Made self-checking testbench

example-prjs/leaky_relu/leaky_relu_test.cpp

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+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+#include <fstream>
+#include <iostream>
+#include <algorithm>
+#include <vector>
+#include <map>
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "firmware/leaky_relu.h"
+#include "firmware/nnet_utils/nnet_helpers.h"
+// #include "firmware/parameters.h"
+
+#include <mc_scverify.h>
+
+//hls-fpga-machine-learning insert bram
+
+//hls-fpga-machine-learning insert declare weights
+
+namespace nnet {
+    bool trace_enabled = true;
+    std::map<std::string, void *> *trace_outputs = NULL;
+    size_t trace_type_size = sizeof(double);
+}
+
+CCS_MAIN(int argc, char *argv[])
+{
+  //load input data from text file
+  std::ifstream fin("tb_data/tb_input_features.dat");
+  //load predictions from text file
+  std::ifstream fpr("tb_data/tb_output_predictions.dat");
+
+#ifdef RTL_SIM
+  std::string RESULTS_LOG = "tb_data/rtl_cosim_results.log";
+#else
+  std::string RESULTS_LOG = "tb_data/csim_results.log";
+#endif
+  std::ofstream fout(RESULTS_LOG);
+
+#ifndef __SYNTHESIS__
+    static bool loaded_weights = false;
+    if (!loaded_weights) {
+        //hls-fpga-machine-learning insert load weights
+        loaded_weights = true;
+    }
+#endif
+  std::string iline;
+  std::string pline;
+
+  if (fin.is_open() && fpr.is_open()) {
+    while ( std::getline(fin,iline) && std::getline (fpr,pline) ) {
+      char* cstr=const_cast<char*>(iline.c_str());
+      char* current;
+      std::vector<float> in;
+      current=strtok(cstr," ");
+      while(current!=NULL) {
+        in.push_back(atof(current));
+        current=strtok(NULL," ");
+      }
+      cstr=const_cast<char*>(pline.c_str());
+      std::vector<float> pr;
+      current=strtok(cstr," ");
+      while(current!=NULL) {
+        pr.push_back(atof(current));
+        current=strtok(NULL," ");
+      }
+//    std::cout << "    Input feature map size = " << in.size() << " Output predictions size = " << pr.size() << std::endl;
+
+      //hls-fpga-machine-learning insert data
+      ac_channel<input_t> input_1/*("input_1")*/;
+      nnet::copy_data<float, input_t, 0, N_INPUT_1_1>(in, input_1);
+      ac_channel<result_t> layer2_out/*("layer2_out")*/;
+
+      //hls-fpga-machine-learning insert top-level-function
+      leaky_relu(input_1,layer2_out);
+
+      result_t tmp = layer2_out[0];
+      for(int i = 0; i < N_INPUT_1_1; i++)
+      {
+	if(fabs(pr[i]-tmp[i].to_double() >= 0.001))
+	  {
+		std::cout << "FAILURE" << std::endl;
+		std::cout << "Expected: " <<  pr[i] << " Actual: " << tmp[i].to_double() << std::endl;
+		return 1;
+	  }
+      }
+
+      //hls-fpga-machine-learning insert tb-output
+      nnet::print_result<result_t, N_INPUT_1_1>(layer2_out, fout);
+    }
+    fin.close();
+    fpr.close();
+  } else {
+    std::cout << "INFO: Unable to open input/predictions file, using default input." << std::endl;
+
+    //hls-fpga-machine-learning insert zero
+    ac_channel<input_t> input_1/*("input_1")*/;
+    nnet::fill_zero<input_t, N_INPUT_1_1>(input_1);
+    ac_channel<result_t> layer2_out/*("layer2_out")*/;
+
+    //hls-fpga-machine-learning insert top-level-function
+    leaky_relu(input_1,layer2_out);
+
+    //hls-fpga-machine-learning insert output
+
+    //hls-fpga-machine-learning insert tb-output
+    nnet::print_result<result_t, N_INPUT_1_1>(layer2_out, fout);
+
+  }
+
+  fout.close();
+  std::cout << "INFO: Saved inference results to file: " << RESULTS_LOG << std::endl;
+
+  return 0;
+}