private static Layer parse_connected(KeyValuePair[] options, SizeParams parameters)
{
int output = OptionList.option_find_int(options, "output", 1);
string activationS = OptionList.option_find_str(options, "activation", "logistic");
Activation activation = ActivationsHelper.Get_activation(activationS);
bool batchNormalize = OptionList.option_find_int_quiet(options, "batch_normalize", 0) != 0;
return(Layer.make_connected_layer(parameters.Batch, parameters.Inputs, output, activation, batchNormalize));
}
private static Layer parse_crnn(KeyValuePair[] options, SizeParams parameters)
{
int outputFilters = OptionList.option_find_int(options, "output_filters", 1);
int hiddenFilters = OptionList.option_find_int(options, "hidden_filters", 1);
string activationS = OptionList.option_find_str(options, "activation", "logistic");
Activation activation = ActivationsHelper.Get_activation(activationS);
bool batchNormalize = OptionList.option_find_int_quiet(options, "batch_normalize", 0) != 0;
Layer l = Layer.make_crnn_layer(parameters.Batch, parameters.W, parameters.H, parameters.C, hiddenFilters, outputFilters, parameters.TimeSteps, activation, batchNormalize);
l.Shortcut = OptionList.option_find_int_quiet(options, "shortcut", 0) != 0;
return(l);
}
private static Layer parse_activation(KeyValuePair[] options, SizeParams parameters)
{
string activationS = OptionList.option_find_str(options, "activation", "linear");
Activation activation = ActivationsHelper.Get_activation(activationS);
Layer l = Layer.make_activation_layer(parameters.Batch, parameters.Inputs, activation);
l.OutH = parameters.H;
l.OutW = parameters.W;
l.OutC = parameters.C;
l.H = parameters.H;
l.W = parameters.W;
l.C = parameters.C;
return(l);
}
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 Layer parse_shortcut(KeyValuePair[] options, SizeParams parameters, Network net)
{
string l = OptionList.option_find(options, "from");
int index = int.Parse(l);
if (index < 0)
{
index = parameters.Index + index;
}
int batch = parameters.Batch;
Layer from = net.Layers[index];
Layer s = Layer.make_shortcut_layer(batch, index, parameters.W, parameters.H, parameters.C, from.OutW, from.OutH, from.OutC);
string activationS = OptionList.option_find_str(options, "activation", "linear");
Activation activation = ActivationsHelper.Get_activation(activationS);
s.Activation = activation;
return(s);
}
private static Layer parse_local(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(options, "pad", 0);
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 local Layer must output image.");
}
Layer layer = new Layer(batch, h, w, c, n, size, stride, pad, activation);
return(layer);
}
