From 04ad083e246674109207802d77a2549be5d4b360 Mon Sep 17 00:00:00 2001
From: Wook Song <wook16.song@samsung.com>
Date: Thu, 5 Sep 2024 13:35:16 +0900
Subject: [PATCH] Data/Model: Add a method to get NNStreamer filter description

This patch adds a method to get the filter description for the
NNStreamer pipeline from the Model class.

Signed-off-by: Wook Song <wook16.song@samsung.com>
---
 .../ml/inference/offloading/MainService.kt    | 19 +----------
 .../ml/inference/offloading/data/Model.kt     | 33 +++++++++++++++++++
 2 files changed, 34 insertions(+), 18 deletions(-)

diff --git a/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/MainService.kt b/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/MainService.kt
index 4d6d4de..7e4ea9b 100644
--- a/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/MainService.kt
+++ b/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/MainService.kt
@@ -375,24 +375,7 @@ class MainService : Service() {
                     val serviceId = runBlocking {
                         preferencesDataStore.getIncrementalCounter()
                     }
-                    // TODO: The following code for generation of the NNStreamer pipeline string
-                    val mFile = App.context().getExternalFilesDir("models")?.resolve(model.models)
-                    val inTypes = model.inputInfo["type"]?.let {
-                        "types=${model.inputInfo["type"]?.joinToString(",")}"
-                    } ?: ""
-                    val inDims = model.inputInfo["dimension"]?.let {
-                        "dimensions=(string)${model.inputInfo["dimension"]?.joinToString(",")}"
-                    } ?: ""
-                    val outTypes = model.outputInfo["type"]?.let {
-                        "types=${model.outputInfo["type"]?.joinToString(",")}"
-                    } ?: ""
-                    val outDims = model.outputInfo["dimension"]?.let {
-                        "dimensions=(string)${model.outputInfo["dimension"]?.joinToString(",")}"
-                    } ?: ""
-                    val filter =
-                        "other/tensors,num_tensors=${model.inputInfo["type"]?.size ?: 1},format=static,${inDims},${inTypes},framerate=0/1 ! " +
-                                "tensor_filter framework=tensorflow-lite model=${mFile} ! " +
-                                "other/tensors,num_tensors=${model.outputInfo["type"]?.size ?: 1},format=static,${outDims},${outTypes},framerate=0/1"
+                    val filter = model.getNNSFilterDesc()
                     val port = findPort()
                     val desc =
                         "tensor_query_serversrc id=" + serviceId.toString() + " host=" + hostAddress + " port=" +
diff --git a/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/data/Model.kt b/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/data/Model.kt
index 88d30be..7856a44 100644
--- a/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/data/Model.kt
+++ b/ml_inference_offloading/src/main/java/ai/nnstreamer/ml/inference/offloading/data/Model.kt
@@ -1,5 +1,6 @@
 package ai.nnstreamer.ml.inference.offloading.data
 
+import ai.nnstreamer.ml.inference.offloading.App
 import androidx.room.ColumnInfo
 import androidx.room.Entity
 import androidx.room.PrimaryKey
@@ -111,4 +112,36 @@ data class Model(
     var outputInfo: Map<String, List<String>> = mapOf()
 
     var optionalInfo: Map<String, String> = mapOf()
+
+    /**
+     * Get NNS filter description string for the given model. This is used to create a new NNS pipeline instance.
+     * @return NNS filter description string. It includes paths of the model files and other information.
+     */
+    fun getNNSFilterDesc(): String {
+        val basePath = App.context().getExternalFilesDir("models")
+        val modelPaths = models.split(",").run {
+            joinToString(
+                separator = ",",
+                transform = { basePath?.resolve(it).toString() }
+            )
+        }
+        val inTypes = inputInfo["type"]?.let {
+            "types=${inputInfo["type"]?.joinToString(",")}"
+        } ?: ""
+        val inDims = inputInfo["dimension"]?.let {
+            "dimensions=(string)${inputInfo["dimension"]?.joinToString(",")}"
+        } ?: ""
+        val outTypes = outputInfo["type"]?.let {
+            "types=${outputInfo["type"]?.joinToString(",")}"
+        } ?: ""
+        val outDims = outputInfo["dimension"]?.let {
+            "dimensions=(string)${outputInfo["dimension"]?.joinToString(",")}"
+        } ?: ""
+        val filter =
+            "other/tensors,num_tensors=${inputInfo["type"]?.size ?: 1},format=static,${inDims},${inTypes},framerate=0/1 ! " +
+                    "tensor_filter framework=tensorflow-lite model=${modelPaths} ! " +
+                    "other/tensors,num_tensors=${outputInfo["type"]?.size ?: 1},format=static,${outDims},${outTypes},framerate=0/1"
+
+        return filter
+    }
 }