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keras_model.cc
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keras_model.cc
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#include "keras_model.h"
#include <iostream>
#include <fstream>
#include <algorithm>
#include <math.h>
using namespace std;
std::vector<float> keras::read_1d_array(std::ifstream &fin, int cols) {
vector<float> arr;
float tmp_float;
char tmp_char;
fin >> tmp_char; // for '['
for(int n = 0; n < cols; ++n) {
fin >> tmp_float;
arr.push_back(tmp_float);
}
fin >> tmp_char; // for ']'
return arr;
}
void keras::DataChunk2D::read_from_file(const std::string &fname) {
ifstream fin(fname.c_str());
fin >> m_depth >> m_rows >> m_cols;
for(int d = 0; d < m_depth; ++d) {
vector<vector<float> > tmp_single_depth;
for(int r = 0; r < m_rows; ++r) {
vector<float> tmp_row = keras::read_1d_array(fin, m_cols);
tmp_single_depth.push_back(tmp_row);
}
data.push_back(tmp_single_depth);
}
fin.close();
}
void keras::LayerConv2D::load_weights(std::ifstream &fin) {
char tmp_char = ' ';
string tmp_str = "";
float tmp_float;
bool skip = false;
fin >> m_kernels_cnt >> m_depth >> m_rows >> m_cols >> m_border_mode;
if (m_border_mode == "[") { m_border_mode = "valid"; skip = true; }
//cout << "LayerConv2D " << m_kernels_cnt << "x" << m_depth << "x" << m_rows <<
// "x" << m_cols << " border_mode " << m_border_mode << endl;
// reading kernel weights
for(int k = 0; k < m_kernels_cnt; ++k) {
vector<vector<vector<float> > > tmp_depths;
for(int d = 0; d < m_depth; ++d) {
vector<vector<float> > tmp_single_depth;
for(int r = 0; r < m_rows; ++r) {
if (!skip) { fin >> tmp_char; } // for '['
else { skip = false; }
vector<float> tmp_row;
for(int c = 0; c < m_cols; ++c) {
fin >> tmp_float;
tmp_row.push_back(tmp_float);
}
fin >> tmp_char; // for ']'
tmp_single_depth.push_back(tmp_row);
}
tmp_depths.push_back(tmp_single_depth);
}
m_kernels.push_back(tmp_depths);
}
// reading kernel biases
fin >> tmp_char; // for '['
for(int k = 0; k < m_kernels_cnt; ++k) {
fin >> tmp_float;
m_bias.push_back(tmp_float);
}
fin >> tmp_char; // for ']'
}
void keras::LayerActivation::load_weights(std::ifstream &fin) {
fin >> m_activation_type;
//cout << "Activation type " << m_activation_type << endl;
}
void keras::LayerMaxPooling::load_weights(std::ifstream &fin) {
fin >> m_pool_x >> m_pool_y;
//cout << "MaxPooling " << m_pool_x << "x" << m_pool_y << endl;
}
void keras::LayerDense::load_weights(std::ifstream &fin) {
fin >> m_input_cnt >> m_neurons;
float tmp_float;
char tmp_char = ' ';
for(int i = 0; i < m_input_cnt; ++i) {
vector<float> tmp_n;
fin >> tmp_char; // for '['
for(int n = 0; n < m_neurons; ++n) {
fin >> tmp_float;
tmp_n.push_back(tmp_float);
}
fin >> tmp_char; // for ']'
m_weights.push_back(tmp_n);
}
//cout << "weights " << m_weights.size() << endl;
fin >> tmp_char; // for '['
for(int n = 0; n < m_neurons; ++n) {
fin >> tmp_float;
m_bias.push_back(tmp_float);
}
fin >> tmp_char; // for ']'
//cout << "bias " << m_bias.size() << endl;
}
keras::KerasModel::KerasModel(const string &input_fname, bool verbose)
: m_verbose(verbose) {
load_weights(input_fname);
}
keras::DataChunk* keras::LayerFlatten::compute_output(keras::DataChunk* dc) {
vector<vector<vector<float> > > im = dc->get_3d();
size_t csize = im[0].size();
size_t rsize = im[0][0].size();
size_t size = im.size() * csize * rsize;
keras::DataChunkFlat *out = new DataChunkFlat(size);
float * y_ret = out->get_1d_rw().data();
for(size_t i = 0, dst = 0; i < im.size(); ++i) {
for(size_t j = 0; j < csize; ++j) {
float * row = im[i][j].data();
for(size_t k = 0; k < rsize; ++k) {
y_ret[dst++] = row[k];
}
}
}
return out;
}
keras::DataChunk* keras::LayerMaxPooling::compute_output(keras::DataChunk* dc) {
vector<vector<vector<float> > > im = dc->get_3d();
vector<vector<vector<float> > > y_ret;
for(unsigned int i = 0; i < im.size(); ++i) {
vector<vector<float> > tmp_y;
for(unsigned int j = 0; j < (unsigned int)(im[0].size()/m_pool_x); ++j) {
tmp_y.push_back(vector<float>((int)(im[0][0].size()/m_pool_y), 0.0));
}
y_ret.push_back(tmp_y);
}
for(unsigned int d = 0; d < y_ret.size(); ++d) {
for(unsigned int x = 0; x < y_ret[0].size(); ++x) {
unsigned int start_x = x*m_pool_x;
unsigned int end_x = start_x + m_pool_x;
for(unsigned int y = 0; y < y_ret[0][0].size(); ++y) {
unsigned int start_y = y*m_pool_y;
unsigned int end_y = start_y + m_pool_y;
vector<float> values;
for(unsigned int i = start_x; i < end_x; ++i) {
for(unsigned int j = start_y; j < end_y; ++j) {
values.push_back(im[d][i][j]);
}
}
y_ret[d][x][y] = *max_element(values.begin(), values.end());
}
}
}
keras::DataChunk *out = new keras::DataChunk2D();
out->set_data(y_ret);
return out;
}
void keras::missing_activation_impl(const string &act) {
cout << "Activation " << act << " not defined!" << endl;
cout << "Please add its implementation before use." << endl;
exit(1);
}
keras::DataChunk* keras::LayerActivation::compute_output(keras::DataChunk* dc) {
if (dc->get_data_dim() == 3) {
vector<vector<vector<float> > > y = dc->get_3d();
if(m_activation_type == "relu") {
for(unsigned int i = 0; i < y.size(); ++i) {
for(unsigned int j = 0; j < y[0].size(); ++j) {
for(unsigned int k = 0; k < y[0][0].size(); ++k) {
if(y[i][j][k] < 0) y[i][j][k] = 0;
}
}
}
keras::DataChunk *out = new keras::DataChunk2D();
out->set_data(y);
return out;
} else {
keras::missing_activation_impl(m_activation_type);
}
} else if (dc->get_data_dim() == 1) { // flat data, use 1D
vector<float> y = dc->get_1d();
if(m_activation_type == "relu") {
for(unsigned int k = 0; k < y.size(); ++k) {
if(y[k] < 0) y[k] = 0;
}
} else if(m_activation_type == "softmax") {
float sum = 0.0;
for(unsigned int k = 0; k < y.size(); ++k) {
y[k] = exp(y[k]);
sum += y[k];
}
for(unsigned int k = 0; k < y.size(); ++k) {
y[k] /= sum;
}
} else {
keras::missing_activation_impl(m_activation_type);
}
keras::DataChunk *out = new DataChunkFlat();
out->set_data(y);
return out;
} else { throw "data dim not supported"; }
return dc;
}
// with border mode = valid
std::vector< std::vector<float> > keras::conv_single_depth_valid(
std::vector< std::vector<float> > const & im,
std::vector< std::vector<float> > const & k)
{
size_t k1_size = k.size(), k2_size = k[0].size();
unsigned int st_x = (k1_size - 1) >> 1;
unsigned int st_y = (k2_size - 1) >> 1;
std::vector< std::vector<float> > y(im.size() - 2*st_x, vector<float>(im[0].size() - 2*st_y, 0));
for(unsigned int i = st_x; i < im.size()-st_x; ++i) {
for(unsigned int j = st_y; j < im[0].size()-st_y; ++j) {
float sum = 0;
for(unsigned int k1 = 0; k1 < k.size(); ++k1) {
const float * k_data = k[k1_size-k1-1].data();
const float * im_data = im[i-st_x+k1].data();
for(unsigned int k2 = 0; k2 < k[0].size(); ++k2) {
sum += k_data[k2_size-k2-1] * im_data[j-st_y+k2];
}
}
y[i-st_x][j-st_y] = sum;
}
}
return y;
}
// with border mode = same
std::vector< std::vector<float> > keras::conv_single_depth_same(
std::vector< std::vector<float> > const & im,
std::vector< std::vector<float> > const & k)
{
size_t k1_size = k.size(), k2_size = k[0].size();
unsigned int st_x = (k1_size - 1) >> 1;
unsigned int st_y = (k2_size - 1) >> 1;
size_t max_imc = im.size() - 1;
size_t max_imr = im[0].size() - 1;
std::vector< std::vector<float> > y(im.size(), vector<float>(im[0].size(), 0));
for(unsigned int i = 0; i < im.size(); ++i) {
for(unsigned int j = 0; j < im[0].size(); ++j) {
float sum = 0;
for(unsigned int k1 = 0; k1 < k.size(); ++k1) {
const float * k_data = k[k1_size-k1-1].data();
const float * im_data = im[i-st_x+k1].data();
for(unsigned int k2 = 0; k2 < k[0].size(); ++k2) {
if(i-st_x+k1 < 0) continue;
if(i-st_x+k1 > max_imc) continue;
if(j-st_y+k2 < 0) continue;
if(j-st_y+k2 > max_imr) continue;
sum += k_data[k2_size-k2-1] * im_data[j-st_y+k2];
}
}
y[i][j] = sum;
}
}
return y;
}
keras::DataChunk* keras::LayerConv2D::compute_output(keras::DataChunk* dc) {
unsigned int st_x = (m_kernels[0][0].size()-1) >> 1;
unsigned int st_y = (m_kernels[0][0][0].size()-1) >> 1;
vector< vector< vector<float> > > y_ret;
auto const & im = dc->get_3d();
size_t size_x = (m_border_mode == "valid")? im[0].size() - 2 * st_x : im[0].size();
size_t size_y = (m_border_mode == "valid")? im[0][0].size() - 2 * st_y: im[0][0].size();
for(unsigned int i = 0; i < m_kernels.size(); ++i) { // depth
vector<vector<float> > tmp;
tmp.reserve(size_x);
for(unsigned int j = 0; j < size_x; ++j) { // rows
tmp.emplace_back(vector<float>(size_y, 0.0));
}
y_ret.push_back(tmp);
}
for(unsigned int j = 0; j < m_kernels.size(); ++j) { // loop over kernels
for(unsigned int m = 0; m < im.size(); ++m) { // loope over image depth
vector<vector<float> > tmp_w = (m_border_mode == "valid")?
keras::conv_single_depth_valid(im[m], m_kernels[j][m]) :
keras::conv_single_depth_same(im[m], m_kernels[j][m]);
for(unsigned int x = 0; x < tmp_w.size(); ++x) {
for(unsigned int y = 0; y < tmp_w[0].size(); ++y) {
y_ret[j][x][y] += tmp_w[x][y];
}
}
}
for(unsigned int x = 0; x < y_ret[0].size(); ++x) {
for(unsigned int y = 0; y < y_ret[0][0].size(); ++y) {
y_ret[j][x][y] += m_bias[j];
}
}
}
keras::DataChunk *out = new keras::DataChunk2D();
out->set_data(y_ret);
return out;
}
keras::DataChunk* keras::LayerDense::compute_output(keras::DataChunk* dc) {
//cout << "weights: input size " << m_weights.size() << endl;
//cout << "weights: neurons size " << m_weights[0].size() << endl;
//cout << "bias " << m_bias.size() << endl;
size_t size = m_weights[0].size();
size_t size8 = size >> 3;
keras::DataChunkFlat *out = new DataChunkFlat(size, 0);
float * y_ret = out->get_1d_rw().data();
auto const & im = dc->get_1d();
for (size_t j = 0; j < m_weights.size(); ++j) { // iter over input
const float * w = m_weights[j].data();
float p = im[j];
size_t k = 0;
for (size_t i = 0; i < size8; ++i) { // iter over neurons
y_ret[k] += w[k] * p; // vectorize if you can
y_ret[k+1] += w[k+1] * p;
y_ret[k+2] += w[k+2] * p;
y_ret[k+3] += w[k+3] * p;
y_ret[k+4] += w[k+4] * p;
y_ret[k+5] += w[k+5] * p;
y_ret[k+6] += w[k+6] * p;
y_ret[k+7] += w[k+7] * p;
k += 8;
}
while (k < size) { y_ret[k] += w[k] * p; ++k; }
}
for (size_t i = 0; i < size; ++i) { // add biases
y_ret[i] += m_bias[i];
}
return out;
}
std::vector<float> keras::KerasModel::compute_output(keras::DataChunk *dc) {
//cout << endl << "KerasModel compute output" << endl;
//cout << "Input data size:" << endl;
//dc->show_name();
keras::DataChunk *inp = dc;
keras::DataChunk *out = 0;
for(int l = 0; l < (int)m_layers.size(); ++l) {
//cout << "Processing layer " << m_layers[l]->get_name() << endl;
out = m_layers[l]->compute_output(inp);
//cout << "Input" << endl;
//inp->show_name();
//cout << "Output" << endl;
//out->show_name();
if(inp != dc) delete inp;
//delete inp;
inp = 0L;
inp = out;
}
std::vector<float> flat_out = out->get_1d();
delete out;
return flat_out;
}
void keras::KerasModel::load_weights(const string &input_fname) {
if(m_verbose) cout << "Reading model from " << input_fname << endl;
ifstream fin(input_fname.c_str());
string layer_type = "";
string tmp_str = "";
int tmp_int = 0;
fin >> tmp_str >> m_layers_cnt;
if(m_verbose) cout << "Layers " << m_layers_cnt << endl;
for(int layer = 0; layer < m_layers_cnt; ++layer) { // iterate over layers
fin >> tmp_str >> tmp_int >> layer_type;
if(m_verbose) cout << "Layer " << tmp_int << " " << layer_type << endl;
Layer *l = 0L;
if(layer_type == "Convolution2D") {
l = new LayerConv2D();
} else if(layer_type == "Activation") {
l = new LayerActivation();
} else if(layer_type == "MaxPooling2D") {
l = new LayerMaxPooling();
} else if(layer_type == "Flatten") {
l = new LayerFlatten();
} else if(layer_type == "Dense") {
l = new LayerDense();
} else if(layer_type == "Dropout") {
continue; // we dont need dropout layer in prediciton mode
}
if(l == 0L) {
cout << "Layer is empty, maybe it is not defined? Cannot define network." << endl;
return;
}
l->load_weights(fin);
m_layers.push_back(l);
}
fin.close();
}
keras::KerasModel::~KerasModel() {
for(int i = 0; i < (int)m_layers.size(); ++i) {
delete m_layers[i];
}
}
int keras::KerasModel::get_output_length() const
{
int i = m_layers.size() - 1;
while ((i > 0) && (m_layers[i]->get_output_units() == 0)) --i;
return m_layers[i]->get_output_units();
}