Skip to content
Snippets Groups Projects
Commit 9e330433 authored by Hashim Sharif's avatar Hashim Sharif
Browse files

Merging

parents c2168c03 e7b8f86f
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_promise.cc 0 → 100644
+ 137
− 0
View file @ 9e330433
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <string.h>
#include "../../../tensor_runtime/include/tensor_runtime.h"
#include "../../include/utils.h"
int main(){
llvm_hpvm_initTensorRt(3);
int total_runs = 1;
for (int i = 0 ; i < total_runs; i++){
startMemTracking();
int test_input_size = 4000;
//int batch_size = 2500;
int batch_size = 4000;
int offset = 5000;
int batch_count = test_input_size / batch_size;
float final_accuracy = 0.0;
for(int i = 0; i < batch_count; i++){
std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
std::string input_path = dir_prefix + std::string("input.bin");
std::string labels_path = dir_prefix + std::string("labels.bin");
std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
int start = i * batch_size + offset;
int end = (i + 1) * batch_size + offset;
void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
void* var_15 = tensorSoftmax(var_14);
uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
// float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
float accuracy = computeTop5Accuracy(labels, batch_size, var_15, 100);
final_accuracy += accuracy;
freeBatchMemory();
}
final_accuracy = final_accuracy / batch_count;
dumpFinalAccuracy(final_accuracy);
}
dumpExecutionAccuracies();
llvm_hpvm_cleanupTensorRt();
return 0;
}
llvm/projects/hpvm-tensor-rt/dnn_sources/src/promise/vgg16_cifar100_top5_valid.cc 0 → 100644
+ 136
− 0
View file @ 9e330433
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <string.h>
#include "../../../tensor_runtime/include/tensor_runtime.h"
#include "../../include/utils.h"
int main(){
llvm_hpvm_initTensorRt(2);
int total_runs = 20;
for (int i = 0 ; i < total_runs; i++){
startMemTracking();
int test_input_size = 5000;
int batch_size = 5000;
int offset = 0;
int batch_count = test_input_size / batch_size;
float final_accuracy = 0.0;
for(int i = 0; i < batch_count; i++){
std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
std::string input_path = dir_prefix + std::string("input.bin");
std::string labels_path = dir_prefix + std::string("labels.bin");
std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
int start = i * batch_size + offset;
int end = (i + 1) * batch_size + offset;
void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
void* var_0 = ConvLayer_PROMISE(input, -1.7829767, 1.9456929, conv2d_1_w, -0.7450515, 0.71249133, conv2d_1_b, -1.5885142, 0.275554, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.7384350299835205, 9);
void* var_1 = ConvLayer_PROMISE(var_0, 0.0, 1.7384350299835205, conv2d_2_w, -0.30790088, 0.43504623, conv2d_2_b, -1.4242363, 1.2602744, 1, 1, 1, 1, 0, 2, 1, 0.0, 4.417154796123498, 9);
void* var_2 = ConvLayer_PROMISE(var_1, 0.0, 4.417154796123498, conv2d_3_w, -0.29189092, 0.26958522, conv2d_3_b, -1.0527138, 0.9075671, 1, 1, 1, 1, -1, 0, 1, 0.0, 3.1919608163833573, 9);
void* var_3 = ConvLayer_PROMISE(var_2, 0.0, 3.1919608163833573, conv2d_4_w, -0.15521508, 0.1829038, conv2d_4_b, -0.845419, 1.9358484, 1, 1, 1, 1, 0, 2, 1, 0.0, 5.108994026184064, 9);
void* var_4 = ConvLayer_PROMISE(var_3, 0.0, 5.108994026184064, conv2d_5_w, -0.13149762, 0.14811686, conv2d_5_b, -0.7162557, 1.0370971, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.8264513099193493, 9);
void* var_5 = ConvLayer_PROMISE(var_4, 0.0, 2.8264513099193493, conv2d_6_w, -0.06236292, 0.08321518, conv2d_6_b, -0.9067523, 0.9922458, 1, 1, 1, 1, -1, 0, 1, 0.0, 2.507186658382409, 9);
void* var_6 = ConvLayer_PROMISE(var_5, 0.0, 2.507186658382409, conv2d_7_w, -0.06471479, 0.1024472, conv2d_7_b, -0.15943134, 0.7988499, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.550416946411133, 9);
void* var_7 = ConvLayer_PROMISE(var_6, 0.0, 2.550416946411133, conv2d_8_w, -0.06320205, 0.08291938, conv2d_8_b, -0.32540628, 0.5203079, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.7303829237818675, 9);
void* var_8 = ConvLayer_PROMISE(var_7, 0.0, 0.7303829237818675, conv2d_9_w, -0.037707984, 0.051601283, conv2d_9_b, -0.25622904, 0.11251946, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.32286912292241965, 9);
void* var_9 = ConvLayer_PROMISE(var_8, 0.0, 0.32286912292241965, conv2d_10_w, -0.056007143, 0.09549151, conv2d_10_b, -0.11591503, 0.06267536, 1, 1, 1, 1, 0, 2, 1, 0.0, 0.47936276525258825, 9);
void* var_10 = ConvLayer_PROMISE(var_9, 0.0, 0.47936276525258825, conv2d_11_w, -0.060094673, 0.10868926, conv2d_11_b, -0.105962686, 0.09584572, 1, 1, 1, 1, -1, 0, 1, 0.0, 0.6409912902116734, 9);
void* var_11 = ConvLayer_PROMISE(var_10, 0.0, 0.6409912902116734, conv2d_12_w, -0.034618977, 0.05792674, conv2d_12_b, -0.4237576, 0.11035452, 1, 1, 1, 1, -1, 0, 1, 0.0, 1.1027569955587349, 9);
void* var_12 = ConvLayer_PROMISE(var_11, 0.0, 1.1027569955587349, conv2d_13_w, -0.035480656, 0.058295887, conv2d_13_b, -0.21477045, 0.14263579, 1, 1, 1, 1, 0, 2, 1, 0.0, 2.4708798038959503, 9);
void* var_13 = FCLayer_PROMISE(var_12, 0.0, 2.4708798038959503, dense_1_w, -0.08929961, 0.11301676, dense_1_b, -0.20798548, 0.47405547, 1, 0.0, 2.8148007798194876, 9);
void* var_14 = FCLayer_PROMISE(var_13, 0.0, 2.8148007798194876, dense_2_w, -0.6627122, 0.35539475, dense_2_b, -1.0631907, 0.9830786, -1, -21.189617557525633, 22.645009384155276, 9);
void* var_15 = tensorSoftmax(var_14);
uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
//-- float accuracy = computeAccuracy2(labels, batch_size, var_15, 100);
float accuracy = computeTop5Accuracy(labels, batch_size, var_15, 100);
final_accuracy += accuracy;
freeBatchMemory();
}
final_accuracy = final_accuracy / batch_count;
dumpFinalAccuracy(final_accuracy);
}
dumpExecutionAccuracies();
llvm_hpvm_cleanupTensorRt();
return 0;
}
llvm/projects/hpvm-tensor-rt/dnn_sources/src/vgg16_cifar100_top5_tuner.cc 0 → 100644
+ 167
− 0
View file @ 9e330433
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <string.h>
#include "../../tensor_runtime/include/tensor_runtime.h"
#include "../include/utils.h"
int main(){
llvm_hpvm_initTensorRt(0);
std::string dir_prefix = std::string("../model_params/vgg16_cifar100_front/");
//std::string input_path = dir_prefix + std::string("vgg16_cifar100_calib.bin");
//std::string labels_path = dir_prefix + std::string("vgg16_cifar100_train_labels.bin");
std::string input_path = dir_prefix + std::string("input.bin");
std::string labels_path = dir_prefix + std::string("labels.bin");
std::string conv2d_1_w_path = dir_prefix + std::string("conv2d_1_w.bin");
void* conv2d_1_w = readTrainedWeights(conv2d_1_w_path.c_str(), 0,64,3,3,3);
std::string conv2d_1_b_path = dir_prefix + std::string("conv2d_1_b.bin");
void* conv2d_1_b = readTrainedWeights(conv2d_1_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_2_w_path = dir_prefix + std::string("conv2d_2_w.bin");
void* conv2d_2_w = readTrainedWeights(conv2d_2_w_path.c_str(), 0,64,64,3,3);
std::string conv2d_2_b_path = dir_prefix + std::string("conv2d_2_b.bin");
void* conv2d_2_b = readTrainedWeights(conv2d_2_b_path.c_str(), 0,1,64,1,1);
std::string conv2d_3_w_path = dir_prefix + std::string("conv2d_3_w.bin");
void* conv2d_3_w = readTrainedWeights(conv2d_3_w_path.c_str(), 0,128,64,3,3);
std::string conv2d_3_b_path = dir_prefix + std::string("conv2d_3_b.bin");
void* conv2d_3_b = readTrainedWeights(conv2d_3_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_4_w_path = dir_prefix + std::string("conv2d_4_w.bin");
void* conv2d_4_w = readTrainedWeights(conv2d_4_w_path.c_str(), 0,128,128,3,3);
std::string conv2d_4_b_path = dir_prefix + std::string("conv2d_4_b.bin");
void* conv2d_4_b = readTrainedWeights(conv2d_4_b_path.c_str(), 0,1,128,1,1);
std::string conv2d_5_w_path = dir_prefix + std::string("conv2d_5_w.bin");
void* conv2d_5_w = readTrainedWeights(conv2d_5_w_path.c_str(), 0,256,128,3,3);
std::string conv2d_5_b_path = dir_prefix + std::string("conv2d_5_b.bin");
void* conv2d_5_b = readTrainedWeights(conv2d_5_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_6_w_path = dir_prefix + std::string("conv2d_6_w.bin");
void* conv2d_6_w = readTrainedWeights(conv2d_6_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_6_b_path = dir_prefix + std::string("conv2d_6_b.bin");
void* conv2d_6_b = readTrainedWeights(conv2d_6_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_7_w_path = dir_prefix + std::string("conv2d_7_w.bin");
void* conv2d_7_w = readTrainedWeights(conv2d_7_w_path.c_str(), 0,256,256,3,3);
std::string conv2d_7_b_path = dir_prefix + std::string("conv2d_7_b.bin");
void* conv2d_7_b = readTrainedWeights(conv2d_7_b_path.c_str(), 0,1,256,1,1);
std::string conv2d_8_w_path = dir_prefix + std::string("conv2d_8_w.bin");
void* conv2d_8_w = readTrainedWeights(conv2d_8_w_path.c_str(), 0,512,256,3,3);
std::string conv2d_8_b_path = dir_prefix + std::string("conv2d_8_b.bin");
void* conv2d_8_b = readTrainedWeights(conv2d_8_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_9_w_path = dir_prefix + std::string("conv2d_9_w.bin");
void* conv2d_9_w = readTrainedWeights(conv2d_9_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_9_b_path = dir_prefix + std::string("conv2d_9_b.bin");
void* conv2d_9_b = readTrainedWeights(conv2d_9_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_10_w_path = dir_prefix + std::string("conv2d_10_w.bin");
void* conv2d_10_w = readTrainedWeights(conv2d_10_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_10_b_path = dir_prefix + std::string("conv2d_10_b.bin");
void* conv2d_10_b = readTrainedWeights(conv2d_10_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_11_w_path = dir_prefix + std::string("conv2d_11_w.bin");
void* conv2d_11_w = readTrainedWeights(conv2d_11_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_11_b_path = dir_prefix + std::string("conv2d_11_b.bin");
void* conv2d_11_b = readTrainedWeights(conv2d_11_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_12_w_path = dir_prefix + std::string("conv2d_12_w.bin");
void* conv2d_12_w = readTrainedWeights(conv2d_12_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_12_b_path = dir_prefix + std::string("conv2d_12_b.bin");
void* conv2d_12_b = readTrainedWeights(conv2d_12_b_path.c_str(), 0,1,512,1,1);
std::string conv2d_13_w_path = dir_prefix + std::string("conv2d_13_w.bin");
void* conv2d_13_w = readTrainedWeights(conv2d_13_w_path.c_str(), 0,512,512,3,3);
std::string conv2d_13_b_path = dir_prefix + std::string("conv2d_13_b.bin");
void* conv2d_13_b = readTrainedWeights(conv2d_13_b_path.c_str(), 0,1,512,1,1);
std::string dense_1_w_path = dir_prefix + std::string("dense_1_w.bin");
void* dense_1_w = readTrainedWeights(dense_1_w_path.c_str(), 0,1,1,512,512);
std::string dense_1_b_path = dir_prefix + std::string("dense_1_b.bin");
void* dense_1_b = readTrainedWeights(dense_1_b_path.c_str(), 0,1,512,1,1);
std::string dense_2_w_path = dir_prefix + std::string("dense_2_w.bin");
void* dense_2_w = readTrainedWeights(dense_2_w_path.c_str(), 0,1,1,512,100);
std::string dense_2_b_path = dir_prefix + std::string("dense_2_b.bin");
void* dense_2_b = readTrainedWeights(dense_2_b_path.c_str(), 0,1,100,1,1);
startMemTracking();
int test_input_size = 4000;
int batch_size = 4000;
int offset = 5000;
int batch_count = test_input_size / batch_size;
float final_accuracy = 0.0;
for(int i = 0; i < batch_count; i++){
int start = i * batch_size + offset;
int end = (i + 1) * batch_size + offset;
void* input = readInputBatch(input_path.c_str(),0,start,end,3,32,32);
void* var_0 = tensorConvolution(input, conv2d_1_w, 1, 1, 1, 1, 1, 0);
void* var_1 = tensorAdd(var_0, conv2d_1_b);
void* var_2 = tensorRelu(var_1);
void* var_4 = tensorConvolution(var_2, conv2d_2_w, 1, 1, 1, 1, 1, 0);
void* var_5 = tensorAdd(var_4, conv2d_2_b);
void* var_6 = tensorRelu(var_5);
void* var_7 = tensorPooling(var_6,0,2,2,0,0,2,2);
void* var_8 = tensorConvolution(var_7, conv2d_3_w, 1, 1, 1, 1, 1, 0);
void* var_9 = tensorAdd(var_8, conv2d_3_b);
void* var_10 = tensorRelu(var_9);
void* var_12 = tensorConvolution(var_10, conv2d_4_w, 1, 1, 1, 1, 1, 0);
void* var_13 = tensorAdd(var_12, conv2d_4_b);
void* var_14 = tensorRelu(var_13);
void* var_15 = tensorPooling(var_14,0,2,2,0,0,2,2);
void* var_16 = tensorConvolution(var_15, conv2d_5_w, 1, 1, 1, 1, 1, 0);
void* var_17 = tensorAdd(var_16, conv2d_5_b);
void* var_18 = tensorRelu(var_17);
void* var_20 = tensorConvolution(var_18, conv2d_6_w, 1, 1, 1, 1, 1, 0);
void* var_21 = tensorAdd(var_20, conv2d_6_b);
void* var_22 = tensorRelu(var_21);
void* var_24 = tensorConvolution(var_22, conv2d_7_w, 1, 1, 1, 1, 1, 0);
void* var_25 = tensorAdd(var_24, conv2d_7_b);
void* var_26 = tensorRelu(var_25);
void* var_27 = tensorPooling(var_26,0,2,2,0,0,2,2);
void* var_28 = tensorConvolution(var_27, conv2d_8_w, 1, 1, 1, 1, 1, 0);
void* var_29 = tensorAdd(var_28, conv2d_8_b);
void* var_30 = tensorRelu(var_29);
void* var_32 = tensorConvolution(var_30, conv2d_9_w, 1, 1, 1, 1, 1, 0);
void* var_33 = tensorAdd(var_32, conv2d_9_b);
void* var_34 = tensorRelu(var_33);
void* var_36 = tensorConvolution(var_34, conv2d_10_w, 1, 1, 1, 1, 1, 0);
void* var_37 = tensorAdd(var_36, conv2d_10_b);
void* var_38 = tensorRelu(var_37);
void* var_39 = tensorPooling(var_38,0,2,2,0,0,2,2);
void* var_40 = tensorConvolution(var_39, conv2d_11_w, 1, 1, 1, 1, 1, 0);
void* var_41 = tensorAdd(var_40, conv2d_11_b);
void* var_42 = tensorRelu(var_41);
void* var_44 = tensorConvolution(var_42, conv2d_12_w, 1, 1, 1, 1, 1, 0);
void* var_45 = tensorAdd(var_44, conv2d_12_b);
void* var_46 = tensorRelu(var_45);
void* var_48 = tensorConvolution(var_46, conv2d_13_w, 1, 1, 1, 1, 1, 0);
void* var_49 = tensorAdd(var_48, conv2d_13_b);
void* var_50 = tensorRelu(var_49);
void* var_51 = tensorPooling(var_50,0,2,2,0,0,2,2);
void* var_54 = tensorGemmGPU(var_51, dense_1_w);
void* var_55 = tensorAdd(var_54, dense_1_b);
void* var_56 = tensorRelu(var_55);
void* var_58 = tensorGemmGPU(var_56, dense_2_w);
void* var_59 = tensorAdd(var_58, dense_2_b);
void* var_60 = tensorSoftmax(var_59);
uint8_t* labels = readLabelsBatch(labels_path.c_str(),start,end);
//float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
float accuracy = computeTop5Accuracy(labels, batch_size, var_60, 100);
final_accuracy += accuracy;
freeBatchMemory();
}
final_accuracy = final_accuracy / batch_count;
dumpFinalAccuracy(final_accuracy);
llvm_hpvm_cleanupTensorRt();
return 0;
}
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment