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Author: benITo47

docs/docs/04-typescript-api/02-computer-vision/InstanceSegmentationModule.md

@@ -60,9 +60,15 @@ Use [`fromCustomConfig`](../../06-api-reference/classes/InstanceSegmentationModu
   - `postprocessorConfig` (optional) - Postprocessing settings (`applyNMS`).
   - `defaultConfidenceThreshold` (optional) - Default confidence threshold.
   - `defaultIouThreshold` (optional) - Default IoU threshold.
-  - `availableInputSizes` and `defaultInputSize` (optional) - Supported input sizes and the default.
+  - `availableInputSizes` and `defaultInputSize` - **Required** if your model supports multiple input sizes (i.e., exports multiple forward methods like `forward_384`, `forward_512`, `forward_640`). Both must be specified together or omitted together.
 - `onDownloadProgress` (optional) - Callback to track download progress.
 
+:::tip
+If your model supports **multiple input sizes**, you must specify both `availableInputSizes` (an array of supported sizes) and `defaultInputSize` (the default size to use when no `inputSize` is provided in options). The model must expose separate methods named `forward_{inputSize}` for each size.
+
+If your model supports only **one input size**, omit both fields and export a single `forward` method.
+:::
+
 ```typescript
 const MyLabels = { GRAPE_GREEN: 0, GRAPE_RED: 1, LEAF: 2 } as const;
 

packages/react-native-executorch/common/rnexecutorch/data_processing/ImageProcessing.cpp

@@ -249,5 +249,13 @@ readImageToTensor(const std::string &path,
   }
   return {image_processing::getTensorFromMatrix(tensorDims, input), imageSize};
 }
+
+cv::Mat applySigmoid(const cv::Mat &logits) {
+  cv::Mat probMat;
+  cv::exp(-logits, probMat);
+  probMat = 255.0f / (1.0f + probMat);
+  probMat.convertTo(probMat, CV_8UC1);
+  return probMat;
+}
 } // namespace image_processing
 } // namespace rnexecutorch

packages/react-native-executorch/common/rnexecutorch/data_processing/ImageProcessing.h

@@ -54,4 +54,10 @@ readImageToTensor(const std::string &path,
                   bool maintainAspectRatio = false,
                   std::optional<cv::Scalar> normMean = std::nullopt,
                   std::optional<cv::Scalar> normStd = std::nullopt);
+/**
+ * @brief Applies sigmoid activation to logits and converts to uint8 binary mask
+ * @param logits Input matrix containing raw logits (pre-sigmoid)
+ * @return 8-bit unsigned integer matrix with values scaled to [0, 255]
+ */
+cv::Mat applySigmoid(const cv::Mat &logits);
 } // namespace rnexecutorch::image_processing

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

@@ -53,9 +53,15 @@ std::vector<types::Instance> BaseInstanceSegmentation::runInference(
   ensureMethodLoaded(methodName);
 
   auto inputShapes = getAllInputShapes(methodName);
-  modelInputShape_ = inputShapes[0];
+  if (inputShapes.empty() || inputShapes[0].empty()) {
+    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
+                            "Method '" + methodName +
+                                "' has invalid input tensor shape.");
+  }
 
-  cv::Size modelInputSize = getInputSize(methodName);
+  modelInputShape_ = inputShapes[0];
+  const auto &shape = modelInputShape_;
+  cv::Size modelInputSize(shape[shape.size() - 1], shape[shape.size() - 2]);
   cv::Size originalSize(image.cols, image.rows);
 
   cv::Mat preprocessed = preprocess(image);
@@ -156,14 +162,6 @@ cv::Rect BaseInstanceSegmentation::addPaddingToRect(const cv::Rect &rect,
   return cv::Rect(x1, y1, x2 - x1, y2 - y1);
 }
 
-cv::Mat BaseInstanceSegmentation::applySigmoid(const cv::Mat &logits) {
-  cv::Mat probMat;
-  cv::exp(-logits, probMat);
-  probMat = 255.0f / (1.0f + probMat);
-  probMat.convertTo(probMat, CV_8UC1);
-  return probMat;
-}
-
 cv::Mat BaseInstanceSegmentation::warpToOriginalResolution(
     const cv::Mat &probMat, const cv::Rect &maskRect, cv::Size originalSize,
     cv::Size maskSize, const utils::computer_vision::BBox &bboxOriginal) {
@@ -202,7 +200,7 @@ cv::Mat BaseInstanceSegmentation::processMaskFromLogits(
   }
 
   cv::Mat cropped = logitsMat(cropRect);
-  cv::Mat probMat = applySigmoid(cropped);
+  cv::Mat probMat = image_processing::applySigmoid(cropped);
 
   if (warpToOriginal) {
     probMat = warpToOriginalResolution(probMat, cropRect, originalSize,
@@ -260,13 +258,30 @@ void BaseInstanceSegmentation::ensureMethodLoaded(
       module_->unload_method(currentlyLoadedMethod_);
     }
     currentlyLoadedMethod_ = methodName;
-    module_->load_method(methodName);
+    auto loadResult = module_->load_method(methodName);
+    if (loadResult != executorch::runtime::Error::Ok) {
+      throw RnExecutorchError(
+          loadResult, "Failed to load method '" + methodName +
+                          "'. Ensure the method exists in the exported model.");
+    }
   }
 }
 
 cv::Size BaseInstanceSegmentation::getInputSize(const std::string &methodName) {
   auto inputShapes = getAllInputShapes(methodName);
-  std::vector<int32_t> inputShape = inputShapes[0];
+  if (inputShapes.empty()) {
+    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
+                            "Method '" + methodName +
+                                "' has no input tensors.");
+  }
+
+  const auto &inputShape = inputShapes[0];
+  if (inputShape.empty()) {
+    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
+                            "Method '" + methodName +
+                                "' input tensor has no dimensions.");
+  }
+
   int32_t inputSize = inputShape[inputShape.size() - 1];
   return cv::Size(inputSize, inputSize);
 }

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

@@ -80,8 +80,6 @@ class BaseInstanceSegmentation : public VisionModel {
 
   cv::Rect addPaddingToRect(const cv::Rect &rect, cv::Size maskSize);
 
-  cv::Mat applySigmoid(const cv::Mat &logits);
-
   cv::Mat
   warpToOriginalResolution(const cv::Mat &probMat, const cv::Rect &maskRect,
                            cv::Size originalSize, cv::Size maskSize,