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Merged
sdatkinson merged 1 commit into from
Jun 20, 2023
Merged

Fix compiler warnings #53

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14 changes: 7 additions & 7 deletions NAM/convnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,7 @@ convnet::ConvNet::ConvNet(const double loudness, const int channels, const std::
this->_verify_params(channels, dilations, batchnorm, params.size());
this->_blocks.resize(dilations.size());
std::vector<float>::iterator it = params.begin();
for (int i = 0; i < dilations.size(); i++)
for (size_t i = 0; i < dilations.size(); i++)
this->_blocks[i].set_params_(i == 0 ? 1 : channels, channels, dilations[i], batchnorm, activation, it);
this->_block_vals.resize(this->_blocks.size() + 1);
this->_head = _Head(channels, it);
Expand All @@ -125,7 +125,7 @@ void convnet::ConvNet::_process_core_()
// TODO one unnecessary copy :/ #speed
for (auto i = i_start; i < i_end; i++)
this->_block_vals[0](0, i) = this->_input_buffer[i];
for (auto i = 0; i < this->_blocks.size(); i++)
for (size_t i = 0; i < this->_blocks.size(); i++)
this->_blocks[i].process_(this->_block_vals[i], this->_block_vals[i + 1], i_start, i_end);
// TODO clean up this allocation
this->_head.process_(this->_block_vals[this->_blocks.size()], this->_head_output, i_start, i_end);
Expand All @@ -145,9 +145,9 @@ void convnet::ConvNet::_verify_params(const int channels, const std::vector<int>
void convnet::ConvNet::_update_buffers_()
{
this->Buffer::_update_buffers_();
const long buffer_size = this->_input_buffer.size();
const size_t buffer_size = this->_input_buffer.size();
this->_block_vals[0].resize(1, buffer_size);
for (long i = 1; i < this->_block_vals.size(); i++)
for (size_t i = 1; i < this->_block_vals.size(); i++)
this->_block_vals[i].resize(this->_blocks[i - 1].get_out_channels(), buffer_size);
}

Expand All @@ -157,7 +157,7 @@ void convnet::ConvNet::_rewind_buffers_()
// resets the offset index
// The last _block_vals is the output of the last block and doesn't need to be
// rewound.
for (long k = 0; k < this->_block_vals.size() - 1; k++)
for (size_t k = 0; k < this->_block_vals.size() - 1; k++)
{
// We actually don't need to pull back a lot...just as far as the first
// input sample would grab from dilation
Expand All @@ -176,7 +176,7 @@ void convnet::ConvNet::_anti_pop_()
if (this->_anti_pop_countdown >= this->_anti_pop_ramp)
return;
const float slope = 1.0f / float(this->_anti_pop_ramp);
for (int i = 0; i < this->_core_dsp_output.size(); i++)
for (size_t i = 0; i < this->_core_dsp_output.size(); i++)
{
if (this->_anti_pop_countdown >= this->_anti_pop_ramp)
break;
Expand All @@ -190,7 +190,7 @@ void convnet::ConvNet::_reset_anti_pop_()
{
// You need the "real" receptive field, not the buffers.
long receptive_field = 1;
for (int i = 0; i < this->_blocks.size(); i++)
for (size_t i = 0; i < this->_blocks.size(); i++)
receptive_field += this->_blocks[i].conv.get_dilation();
this->_anti_pop_countdown = -receptive_field;
}
24 changes: 12 additions & 12 deletions NAM/dsp.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ void DSP::_apply_input_level_(double** inputs, const int num_channels, const int
{
// Must match exactly; we're going to use the size of _input_post_gain later
// for num_frames.
if (this->_input_post_gain.size() != num_frames)
if ((int)this->_input_post_gain.size() != num_frames)
this->_input_post_gain.resize(num_frames);
// MONO ONLY
const int channel = 0;
Expand All @@ -79,7 +79,7 @@ void DSP::_ensure_core_dsp_output_ready_()
void DSP::_process_core_()
{
// Default implementation is the null operation
for (int i = 0; i < this->_input_post_gain.size(); i++)
for (size_t i = 0; i < this->_input_post_gain.size(); i++)
this->_core_dsp_output[i] = this->_input_post_gain[i];
}

Expand Down Expand Up @@ -124,7 +124,7 @@ void Buffer::_update_buffers_()
// frames needed!
{
const long minimum_input_buffer_size = (long)this->_receptive_field + _INPUT_BUFFER_SAFETY_FACTOR * num_frames;
if (this->_input_buffer.size() < minimum_input_buffer_size)
if ((long)this->_input_buffer.size() < minimum_input_buffer_size)
{
long new_buffer_size = 2;
while (new_buffer_size < minimum_input_buffer_size)
Expand All @@ -135,7 +135,7 @@ void Buffer::_update_buffers_()

// If we'd run off the end of the input buffer, then we need to move the data
// back to the start of the buffer and start again.
if (this->_input_buffer_offset + num_frames > this->_input_buffer.size())
if (this->_input_buffer_offset + num_frames > (long)this->_input_buffer.size())
this->_rewind_buffers_();
// Put the new samples into the input buffer
for (long i = this->_input_buffer_offset, j = 0; j < num_frames; i++, j++)
Expand Down Expand Up @@ -179,7 +179,7 @@ Linear::Linear(const int receptive_field, const bool _bias, const std::vector<fl
Linear::Linear(const double loudness, const int receptive_field, const bool _bias, const std::vector<float>& params)
: Buffer(loudness, receptive_field)
{
if (params.size() != (receptive_field + (_bias ? 1 : 0)))
if ((int)params.size() != (receptive_field + (_bias ? 1 : 0)))
throw std::runtime_error(
"Params vector does not match expected size based "
"on architecture parameters");
Expand All @@ -196,9 +196,9 @@ void Linear::_process_core_()
this->Buffer::_update_buffers_();

// Main computation!
for (long i = 0; i < this->_input_post_gain.size(); i++)
for (size_t i = 0; i < this->_input_post_gain.size(); i++)
{
const long offset = this->_input_buffer_offset - this->_weight.size() + i + 1;
const size_t offset = this->_input_buffer_offset - this->_weight.size() + i + 1;
auto input = Eigen::Map<const Eigen::VectorXf>(&this->_input_buffer[offset], this->_receptive_field);
this->_core_dsp_output[i] = this->_bias + this->_weight.dot(input);
}
Expand All @@ -215,18 +215,18 @@ void Conv1D::set_params_(std::vector<float>::iterator& params)
// Crazy ordering because that's how it gets flattened.
for (auto i = 0; i < out_channels; i++)
for (auto j = 0; j < in_channels; j++)
for (auto k = 0; k < this->_weight.size(); k++)
for (size_t k = 0; k < this->_weight.size(); k++)
this->_weight[k](i, j) = *(params++);
}
for (int i = 0; i < this->_bias.size(); i++)
for (long i = 0; i < this->_bias.size(); i++)
this->_bias(i) = *(params++);
}

void Conv1D::set_size_(const int in_channels, const int out_channels, const int kernel_size, const bool do_bias,
const int _dilation)
{
this->_weight.resize(kernel_size);
for (int i = 0; i < this->_weight.size(); i++)
for (size_t i = 0; i < this->_weight.size(); i++)
this->_weight[i].resize(out_channels,
in_channels); // y = Ax, input array (C,L)
if (do_bias)
Expand All @@ -247,7 +247,7 @@ void Conv1D::process_(const Eigen::MatrixXf& input, Eigen::MatrixXf& output, con
const long j_start) const
{
// This is the clever part ;)
for (long k = 0; k < this->_weight.size(); k++)
for (size_t k = 0; k < this->_weight.size(); k++)
{
const long offset = this->_dilation * (k + 1 - this->_weight.size());
if (k == 0)
Expand All @@ -262,7 +262,7 @@ void Conv1D::process_(const Eigen::MatrixXf& input, Eigen::MatrixXf& output, con
long Conv1D::get_num_params() const
{
long num_params = this->_bias.size();
for (long i = 0; i < this->_weight.size(); i++)
for (size_t i = 0; i < this->_weight.size(); i++)
num_params += this->_weight[i].size();
return num_params;
}
Expand Down
6 changes: 3 additions & 3 deletions NAM/get_dsp.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ std::unique_ptr<DSP> get_dsp(dspData& conf)
const int channels = config["channels"];
const bool batchnorm = config["batchnorm"];
std::vector<int> dilations;
for (int i = 0; i < config["dilations"].size(); i++)
for (size_t i = 0; i < config["dilations"].size(); i++)
dilations.push_back(config["dilations"][i]);
const std::string activation = config["activation"];
return std::make_unique<convnet::ConvNet>(loudness, channels, dilations, batchnorm, activation, params);
Expand All @@ -173,11 +173,11 @@ std::unique_ptr<DSP> get_dsp(dspData& conf)
else if (architecture == "WaveNet" || architecture == "CatWaveNet")
{
std::vector<wavenet::LayerArrayParams> layer_array_params;
for (int i = 0; i < config["layers"].size(); i++)
for (size_t i = 0; i < config["layers"].size(); i++)
{
nlohmann::json layer_config = config["layers"][i];
std::vector<int> dilations;
for (int j = 0; j < layer_config["dilations"].size(); j++)
for (size_t j = 0; j < layer_config["dilations"].size(); j++)
dilations.push_back(layer_config["dilations"][j]);
layer_array_params.push_back(
wavenet::LayerArrayParams(layer_config["input_size"], layer_config["condition_size"], layer_config["head_size"],
Expand Down
4 changes: 2 additions & 2 deletions NAM/lstm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -111,7 +111,7 @@ void lstm::LSTM::_process_core_()
this->_stale_params = false;
}
// Process samples, placing results in the required output location
for (int i = 0; i < this->_input_post_gain.size(); i++)
for (size_t i = 0; i < this->_input_post_gain.size(); i++)
this->_core_dsp_output[i] = this->_process_sample(this->_input_post_gain[i]);
}

Expand All @@ -121,7 +121,7 @@ float lstm::LSTM::_process_sample(const float x)
return x;
this->_input_and_params(0) = x;
this->_layers[0].process_(this->_input_and_params);
for (int i = 1; i < this->_layers.size(); i++)
for (size_t i = 1; i < this->_layers.size(); i++)
this->_layers[i].process_(this->_layers[i - 1].get_hidden_state());
return this->_head_weight.dot(this->_layers[this->_layers.size() - 1].get_hidden_state()) + this->_head_bias;
}
50 changes: 25 additions & 25 deletions NAM/wavenet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -61,10 +61,10 @@ wavenet::_LayerArray::_LayerArray(const int input_size, const int condition_size
: _rechannel(input_size, channels, false)
, _head_rechannel(channels, head_size, head_bias)
{
for (int i = 0; i < dilations.size(); i++)
for (size_t i = 0; i < dilations.size(); i++)
this->_layers.push_back(_Layer(condition_size, channels, kernel_size, dilations[i], activation, gated));
const long receptive_field = this->_get_receptive_field();
for (int i = 0; i < dilations.size(); i++)
for (size_t i = 0; i < dilations.size(); i++)
{
this->_layer_buffers.push_back(Eigen::MatrixXf(channels, LAYER_ARRAY_BUFFER_SIZE + receptive_field - 1));
this->_layer_buffers[i].setZero();
Expand All @@ -80,7 +80,7 @@ void wavenet::_LayerArray::advance_buffers_(const int num_frames)
long wavenet::_LayerArray::get_receptive_field() const
{
long result = 0;
for (int i = 0; i < this->_layers.size(); i++)
for (size_t i = 0; i < this->_layers.size(); i++)
result += this->_layers[i].get_dilation() * (this->_layers[i].get_kernel_size() - 1);
return result;
}
Expand All @@ -103,8 +103,8 @@ void wavenet::_LayerArray::process_(const Eigen::MatrixXf& layer_inputs, const E
Eigen::MatrixXf& head_outputs)
{
this->_layer_buffers[0].middleCols(this->_buffer_start, layer_inputs.cols()) = this->_rechannel.process(layer_inputs);
const long last_layer = this->_layers.size() - 1;
for (auto i = 0; i < this->_layers.size(); i++)
const size_t last_layer = this->_layers.size() - 1;
for (size_t i = 0; i < this->_layers.size(); i++)
{
this->_layers[i].process_(this->_layer_buffers[i], condition, head_inputs,
i == last_layer ? layer_outputs : this->_layer_buffers[i + 1], this->_buffer_start,
Expand All @@ -125,14 +125,14 @@ void wavenet::_LayerArray::set_num_frames_(const long num_frames)
<< "); copy errors could occur!\n";
throw std::runtime_error(ss.str().c_str());
}
for (int i = 0; i < this->_layers.size(); i++)
for (size_t i = 0; i < this->_layers.size(); i++)
this->_layers[i].set_num_frames_(num_frames);
}

void wavenet::_LayerArray::set_params_(std::vector<float>::iterator& params)
{
this->_rechannel.set_params_(params);
for (int i = 0; i < this->_layers.size(); i++)
for (size_t i = 0; i < this->_layers.size(); i++)
this->_layers[i].set_params_(params);
this->_head_rechannel.set_params_(params);
}
Expand All @@ -146,7 +146,7 @@ long wavenet::_LayerArray::_get_receptive_field() const
{
// TODO remove this and use get_receptive_field() instead!
long res = 1;
for (int i = 0; i < this->_layers.size(); i++)
for (size_t i = 0; i < this->_layers.size(); i++)
res += (this->_layers[i].get_kernel_size() - 1) * this->_layers[i].get_dilation();
return res;
}
Expand All @@ -156,7 +156,7 @@ void wavenet::_LayerArray::_rewind_buffers_()
// Can make this smaller--largest dilation, not receptive field!
{
const long start = this->_get_receptive_field() - 1;
for (int i = 0; i < this->_layer_buffers.size(); i++)
for (size_t i = 0; i < this->_layer_buffers.size(); i++)
{
const long d = (this->_layers[i].get_kernel_size() - 1) * this->_layers[i].get_dilation();
this->_layer_buffers[i].middleCols(start - d, d) = this->_layer_buffers[i].middleCols(this->_buffer_start - d, d);
Expand All @@ -168,8 +168,8 @@ void wavenet::_LayerArray::_rewind_buffers_()

wavenet::_Head::_Head(const int input_size, const int num_layers, const int channels, const std::string activation)
: _channels(channels)
, _activation(activations::Activation::get_activation(activation))
, _head(num_layers > 0 ? channels : input_size, 1, true)
, _activation(activations::Activation::get_activation(activation))
{
assert(num_layers > 0);
int dx = input_size;
Expand All @@ -184,7 +184,7 @@ wavenet::_Head::_Head(const int input_size, const int num_layers, const int chan

void wavenet::_Head::set_params_(std::vector<float>::iterator& params)
{
for (int i = 0; i < this->_layers.size(); i++)
for (size_t i = 0; i < this->_layers.size(); i++)
this->_layers[i].set_params_(params);
}

Expand All @@ -197,7 +197,7 @@ void wavenet::_Head::process_(Eigen::MatrixXf& inputs, Eigen::MatrixXf& outputs)
else
{
this->_buffers[0] = this->_layers[0].process(inputs);
for (int i = 1; i < num_layers; i++)
for (size_t i = 1; i < num_layers; i++)
{ // Asserted > 0 layers
this->_apply_activation_(this->_buffers[i - 1]);
if (i < num_layers - 1)
Expand All @@ -210,7 +210,7 @@ void wavenet::_Head::process_(Eigen::MatrixXf& inputs, Eigen::MatrixXf& outputs)

void wavenet::_Head::set_num_frames_(const long num_frames)
{
for (int i = 0; i < this->_buffers.size(); i++)
for (size_t i = 0; i < this->_buffers.size(); i++)
this->_buffers[i].resize(this->_channels, num_frames);
}

Expand All @@ -237,7 +237,7 @@ wavenet::WaveNet::WaveNet(const double loudness, const std::vector<wavenet::Laye
if (with_head)
throw std::runtime_error("Head not implemented!");
this->_init_parametric_(parametric);
for (int i = 0; i < layer_array_params.size(); i++)
for (size_t i = 0; i < layer_array_params.size(); i++)
{
this->_layer_arrays.push_back(wavenet::_LayerArray(
layer_array_params[i].input_size, layer_array_params[i].condition_size, layer_array_params[i].head_size,
Expand Down Expand Up @@ -270,14 +270,14 @@ void wavenet::WaveNet::finalize_(const int num_frames)
void wavenet::WaveNet::set_params_(std::vector<float>& params)
{
std::vector<float>::iterator it = params.begin();
for (int i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].set_params_(it);
// this->_head.set_params_(it);
this->_head_scale = *(it++);
if (it != params.end())
{
std::stringstream ss;
for (int i = 0; i < params.size(); i++)
for (size_t i = 0; i < params.size(); i++)
if (params[i] == *it)
{
ss << "Parameter mismatch: assigned " << i + 1 << " parameters, but " << params.size() << " were provided.";
Expand All @@ -290,7 +290,7 @@ void wavenet::WaveNet::set_params_(std::vector<float>& params)

void wavenet::WaveNet::_advance_buffers_(const int num_frames)
{
for (int i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].advance_buffers_(num_frames);
}

Expand All @@ -304,7 +304,7 @@ void wavenet::WaveNet::_init_parametric_(nlohmann::json& parametric)

void wavenet::WaveNet::_prepare_for_frames_(const long num_frames)
{
for (auto i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].prepare_for_frames_(num_frames);
}

Expand All @@ -326,15 +326,15 @@ void wavenet::WaveNet::_process_core_()
this->_condition(0, j) = this->_input_post_gain[j];
if (this->_stale_params) // Column-major assignment; good for Eigen. Let the
// compiler optimize this.
for (int i = 0; i < this->_param_names.size(); i++)
for (size_t i = 0; i < this->_param_names.size(); i++)
this->_condition(i + 1, j) = (float)this->_params[this->_param_names[i]];
}

// Main layer arrays:
// Layer-to-layer
// Sum on head output
this->_head_arrays[0].setZero();
for (int i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].process_(i == 0 ? this->_condition : this->_layer_array_outputs[i - 1], this->_condition,
this->_head_arrays[i], this->_layer_array_outputs[i], this->_head_arrays[i + 1]);
// this->_head.process_(
Expand Down Expand Up @@ -365,13 +365,13 @@ void wavenet::WaveNet::_set_num_frames_(const long num_frames)
return;

this->_condition.resize(1 + this->_param_names.size(), num_frames);
for (int i = 0; i < this->_head_arrays.size(); i++)
for (size_t i = 0; i < this->_head_arrays.size(); i++)
this->_head_arrays[i].resize(this->_head_arrays[i].rows(), num_frames);
for (int i = 0; i < this->_layer_array_outputs.size(); i++)
for (size_t i = 0; i < this->_layer_array_outputs.size(); i++)
this->_layer_array_outputs[i].resize(this->_layer_array_outputs[i].rows(), num_frames);
this->_head_output.resize(this->_head_output.rows(), num_frames);

for (int i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
this->_layer_arrays[i].set_num_frames_(num_frames);
// this->_head.set_num_frames_(num_frames);
this->_num_frames = num_frames;
Expand All @@ -382,7 +382,7 @@ void wavenet::WaveNet::_anti_pop_()
if (this->_anti_pop_countdown >= this->_anti_pop_ramp)
return;
const float slope = 1.0f / float(this->_anti_pop_ramp);
for (int i = 0; i < this->_core_dsp_output.size(); i++)
for (size_t i = 0; i < this->_core_dsp_output.size(); i++)
{
if (this->_anti_pop_countdown >= this->_anti_pop_ramp)
break;
Expand All @@ -396,7 +396,7 @@ void wavenet::WaveNet::_reset_anti_pop_()
{
// You need the "real" receptive field, not the buffers.
long receptive_field = 1;
for (int i = 0; i < this->_layer_arrays.size(); i++)
for (size_t i = 0; i < this->_layer_arrays.size(); i++)
receptive_field += this->_layer_arrays[i].get_receptive_field();
this->_anti_pop_countdown = -receptive_field;
}
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