diff --git a/runtimes/huggingface/mlserver_huggingface/codecs/__init__.py b/runtimes/huggingface/mlserver_huggingface/codecs/__init__.py index 4a1d67025..c4004d3d6 100644 --- a/runtimes/huggingface/mlserver_huggingface/codecs/__init__.py +++ b/runtimes/huggingface/mlserver_huggingface/codecs/__init__.py @@ -6,6 +6,7 @@ from .conversation import HuggingfaceConversationCodec from .raw import RawCodec from .utils import EqualUtil +from .chariot import ChariotImgModelOutputCodec __all__ = [ "MultiInputRequestCodec", @@ -14,6 +15,7 @@ "HuggingfaceSingleJSONCodec", "HuggingfaceListJSONCodec", "HuggingfaceConversationCodec", + "ChariotImgModelOutputCodec", "NumpyListCodec", "RawCodec", "EqualUtil", diff --git a/runtimes/huggingface/mlserver_huggingface/codecs/chariot.py b/runtimes/huggingface/mlserver_huggingface/codecs/chariot.py new file mode 100644 index 000000000..3bbb1bfc8 --- /dev/null +++ b/runtimes/huggingface/mlserver_huggingface/codecs/chariot.py @@ -0,0 +1,113 @@ +import numpy as np +from mlserver.codecs.lists import is_list_of +import json + + +def get_det_dict_from_hf_obj_detect(obj_detect): + """Convert hf object detection output to standard chariot object detection output""" + det_dict = { + "num_detections": 0, + "detection_classes": [], + "detection_boxes": [], + "detection_scores": [], + } + for det in obj_detect: + conf, cls = det["score"], det["label"] + y1, x1, y2, x2 = ( + det["box"]["ymin"], + det["box"]["xmin"], + det["box"]["ymax"], + det["box"]["xmax"], + ) + det_dict["num_detections"] += 1 + det_dict["detection_classes"].append(cls) + det_dict["detection_scores"].append(conf) + det_dict["detection_boxes"].append([y1, x1, y2, x2]) + return det_dict + + +def get_chariot_seg_mask_from_hf_seg_output(seg_pred, class_int_to_str): + """Convert hf segmentation output to standard chariot segmentation output""" + mask_shape = np.array(seg_pred[0]["mask"]).shape + class_str_to_int = {v: k for k, v in class_int_to_str.items()} + # Create an empty mask + combined_mask = np.full(mask_shape, None) + for i in seg_pred: + # Convert mask from PIL image to numpy array + mask = np.array(i["mask"]) + class_str = i["label"] + class_int = class_str_to_int[class_str] + combined_mask[np.where(mask > 0)] = class_int + predictions = combined_mask.tolist() + return predictions + + +class ChariotImgModelOutputCodec: + """Encoder that converts HF model output to the standard Chariot model output""" + + @classmethod + def encode_output( + cls, predictions, task_type, class_int_to_str, predict_proba=False + ): + if is_list_of(predictions, dict): + predictions = [predictions] + if task_type == "image-classification": + + if predict_proba: + # class_int_to_str: {0:"Egyptian cat", + # 1:"tabby, tabby cat", + # 2:"tiger cat"} + # convert HF output: [[{"label": "tabby, tabby cat", "score": 0.94}, + # {"label": "tiger cat", "score"': 0.04}, + # {"label": "Egyptian cat", "score": 0.02}]] + # to standard Chariot probability output: [[0.02,0.94,0.04]] + # The probability scores are ordered by class id + num_labels = len(class_int_to_str) + class_to_proba = [ + {d["label"]: d["score"] for d in p} for p in predictions + ] + predictions = [ + [d.get(class_int_to_str[i]) for i in range(num_labels)] + for d in class_to_proba + ] + else: + # get Top-1 predicted class + # convert HF output: [[{"label": "tabby, tabby cat", "score": 0.94}, + # {"label": "tiger cat", "score": 0.04}, + # {"label": "Egyptian cat", "score": 0.02}]] + # to standard Chariot output: ['"tabby, tabby cat"'] + predictions = [json.dumps(p[0]["label"]) for p in predictions] + elif task_type == "object-detection": + + # convert HF output: [[{"score": 0.9897010326385498, + # "label": 'cat', + # "box": {"xmin": 53, "ymin": 313, + # "xmax": 697, "ymax": 986}}, + # {"score": 0.9896764159202576, + # "label": "cat", + # "box": {"xmin": 974, "ymin": 221, + # "xmax": 1526, "ymax": 1071}}]] + + # to standard Chariot output: [{"num_detections":2, + # "detection_classes":["cat","cat"], + # "detection_scores":[0.9897010326385498,0.9896764159202576], + # "detection_boxes":[[313,53,986,697], + # [221,974,1071,1562]]}] + predictions = [get_det_dict_from_hf_obj_detect(p) for p in predictions] + + elif task_type == "image-segmentation": + + # convert HF output: [[{"score": None, + # "label": "wall", + # "mask": }, + # {"score": None, + # "label": "floor", + # "mask": }]] + # to standard Chariot output: [[[0,0,...,0],...,[0,0,0,...,0]]] + # 2d array with size of the original image. Each pixel is a class int + # Background uses class_int 0 + predictions = [ + get_chariot_seg_mask_from_hf_seg_output(p, class_int_to_str) + for p in predictions + ] + return predictions diff --git a/runtimes/huggingface/mlserver_huggingface/common.py b/runtimes/huggingface/mlserver_huggingface/common.py index e53d7f6e2..67ca70e78 100644 --- a/runtimes/huggingface/mlserver_huggingface/common.py +++ b/runtimes/huggingface/mlserver_huggingface/common.py @@ -33,8 +33,6 @@ def load_pipeline_from_settings( if not model: model = settings.parameters.uri # type: ignore tokenizer = hf_settings.pretrained_tokenizer - if not tokenizer: - tokenizer = hf_settings.pretrained_model if hf_settings.framework == "tf": if hf_settings.inter_op_threads is not None: tf.config.threading.set_inter_op_parallelism_threads( @@ -49,7 +47,8 @@ def load_pipeline_from_settings( torch.set_num_interop_threads(hf_settings.inter_op_threads) if hf_settings.intra_op_threads is not None: torch.set_num_threads(hf_settings.intra_op_threads) - + # If no tokenizer is provided in the config + # HF pipeline would automatically load tokenizer from model directory hf_pipeline = pipeline( hf_settings.task_name, model=model, @@ -63,15 +62,17 @@ def load_pipeline_from_settings( # If max_batch_size > 1 we need to ensure tokens are padded if settings.max_batch_size > 1: model = hf_pipeline.model - if not hf_pipeline.tokenizer.pad_token_id: - eos_token_id = model.config.eos_token_id # type: ignore - if eos_token_id: - hf_pipeline.tokenizer.pad_token_id = [str(eos_token_id)] # type: ignore - else: - logger.warning( - "Model has neither pad_token or eos_token, setting batch size to 1" - ) - hf_pipeline._batch_size = 1 + if hf_pipeline.tokenizer is not None: + if not hf_pipeline.tokenizer.pad_token_id: + eos_token_id = model.config.eos_token_id # type: ignore + if eos_token_id: + hf_pipeline.tokenizer.pad_token_id = [str(eos_token_id)] + else: + logger.warning( + "Model has neither pad_token or eos_token, \ + setting batch size to 1" + ) + hf_pipeline._batch_size = 1 return hf_pipeline diff --git a/runtimes/huggingface/mlserver_huggingface/runtime.py b/runtimes/huggingface/mlserver_huggingface/runtime.py index c21d4c141..9d3094d10 100644 --- a/runtimes/huggingface/mlserver_huggingface/runtime.py +++ b/runtimes/huggingface/mlserver_huggingface/runtime.py @@ -1,6 +1,6 @@ import asyncio import torch - +from typing import Any from mlserver.model import MLModel from mlserver.settings import ModelSettings from mlserver.logging import logger @@ -11,9 +11,15 @@ from .settings import get_huggingface_settings from .common import load_pipeline_from_settings -from .codecs import HuggingfaceRequestCodec +from .codecs import HuggingfaceRequestCodec, ChariotImgModelOutputCodec from .metadata import METADATA +CHARIOT_IMAGE_TASK = [ + "image-classification", + "image-segmentation", + "object-detection", +] + class HuggingFaceRuntime(MLModel): """Runtime class for specific Huggingface models""" @@ -40,15 +46,45 @@ async def predict(self, payload: InferenceRequest) -> InferenceResponse: # TODO: convert and validate? kwargs = HuggingfaceRequestCodec.decode_request(payload) args = kwargs.pop("args", []) - array_inputs = kwargs.pop("array_inputs", []) if array_inputs: args = [list(array_inputs)] + args - prediction = self._model(*args, **kwargs) - - return self.encode_response( - payload=prediction, default_codec=HuggingfaceRequestCodec + predict_proba, predict_proba_kwargs = self.get_predict_proba_kwargs(payload) + predictions = self._model(*args, **kwargs, **predict_proba_kwargs) + if self.hf_settings.task in CHARIOT_IMAGE_TASK: + predictions = ChariotImgModelOutputCodec.encode_output( + predictions, + task_type=self.hf_settings.task, + class_int_to_str=self._model.model.config.id2label, + predict_proba=predict_proba, + ) + response = self.encode_response( + payload=predictions, default_codec=HuggingfaceRequestCodec ) + return response + + def get_predict_proba_kwargs( + self, payload: InferenceRequest + ) -> tuple[bool, dict[str, Any]]: + actions = { + ( + getattr(request_input.parameters, "action", "predict") + if request_input.parameters + else "predict" + ) + for request_input in payload.inputs + } + if len(actions) > 1: + raise ValueError( + f"If processing a batch all 'actions' must be the same \ + but got 'actions': {actions}" + ) + action = actions.pop() + predict_proba = action == "predict_proba" + predict_proba_kwargs = dict() + if predict_proba and self.hf_settings.task == "image-classification": + predict_proba_kwargs["top_k"] = self._model.model.config.num_labels + return predict_proba, predict_proba_kwargs async def unload(self) -> bool: # TODO: Free up Tensorflow's GPU memory diff --git a/runtimes/huggingface/mlserver_huggingface/version.py b/runtimes/huggingface/mlserver_huggingface/version.py index 8cb37b588..9d85eb132 100644 --- a/runtimes/huggingface/mlserver_huggingface/version.py +++ b/runtimes/huggingface/mlserver_huggingface/version.py @@ -1 +1 @@ -__version__ = "2.0.8" +__version__ = "2.0.11" diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index 7ffbfcb93..ba8c161c5 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -339,6 +339,10 @@ files = [ {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:a37b8f0391212d29b3a91a799c8e4a2855e0576911cdfb2515487e30e322253d"}, {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e84799f09591700a4154154cab9787452925578841a94321d5ee8fb9a9a328f0"}, {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f66b5337fa213f1da0d9000bc8dc0cb5b896b726eefd9c6046f699b169c41b9e"}, + {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5dab0844f2cf82be357a0eb11a9087f70c5430b2c241493fc122bb6f2bb0917c"}, + {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e4fe605b917c70283db7dfe5ada75e04561479075761a0b3866c081d035b01c1"}, + {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:1e9a65b5736232e7a7f91ff3d02277f11d339bf34099a56cdab6a8b3410a02b2"}, + {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:58d4b711689366d4a03ac7957ab8c28890415e267f9b6589969e74b6e42225ec"}, {file = "Brotli-1.1.0-cp310-cp310-win32.whl", hash = "sha256:be36e3d172dc816333f33520154d708a2657ea63762ec16b62ece02ab5e4daf2"}, {file = "Brotli-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:0c6244521dda65ea562d5a69b9a26120769b7a9fb3db2fe9545935ed6735b128"}, {file = "Brotli-1.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a3daabb76a78f829cafc365531c972016e4aa8d5b4bf60660ad8ecee19df7ccc"}, @@ -351,8 +355,14 @@ files = [ {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:19c116e796420b0cee3da1ccec3b764ed2952ccfcc298b55a10e5610ad7885f9"}, {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:510b5b1bfbe20e1a7b3baf5fed9e9451873559a976c1a78eebaa3b86c57b4265"}, {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:a1fd8a29719ccce974d523580987b7f8229aeace506952fa9ce1d53a033873c8"}, + {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c247dd99d39e0338a604f8c2b3bc7061d5c2e9e2ac7ba9cc1be5a69cb6cd832f"}, + {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:1b2c248cd517c222d89e74669a4adfa5577e06ab68771a529060cf5a156e9757"}, + {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:2a24c50840d89ded6c9a8fdc7b6ed3692ed4e86f1c4a4a938e1e92def92933e0"}, + {file = "Brotli-1.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f31859074d57b4639318523d6ffdca586ace54271a73ad23ad021acd807eb14b"}, {file = "Brotli-1.1.0-cp311-cp311-win32.whl", hash = "sha256:39da8adedf6942d76dc3e46653e52df937a3c4d6d18fdc94a7c29d263b1f5b50"}, {file = "Brotli-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:aac0411d20e345dc0920bdec5548e438e999ff68d77564d5e9463a7ca9d3e7b1"}, + {file = "Brotli-1.1.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:32d95b80260d79926f5fab3c41701dbb818fde1c9da590e77e571eefd14abe28"}, + {file = "Brotli-1.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b760c65308ff1e462f65d69c12e4ae085cff3b332d894637f6273a12a482d09f"}, {file = "Brotli-1.1.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:316cc9b17edf613ac76b1f1f305d2a748f1b976b033b049a6ecdfd5612c70409"}, {file = "Brotli-1.1.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:caf9ee9a5775f3111642d33b86237b05808dafcd6268faa492250e9b78046eb2"}, {file = "Brotli-1.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:70051525001750221daa10907c77830bc889cb6d865cc0b813d9db7fefc21451"}, @@ -363,8 +373,24 @@ files = [ {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:4093c631e96fdd49e0377a9c167bfd75b6d0bad2ace734c6eb20b348bc3ea180"}, {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:7e4c4629ddad63006efa0ef968c8e4751c5868ff0b1c5c40f76524e894c50248"}, {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:861bf317735688269936f755fa136a99d1ed526883859f86e41a5d43c61d8966"}, + {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:87a3044c3a35055527ac75e419dfa9f4f3667a1e887ee80360589eb8c90aabb9"}, + {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:c5529b34c1c9d937168297f2c1fde7ebe9ebdd5e121297ff9c043bdb2ae3d6fb"}, + {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:ca63e1890ede90b2e4454f9a65135a4d387a4585ff8282bb72964fab893f2111"}, + {file = "Brotli-1.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e79e6520141d792237c70bcd7a3b122d00f2613769ae0cb61c52e89fd3443839"}, {file = "Brotli-1.1.0-cp312-cp312-win32.whl", hash = "sha256:5f4d5ea15c9382135076d2fb28dde923352fe02951e66935a9efaac8f10e81b0"}, {file = "Brotli-1.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:906bc3a79de8c4ae5b86d3d75a8b77e44404b0f4261714306e3ad248d8ab0951"}, + {file = "Brotli-1.1.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8bf32b98b75c13ec7cf774164172683d6e7891088f6316e54425fde1efc276d5"}, + {file = "Brotli-1.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7bc37c4d6b87fb1017ea28c9508b36bbcb0c3d18b4260fcdf08b200c74a6aee8"}, + {file = "Brotli-1.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3c0ef38c7a7014ffac184db9e04debe495d317cc9c6fb10071f7fefd93100a4f"}, + {file = "Brotli-1.1.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:91d7cc2a76b5567591d12c01f019dd7afce6ba8cba6571187e21e2fc418ae648"}, + {file = "Brotli-1.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a93dde851926f4f2678e704fadeb39e16c35d8baebd5252c9fd94ce8ce68c4a0"}, + {file = "Brotli-1.1.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f0db75f47be8b8abc8d9e31bc7aad0547ca26f24a54e6fd10231d623f183d089"}, + {file = "Brotli-1.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6967ced6730aed543b8673008b5a391c3b1076d834ca438bbd70635c73775368"}, + {file = "Brotli-1.1.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:7eedaa5d036d9336c95915035fb57422054014ebdeb6f3b42eac809928e40d0c"}, + {file = "Brotli-1.1.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:d487f5432bf35b60ed625d7e1b448e2dc855422e87469e3f450aa5552b0eb284"}, + {file = "Brotli-1.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:832436e59afb93e1836081a20f324cb185836c617659b07b129141a8426973c7"}, + {file = "Brotli-1.1.0-cp313-cp313-win32.whl", hash = "sha256:43395e90523f9c23a3d5bdf004733246fba087f2948f87ab28015f12359ca6a0"}, + {file = "Brotli-1.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:9011560a466d2eb3f5a6e4929cf4a09be405c64154e12df0dd72713f6500e32b"}, {file = "Brotli-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:a090ca607cbb6a34b0391776f0cb48062081f5f60ddcce5d11838e67a01928d1"}, {file = "Brotli-1.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2de9d02f5bda03d27ede52e8cfe7b865b066fa49258cbab568720aa5be80a47d"}, {file = "Brotli-1.1.0-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2333e30a5e00fe0fe55903c8832e08ee9c3b1382aacf4db26664a16528d51b4b"}, @@ -374,6 +400,10 @@ files = [ {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:fd5f17ff8f14003595ab414e45fce13d073e0762394f957182e69035c9f3d7c2"}, {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_1_ppc64le.whl", hash = "sha256:069a121ac97412d1fe506da790b3e69f52254b9df4eb665cd42460c837193354"}, {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:e93dfc1a1165e385cc8239fab7c036fb2cd8093728cbd85097b284d7b99249a2"}, + {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_aarch64.whl", hash = "sha256:aea440a510e14e818e67bfc4027880e2fb500c2ccb20ab21c7a7c8b5b4703d75"}, + {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_i686.whl", hash = "sha256:6974f52a02321b36847cd19d1b8e381bf39939c21efd6ee2fc13a28b0d99348c"}, + {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_ppc64le.whl", hash = "sha256:a7e53012d2853a07a4a79c00643832161a910674a893d296c9f1259859a289d2"}, + {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_x86_64.whl", hash = "sha256:d7702622a8b40c49bffb46e1e3ba2e81268d5c04a34f460978c6b5517a34dd52"}, {file = "Brotli-1.1.0-cp36-cp36m-win32.whl", hash = "sha256:a599669fd7c47233438a56936988a2478685e74854088ef5293802123b5b2460"}, {file = "Brotli-1.1.0-cp36-cp36m-win_amd64.whl", hash = "sha256:d143fd47fad1db3d7c27a1b1d66162e855b5d50a89666af46e1679c496e8e579"}, {file = "Brotli-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:11d00ed0a83fa22d29bc6b64ef636c4552ebafcef57154b4ddd132f5638fbd1c"}, @@ -385,6 +415,10 @@ files = [ {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:919e32f147ae93a09fe064d77d5ebf4e35502a8df75c29fb05788528e330fe74"}, {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:23032ae55523cc7bccb4f6a0bf368cd25ad9bcdcc1990b64a647e7bbcce9cb5b"}, {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:224e57f6eac61cc449f498cc5f0e1725ba2071a3d4f48d5d9dffba42db196438"}, + {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:cb1dac1770878ade83f2ccdf7d25e494f05c9165f5246b46a621cc849341dc01"}, + {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_i686.whl", hash = "sha256:3ee8a80d67a4334482d9712b8e83ca6b1d9bc7e351931252ebef5d8f7335a547"}, + {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_ppc64le.whl", hash = "sha256:5e55da2c8724191e5b557f8e18943b1b4839b8efc3ef60d65985bcf6f587dd38"}, + {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:d342778ef319e1026af243ed0a07c97acf3bad33b9f29e7ae6a1f68fd083e90c"}, {file = "Brotli-1.1.0-cp37-cp37m-win32.whl", hash = "sha256:587ca6d3cef6e4e868102672d3bd9dc9698c309ba56d41c2b9c85bbb903cdb95"}, {file = "Brotli-1.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:2954c1c23f81c2eaf0b0717d9380bd348578a94161a65b3a2afc62c86467dd68"}, {file = "Brotli-1.1.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:efa8b278894b14d6da122a72fefcebc28445f2d3f880ac59d46c90f4c13be9a3"}, @@ -397,6 +431,10 @@ files = [ {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1ab4fbee0b2d9098c74f3057b2bc055a8bd92ccf02f65944a241b4349229185a"}, {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:141bd4d93984070e097521ed07e2575b46f817d08f9fa42b16b9b5f27b5ac088"}, {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fce1473f3ccc4187f75b4690cfc922628aed4d3dd013d047f95a9b3919a86596"}, + {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d2b35ca2c7f81d173d2fadc2f4f31e88cc5f7a39ae5b6db5513cf3383b0e0ec7"}, + {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:af6fa6817889314555aede9a919612b23739395ce767fe7fcbea9a80bf140fe5"}, + {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:2feb1d960f760a575dbc5ab3b1c00504b24caaf6986e2dc2b01c09c87866a943"}, + {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4410f84b33374409552ac9b6903507cdb31cd30d2501fc5ca13d18f73548444a"}, {file = "Brotli-1.1.0-cp38-cp38-win32.whl", hash = "sha256:db85ecf4e609a48f4b29055f1e144231b90edc90af7481aa731ba2d059226b1b"}, {file = "Brotli-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:3d7954194c36e304e1523f55d7042c59dc53ec20dd4e9ea9d151f1b62b4415c0"}, {file = "Brotli-1.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5fb2ce4b8045c78ebbc7b8f3c15062e435d47e7393cc57c25115cfd49883747a"}, @@ -409,6 +447,10 @@ files = [ {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:949f3b7c29912693cee0afcf09acd6ebc04c57af949d9bf77d6101ebb61e388c"}, {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:89f4988c7203739d48c6f806f1e87a1d96e0806d44f0fba61dba81392c9e474d"}, {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:de6551e370ef19f8de1807d0a9aa2cdfdce2e85ce88b122fe9f6b2b076837e59"}, + {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0737ddb3068957cf1b054899b0883830bb1fec522ec76b1098f9b6e0f02d9419"}, + {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:4f3607b129417e111e30637af1b56f24f7a49e64763253bbc275c75fa887d4b2"}, + {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:6c6e0c425f22c1c719c42670d561ad682f7bfeeef918edea971a79ac5252437f"}, + {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:494994f807ba0b92092a163a0a283961369a65f6cbe01e8891132b7a320e61eb"}, {file = "Brotli-1.1.0-cp39-cp39-win32.whl", hash = "sha256:f0d8a7a6b5983c2496e364b969f0e526647a06b075d034f3297dc66f3b360c64"}, {file = "Brotli-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cdad5b9014d83ca68c25d2e9444e28e967ef16e80f6b436918c700c117a85467"}, {file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"}, @@ -1681,11 +1723,14 @@ orjson = "*" pandas = "*" protobuf = "*" py-grpc-prometheus = "*" +pyarrow = ">=14.0.1" pydantic = "2.7.1" pydantic-settings = "2.2.1" python-dotenv = "*" python-multipart = "*" starlette-exporter = "*" +tensorflow = ">2.12" +transformers = ">=4.36.0" tritonclient = {version = ">=2.42", extras = ["http"]} uvicorn = "*" uvloop = {version = "*", markers = "(sys_platform != \"win32\" and sys_platform != \"cygwin\") and platform_python_implementation != \"PyPy\""} @@ -3123,7 +3168,6 @@ files = [ {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"}, {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"}, {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"}, - {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"}, {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"}, {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"}, {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"}, @@ -3131,16 +3175,8 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"}, {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"}, {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"}, - {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"}, {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"}, {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, - {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, - {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, - {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, - {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, - {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, - {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, - {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"}, {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"}, {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"}, {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"}, @@ -3157,7 +3193,6 @@ files = [ {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"}, {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"}, {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"}, - {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"}, {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"}, {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"}, {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"}, @@ -3165,7 +3200,6 @@ files = [ {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"}, {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"}, {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"}, - {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"}, {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"}, {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"}, {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"}, @@ -3831,6 +3865,24 @@ files = [ {file = "threadpoolctl-3.3.0.tar.gz", hash = "sha256:5dac632b4fa2d43f42130267929af3ba01399ef4bd1882918e92dbc30365d30c"}, ] +[[package]] +name = "timm" +version = "1.0.14" +description = "PyTorch Image Models" +optional = false +python-versions = ">=3.8" +files = [ + {file = "timm-1.0.14-py3-none-any.whl", hash = "sha256:16653695ff420cf4e87e384c8654f2ee6be2070bcb4a0e840b5c6764ee0547ec"}, + {file = "timm-1.0.14.tar.gz", hash = "sha256:00a7f2cc04ce3ed8f80476bbb7eea27eac8cf6f2d59b5e9aa9cdd375dd6550db"}, +] + +[package.dependencies] +huggingface_hub = "*" +pyyaml = "*" +safetensors = "*" +torch = "*" +torchvision = "*" + [[package]] name = "tokenizers" version = "0.19.1" @@ -4060,6 +4112,85 @@ typing-extensions = ">=4.8.0" opt-einsum = ["opt-einsum (>=3.3)"] optree = ["optree (>=0.9.1)"] +[[package]] +name = "torchvision" +version = "0.17.2" +description = "image and video datasets and models for torch deep learning" +optional = false +python-versions = ">=3.8" +files = [ + {file = "torchvision-0.17.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:1f2910fe3c21ad6875b2720d46fad835b2e4b336e9553d31ca364d24c90b1d4f"}, + {file = "torchvision-0.17.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ecc1c503fa8a54fbab777e06a7c228032b8ab78efebf35b28bc8f22f544f51f1"}, + {file = "torchvision-0.17.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:f400145fc108833e7c2fc28486a04989ca742146d7a2a2cc48878ebbb40cdbbd"}, + {file = "torchvision-0.17.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:e9e4bed404af33dfc92eecc2b513d21ddc4c242a7fd8708b3b09d3a26aa6f444"}, + {file = "torchvision-0.17.2-cp310-cp310-win_amd64.whl", hash = "sha256:ba2e62f233eab3d42b648c122a3a29c47cc108ca314dfd5cbb59cd3a143fd623"}, + {file = "torchvision-0.17.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:9b83e55ee7d0a1704f52b9c0ac87388e7a6d1d98a6bde7b0b35f9ab54d7bda54"}, + {file = "torchvision-0.17.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e031004a1bc432c980a7bd642f6c189a3efc316e423fc30b5569837166a4e28d"}, + {file = "torchvision-0.17.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3bbc24b7713e8f22766992562547d8b4b10001208d372fe599255af84bfd1a69"}, + {file = "torchvision-0.17.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:833fd2e4216ced924c8aca0525733fe727f9a1af66dfad7c5be7257e97c39678"}, + {file = "torchvision-0.17.2-cp311-cp311-win_amd64.whl", hash = "sha256:6835897df852fad1015e6a106c167c83848114cbcc7d86112384a973404e4431"}, + {file = "torchvision-0.17.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:14fd1d4a033c325bdba2d03a69c3450cab6d3a625f85cc375781d9237ca5d04d"}, + {file = "torchvision-0.17.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9c3acbebbe379af112b62b535820174277b1f3eed30df264a4e458d58ee4e5b2"}, + {file = "torchvision-0.17.2-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:77d680adf6ce367166a186d2c7fda3a73807ab9a03b2c31a03fa8812c8c5335b"}, + {file = "torchvision-0.17.2-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:f1c9ab3152cfb27f83aca072cac93a3a4c4e4ab0261cf0f2d516b9868a4e96f3"}, + {file = "torchvision-0.17.2-cp312-cp312-win_amd64.whl", hash = "sha256:3f784381419f3ed3f2ec2aa42fb4aeec5bf4135e298d1631e41c926e6f1a0dff"}, + {file = "torchvision-0.17.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:b83aac8d78f48981146d582168d75b6c947cfb0a7693f76e219f1926f6e595a3"}, + {file = "torchvision-0.17.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1ece40557e122d79975860a005aa7e2a9e2e6c350a03e78a00ec1450083312fd"}, + {file = "torchvision-0.17.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:32dbeba3987e20f2dc1bce8d1504139fff582898346dfe8ad98d649f97ca78fa"}, + {file = "torchvision-0.17.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:35ba5c1600c3203549d2316422a659bd20c0cfda1b6085eec94fb9f35f55ca43"}, + {file = "torchvision-0.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:2f69570f50b1d195e51bc03feffb7b7728207bc36efcfb1f0813712b2379d881"}, + {file = "torchvision-0.17.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:4868bbfa55758c8107e69a0e7dd5e77b89056035cd38b767ad5b98cdb71c0f0d"}, + {file = "torchvision-0.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:efd6d0dd0668e15d01a2cffadc74068433b32cbcf5692e0c4aa15fc5cb250ce7"}, + {file = "torchvision-0.17.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7dc85b397f6c6d9ef12716ce0d6e11ac2b803f5cccff6fe3966db248e7774478"}, + {file = "torchvision-0.17.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d506854c5acd69b20a8b6641f01fe841685a21c5406b56813184f1c9fc94279e"}, + {file = "torchvision-0.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:067095e87a020a7a251ac1d38483aa591c5ccb81e815527c54db88a982fc9267"}, +] + +[package.dependencies] +numpy = "*" +pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0" +torch = "2.2.2" + +[package.extras] +scipy = ["scipy"] + +[[package]] +name = "torchvision" +version = "0.18.1" +description = "image and video datasets and models for torch deep learning" +optional = false +python-versions = ">=3.8" +files = [ + {file = "torchvision-0.18.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3e694e54b0548dad99c12af6bf0c8e4f3350137d391dcd19af22a1c5f89322b3"}, + {file = "torchvision-0.18.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:0b3bda0aa5b416eeb547143b8eeaf17720bdba9cf516dc991aacb81811aa96a5"}, + {file = "torchvision-0.18.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:573ff523c739405edb085f65cb592f482d28a30e29b0be4c4ba08040b3ae785f"}, + {file = "torchvision-0.18.1-cp310-cp310-win_amd64.whl", hash = "sha256:ef7bbbc60b38e831a75e547c66ca1784f2ac27100f9e4ddbe9614cef6cbcd942"}, + {file = "torchvision-0.18.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:80b5d794dd0fdba787adc22f1a367a5ead452327686473cb260dd94364bc56a6"}, + {file = "torchvision-0.18.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:9077cf590cdb3a5e8fdf5cdb71797f8c67713f974cf0228ecb17fcd670ab42f9"}, + {file = "torchvision-0.18.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:ceb993a882f1ae7ae373ed39c28d7e3e802205b0e59a7ed84ef4028f0bba8d7f"}, + {file = "torchvision-0.18.1-cp311-cp311-win_amd64.whl", hash = "sha256:52f7436140045dc2239cdc502aa76b2bd8bd676d64244ff154d304aa69852046"}, + {file = "torchvision-0.18.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2be6f0bf7c455c89a51a1dbb6f668d36c6edc479f49ac912d745d10df5715657"}, + {file = "torchvision-0.18.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:f118d887bfde3a948a41d56587525401e5cac1b7db2eaca203324d6ed2b1caca"}, + {file = "torchvision-0.18.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:13d24d904f65e62d66a1e0c41faec630bc193867b8a4a01166769e8a8e8df8e9"}, + {file = "torchvision-0.18.1-cp312-cp312-win_amd64.whl", hash = "sha256:ed6340b69a63a625e512a66127210d412551d9c5f2ad2978130c6a45bf56cd4a"}, + {file = "torchvision-0.18.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b1c3864fa9378c88bce8ad0ef3599f4f25397897ce612e1c245c74b97092f35e"}, + {file = "torchvision-0.18.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:02085a2ffc7461f5c0edb07d6f3455ee1806561f37736b903da820067eea58c7"}, + {file = "torchvision-0.18.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:9726c316a2501df8503e5a5dc46a631afd4c515a958972e5b7f7b9c87d2125c0"}, + {file = "torchvision-0.18.1-cp38-cp38-win_amd64.whl", hash = "sha256:64a2662dbf30db9055d8b201d6e56f312a504e5ccd9d144c57c41622d3c524cb"}, + {file = "torchvision-0.18.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:975b8594c0f5288875408acbb74946eea786c5b008d129c0d045d0ead23742bc"}, + {file = "torchvision-0.18.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:da83c8bbd34d8bee48bfa1d1b40e0844bc3cba10ed825a5a8cbe3ce7b62264cd"}, + {file = "torchvision-0.18.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:54bfcd352abb396d5c9c237d200167c178bd136051b138e1e8ef46ce367c2773"}, + {file = "torchvision-0.18.1-cp39-cp39-win_amd64.whl", hash = "sha256:5c8366a1aeee49e9ea9e64b30d199debdf06b1bd7610a76165eb5d7869c3bde5"}, +] + +[package.dependencies] +numpy = "*" +pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0" +torch = "2.3.1" + +[package.extras] +scipy = ["scipy"] + [[package]] name = "tqdm" version = "4.66.4" @@ -4740,4 +4871,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "24af455593b387d8ac5e9ee82e55de1dbbd0cb96230453bd17deec76b014439c" +content-hash = "9861bc587b5154cd19bcaa5fe0c3bafe04e08e2240d6f9d91d3cd13e9e78c466" diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 44ec8f21e..95813a623 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "mlserver-huggingface" -version = "2.0.8" +version = "2.0.11" description = "HuggingFace runtime for MLServer" authors = ["Seldon Technologies Ltd. "] license = "Apache-2.0" @@ -18,6 +18,7 @@ accelerate = "^0.27.2" bitsandbytes = "^0.42.0" sentence-transformers = "2.5.1" transformers = "4.41.2" +timm ="^1.0.13" [tool.poetry.group.dev.dependencies] mlserver = {path = "../..", develop = true} diff --git a/runtimes/huggingface/tests/test_codecs/test_chariot.py b/runtimes/huggingface/tests/test_codecs/test_chariot.py new file mode 100644 index 000000000..6267dc2c6 --- /dev/null +++ b/runtimes/huggingface/tests/test_codecs/test_chariot.py @@ -0,0 +1,111 @@ +import pytest +from mlserver_huggingface.codecs import ChariotImgModelOutputCodec +from PIL import Image +import numpy as np + + +@pytest.mark.parametrize( + "task_type,class_int_to_str,predict_proba, hf_prediction,expected_chariot_output", + [ + ( + "image-classification", + None, + False, + [ + [ + {"label": "tabby, tabby cat", "score": 0.94}, + {"label": "tiger cat", "score": 0.04}, + {"label": "Egyptian cat", "score": 0.02}, + ] + ], + ['"tabby, tabby cat"'], + ), + ( + "image-classification", + {0: "Egyptian cat", 1: "tabby, tabby cat", 2: "tiger cat"}, + True, + [ + [ + {"label": "tabby, tabby cat", "score": 0.94}, + {"label": "tiger cat", "score": 0.04}, + {"label": "Egyptian cat", "score": 0.02}, + ] + ], + [[0.02, 0.94, 0.04]], + ), + ( + "object-detection", + None, + False, + [ + [ + { + "score": 0.9897010326385498, + "label": "cat", + "box": {"xmin": 53, "ymin": 313, "xmax": 697, "ymax": 986}, + }, + { + "score": 0.9896764159202576, + "label": "cat", + "box": {"xmin": 974, "ymin": 221, "xmax": 1526, "ymax": 1071}, + }, + ] + ], + [ + { + "num_detections": 2, + "detection_classes": ["cat", "cat"], + "detection_scores": [0.9897010326385498, 0.9896764159202576], + "detection_boxes": [[313, 53, 986, 697], [221, 974, 1071, 1526]], + } + ], + ), + ( + "image-segmentation", + {0: "class_0", 1: "class_1", 2: "class_2"}, + False, + [ + [ + { + "score": None, + "label": "class_0", + "mask": Image.fromarray( + np.array( + [[255, 255, 255], [255, 0, 0], [255, 0, 255]] + ).astype("uint8"), + mode="L", + ), + }, + { + "score": None, + "label": "class_1", + "mask": Image.fromarray( + np.array([[0, 0, 0], [0, 255, 255], [0, 0, 0]]).astype( + "uint8" + ), + mode="L", + ), + }, + { + "score": None, + "label": "class_2", + "mask": Image.fromarray( + np.array([[0, 0, 0], [0, 0, 0], [0, 255, 0]]).astype( + "uint8" + ), + mode="L", + ), + }, + ] + ], + [[[0, 0, 0], [0, 1, 1], [0, 2, 0]]], + ), + ], +) +def test_encode_input( + task_type, class_int_to_str, predict_proba, hf_prediction, expected_chariot_output +): + chariot_output = ChariotImgModelOutputCodec.encode_output( + hf_prediction, task_type, class_int_to_str, predict_proba + ) + assert chariot_output == expected_chariot_output