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Cleanup

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This commit is contained in:
Manav Rathi 2024-04-11 14:39:59 +05:30
parent a1d6ef43b4
commit 9ff4989d81
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3 changed files with 6 additions and 48 deletions
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4
desktop/src/main/services/ml-clip.ts
View file

@ -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);

45
desktop/src/main/services/ml-face.ts
View file

@ -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);
// };

5
web/apps/photos/src/services/machineLearning/faceService.ts
View file

@ -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(