/// <summary>
/// Calculates the kernel and stride dimensions for the pooling layer,
/// returns a correctly configured LayerParameter for a PoolingLayer.
/// </summary>
/// <param name="nPyramidLevel">Specifies the pyramid level.</param>
/// <param name="nBottomH">Specifies the bottom height.</param>
/// <param name="nBottomW">Specifies the bottom width.</param>
/// <param name="spp_param">Specifies the SPPParameter used.</param>
/// <returns>The pooling parameter is returned.</returns>
protected virtual LayerParameter getPoolingParam(int nPyramidLevel, int nBottomH, int nBottomW, SPPParameter spp_param)
{
LayerParameter pool_param = new param.LayerParameter(LayerParameter.LayerType.POOLING);
int nNumBins = (int)Math.Pow(2, nPyramidLevel);
// find padding and kernel size so that the pooling is
// performed across the entrie image
int nKernelH = (int)Math.Ceiling(nBottomH / (double)nNumBins);
int nKernelW = (int)Math.Ceiling(nBottomW / (double)nNumBins);
// remainder_H is the min number of pixels that need to be padded before
// entire image height is pooled over with the chosen kernel simension
int nRemainderH = nKernelH * nNumBins - nBottomH;
int nRemainderW = nKernelW * nNumBins - nBottomW;
// pooling layer pads (2 * pad_h) pixels on the top and bottom of the
// image.
int nPadH = (nRemainderH + 1) / 2;
int nPadW = (nRemainderW + 1) / 2;
pool_param.pooling_param.pad_h = (uint)nPadH;
pool_param.pooling_param.pad_w = (uint)nPadW;
pool_param.pooling_param.kernel_h = (uint)nKernelH;
pool_param.pooling_param.kernel_w = (uint)nKernelW;
pool_param.pooling_param.stride_h = (uint)nKernelH;
pool_param.pooling_param.stride_w = (uint)nKernelW;
pool_param.pooling_param.pool = spp_param.pool;
return(pool_param);
}
/// <summary>
/// The BinaryHashLayer constructor.
/// </summary>
/// <param name="cuda">Specifies the CudaDnn connection to Cuda.</param>
/// <param name="log">Specifies the Log for output.</param>
/// <param name="p">Specifies the LayerParameter of type GRN.
/// </param>
public BinaryHashLayer(CudaDnn <T> cuda, Log log, LayerParameter p)
: base(cuda, log, p)
{
m_type = LayerParameter.LayerType.BINARYHASH;
m_blobDebug = new common.Blob <T>(cuda, log, false);
m_blobDebug.Name = "debug";
m_blobWork = new common.Blob <T>(cuda, log);
m_blobWork.Name = "work";
m_blobNormalized = new common.Blob <T>(cuda, log);
m_blobNormalized.Name = "normalized";
LayerParameter paramSigmoid = new param.LayerParameter(LayerParameter.LayerType.SIGMOID);
paramSigmoid.sigmoid_param.engine = EngineParameter.Engine.CUDNN;
}
/// <summary>
/// Fills the NetParameter with the LSTM network architecture.
/// </summary>
/// <param name="net_param"></param>
protected override void FillUnrolledNet(NetParameter net_param)
{
uint nNumOutput = m_param.recurrent_param.num_output;
m_log.CHECK_GT(nNumOutput, 0, "num_output must be positive.");
FillerParameter weight_filler = m_param.recurrent_param.weight_filler;
FillerParameter bias_filler = m_param.recurrent_param.bias_filler;
// Add generic LayerParameter's (without bottoms/tops) of layer types we'll
// use to save redundant code.
LayerParameter hidden_param = new param.LayerParameter(LayerParameter.LayerType.INNERPRODUCT);
hidden_param.inner_product_param.num_output = nNumOutput * 4;
hidden_param.inner_product_param.bias_term = false;
hidden_param.inner_product_param.axis = 2;
hidden_param.inner_product_param.weight_filler = weight_filler.Clone();
LayerParameter biased_hidden_param = hidden_param.Clone(false);
biased_hidden_param.inner_product_param.bias_term = true;
biased_hidden_param.inner_product_param.bias_filler = bias_filler.Clone();
LayerParameter sum_param = new param.LayerParameter(LayerParameter.LayerType.ELTWISE);
sum_param.eltwise_param.operation = EltwiseParameter.EltwiseOp.SUM;
LayerParameter scale_param = new LayerParameter(LayerParameter.LayerType.SCALE);
scale_param.scale_param.axis = 0;
LayerParameter slice_param = new LayerParameter(LayerParameter.LayerType.SLICE);
slice_param.slice_param.axis = 0;
LayerParameter split_param = new LayerParameter(LayerParameter.LayerType.SPLIT);
List <BlobShape> rgInputShapes = new List <BlobShape>();
RecurrentInputShapes(rgInputShapes);
m_log.CHECK_EQ(2, rgInputShapes.Count, "There should be 2 input shapes.");
//--- Add the layers ---
LayerParameter input_layer_param = new LayerParameter(LayerParameter.LayerType.INPUT);
input_layer_param.top.Add("c_0");
input_layer_param.input_param.shape.Add(rgInputShapes[0].Clone());
input_layer_param.top.Add("h_0");
input_layer_param.input_param.shape.Add(rgInputShapes[1].Clone());
net_param.layer.Add(input_layer_param);
LayerParameter cont_slice_param = slice_param.Clone(false);
cont_slice_param.name = "cont_slice";
cont_slice_param.bottom.Add("cont");
cont_slice_param.slice_param.axis = 0;
net_param.layer.Add(cont_slice_param);
// Add layer to transform all timesteps of x to the hidden state dimension.
// W_xc_x = W_xc * x + b_c
{
LayerParameter x_transform_param = biased_hidden_param.Clone(false);
x_transform_param.name = "x_transform";
x_transform_param.parameters.Add(new ParamSpec("W_xc"));
x_transform_param.parameters.Add(new ParamSpec("b_c"));
x_transform_param.bottom.Add("x");
x_transform_param.top.Add("W_xc_x");
x_transform_param.propagate_down.Add(true);
net_param.layer.Add(x_transform_param);
}
if (m_bStaticInput)
{
// Add layer to transform x_static to the hidden state dimension.
// W_xc_x_static = W_xc_static * x_static
LayerParameter x_static_transform_param = hidden_param.Clone(false);
x_static_transform_param.inner_product_param.axis = 1;
x_static_transform_param.name = "W_xc_x_static";
x_static_transform_param.parameters.Add(new ParamSpec("W_xc_static"));
x_static_transform_param.bottom.Add("x_static");
x_static_transform_param.top.Add("W_xc_x_static_preshape");
x_static_transform_param.propagate_down.Add(true);
net_param.layer.Add(x_static_transform_param);
LayerParameter reshape_param = new LayerParameter(LayerParameter.LayerType.RESHAPE);
BlobShape new_shape = reshape_param.reshape_param.shape;
new_shape.dim.Add(1); // One timestep.
new_shape.dim.Add(-1); // Should infer m_nN as the dimension so we can reshape on batch size.
new_shape.dim.Add((int)x_static_transform_param.inner_product_param.num_output);
reshape_param.name = "W_xc_x_static_reshape";
reshape_param.bottom.Add("W_xc_x_static_preshape");
reshape_param.top.Add("W_xc_x_static");
net_param.layer.Add(reshape_param);
}
LayerParameter x_slice_param = slice_param.Clone(false);
x_slice_param.name = "W_xc_x_slice";
x_slice_param.bottom.Add("W_xc_x");
net_param.layer.Add(x_slice_param);
LayerParameter output_concat_layer = new LayerParameter(LayerParameter.LayerType.CONCAT);
output_concat_layer.name = "h_concat";
output_concat_layer.top.Add("h");
output_concat_layer.concat_param.axis = 0;
for (int t = 1; t <= m_nT; t++)
{
string tm1s = (t - 1).ToString();
string ts = t.ToString();
cont_slice_param.top.Add("cont_" + ts);
x_slice_param.top.Add("W_xc_x_" + ts);
// Add layer to flush the hidden state when beginning a new sequence,
// as indicated by cont_t.
// h_conted_{t-1} := cont_t * h_{t-1}
//
// Normally, cont_t is binary (i.e., 0 or 1), so:
// h_conted_{t-1} := h_{t-1} if cont_t == 1
// 0 otherwise.
{
LayerParameter cont_h_param = scale_param.Clone(false);
cont_h_param.group_start = true;
cont_h_param.name = "h_conted_" + tm1s;
cont_h_param.bottom.Add("h_" + tm1s);
cont_h_param.bottom.Add("cont_" + ts);
cont_h_param.top.Add("h_conted_" + tm1s);
net_param.layer.Add(cont_h_param);
}
// Add layer to compute
// W_hc_h_{t-1} := W_hc * h_conted_{t-1}
{
LayerParameter w_param = hidden_param.Clone(false);
w_param.name = "transform_" + ts;
w_param.parameters.Add(new ParamSpec("W_hc"));
w_param.bottom.Add("h_conted_" + tm1s);
w_param.top.Add("W_hc_h_" + tm1s);
w_param.inner_product_param.axis = 2;
net_param.layer.Add(w_param);
}
// Add the outputs of the linear transformations to compute the gate input.
// get_input_t := W_hc * h_conted_{t-1} + W_xc * x_t + b_c
// = W_hc_h_{t-1} + W_xc_x_t + b_c
{
LayerParameter input_sum_layer = sum_param.Clone(false);
input_sum_layer.name = "gate_input_" + ts;
input_sum_layer.bottom.Add("W_hc_h_" + tm1s);
input_sum_layer.bottom.Add("W_xc_x_" + ts);
if (m_bStaticInput)
{
input_sum_layer.bottom.Add("W_xc_x_static");
}
input_sum_layer.top.Add("gate_input_" + ts);
net_param.layer.Add(input_sum_layer);
}
// Add LSTMUnit layer to compute the cell & hidden vectors c_t and h_t.
// Inputs: c_{t-1}, gate_input_t = (i_t, f_t, o_t, g_t), cont_t
// Outputs: c_t, h_t
// [ i_t' ]
// [ f_t' ] := gate_input_t
// [ o_t' ]
// [ g_t' ]
// i_t := \sigmoid[i_t']
// f_t := \sigmoid[f_t']
// o_t := \sigmoid[o_t']
// g_t := \tanh[g_t']
// c_t := cont_t * (f_t .* c_{t-1}) + (i_t .* g_t)
// h_t := o_t .* \tanh[c_t]
{
LayerParameter lstm_unit_param = new LayerParameter(LayerParameter.LayerType.LSTM_UNIT);
lstm_unit_param.bottom.Add("c_" + tm1s);
lstm_unit_param.bottom.Add("gate_input_" + ts);
lstm_unit_param.bottom.Add("cont_" + ts);
lstm_unit_param.top.Add("c_" + ts);
lstm_unit_param.top.Add("h_" + ts);
lstm_unit_param.name = "unit_" + ts;
net_param.layer.Add(lstm_unit_param);
}
output_concat_layer.bottom.Add("h_" + ts);
}
{
LayerParameter c_T_copy_param = split_param.Clone(false);
c_T_copy_param.bottom.Add("c_" + m_nT.ToString());
c_T_copy_param.top.Add("c_T");
net_param.layer.Add(c_T_copy_param);
}
net_param.layer.Add(output_concat_layer.Clone(false));
}
/// <summary>
/// Fills the NetParameter with the RNN network architecture.
/// </summary>
/// <param name="net_param"></param>
protected override void FillUnrolledNet(NetParameter net_param)
{
uint nNumOutput = m_param.recurrent_param.num_output;
m_log.CHECK_GT(nNumOutput, 0, "num_output must be positive.");
FillerParameter weight_filler = m_param.recurrent_param.weight_filler;
FillerParameter bias_filler = m_param.recurrent_param.bias_filler;
// Add generic LayerParameter's (without bottoms/tops) of layer types we'll
// use to save redundant code.
LayerParameter hidden_param = new param.LayerParameter(LayerParameter.LayerType.INNERPRODUCT);
hidden_param.inner_product_param.num_output = nNumOutput;
hidden_param.inner_product_param.bias_term = false;
hidden_param.inner_product_param.axis = 2;
hidden_param.inner_product_param.weight_filler = weight_filler.Clone();
LayerParameter biased_hidden_param = hidden_param.Clone(false);
biased_hidden_param.inner_product_param.bias_term = true;
biased_hidden_param.inner_product_param.bias_filler = bias_filler.Clone();
LayerParameter sum_param = new param.LayerParameter(LayerParameter.LayerType.ELTWISE);
sum_param.eltwise_param.operation = EltwiseParameter.EltwiseOp.SUM;
LayerParameter tanh_param = new LayerParameter(LayerParameter.LayerType.TANH);
LayerParameter scale_param = new LayerParameter(LayerParameter.LayerType.SCALE);
scale_param.scale_param.axis = 0;
LayerParameter slice_param = new LayerParameter(LayerParameter.LayerType.SLICE);
slice_param.slice_param.axis = 0;
List <BlobShape> rgInputShapes = new List <BlobShape>();
RecurrentInputShapes(rgInputShapes);
m_log.CHECK_EQ(1, rgInputShapes.Count, "There should only be one input shape.");
//--- Add the layers ---
LayerParameter input_layer_param = new LayerParameter(LayerParameter.LayerType.INPUT);
input_layer_param.top.Add("h_0");
input_layer_param.input_param.shape.Add(rgInputShapes[0]);
net_param.layer.Add(input_layer_param);
LayerParameter cont_slice_param = slice_param.Clone(false);
cont_slice_param.name = "cont_slice";
cont_slice_param.bottom.Add("cont");
cont_slice_param.slice_param.axis = 0;
net_param.layer.Add(cont_slice_param);
// Add layer to transform all timesteps of x to the hidden state dimension.
// W_xh_x = W_xh * x + b_h
{
LayerParameter x_transform_param = biased_hidden_param.Clone(false);
x_transform_param.name = "x_transform";
x_transform_param.parameters.Add(new ParamSpec("W_xh"));
x_transform_param.parameters.Add(new ParamSpec("b_h"));
x_transform_param.bottom.Add("x");
x_transform_param.top.Add("W_xh_x");
x_transform_param.propagate_down.Add(true);
net_param.layer.Add(x_transform_param);
}
if (m_bStaticInput)
{
// Add layer to transform x_static to the hidden state dimension.
// W_xh_x_static = W_xh_static * x_static
LayerParameter x_static_transform_param = hidden_param.Clone(false);
x_static_transform_param.inner_product_param.axis = 1;
x_static_transform_param.name = "W_xh_x_static";
x_static_transform_param.parameters.Add(new ParamSpec("W_xh_static"));
x_static_transform_param.bottom.Add("x_static");
x_static_transform_param.top.Add("W_xh_x_static_preshape");
x_static_transform_param.propagate_down.Add(true);
net_param.layer.Add(x_static_transform_param);
LayerParameter reshape_param = new LayerParameter(LayerParameter.LayerType.RESHAPE);
BlobShape new_shape = reshape_param.reshape_param.shape;
new_shape.dim.Add(1); // One timestep.
new_shape.dim.Add(-1); // Should infer m_nN as the dimension so we can reshape on batch size.
new_shape.dim.Add((int)x_static_transform_param.inner_product_param.num_output);
reshape_param.name = "W_xh_x_static_reshape";
reshape_param.bottom.Add("W_xh_x_static_preshape");
reshape_param.top.Add("W_xh_x_static");
net_param.layer.Add(reshape_param);
}
LayerParameter x_slice_param = slice_param.Clone(false);
x_slice_param.name = "W_xh_x_slice";
x_slice_param.bottom.Add("W_xh_x");
net_param.layer.Add(x_slice_param);
LayerParameter output_concat_layer = new LayerParameter(LayerParameter.LayerType.CONCAT);
output_concat_layer.name = "o_concat";
output_concat_layer.top.Add("o");
output_concat_layer.concat_param.axis = 0;
for (int t = 1; t <= m_nT; t++)
{
string tm1s = (t - 1).ToString();
string ts = t.ToString();
cont_slice_param.top.Add("cont_" + ts);
x_slice_param.top.Add("W_xh_x_" + ts);
// Add layer to flush the hidden state when beginning a new sequence,
// as indicated by cont_t.
// h_conted_{t-1} := cont_t * h_{t-1}
//
// Normally, cont_t is binary (i.e., 0 or 1), so:
// h_conted_{t-1} := h_{t-1} if cont_t == 1
// 0 otherwise.
{
LayerParameter cont_h_param = scale_param.Clone(false);
cont_h_param.name = "h_conted_" + tm1s;
cont_h_param.bottom.Add("h_" + tm1s);
cont_h_param.bottom.Add("cont_" + ts);
cont_h_param.top.Add("h_conted_" + tm1s);
net_param.layer.Add(cont_h_param);
}
// Add layer to compute
// W_hh_h_{t-1} := W_hh * h_conted_{t-1}
{
LayerParameter w_param = hidden_param.Clone(false);
w_param.name = "W_hh_h_" + tm1s;
w_param.parameters.Add(new ParamSpec("W_hh"));
w_param.bottom.Add("h_conted_" + tm1s);
w_param.top.Add("W_hh_h_" + tm1s);
w_param.inner_product_param.axis = 2;
net_param.layer.Add(w_param);
}
// Add layers to compute
// h_t := \tanh( W_hh * h_conted_t{t-1} + W_xh * x_t + b_h )
// = \tanh( W_hh_h_{t-1} + W_xh_t )
{
LayerParameter h_input_sum_param = sum_param.Clone(false);
h_input_sum_param.name = "h_input_sum_" + ts;
h_input_sum_param.bottom.Add("W_hh_h_" + tm1s);
h_input_sum_param.bottom.Add("W_xh_x_" + ts);
if (m_bStaticInput)
{
h_input_sum_param.bottom.Add("W_xh_x_static");
}
h_input_sum_param.top.Add("h_neuron_input_" + ts);
net_param.layer.Add(h_input_sum_param);
}
{
LayerParameter h_neuron_param = tanh_param.Clone(false);
h_neuron_param.name = "h_neuron_input_" + ts;
h_neuron_param.bottom.Add("h_neuron_input_" + ts);
h_neuron_param.top.Add("h_" + ts);
net_param.layer.Add(h_neuron_param);
}
// Add layer to compute
// W_ho_h_t := W_ho * h_t + b_o
{
LayerParameter w_param = biased_hidden_param.Clone(false);
w_param.name = "W_ho_h_" + ts;
w_param.parameters.Add(new ParamSpec("W_ho"));
w_param.parameters.Add(new ParamSpec("b_o"));
w_param.bottom.Add("h_" + ts);
w_param.top.Add("W_ho_h_" + ts);
w_param.inner_product_param.axis = 2;
net_param.layer.Add(w_param);
}
// Add layer to compute
// o_t := \tanh( W_ho * h_t + b_o
// = \tanh( W_ho_h_t )
{
LayerParameter o_neuron_param = tanh_param.Clone(false);
o_neuron_param.name = "o_neuron_" + ts;
o_neuron_param.bottom.Add("W_ho_h_" + ts);
o_neuron_param.top.Add("o_" + ts);
net_param.layer.Add(o_neuron_param);
}
output_concat_layer.bottom.Add("o_" + ts);
}
net_param.layer.Add(output_concat_layer.Clone(false));
}
/// <summary>
/// Setup the layer.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void LayerSetUp(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
m_nNum = colBottom[0].num;
m_nChannels = colBottom[0].channels;
m_nBottomH = colBottom[0].height;
m_nBottomW = colBottom[0].width;
m_bReshapedFirstTime = false;
m_log.CHECK_GT(m_nBottomH, 0, "Input dimensions cannot be zero.");
m_log.CHECK_GT(m_nBottomW, 0, "Input dimensions cannot be zero.");
m_nPyramidHeight = (int)m_param.spp_param.pyramid_height;
m_colBlobSplitTopVec = new BlobCollection <T>();
m_rgPoolingBottomVec = new List <BlobCollection <T> >();
m_rgPoolingLayers = new List <PoolingLayer <T> >();
m_rgPoolingTopVecs = new List <BlobCollection <T> >();
m_colBlobPoolingOutputs = new BlobCollection <T>();
m_rgFlattenLayers = new List <FlattenLayer <T> >();
m_rgFlattenLayerTopVecs = new List <BlobCollection <T> >();
m_colBlobFlattenOutputs = new BlobCollection <T>();
m_colBlobConcatBottomVec = new BlobCollection <T>();
if (m_nPyramidHeight == 1)
{
// pooling layer setup
LayerParameter pp = getPoolingParam(0, m_nBottomH, m_nBottomW, m_param.spp_param);
m_rgPoolingLayers.Add(new PoolingLayer <T>(m_cuda, m_log, pp));
m_rgPoolingLayers[0].Setup(colBottom, colTop);
return;
}
// split layer output holders setup
for (int i = 0; i < m_nPyramidHeight; i++)
{
m_colBlobSplitTopVec.Add(new Blob <T>(m_cuda, m_log));
}
// split layer setup
LayerParameter split_param = new param.LayerParameter(LayerParameter.LayerType.SPLIT);
m_split_layer = new SplitLayer <T>(m_cuda, m_log, split_param);
m_split_layer.Setup(colBottom, m_colBlobSplitTopVec);
for (int i = 0; i < m_nPyramidHeight; i++)
{
// pooling layer input holders setup
m_rgPoolingBottomVec.Add(new BlobCollection <T>());
m_rgPoolingBottomVec[i].Add(m_colBlobSplitTopVec[i]);
// pooling layer output holders setup
m_colBlobPoolingOutputs.Add(new Blob <T>(m_cuda, m_log));
m_rgPoolingTopVecs.Add(new BlobCollection <T>());
m_rgPoolingTopVecs[i].Add(m_colBlobPoolingOutputs[i]);
// pooling layer setup
LayerParameter pooling_param = getPoolingParam(i, m_nBottomH, m_nBottomW, m_param.spp_param);
m_rgPoolingLayers.Add(new PoolingLayer <T>(m_cuda, m_log, pooling_param));
m_rgPoolingLayers[i].Setup(m_rgPoolingBottomVec[i], m_rgPoolingTopVecs[i]);
// flatten layer output holders setup
m_colBlobFlattenOutputs.Add(new Blob <T>(m_cuda, m_log));
m_rgFlattenLayerTopVecs.Add(new BlobCollection <T>());
m_rgFlattenLayerTopVecs[i].Add(m_colBlobFlattenOutputs[i]);
// flatten layer setup
LayerParameter flatten_param = new LayerParameter(LayerParameter.LayerType.FLATTEN);
m_rgFlattenLayers.Add(new FlattenLayer <T>(m_cuda, m_log, flatten_param));
m_rgFlattenLayers[i].Setup(m_rgPoolingTopVecs[i], m_rgFlattenLayerTopVecs[i]);
// concat layer input holders setup
m_colBlobConcatBottomVec.Add(m_colBlobFlattenOutputs[i]);
}
// concat layer setup
LayerParameter concat_param = new LayerParameter(LayerParameter.LayerType.CONCAT);
m_concat_layer = new ConcatLayer <T>(m_cuda, m_log, concat_param);
m_concat_layer.Setup(m_colBlobConcatBottomVec, colTop);
}
