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DTLNImpl.cpp
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DTLNImpl.cpp
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#include "DTLN_defs.h"
#include "pocketfft_hdronly.h"
#include "AudioFile.h"
using namespace std;
typedef complex<double> cpx_type;
void ExportWAV(
const std::string & Filename,
const std::vector<float>& Data,
unsigned SampleRate) {
AudioFile<float>::AudioBuffer Buffer;
Buffer.resize(1);
Buffer[0] = Data;
size_t BufSz = Data.size();
AudioFile<float> File;
File.setAudioBuffer(Buffer);
File.setAudioBufferSize(1, (int)BufSz);
File.setNumSamplesPerChannel((int)BufSz);
File.setNumChannels(1);
File.setBitDepth(16);
File.setSampleRate(SAMEPLERATE);
File.save(Filename, AudioFileFormat::Wave);
}
void DTLN_RKNN(){
trg_engine* m_pEngine;
m_pEngine = new trg_engine;
m_pEngine->statesa.clear();
m_pEngine->statesb.clear();
m_pEngine->statesa.resize(4);
m_pEngine->statesb.resize(4);
for (int i=0;i<4;i++){
m_pEngine->statesa[i].clear();
m_pEngine->statesb[i].clear();
m_pEngine->statesa[i].resize(DTLN_UNIT_SIZE);
m_pEngine->statesb[i].resize(DTLN_UNIT_SIZE);
memset(m_pEngine->statesa[i].data(),0,DTLN_UNIT_SIZE*sizeof(float));
memset(m_pEngine->statesb[i].data(),0,DTLN_UNIT_SIZE*sizeof(float));
}
int ret=0;
int model_len1 = 0;
unsigned char* model1 = __ReadModel(DTLN_MODEL_A, &model_len1);
rknn_context m_ctx[2];
rknn_input_output_num m_ioNum[2];
ret=rknn_init(&(m_ctx[0]),model1,model_len1,0,NULL);
if (ret < 0) {
printf("rknn_init fail1! ret=%d\n", ret);
return -1;
}
int model_len2 = 0;
unsigned char* model2 = __ReadModel(DTLN_MODEL_B, &model_len2);
ret=rknn_init(&(m_ctx[1]),model2,model_len2,0,NULL);
if (ret < 0) {
printf("rknn_init fail2! ret=%d\n", ret);
return -1;
}
ret = rknn_query(m_ctx[0], RKNN_QUERY_IN_OUT_NUM, &(m_ioNum[0]), sizeof(rknn_input_output_num));
if (ret != RKNN_SUCC) {
printf("rknn_query1 fail1! ret=%d\n", ret);
return -1;
}
ret = rknn_query(m_ctx[1], RKNN_QUERY_IN_OUT_NUM, &(m_ioNum[1]), sizeof(rknn_input_output_num));
if (ret != RKNN_SUCC) {
printf("rknn_query1 fail2! ret=%d\n", ret);
return -1;
}
rknn_tensor_attr input_attrs_a[m_ioNum[0].n_input];
memset(input_attrs_a,0,m_ioNum[0].n_input*sizeof(rknn_tensor_attr));
rknn_tensor_attr input_attrs_b[m_ioNum[1].n_input];
memset(input_attrs_b,0,m_ioNum[1].n_input*sizeof(rknn_tensor_attr));
int i=0;
for (i=0;i<m_ioNum[0].n_input;i++){
input_attrs_a[i].index=i;
ret= rknn_query(m_ctx[0], RKNN_QUERY_INPUT_ATTR, &(input_attrs_a[i]), sizeof(rknn_tensor_attr));
if (ret < 0) {
printf("rknn_init error1! ret=%d\n", ret);
return -1;
}
input_attrs_b[i].index=i;
ret= rknn_query(m_ctx[1], RKNN_QUERY_INPUT_ATTR, &(input_attrs_b[i]), sizeof(rknn_tensor_attr));
if (ret < 0) {
printf("rknn_init error3! ret=%d\n", ret);
return -1;
}
}
rknn_tensor_attr output_attrs_a[m_ioNum[0].n_output], output_attrs_b[m_ioNum[1].n_output];
memset(output_attrs_a,0,m_ioNum[0].n_output*sizeof(rknn_tensor_attr));
memset(output_attrs_b,0,m_ioNum[1].n_output*sizeof(rknn_tensor_attr));
for (i=0;i<m_ioNum[0].n_output;i++){
output_attrs_a[i].index=i;
ret = rknn_query(m_ctx[0], RKNN_QUERY_OUTPUT_ATTR, &(output_attrs_a[i]), sizeof(rknn_tensor_attr));
if (ret != RKNN_SUCC) {
printf("rknn_query2 fail1! ret=%d\n", ret);
return -1;
}
output_attrs_b[i].index=i;
ret = rknn_query(m_ctx[1], RKNN_QUERY_OUTPUT_ATTR, &(output_attrs_b[i]), sizeof(rknn_tensor_attr));
if (ret != RKNN_SUCC) {
printf("rknn_query2 fail3! ret=%d\n", ret);
return -1;
}
}
float f32_output[DTLN_BLOCK_LEN];
std::vector<float> testoutdata; //vector used to store enhanced data in a wav file
AudioFile<float> inputwavfile;
std::string testfile="./wav/testwav.wav";
inputwavfile.load(testfile);
int audiolen=inputfile.getNumSamplesPerChannel();
int process_num=(audiolen-DTLN_BLOCK_LEN)/DTLN_BLOCK_SHIFT;
for(int i=0;i<process_num;i++)
{
memmove(m_pEngine->in_buffer, m_pEngine->in_buffer + DTLN_BLOCK_SHIFT, (DTLN_BLOCK_LEN - DTLN_BLOCK_SHIFT) * sizeof(float));
for(int n=0;n<BLOCK_SHIFT;n++){
m_pEngine->in_buffer[n+DTLN_BLOCK_LEN-DTLN_BLOCK_SHIFT]=inputmicfile.samples[0][n+i*DTLN_BLOCK_SHIFT];
}
RKNNInfer(m_pEngine,m_ctx,m_ioNum);
for(int j=0;j<DTLN_BLOCK_SHIFT;j++){
testoutdata.push_back(m_pEngine->out_buffer[j]); //for one forward process save first BLOCK_SHIFT model output samples
}
}
ExportWAV("rknntest.wav",testoutdata,SAMEPLERATE);
for (int i=0;i<2:i++){
if(m_ctx[i]>0){
rknn_destroy(m_ctx[i]);
}
}
if (model1) {
free(model1);
}
if (model2) {
free(model2);
}
delete m_pEngine;
}
void RKNNInfer(trg_engine* m_pEngine, rknn_context* m_ctx,rknn_input_output_num *m_ionum) {
float in_mag[FFT_OUT_SIZE] ={0}; ;
float in_phase[FFT_OUT_SIZE] = { 0 };
float estimated_block[DTLN_BLOCK_LEN] ={0};
double fft_in[DTLN_BLOCK_LEN];
std::vector<cpx_type> fft_res(DTLN_BLOCK_LEN);
std::vector<size_t> shape;
shape.push_back(DTLN_BLOCK_LEN);
std::vector<size_t> axes;
axes.push_back(0);
std::vector<ptrdiff_t> stridel, strideo;
strideo.push_back(sizeof(cpx_type));
stridel.push_back(sizeof(double));
float out_mask[FFT_OUT_SIZE] ={0};
float out_block[DTLN_BLOCK_LEN]={0} ;
int res2;
for (int i = 0; i < DTLN_BLOCK_LEN; i++){
fft_in[i] = m_pEngine->in_buffer[i];
}
pocketfft::r2c(shape, stridel, strideo, axes, pocketfft::FORWARD, fft_in, fft_res.data(), 1.0);
__calc_mag_phase(fft_res, in_mag, in_phase, FFT_OUT_SIZE);
rknn_input input_details_a[m_ioNum[0].n_input], input_details_b[m_ioNum[1].n_input];
rknn_output output_details_a[m_ioNum[0].n_output], output_details_b[m_ioNum[1].n_output];
memset(input_details_a,0,m_ioNum[0].n_input*sizeof(rknn_input));
memset(input_details_b,0,m_ioNum[1].n_input*sizeof(rknn_input));
for (int i=0;i<m_ioNum[0].n_input;i++){
input_details_a[i].index=i;
input_details_a[i].type=RKNN_TENSOR_FLOAT32;
input_details_a[i].fmt=RKNN_TENSOR_NHWC;
input_details_b[i].index=i;
input_details_b[i].type=RKNN_TENSOR_FLOAT32;
input_details_b[i].fmt=RKNN_TENSOR_NHWC;
if(i<1){
input_details_a[0].size=FFT_OUT_SIZE*sizeof(float);
input_details_b[0].size=DTLN_BLOCK_LEN*sizeof(float);
}else{
input_details_a[i].size=DTLN_UNIT_SIZE*sizeof(float);
input_details_b[i].size=DTLN_UNIT_SIZE*sizeof(float);
}
}
memset(output_details_a,0,m_ioNum[0].n_output*sizeof(rknn_output));
memset(output_details_b,0,m_ioNum[1].n_output*sizeof(rknn_output));
// memset(output_details_b,0,m_ioNum[1].n_output*sizeof(rknn_output));
for (int i=0;i<m_ioNum[0].n_output;i++){
output_details_a[i].index=i;
output_details_a[i].want_float=1;
output_details_a[i].is_prealloc = 0;
output_details_b[i].index=i;
output_details_b[i].want_float=1;
output_details_b[i].is_prealloc = 0;
}
input_details_a[0].buf=in_mag;
for (int i=0;i<4;i++){
m_input_details_a[1+i].buf=m_pEngine->statesa[i].data();
}
res2=rknn_inputs_set(m_ctx[0], m_ioNum[0].n_input,input_details_a);
if (res2 < 0) {
LOGW("%s:Model a can't set input,break",__FUNCTION__);
ResetInout();
return -1;
}
res2=rknn_run(m_ctx[0], nullptr);
res2 = rknn_outputs_get(m_ctx[0], m_ioNum[0].n_output,output_details_a, NULL);
out_mask =(float*)(output_details_a[0].buf);
for (int i=0;i<4;i++){
memcpy(m_pEngine->statesa[i].data(),(float*)(m_output_details_a[i+1].buf),DTLN_UNIT_SIZE*sizeof(float));
}
//memcpy(m_pEngine->states_a,outstatea,DTLN_STATE_SIZE*sizeof(float));
for (int i = 0; i < FFT_OUT_SIZE; i++) {
fft_res[i] = cpx_type( out_mask[i] * cosf(in_phase[i]), out_mask[i] * sinf(in_phase[i]));
}
pocketfft::c2r(shape, strideo, stridel,axes, pocketfft::BACKWARD, fft_res.data(), fft_in, 1.0);
for (int i = 0; i < DTLN_BLOCK_LEN; i++)
{
estimated_block[i] = fft_in[i] / DTLN_BLOCK_LEN;
}
res2 = rknn_outputs_release(m_ctx[0], m_ioNum[0].n_output, output_details_a);
input_details_b[0].buf = estimated_block;
for (int i=0;i<4;i++){
m_input_details_b[1+i].buf=m_pEngine->statesb[i].data();
}
res2=rknn_inputs_set(m_ctx[1], m_ioNum[1].n_input, input_details_b);
res2=rknn_run(m_ctx[1], nullptr);
res2 = rknn_outputs_get(m_ctx[1], m_ioNum[1].n_output, output_details_b, NULL);
// res2 =rknn_query(m_ctx[1], RKNN_QUERY_PERF_RUN, &(runtime2), sizeof(rknn_perf_run));
// m_duration +=runtime2.run_duration;
out_block =(float*)(output_details_b[0].buf);
for (int i=0;i<4;i++){
memcpy(m_pEngine->statesb[i].data(),(float*)(m_output_details_b[i+1].buf),DTLN_UNIT_SIZE*sizeof(float));
}
res2 = rknn_outputs_release(m_ctx[1], m_ioNum[1].n_output, output_details_b);
memmove(m_pEngine->out_buffer,
m_pEngine->out_buffer + DTLN_BLOCK_SHIFT,
(DTLN_BLOCK_LEN - DTLN_BLOCK_SHIFT) * sizeof(float));
memset(m_pEngine->out_buffer + (DTLN_BLOCK_LEN - DTLN_BLOCK_SHIFT), 0, DTLN_BLOCK_SHIFT * sizeof(float));
for (int i = 0; i < DTLN_BLOCK_LEN; i++){
m_pEngine->out_buffer[i] += out_block[i];
}
}
void __calc_mag_phase(std::vector<cpx_type> fft_res,
float* in_mag, float* in_phase, int count) {
for (int i = 0; i < count; i++) {
in_mag[i] = sqrtf(fft_res[i].real() * fft_res[i].real() + fft_res[i].imag() * fft_res[i].imag());
in_phase[i] = atan2f(fft_res[i].imag(), fft_res[i].real());
}
}
unsigned char* __ReadModel(char *filename, int *model_size) {
FILE *fp = fopen(filename, "rb");
if(fp == nullptr) {
printf("fopen %s fail!\n", filename);
return NULL;
}
fseek(fp, 0, SEEK_END);
int model_len = ftell(fp);
unsigned char *model = (unsigned char*)malloc(model_len);
fseek(fp, 0, SEEK_SET);
if(model_len != fread(model, 1, model_len, fp)) {
printf("fread %s fail!\n", filename);
free(model);
return NULL;
}
*model_size = model_len;
if(fp) {
fclose(fp);
}
return model;
}