import openvino as ov from transformers import AutoConfig, AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""} model_dir = "d:\llama3_2_1B_int4" #llama3.2 1B int4模型路径 DEVICE = "...
..\Intel\openvino_2021.4.752\deployment_tools\open_model_zoo\models\public\mobilenet-v2-pytorch\model.yml 1. 相关信息: framework: pytorch conversion_to_onnx_args: –model-name=mobilenet_v2 –weights=dl_dir/mobilenet_v2-b0353104.pth –import-module=torchvision.models –input-shape=1,3,224,224...
·read_model():将模型从硬盘载入内存,并返回model对象。model对象可以通过compile_model()方法编译为可以在目标设备上执行的compile_model对象。 ·compile_model():将模型从硬盘载入内存,编译模型,并返回compile_model对象。 fromopenvino.runtimeimport Core ie= Core() classification_model_xml="model/classification...
1 import cv2 2 import numpy as np 3 from openvino.preprocess import PrePostProcessor, ResizeAlgorithm 4 from openvino.runtime import Core, Layout, Type 5 6 def main(): 7 model_path = "resnet50.xml" 8 image_path = "test.jpg" 9 device_name = "CPU" 10 11 # 初始化OpenVINO运行时引...
mo --input_model Domino_best.onnx 在ONNX转换完成后,可以发现我们的目录下已经出现了bin文件和xml文件,在后续Vino的推理Core上就可以使用啦! 在进行推理前我们可以先通过Vino带的推理Core对我们硬件上支持的推理设备进行一个查找: importopenvino.inference_engine as ie ...
onnx.export(model,(dummy_input,),'model.onnx') 2.由于各深度学习框架训练导出的模型格式不一,需利用OpenVINO的模型优化器将模型转换为统一格式; """方式一""" from openvino.runtime import serialize serialize(model_onnx,xml_path='model/exported_onnx_model.xml') """方式二""" # 在终端使用输入...
net = ie.read_network(model='model.xml', weights='model.bin') exec_net = ie.Core().load_network(network=net, device_name='CPU') 预处理输入数据 根据模型要求,对输入图像进行预处理(例如缩放、归一化等)。 import cv2 import numpy as np image = cv2.imread('input.jpg') image = cv2.resize...
import tensorflow as tf import time mnist = tf.keras.dataset***nist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.kera***odels.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), ...
在240-dolly-2-instruction-following.ipynb中“Download and Convert Model”代码块更改如下所示: from pathlib import Path from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "databricks/dolly-v2-3b" ...
1. from openvino.runtime import Core, Model 2. 3. core = Core() 4. ov_model = core.read_model(model_path) 5. device = "CPU" # GPU 6. if device != "CPU": 7. ov_model.reshape({0: [1, 3, 640, 640]}) 8. compiled_model = core.compile_model(ov_model, device) ...