mertalev
commented
8 months ago Can you check if setting the concurrency to 1 and restarting helps? And if that doesn't work, deleting the model cache and restarting?
Closed LeoAdL closed 4 months ago
mertalev
commented
8 months ago Can you check if setting the concurrency to 1 and restarting helps? And if that doesn't work, deleting the model cache and restarting?
LeoAdL
commented
8 months ago Setting the concurrency to 1, I almost get immediately a similar error:
immich_redis | 1:M 24 Mar 2024 00:46:41.343 * Background saving terminated with success
immich_machine_learning | [03/24/24 00:46:43] INFO Setting 'buffalo_l' execution providers to
immich_machine_learning | ['OpenVINOExecutionProvider',
immich_machine_learning | 'CPUExecutionProvider'], in descending order of
immich_machine_learning | preference
immich_machine_learning | [03/24/24 00:46:43] INFO Loading facial recognition model 'buffalo_l' to
immich_machine_learning | memory
immich_machine_learning | [03/24/24 00:46:45] ERROR Exception in ASGI application
immich_machine_learning |
immich_machine_learning | ╭─────── Traceback (most recent call last) ───────╮
immich_machine_learning | │ /usr/src/app/main.py:118 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 115 │ │
immich_machine_learning | │ 116 │ model = await load(await model_cache. │
immich_machine_learning | │ ttl=settings.model_ttl, **kwargs)) │
immich_machine_learning | │ 117 │ model.configure(**kwargs) │
immich_machine_learning | │ ❱ 118 │ outputs = await run(model.predict, in │
immich_machine_learning | │ 119 │ return ORJSONResponse(outputs) │
immich_machine_learning | │ 120 │
immich_machine_learning | │ 121 │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/main.py:125 in run │
immich_machine_learning | │ │
immich_machine_learning | │ 122 async def run(func: Callable[..., Any], i │
immich_machine_learning | │ 123 │ if thread_pool is None: │
immich_machine_learning | │ 124 │ │ return func(inputs) │
immich_machine_learning | │ ❱ 125 │ return await asyncio.get_running_loop │
immich_machine_learning | │ 126 │
immich_machine_learning | │ 127 │
immich_machine_learning | │ 128 async def load(model: InferenceModel) -> │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/lib/python3.10/concurrent/futures/thread.p │
immich_machine_learning | │ y:58 in run │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/base.py:59 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 56 │ │ self.load() │
immich_machine_learning | │ 57 │ │ if model_kwargs: │
immich_machine_learning | │ 58 │ │ │ self.configure(**model_kwargs │
immich_machine_learning | │ ❱ 59 │ │ return self._predict(inputs) │
immich_machine_learning | │ 60 │ │
immich_machine_learning | │ 61 │ @abstractmethod │
immich_machine_learning | │ 62 │ def _predict(self, inputs: Any) -> An │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/facial_recognition.py:49 in │
immich_machine_learning | │ _predict │
immich_machine_learning | │ │
immich_machine_learning | │ 46 │ │ else: │
immich_machine_learning | │ 47 │ │ │ decoded_image = image │
immich_machine_learning | │ 48 │ │ assert is_ndarray(decoded_image, n │
immich_machine_learning | │ ❱ 49 │ │ bboxes, kpss = self.det_model.dete │
immich_machine_learning | │ 50 │ │ if bboxes.size == 0: │
immich_machine_learning | │ 51 │ │ │ return [] │
immich_machine_learning | │ 52 │ │ assert is_ndarray(kpss, np.float32 │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │
immich_machine_learning | │ ace/model_zoo/retinaface.py:224 in detect │
immich_machine_learning | │ │
immich_machine_learning | │ 221 │ │ det_img = np.zeros( (input_size[1 │
immich_machine_learning | │ 222 │ │ det_img[:new_height, :new_width, │
immich_machine_learning | │ 223 │ │ │
immich_machine_learning | │ ❱ 224 │ │ scores_list, bboxes_list, kpss_li │
immich_machine_learning | │ 225 │ │ │
immich_machine_learning | │ 226 │ │ scores = np.vstack(scores_list) │
immich_machine_learning | │ 227 │ │ scores_ravel = scores.ravel() │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │
immich_machine_learning | │ ace/model_zoo/retinaface.py:152 in forward │
immich_machine_learning | │ │
immich_machine_learning | │ 149 │ │ kpss_list = [] │
immich_machine_learning | │ 150 │ │ input_size = tuple(img.shape[0:2] │
immich_machine_learning | │ 151 │ │ blob = cv2.dnn.blobFromImage(img, │
immich_machine_learning | │ (self.input_mean, self.input_mean, self.i │
immich_machine_learning | │ ❱ 152 │ │ net_outs = self.session.run(self. │
immich_machine_learning | │ 153 │ │ │
immich_machine_learning | │ 154 │ │ input_height = blob.shape[2] │
immich_machine_learning | │ 155 │ │ input_width = blob.shape[3] │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/onnxrunt │
immich_machine_learning | │ ime/capi/onnxruntime_inference_collection.py:22 │
immich_machine_learning | │ 0 in run │
immich_machine_learning | │ │
immich_machine_learning | │ 217 │ │ if not output_names: │
immich_machine_learning | │ 218 │ │ │ output_names = [output.name │
immich_machine_learning | │ 219 │ │ try: │
immich_machine_learning | │ ❱ 220 │ │ │ return self._sess.run(output │
immich_machine_learning | │ 221 │ │ except C.EPFail as err: │
immich_machine_learning | │ 222 │ │ │ if self._enable_fallback: │
immich_machine_learning | │ 223 │ │ │ │ print(f"EP Error: {err!s │
immich_machine_learning | ╰─────────────────────────────────────────────────╯
immich_machine_learning | RuntimeException: [ONNXRuntimeError] : 6 :
immich_machine_learning | RUNTIME_EXCEPTION : Encountered unknown exception
immich_machine_learning | in Run()
immich_microservices | [Nest] 7 - 03/24/2024, 12:46:45 AM ERROR [JobService] Unable to run job handler (faceDetection/face-detection): Error: Machine learning request for facial recognition failed with status 500: Internal Server Error
immich_microservices | [Nest] 7 - 03/24/2024, 12:46:45 AM ERROR [JobService] Error: Machine learning request for facial recognition failed with status 500: Internal Server Error
immich_microservices | at MachineLearningRepository.predict (/usr/src/app/dist/infra/repositories/machine-learning.repository.js:23:19)
immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
immich_microservices | at async PersonService.handleDetectFaces (/usr/src/app/dist/domain/person/person.service.js:248:23)
immich_microservices | at async /usr/src/app/dist/domain/job/job.service.js:137:36
immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28)
immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24)
immich_microservices | [Nest] 7 - 03/24/2024, 12:46:45 AM ERROR [JobService] Object:
immich_microservices | {
immich_microservices | "id": "9e1d4bbf-84c2-40dd-9aec-c913e5a1a662"
immich_microservices | }
immich_microservices | Deleting the model cache did not help neither.
mertalev
commented
8 months ago Can you try again with these env variables set?
ORT_OPENVINO_ENABLE_CI_LOG=1
ORT_OPENVINO_ENABLE_DEBUG=1
OPENVINO_LOG_LEVEL=5
LOG_LEVEL=debugThis will help get more info on what's causing the error.
edjacob25
commented
8 months ago I have the same issue with the N100 too. I tried changing the model to see if that was a workaround but I'm still getting the issue. After adding those variables I got this:
Hope this helps!
LeoAdL
commented
8 months ago Here are my logs, I think very similar:
immich_machine_learning | [03/24/24 16:17:42] DEBUG Available ORT providers:
immich_machine_learning | {'OpenVINOExecutionProvider',
immich_machine_learning | 'CPUExecutionProvider'}
immich_machine_learning | [03/24/24 16:17:42] DEBUG Available OpenVINO devices: ['CPU', 'GPU']
immich_machine_learning | [03/24/24 16:17:42] INFO Setting 'buffalo_l' execution providers to
immich_machine_learning | ['OpenVINOExecutionProvider',
immich_machine_learning | 'CPUExecutionProvider'], in descending order of
immich_machine_learning | preference
immich_machine_learning | [03/24/24 16:17:42] DEBUG Setting execution provider options to
immich_machine_learning | [{'device_type': 'GPU_FP32', 'cache_dir':
immich_machine_learning | '/cache/facial-recognition/buffalo_l/openvino'},
immich_machine_learning | {'arena_extend_strategy': 'kSameAsRequested'}]
immich_machine_learning | [03/24/24 16:17:42] DEBUG Setting execution_mode to ORT_SEQUENTIAL
immich_machine_learning | [03/24/24 16:17:42] DEBUG Setting inter_op_num_threads to 0
immich_machine_learning | [03/24/24 16:17:42] DEBUG Setting intra_op_num_threads to 0
immich_machine_learning | [03/24/24 16:17:42] DEBUG Setting preferred runtime to onnx
immich_machine_learning | [03/24/24 16:17:42] INFO Loading facial recognition model 'buffalo_l' to
immich_machine_learning | memory
immich_machine_learning | In the OpenVINO EP
immich_machine_learning | Model is fully supported on OpenVINO
immich_machine_learning | CreateNgraphFunc
immich_machine_learning | In the OpenVINO EP
immich_machine_learning | Model is fully supported on OpenVINO
immich_machine_learning | CreateNgraphFunc
immich_machine_learning | [03/24/24 16:17:44] ERROR Exception in ASGI application
immich_machine_learning |
immich_machine_learning | ╭─────── Traceback (most recent call last) ───────╮
immich_machine_learning | │ /usr/src/app/main.py:118 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 115 │ │
immich_machine_learning | │ 116 │ model = await load(await model_cache. │
immich_machine_learning | │ ttl=settings.model_ttl, **kwargs)) │
immich_machine_learning | │ 117 │ model.configure(**kwargs) │
immich_machine_learning | │ ❱ 118 │ outputs = await run(model.predict, in │
immich_machine_learning | │ 119 │ return ORJSONResponse(outputs) │
immich_machine_learning | │ 120 │
immich_machine_learning | │ 121 │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ image = UploadFile(filename='blob', │ │
immich_machine_learning | │ │ size=591599, │ │
immich_machine_learning | │ │ headers=Headers({'content-dis… │ │
immich_machine_learning | │ │ 'form-data; name="image"; │ │
immich_machine_learning | │ │ filename="blob"', │ │
immich_machine_learning | │ │ 'content-type': │ │
immich_machine_learning | │ │ 'application/octet-stream'})) │ │
immich_machine_learning | │ │ inputs = b'\xff\xd8\xff\xe2\x01\xf0ICC… │ │
immich_machine_learning | │ │ \x00\x00mntrRGB XYZ │ │
immich_machine_learning | │ │ \x07\xe2\x00\x03\x00\x14\x00\… │ │
immich_machine_learning | │ │ kwargs = { │ │
immich_machine_learning | │ │ │ 'minScore': 0.7, │ │
immich_machine_learning | │ │ │ 'maxDistance': 0.5, │ │
immich_machine_learning | │ │ │ 'minFaces': 3 │ │
immich_machine_learning | │ │ } │ │
immich_machine_learning | │ │ model = <app.models.facial_recognitio… │ │
immich_machine_learning | │ │ object at 0x7ab23c362fe0> │ │
immich_machine_learning | │ │ model_name = 'buffalo_l' │ │
immich_machine_learning | │ │ model_type = <ModelType.FACIAL_RECOGNITION: │ │
immich_machine_learning | │ │ 'facial-recognition'> │ │
immich_machine_learning | │ │ options = '{"minScore":0.7,"maxDistance… │ │
immich_machine_learning | │ │ text = None │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/main.py:125 in run │
immich_machine_learning | │ │
immich_machine_learning | │ 122 async def run(func: Callable[..., Any], i │
immich_machine_learning | │ 123 │ if thread_pool is None: │
immich_machine_learning | │ 124 │ │ return func(inputs) │
immich_machine_learning | │ ❱ 125 │ return await asyncio.get_running_loop │
immich_machine_learning | │ 126 │
immich_machine_learning | │ 127 │
immich_machine_learning | │ 128 async def load(model: InferenceModel) -> │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ func = <bound method │ │
immich_machine_learning | │ │ InferenceModel.predict of │ │
immich_machine_learning | │ │ <app.models.facial_recognition.Fa… │ │
immich_machine_learning | │ │ object at 0x7ab23c362fe0>> │ │
immich_machine_learning | │ │ inputs = b'\xff\xd8\xff\xe2\x01\xf0ICC_PRO… │ │
immich_machine_learning | │ │ \x00\x00mntrRGB XYZ │ │
immich_machine_learning | │ │ \x07\xe2\x00\x03\x00\x14\x00\t\x0… │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/lib/python3.10/concurrent/futures/thread.p │
immich_machine_learning | │ y:58 in run │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/base.py:59 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 56 │ │ self.load() │
immich_machine_learning | │ 57 │ │ if model_kwargs: │
immich_machine_learning | │ 58 │ │ │ self.configure(**model_kwargs │
immich_machine_learning | │ ❱ 59 │ │ return self._predict(inputs) │
immich_machine_learning | │ 60 │ │
immich_machine_learning | │ 61 │ @abstractmethod │
immich_machine_learning | │ 62 │ def _predict(self, inputs: Any) -> An │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ inputs = b'\xff\xd8\xff\xe2\x01\xf0I… │ │
immich_machine_learning | │ │ \x00\x00mntrRGB XYZ │ │
immich_machine_learning | │ │ \x07\xe2\x00\x03\x00\x14\x0… │ │
immich_machine_learning | │ │ model_kwargs = {} │ │
immich_machine_learning | │ │ self = <app.models.facial_recognit… │ │
immich_machine_learning | │ │ object at 0x7ab23c362fe0> │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/facial_recognition.py:49 in │
immich_machine_learning | │ _predict │
immich_machine_learning | │ │
immich_machine_learning | │ 46 │ │ else: │
immich_machine_learning | │ 47 │ │ │ decoded_image = image │
immich_machine_learning | │ 48 │ │ assert is_ndarray(decoded_image, n │
immich_machine_learning | │ ❱ 49 │ │ bboxes, kpss = self.det_model.dete │
immich_machine_learning | │ 50 │ │ if bboxes.size == 0: │
immich_machine_learning | │ 51 │ │ │ return [] │
immich_machine_learning | │ 52 │ │ assert is_ndarray(kpss, np.float32 │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ decoded_image = array([[[ 0, 17, 34], │ │
immich_machine_learning | │ │ │ │ [ 0, 17, 34], │ │
immich_machine_learning | │ │ │ │ [ 0, 18, 35], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 8, 7, 9], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 19, 18, 20]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 0, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 2, 20, 37], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 7, 6, 8], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 20, 19, 21]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 2, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 4, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 4, 22, 39], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 5, 4, 6], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 22, 21, 23]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 32, 56, 86], │ │
immich_machine_learning | │ │ │ │ [ 36, 60, 90], │ │
immich_machine_learning | │ │ │ │ [ 42, 66, 96], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 28, 54, 71], │ │
immich_machine_learning | │ │ │ │ [ 31, 57, 74], │ │
immich_machine_learning | │ │ │ │ [ 36, 62, 79]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 37, 59, 87], │ │
immich_machine_learning | │ │ │ │ [ 43, 65, 93], │ │
immich_machine_learning | │ │ │ │ [ 53, 75, 103], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70], │ │
immich_machine_learning | │ │ │ │ [ 28, 54, 71], │ │
immich_machine_learning | │ │ │ │ [ 32, 58, 75]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 44, 66, 94], │ │
immich_machine_learning | │ │ │ │ [ 50, 72, 100], │ │
immich_machine_learning | │ │ │ │ [ 61, 83, 111], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70], │ │
immich_machine_learning | │ │ │ │ [ 25, 51, 68], │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70]]], │ │
immich_machine_learning | │ │ dtype=uint8) │ │
immich_machine_learning | │ │ image = b'\xff\xd8\xff\xe2\x01\xf0… │ │
immich_machine_learning | │ │ \x00\x00mntrRGB XYZ │ │
immich_machine_learning | │ │ \x07\xe2\x00\x03\x00\x14\x… │ │
immich_machine_learning | │ │ self = <app.models.facial_recogni… │ │
immich_machine_learning | │ │ object at 0x7ab23c362fe0> │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │
immich_machine_learning | │ ace/model_zoo/retinaface.py:224 in detect │
immich_machine_learning | │ │
immich_machine_learning | │ 221 │ │ det_img = np.zeros( (input_size[1 │
immich_machine_learning | │ 222 │ │ det_img[:new_height, :new_width, │
immich_machine_learning | │ 223 │ │ │
immich_machine_learning | │ ❱ 224 │ │ scores_list, bboxes_list, kpss_li │
immich_machine_learning | │ 225 │ │ │
immich_machine_learning | │ 226 │ │ scores = np.vstack(scores_list) │
immich_machine_learning | │ 227 │ │ scores_ravel = scores.ravel() │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ det_img = array([[[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 13, 31], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 6, 24, 41], │ │
immich_machine_learning | │ │ │ │ [ 6, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 1, 15, 33], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 14, 32], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 40, 67, 101], │ │
immich_machine_learning | │ │ │ │ [ 28, 55, 89], │ │
immich_machine_learning | │ │ │ │ [ 33, 60, 94], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 35, 60, 92], │ │
immich_machine_learning | │ │ │ │ [ 33, 58, 90], │ │
immich_machine_learning | │ │ │ │ [ 43, 68, 100], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 43, 65, 93], │ │
immich_machine_learning | │ │ │ │ [ 69, 91, 119], │ │
immich_machine_learning | │ │ │ │ [ 64, 86, 114], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]]], │ │
immich_machine_learning | │ │ dtype=uint8) │ │
immich_machine_learning | │ │ det_scale = 0.3333333333333333 │ │
immich_machine_learning | │ │ im_ratio = 1.3333333333333333 │ │
immich_machine_learning | │ │ img = array([[[ 0, 17, 34], │ │
immich_machine_learning | │ │ │ │ [ 0, 17, 34], │ │
immich_machine_learning | │ │ │ │ [ 0, 18, 35], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 8, 7, 9], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 19, 18, 20]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 0, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 2, 20, 37], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 7, 6, 8], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 20, 19, 21]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 2, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 4, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 4, 22, 39], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 5, 4, 6], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15], │ │
immich_machine_learning | │ │ │ │ [ 22, 21, 23]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 32, 56, 86], │ │
immich_machine_learning | │ │ │ │ [ 36, 60, 90], │ │
immich_machine_learning | │ │ │ │ [ 42, 66, 96], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 28, 54, 71], │ │
immich_machine_learning | │ │ │ │ [ 31, 57, 74], │ │
immich_machine_learning | │ │ │ │ [ 36, 62, 79]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 37, 59, 87], │ │
immich_machine_learning | │ │ │ │ [ 43, 65, 93], │ │
immich_microservices | [Nest] 7 - 03/24/2024, 4:17:44 PM ERROR [JobService] Unable to run job handler (faceDetection/face-detection): Error: Machine learning request for facial recognition failed with status 500: Internal Server Error
immich_microservices | [Nest] 7 - 03/24/2024, 4:17:44 PM ERROR [JobService] Error: Machine learning request for facial recognition failed with status 500: Internal Server Error
immich_microservices | at MachineLearningRepository.predict (/usr/src/app/dist/infra/repositories/machine-learning.repository.js:23:19)
immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
immich_microservices | at async PersonService.handleDetectFaces (/usr/src/app/dist/domain/person/person.service.js:248:23)
immich_microservices | at async /usr/src/app/dist/domain/job/job.service.js:137:36
immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28)
immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24)
immich_microservices | [Nest] 7 - 03/24/2024, 4:17:44 PM ERROR [JobService] Object:
immich_microservices | {
immich_microservices | "id": "ea7fe00a-068b-46d3-8ae1-61dd6146286f"
immich_microservices | }
immich_microservices |
immich_machine_learning | │ │ │ │ [ 53, 75, 103], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70], │ │
immich_machine_learning | │ │ │ │ [ 28, 54, 71], │ │
immich_machine_learning | │ │ │ │ [ 32, 58, 75]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 44, 66, 94], │ │
immich_machine_learning | │ │ │ │ [ 50, 72, 100], │ │
immich_machine_learning | │ │ │ │ [ 61, 83, 111], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70], │ │
immich_machine_learning | │ │ │ │ [ 25, 51, 68], │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70]]], │ │
immich_machine_learning | │ │ dtype=uint8) │ │
immich_machine_learning | │ │ input_size = (640, 640) │ │
immich_machine_learning | │ │ max_num = 0 │ │
immich_machine_learning | │ │ metric = 'default' │ │
immich_machine_learning | │ │ model_ratio = 1.0 │ │
immich_machine_learning | │ │ new_height = 640 │ │
immich_machine_learning | │ │ new_width = 480 │ │
immich_machine_learning | │ │ resized_img = array([[[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 13, 31], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 2, 1, 3], │ │
immich_machine_learning | │ │ │ │ [ 2, 1, 3], │ │
immich_machine_learning | │ │ │ │ [ 14, 13, 15]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 6, 24, 41], │ │
immich_machine_learning | │ │ │ │ [ 6, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 1, 15, 33], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 1], │ │
immich_machine_learning | │ │ │ │ [ 4, 3, 5], │ │
immich_machine_learning | │ │ │ │ [ 16, 15, 17]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 14, 32], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 2, 1, 3], │ │
immich_machine_learning | │ │ │ │ [ 2, 1, 3], │ │
immich_machine_learning | │ │ │ │ [ 18, 17, 19]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 40, 67, 101], │ │
immich_machine_learning | │ │ │ │ [ 28, 55, 89], │ │
immich_machine_learning | │ │ │ │ [ 33, 60, 94], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 49, 75, 92], │ │
immich_machine_learning | │ │ │ │ [ 37, 63, 80], │ │
immich_machine_learning | │ │ │ │ [ 32, 58, 75]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 35, 60, 92], │ │
immich_machine_learning | │ │ │ │ [ 33, 58, 90], │ │
immich_machine_learning | │ │ │ │ [ 43, 68, 100], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 29, 55, 72], │ │
immich_machine_learning | │ │ │ │ [ 33, 59, 76], │ │
immich_machine_learning | │ │ │ │ [ 33, 59, 76]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 43, 65, 93], │ │
immich_machine_learning | │ │ │ │ [ 69, 91, 119], │ │
immich_machine_learning | │ │ │ │ [ 64, 86, 114], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 27, 53, 70], │ │
immich_machine_learning | │ │ │ │ [ 30, 56, 73], │ │
immich_machine_learning | │ │ │ │ [ 28, 54, 71]]], │ │
immich_machine_learning | │ │ dtype=uint8) │ │
immich_machine_learning | │ │ self = <insightface.model_zoo.retin… │ │
immich_machine_learning | │ │ object at 0x7ab23c362dd0> │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │
immich_machine_learning | │ ace/model_zoo/retinaface.py:152 in forward │
immich_machine_learning | │ │
immich_machine_learning | │ 149 │ │ kpss_list = [] │
immich_machine_learning | │ 150 │ │ input_size = tuple(img.shape[0:2] │
immich_machine_learning | │ 151 │ │ blob = cv2.dnn.blobFromImage(img, │
immich_machine_learning | │ (self.input_mean, self.input_mean, self.i │
immich_machine_learning | │ ❱ 152 │ │ net_outs = self.session.run(self. │
immich_machine_learning | │ 153 │ │ │
immich_machine_learning | │ 154 │ │ input_height = blob.shape[2] │
immich_machine_learning | │ 155 │ │ input_width = blob.shape[3] │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ bboxes_list = [] │ │
immich_machine_learning | │ │ blob = array([[[[-0.71484375, │ │
immich_machine_learning | │ │ -0.71484375, -0.75390625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.67578125, │ │
immich_machine_learning | │ │ -0.69140625, -0.73828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.71484375, │ │
immich_machine_learning | │ │ -0.71484375, -0.74609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.20703125, │ │
immich_machine_learning | │ │ -0.30078125, -0.26171875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.27734375, │ │
immich_machine_learning | │ │ -0.29296875, -0.21484375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.26953125, │ │
immich_machine_learning | │ │ -0.06640625, -0.10546875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]], │ │
immich_machine_learning | │ │ │ │ │ │
immich_machine_learning | │ │ │ │ [[-0.84765625, │ │
immich_machine_learning | │ │ -0.84765625, -0.89453125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.80859375, │ │
immich_machine_learning | │ │ -0.82421875, -0.87890625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.84765625, │ │
immich_machine_learning | │ │ -0.84765625, -0.88671875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.47265625, │ │
immich_machine_learning | │ │ -0.56640625, -0.52734375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.52734375, │ │
immich_machine_learning | │ │ -0.54296875, -0.46484375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.48828125, │ │
immich_machine_learning | │ │ -0.28515625, -0.32421875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]], │ │
immich_machine_learning | │ │ │ │ │ │
immich_machine_learning | │ │ │ │ [[-0.98828125, │ │
immich_machine_learning | │ │ -0.97265625, -0.99609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.94921875, │ │
immich_machine_learning | │ │ -0.94921875, -0.98828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.98828125, │ │
immich_machine_learning | │ │ -0.97265625, -0.99609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.68359375, │ │
immich_machine_learning | │ │ -0.77734375, -0.73828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.72265625, │ │
immich_machine_learning | │ │ -0.73828125, -0.66015625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.66015625, │ │
immich_machine_learning | │ │ -0.45703125, -0.49609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]]]], │ │
immich_machine_learning | │ │ dtype=float32) │ │
immich_machine_learning | │ │ img = array([[[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 13, 31], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 6, 24, 41], │ │
immich_machine_learning | │ │ │ │ [ 6, 22, 39], │ │
immich_machine_learning | │ │ │ │ [ 1, 15, 33], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 1, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 3, 19, 36], │ │
immich_machine_learning | │ │ │ │ [ 0, 14, 32], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 40, 67, 101], │ │
immich_machine_learning | │ │ │ │ [ 28, 55, 89], │ │
immich_machine_learning | │ │ │ │ [ 33, 60, 94], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 35, 60, 92], │ │
immich_machine_learning | │ │ │ │ [ 33, 58, 90], │ │
immich_machine_learning | │ │ │ │ [ 43, 68, 100], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]], │ │
immich_machine_learning | │ │ │ │ │
immich_machine_learning | │ │ │ [[ 43, 65, 93], │ │
immich_machine_learning | │ │ │ │ [ 69, 91, 119], │ │
immich_machine_learning | │ │ │ │ [ 64, 86, 114], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0], │ │
immich_machine_learning | │ │ │ │ [ 0, 0, 0]]], │ │
immich_machine_learning | │ │ dtype=uint8) │ │
immich_machine_learning | │ │ input_size = (640, 640) │ │
immich_machine_learning | │ │ kpss_list = [] │ │
immich_machine_learning | │ │ scores_list = [] │ │
immich_machine_learning | │ │ self = <insightface.model_zoo.retin… │ │
immich_machine_learning | │ │ object at 0x7ab23c362dd0> │ │
immich_machine_learning | │ │ threshold = 0.7 │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/onnxrunt │
immich_machine_learning | │ ime/capi/onnxruntime_inference_collection.py:22 │
immich_machine_learning | │ 0 in run │
immich_machine_learning | │ │
immich_machine_learning | │ 217 │ │ if not output_names: │
immich_machine_learning | │ 218 │ │ │ output_names = [output.name │
immich_machine_learning | │ 219 │ │ try: │
immich_machine_learning | │ ❱ 220 │ │ │ return self._sess.run(output │
immich_machine_learning | │ 221 │ │ except C.EPFail as err: │
immich_machine_learning | │ 222 │ │ │ if self._enable_fallback: │
immich_machine_learning | │ 223 │ │ │ │ print(f"EP Error: {err!s │
immich_machine_learning | │ │
immich_machine_learning | │ ╭────────────────── locals ───────────────────╮ │
immich_machine_learning | │ │ input_feed = { │ │
immich_machine_learning | │ │ │ 'input.1': │ │
immich_machine_learning | │ │ array([[[[-0.71484375, │ │
immich_machine_learning | │ │ -0.71484375, -0.75390625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.67578125, │ │
immich_machine_learning | │ │ -0.69140625, -0.73828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.71484375, │ │
immich_machine_learning | │ │ -0.71484375, -0.74609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.20703125, │ │
immich_machine_learning | │ │ -0.30078125, -0.26171875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.27734375, │ │
immich_machine_learning | │ │ -0.29296875, -0.21484375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.26953125, │ │
immich_machine_learning | │ │ -0.06640625, -0.10546875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]], │ │
immich_machine_learning | │ │ │ │ │ │
immich_machine_learning | │ │ │ │ [[-0.84765625, │ │
immich_machine_learning | │ │ -0.84765625, -0.89453125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.80859375, │ │
immich_machine_learning | │ │ -0.82421875, -0.87890625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.84765625, │ │
immich_machine_learning | │ │ -0.84765625, -0.88671875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.47265625, │ │
immich_machine_learning | │ │ -0.56640625, -0.52734375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.52734375, │ │
immich_machine_learning | │ │ -0.54296875, -0.46484375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.48828125, │ │
immich_machine_learning | │ │ -0.28515625, -0.32421875, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]], │ │
immich_machine_learning | │ │ │ │ │ │
immich_machine_learning | │ │ │ │ [[-0.98828125, │ │
immich_machine_learning | │ │ -0.97265625, -0.99609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.94921875, │ │
immich_machine_learning | │ │ -0.94921875, -0.98828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.98828125, │ │
immich_machine_learning | │ │ -0.97265625, -0.99609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ ..., │ │
immich_machine_learning | │ │ │ │ [-0.68359375, │ │
immich_machine_learning | │ │ -0.77734375, -0.73828125, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.72265625, │ │
immich_machine_learning | │ │ -0.73828125, -0.66015625, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375], │ │
immich_machine_learning | │ │ │ │ [-0.66015625, │ │
immich_machine_learning | │ │ -0.45703125, -0.49609375, │ │
immich_machine_learning | │ │ ..., -0.99609375, │ │
immich_machine_learning | │ │ │ │ -0.99609375, │ │
immich_machine_learning | │ │ -0.99609375]]]], │ │
immich_machine_learning | │ │ dtype=float32) │ │
immich_machine_learning | │ │ } │ │
immich_machine_learning | │ │ output_names = [ │ │
immich_machine_learning | │ │ │ '448', │ │
immich_machine_learning | │ │ │ '471', │ │
immich_machine_learning | │ │ │ '494', │ │
immich_machine_learning | │ │ │ '451', │ │
immich_machine_learning | │ │ │ '474', │ │
immich_machine_learning | │ │ │ '497', │ │
immich_machine_learning | │ │ │ '454', │ │
immich_machine_learning | │ │ │ '477', │ │
immich_machine_learning | │ │ │ '500' │ │
immich_machine_learning | │ │ ] │ │
immich_machine_learning | │ │ run_options = None │ │
immich_machine_learning | │ │ self = <onnxruntime.capi.onnxrunti… │ │
immich_machine_learning | │ │ object at 0x7ab23c362b00> │ │
immich_machine_learning | │ ╰─────────────────────────────────────────────╯ │
immich_machine_learning | ╰─────────────────────────────────────────────────╯
immich_machine_learning | RuntimeException: [ONNXRuntimeError] : 6 :
immich_machine_learning | RUNTIME_EXCEPTION : Encountered unknown exception
immich_machine_learning | in Run()
immich_machine_learning | [03/24/24 16:17:48] DEBUG Checking for inactivity... I made an upstream issue for this based on the info here. Let's wait for their response.
yu-jingrui
commented
8 months ago I am having this problem too.
diogovalentte
commented
8 months ago I'm having the same issue in 1.100.0 too.
mertalev
commented
8 months ago For future reference, it's better to leave a thumbs up for issues if they affect you. Leaving a comment notifies every participant in the issue, so it should be reserved for comments that drive the issue forward.
kaienli
commented
8 months ago same issue in 1.100.0 too. on wsl2，my config is immich-machine-learning: container_name: immich_machine_learning restart: always image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}-openvino devices:
agrawalsourav98
commented
7 months ago I am also facing this issue. It runs fine on my laptop which has i5-1145G7 but doesn't work on my immich desktop which has i3-12100.
Can we provide an option to disable OpenVINO runtime for where it fails only for the facial recognition model? Because the smart search models work fine.
mertalev
commented
7 months ago The i5-1145G7 has Iris graphics, same with the CPU I tested with. It seems to be that UHD graphics doesn't work, but Iris does.
jsapede
commented
7 months ago same here with 1.101.0 on i5 Gen 7 was perfectly working on older releases
plmsuper8
commented
7 months ago similar here with smart search. 1.102.3 on N5095. sadly the smart search nerver works for me.
good news, when change from immich-machine-learning:${IMMICH_VERSION}-openvino to immich-machine-learning:${IMMICH_VERSION}, it works! (related https://github.com/immich-app/immich/issues/8918)
jsapede
commented
7 months ago same error on 1.102.3, was working until 1.099
immich_machine_learning | [04/29/24 12:59:22] INFO Setting 'buffalo_l' execution providers to immich_machine_learning | ['OpenVINOExecutionProvider', immich_machine_learning | 'CPUExecutionProvider'], in descending order of immich_machine_learning | preference immich_machine_learning | [04/29/24 12:59:22] INFO Loading facial recognition model 'buffalo_l' to immich_machine_learning | memory immich_machine_learning | [04/29/24 12:59:29] ERROR Exception in ASGI application immich_machine_learning | immich_machine_learning | ╭─────── Traceback (most recent call last) ───────╮ immich_machine_learning | │ /usr/src/app/main.py:118 in predict │ immich_machine_learning | │ │ immich_machine_learning | │ 115 │ │ immich_machine_learning | │ 116 │ model = await load(await model_cache. │ immich_machine_learning | │ ttl=settings.model_ttl, **kwargs)) │ immich_machine_learning | │ 117 │ model.configure(**kwargs) │ immich_machine_learning | │ ❱ 118 │ outputs = await run(model.predict, in │ immich_machine_learning | │ 119 │ return ORJSONResponse(outputs) │ immich_machine_learning | │ 120 │ immich_machine_learning | │ 121 │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/main.py:125 in run │ immich_machine_learning | │ │ immich_machine_learning | │ 122 async def run(func: Callable[..., Any], i │ immich_machine_learning | │ 123 │ if thread_pool is None: │ immich_machine_learning | │ 124 │ │ return func(inputs) │ immich_machine_learning | │ ❱ 125 │ return await asyncio.get_running_loop │ immich_machine_learning | │ 126 │ immich_machine_learning | │ 127 │ immich_machine_learning | │ 128 async def load(model: InferenceModel) -> │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/lib/python3.10/concurrent/futures/thread.p │ immich_machine_learning | │ y:58 in run │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/models/base.py:59 in predict │ immich_machine_learning | │ │ immich_machine_learning | │ 56 │ │ self.load() │ immich_machine_learning | │ 57 │ │ if model_kwargs: │ immich_machine_learning | │ 58 │ │ │ self.configure(**model_kwargs │ immich_machine_learning | │ ❱ 59 │ │ return self._predict(inputs) │ immich_machine_learning | │ 60 │ │ immich_machine_learning | │ 61 │ @abstractmethod │ immich_machine_learning | │ 62 │ def _predict(self, inputs: Any) -> An │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/models/facial_recognition.py:49 in │ immich_machine_learning | │ _predict │ immich_machine_learning | │ │ immich_machine_learning | │ 46 │ │ else: │ immich_machine_learning | │ 47 │ │ │ decoded_image = image │ immich_machine_learning | │ 48 │ │ assert is_ndarray(decoded_image, n │ immich_machine_learning | │ ❱ 49 │ │ bboxes, kpss = self.det_model.dete │ immich_machine_learning | │ 50 │ │ if bboxes.size == 0: │ immich_machine_learning | │ 51 │ │ │ return [] │ immich_machine_learning | │ 52 │ │ assert is_ndarray(kpss, np.float32 │ immich_machine_learning | │ │ immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │ immich_machine_learning | │ ace/model_zoo/retinaface.py:224 in detect │ immich_machine_learning | │ │ immich_machine_learning | │ 221 │ │ det_img = np.zeros( (input_size[1 │ immich_machine_learning | │ 222 │ │ det_img[:new_height, :new_width, │ immich_machine_learning | │ 223 │ │ │ immich_machine_learning | │ ❱ 224 │ │ scores_list, bboxes_list, kpss_li │ immich_machine_learning | │ 225 │ │ │ immich_machine_learning | │ 226 │ │ scores = np.vstack(scores_list) │ immich_machine_learning | │ 227 │ │ scores_ravel = scores.ravel() │ immich_machine_learning | │ │ immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/insightf │ immich_machine_learning | │ ace/model_zoo/retinaface.py:152 in forward │ immich_machine_learning | │ │ immich_machine_learning | │ 149 │ │ kpss_list = [] │ immich_machine_learning | │ 150 │ │ input_size = tuple(img.shape[0:2] │ immich_machine_learning | │ 151 │ │ blob = cv2.dnn.blobFromImage(img, │ immich_machine_learning | │ (self.input_mean, self.input_mean, self.i │ immich_machine_learning | │ ❱ 152 │ │ net_outs = self.session.run(self. │ immich_machine_learning | │ 153 │ │ │ immich_machine_learning | │ 154 │ │ input_height = blob.shape[2] │ immich_machine_learning | │ 155 │ │ input_width = blob.shape[3] │ immich_machine_learning | │ │ immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/onnxrunt │ immich_machine_learning | │ ime/capi/onnxruntime_inference_collection.py:22 │ immich_machine_learning | │ 0 in run │ immich_machine_learning | │ │ immich_machine_learning | │ 217 │ │ if not output_names: │ immich_machine_learning | │ 218 │ │ │ output_names = [output.name │ immich_machine_learning | │ 219 │ │ try: │ immich_machine_learning | │ ❱ 220 │ │ │ return self._sess.run(output │ immich_machine_learning | │ 221 │ │ except C.EPFail as err: │ immich_machine_learning | │ 222 │ │ │ if self._enable_fallback: │ immich_machine_learning | │ 223 │ │ │ │ print(f"EP Error: {err!s │ immich_machine_learning | ╰─────────────────────────────────────────────────╯ immich_machine_learning | RuntimeException: [ONNXRuntimeError] : 6 : immich_machine_learning | RUNTIME_EXCEPTION : Encountered unknown exception immich_machine_learning | in Run() immich_microservices | [Nest] 7 - 04/29/2024, 12:59:29 PM ERROR [JobService] Unable to run job handler (faceDetection/face-detection): Error: Machine learning request for facial recognition failed with status 500: Internal Server Error immich_microservices | [Nest] 7 - 04/29/2024, 12:59:29 PM ERROR [JobService] Error: Machine learning request for facial recognition failed with status 500: Internal Server Error immich_microservices | at MachineLearningRepository.predict (/usr/src/app/dist/repositories/machine-learning.repository.js:23:19) immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5) immich_microservices | at async PersonService.handleDetectFaces (/usr/src/app/dist/services/person.service.js:268:23) immich_microservices | at async /usr/src/app/dist/services/job.service.js:149:36 immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) immich_microservices | [Nest] 7 - 04/29/2024, 12:59:29 PM ERROR [JobService] Object: immich_microservices | { immich_microservices | "id": "541c6706-c6bd-43e0-bf74-d8bbc4d1b664" immich_microservices | } immich_microservices |
nodis
commented
7 months ago me too
engels0n
commented
7 months ago Same problem in new version 1.103.1
`[05/02/24 07:05:03] INFO Setting 'antelopev2' execution providers to ['OpenVINOExecutionProvider', 'CPUExecutionProvider'], in descending order of preference [05/02/24 07:05:03] INFO Loading facial recognition model 'antelopev2' to memory [05/02/24 07:05:11] ERROR Exception in ASGI application
╭─────── Traceback (most recent call last) ───────╮
│ /usr/src/app/main.py:118 in predict │
│ │
│ 115 │ │
│ 116 │ model = await load(await model_cache. │
│ ttl=settings.model_ttl, **kwargs)) │
│ 117 │ model.configure(**kwargs) │
│ ❱ 118 │ outputs = await run(model.predict, in │
│ 119 │ return ORJSONResponse(outputs) │
│ 120 │
│ 121 │
│ │
│ /usr/src/app/main.py:125 in run │
│ │
│ 122 async def run(func: Callable[..., Any], i │
│ 123 │ if thread_pool is None: │
│ 124 │ │ return func(inputs) │
│ ❱ 125 │ return await asyncio.get_running_loop │
│ 126 │
│ 127 │
│ 128 async def load(model: InferenceModel) -> │
│ │
│ /usr/lib/python3.10/concurrent/futures/thread.p │
│ y:58 in run │
│ │
│ /usr/src/app/models/base.py:59 in predict │
│ │
│ 56 │ │ self.load() │
│ 57 │ │ if model_kwargs: │
│ 58 │ │ │ self.configure(**model_kwargs │
│ ❱ 59 │ │ return self._predict(inputs) │
│ 60 │ │
│ 61 │ @abstractmethod │
│ 62 │ def _predict(self, inputs: Any) -> An │
│ │
│ /usr/src/app/models/facial_recognition.py:49 in │
│ _predict │
│ │
│ 46 │ │ else: │
│ 47 │ │ │ decoded_image = image │
│ 48 │ │ assert is_ndarray(decoded_image, n │
│ ❱ 49 │ │ bboxes, kpss = self.det_model.dete │
│ 50 │ │ if bboxes.size == 0: │
│ 51 │ │ │ return [] │
│ 52 │ │ assert is_ndarray(kpss, np.float32 │
│ │
│ /opt/venv/lib/python3.10/site-packages/insightf │
│ ace/model_zoo/retinaface.py:224 in detect │
│ │
│ 221 │ │ det_img = np.zeros( (input_size[1 │
│ 222 │ │ det_img[:new_height, :new_width, │
│ 223 │ │ │
│ ❱ 224 │ │ scores_list, bboxes_list, kpss_li │
│ 225 │ │ │
│ 226 │ │ scores = np.vstack(scores_list) │
│ 227 │ │ scores_ravel = scores.ravel() │
│ │
│ /opt/venv/lib/python3.10/site-packages/insightf │
│ ace/model_zoo/retinaface.py:152 in forward │
│ │
│ 149 │ │ kpss_list = [] │
│ 150 │ │ input_size = tuple(img.shape[0:2] │
│ 151 │ │ blob = cv2.dnn.blobFromImage(img, │
│ (self.input_mean, self.input_mean, self.i │
│ ❱ 152 │ │ net_outs = self.session.run(self. │
│ 153 │ │ │
│ 154 │ │ input_height = blob.shape[2] │
│ 155 │ │ input_width = blob.shape[3] │
│ │
│ /opt/venv/lib/python3.10/site-packages/onnxrunt │
│ ime/capi/onnxruntime_inference_collection.py:22 │
│ 0 in run │
│ │
│ 217 │ │ if not output_names: │
│ 218 │ │ │ output_names = [output.name │
│ 219 │ │ try: │
│ ❱ 220 │ │ │ return self._sess.run(output │
│ 221 │ │ except C.EPFail as err: │
│ 222 │ │ │ if self._enable_fallback: │
│ 223 │ │ │ │ print(f"EP Error: {err!s │
╰─────────────────────────────────────────────────╯
RuntimeException: [ONNXRuntimeError] : 6 :
RUNTIME_EXCEPTION : Encountered unknown exception
in Run()`@plmsuper8
Thanks for the hint and "workaround". But now it obviously uses the CPU instead of the GPU ;) - so the problem still exists.
jsapede
commented
6 months ago still not working in 1.104.0 will there be a solution for openvino use ?
Snuupy
commented
6 months ago I was going to try downgrading onnxruntime to 2024.1 (from the current 2024.3) in the immich-machine-learning Dockerfile. The last version to have working openvino was .98 which used 2024.1. I was going to do a build and test it, but I haven't had time yet.
Edit: nope, not that simple if you try to edit the Dockerfile and change just the onnxruntime version unfortunately.
Snuupy
commented
6 months ago I got it working, give me a bit to upload the image and do a writeup :) Thanks @mertalev for the help!
Snuupy
commented
6 months ago Want to use my image?
In your docker-compose.yml, replace
image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}-openvino with
image: ghcr.io/snuupy/immich-machine-learning:v1.105.0-openvino
https://github.com/Snuupy/immich/pkgs/container/immich-machine-learning
Want to build your own image? Here's how I did it:
install poetry, change onnxruntime-openvino to target 1.15 or 1.16 in the
pyproject.tomlfile, then runpoetry lock --no-update
[tool.poetry.group.openvino.dependencies]
# onnxruntime-openvino = "^1.17.1"
onnxruntime-openvino = ">=1.15.0,<1.16.0"In the machine-learning/Dockerfile, change the line:
FROM openvino/ubuntu22_runtime:2023.3.0@sha256:176646df619032ea6c10faf842867119c393e7497b7f88b5e307e932a0fd5aa8 as builder-openvino (v1.105.0)
to FROM openvino/ubuntu22_runtime:2023.1.0@sha256:002842a9005ba01543b7169ff6f14ecbec82287f09c4d1dd37717f0a8e8754a7 as builder-openvino (v1.98.2)
then do a
docker build --build-arg="DEVICE=openvino" -t NAMESPACE/immich-machine-learning:v1.105.0-openvino .
jsapede
commented
6 months ago tested this morning seems to work, its scrapping !
engels0n
commented
6 months ago Nice ! Thank you @Snuupy
@alextran1502 can you merge it into the next version, please? ;)
Snuupy
commented
6 months ago Nice ! Thank you @Snuupy
@alextran1502 can you merge it into the next version, please? ;)
No because it will break openvino for other users (XE/ARC) afaik
Snuupy
commented
6 months ago I will build 105.1 soon™️
jsapede
commented
6 months ago hello, using snuppy trick did the job for facial recognition however it still stands errors on smart search :
immich_machine_learning | 2024-05-15 07:52:52.642944782 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running OpenVINO-EP-subgraph_2 node. Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgraph_2_0' Status Message: /home/onnxruntimedev/onnxruntime/onnxruntime/core/providers/openvino/ov_interface.cc:53 onnxruntime::openvino_ep::OVExeNetwork onnxruntime::openvino_ep::OVCore::LoadNetwork(const string&, std::string&, ov::AnyMap&, std::string) [OpenVINO-EP] Exception while Loading Network for graph: OpenVINOExecutionProvider_OpenVINO-EP-subgraph_2_0Check 'false' failed at src/inference/src/core.cpp:149: immich_machine_learning | invalid external data: ExternalDataInfo(data_full_path: Constant_2611_attr__value, offset: 0, data_length: 0) immich_machine_learning | immich_machine_learning | immich_machine_learning | [05/15/24 07:52:52] ERROR Exception in ASGI application immich_machine_learning | immich_machine_learning | ╭─────── Traceback (most recent call last) ───────╮ immich_machine_learning | │ /usr/src/app/main.py:118 in predict │ immich_machine_learning | │ │ immich_machine_learning | │ 115 │ │ immich_machine_learning | │ 116 │ model = await load(await model_cache. │ immich_machine_learning | │ ttl=settings.model_ttl, **kwargs)) │ immich_machine_learning | │ 117 │ model.configure(**kwargs) │ immich_machine_learning | │ ❱ 118 │ outputs = await run(model.predict, in │ immich_machine_learning | │ 119 │ return ORJSONResponse(outputs) │ immich_machine_learning | │ 120 │ immich_machine_learning | │ 121 │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/main.py:125 in run │ immich_machine_learning | │ │ immich_machine_learning | │ 122 async def run(func: Callable[..., Any], i │ immich_machine_learning | │ 123 │ if thread_pool is None: │ immich_machine_learning | │ 124 │ │ return func(inputs) │ immich_machine_learning | │ ❱ 125 │ return await asyncio.get_running_loop │ immich_machine_learning | │ 126 │ immich_machine_learning | │ 127 │ immich_machine_learning | │ 128 async def load(model: InferenceModel) -> │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/lib/python3.10/concurrent/futures/thread.p │ immich_machine_learning | │ y:58 in run │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/models/base.py:59 in predict │ immich_machine_learning | │ │ immich_machine_learning | │ 56 │ │ self.load() │ immich_machine_learning | │ 57 │ │ if model_kwargs: │ immich_machine_learning | │ 58 │ │ │ self.configure(**model_kwargs │ immich_machine_learning | │ ❱ 59 │ │ return self._predict(inputs) │ immich_machine_learning | │ 60 │ │ immich_machine_learning | │ 61 │ @abstractmethod │ immich_machine_learning | │ 62 │ def _predict(self, inputs: Any) -> An │ immich_machine_learning | │ │ immich_machine_learning | │ /usr/src/app/models/clip.py:52 in _predict │ immich_machine_learning | │ │ immich_machine_learning | │ 49 │ │ │ case Image.Image(): │ immich_machine_learning | │ 50 │ │ │ │ if self.mode == "text": │ immich_machine_learning | │ 51 │ │ │ │ │ raise TypeError("Cann │ immich_machine_learning | │ ❱ 52 │ │ │ │ outputs: NDArray[np.float │ immich_machine_learning | │ self.transform(image_or_text))[0][0] │ immich_machine_learning | │ 53 │ │ │ case str(): │ immich_machine_learning | │ 54 │ │ │ │ if self.mode == "vision": │ immich_machine_learning | │ 55 │ │ │ │ │ raise TypeError("Cann │ immich_machine_learning | │ │ immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/onnxrunt │ immich_machine_learning | │ ime/capi/onnxruntime_inference_collection.py:21 │ immich_machine_learning | │ 9 in run │ immich_machine_learning | │ │ immich_machine_learning | │ 216 │ │ if not output_names: │ immich_machine_learning | │ 217 │ │ │ output_names = [output.name f │ immich_machine_learning | │ 218 │ │ try: │ immich_machine_learning | │ ❱ 219 │ │ │ return self._sess.run(output_ │ immich_machine_learning | │ 220 │ │ except C.EPFail as err: │ immich_machine_learning | │ 221 │ │ │ if self._enable_fallback: │ immich_machine_learning | │ 222 │ │ │ │ print(f"EP Error: {str(er │ immich_machine_learning | ╰─────────────────────────────────────────────────╯ immich_machine_learning | Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero immich_machine_learning | status code returned while running immich_machine_learning | OpenVINO-EP-subgraph_2 node. immich_machine_learning | Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgrap immich_machine_learning | h_2_0' Status Message: immich_machine_learning | /home/onnxruntimedev/onnxruntime/onnxruntime/core/p immich_machine_learning | roviders/openvino/ov_interface.cc:53 immich_machine_learning | onnxruntime::openvino_ep::OVExeNetwork immich_machine_learning | onnxruntime::openvino_ep::OVCore::LoadNetwork(const immich_machine_learning | string&, std::string&, ov::AnyMap&, std::string) immich_machine_learning | [OpenVINO-EP] Exception while Loading Network for immich_machine_learning | graph: immich_machine_learning | OpenVINOExecutionProvider_OpenVINO-EP-subgraph_2_0C immich_machine_learning | heck 'false' failed at immich_machine_learning | src/inference/src/core.cpp:149: immich_machine_learning | invalid external data: immich_machine_learning | ExternalDataInfo(data_full_path: immich_machine_learning | Constant_2611_attr__value, offset: 0, data_length: immich_machine_learning | 0) immich_machine_learning | immich_machine_learning | immich_microservices | [Nest] 7 - 05/15/2024, 7:52:52 AM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): Error: Machine learning request for clip failed with status 500: Internal Server Error immich_microservices | [Nest] 7 - 05/15/2024, 7:52:52 AM ERROR [ImmichMicroservices] [JobService] Error: Machine learning request for clip failed with status 500: Internal Server Error immich_microservices | at MachineLearningRepository.predict (/usr/src/app/dist/repositories/machine-learning.repository.js:23:19) immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5) immich_microservices | at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:86:31) immich_microservices | at async /usr/src/app/dist/services/job.service.js:145:36 immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) immich_microservices | [Nest] 7 - 05/15/2024, 7:52:52 AM ERROR [ImmichMicroservices] [JobService] Object: immich_microservices | { immich_microservices | "id": "3f326640-2542-47db-b658-66543898ba07" immich_microservices | }
Snuupy
commented
6 months ago hello, using snuppy trick did the job for facial recognition however it still stands errors on smart search :
@jsapede
can you try again? made a change on Dockerfile, looks like I missed a line
jsapede
commented
6 months ago not sur if i understand how to test ^^
Snuupy
commented
6 months ago @jsapede change your docker-compose.yml under immich-machine-learning
image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}-openvino to
image: ghcr.io/snuupy/immich-machine-learning:v1.105.1-openvinook will have a test tonight
jsapede
commented
6 months ago immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | at PostgresQueryRunner.query (/usr/src/app/node_modules/typeorm/driver/postgres/PostgresQueryRunner.js:219:19) immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5) immich_microservices | at async InsertQueryBuilder.execute (/usr/src/app/node_modules/typeorm/query-builder/InsertQueryBuilder.js:106:33) immich_microservices | at async SearchRepository.upsert (/usr/src/app/dist/repositories/search.repository.js:188:9) immich_microservices | at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:91:9) immich_microservices | at async /usr/src/app/dist/services/job.service.js:145:36 immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] Object: immich_microservices | { immich_microservices | "id": "5c3721ca-a64d-4ce2-83a9-fdc15b54f424" immich_microservices | } immich_microservices | immich_postgres | 2024-05-15 11:12:14.288 UTC [65] ERROR: pgvecto.rs: The given vector is invalid for input. immich_postgres | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_postgres | 2024-05-15 11:12:14.288 UTC [65] STATEMENT: INSERT INTO "smart_search"("assetId", "embedding") VALUES ($1, '[-0.00472641,0.05822754,-0.023330688,0.029434204,-0.044403076,-0.011169434,0.051818848,0.002779007,0.04650879,0.017105103,0.010429382,0.008491516,-0.050964355,-0.0010976791,0.005554199,-0.001657486,-0.038330078,0.04360962,0.014129639,-0.043060303,0.07965088,0.020858765,0.022628784,-0.07476807,-0.017929077,-0.020385742,0.0284729,0.035064697,-0.00068330765,0.025421143,0.01612854,0.053527832,-0.0026435852,-0.0013608932,0.023208618,0.0027942657,-0.008598328,0.0051612854,0.019424438,0.084228516,0.02394104,0.022583008,-0.036010742,0.015899658,0.03225708,-0.14123535,-0.02772522,0.020217896,-0.008743286,-0.005756378,0.024810791,-0.020217896,0.020324707,0.0033988953,-0.023208618,0.033691406,-0.00749588,0.0011930466,0.01374054,-0.034851074,-0.048065186,-0.024459839,0.010444641,0.030822754,0.004508972,-0.021942139,0.033996582,0.1083374,0.0110321045,-0.035827637,-0.0021896362,-0.021728516,-0.018951416,-0.055725098,0.0055770874,-0.038513184,-0.010269165,0.0034484863,-0.012397766,-0.031219482,-0.012359619,-0.009246826,0.012069702,-0.016830444,0.033203125,-0.010108948,0.01209259,0.01386261,-0.00075006485,-0.051696777,0.010604858,-0.0041618347,-0.6850586,0.0138168335,-0.008117676,0.021026611,0.011917114,0.00022399426,-0.03262329,-0.10681152,-0.0060424805,-0.010658264,-0.008338928,0.013244629,0.020736694,0.021316528,-0.06512451,0.0107040405,0.0013160706,0.008262634,-0.029266357,-0.027328491,-0.052215576,-0.006549835,-0.018753052,0.012001038,-0.012680054,0.036834717,0.04373169,0.030593872,0.03756714,-0.031036377,-0.055908203,-0.013931274,0.017715454,-0.0008006096,-0.0012874603,0.0026359558,-0.0019264221,0.03286743,0.009559631,0.008552551,-0.0021438599,0.08544922,-0.015174866,0.027923584,0.021194458,-0.027862549,-0.017837524,-0.006389618,0.012397766,0.013069153,-0.0010528564,0.013214111,-0.022491455,-0.005317688,-0.053619385,0.025314331,-0.019546509,0.010269165,0.020004272,0.016921997,0.045654297,-0.042816162,0.031036377,-0.011940002,0.0023460388,-0.03842163,0.059051514,-0.010475159,-0.054382324,-0.035308838,-0.0024986267,0.023513794,-0.006137848,0.0022735596,0.044830322,0.03591919,0.002620697,-0.0032539368,0.0015945435,0.012001038,-0.03353882,-0.021606445,-0.0069999695,-0.015075684,-0.009689331,0.0038013458,-0.07434082,-0.0012989044,0.02355957,-0.0033931732,-0.03579712,0.000954628,0.012786865,-0.024765015,0.008148193,0.012756348,-0.0028266907,-0.012832642,-0.0060768127,-0.014297485,0.029891968,-0.00020158291,-0.078552246,-0.031280518,0.042938232,-0.008842468,0.010803223,-0.01586914,0.002986908,-0.011833191,0.0115356445,0.014930725,0.0009407997,-0.0018815994,-0.04168701,0.01701355,-0.018630981,0.05105591,-0.08270264,0.023925781,0.034301758,0.029724121,0.00020718575,-0.024505615,0.025131226,0.014511108,0.06695557,-0.013328552,-0.021224976,0.07763672,-0.0725708,-0.041625977,-0.0005373955,0.03286743,-0.037963867,0.0001399517,-0.0026435852,0.016723633,-0.05517578,-0.021636963,-0.00907135,-0.03503418,-0.028213501,-0.008598328,-0.017425537,-0.018096924,-0.018005371,-0.0021629333,0.022979736,-0.026779175,0.022659302,-0.030410767,-0.033813477,-0.023254395,0.008255005,0.016021729,-0.03201294,-0.011703491,-0.039520264,0.013671875,0.042907715,-0.0026798248,-0.028137207,0.013076782,-0.05496216,-0.01776123,0.16101074,0.03363037,-0.029525757,0.013900757,0.015037537,0.010681152,-0.015991211,-0.045562744,-0.011459351,0.04196167,0.014511108,-0.03967285,0.005039215,0.018447876,-0.03213501,-0.00409317,0.017608643,0.018585205,0.008155823,-0.015670776,-0.051330566,-0.020141602,0.015899658,-0.0003080368,-0.07092285,0.00058317184,0.006713867,0.022232056,-0.11553955,-0.01423645,-0.032928467,-0.026748657,-0.0037460327,-0.0034217834,-0.025939941,0.020507812,-0.030227661,0.006690979,0.1005249,-0.015808105,0.010948181,-0.0008792877,-0.011711121,0.0023117065,-0.01737976,0.0038661957,0.021224976,-0.0044136047,-0.022323608,-0.00491333,0.021148682,0.04019165,0.004234314,-0.015014648,0.08526611,-0.010047913,0.016784668,0.0048713684,0.027999878,0.055999756,-0.009315491,-0.08337402,0.021362305,0.16870117,-0.009353638,-0.038208008,0.016937256,-0.002922058,0.025909424,-0.0046691895,-0.036987305,0.032104492,-0.015541077,0.0109939575,-0.0032138824,-0.03878784,-0.03414917,-0.0054359436,0.009765625,0.035308838,0.027374268,0.010604858,-0.023605347,-0.031677246,-0.031173706,-0.007675171,-0.024642944,0.0065727234,-0.022827148,-0.008056641,-0.012542725,0.0049591064,0.028625488,-0.0032634735,0.07757568,-0.0029850006,-0.016555786,-0.03186035,-0.020828247,-0.066589355,0.015060425,0.011962891,-0.022323608,-0.017425537,-0.014533997,-0.027542114,-0.09362793,-0.02532959,0.0031356812,-0.066589355,0.0008621216,0.014961243,-0.028930664,0.010726929,0.019302368,-0.02947998,-0.013313293,0.012924194,0.029342651,-0.01725769,-0.0062828064,0.011276245,0.0011253357,-0.008880615,-0.025482178,0.0018310547,0.019363403,0.018859863,0.032287598,0.014457703,-0.039276123,0.105773926,-0.09094238,-0.03074646,0.02015686,-0.008270264,-0.0034713745,-0.008834839,0.05670166,0.027496338,-0.038085938,0.014419556,0.012298584,-0.036193848,0.0054016113,0.012512207,0.002822876,-0.0039863586,-0.0034999847,-0.0034828186,0.070251465,-0.042541504,-0.026657104,0.00856781,-0.0009965897,0.016174316,-0.034423828,0.018356323,0.004737854,-0.008659363,-0.042114258,0.0039749146,-0.019195557,-0.014984131,0.0049095154,-0.04345703,-0.0115356445,-0.004966736,-0.035217285,-0.021820068,-0.017868042,0.03717041,0.024307251,0.033447266,0.04650879,-0.002603531,-0.0231781,0.0026435852,-0.018325806,0.015289307,-0.019256592,-0.008934021,0.004043579,0.009521484,-0.0023288727,-0.031829834,-0.036193848,0.02017212,0.011253357,-0.024719238,-0.006023407,0.0030002594,-0.043792725,-0.023361206,-0.014152527,0.0020446777,0.026657104,-0.006462097,0.011756897,-0.019943237,-0.004043579,0.013961792,0.010429382,0.013305664,-0.045959473,-0.020599365,0.03186035,0.014312744,0.00774765,0.0019035339,-0.03942871,0.0012712479,0.019638062,0.009353638,0.015556335,-0.029830933,0.025390625,-0.053131104,-0.0037784576,-0.024429321,0.010353088,0.0088272095,0.008308411,-0.024505615,0.024719238,0.044830322,0.019927979,-0.015991211,0.011962891,0.01448822,0.0059127808,-0.02658081,0.011604309,-0.0008454323,-0.00381279,-0.07067871,-0.006931305,-0.02557373,0.008361816,0.016799927,-0.025466919,-0.011802673,0.025024414,-0.0066566467,-0.0046844482,0.004814148,0.027282715,-0.010559082,0.020477295,-0.009292603,-0.005634308,0.103759766,-0.037750244,-0.014671326]') ON CONFLICT ( "assetId" ) DO UPDATE SET "assetId" = EXCLUDED."assetId", "embedding" = EXCLUDED."embedding" immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | at PostgresQueryRunner.query (/usr/src/app/node_modules/typeorm/driver/postgres/PostgresQueryRunner.js:219:19) immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5) immich_microservices | at async InsertQueryBuilder.execute (/usr/src/app/node_modules/typeorm/query-builder/InsertQueryBuilder.js:106:33) immich_microservices | at async SearchRepository.upsert (/usr/src/app/dist/repositories/search.repository.js:188:9) immich_microservices | at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:91:9) immich_microservices | at async /usr/src/app/dist/services/job.service.js:145:36 immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) immich_microservices | [Nest] 7 - 05/15/2024, 11:12:14 AM ERROR [ImmichMicroservices] [JobService] Object: immich_microservices | { immich_microservices | "id": "4ee62067-827d-4a1e-aa0c-be0ec78e4833" immich_microservices | } immich_microservices | immich_postgres | 2024-05-15 11:12:14.368 UTC [65] ERROR: pgvecto.rs: The given vector is invalid for input. immich_postgres | ADVICE: Check if dimensions and scalar type of the vector is matched with the index.
jsapede
commented
6 months ago with ViT-B-32__openai
facial recognition works
Snuupy
commented
6 months ago after changing the line in docker-compose.yml did you docker compose down && docker compose up -d?
my logs show no errors upon uploading.
jsapede
commented
6 months ago yes, i did it, dont know where the error is coming form
looks like this : https://github.com/immich-app/immich/discussions/7381
but there seems to be no error on permissions in database.
Snuupy
commented
6 months ago does that happen on a new/separate instance of immich?
jsapede
commented
6 months ago looks better like this : https://github.com/immich-app/immich/discussions/7425 trying to figure how to re-compute search index
jsapede
commented
6 months ago does that happen on a new/separate instance of immich?
https://github.com/immich-app/immich/discussions/7425#discussioncomment-9446357
seems solved !
ilijamt
commented
6 months ago Works with ghcr.io/snuupy/immich-machine-learning:v1.105.1-openvino for me.
LeoAdL
commented
6 months ago It fixes the face detection (thank you @Snuupy!), but then the smart search is not working (while it is the reverse with the current open-vino release).
Snuupy
commented
6 months ago @LeoAdL
It fixes the face detection (thank you @Snuupy!), but then the smart search is not working (while it is the reverse with the current open-vino release).
can you check if it's this issue? https://github.com/immich-app/immich/discussions/7425#discussioncomment-9446151
LeoAdL
commented
6 months ago I am trying to rerun again, but for the moment I get unrelated results and the following error message:
immich_server | [Nest] 7 - 05/19/2024, 5:57:56 PM WARN [ImmichServer] [ExpressAdapter] Content-Type doesn't match Reply body, you might need a custom ExceptionFilter for non-JSON responsesLook at my logs upper in the post to see if its the same pb
LeoAdL
commented
6 months ago I do not get any error messages anymore, but the results are non-relevant as in pre 1.99.0. It works perfectly fine with the CPU loading the same model.
Snuupy
commented
6 months ago I do not get any error messages anymore, but the results are non-relevant as in pre 1.99.0. It works perfectly fine with the CPU loading the same model.
try again with a new instance, does this still happen? you don't have to delete your data but tell me if it works on a fresh instance.
also pretty sure this is an openvino (upstream) issue not an immich issue
jsapede
commented
6 months ago I do not get any error messages anymore, but the results are non-relevant as in pre 1.99.0. It works perfectly fine with the CPU loading the same model.
I'll have a try tomorrow with CPU as i've also observed irrevelency
Although some searches looks cohérent
Snuupy
commented
6 months ago you may want to try a better model, check on discord what people are using but I haven't done much research into it
the default one iirc is not that great but I think it was done because not everyone has beefy hardware
mertalev
commented
6 months ago If it worked well on CPU, but not on OpenVINO, then it indicates a bug in how the model was compiled to OpenVINO format.
jsapede
commented
6 months ago ok, tried this morning with another CLIP multilingual model :
https://huggingface.co/immich-app/nllb-clip-large-siglip__v1
got the following errors :
`immich_machine_learning | 2024-05-20 07:06:14.817966319 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running OpenVINO-EP-subgraph_3 node. Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgraph_3_0' Status Message: /home/onnxruntimedev/onnxruntime/onnxruntime/core/providers/openvino/ov_interface.cc:53 onnxruntime::openvino_ep::OVExeNetwork onnxruntime::openvino_ep::OVCore::LoadNetwork(const string&, std::string&, ov::AnyMap&, std::string) [OpenVINO-EP] Exception while Loading Network for graph: OpenVINOExecutionProvider_OpenVINO-EP-subgraph_3_0Check 'false' failed at src/inference/src/core.cpp:149:
immich_machine_learning | invalid external data: ExternalDataInfo(data_full_path: Constant_1863_attr__value, offset: 0, data_length: 0)
immich_machine_learning |
immich_machine_learning |
immich_machine_learning | [05/20/24 07:06:14] ERROR Exception in ASGI application
immich_machine_learning |
immich_machine_learning | ╭─────── Traceback (most recent call last) ───────╮
immich_machine_learning | │ /usr/src/app/main.py:118 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 115 │ │
immich_machine_learning | │ 116 │ model = await load(await model_cache. │
immich_machine_learning | │ ttl=settings.model_ttl, kwargs)) │
immich_machine_learning | │ 117 │ model.configure(kwargs) │
immich_microservices | [Nest] 7 - 05/20/2024, 7:06:14 AM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): Error: Machine learning request for clip failed with status 500: Internal Server Error
immich_machine_learning | │ ❱ 118 │ outputs = await run(model.predict, in │
immich_microservices | [Nest] 7 - 05/20/2024, 7:06:14 AM ERROR [ImmichMicroservices] [JobService] Error: Machine learning request for clip failed with status 500: Internal Server Error
immich_microservices | at MachineLearningRepository.predict (/usr/src/app/dist/repositories/machine-learning.repository.js:23:19)
immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
immich_machine_learning | │ 119 │ return ORJSONResponse(outputs) │
immich_microservices | at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:86:31)
immich_machine_learning | │ 120 │
immich_microservices | at async /usr/src/app/dist/services/job.service.js:145:36
immich_machine_learning | │ 121 │
immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28)
immich_machine_learning | │ │
immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24)
immich_machine_learning | │ /usr/src/app/main.py:125 in run │
immich_microservices | [Nest] 7 - 05/20/2024, 7:06:14 AM ERROR [ImmichMicroservices] [JobService] Object:
immich_machine_learning | │ │
immich_microservices | {
immich_machine_learning | │ 122 async def run(func: Callable[..., Any], i │
immich_microservices | "id": "aeffa9af-b9af-4bba-9a62-eb939a1b5a28"
immich_machine_learning | │ 123 │ if thread_pool is None: │
immich_machine_learning | │ 124 │ │ return func(inputs) │
immich_microservices | }
immich_machine_learning | │ ❱ 125 │ return await asyncio.get_running_loop │
immich_microservices |
immich_machine_learning | │ 126 │
immich_machine_learning | │ 127 │
immich_machine_learning | │ 128 async def load(model: InferenceModel) -> │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/lib/python3.10/concurrent/futures/thread.p │
immich_machine_learning | │ y:58 in run │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/base.py:59 in predict │
immich_machine_learning | │ │
immich_machine_learning | │ 56 │ │ self.load() │
immich_machine_learning | │ 57 │ │ if model_kwargs: │
immich_machine_learning | │ 58 │ │ │ self.configure(**model_kwargs │
immich_machine_learning | │ ❱ 59 │ │ return self._predict(inputs) │
immich_machine_learning | │ 60 │ │
immich_machine_learning | │ 61 │ @abstractmethod │
immich_machine_learning | │ 62 │ def _predict(self, inputs: Any) -> An │
immich_machine_learning | │ │
immich_machine_learning | │ /usr/src/app/models/clip.py:52 in _predict │
immich_machine_learning | │ │
immich_machine_learning | │ 49 │ │ │ case Image.Image(): │
immich_machine_learning | │ 50 │ │ │ │ if self.mode == "text": │
immich_machine_learning | │ 51 │ │ │ │ │ raise TypeError("Cann │
immich_machine_learning | │ ❱ 52 │ │ │ │ outputs: NDArray[np.float │
immich_machine_learning | │ self.transform(image_or_text))[0][0] │
immich_machine_learning | │ 53 │ │ │ case str(): │
immich_machine_learning | │ 54 │ │ │ │ if self.mode == "vision": │
immich_machine_learning | │ 55 │ │ │ │ │ raise TypeError("Cann │
immich_machine_learning | │ │
immich_machine_learning | │ /opt/venv/lib/python3.10/site-packages/onnxrunt │
immich_machine_learning | │ ime/capi/onnxruntime_inference_collection.py:21 │
immich_machine_learning | │ 9 in run │
immich_machine_learning | │ │
immich_machine_learning | │ 216 │ │ if not output_names: │
immich_machine_learning | │ 217 │ │ │ output_names = [output.name f │
immich_machine_learning | │ 218 │ │ try: │
immich_machine_learning | │ ❱ 219 │ │ │ return self.sess.run(output │
immich_machine_learning | │ 220 │ │ except C.EPFail as err: │
immich_machine_learning | │ 221 │ │ │ if self._enable_fallback: │
immich_machine_learning | │ 222 │ │ │ │ print(f"EP Error: {str(er │
immich_machine_learning | ╰─────────────────────────────────────────────────╯
immich_machine_learning | Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero
immich_machine_learning | status code returned while running
immich_machine_learning | OpenVINO-EP-subgraph_3 node.
immich_machine_learning | Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgrap
immich_machine_learning | h_3_0' Status Message:
immich_machine_learning | /home/onnxruntimedev/onnxruntime/onnxruntime/core/p
immich_machine_learning | roviders/openvino/ov_interface.cc:53
immich_machine_learning | onnxruntime::openvino_ep::OVExeNetwork
immich_machine_learning | onnxruntime::openvino_ep::OVCore::LoadNetwork(const
immich_machine_learning | string&, std::string&, ov::AnyMap&, std::string)
immich_machine_learning | [OpenVINO-EP] Exception while Loading Network for
immich_machine_learning | graph:
immich_machine_learning | OpenVINOExecutionProvider_OpenVINO-EP-subgraph_3_0C
immich_machine_learning | heck 'false' failed at
immich_machine_learning | src/inference/src/core.cpp:149:
immich_machine_learning | invalid external data:
immich_machine_learning | ExternalDataInfo(data_full_path:
immich_machine_learning | Constant_1863_attr__value, offset: 0, data_length:
immich_machine_learning | 0)
immich_machine_learning |
immich_machine_learning |
immich_machine_learning | mimalloc: warning: mi_free: pointer might not point to a valid heap region: 0x75043c020000
immich_machine_learning | (this may still be a valid very large allocation (over 64MiB))
immich_machine_learning | mimalloc: warning: (yes, the previous pointer 0x75043c020000 was valid after all)
immich_machine_learning | mimalloc: warning: mi_usable_size: pointer might not point to a valid heap region: 0x75043c020000
immich_machine_learning | (this may still be a valid very large allocation (over 64MiB))
immich_machine_learning | mimalloc: warning: (yes, the previous pointer 0x75043c020000 was valid after all)
switching to CPU version generates index errors :
immich_postgres | 2024-05-20 07:12:39.522 UTC [58] ERROR: pgvecto.rs: The given vector is invalid for input. immich_postgres | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_postgres | 2024-05-20 07:12:39.522 UTC [58] STATEMENT: INSERT INTO "smart_search"("assetId", "embedding") VALUES ($1, 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ON CONFLICT ( "assetId" ) DO UPDATE SET "assetId" = EXCLUDED."assetId", "embedding" = EXCLUDED."embedding" immich_microservices | [Nest] 6 - 05/20/2024, 7:12:39 AM ERROR [ImmichMicroservices] [JobService] Unable to run job handler (smartSearch/smart-search): QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | [Nest] 6 - 05/20/2024, 7:12:39 AM ERROR [ImmichMicroservices] [JobService] QueryFailedError: pgvecto.rs: The given vector is invalid for input. immich_microservices | ADVICE: Check if dimensions and scalar type of the vector is matched with the index. immich_microservices | at PostgresQueryRunner.query (/usr/src/app/node_modules/typeorm/driver/postgres/PostgresQueryRunner.js:219:19) immich_microservices | at process.processTicksAndRejections (node:internal/process/task_queues:95:5) immich_microservices | at async InsertQueryBuilder.execute (/usr/src/app/node_modules/typeorm/query-builder/InsertQueryBuilder.js:106:33) immich_microservices | at async SearchRepository.upsert (/usr/src/app/dist/repositories/search.repository.js:188:9) immich_microservices | at async SmartInfoService.handleEncodeClip (/usr/src/app/dist/services/smart-info.service.js:91:9) immich_microservices | at async /usr/src/app/dist/services/job.service.js:145:36 immich_microservices | at async Worker.processJob (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:394:28) immich_microservices | at async Worker.retryIfFailed (/usr/src/app/node_modules/bullmq/dist/cjs/classes/worker.js:581:24) immich_microservices | [Nest] 6 - 05/20/2024, 7:12:39 AM ERROR [ImmichMicroservices] [JobService] Object: immich_microservices | { immich_microservices | "id": "17874952-3342-437e-b0f2-ff401bbfe0d5" immich_microservices | } immich_microservices |
even applying https://github.com/immich-app/immich/discussions/7425#discussioncomment-9446151 trick
[EDIT] relaunched smart search CPU on "ALL" seems to compute now ... these machine learnings settings / are quite obscure especially if yuo dont have eye on logs to see if it works
[EDIT 2] CPU version of the https://huggingface.co/immich-app/nllb-clip-large-siglip__v1 works and gives reliable results however OpenVino version still returns errors as on top of this post.
jsapede
commented
6 months ago following the tests, rebuild immich instance from zero, using the simplest model : immich-app/RN50__openai
build immich with CPU smart search and it works and give reliable results. Then rebuilt immich with snuupy openvino and it also works and alos give reliable results
Done the same tests with anorther intermediate CLIP model : LABSE-Vit-L-14
Tried openVino First but failed (error) then switched to CPU and it works, and give reliable results
looks like some models are not available with openvino !
The bug
When I try launching the face detection, whatever model I use, I get the following error:
Regular Smart Search proceeds without issue.
The OS that Immich Server is running on
Proxmox 8.1 (6.5 Linux Kernel)
Version of Immich Server
1.99.0
Version of Immich Mobile App
1.99.0
Platform with the issue
Your docker-compose.yml content
Your .env content
Reproduction steps
Additional information
My processor is an Intel N100. Previously to 1.99.0, the face detection was working, but I had issues with the smart search, so I guess it's hard to get all of it with OpenVino haha.
Thank you for the gigantic work up to now! Leo