Remove console.logs, change error type

Author: benITo47

apps/computer-vision/components/vision_camera/tasks/InstanceSegmentationTask.tsx

@@ -73,27 +73,12 @@ export default function InstanceSegmentationTask({
       imageWidth: number;
       imageHeight: number;
     }) => {
-      console.log(`[${activeModel}] Got ${p.results.length} instances`);
-      if (p.results.length > 0) {
-        const first = p.results[0];
-        console.log(`[${activeModel}] First instance:`, {
-          label: first.label,
-          score: first.score,
-          bbox: first.bbox,
-          maskWidth: first.maskWidth,
-          maskHeight: first.maskHeight,
-          maskSize: first.mask.length,
-        });
-      }
       const displayInstances = buildDisplayInstances(
         p.results.map((inst) => ({
           ...inst,
           label: String(inst.label),
         }))
       );
-      console.log(
-        `[${activeModel}] Built ${displayInstances.length} display instances`
-      );
       setInstances((prev) => {
         // Dispose old mask images
         prev.forEach((inst) => inst.maskImage.dispose());
@@ -105,7 +90,7 @@ export default function InstanceSegmentationTask({
       if (diff > 0) onFpsChange(Math.round(1000 / diff), diff);
       lastFrameTimeRef.current = now;
     },
-    [onFpsChange, activeModel]
+    [onFpsChange]
   );
 
   const frameOutput = useFrameOutput({

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

@@ -2,7 +2,6 @@
 
 #include <cmath>
 #include <cstdint>
-#include <iostream>
 #include <rnexecutorch/Error.h>
 #include <rnexecutorch/ErrorCodes.h>
 #include <rnexecutorch/Log.h>
@@ -234,7 +233,7 @@ void BaseInstanceSegmentation::validateThresholds(double confidenceThreshold,
 void BaseInstanceSegmentation::validateOutputTensors(
     const std::vector<EValue> &tensors) const {
   if (tensors.size() != 3) {
-    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
+    throw RnExecutorchError(RnExecutorchErrorCode::InvalidModelOutput,
                             "Expected 3 output tensors ([1,N,4] + [1,N,2] + "
                             "[1,N,H,W]), got " +
                                 std::to_string(tensors.size()));
@@ -268,13 +267,13 @@ void BaseInstanceSegmentation::ensureMethodLoaded(
   if (!currentlyLoadedMethod_.empty()) {
     module_->unload_method(currentlyLoadedMethod_);
   }
-  currentlyLoadedMethod_ = 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.");
   }
+  currentlyLoadedMethod_ = methodName;
 }
 
 std::vector<types::Instance> BaseInstanceSegmentation::finalizeInstances(

packages/react-native-executorch/src/types/instanceSegmentation.ts

@@ -1,5 +1,5 @@
 import { RnExecutorchError } from '../errors/errorUtils';
-import { LabelEnum, ResourceSource, Triple, Frame } from './common';
+import { LabelEnum, ResourceSource, Triple, Frame, PixelData } from './common';
 import { Bbox } from './objectDetection';
 
 /**
@@ -185,13 +185,13 @@ export interface InstanceSegmentationType<L extends LabelEnum> {
 
   /**
    * Executes the model's forward pass to perform instance segmentation on the provided image.
-   * @param imageSource - A string representing the image source (e.g., a file path, URI, or base64 string) to be processed.
+   * @param imageSource - A string (e.g., a file path, URI ) or PixelData representing the image source to be processed.
    * @param options - Optional configuration for the segmentation process.
    * @returns A Promise resolving to an array of {@link SegmentedInstance} objects.
    * @throws {RnExecutorchError} If the model is not loaded or is currently processing another image.
    */
   forward: (
-    imageSource: string,
+    imageSource: string | PixelData,
     options?: InstanceSegmentationOptions<L>
   ) => Promise<SegmentedInstance<L>[]>;