Merge pull request #318 from leondavi/source_nif

NerlnetRun.sh

@@ -229,6 +229,7 @@ case "$_arg_run_mode" in
 (*) exit 1 ;;
 esac
 
+exit 0
 
 #TODO TO BE REMOVED
 # only for CI debug

src_cpp/common/CMakeLists.txt

@@ -4,6 +4,7 @@ set(NIFPP_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../nifpp/")
 set(SIMPLE_LOGGER_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../simple-cpp-logger/include")
 
 set(SRC_CODE
+    "nerltensor.h"
     "common_definitions.h"
     "nerlLayer.cpp"
     "nerlLayer.h"

src_cpp/common/nerltensor.h

@@ -0,0 +1,22 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+// enum defnitions
+enum {NERL_TENSOR_DIMS = 3, CASE_1D = 1, CASE_2D =2, CASE_3D = 3};
+enum NERLTENSOR_DATA_TYPES_NUM {ATOM_FLOAT, ATOM_DOUBLE, ATOM_INT32, ATOM_INT16, ATOM_UINT8};
+
+
+static std::unordered_map<std::string, int> atom_str_to_enum_map = {
+    {"float", ATOM_FLOAT},
+    {"double", ATOM_DOUBLE},
+    {"int32", ATOM_INT32},
+    {"int16", ATOM_INT16},
+    {"uint8", ATOM_UINT8}
+};
+
+// functions
+inline int atom_str_to_enum(std::string &in_str)
+{
+    return atom_str_to_enum_map[in_str];
+}

src_cpp/opennnBridge/CMakeLists.txt

@@ -28,7 +28,6 @@ set(SRC_CODE
 	"eigenTensorTypes.h"
 	"nifppEigenExtensions.h"
 	"nifppNerltensorEigen.h"
-	"definitionsNN.h"
 	"openNNnif.h"
 	"openNNnif.cpp"
 	"nerlWorkerOpenNN.h"

src_cpp/opennnBridge/ae_red.cpp

@@ -1,15 +1,23 @@
 #include "ae_red.h"
 
-using namespace nerlnet;
-
-AeRed::AeRed(float k , float alpha)
+namespace nerlnet
+{
+
+AeRed::AeRed(float k, float alpha)
 {
     _k = k;
     _alpha = alpha;
+    _ema = 0;
+    _emad = 1;
+    _ema_event = 0;
+    _ema_normal = 0;
+    _prev_ema = 0;
+    _prev_emad = 0;
 }
 
 AeRed::~AeRed()
 {
+
 }
 
 fTensor1DPtr AeRed::update_batch(fTensor1D loss_values)
@@ -38,4 +46,6 @@ float AeRed::update_sample(float loss_value){
 
     if(loss_value > _threshold) return loss_value;
     else return -loss_value;
-}
+}
+
+} // namespace nerlnet

src_cpp/opennnBridge/ae_red.h

@@ -1,6 +1,8 @@
 #pragma once
+
 #define PARAM_K_DEFAULT 1.2f
 #define ALPHA_DEFAULT 0.3f
+
 #include "eigenTensorTypes.h"
 
 
@@ -11,6 +13,7 @@ class AeRed
 {
 
     public:
+
     AeRed(float k = PARAM_K_DEFAULT , float alpha = ALPHA_DEFAULT);
     ~AeRed();
 
@@ -21,12 +24,12 @@ class AeRed
     float _k;
     float _alpha;
     float _threshold;
-    float _ema = 0;
-    float _emad = 1;
-    float _ema_event = 0;
-    float _ema_normal = 0;
-    float _prev_ema = 0;
-    float _prev_emad = 0;
+    float _ema;
+    float _emad;
+    float _ema_event;
+    float _ema_normal;
+    float _prev_ema;
+    float _prev_emad;
 
 };
 

src_cpp/opennnBridge/nifppNerltensorEigen.h

@@ -1,14 +1,11 @@
 #pragma once 
 
+#include "nerltensor.h"
 #include "eigenTensorTypes.h"
-#include "definitionsNN.h"
 #include "nifppEigenExtensions.h"
 
 namespace nifpp
 {
-    enum {NERL_TENSOR_DIMS = 3, CASE_1D = 1, CASE_2D =2, CASE_3D = 3};
-
-    int atom_str_to_enum(str_atom in_str);
     template<typename BasicType> int get_actual_dim(std::vector<BasicType> &dims);
     template<typename BasicType, typename EigenTypePtr, typename EigenType> int get_tensor_1d(ErlNifEnv *env , ERL_NIF_TERM bin_term, EigenTypePtr &tensor_ptr);
     template<typename BasicType, typename EigenTypePtr, typename EigenType> int get_tensor_2d(ErlNifEnv *env , ERL_NIF_TERM bin_term, EigenTypePtr &tensor_ptr);
@@ -263,25 +260,4 @@ namespace nifpp
         vec = std::vector<Type>(bin.data, bin.data + bin.size / sizeof(Type));
         return ret;
     }
-
-    int atom_str_to_enum(str_atom in_str)
-    {
-        if (in_str == "float")
-        {
-            return ATOM_FLOAT;
-        }
-        else if (in_str == "double")
-        {
-            return ATOM_DOUBLE;
-        }
-        else if (in_str == "int32")
-        {
-            return ATOM_INT32;
-        }
-        else if (in_str == "int16")
-        {
-            return ATOM_INT16;
-        }
-        throw("Type is unsupported, update atom_str_to_enum and enum in nifppEigenExtensions.h");
-    }
 }

src_erl/NerlnetApp/src/nerl_tools.erl

@@ -32,7 +32,6 @@ http_router_request(RouterHost, RouterPort, DestinationsList, ActionStr, Body) -
 
 
 http_request(Host, Port, Path, {json, Body}) -> 
-  io:format("Sending Json to ~p:~p~n",[Host,Port]),
   JsonContentType = ?HTTP_CONTENT_TYPE_JSON,
   Json = jsx:encode(Body),
   http_request(Host, Port,Path, JsonContentType, Json);

src_py/apiServer/experiment_flow.py

@@ -124,17 +124,20 @@ def print(self):
         #LOG_INFO(f"CSV dataset: {self.csv_dataset.get_csv_file_path()}")
         LOG_INFO(f"Number of features: {self.csv_dataset.get_num_of_features()}")
         LOG_INFO(f"Number of labels: {self.csv_dataset.get_num_of_labels()}")
-        LOG_INFO("\nPhases:\n")
+        LOG_INFO("")
+        LOG_INFO("Phases:")
         for phase in self.exp_phase_list:
-            LOG_INFO(f"Phase name: {phase.get_name()}")
-            LOG_INFO(f"Phase type: {phase.get_phase_type()}")
-            LOG_INFO(f"Sources: {phase.get_sources_str_list()}")
-            LOG_INFO("\nSource pieces:\n")
+            LOG_INFO(f"   Phase name: {phase.get_name()}")
+            LOG_INFO(f"   Phase type: {phase.get_phase_type()}")
+            LOG_INFO(f"   Sources: {phase.get_sources_str_list()}")
+            LOG_INFO("")
+            LOG_INFO("    Source pieces:")
             for source_piece in phase.get_sources_pieces():
-                LOG_INFO(f"Source name: {source_piece.get_source_name()}")
-                LOG_INFO(f"Batch size: {source_piece.get_batch_size()}")
-                LOG_INFO(f"Phase: {source_piece.get_phase()}")
-                LOG_INFO(f"Starting offset: {source_piece.get_starting_offset()}")
-                LOG_INFO(f"Number of batches: {source_piece.get_num_of_batches()}")
-                LOG_INFO(f"Workers target: {source_piece.get_target_workers()}")
+                LOG_INFO(f"         Source name: {source_piece.get_source_name()}")
+                LOG_INFO(f"         Batch size: {source_piece.get_batch_size()}")
+                LOG_INFO(f"         Phase: {source_piece.get_phase()}")
+                LOG_INFO(f"         Starting offset: {source_piece.get_starting_offset()}")
+                LOG_INFO(f"         Number of batches: {source_piece.get_num_of_batches()}")
+                LOG_INFO(f"         Workers target: {source_piece.get_target_workers()}")
+                LOG_INFO("      ----------------------")
                 LOG_INFO("")