////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Model load. </summary>
///
/// <param name="path"> Full pathname of the file. </param>
/// <param name="model"> The model. </param>
////////////////////////////////////////////////////////////////////////////////////////////////////
public static void ModelLoad(string path, FunctionStack model)
{
var modelData = new NpzDictionary(path);
foreach (var function in model.Functions)
{
SetParams(function, modelData);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Sets the parameters. </summary>
///
/// <param name="func"> The function. </param>
/// <param name="modelData"> Information describing the model. </param>
////////////////////////////////////////////////////////////////////////////////////////////////////
static void SetParams(Function func, NpzDictionary modelData)
{
if (func is Linear)
{
Linear linear = (Linear)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), linear.Weight.Data, linear.Weight.Data.Length);
if (!linear.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), linear.Bias.Data, linear.Bias.Data.Length);
}
}
else if (func is Convolution2D)
{
Convolution2D conv2D = (Convolution2D)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), conv2D.Weight.Data, conv2D.Weight.Data.Length);
if (!conv2D.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), conv2D.Bias.Data, conv2D.Bias.Data.Length);
}
}
else if (func is Deconvolution2D)
{
Deconvolution2D deconv2D = (Deconvolution2D)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), deconv2D.Weight.Data, deconv2D.Weight.Data.Length);
if (!deconv2D.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), deconv2D.Bias.Data, deconv2D.Bias.Data.Length);
}
}
else if (func is EmbedID)
{
EmbedID embed = (EmbedID)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), embed.Weight.Data, embed.Weight.Data.Length);
}
else if (func is BatchNormalization)
{
BatchNormalization bn = (BatchNormalization)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/beta.npy"]), bn.Beta.Data, bn.Beta.Data.Length);
Array.Copy(Real.GetArray(modelData[func.Name + "/gamma.npy"]), bn.Gamma.Data, bn.Gamma.Data.Length);
if (bn.IsTrain)
{
if (modelData.ContainsKey(func.Name + "/avg_mean.npy"))
{
Array.Copy(Real.GetArray(modelData[func.Name + "/avg_mean.npy"]), bn.AvgMean.Data, bn.AvgMean.Data.Length);
}
if (modelData.ContainsKey(func.Name + "/avg_var.npy"))
{
Array.Copy(Real.GetArray(modelData[func.Name + "/avg_var.npy"]), bn.AvgVar.Data, bn.AvgVar.Data.Length);
}
}
}
else if (func is MultiplyScale)
{
MultiplyScale scale = (MultiplyScale)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), scale.Weight.Data, scale.Weight.Data.Length);
if (scale.BiasTerm)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/bias/b.npy"]), scale.Bias.Data, scale.Bias.Data.Length);
}
}
}
public static NpzDictionary <T> Load <T>(Stream stream, out NpzDictionary <T> value) where T : class, ICloneable, IList, ICollection, IEnumerable, IStructuralComparable, IStructuralEquatable
{
return(value = Load <T>(stream));
}
public static NpzDictionary <T> Load <T>(string path, out NpzDictionary <T> value) where T : class, ICloneable, IList, ICollection, IEnumerable, IStructuralComparable, IStructuralEquatable
{
return(value = Load <T>(new FileStream(path, FileMode.Open)));
}
