Add MocapNET

2026-10-16 00:13:07 +00:00 · 2023-12-15 04:22:41 +05:30
parent 8434144ded
commit 65e8934d5e
336 changed files with 100584 additions and 0 deletions
@@ -0,0 +1,219 @@
+#include "neuralNetworkAbstraction.hpp"
+
+#include <stdio.h>
+
+
+void neuralNetworkPrintVersion()
+{
+  #if USE_TENSORFLOW1
+    //=========================
+    //=========================
+    fprintf(stderr,"TF1 wrapper / Tensorflow version %s\n",TF_Version());
+    //=========================
+    //=========================
+  #elif USE_TENSORFLOW2
+    //=========================
+    //=========================
+    fprintf(stderr,"TF2 wrapper / Tensorflow version %s\n",TF_Version());
+    //=========================
+    //=========================
+  #else
+    fprintf(stderr,"No Tensorflow support\n");
+  #endif
+}
+
+char * neuralNetworkGetPath(struct neuralNetworkModel * nn)
+{
+  #if USE_TENSORFLOW1
+    //=========================
+    //=========================
+    return nn->model.modelPath;
+    //=========================
+    //=========================
+  #elif USE_TENSORFLOW2
+    //=========================
+    //=========================
+    return nn->model.modelPath;
+    //=========================
+    //=========================
+  #else
+    return 0;
+  #endif
+}
+
+int neuralNetworkLoad(
+                      struct neuralNetworkModel * nn,
+                      const char * filename,
+                      const char * inputTensor,
+                      const char * outputTensor,
+                      unsigned int numberOfInputElements,
+                      unsigned int numberOfOutputElements,
+                      unsigned int forceCPU
+                     )
+{
+  #ifdef USE_TENSORFLOW1
+    //=========================
+    //=========================
+    return  loadTensorflowInstance(&nn->model,filename,inputTensor,outputTensor,forceCPU);
+    //=========================
+    //=========================
+  #elif USE_TENSORFLOW2 
+    //=========================
+    //=========================
+    char checkForTF2Path[2048];
+    snprintf(checkForTF2Path,2048,"%s/saved_model.pb",filename);
+    
+    if (tf2_fileExists(checkForTF2Path))
+    {
+    fprintf(stderr,"neuralNetworkLoad: attempting to load a TF2 style saved model (%s)..\n",checkForTF2Path);
+     //This means we are talking about a TF2 "saved model" model
+     return tf2_loadModel(&nn->model,filename,inputTensor,numberOfInputElements,numberOfOutputElements,forceCPU);
+    } else
+    if (tf2_fileExists(filename))
+    {
+     //This means we are talking about a TF1 style "frozen graph" model
+     return tf2_loadFrozenGraph(&nn->model,filename,inputTensor,outputTensor,numberOfInputElements,numberOfOutputElements,forceCPU);     
+    } else
+    {
+    fprintf(stderr,"Error: neuralNetworkLoad could not find a tf2/tf1 model to load..\n");
+    }
+    //=========================
+    //=========================
+  #endif 
+  return 0;
+}
+
+
+int neuralNetworkUnload(
+                        struct neuralNetworkModel * nn
+                       )
+{
+  #ifdef USE_TENSORFLOW1
+    //=========================
+    //=========================
+    return unloadTensorflow(&nn->model);
+    //=========================
+    //=========================
+  #elif USE_TENSORFLOW2 
+    //=========================
+    //=========================
+    return tf2_unloadModel(&nn->model); 
+    //=========================
+    //=========================
+ #else
+   return 0;
+  #endif
+}
+
+std::vector<float> neuralNetworkExecute(struct neuralNetworkModel * nn,std::vector<float> input)
+{
+  std::vector<float> result;
+  result.clear();
+  
+  
+  #ifdef USE_TENSORFLOW1
+    //=========================
+    //========================= 
+    result = predictTensorflow(&nn->model,input);
+    //=========================
+    //========================= 
+  #elif USE_TENSORFLOW2
+    //=========================
+    //=========================
+    if (input.size() != nn->model.inputElements)
+    {
+      fprintf(stderr,"neuralNetworkExecute: Inconsistent input .. \n");
+      return result;
+    }
+    
+    //Cast input vector in a regular C memory block and hold the dimension data..
+    //-------------------------------------------------------------------------------------------
+    unsigned int ndata = sizeof(float)*1*nn->model.inputElements; //number of bytes not number of element
+    int ndims = 2;
+    int64_t dims[] = {1,nn->model.inputElements};
+    
+    
+    unsigned int newBufferSize = ndata;
+    if ( (nn->model.buffer!=0) && (nn->model.bufferSize < newBufferSize) )
+    {
+      //Do reallocation here.. 
+      float * newBuffer = (float*) realloc (nn->model.buffer,newBufferSize);
+      if (newBuffer!=nn->model.buffer)
+      {
+        nn->model.bufferSize = newBufferSize;
+      }
+    } else
+    if (nn->model.buffer==0)
+    {
+        newBufferSize = ndata;
+        nn->model.bufferSize = newBufferSize;
+        nn->model.buffer = (float *) malloc(newBufferSize);
+    }
+     
+    if (nn->model.buffer!=0)
+    {
+      for(int i=0; i<nn->model.inputElements; i++)
+        {
+          nn->model.buffer[i] = input[i];
+        }
+    //-------------------------------------------------------------------------------------------
+        
+    //Actually run the neural network..
+    //-------------------------------------------------------------------------------------------
+    if (
+        tf2_run(
+              &nn->model,
+              dims,
+              ndims,
+              nn->model.buffer,
+              nn->model.bufferSize
+             )
+         )
+    //-------------------------------------------------------------------------------------------
+    {
+    //Cast ouput from TF tensor data to a vector 
+    //-------------------------------------------------------------------------------------------
+      void* buff = TF_TensorData(nn->model.outputValues[0]); 
+      size_t tensorByteSize = TF_TensorByteSize(nn->model.outputValues[0]);
+      unsigned long tensorElements = TF_TensorElementCount(nn->model.outputValues[0]);
+      
+      if (tensorElements<nn->model.outputElements)
+      {
+        fprintf(stderr,"neuralNetworkExecute: Incorrect number of output elements, expected %u found %lu \n",nn->model.outputElements,tensorElements);
+      } else
+      if (buff!=0)
+      { //If we have a buffer
+        if (nn->model.outputIsHalfFloats)
+          {
+           //Convert float16 back to float32
+           float16* output = (float16*) buff; 
+           for (unsigned int i=0; i<nn->model.outputElements; i++)
+               {
+                result.push_back(convertFloat16ToFloat32(output[i]));
+               }
+          } else
+          {
+           //Regular float32 output that can be immediately pushed back to the vector
+           float* output = (float*) buff; 
+           for (unsigned int i=0; i<nn->model.outputElements; i++)
+               { 
+                result.push_back(output[i]);
+               }
+          }
+      }
+      } else
+      {
+        fprintf(stderr,"Failed running the network..\n");
+      }
+    //------------------------------------------------------------------------------------------- 
+    } //We have a data buffer.. 
+     else
+    {
+      fprintf(stderr,"Could not allocate enough memory to run %s\n",nn->model.modelPath);
+    }
+    //=========================
+    //=========================
+  #endif
+  
+  return result;
+}