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Anirudh B HAnirudhBHarish
Anirudh B H
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AnirudhBHarish
committed
Added conv1d layers with tests
1 parent 92f9654 commit 664eaa9

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16 files changed

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‎c_reference/include/conv1d.h‎

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#include<stdlib.h>
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#ifndef __CONVLAYERS_H__
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#define __CONVLAYERS_H__
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#define ERR_INTERMIDIATE_NOT_INIT -1
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#define ERR_TEMPW_NOT_INIT -2
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#define ERR_TEMPLRU_NOT_INIT -3
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#define ERR_NORMFEATURES_NOT_INIT -4
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/**
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* @brief Model paramters for the 1D Convolution Layer
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* @var mean pointer to mean of input vector for normalization, size inputDims
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* @var stdDev pointer to standard dev of input for normalization, size inputDims*steps
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* @var W pointer to convolutional weights W
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* @var B pointer to the bias vector for the convolution
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*/
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typedef struct ConvLayers_Params{
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float* W;
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float* B;
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} ConvLayers_Params;
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int Conv1D(float *output_signal, unsigned out_T, unsigned out_channels, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations);
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int Conv1D_Depth(float *output_signal, unsigned out_T, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations);
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// Low Rank
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/**
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* @brief Model paramters for the Low Rank 1D Convolution Layer
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* @var mean pointer to mean of input vector for normalization, size inputDims
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* @var stdDev pointer to standard dev of input for normalization, size inputDims
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* @var W1 pointer to first low-rank component of the convolutional weight W
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* @var W2 pointer to second low-rank component of the convolutional weight W
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* @var Rank rank of W matrix
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* @var B pointer to the bias vector for the convolution
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*/
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typedef struct ConvLayers_LR_Params{
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float* W1;
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float* W2;
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float* B;
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unsigned rank;
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} ConvLayers_LR_Params;
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int Conv1D_LR(float *output_signal, unsigned out_T, unsigned out_channels, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations);
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int Conv1D_Depth_LR(float *output_signal, unsigned out_T, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations);
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//Pool
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int AvgPool1D(float *output_signal, unsigned out_T, const float *input_signal, unsigned in_T, unsigned in_channels,
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int padding, unsigned kernel_size, int activations);
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#endif

‎c_reference/include/conv_utils.h‎

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#ifndef __CONVLAYER_UTILS__
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#define __CONVLAYER_UTILS__
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#include <math.h>
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#include <float.h>
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int prepareLowRankConvMat(float* out, float* W1, float* W2, unsigned rank, unsigned I, unsigned J);
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float sigmoid(float x);
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float relu(float x);
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#endif

‎c_reference/src/Makefile‎

Lines changed: 7 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,13 @@ include ../config.mk
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INCLUDE_DIR=../include
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IFLAGS = -I $(INCLUDE_DIR)
88

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all: utils.o fastgrnn.o classifier.o rnnpool.o quantized_utils.o quantized_fastgrnn.o quantized_rnnpool.o quantized_mbconv.o
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all: conv1d.o conv_utils.o utils.o fastgrnn.o classifier.o rnnpool.o quantized_utils.o quantized_fastgrnn.o quantized_rnnpool.o quantized_mbconv.o
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conv_utils.o : conv_utils.c
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$(CC) -o $@ $(IFLAGS) $(CFLAGS) -c $^
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conv1d.o : conv1d.c
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$(CC) -o $@ $(IFLAGS) $(CFLAGS) -c $^
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utils.o: utils.c
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$(CC) -o $@ $(IFLAGS) $(CFLAGS) -c $^

‎c_reference/src/conv1d.c‎

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#include"conv1d.h"
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#include"conv_utils.h"
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#include<stdlib.h>
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#include<math.h>
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#include<stdio.h>
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int Conv1D_LR(float *output_signal, unsigned out_T, unsigned out_channels, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations){
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const ConvLayers_LR_Params* tparams= (ConvLayers_LR_Params*) params;
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if(padding == -1){
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padding = kernel_size >> 1;
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}
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float* tempW = (float*)malloc(out_channels * in_channels * kernel_size * sizeof(float)) ;
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prepareLowRankConvMat(tempW, tparams->W1, tparams->W2, tparams->rank, out_channels, in_channels * kernel_size);
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// Perform the Convolution
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// input.shape = [in_T, in_channels]
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// output.shape = [out_T, out_channels]
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// filter.shape = [out_channels, in_channels, kernel_size]
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for(int t = 0; t < out_T; t++){
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for(int co = 0; co < out_channels ; co++){
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float sum = 0;
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for(int tf = 0 ; tf < kernel_size ; tf++ ){
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for(int ci = 0 ; ci < in_channels ; ci++){
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if(((t + tf) < padding) || ((t + tf) >= (in_T + padding)))
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continue;
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else
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sum += (input_signal[((tf + t) - padding) * in_channels + ci] * tempW[co * in_channels * kernel_size + ci * kernel_size + tf]);
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}
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}
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if(activations == 1){
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output_signal[t * out_channels + co] = sigmoid(sum + tparams->B[co]);
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}
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else if(activations == 2){
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output_signal[t * out_channels + co] = tanh(sum + tparams->B[co]);
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}
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else if(activations == 3){
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output_signal[t * out_channels + co] = relu(sum + tparams->B[co]);
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}
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else{
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output_signal[t * out_channels + co] = sum + tparams->B[co];
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}
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}
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}
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free(tempW);
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return 0;
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}
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int Conv1D_Depth_LR(float *output_signal, unsigned out_T, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
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const void* params, int activations){
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const ConvLayers_LR_Params* tparams= (ConvLayers_LR_Params*) params;
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if(padding == -1){
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padding = kernel_size >> 1;
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}
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float* tempW = (float*)malloc(in_channels * kernel_size * sizeof(float)) ;
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prepareLowRankConvMat(tempW, tparams->W1, tparams->W2, tparams->rank, in_channels, kernel_size);
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// Perform the Convolution
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// input.shape = [N, in_T, in_channels]
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// output.shape = [N, out_T, in_channels]
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// filter.shape = [(out)in_channels, in_channels, kernel_size]
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for(int t = 0; t < out_T; t++){
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for(int ci = 0; ci < in_channels ; ci++){
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float sum = 0;
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for(int tf = 0 ; tf < kernel_size ; tf++ ){
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if(((t + tf) < padding) || ((t + tf) >= (in_T + padding)))
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continue;
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else
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sum += (input_signal[((tf + t) - padding) * in_channels + ci] * tempW[ci * kernel_size + tf]);
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}
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if(activations == 1){
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output_signal[t * in_channels + ci] = sigmoid(sum + tparams->B[ci]);
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}
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else if(activations == 2){
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output_signal[t * in_channels + ci] = tanh(sum + tparams->B[ci]);
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}
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else if(activations == 3){
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output_signal[t * in_channels + ci] = relu(sum + tparams->B[ci]);
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}
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else{
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output_signal[t * in_channels + ci] = sum + tparams->B[ci];
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}
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}
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}
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free(tempW);
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return 0;
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}
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int Conv1D(float *output_signal, unsigned out_T, unsigned out_channels, const float *input_signal,
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unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
99+
const void* params, int activations){
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const ConvLayers_Params* tparams= (ConvLayers_Params*) params;
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103+
if(padding == -1){
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padding = kernel_size >> 1;
105+
}
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float sum;
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// Perform the Convolution
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// input.shape = [N, in_T, in_channels]
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// output.shape = [N, out_T, out_channels]
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// filter.shape = [out_channels, in_channels, kernel_size]
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for(int t = 0; t < out_T; t++){
112+
for(int co = 0; co < out_channels ; co++){
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sum = 0;
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for(int tf = 0 ; tf < kernel_size ; tf++ ){
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for(int ci = 0 ; ci < in_channels ; ci++){
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if(((t + tf) < padding) || ((t + tf) >= (in_T + padding)))
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continue;
118+
else
119+
sum += (input_signal[((tf + t) - padding) * in_channels + ci] * tparams->W[co * in_channels * kernel_size + ci * kernel_size + tf]);
120+
}
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}
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if(activations == 1){
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output_signal[t * out_channels + co] = sigmoid(sum + tparams->B[co]);
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}
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else if(activations == 2){
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output_signal[t * out_channels + co] = tanh(sum + tparams->B[co]);
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}
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else if(activations == 3){
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output_signal[t * out_channels + co] = relu(sum + tparams->B[co]);
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}
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else{
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output_signal[t * out_channels + co] = sum + tparams->B[co];
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}
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}
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}
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return 0;
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}
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int Conv1D_Depth(float *output_signal, unsigned out_T, const float *input_signal,
140+
unsigned in_T, unsigned in_channels, int padding, unsigned kernel_size,
141+
const void* params, int activations){
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const ConvLayers_Params* tparams= (ConvLayers_Params*) params;
144+
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if(padding == -1){
146+
padding = kernel_size >> 1;
147+
}
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// Perform the Convolution
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// input.shape = [N, in_T, in_channels]
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// output.shape = [N, out_T, in_channels]
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// filter.shape = [(out)in_channels, in_channels, kernel_size]
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for(int t = 0; t < out_T; t++){
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for(int ci = 0; ci < in_channels ; ci++){
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float sum = 0;
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for(int tf = 0 ; tf < kernel_size ; tf++ ){
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if(((t + tf) < padding) || ((t + tf) >= (in_T + padding)))
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continue;
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else
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sum += (input_signal[((tf + t) - padding) * in_channels + ci] * tparams->W[ci * kernel_size + tf]);
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}
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if(activations == 1){
163+
output_signal[t * in_channels + ci] = sigmoid(sum + tparams->B[ci]);
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}
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else if(activations == 2){
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output_signal[t * in_channels + ci] = tanh(sum + tparams->B[ci]);
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}
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else if(activations == 3){
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output_signal[t * in_channels + ci] = relu(sum + tparams->B[ci]);
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}
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else{
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output_signal[t * in_channels + ci] = sum + tparams->B[ci];
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}
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}
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}
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return 0;
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}
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int AvgPool1D(float *output_signal, unsigned out_T, const float *input_signal, unsigned in_T, unsigned in_channels,
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int padding, unsigned kernel_size, int activations){
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if(padding == -1){
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padding = kernel_size >> 1;
184+
}
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for(int t = 0; t < out_T; t++){
187+
for(int ci = 0 ; ci < in_channels; ci++){
188+
float sum = 0;
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for(int tf = 0; tf < kernel_size ; tf++){
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if(((t + tf) < padding) || ((t + tf) >= (in_T + padding)))
191+
continue;
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else
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sum += (input_signal[((tf + t) - padding) * in_channels + ci]);
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}
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if(activations == 1){
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output_signal[t * in_channels + ci] = sigmoid(sum/(float)kernel_size);
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}
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else if(activations == 2){
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output_signal[t * in_channels + ci] = tanh(sum/(float)kernel_size);
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}
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else if(activations == 3){
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output_signal[t * in_channels + ci] = relu(sum/(float)kernel_size);
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}
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else{
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output_signal[t * in_channels + ci] = sum/(float)kernel_size;
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}
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}
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}
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return 0;
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}

‎c_reference/src/conv_utils.c‎

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#include"conv_utils.h"
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#include <math.h>
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#include <float.h>
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int prepareLowRankConvMat(float* out, float* W1, float* W2, unsigned rank, unsigned I, unsigned J){
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for(int i = 0 ; i < I; i++){
7+
for(int j = 0 ; j < J; j++){
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float sum = 0;
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for(int k = 0; k < rank ; k++){
10+
sum += (W1[i * rank + k] * W2[k * J + j]);
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}
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out[i * J + j] = sum;
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}
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}
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return 0;
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}
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float relu(float x) {
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if (x < 0.0) return 0.0;
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else return x;
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}
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float sigmoid(float x) {
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return 1.0f / (1.0f + expf(-1.0f * x));
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}

‎c_reference/tests/Makefile‎

Lines changed: 14 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,19 @@ MODEL_DIR=../models
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SRC_DIR=../src
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IFLAGS = -I $(INCLUDE_DIR) -I $(MODEL_DIR)
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all: test_fastgrnn_lr test_rnnpool test_quantized_utils test_quantized_fastgrnn test_quantized_rnnpool test_quantized_mbconv test_quantized_face_detection test_quantized_face_detection_fast
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all: test_avg_pool test_conv1d test_conv1d_depth test_conv1d_lr test_conv1d_lr_depth test_fastgrnn_lr test_rnnpool test_quantized_utils test_quantized_fastgrnn test_quantized_rnnpool test_quantized_mbconv test_quantized_face_detection test_quantized_face_detection_fast
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CONV1D_DIR=conv1d
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test_conv1d: $(CONV1D_DIR)/conv1d_regular/test_conv1d.c $(SRC_DIR)/conv_utils.o $(SRC_DIR)/conv1d.o
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$(CC) -o $@ $^ $(IFLAGS) $(CFLAGS) -lm
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test_conv1d_depth: $(CONV1D_DIR)/conv1d_depthwise/test_conv1d_depth.c $(SRC_DIR)/conv_utils.o $(SRC_DIR)/conv1d.o
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$(CC) -o $@ $^ $(IFLAGS) $(CFLAGS) -lm
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test_conv1d_lr: $(CONV1D_DIR)/conv1d_lr/test_conv1d_lr.c $(SRC_DIR)/conv_utils.o $(SRC_DIR)/conv1d.o
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$(CC) -o $@ $^ $(IFLAGS) $(CFLAGS) -lm
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test_conv1d_lr_depth: $(CONV1D_DIR)/conv1d_lr_depthwise/test_conv1d_lr_depth.c $(SRC_DIR)/conv_utils.o $(SRC_DIR)/conv1d.o
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$(CC) -o $@ $^ $(IFLAGS) $(CFLAGS) -lm
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test_avg_pool: $(CONV1D_DIR)/avg_pool/test_avg_pool.c $(SRC_DIR)/conv_utils.o $(SRC_DIR)/conv1d.o
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$(CC) -o $@ $^ $(IFLAGS) $(CFLAGS) -lm
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1325
FASTGRNN_DIR=fastgrnn
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test_fastgrnn_lr: $(FASTGRNN_DIR)/test_fastgrnn_lr.c $(SRC_DIR)/utils.o $(SRC_DIR)/fastgrnn.o $(SRC_DIR)/classifier.o
@@ -39,7 +51,7 @@ test_quantized_face_detection_fast: $(FACE_DETECTION_DIR)/test_quantized_face_de
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.PHONY: clean cleanest
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clean:
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rm -f *.o *.gch test_fastgrnn_lr test_rnnpool test_quantized_utils test_quantized_fastgrnn test_quantized_rnnpool test_quantized_mbconv test_quantized_face_detection test_quantized_face_detection_fast
54+
rm -f *.o *.gch test_avg_pool test_conv1d test_conv1d_depth test_conv1d_lr test_conv1d_lr_depth test_fastgrnn_lr test_rnnpool test_quantized_utils test_quantized_fastgrnn test_quantized_rnnpool test_quantized_mbconv test_quantized_face_detection test_quantized_face_detection_fast
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4456
cleanest: clean
4557
rm *~

‎c_reference/tests/conv1d/avg_pool/avg_io.h‎

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Large diffs are not rendered by default.

‎c_reference/tests/conv1d/avg_pool/test_avg_pool.c‎

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#include<stdio.h>
2+
#include<stdlib.h>
3+
4+
#include"avg_io.h"
5+
#include"conv1d.h"
6+
#include"conv_utils.h"
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int main(){
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10+
float pred[O_T * O_F] = {};
11+
AvgPool1D(pred, O_T, INPUT, I_T, I_F, PAD, FILT, ACT);
12+
float error = 0;
13+
for(int t = 0 ; t < O_T ; t++){
14+
for(int d = 0 ; d < O_F ; d++){
15+
error += ((pred[t * O_F + d] - OUTPUT[t * O_F + d]) * (pred[t * O_F + d] - OUTPUT[t * O_F + d]));
16+
}
17+
}
18+
float avg_error = error/(O_T*O_F);
19+
printf("Testing Average Pool\n");
20+
printf("Squared Error : %f \t ; MSE : %f \n", error, avg_error);
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return 0 ;
23+
}

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