Pusing 4/20 fixed rutnime sources - more left

hpvm/projects/hpvm-tensor-rt/dnn_sources/include/utils.h

@@ -16,11 +16,10 @@
 #include <string.h>
 
 std::vector<float> run_accuracies;
-std::string model_params_path = "../../../build/model_params/";
+std::string model_params_path =  "../../test/dnn_benchmarks/model_params/";
 
 // FIXIT: Move this to debug.h and include in all files
 void dumpWeightsToFile(const char *file_name, void *weights_ptr) {
-
   struct Tensor *weights = (Tensor *)weights_ptr;
   // Move data back to host
   hpvm_request_tensor(weights, 0);

hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet2_cifar10_half.cc

@@ -10,10 +10,9 @@ void testCifarNet() {
 
   std::string dir_prefix =
       model_params_path + std::string("/alexnet2_cifar10/");
-  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 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);
@@ -119,9 +118,9 @@ void testCifarNet() {
     void *gemm1biasout = tensorHalfAdd(gemm1out, dense_1_b);
     void *result = tensorSoftmax(gemm1biasout);
 
-    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, result);
+    float accuracy = computeAccuracy3(labels, result);
     final_accuracy += accuracy;
 
     freeBatchMemory();

hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp16/alexnet_cifar10_half.cc

@@ -9,9 +9,8 @@ int main() {
 
   std::string dir_prefix = model_params_path + std::string("/alexnet_cifar10/");
 
-  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 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, 11, 11);
@@ -86,9 +85,9 @@ int main() {
     void *var_23 = tensorHalfAdd(var_22, dense_1_b);
     void *var_24 = tensorSoftmax(var_23);
 
-    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_24);
+    float accuracy = computeAccuracy3(labels, var_24);
     final_accuracy += accuracy;
 
     freeBatchMemory();

hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet2_cifar10.cc

@@ -8,11 +8,9 @@ void testCifarNet() {
 
   printf("********* Alexnet2 CIFAR-10 DNN ********** \n");
 
-  std::string dir_prefix =
-      model_params_path + std::string("/alexnet2_cifar10/");
-  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("/alexnet2_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 =
@@ -62,8 +60,8 @@ void testCifarNet() {
 
   startMemTracking();
 
-  int test_input_size = 2000;
-  int batch_size = 2000;
+  int test_input_size = 5000;
+  int batch_size = 1000;
   int batch_count = test_input_size / batch_size;
   float final_accuracy = 0.0;
 
@@ -119,9 +117,9 @@ void testCifarNet() {
     void *gemm1biasout = tensorAdd(gemm1out, dense_1_b);
     void *result = tensorSoftmax(gemm1biasout);
 
-    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, result);
+    float accuracy = computeAccuracy3(labels, result);
     final_accuracy += accuracy;
 
     freeBatchMemory();

hpvm/projects/hpvm-tensor-rt/dnn_sources/src/fp32/alexnet_cifar10.cc

@@ -9,9 +9,9 @@ int main() {
 
   std::string dir_prefix = model_params_path + std::string("/alexnet_cifar10/");
 
-  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 input_path = dir_prefix + std::string("test_input.bin");
+  //std::string labels_path = dir_prefix + std::string("labels.bin");
+  std::string labels32_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, 11, 11);
@@ -86,9 +86,9 @@ int main() {
     void *var_23 = tensorAdd(var_22, dense_1_b);
     void *var_24 = tensorSoftmax(var_23);
 
-    uint8_t *labels = readLabelsBatch(labels_path.c_str(), start, end);
+    uint32_t *labels = readLabelsBatch3(labels32_path.c_str(), start, end);
 
-    float accuracy = computeAccuracy2(labels, batch_size, var_24);
+    float accuracy = computeAccuracy3(labels, var_24);
     final_accuracy += accuracy;
 
     freeBatchMemory();