lines.Length;
}
else if (net.Policy == LearningRatePolicy.Exp)
{
net.Gamma = OptionList.option_find_float(options, "gamma", 1);
}
else if (net.Policy == LearningRatePolicy.Sig)
{
net.Gamma = OptionList.option_find_float(options, "gamma", 1);
net.Step = OptionList.option_find_int(options, "step", 1);
}
else if (net.Policy == LearningRatePolicy.Poly || net.Policy == LearningRatePolicy.Random)
{
net.Power = OptionList.option_find_float(options, "power", 1);
}
net.MaxBatches = OptionList.option_find_int(options, "max_batches", 0);
}
