如何转换 TensorFlow 模型并在 OpenVINO™ 工具包中使用

要使用 OpenVINO™ 工具包 运行网络，首先需要将其转换为 中间表示 (IR)。为此，您需要 模型优化器，它是 OpenVINO™ 工具包开发者包中的一个命令行工具。最简单的方法是通过 PyPi 获取它。

pip install openvino-dev

模型优化器直接支持 TensorFlow 模型，因此下一步是在终端中使用以下命令

mo --input_model v3-small_224_1.0_float.pb --input_shape "[1,224,224,3]"

这意味着您正在将 v3-small_224_1.0_float.pb 模型转换为一个尺寸为 224x224 的 RGB 图像。当然，您可以指定更多参数，例如预处理步骤或所需的模型精度（FP32 或 FP16）。

mo --input_model v3-small_224_1.0_float.pb --input_shape "[1,224,224,3]" --mean_values="[127.5,127.5,127.5]" --scale_values="[127.5]" --data_type FP16

您的模型会将所有像素归一化到 [-1,1] 的值范围，并且推理将以 FP16 模式进行。运行后，您应该会看到如下内容，其中包含所有显式和隐式参数，例如模型路径、输入形状、所选精度、通道反转、均值和缩放值、转换参数等等。

Exporting TensorFlow model to IR… This may take a few minutes.
Model Optimizer arguments:
Common parameters:
    — Path to the Input Model: /home/adrian/repos/openvino_notebooks/notebooks/101-tensorflow-to-openvino/model/v3-small_224_1.0_float.pb
    — Path for generated IR: /home/adrian/repos/openvino_notebooks/notebooks/101-tensorflow-to-openvino/model
    — IR output name: v3-small_224_1.0_float
    — Log level: ERROR
    — Batch: Not specified, inherited from the model
    — Input layers: Not specified, inherited from the model
    — Output layers: Not specified, inherited from the model
    — Input shapes: [1,224,224,3]
    — Mean values: [127.5,127.5,127.5]
    — Scale values: [127.5]
    — Scale factor: Not specified
    — Precision of IR: FP16
    — Enable fusing: True
    — Enable grouped convolutions fusing: True
    — Move mean values to preprocess section: None
    — Reverse input channels: False
TensorFlow specific parameters:
    — Input model in text protobuf format: False
    — Path to model dump for TensorBoard: None
    — List of shared libraries with TensorFlow custom layers implementation: None
    — Update the configuration file with input/output node names: None
    — Use configuration file used to generate the model with Object Detection API: None
    — Use the config file: None
    — Inference Engine found in: /home/adrian/repos/openvino_notebooks/openvino_env/lib/python3.8/site-packages/openvino
Inference Engine version: 2021.4.1–3926–14e67d86634-releases/2021/4
Model Optimizer version: 2021.4.1–3926–14e67d86634-releases/2021/4
[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: /home/adrian/repos/openvino_notebooks/notebooks/101-tensorflow-to-openvino/model/v3-small_224_1.0_float.xml
[ SUCCESS ] BIN file: /home/adrian/repos/openvino_notebooks/notebooks/101-tensorflow-to-openvino/model/v3-small_224_1.0_float.bin
[ SUCCESS ] Total execution time: 9.97 seconds. 
[ SUCCESS ] Memory consumed: 374 MB.

SUCCESS 表示一切都已成功转换。您应该获得 IR，它由两个文件组成：.xml 和 .bin。现在，您可以将此网络加载到推理引擎并运行推理。下面的代码假定您的模型用于 ImageNet 分类。

import cv2 
import numpy as np 
from openvino.inference_engine import IECore 
 
# Load the model 
ie = IECore() 
net = ie.read_network(model="v3-small_224_1.0_float.xml", weights="v3-small_224_1.0_float.bin") 
exec_net = ie.load_network(network=net, device_name="CPU")
 
input_key = next(iter(exec_net.input_info)) 
output_key = next(iter(exec_net.outputs.keys())) 
 
# Load the image 
# The MobileNet network expects images in RGB format 
image = cv2.cvtColor(cv2.imread(filename="image.jpg"), code=cv2.COLOR_BGR2RGB) 
 
# resize to MobileNet image shape 
input_image = cv2.resize(src=image, dsize=(224, 224)) 
 
# reshape to network input shape 
input_image = np.expand_dims(input_image.transpose(2, 0, 1), axis=0) 
 
# Do inference 
result = exec_net.infer(inputs={input_key: input_image})[output_key] 
result_index = np.argmax(result) 
 
# Convert the inference result to a class name. 
imagenet_classes = open("imagenet_2012.txt").read().splitlines() 
 
# The model description states that for this model, class 0 is background, 
# so we add background at the beginning of imagenet_classes 
imagenet_classes = ["background"] + imagenet_classes 
 
print(imagenet_classes[result_index])

它有效！您将获得图像的类别（例如下图中的平毛寻回犬）。您可以使用此 演示 亲自尝试。

如果您想以更有限的方式尝试 OpenVINO，并且对代码进行更少的更改，请查看我们的 TensorFlow 集成 插件。