Fixing LeNet Paths

llvm/projects/hpvm-tensor-rt/dnn_sources/src/half/lenet_keras_half.cc

@@ -9,8 +9,8 @@
 #include <string.h>
 
 
-#include "../../../tensor_runtime/include/tensor_runtime.h"
-#include "../../include/utils.h"
+#include "tensor_runtime.h"
+#include "utils.h"
 
 
 bool Opentuner_run = false;
@@ -30,15 +30,16 @@ void testLenetTanh(){
 
   int test_batch_size = 5000;
 
-  uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
-  
-  void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
-				CUDNN_DATA_FLOAT,
-				test_batch_size, 1, 28, 28);
+  std::string dir_prefix = std::string("../model_params/lenet_keras/");   
 
-  // NOTE: Filter descriptors do NOT have batch size
-  // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
-  // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
+  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");
+  
+  // 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_keras/conv1.bin",
 					  float_type, 32, 1, 5, 5);    
   void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",

llvm/projects/hpvm-tensor-rt/dnn_sources/src/lenet_keras.cc

@@ -9,8 +9,8 @@
 #include <string.h>
 
 
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
+#include "tensor_runtime.h"
+#include "utils.h"
 
 
 bool Opentuner_run = false;
@@ -29,17 +29,20 @@ void testLenetTanh(){
   printf("********* Lenet-2 Architecture ********** \n");
   // FIXIT: Extend this to batch of images - currently 5 images
 
-  int test_batch_size = 2000;
+  int test_batch_size = 5000;
+
+  std::string dir_prefix = std::string("../model_params/lenet_keras/");   
+
+  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");
 
-  uint8_t* labels = readLabels("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte", test_batch_size);
   
-  void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
-				CUDNN_DATA_FLOAT,
-				test_batch_size, 1, 28, 28);
+  // 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); 
+    
 
-  // NOTE: Filter descriptors do NOT have batch size
-  // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
-  // IMP: The output channels matches the trained model - not the Lenet arch proposed in Andrew Ng's class
   void* conv1_filter = readTrainedWeights("../model_params/lenet_keras/conv1.bin",
 					  float_type, 32, 1, 5, 5);    
   void* conv1_bias = readTrainedWeights("../model_params/lenet_keras/conv1_bias.bin",