moving more outdated files to ./legacy

llvm/projects/hpvm-tensor-rt/dnn_sources/src/test_lenet2.cc

-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-
-#include "../../tensor_runtime/include/tensor_runtime.h"
-#include "../include/utils.h"
-
-
-
-/* NOTE: Reference Architecture to use for profiling */
-void testLenet2Arch(){
-
-  printf("********* Lenet-2 Architecture ********** \n");
-  // FIXIT: Extend this to batch of images - currently 5 images
-
-  int test_batch_size = 10000;  
-  void* input = readInputTensor("../model_params/lenet_params/datasets/t10k-images-idx3-ubyte",
-				CUDNN_DATA_FLOAT,
-				test_batch_size, 1, 28, 28);
-
-  // 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/lenet2_params/conv1.bin",
-					  float_type, 32, 1, 5, 5);    
-  void* conv1_bias = readTrainedWeights("../model_params/lenet2_params/conv1_bias.bin",
-					float_type, 1, 32, 1, 1);  
-  void* conv2_filter = readTrainedWeights("../model_params/lenet2_params/conv2.bin",
-					  float_type, 64, 32, 5, 5);  
-  void* conv2_bias = readTrainedWeights("../model_params/lenet2_params/conv2_bias.bin",
-					float_type, 1, 64, 1, 1);  
-  void* fc1_weights = readTrainedWeights("../model_params/lenet2_params/fc1.bin",
-					 float_type, 1, 1, 7*7*64, 1024);  
-  void* fc1_bias = readTrainedWeights("../model_params/lenet2_params/fc1_bias.bin",
-				      float_type, 1, 1024, 1, 1);  
-  void* fc2_weights = readTrainedWeights("../model_params/lenet2_params/fc2.bin",
-					 float_type, 1, 1, 1024, 10);  
-  void* fc2_bias = readTrainedWeights("../model_params/lenet2_params/fc2_bias.bin",
-				      float_type, 1, 10, 1, 1);  
-
-
-  // Start power and performnce profiling 
-  startProfiling();
-  
-  int conv_mode = 1; // NOTE: using CROSS_CORRELATION
-  int conv_precision = 0; // NOTE: using Float as compute precision. FIXIT: use enum
-
-  // NOTE: 'SAME' convolution
-  void* conv1out = tensorConvolution(input, conv1_filter, 2, 2, 1, 1,
-				     conv_mode, conv_precision);
-
-  // NOTE: For tensorAdd, the only dimension that MUST match is channels  
-  tensorAdd(conv1out, conv1_bias); // NOTE: In place operation
-  printTensorDims(conv1out);
-
-  void* conv1_reluout = tensorRelu(conv1out);
-  //dumpWeightsToFile("tensors_out/conv1.out", conv1_reluout);  
-
-  void* pool1out = tensorPooling(conv1_reluout, 0, 2, 2, 0, 0, 2, 2);
-  printTensorDims(pool1out);
-  // NOTE: input channels have to match between tensor op inputs and outputs 
-  void* conv2out = tensorConvolution(pool1out, conv2_filter, 2, 2, 1, 1,
-				     conv_mode, conv_precision);
-  tensorAdd(conv2out, conv2_bias); // NOTE: In place operation
-  printTensorDims(conv2out);
-
-  void* conv2_reluout = tensorRelu(conv2out);
-  //dumpWeightsToFile("tensors_out/conv2.out", conv2_reluout);  
-
-  void* pool2out = tensorPooling(conv2_reluout, 0, 2, 2, 0, 0, 2, 2);
-  printTensorDims(pool2out);
-  //dumpWeightsToFile("tensors_out/maxpool2.out", pool2out);  
-  
-  void* gemm1out = tensorGemmGPU(pool2out, fc1_weights);  
-  printTensorDims(gemm1out);
-  //dumpWeightsToFile("tensors_out/gemm1.out", gemm1out);  
-  
-  void* gemm1biasout = tensorAdd(gemm1out, fc1_bias);
-  printTensorDims(gemm1biasout);
-
-  void* relu1out = tensorRelu(gemm1biasout);
-  printTensorDims(relu1out);
-  
-  void* gemm2out = tensorGemmGPU(relu1out, fc2_weights);  
-  printTensorDims(gemm2out);
-  
-  void* gemm2_biasout = tensorAdd(gemm2out, fc2_bias);
-  printTensorDims(gemm2_biasout);
-  
-  void* result = tensorSoftmax(gemm2_biasout);
-  printTensorDims(result);
-
-  // End profiling and dump output to profile.txt
-  stopProfiling();
-  
-  computeAccuracy("../model_params/lenet_params/datasets/t10k-labels-idx1-ubyte",
-		  test_batch_size, result);
-  // THINK: I believe that comparing the results do not need to be part of the HPVM graph
-}
-
-
-int main(){
-
-  llvm_hpvm_initTensorRt(0);
-
-  testLenet2Arch();
-  
-  return 0;
-}
-