diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
index 0fb39cbe84af998ad42c9c14915e272aa3dab88d..54cbbed01475ab0f13c3ff9b131bf174ba8e8e12 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/lenet_mnist_half.cc
@@ -11,33 +11,40 @@ void testLenetTanh() {
int test_batch_size = 5000;
- std::string dir_prefix = model_params_path + std::string("/lenet_mnist/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string labels32_path = dir_prefix + std::string("labels32.bin");
+ std::string dir_prefix =
+ model_params_path + std::string("/lenet_mnist/");
+
+ std::string input_path = dir_prefix + std::string("test_input.bin");
+ std::string labels_path = dir_prefix + std::string("test_labels.bin");
// Loading Input Batch
void *input =
readInputBatch(input_path.c_str(), 0, 0, test_batch_size, 1, 28, 28);
- uint8_t *labels = readLabelsBatch(labels_path.c_str(), 0, test_batch_size);
-
- void *conv1_filter = readTrainedWeights(
- "../model_params/lenet_mnist/conv1.bin", float_type, 32, 1, 5, 5);
- void *conv1_bias = readTrainedWeights(
- "../model_params/lenet_mnist/conv1_bias.bin", float_type, 1, 32, 1, 1);
- void *conv2_filter = readTrainedWeights(
- "../model_params/lenet_mnist/conv2.bin", float_type, 64, 32, 5, 5);
- void *conv2_bias = readTrainedWeights(
- "../model_params/lenet_mnist/conv2_bias.bin", float_type, 1, 64, 1, 1);
- void *fc1_weights = readTrainedWeights("../model_params/lenet_mnist/fc1.bin",
+
+ uint32_t *labels = readLabelsBatch3(labels_path.c_str(), 0, test_batch_size);
+
+ void *conv1_filter = readTrainedWeights((dir_prefix + std::string("/conv2d_1_w.bin")).c_str(),
+ float_type, 32, 1, 5, 5);
+ void *conv1_bias = readTrainedWeights((dir_prefix + std::string("/conv2d_1_b.bin")).c_str(),
+ float_type, 1, 32, 1, 1);
+ void *conv2_filter = readTrainedWeights((dir_prefix + std::string("/conv2d_2_w.bin")).c_str(),
+ float_type, 64, 32, 5, 5);
+
+ void *conv2_bias = readTrainedWeights((dir_prefix + std::string("/conv2d_2_b.bin")).c_str(),
+ float_type, 1, 64, 1, 1);
+
+ void *fc1_weights = readTrainedWeights((dir_prefix + std::string("/dense_1_w.bin")).c_str(),
float_type, 1, 1, 7 * 7 * 64, 1024);
- void *fc1_bias = readTrainedWeights(
- "../model_params/lenet_mnist/fc1_bias.bin", float_type, 1, 1024, 1, 1);
- void *fc2_weights = readTrainedWeights("../model_params/lenet_mnist/fc2.bin",
+
+ void *fc1_bias = readTrainedWeights((dir_prefix + std::string("/dense_1_b.bin")).c_str(),
+ float_type, 1, 1024, 1, 1);
+
+ void *fc2_weights = readTrainedWeights((dir_prefix + std::string("/dense_2_w.bin")).c_str(),
float_type, 1, 1, 1024, 10);
- void *fc2_bias = readTrainedWeights(
- "../model_params/lenet_mnist/fc2_bias.bin", float_type, 1, 10, 1, 1);
+
+ void *fc2_bias = readTrainedWeights((dir_prefix + std::string("/dense_2_b.bin")).c_str(),
+ float_type, 1, 10, 1, 1);
+
clearTensorMap();
@@ -88,7 +95,7 @@ void testLenetTanh() {
// End profiling and dump output to profile.txt
stopProfiling();
- computeAccuracy2(labels, test_batch_size, result);
+ computeAccuracy3(labels, result);
dumpAccuracyNorms();
freeOutputTensors();
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
index 7722447047aaac6dc679fb02c16e6b2c20c2c049..7340e52c8d60453a1fb6f68f718c1c08c478c058 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/mobilenet_half.cc
@@ -7,9 +7,11 @@ int main() {
llvm_hpvm_initTensorRt(0);
- std::string dir_prefix = model_params_path + std::string("/mobilenet/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
+ std::string dir_prefix = model_params_path + std::string("/mobilenet_cifar10/");
+
+ std::string input_path = dir_prefix + std::string("test_input.bin");
+ std::string labels_path = dir_prefix + std::string("test_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, 32, 3, 3, 3);
@@ -710,9 +712,9 @@ int main() {
void *var_102 = tensorHalfAdd(var_101, dense_1_b);
void *var_103 = tensorSoftmax(var_102);
- uint8_t *labels = readLabelsBatch(labels_path.c_str(), start, end);
+ uint32_t *labels = readLabelsBatch3(labels_path.c_str(), start, end);
- float accuracy = computeAccuracy2(labels, batch_size, var_103);
+ float accuracy = computeAccuracy3(labels, var_103);
final_accuracy += accuracy;
freeBatchMemory();
}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
index 1bd79c7fb71400edd900bceb42413cf4320005fe..a5b703ab6e2461f67c2d05b077ca2bdade86a5f9 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar100_half.cc
@@ -8,8 +8,10 @@ int main() {
llvm_hpvm_initTensorRt(0);
std::string dir_prefix = model_params_path + std::string("/vgg16_cifar100/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
+
+ std::string input_path = dir_prefix + std::string("test_input.bin");
+ std::string labels_path = dir_prefix + std::string("test_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);
@@ -164,9 +166,9 @@ int main() {
void *var_59 = tensorHalfAdd(var_58, dense_2_b);
void *var_60 = tensorSoftmax(var_59);
- uint8_t *labels = readLabelsBatch(labels_path.c_str(), start, end);
+ uint32_t *labels = readLabelsBatch3(labels_path.c_str(), start, end);
- float accuracy = computeAccuracy2(labels, batch_size, var_60, 100);
+ float accuracy = computeAccuracy3(labels, var_60);
final_accuracy += accuracy;
freeBatchMemory();
}
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
index 22d2a3614cb668a668f60c7a3941e06d92ebf4de..2a4b5a6a914698f621284e0f5b19843b817068df 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/vgg16_cifar10_half.cc
@@ -8,9 +8,10 @@ int main() {
llvm_hpvm_initTensorRt(0);
std::string dir_prefix = model_params_path + std::string("/vgg16_cifar10/");
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
+ std::string input_path = dir_prefix + std::string("test_input.bin");
+ std::string labels_path = dir_prefix + std::string("test_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");
@@ -167,9 +168,9 @@ int main() {
void *var_59 = tensorHalfAdd(var_58, dense_2_b);
void *var_60 = tensorSoftmax(var_59);
- uint8_t *labels = readLabelsBatch(labels_path.c_str(), start, end);
+ uint32_t *labels = readLabelsBatch3(labels_path.c_str(), start, end);
- float accuracy = computeAccuracy2(labels, batch_size, var_60);
+ float accuracy = computeAccuracy3(labels, var_60);
final_accuracy += accuracy;
freeBatchMemory();
diff --git a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
index cb6593f7d5cac872159c909c99fbde478729df29..e1550c715cb36dca8eec1fd24e82e038014039a0 100644
--- a/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
+++ b/hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/lenet_mnist.cc
@@ -1,50 +1,54 @@
#include "tensor_runtime.h"
-#include "utils.h"
+#include "../include/utils.h"
int total_runs = 1;
-/* NOTE: Reference Architecture to use for profiling */
+
void testLenetTanh() {
printf("********* Lenet-2 Architecture ********** \n");
- // FIXIT: Extend this to batch of images - currently 5 images
int test_batch_size = 5000;
- std::string dir_prefix = model_params_path + std::string("/lenet_mnist/");
-
- std::string input_path = dir_prefix + std::string("input.bin");
- std::string labels_path = dir_prefix + std::string("labels.bin");
- std::string labels32_path = dir_prefix + std::string("labels32.bin");
+ std::string dir_prefix =
+ model_params_path + std::string("/lenet_mnist/");
+
+ std::string input_path = dir_prefix + std::string("test_input.bin");
+ std::string labels_path = dir_prefix + std::string("test_labels.bin");
// Loading Input Batch
void *input =
readInputBatch(input_path.c_str(), 0, 0, test_batch_size, 1, 28, 28);
- uint8_t *labels = readLabelsBatch(labels_path.c_str(), 0, test_batch_size);
-
- void *conv1_filter = readTrainedWeights(
- "../model_params/lenet_mnist/conv1.bin", float_type, 32, 1, 5, 5);
- void *conv1_bias = readTrainedWeights(
- "../model_params/lenet_mnist/conv1_bias.bin", float_type, 1, 32, 1, 1);
- void *conv2_filter = readTrainedWeights(
- "../model_params/lenet_mnist/conv2.bin", float_type, 64, 32, 5, 5);
- void *conv2_bias = readTrainedWeights(
- "../model_params/lenet_mnist/conv2_bias.bin", float_type, 1, 64, 1, 1);
- void *fc1_weights = readTrainedWeights("../model_params/lenet_mnist/fc1.bin",
+
+ uint32_t *labels = readLabelsBatch3(labels_path.c_str(), 0, test_batch_size);
+
+ void *conv1_filter = readTrainedWeights((dir_prefix + std::string("/conv2d_1_w.bin")).c_str(),
+ float_type, 32, 1, 5, 5);
+ void *conv1_bias = readTrainedWeights((dir_prefix + std::string("/conv2d_1_b.bin")).c_str(),
+ float_type, 1, 32, 1, 1);
+ void *conv2_filter = readTrainedWeights((dir_prefix + std::string("/conv2d_2_w.bin")).c_str(),
+ float_type, 64, 32, 5, 5);
+
+ void *conv2_bias = readTrainedWeights((dir_prefix + std::string("/conv2d_2_b.bin")).c_str(),
+ float_type, 1, 64, 1, 1);
+
+ void *fc1_weights = readTrainedWeights((dir_prefix + std::string("/dense_1_w.bin")).c_str(),
float_type, 1, 1, 7 * 7 * 64, 1024);
- void *fc1_bias = readTrainedWeights(
- "../model_params/lenet_mnist/fc1_bias.bin", float_type, 1, 1024, 1, 1);
- void *fc2_weights = readTrainedWeights("../model_params/lenet_mnist/fc2.bin",
+
+ void *fc1_bias = readTrainedWeights((dir_prefix + std::string("/dense_1_b.bin")).c_str(),
+ float_type, 1, 1024, 1, 1);
+
+ void *fc2_weights = readTrainedWeights((dir_prefix + std::string("/dense_2_w.bin")).c_str(),
float_type, 1, 1, 1024, 10);
- void *fc2_bias = readTrainedWeights(
- "../model_params/lenet_mnist/fc2_bias.bin", float_type, 1, 10, 1, 1);
+
+ void *fc2_bias = readTrainedWeights((dir_prefix + std::string("/dense_2_b.bin")).c_str(),
+ float_type, 1, 10, 1, 1);
clearTensorMap();
for (int i = 0; i < total_runs; i++) {
- readOpenTunerFlags("opentuner_flags"); // Resets the OpenTuner counters
-
+
// Start power and performnce profiling
startProfiling();
@@ -89,7 +93,7 @@ void testLenetTanh() {
// End profiling and dump output to profile.txt
stopProfiling();
- float accuracy = computeAccuracy2(labels, test_batch_size, result);
+ float accuracy = computeAccuracy3(labels, result);
dumpFinalAccuracy(accuracy);
// FIXME: remove the comment below to use piped autotuner