[Example] Change user scenario

In this patch, user draws smile face and sad face respectively for 5 times. It is splited half to trainset/validationSet and used for training. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: Jihoon Lee <[email protected]>
nnstreamer · Aug 10, 2020 · 13ae17b · 13ae17b
1 parent 418dc57
commit 13ae17b
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diff --git a/Applications/Tizen_native/CustomShortcut/inc/data.h b/Applications/Tizen_native/CustomShortcut/inc/data.h
@@ -30,14 +30,13 @@
 
 #define EDJ_PATH "edje/main.edj"
 #define TRAIN_SET_PATH "trainingSet.dat"
+#define VALIDATION_SET_PATH "validationSet.dat"
 
-#define MAX_TRIES 5
+#define MAX_TRAIN_TRIES 5
+#define MAX_TRIES 10
 
-typedef enum _DRAW_MODE {
-  INFER = 0,
-  TRAIN_SMILE,
-  TRAIN_SAD,
-} DRAW_MODE;
+#define FEATURE_SIZE 62720
+#define NUM_CLASS 2
 typedef struct appdata {
   Evas_Object *win;
   Evas_Object *conform;
@@ -60,7 +59,6 @@ typedef struct appdata {
 
   cairo_surface_t *cr_surface; /**< cairo surface for the canvas */
   cairo_t *cr;                 /**< cairo engine for the canvas */
-  DRAW_MODE mode;              /**< drawing mode of current canvas */
   int tries;                   /**< tells how many data has been labeled */
 
   /**< ML related */

diff --git a/Applications/Tizen_native/CustomShortcut/res/edje/main.edc b/Applications/Tizen_native/CustomShortcut/res/edje/main.edc
@@ -94,32 +94,17 @@ collections {
   group {
     name: "home";
     parts {
-      PART_TITLE("home/title", "Select actions...")
-      PART_BUTTON("home/to_train", "train", 0.55, 0.3, 0.9, 0.7)
-      PART_BUTTON("home/to_test", "test", 0.1, 0.3, 0.45, 0.7)
+      PART_TITLE("home/title", "NNTrainer Demo")
+      PART_BUTTON("home/proceed", "start draw", 0.1, 0.3, 0.9, 0.7)
     }
     programs {
-      PROGRAM_BUTTON("home/to_train", "routes/to", "select")
-      PROGRAM_BUTTON("home/to_test", "routes/to", "draw:inference")
+      PROGRAM_BUTTON("home/proceed", "routes/to", "draw")
     }
   }
-  group {
-    name: "select";
-    parts {
-      PART_TITLE("select/title", "Select target emoji...")
-      PART_BUTTON("select/sad", "😢", 0.1, 0.3, 0.45, 0.7)
-      PART_BUTTON("select/smile", "😊", 0.55, 0.3, 0.9, 0.7)
-    }
-    programs {
-      PROGRAM_BUTTON("select/smile", "routes/to", "draw:smile")
-      PROGRAM_BUTTON("select/sad", "routes/to", "draw:sad")
-    }
-  }
-  // this group is meant to be used for train / test, if it is not applicable, this group is reserved for training part
   group {
     name: "draw";
     parts {
-      PART_TITLE("draw/title", "draw your symbol")
+      PART_TITLE("draw/title", "draw for 😊")
       part {
         name: "draw/canvas";
         type: SWALLOW;
@@ -143,12 +128,4 @@ collections {
       PART_BUTTON("train_result/go_back", "go back", 0.1, 0.3, 0.9, 0.7)
     }
   }
-  group {
-    name: "test_result";
-    parts {
-      PART_TITLE("test_result/title", "test is successfully done")
-      PART_BUTTON("test_result/go_back", "test result: 😊", 0.1, 0.3, 0.9, 0.7)
-      // reserve a text area to show the guess from the model
-    }
-  }
 }
diff --git a/Applications/Tizen_native/CustomShortcut/src/data.c b/Applications/Tizen_native/CustomShortcut/src/data.c
@@ -111,28 +111,40 @@ static void _on_data_receive(ml_tensors_data_h data,
     goto CLEAN;
   }
 
-  file = fopen(ad->pipe_dst, ad->tries == 1 ? "wb" : "wb+");
+  LOG_I("current tries %d", ad->tries);
+
+  if (ad->tries == MAX_TRAIN_TRIES || ad->tries == 0)
+    file = fopen(ad->pipe_dst, "wb+");
+  else
+    file = fopen(ad->pipe_dst, "ab+");
 
   if (file == NULL) {
     LOG_E("cannot open file");
     goto CLEAN;
   }
 
-  if (write_result = fwrite(raw_data, data_size, 1, file) < 0) {
+  if ((write_result = fwrite(raw_data, 1, data_size, file)) < 0) {
     LOG_E("write error happend");
   }
 
   if (write_result < data_size) {
-    LOG_E("data was not fully written to file");
+    LOG_E("data was not fully written to file, result = %d, data_size = %d",
+          write_result, data_size);
   }
 
-  if (ad->mode != INFER) {
-    LOG_D("writing label");
-    label = ad->mode == TRAIN_SMILE ? 1 : 0;
-    if (fwrite(&label, sizeof(float), 1, file) < 0) {
-      LOG_E("write error happend");
-    };
-  }
+  bool target_label = ad->tries % 2;
+  LOG_D("writing one-hot encoded label");
+  label = target_label;
+  if (fwrite(&label, sizeof(float), 1, file) < 0) {
+    LOG_E("write error happend");
+  };
+
+  label = !target_label;
+  if (fwrite(&label, sizeof(float), 1, file) < 0) {
+    LOG_E("write error happend");
+  };
+
+  LOG_I("file dst: %s size: %ld", ad->pipe_dst, ftell(file));
 
   if (fclose(file) < 0) {
     LOG_E("there was error closing");
@@ -245,6 +257,7 @@ int data_extract_feature(appdata_s *ad, const char *dst, bool append) {
 
 void data_train_model() {
   ml_train_model_h model;
+
   char model_conf_path[PATH_MAX];
   char label_path[PATH_MAX];
   int status = ML_ERROR_NONE;
@@ -284,6 +297,8 @@ void data_train_model() {
     goto CLEAN_UP;
   }
 
+  freopen("out.txt", "a+", stdout);
+
   status = ml_train_model_run(model, NULL);
   if (status != ML_ERROR_NONE) {
     LOG_E("run model failed %d", status);

diff --git a/Applications/Tizen_native/CustomShortcut/src/view.c b/Applications/Tizen_native/CustomShortcut/src/view.c
@@ -264,36 +264,29 @@ static void _on_draw_proceed(void *data, Evas_Object *obj, const char *emission,
   int status = APP_ERROR_NONE;
 
   char buf[256];
-  ad->tries++;
 
-  if (ad->mode != INFER) {
-    LOG_D("labeling proceed");
-    if (ad->tries >= MAX_TRIES) {
-      ad->tries = 0;
-      elm_naviframe_item_pop(ad->naviframe);
-      data_train_model();
-      view_routes_to(ad, "train_result");
-      return;
-    }
-
-    sprintf(buf, "draw your symbol [%d/%d]", ad->tries, MAX_TRIES);
-    elm_object_part_text_set(obj, "draw/title", buf);
-    LOG_D("starting extraction");
-
-    status = data_extract_feature(ad, TRAIN_SET_PATH, true);
-    if (status != APP_ERROR_NONE) {
-      LOG_E("feature extraction failed");
-    }
-  } else {
-    LOG_D("infer proceed");
-    status = data_extract_feature(ad, "test.dat", true);
-    if (status != APP_ERROR_NONE) {
-      LOG_E("feature extraction failed");
-    }
-
-    view_routes_to(ad, "test_result");
+  LOG_D("labeling proceed");
+  status = data_extract_feature(
+    ad, ad->tries < MAX_TRAIN_TRIES ? TRAIN_SET_PATH : VALIDATION_SET_PATH,
+    true);
+  if (status != APP_ERROR_NONE) {
+    LOG_E("feature extraction failed");
+  }
+
+  if (ad->tries == MAX_TRIES - 1) {
+    ad->tries = 0;
+    elm_naviframe_item_pop(ad->naviframe);
+    data_train_model();
+    view_routes_to(ad, "train_result");
+    return;
   }
 
+  sprintf(buf, "draw for %s [%d/%d]", ad->tries % NUM_CLASS ? "😊" : "😢",
+          ad->tries + 2, MAX_TRIES);
+  elm_object_part_text_set(obj, "draw/title", buf);
+  LOG_D("starting extraction");
+
+  ad->tries++;
   _canvas_erase_all(ad);
 }
 
@@ -303,14 +296,6 @@ static int _create_canvas(appdata_s *ad, const char *draw_mode) {
 
   Evas_Object *frame = elm_layout_add(ad->layout);
 
-  if (!strcmp(draw_mode, "inference")) {
-    ad->mode = INFER;
-  } else if (!strcmp(draw_mode, "smile")) {
-    ad->mode = TRAIN_SMILE;
-  } else if (!strcmp(draw_mode, "sad")) {
-    ad->mode = TRAIN_SAD;
-  }
-
   status = elm_layout_content_set(ad->layout, "draw/canvas", frame);
   if (status == EINA_FALSE) {
     LOG_E("failed to get canvas object");