tokenizer = AutoTokenizer.from_pretrained(model_id) # load config model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(model, feature=feature) onnx_config = model_onnx_config(model.config) # export onnx_inputs, onnx_outputs = transformers.onnx.export( preprocessor=...
transformers.onnx包的源码地址:https://github.com/huggingface/transformers/tree/main/src/transformers/onnx,代码结构如下: 其中,config.py是onnx提供的配置相关代码。 3.2 onnx的相关配置 transformers提供了三个抽象类供使用者集成,我们可以根据希望导出的模型体系结构的类型来选择集成哪一个。 Encoder-based models...
GPUOptions(per_process_gpu_memory_fraction=0.7) # defalut 0.5 tfconfig = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options) ... with tf.Session(config=tfconfig) as persisted_sess: persisted_sess.graph.as_default() tf.import_graph_def(tf_rep.graph.as_graph_def(), name='...
model_name ='bert'x = import_module('models.'+ model_name) config = x.Config('THUCNews') model = x.Model(config).to(config.device) model.load_state_dict(torch.load(config.save_path, map_location='cpu')) // bert输入向量化过程 defbuild_predict_text(text): token = config.tokenizer.to...
config.int8_calibrator = int8_calib # 构建TensorRT engine engine = builder.build_cuda_engine(network) # 序列化并保存engine with open(engine_file_path, 'wb') as f: f.write(engine.serialize()) 执行量化脚本:运行量化脚本,生成INT8量化的TensorRT engine。 部署和推理:将量化的TensorRT engine部署到...
--calibration_config 功能说明 训练后量化简易配置文件路径与文件名。 如果用户自己构造了训练后量化简易配置文件:即用户自行决定哪些层进行量化,哪些层不量化,则需要使用该参数。 关联参数 该参数不能与--batch_num参数同时使用。 参数取值 参数值:量化简易配置文件路径与文件名。
engine = builder->buildEngineWithConfig(*network, *config); assert(engine != nullptr); // 序列化引擎并保存到文件 std::string enginePath = "./deepsort.engine"; std::ofstream engineFile(enginePath, std::ios::binary); if (engineFile) ...
To create the container, we pull theappropriate imagefrom Amazon ECR for Triton Server. SageMaker allows us to customize and inject various environment variables. Some of the key features are the ability to set theBATCH_SIZE; we can set this per model in theconfig.pbtxtfile, or...
raw_img_info['file_name'] = raw_img_info['coco_url'].replace( KeyError: 'coco_url' Process finished with exit code 1 这是用segment里面的config作为配置文件,python train.pyd 的报错。不知道你们那边有这个没有。修改datasetstype为yolov5cocodatasets之后也是同样的问题。Sign...
from azure.identity import DefaultAzureCredential from azure.ai.ml import MLClient mlflow_client = MlflowClient() credential = DefaultAzureCredential() ml_client = None try: ml_client = MLClient.from_config(credential) except Exception as ex: print(ex) # Enter details of your Azur...