Continue refactoring for creating common executionContext providing required data in various context

Related-To #137

Author: michalharakal

skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/context/ExecutionContext.kt

@@ -8,61 +8,3 @@ public interface ExecutionContext<V> {
     public val ops: TensorOps<V>
     public val tensorDataFactory: TensorDataFactory
 }
-
-
-/**
- * Memory usage information
- */
-public data class MemoryInfo(
-    /**
-     * Total memory available on the device
-     */
-    public val totalMemory: Long,
-
-    /**
-     * Memory currently in use
-     */
-    public val usedMemory: Long,
-
-    /**
-     * Free memory available
-     */
-    public val freeMemory: Long = totalMemory - usedMemory,
-
-    /**
-     * Memory usage as percentage
-     */
-    public val usagePercentage: Double = (usedMemory.toDouble() / totalMemory) * 100.0
-)
-
-/**
- * Execution statistics
- */
-public data class ExecutionStats(
-    /**
-     * Total number of operations executed
-     */
-    public val operationsExecuted: Long = 0,
-
-    /**
-     * Total execution time in milliseconds
-     */
-    public val totalExecutionTime: Long = 0,
-
-    /**
-     * Average execution time per operation
-     */
-    public val averageExecutionTime: Double =
-        if (operationsExecuted > 0) totalExecutionTime.toDouble() / operationsExecuted else 0.0,
-
-    /**
-     * Number of tensors created
-     */
-    public val tensorsCreated: Long = 0,
-
-    /**
-     * Peak memory usage
-     */
-    public val peakMemoryUsage: Long = 0
-)
-

…/sk/ainet/lang/graph/ExecutionContext.kt …k/ainet/context/GraphExecutionContext.ktskainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/graph/ExecutionContext.kt renamed to skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/context/GraphExecutionContext.kt skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/graph/ExecutionContext.kt renamed to skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/context/GraphExecutionContext.kt

@@ -1,11 +1,7 @@
-package sk.ainet.lang.graph
-
-import sk.ainet.context.ExecutionContext
-import sk.ainet.context.ExecutionStats
-import sk.ainet.context.MemoryInfo
-import sk.ainet.lang.tensor.Tensor
-import sk.ainet.lang.types.DType
+package sk.ainet.context
 
+import sk.ainet.lang.graph.ExecutionTape
+import sk.ainet.lang.graph.TapeStack
 
 /**
  * Context for managing execution state, including mode switching,
@@ -49,4 +45,4 @@ public interface GraphExecutionContext<V> : ExecutionContext<V> {
      * Reset execution statistics
      */
     public fun resetExecutionStats()
-}
+}

skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/context/stats.kt

@@ -0,0 +1,59 @@
+package sk.ainet.context
+
+/**
+ * Memory usage information
+ */
+public data class MemoryInfo(
+    /**
+     * Total memory available on the device
+     */
+    public val totalMemory: Long,
+
+    /**
+     * Memory currently in use
+     */
+    public val usedMemory: Long,
+
+    /**
+     * Free memory available
+     */
+    public val freeMemory: Long = totalMemory - usedMemory,
+
+    /**
+     * Memory usage as percentage
+     */
+    public val usagePercentage: Double = (usedMemory.toDouble() / totalMemory) * 100.0
+)
+
+/**
+ * Execution statistics
+ */
+public data class ExecutionStats(
+    /**
+     * Total number of operations executed
+     */
+    public val operationsExecuted: Long = 0,
+
+    /**
+     * Total execution time in milliseconds
+     */
+    public val totalExecutionTime: Long = 0,
+
+    /**
+     * Average execution time per operation
+     */
+    public val averageExecutionTime: Double =
+        if (operationsExecuted > 0) totalExecutionTime.toDouble() / operationsExecuted else 0.0,
+
+    /**
+     * Number of tensors created
+     */
+    public val tensorsCreated: Long = 0,
+
+    /**
+     * Peak memory usage
+     */
+    public val peakMemoryUsage: Long = 0
+)
+
+

skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/graph/DefaultExecutionContext.kt

@@ -1,6 +1,7 @@
 package sk.ainet.lang.graph
 
 import sk.ainet.context.ExecutionStats
+import sk.ainet.context.GraphExecutionContext
 import sk.ainet.lang.tensor.data.TensorDataFactory
 import sk.ainet.lang.tensor.data.DenseTensorDataFactory
 import sk.ainet.lang.types.DType
@@ -36,7 +37,7 @@ public data class GraphExecutionResult<V>(
 public fun <V, R> exec(
     dataFactory: TensorDataFactory = DenseTensorDataFactory(),
     block: GraphExecutionContext<V>.() -> R
-): GraphExecutionResult<V> {
+) {//}: GraphExecutionResult<V> {
     /*
 
     return try {

skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/graph/GraphExecutionContext.kt

@@ -0,0 +1,41 @@
+package sk.ainet.lang.tensor.dsl
+
+import sk.ainet.lang.tensor.Tensor
+import sk.ainet.lang.tensor.dsl.tensor
+import sk.ainet.lang.types.FP32
+import sk.ainet.lang.types.Int32
+
+/**
+ * Image normalization utilities for BCHW tensors with integer pixel values.
+ */
+public object ImageNormalization {
+    /** Default max pixel value for 8-bit images */
+    public const val DEFAULT_MAX_PIXEL: Float = 255f
+}
+
+/**
+ * Normalize a BCHW tensor with integer pixel values to the [0, 1] range.
+ *
+ * Steps:
+ * - Validates BCHW rank (4D)
+ * - Builds a new FP32 tensor with the same shape where each element is value/maxValue
+ */
+public fun Tensor<Int32, Int>.normalizePixels(maxValue: Float = ImageNormalization.DEFAULT_MAX_PIXEL): Tensor<FP32, Float> {
+    require(this.rank == 4) { "Expected BCHW tensor with rank 4 (BCHW), but got rank=${this.rank} and shape=${this.shape}" }
+
+    val src = this
+    val shape = src.shape
+
+    // Build a new FP32 tensor by mapping each Int pixel to Float/Max
+    return tensor<FP32, Float> {
+        shape(shape) {
+            init { indices ->
+                val b = indices[0]
+                val c = indices[1]
+                val h = indices[2]
+                val w = indices[3]
+                (src.data[b, c, h, w].toFloat() / maxValue)
+            }
+        }
+    }
+}