Cleanup

desktop/src/main/services/ml-clip.ts

@@ -134,7 +134,7 @@ const clipImageEmbedding_ = async (jpegFilePath: string) => {
     const results = await imageSession.run(feeds);
     log.debug(
         () =>
-            `CLIP image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `onnx/clip image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
     );
     const imageEmbedding = results["output"].data; // Float32Array
     return normalizeEmbedding(imageEmbedding);
@@ -241,7 +241,7 @@ export const clipTextEmbedding = async (text: string) => {
     const results = await imageSession.run(feeds);
     log.debug(
         () =>
-            `CLIP text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
+            `onnx/clip text embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
     );
     const textEmbedding = results["output"].data;
     return normalizeEmbedding(textEmbedding);

desktop/src/main/services/ml-face.ts

@@ -2,7 +2,7 @@
  * @file Various face recognition related tasks.
  *
  * - Face detection with the YOLO model.
- * - Face embedding with the mobilefacenet model.
+ * - Face embedding with the MobileFaceNet model.
  *
  * The runtime used is ONNX.
  */
@@ -99,51 +99,10 @@ export const detectFaces = async (input: Float32Array) => {
         input: new ort.Tensor("float32", input, [1, 3, 640, 640]),
     };
     const results = await session.run(feeds);
-    log.debug(() => `onnx/yolo inference took ${Date.now() - t} ms`);
+    log.debug(() => `onnx/yolo face detection took ${Date.now() - t} ms`);
     return results["output"].data;
 };
 
 export const faceEmbedding = async (input: Float32Array) => {
     throw new Error("test");
 };
-
-/*
-
-private async initOnnx() {
-}
-
-private async getOnnxInferenceSession() {
-    if (!this.onnxInferenceSession) {
-        await this.initOnnx();
-    }
-    return this.onnxInferenceSession;
-}
-*/
-
-// export const clipImageEmbedding = async (jpegImageData: Uint8Array) => {
-//     const tempFilePath = await generateTempFilePath("");
-//     const imageStream = new Response(jpegImageData.buffer).body;
-//     await writeStream(tempFilePath, imageStream);
-//     try {
-//         return await clipImageEmbedding_(tempFilePath);
-//     } finally {
-//         await deleteTempFile(tempFilePath);
-//     }
-// };
-
-// const clipImageEmbedding_ = async (jpegFilePath: string) => {
-//     const imageSession = await onnxImageSession();
-//     const t1 = Date.now();
-//     const rgbData = await getRGBData(jpegFilePath);
-//     const feeds = {
-//         input: new ort.Tensor("float32", rgbData, [1, 3, 224, 224]),
-//     };
-//     const t2 = Date.now();
-//     const results = await imageSession.run(feeds);
-//     log.debug(
-//         () =>
-//             `CLIP image embedding took ${Date.now() - t1} ms (prep: ${t2 - t1} ms, inference: ${Date.now() - t2} ms)`,
-//     );
-//     const imageEmbedding = results["output"].data; // Float32Array
-//     return normalizeEmbedding(imageEmbedding);
-// };

web/apps/photos/src/services/machineLearning/faceService.ts

@@ -55,7 +55,7 @@ class FaceService {
             await syncContext.faceDetectionService.detectFaces(imageBitmap);
         console.timeEnd(timerId);
         console.log("faceDetections: ", faceDetections?.length);
-        // log.info('3 TF Memory stats: ',JSON.stringify(tf.memory()));
+
         // TODO: reenable faces filtering based on width
         const detectedFaces = faceDetections?.map((detection) => {
             return {
@@ -150,7 +150,7 @@ class FaceService {
 
         imageBitmap.close();
         log.info("[MLService] alignedFaces: ", newMlFile.faces?.length);
-        // log.info('4 TF Memory stats: ',JSON.stringify(tf.memory()));
+
         return faceImages;
     }
 
@@ -187,7 +187,6 @@ class FaceService {
         newMlFile.faces.forEach((f, i) => (f.embedding = embeddings[i]));
 
         log.info("[MLService] facesWithEmbeddings: ", newMlFile.faces.length);
-        // log.info('5 TF Memory stats: ',JSON.stringify(tf.memory()));
     }
 
     async syncFileFaceMakeRelativeDetections(