private LayerParameter addBody(bool bDeploy, string strDataName, bool bNamedParams = false, string strLayerPostfix = "", Phase phaseExclude = Phase.NONE)
{
LayerParameter lastLayer;
switch (m_model)
{
case MODEL.RESNET26:
lastLayer = addResNetOctConvBody(strDataName, new int[] { 2, 2, 2, 2 });
break;
default:
throw new Exception("The model type '" + m_model.ToString() + "' is not supported.");
}
for (int i = 0; i < m_rgIpLayers.Count; i++)
{
LayerParameter ip = new LayerParameter(LayerParameter.LayerType.INNERPRODUCT);
string strName = "fc" + (i + 1).ToString();
ip.name = strName + strLayerPostfix;
ip.inner_product_param.axis = 1;
ip.inner_product_param.num_output = (uint)m_rgIpLayers[i].Item1;
ip.inner_product_param.enable_noise = m_rgIpLayers[i].Item2;
ip.top.Add(ip.name);
ip.parameters.Add(new ParamSpec(1, 1, (bNamedParams) ? strName + "_w" : null));
ip.parameters.Add(new ParamSpec(1, 2, (bNamedParams) ? strName + "_b" : null));
addExclusion(ip, phaseExclude);
lastLayer = connectAndAddLayer(lastLayer, ip);
}
return(lastLayer);
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="strBaseDirectory">Specifies the base directory that contains the data and models.</param>
/// <param name="strDataset">Specifies the dataset that the model will run on.</param>
/// <param name="nChannels">Specifies the number of channels in the data set (e.g. color = 3, b/w = 1).</param>
/// <param name="bSiamese">Specifies whether or not to create a Siamese network."</param>
/// <param name="rgIpLayers">Specifies a list of inner product layers added to the end of the network where each entry specifies the number of output and whether or not Noise is enabled for the layer.</param>
/// <param name="bUsePool5">Specifies whether or not to use the Pool layer as the last layer.</param>
/// <param name="bUseDilationConv5">Specifies whether or not to use dilation on block 5 layers.</param>
/// <param name="model">Specifies the type of ResNet model to create.</param>
/// <param name="nBatchSize">Optionally, specifies the batch size (default = 32).</param>
/// <param name="nAccumBatchSize">Optionally, specifies the accumulation batch size (default = 32).</param>
/// <param name="rgGpuId">Optionally, specifies a set of GPU ID's to use (when null, GPU=0 is used).</param>
/// <param name="net">Specifies the 'base' net parameter that is to be altered.</param>
public ResNetModelBuilder(string strBaseDirectory, string strDataset, int nChannels, bool bSiamese, List <Tuple <int, bool> > rgIpLayers, bool bUsePool5, bool bUseDilationConv5, MODEL model, int nBatchSize = 32, int nAccumBatchSize = 32, List <int> rgGpuId = null, NetParameter net = null)
: base(strBaseDirectory, net)
{
if (rgGpuId == null)
{
m_rgGpuID.Add(0);
}
else
{
m_rgGpuID = new List <int>(rgGpuId);
}
m_nChannels = nChannels;
m_bSiamese = bSiamese;
m_rgIpLayers = rgIpLayers;
m_model = model;
m_strModel = model.ToString();
m_nBatchSize = nBatchSize;
m_nAccumBatchSize = nAccumBatchSize;
m_nIterSize = m_nAccumBatchSize / m_nBatchSize;
m_nBatchSizePerDevice = (m_rgGpuID.Count == 1) ? m_nBatchSize : m_nBatchSize / m_rgGpuID.Count;
m_nIterSize = (int)Math.Ceiling((float)m_nAccumBatchSize / (m_nBatchSizePerDevice * m_rgGpuID.Count));
m_nGpuID = m_rgGpuID[0];
m_dfBaseLr = 0.001;
m_bUseDilationConv5 = bUseDilationConv5;
m_bUsePool5 = bUsePool5;
m_strDataset = strDataset;
//-------------------------------------------------------
// Create the transformer for Training.
//-------------------------------------------------------
m_transformTrain = new TransformationParameter();
m_transformTrain.mirror = true;
m_transformTrain.color_order = TransformationParameter.COLOR_ORDER.BGR; // to support caffe models.
m_transformTrain.mean_value = new List <double>();
m_transformTrain.mean_value.Add(104);
m_transformTrain.mean_value.Add(117);
m_transformTrain.mean_value.Add(123);
//-------------------------------------------------------
// Create the transformer for Testing.
//-------------------------------------------------------
m_transformTest = new TransformationParameter();
m_transformTest.color_order = TransformationParameter.COLOR_ORDER.BGR; // to support caffe models.
m_transformTest.mean_value = new List <double>();
m_transformTest.mean_value.Add(104);
m_transformTest.mean_value.Add(117);
m_transformTest.mean_value.Add(123);
}
/// <summary>
/// The constructor.
/// </summary>
/// <param name="strBaseDirectory">Specifies the base directory that contains the data and models.</param>
/// <param name="strDataset">Specifies the dataset that the model will run on.</param>
/// <param name="rgIpLayers">Specifies a list of inner product layers added to the end of the network where each entry specifies the number of output and whether or not Noise is enabled for the layer.</param>
/// <param name="model">Specifies the type of ResNet model to create.</param>
/// <param name="nBatchSize">Optionally, specifies the batch size (default = 32).</param>
/// <param name="rgGpuId">Optionally, specifies a set of GPU ID's to use (when null, GPU=0 is used).</param>
/// <param name="net">Specifies the 'base' net parameter that is to be altered.</param>
public ResNetOctConvModelBuilder(string strBaseDirectory, string strDataset, List <Tuple <int, bool> > rgIpLayers, MODEL model, int nBatchSize = 32, List <int> rgGpuId = null, NetParameter net = null)
: base(strBaseDirectory, net)
{
if (rgGpuId == null)
{
m_rgGpuID.Add(0);
}
else
{
m_rgGpuID = new List <int>(rgGpuId);
}
m_rgIpLayers = rgIpLayers;
m_model = model;
m_strModel = model.ToString();
m_nBatchSize = nBatchSize;
m_nIterSize = 1;
m_nGpuID = m_rgGpuID[0];
m_dfBaseLr = 0.001;
m_strDataset = strDataset;
//-------------------------------------------------------
// Create the transformer for Training.
//-------------------------------------------------------
m_transformTrain = new TransformationParameter();
m_transformTrain.mirror = true;
m_transformTrain.color_order = TransformationParameter.COLOR_ORDER.BGR; // to support caffe models.
m_transformTrain.mean_value = new List <double>();
m_transformTrain.mean_value.Add(104);
m_transformTrain.mean_value.Add(117);
m_transformTrain.mean_value.Add(123);
//-------------------------------------------------------
// Create the transformer for Testing.
//-------------------------------------------------------
m_transformTest = new TransformationParameter();
m_transformTest.color_order = TransformationParameter.COLOR_ORDER.BGR; // to support caffe models.
m_transformTest.mean_value = new List <double>();
m_transformTest.mean_value.Add(104);
m_transformTest.mean_value.Add(117);
m_transformTest.mean_value.Add(123);
}
