Migrate vector<uint8> to OwningArrayBuffer

Author: benITo47

packages/react-native-executorch/common/rnexecutorch/host_objects/JsiConversions.h

@@ -471,10 +471,8 @@ getJsiValue(const std::vector<models::instance_segmentation::types::Instance>
     instance.setProperty(runtime, "bbox",
                          getJsiValue(instances[i].bbox, runtime));
 
-    // Mask as Uint8Array
-    auto maskBuffer = std::make_shared<OwningArrayBuffer>(
-        instances[i].mask.data(), instances[i].mask.size());
-    jsi::ArrayBuffer arrayBuffer(runtime, maskBuffer);
+    // Mask as Uint8Array - reuse existing OwningArrayBuffer
+    jsi::ArrayBuffer arrayBuffer(runtime, instances[i].mask);
     auto uint8ArrayCtor =
         runtime.global().getPropertyAsFunction(runtime, "Uint8Array");
     auto uint8Array = uint8ArrayCtor.callAsConstructor(runtime, arrayBuffer)

packages/react-native-executorch/common/rnexecutorch/models/instance_segmentation/BaseInstanceSegmentation.cpp

@@ -364,11 +364,10 @@ std::vector<types::Instance> BaseInstanceSegmentation::postprocess(
         logitsMat, bboxModel, bboxOriginal, modelInputSize, originalSize,
         returnMaskAtOriginalResolution);
 
-    instances.emplace_back(
-        bboxOriginal,
-        std::vector<uint8_t>(binaryMask.data,
-                             binaryMask.data + binaryMask.total()),
-        binaryMask.cols, binaryMask.rows, labelIdx, score);
+    instances.emplace_back(bboxOriginal,
+                           std::make_shared<OwningArrayBuffer>(
+                               binaryMask.data, binaryMask.total()),
+                           binaryMask.cols, binaryMask.rows, labelIdx, score);
   }
 
   return finalizeInstances(std::move(instances), iouThreshold, maxInstances);

packages/react-native-executorch/common/rnexecutorch/models/instance_segmentation/Types.h

@@ -1,8 +1,9 @@
 #pragma once
 
 #include <cstdint>
+#include <memory>
+#include <rnexecutorch/jsi/OwningArrayBuffer.h>
 #include <rnexecutorch/utils/computer_vision/Types.h>
-#include <vector>
 
 namespace rnexecutorch::models::instance_segmentation::types {
 
@@ -13,17 +14,17 @@ namespace rnexecutorch::models::instance_segmentation::types {
  * and confidence score.
  */
 struct Instance {
-  utils::computer_vision::BBox bbox; ///< Bounding box coordinates
-  std::vector<uint8_t> mask;         ///< Binary mask (0 or 1) for the instance
-  int32_t maskWidth;                 ///< Width of the mask array
-  int32_t maskHeight;                ///< Height of the mask array
-  int32_t classIndex;                ///< Model output class index
-  float score;                       ///< Confidence score [0, 1]
+  utils::computer_vision::BBox bbox;
+  std::shared_ptr<OwningArrayBuffer> mask;
+  int32_t maskWidth;
+  int32_t maskHeight;
+  int32_t classIndex;
+  float score;
 
   Instance() = default;
-  Instance(utils::computer_vision::BBox bbox, std::vector<uint8_t> mask,
-           int32_t maskWidth, int32_t maskHeight, int32_t classIndex,
-           float score)
+  Instance(utils::computer_vision::BBox bbox,
+           std::shared_ptr<OwningArrayBuffer> mask, int32_t maskWidth,
+           int32_t maskHeight, int32_t classIndex, float score)
       : bbox(bbox), mask(std::move(mask)), maskWidth(maskWidth),
         maskHeight(maskHeight), classIndex(classIndex), score(score) {}
 };

packages/react-native-executorch/common/rnexecutorch/tests/integration/InstanceSegmentationTest.cpp

@@ -161,10 +161,11 @@ TEST(InstanceSegResultTests, InstancesHaveValidMasks) {
   for (const auto &inst : results) {
     EXPECT_GT(inst.maskWidth, 0);
     EXPECT_GT(inst.maskHeight, 0);
-    EXPECT_EQ(inst.mask.size(),
+    EXPECT_EQ(inst.mask->size(),
               static_cast<size_t>(inst.maskWidth) * inst.maskHeight);
 
-    for (uint8_t val : inst.mask) {
+    for (size_t i = 0; i < inst.mask->size(); ++i) {
+      uint8_t val = inst.mask->data()[i];
       EXPECT_TRUE(val == 0 || val == 1);
     }
   }
@@ -222,7 +223,7 @@ TEST(InstanceSegMaskTests, LowResMaskIsSmallerThanOriginal) {
                               kMethodName);
 
   if (!hiRes.empty() && !loRes.empty()) {
-    EXPECT_LE(loRes[0].mask.size(), hiRes[0].mask.size());
+    EXPECT_LE(loRes[0].mask->size(), hiRes[0].mask->size());
   }
 }