from torchvision.models import resnet50
from openvino.runtime import Core
import torch
from pathlib import Path

model = resnet50()
model.eval()

dummy_input = torch.randn((1, 3, 224, 224),
                             generator=None,
                             device="cpu",
                             dtype=torch.float32)
onnx_path = Path("resnet50.onnx")

if not onnx_path.exists():
   torch.onnx.export(
           model,
           dummy_input,
           onnx_path,
           opset_version=11,
           do_constant_folding=False,
       )

mo --input_model "resnet50.onnx" --input_shape "[1,3, 224, 224]" --mean_values="[123.675, 116.28 , 103.53]" --scale_values="[58.395, 57.12 , 57.375]" --data_type FP32 --output_dir model

export LD_PRELOAD=conda_env_home/lib/libiomp5.so # like /usr/local/envs/test/lib/libiomp5.so
export KMP_AFFINITY=granularity=fine,compact,1,0

from openvino.runtime import Core
from pathlib import Path
import numpy as np

dummy_input = np.random.randn(1, 3, 224, 224)

config = {
        "CPU_THREADS_NUM": "48"
    }

ie = Core()
classification_model_xml = "model/resnet50.xml"
model = ie.read_model(model=classification_model_xml)

import torch # if import torch here, openvino will use only one core.

compiled_model = ie.compile_model(model, "CPU", config)

# import torch # if import torch here, openvino will use 48 cores.

input_layer = compiled_model.input(0)
output_layer = compiled_model.output(0)
ir = compiled_model.create_infer_request()
import time
s = time.time()
for i in range(200):
    ir.infer(inputs={input_layer.any_name: dummy_input})

print("time cost: " + str(time.time() - s))

conda create -n test -y python==3.7.10 setuptools==58.0.4 
conda activate test
conda install intel-openmp
pip3 install torch torchvision
pip3 install openvino openvino-dev