自贡建设监督质量网站,淘客采集网站怎么做的,天津专业网站建设公司,统计网站怎么做一、openvino安装
参看官方文档https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html 安装命令是根据上面的选择生成。这里安装了pytorch和onnx依赖。
二、pytorch模型转opnvino模型推理
import os
import time
import cv2
import nu…一、openvino安装
参看官方文档https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/download.html 安装命令是根据上面的选择生成。这里安装了pytorch和onnx依赖。
二、pytorch模型转opnvino模型推理
import os
import time
import cv2
import numpy as np
import torchfrom openvino.runtime import Core
from openvino.tools import moimg_path r./000000002306.jpg
model_path pure_pose.pt
## 加载onnx模型
model torch.load(model_path)
model.eval()
## onnx模型转openvino
model_ir mo.convert_model(model, input_shape[1,3, 256, 192],mean_values [123.675, 116.28 , 103.53],scale_values[58.395, 57.12 , 57.375],compress_to_fp16True
)## 图片预处理
image cv2.cvtColor(srccv2.imread(filenamestr(img_path)),codecv2.COLOR_BGR2RGB,
)
resized_image,ratio, (dw, dh) letterbox(image,new_shape(256,192))# # # Convert the image shape to a shape and a data type expected by the network
# # # for OpenVINO IR model: (1, 3, 512, 512).
input_image np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)ie Core()
compiled_model_ir ie.compile_model(modelmodel_ir, device_nameCPU)
# Get the names of input and output layers.
input_layer_ir compiled_model_ir.input(0)
output_layer_ir compiled_model_ir.output(0)# Do inference on the input image.
start_time time.perf_counter()
result compiled_model_ir([input_image])[output_layer_ir]
end_time time.perf_counter()
print(fInference finished. Inference time: {end_time-start_time:.3f} seconds, fFPS: {1/(end_time-start_time):.2f}.
)
在pytorch转openvino模型的时候已包含归一化操作过程。在推理的时候不需要对输入图片做归一化操作。
三、onnx模型转opnvino模型推理
1. onnx模型转openvino模型
在上面的安装文件夹openvino_env文件夹下找到mo_onnx.py文件。我的路径openvino_env/lib/python3.9/site-packages/openvino/tools/mo/mo_onnx.py 根据下面的脚本将onnx模型转成openvino模型
python ../openvino_env/lib/python3.9/site-packages/openvino/tools/mo/mo_onnx.py \--input_model ./mobilenet_load.onnx \--output_dir ./openvino_model \--input_shape [1,3,256,192] \--mean_values[123.675, 116.28 , 103.53] \--scale_values[58.395, 57.12 , 57.375] \--data_type FP16生成下面三个文件
2. 调用openvino模型进行推理
import time
import cv2
import numpy as np
from openvino.runtime import Core
img_path r./000000002306.jpg
from utils import letterboximage cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)# Convert the resized images to network input shape
resized_image,ratio, (dw, dh) letterbox(image,new_shape(256,192))
h, w, c resized_image.shape
input_image np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)# Load the network in Inference Engine
core Core()
model_ir core.read_model(modelopenvino_model/mobilenet_load.xml)
compiled_model_ir core.compile_model(modelmodel_ir, device_nameCPU)# Get output layer
output_layer_ir compiled_model_ir.output(0)# Run inference on the input image
# Do inference on the input image.
start_time time.perf_counter()
res_ir compiled_model_ir([input_image])[output_layer_ir]
end_time time.perf_counter()
print(fInference finished. Inference time: {end_time-start_time:.3f} seconds, fFPS: {1/(end_time-start_time):.2f}.
)
比较通过pytorch和onnx转成的openvino模型的推理时间差不多。openvino模型推理时间大概是pytorch模型推理时间的1/5.
模型推理时间(s)openvino0.010onnx0.015pytorch0.048
