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Add nam namespace #93

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Dec 2, 2023
Merged

Add nam namespace #93

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26 changes: 13 additions & 13 deletions NAM/activations.cpp
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Original file line number Diff line number Diff line change
@@ -1,29 +1,29 @@
#include "activations.h"

activations::ActivationTanh _TANH = activations::ActivationTanh();
activations::ActivationFastTanh _FAST_TANH = activations::ActivationFastTanh();
activations::ActivationHardTanh _HARD_TANH = activations::ActivationHardTanh();
activations::ActivationReLU _RELU = activations::ActivationReLU();
activations::ActivationSigmoid _SIGMOID = activations::ActivationSigmoid();
nam::activations::ActivationTanh _TANH = nam::activations::ActivationTanh();
nam::activations::ActivationFastTanh _FAST_TANH = nam::activations::ActivationFastTanh();
nam::activations::ActivationHardTanh _HARD_TANH = nam::activations::ActivationHardTanh();
nam::activations::ActivationReLU _RELU = nam::activations::ActivationReLU();
nam::activations::ActivationSigmoid _SIGMOID = nam::activations::ActivationSigmoid();

bool activations::Activation::using_fast_tanh = false;
bool nam::activations::Activation::using_fast_tanh = false;

std::unordered_map<std::string, activations::Activation*> activations::Activation::_activations =
std::unordered_map<std::string, nam::activations::Activation*> nam::activations::Activation::_activations =
{{"Tanh", &_TANH}, {"Hardtanh", &_HARD_TANH}, {"Fasttanh", &_FAST_TANH}, {"ReLU", &_RELU}, {"Sigmoid", &_SIGMOID}};

activations::Activation* tanh_bak = nullptr;
nam::activations::Activation* tanh_bak = nullptr;

activations::Activation* activations::Activation::get_activation(const std::string name)
nam::activations::Activation* nam::activations::Activation::get_activation(const std::string name)
{
if (_activations.find(name) == _activations.end())
return nullptr;

return _activations[name];
}

void activations::Activation::enable_fast_tanh()
void nam::activations::Activation::enable_fast_tanh()
{
activations::Activation::using_fast_tanh = true;
nam::activations::Activation::using_fast_tanh = true;

if (_activations["Tanh"] != _activations["Fasttanh"])
{
Expand All @@ -32,9 +32,9 @@ void activations::Activation::enable_fast_tanh()
}
}

void activations::Activation::disable_fast_tanh()
void nam::activations::Activation::disable_fast_tanh()
{
activations::Activation::using_fast_tanh = false;
nam::activations::Activation::using_fast_tanh = false;

if (_activations["Tanh"] == _activations["Fasttanh"])
{
Expand Down
6 changes: 4 additions & 2 deletions NAM/activations.h
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,8 @@
#include <unordered_map>
#include <Eigen/Dense>

namespace nam
{
namespace activations
{
inline float relu(float x)
Expand Down Expand Up @@ -119,5 +121,5 @@ class ActivationSigmoid : public Activation
}
}
};

}; // namespace activations
}; // namespace activations
}; // namespace nam
37 changes: 19 additions & 18 deletions NAM/convnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
#include "util.h"
#include "convnet.h"

convnet::BatchNorm::BatchNorm(const int dim, std::vector<float>::iterator& params)
nam::convnet::BatchNorm::BatchNorm(const int dim, std::vector<float>::iterator& params)
{
// Extract from param buffer
Eigen::VectorXf running_mean(dim);
Expand All @@ -37,7 +37,7 @@ convnet::BatchNorm::BatchNorm(const int dim, std::vector<float>::iterator& param
this->loc = _bias - this->scale.cwiseProduct(running_mean);
}

void convnet::BatchNorm::process_(Eigen::MatrixXf& x, const long i_start, const long i_end) const
void nam::convnet::BatchNorm::process_(Eigen::MatrixXf& x, const long i_start, const long i_end) const
{
// todo using colwise?
// #speed but conv probably dominates
Expand All @@ -48,9 +48,9 @@ void convnet::BatchNorm::process_(Eigen::MatrixXf& x, const long i_start, const
}
}

void convnet::ConvNetBlock::set_params_(const int in_channels, const int out_channels, const int _dilation,
const bool batchnorm, const std::string activation,
std::vector<float>::iterator& params)
void nam::convnet::ConvNetBlock::set_params_(const int in_channels, const int out_channels, const int _dilation,
const bool batchnorm, const std::string activation,
std::vector<float>::iterator& params)
{
this->_batchnorm = batchnorm;
// HACK 2 kernel
Expand All @@ -60,8 +60,8 @@ void convnet::ConvNetBlock::set_params_(const int in_channels, const int out_cha
this->activation = activations::Activation::get_activation(activation);
}

void convnet::ConvNetBlock::process_(const Eigen::MatrixXf& input, Eigen::MatrixXf& output, const long i_start,
const long i_end) const
void nam::convnet::ConvNetBlock::process_(const Eigen::MatrixXf& input, Eigen::MatrixXf& output, const long i_start,
const long i_end) const
{
const long ncols = i_end - i_start;
this->conv.process_(input, output, i_start, ncols, i_start);
Expand All @@ -71,30 +71,31 @@ void convnet::ConvNetBlock::process_(const Eigen::MatrixXf& input, Eigen::Matrix
this->activation->apply(output.middleCols(i_start, ncols));
}

long convnet::ConvNetBlock::get_out_channels() const
long nam::convnet::ConvNetBlock::get_out_channels() const
{
return this->conv.get_out_channels();
}

convnet::_Head::_Head(const int channels, std::vector<float>::iterator& params)
nam::convnet::_Head::_Head(const int channels, std::vector<float>::iterator& params)
{
this->_weight.resize(channels);
for (int i = 0; i < channels; i++)
this->_weight[i] = *(params++);
this->_bias = *(params++);
}

void convnet::_Head::process_(const Eigen::MatrixXf& input, Eigen::VectorXf& output, const long i_start,
const long i_end) const
void nam::convnet::_Head::process_(const Eigen::MatrixXf& input, Eigen::VectorXf& output, const long i_start,
const long i_end) const
{
const long length = i_end - i_start;
output.resize(length);
for (long i = 0, j = i_start; i < length; i++, j++)
output(i) = this->_bias + input.col(j).dot(this->_weight);
}

convnet::ConvNet::ConvNet(const int channels, const std::vector<int>& dilations, const bool batchnorm,
const std::string activation, std::vector<float>& params, const double expected_sample_rate)
nam::convnet::ConvNet::ConvNet(const int channels, const std::vector<int>& dilations, const bool batchnorm,
const std::string activation, std::vector<float>& params,
const double expected_sample_rate)
: Buffer(*std::max_element(dilations.begin(), dilations.end()), expected_sample_rate)
{
this->_verify_params(channels, dilations, batchnorm, params.size());
Expand All @@ -116,7 +117,7 @@ convnet::ConvNet::ConvNet(const int channels, const std::vector<int>& dilations,
}


void convnet::ConvNet::process(NAM_SAMPLE* input, NAM_SAMPLE* output, const int num_frames)
void nam::convnet::ConvNet::process(NAM_SAMPLE* input, NAM_SAMPLE* output, const int num_frames)

{
this->_update_buffers_(input, num_frames);
Expand All @@ -135,13 +136,13 @@ void convnet::ConvNet::process(NAM_SAMPLE* input, NAM_SAMPLE* output, const int
output[s] = this->_head_output(s);
}

void convnet::ConvNet::_verify_params(const int channels, const std::vector<int>& dilations, const bool batchnorm,
const size_t actual_params)
void nam::convnet::ConvNet::_verify_params(const int channels, const std::vector<int>& dilations, const bool batchnorm,
const size_t actual_params)
{
// TODO
}

void convnet::ConvNet::_update_buffers_(NAM_SAMPLE* input, const int num_frames)
void nam::convnet::ConvNet::_update_buffers_(NAM_SAMPLE* input, const int num_frames)
{
this->Buffer::_update_buffers_(input, num_frames);

Expand All @@ -163,7 +164,7 @@ void convnet::ConvNet::_update_buffers_(NAM_SAMPLE* input, const int num_frames)
}
}

void convnet::ConvNet::_rewind_buffers_()
void nam::convnet::ConvNet::_rewind_buffers_()
{
// Need to rewind the block vals first because Buffer::rewind_buffers()
// resets the offset index
Expand Down
3 changes: 3 additions & 0 deletions NAM/convnet.h
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@

#include <Eigen/Dense>

namespace nam
{
namespace convnet
{
// Custom Conv that avoids re-computing on pieces of the input and trusts
Expand Down Expand Up @@ -82,3 +84,4 @@ class ConvNet : public Buffer
void process(NAM_SAMPLE* input, NAM_SAMPLE* output, const int num_frames) override;
};
}; // namespace convnet
}; // namespace nam
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