Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Pass in const reference to weights #97

Draft
wants to merge 1 commit into
base: main
Choose a base branch
from
Draft

Pass in const reference to weights #97

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions NAM/convnet.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
#include "dsp.h"
#include "convnet.h"

nam::convnet::BatchNorm::BatchNorm(const int dim, std::vector<float>::iterator& weights)
nam::convnet::BatchNorm::BatchNorm(const int dim, std::vector<float>::const_iterator& weights)
{
// Extract from param buffer
Eigen::VectorXf running_mean(dim);
Expand Down Expand Up @@ -48,7 +48,7 @@ void nam::convnet::BatchNorm::process_(Eigen::MatrixXf& x, const long i_start, c

void nam::convnet::ConvNetBlock::set_weights_(const int in_channels, const int out_channels, const int _dilation,
const bool batchnorm, const std::string activation,
std::vector<float>::iterator& weights)
std::vector<float>::const_iterator& weights)
{
this->_batchnorm = batchnorm;
// HACK 2 kernel
Expand All @@ -74,7 +74,7 @@ long nam::convnet::ConvNetBlock::get_out_channels() const
return this->conv.get_out_channels();
}

nam::convnet::_Head::_Head(const int channels, std::vector<float>::iterator& weights)
nam::convnet::_Head::_Head(const int channels, std::vector<float>::const_iterator& weights)
{
this->_weight.resize(channels);
for (int i = 0; i < channels; i++)
Expand All @@ -92,13 +92,13 @@ void nam::convnet::_Head::process_(const Eigen::MatrixXf& input, Eigen::VectorXf
}

nam::convnet::ConvNet::ConvNet(const int channels, const std::vector<int>& dilations, const bool batchnorm,
const std::string activation, std::vector<float>& weights,
const std::string activation, const std::vector<float>& weights,
const double expected_sample_rate)
: Buffer(*std::max_element(dilations.begin(), dilations.end()), expected_sample_rate)
{
this->_verify_weights(channels, dilations, batchnorm, weights.size());
this->_blocks.resize(dilations.size());
std::vector<float>::iterator it = weights.begin();
std::vector<float>::const_iterator it = weights.begin();
for (size_t i = 0; i < dilations.size(); i++)
this->_blocks[i].set_weights_(i == 0 ? 1 : channels, channels, dilations[i], batchnorm, activation, it);
this->_block_vals.resize(this->_blocks.size() + 1);
Expand Down
8 changes: 4 additions & 4 deletions NAM/convnet.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ class BatchNorm
{
public:
BatchNorm(){};
BatchNorm(const int dim, std::vector<float>::iterator& weights);
BatchNorm(const int dim, std::vector<float>::const_iterator& weights);
void process_(Eigen::MatrixXf& input, const long i_start, const long i_end) const;

private:
Expand All @@ -41,7 +41,7 @@ class ConvNetBlock
public:
ConvNetBlock(){};
void set_weights_(const int in_channels, const int out_channels, const int _dilation, const bool batchnorm,
const std::string activation, std::vector<float>::iterator& weights);
const std::string activation, std::vector<float>::const_iterator& weights);
void process_(const Eigen::MatrixXf& input, Eigen::MatrixXf& output, const long i_start, const long i_end) const;
long get_out_channels() const;
Conv1D conv;
Expand All @@ -56,7 +56,7 @@ class _Head
{
public:
_Head(){};
_Head(const int channels, std::vector<float>::iterator& weights);
_Head(const int channels, std::vector<float>::const_iterator& weights);
void process_(const Eigen::MatrixXf& input, Eigen::VectorXf& output, const long i_start, const long i_end) const;

private:
Expand All @@ -68,7 +68,7 @@ class ConvNet : public Buffer
{
public:
ConvNet(const int channels, const std::vector<int>& dilations, const bool batchnorm, const std::string activation,
std::vector<float>& weights, const double expected_sample_rate = -1.0);
const std::vector<float>& weights, const double expected_sample_rate = -1.0);
~ConvNet() = default;

protected:
Expand Down
6 changes: 3 additions & 3 deletions NAM/dsp.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,7 @@ void nam::Linear::process(NAM_SAMPLE* input, NAM_SAMPLE* output, const int num_f

// NN modules =================================================================

void nam::Conv1D::set_weights_(std::vector<float>::iterator& weights)
void nam::Conv1D::set_weights_(std::vector<float>::const_iterator& weights)
{
if (this->_weight.size() > 0)
{
Expand Down Expand Up @@ -198,7 +198,7 @@ void nam::Conv1D::set_size_(const int in_channels, const int out_channels, const
}

void nam::Conv1D::set_size_and_weights_(const int in_channels, const int out_channels, const int kernel_size,
const int _dilation, const bool do_bias, std::vector<float>::iterator& weights)
const int _dilation, const bool do_bias, std::vector<float>::const_iterator& weights)
{
this->set_size_(in_channels, out_channels, kernel_size, do_bias, _dilation);
this->set_weights_(weights);
Expand Down Expand Up @@ -236,7 +236,7 @@ nam::Conv1x1::Conv1x1(const int in_channels, const int out_channels, const bool
this->_bias.resize(out_channels);
}

void nam::Conv1x1::set_weights_(std::vector<float>::iterator& weights)
void nam::Conv1x1::set_weights_(std::vector<float>::const_iterator& weights)
{
for (int i = 0; i < this->_weight.rows(); i++)
for (int j = 0; j < this->_weight.cols(); j++)
Expand Down
6 changes: 3 additions & 3 deletions NAM/dsp.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -124,11 +124,11 @@ class Conv1D
{
public:
Conv1D() { this->_dilation = 1; };
void set_weights_(std::vector<float>::iterator& weights);
void set_weights_(std::vector<float>::const_iterator& weights);
void set_size_(const int in_channels, const int out_channels, const int kernel_size, const bool do_bias,
const int _dilation);
void set_size_and_weights_(const int in_channels, const int out_channels, const int kernel_size, const int _dilation,
const bool do_bias, std::vector<float>::iterator& weights);
const bool do_bias, std::vector<float>::const_iterator& weights);
// Process from input to output
// Rightmost indices of input go from i_start to i_end,
// Indices on output for from j_start (to j_start + i_end - i_start)
Expand All @@ -153,7 +153,7 @@ class Conv1x1
{
public:
Conv1x1(const int in_channels, const int out_channels, const bool _bias);
void set_weights_(std::vector<float>::iterator& weights);
void set_weights_(std::vector<float>::const_iterator& weights);
// :param input: (N,Cin) or (Cin,)
// :return: (N,Cout) or (Cout,), respectively
Eigen::MatrixXf process(const Eigen::MatrixXf& input) const;
Expand Down
6 changes: 3 additions & 3 deletions NAM/lstm.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

#include "lstm.h"

nam::lstm::LSTMCell::LSTMCell(const int input_size, const int hidden_size, std::vector<float>::iterator& weights)
nam::lstm::LSTMCell::LSTMCell(const int input_size, const int hidden_size, std::vector<float>::const_iterator& weights)
{
// Resize arrays
this->_w.resize(4 * hidden_size, input_size + hidden_size);
Expand Down Expand Up @@ -63,12 +63,12 @@ void nam::lstm::LSTMCell::process_(const Eigen::VectorXf& x)
}
}

nam::lstm::LSTM::LSTM(const int num_layers, const int input_size, const int hidden_size, std::vector<float>& weights,
nam::lstm::LSTM::LSTM(const int num_layers, const int input_size, const int hidden_size, const std::vector<float>& weights,
const double expected_sample_rate)
: DSP(expected_sample_rate)
{
this->_input.resize(1);
std::vector<float>::iterator it = weights.begin();
std::vector<float>::const_iterator it = weights.begin();
for (int i = 0; i < num_layers; i++)
this->_layers.push_back(LSTMCell(i == 0 ? input_size : hidden_size, hidden_size, it));
this->_head_weight.resize(hidden_size);
Expand Down
4 changes: 2 additions & 2 deletions NAM/lstm.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ namespace lstm
class LSTMCell
{
public:
LSTMCell(const int input_size, const int hidden_size, std::vector<float>::iterator& weights);
LSTMCell(const int input_size, const int hidden_size, std::vector<float>::const_iterator& weights);
Eigen::VectorXf get_hidden_state() const { return this->_xh(Eigen::placeholders::lastN(this->_get_hidden_size())); };
void process_(const Eigen::VectorXf& x);

Expand Down Expand Up @@ -50,7 +50,7 @@ class LSTMCell
class LSTM : public DSP
{
public:
LSTM(const int num_layers, const int input_size, const int hidden_size, std::vector<float>& weights,
LSTM(const int num_layers, const int input_size, const int hidden_size, const std::vector<float>& weights,
const double expected_sample_rate = -1.0);
~LSTM() = default;

Expand Down
12 changes: 6 additions & 6 deletions NAM/wavenet.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ nam::wavenet::_DilatedConv::_DilatedConv(const int in_channels, const int out_ch
this->set_size_(in_channels, out_channels, kernel_size, bias, dilation);
}

void nam::wavenet::_Layer::set_weights_(std::vector<float>::iterator& weights)
void nam::wavenet::_Layer::set_weights_(std::vector<float>::const_iterator& weights)
{
this->_conv.set_weights_(weights);
this->_input_mixin.set_weights_(weights);
Expand Down Expand Up @@ -133,7 +133,7 @@ void nam::wavenet::_LayerArray::set_num_frames_(const long num_frames)
this->_layers[i].set_num_frames_(num_frames);
}

void nam::wavenet::_LayerArray::set_weights_(std::vector<float>::iterator& weights)
void nam::wavenet::_LayerArray::set_weights_(std::vector<float>::const_iterator& weights)
{
this->_rechannel.set_weights_(weights);
for (size_t i = 0; i < this->_layers.size(); i++)
Expand Down Expand Up @@ -186,7 +186,7 @@ nam::wavenet::_Head::_Head(const int input_size, const int num_layers, const int
}
}

void nam::wavenet::_Head::set_weights_(std::vector<float>::iterator& weights)
void nam::wavenet::_Head::set_weights_(std::vector<float>::const_iterator& weights)
{
for (size_t i = 0; i < this->_layers.size(); i++)
this->_layers[i].set_weights_(weights);
Expand Down Expand Up @@ -231,7 +231,7 @@ void nam::wavenet::_Head::_apply_activation_(Eigen::MatrixXf& x)
// WaveNet ====================================================================

nam::wavenet::WaveNet::WaveNet(const std::vector<nam::wavenet::LayerArrayParams>& layer_array_params,
const float head_scale, const bool with_head, std::vector<float> weights,
const float head_scale, const bool with_head, const std::vector<float>& weights,
const double expected_sample_rate)
: DSP(expected_sample_rate)
, _num_frames(0)
Expand Down Expand Up @@ -272,9 +272,9 @@ void nam::wavenet::WaveNet::finalize_(const int num_frames)
this->_advance_buffers_(num_frames);
}

void nam::wavenet::WaveNet::set_weights_(std::vector<float>& weights)
void nam::wavenet::WaveNet::set_weights_(const std::vector<float>& weights)
{
std::vector<float>::iterator it = weights.begin();
std::vector<float>::const_iterator it = weights.begin();
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].set_weights_(it);
// this->_head.set_params_(it);
Expand Down
10 changes: 5 additions & 5 deletions NAM/wavenet.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ class _Layer
, _1x1(channels, channels, true)
, _activation(activations::Activation::get_activation(activation))
, _gated(gated){};
void set_weights_(std::vector<float>::iterator& weights);
void set_weights_(std::vector<float>::const_iterator& weights);
// :param `input`: from previous layer
// :param `output`: to next layer
void process_(const Eigen::MatrixXf& input, const Eigen::MatrixXf& condition, Eigen::MatrixXf& head_input,
Expand Down Expand Up @@ -108,7 +108,7 @@ class _LayerArray
Eigen::MatrixXf& head_outputs // post head-rechannel
);
void set_num_frames_(const long num_frames);
void set_weights_(std::vector<float>::iterator& it);
void set_weights_(std::vector<float>::const_iterator& it);

// "Zero-indexed" receptive field.
// E.g. a 1x1 convolution has a z.i.r.f. of zero.
Expand Down Expand Up @@ -144,7 +144,7 @@ class _Head
{
public:
_Head(const int input_size, const int num_layers, const int channels, const std::string activation);
void set_weights_(std::vector<float>::iterator& weights);
void set_weights_(std::vector<float>::const_iterator& weights);
// NOTE: the head transforms the provided input by applying a nonlinearity
// to it in-place!
void process_(Eigen::MatrixXf& inputs, Eigen::MatrixXf& outputs);
Expand All @@ -169,11 +169,11 @@ class WaveNet : public DSP
{
public:
WaveNet(const std::vector<LayerArrayParams>& layer_array_params, const float head_scale, const bool with_head,
std::vector<float> weights, const double expected_sample_rate = -1.0);
const std::vector<float>& weights, const double expected_sample_rate = -1.0);
~WaveNet() = default;

void finalize_(const int num_frames) override;
void set_weights_(std::vector<float>& weights);
void set_weights_(const std::vector<float>& weights);

private:
long _num_frames;
Expand Down