JBAI-5829 [examples] Refactored resource path usage to cache directory.

examples/src/jvmMain/kotlin/io/kinference/examples/Utils.kt

@@ -12,38 +12,70 @@ import io.ktor.util.cio.writeChannel
 import io.ktor.utils.io.copyAndClose
 import java.io.File
 
-val resourcesPath = System.getProperty("user.dir") + "/cache/"
+/**
+ * Directory used to store cached files.
+ *
+ * This variable combines the user's current working directory
+ * with a "cache" subdirectory to create the path for storing cache files.
+ * It is used in various functions to check for existing files or directories,
+ * create new ones if they do not exist, and manage the caching of downloaded files.
+ */
+val cacheDirectory = System.getProperty("user.dir") + "/cache/"
 
 /**
- * Downloads a file from the specified URL and saves it to the given output path.
- * If the file already exists at the output path, the download is skipped.
+ * Downloads a file from the given URL and saves it with the specified file name.
  *
- * @param url The URL from which the file will be downloaded.
- * @param outputPath The path to which the downloaded file will be saved.
+ * Checks if the directory specified by `cacheDirectory` exists.
+ * If not, it creates the directory. If the file already exists,
+ * the download is skipped. Otherwise, the file is downloaded
+ * using an HTTP client with a 10-minute timeout setting.
+ *
+ * @param url The URL from which to download the file.
+ * @param fileName The name to use for the downloaded file.
+ * @param timeout Optional timeout duration for the download request, in milliseconds.
+ * Defaults to 600,000 milliseconds (10 minutes).
+ * Increase the timeout if you are not sure that download for the particular model with fit into the default timeout.
  */
-suspend fun downloadFile(url: String, outputPath: String) {
+suspend fun downloadFile(url: String, fileName: String, timeout: Long = 600_000) {
+    // Ensure the predefined path is treated as a directory
+    val directory = File(cacheDirectory)
+
+    // Check if the directory exists, if not create it
+    if (!directory.exists()) {
+        println("Predefined directory doesn't exist. Creating directory at $cacheDirectory.")
+        directory.mkdirs() // Create the directory if it doesn't exist
+    }
+
     // Check if the file already exists
-    val file = File(outputPath)
+    val file = File(directory, fileName)
     if (file.exists()) {
-        println("File already exists at $outputPath. Skipping download.")
+        println("File already exists at ${file.absolutePath}. Skipping download.")
         return // Exit the function if the file exists
     }
 
     // Create an instance of HttpClient with custom timeout settings
     val client = HttpClient {
         install(HttpTimeout) {
-            requestTimeoutMillis = 600_000 // Set timeout to 10 minutes (600,000 milliseconds)
+            requestTimeoutMillis = timeout
         }
     }
 
     // Download the file and write to the specified output path
     client.prepareRequest(url).execute { response ->
-        response.bodyAsChannel().copyAndClose(File(outputPath).writeChannel())
+        response.bodyAsChannel().copyAndClose(file.writeChannel())
     }
 
     client.close()
 }
 
+/**
+ * Extracts the token ID with the highest probability from the output tensor.
+ *
+ * @param output A map containing the output tensors identified by their names.
+ * @param tokensSize The number of tokens in the sequence.
+ * @param outputName The name of the tensor containing the logits.
+ * @return The ID of the top token.
+ */
 suspend fun extractTopToken(output: Map<String, KIONNXData<*>>, tokensSize: Int, outputName: String): Long {
     val logits = output[outputName]!! as KITensor
     val sliced = logits.data.slice(

examples/src/jvmMain/kotlin/io/kinference/examples/classification/KIClassificationMain.kt

@@ -4,7 +4,7 @@ import io.kinference.core.KIEngine
 import io.kinference.core.data.tensor.KITensor
 import io.kinference.core.data.tensor.asTensor
 import io.kinference.examples.downloadFile
-import io.kinference.examples.resourcesPath
+import io.kinference.examples.cacheDirectory
 import io.kinference.ndarray.arrays.*
 import io.kinference.ndarray.arrays.FloatNDArray.Companion.invoke
 import io.kinference.utils.CommonDataLoader
@@ -100,12 +100,12 @@ suspend fun main() {
     val modelName = "CaffeNet"
 
     println("Downloading model from: $modelUrl")
-    downloadFile(modelUrl, "$resourcesPath/$modelName.onnx")
+    downloadFile(modelUrl, "$modelName.onnx")
     println("Downloading synset from: $synsetUrl")
-    downloadFile(synsetUrl, "$resourcesPath/synset.txt")
+    downloadFile(synsetUrl, "synset.txt")
 
-    val modelBytes = CommonDataLoader.bytes("$resourcesPath/$modelName.onnx".toPath())
-    val classLabels = File("$resourcesPath/synset.txt").readLines()
+    val modelBytes = CommonDataLoader.bytes("$cacheDirectory/$modelName.onnx".toPath())
+    val classLabels = File("$cacheDirectory/synset.txt").readLines()
 
     println("Loading model...")
     val model = KIEngine.loadModel(modelBytes, optimize = true, predictionConfig = PredictionConfigs.DefaultAutoAllocator)

examples/src/jvmMain/kotlin/io/kinference/examples/classification/ORTClassificationMain.kt

@@ -1,7 +1,7 @@
 package io.kinference.examples.classification
 
 import io.kinference.examples.downloadFile
-import io.kinference.examples.resourcesPath
+import io.kinference.examples.cacheDirectory
 import io.kinference.ndarray.arrays.*
 import io.kinference.ndarray.arrays.FloatNDArray.Companion.invoke
 import io.kinference.ort.ORTEngine
@@ -99,12 +99,12 @@ suspend fun main() {
     val modelName = "CaffeNet"
 
     println("Downloading model from: $modelUrl")
-    downloadFile(modelUrl, "$resourcesPath/$modelName.onnx")
+    downloadFile(modelUrl, "$modelName.onnx")
     println("Downloading synset from: $synsetUrl")
-    downloadFile(synsetUrl, "$resourcesPath/synset.txt")
+    downloadFile(synsetUrl, "synset.txt")
 
-    val modelBytes = CommonDataLoader.bytes("$resourcesPath/$modelName.onnx".toPath())
-    val classLabels = File("$resourcesPath/synset.txt").readLines()
+    val modelBytes = CommonDataLoader.bytes("$cacheDirectory/$modelName.onnx".toPath())
+    val classLabels = File("$cacheDirectory/synset.txt").readLines()
 
     println("Loading model...")
     val model = ORTEngine.loadModel(modelBytes)

examples/src/jvmMain/kotlin/io/kinference/examples/lm/KIGPT2Main.kt

@@ -5,7 +5,7 @@ import io.kinference.core.KIEngine
 import io.kinference.core.data.tensor.asTensor
 import io.kinference.examples.downloadFile
 import io.kinference.examples.extractTopToken
-import io.kinference.examples.resourcesPath
+import io.kinference.examples.cacheDirectory
 import io.kinference.ndarray.arrays.LongNDArray
 import io.kinference.ndarray.arrays.NDArrayCore
 import io.kinference.utils.CommonDataLoader
@@ -22,9 +22,9 @@ suspend fun main() {
     val modelName = "gpt2-lm-head-10"
 
     println("Downloading model from: $modelUrl")
-    downloadFile(modelUrl, "$resourcesPath/$modelName.onnx")
+    downloadFile(modelUrl, "$modelName.onnx")  //GPT-2 from model zoo is around 650 Mb, adjust your timeout if needed
 
-    val modelBytes = CommonDataLoader.bytes("${resourcesPath}/$modelName.onnx".toPath())
+    val modelBytes = CommonDataLoader.bytes("${cacheDirectory}/$modelName.onnx".toPath())
 
     println("Loading model...")
     val model = KIEngine.loadModel(modelBytes, optimize = true, predictionConfig = PredictionConfigs.DefaultAutoAllocator)

examples/src/jvmMain/kotlin/io/kinference/examples/lm/ORTGPT2Main.kt

@@ -5,7 +5,7 @@ import io.kinference.core.data.tensor.KITensor
 import io.kinference.core.data.tensor.asTensor
 import io.kinference.examples.downloadFile
 import io.kinference.examples.extractTopToken
-import io.kinference.examples.resourcesPath
+import io.kinference.examples.cacheDirectory
 import io.kinference.ndarray.arrays.FloatNDArray
 import io.kinference.ndarray.arrays.FloatNDArray.Companion.invoke
 import io.kinference.ort.ORTData
@@ -25,9 +25,9 @@ suspend fun main() {
     val modelName = "gpt2-lm-head-10"
 
     println("Downloading model from: $modelUrl")
-    downloadFile(modelUrl, "$resourcesPath/$modelName.onnx")
+    downloadFile(modelUrl, "$modelName.onnx") //GPT-2 from model zoo is around 650 Mb, adjust your timeout if needed
 
-    val modelBytes = CommonDataLoader.bytes("${resourcesPath}/$modelName.onnx".toPath())
+    val modelBytes = CommonDataLoader.bytes("${cacheDirectory}/$modelName.onnx".toPath())
 
     println("Loading model...")
     val model = ORTEngine.loadModel(modelBytes)