Convert a TensorFlow Model to OpenVINO™

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the “launch binder” button.

This short tutorial shows how to convert a TensorFlow MobileNetV3 image classification model to OpenVINO Intermediate Representation (OpenVINO IR) format, using model conversion API. After creating the OpenVINO IR, load the model in OpenVINO Runtime and do inference with a sample image.

.. _top:

Table of contents:

• Imports

• Settings

• Download model

• Convert a Model to OpenVINO IR Format

  • Convert a TensorFlow Model to OpenVINO IR Format

• Test Inference on the Converted Model

  • Load the Model

• Select inference device

  • Get Model Information

  • Load an Image

  • Do Inference

• Timing

Imports ⇑

import time
from pathlib import Path

import cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from openvino.runtime import Core, serialize
from openvino.tools import mo

2023-08-15 22:26:34.199621: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2023-08-15 22:26:34.233464: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-15 22:26:34.746193: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Settings ⇑

# The paths of the source and converted models.
model_dir = Path("model")
model_dir.mkdir(exist_ok=True)

model_path = Path("model/v3-small_224_1.0_float")

ir_path = Path("model/v3-small_224_1.0_float.xml")

Download model ⇑

Load model using tf.keras.applications api and save it to the disk.

model = tf.keras.applications.MobileNetV3Small()
model.save(model_path)

WARNING:tensorflow:input_shape is undefined or non-square, or rows is not 224. Weights for input shape (224, 224) will be loaded as the default.

2023-08-15 22:26:35.659386: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. model.compile_metrics will be empty until you train or evaluate the model.

2023-08-15 22:26:39.846021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
     [[{{node inputs}}]]
2023-08-15 22:26:42.992490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
     [[{{node inputs}}]]
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 54). These functions will not be directly callable after loading.

INFO:tensorflow:Assets written to: model/v3-small_224_1.0_float/assets

INFO:tensorflow:Assets written to: model/v3-small_224_1.0_float/assets

Convert a Model to OpenVINO IR Format ⇑

Convert a TensorFlow Model to OpenVINO IR Format ⇑

Use the model conversion Python API to convert the TensorFlow model to OpenVINO IR. The mo.convert_model function accept path to saved model directory and returns OpenVINO Model class instance which represents this model. Obtained model is ready to use and to be loaded on a device using compile_model or can be saved on a disk using the serialize function. See the tutorial for more information about using model conversion API with TensorFlow models.

# Run model conversion API if the IR model file does not exist
if not ir_path.exists():
    print("Exporting TensorFlow model to IR... This may take a few minutes.")
    ov_model = mo.convert_model(saved_model_dir=model_path, input_shape=[[1, 224, 224, 3]], compress_to_fp16=True)
    serialize(ov_model, ir_path)
else:
    print(f"IR model {ir_path} already exists.")

Exporting TensorFlow model to IR... This may take a few minutes.

Test Inference on the Converted Model ⇑

Load the Model ⇑

core = Core()
model = core.read_model(ir_path)

Select inference device ⇑

Select device from dropdown list for running inference using OpenVINO:

import ipywidgets as widgets

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

compiled_model = core.compile_model(model=model, device_name=device.value)

Get Model Information ⇑

input_key = compiled_model.input(0)
output_key = compiled_model.output(0)
network_input_shape = input_key.shape

Load an Image ⇑

Load an image, resize it, and convert it to the input shape of the network.

# The MobileNet network expects images in RGB format.
image = cv2.cvtColor(cv2.imread(filename="../data/image/coco.jpg"), code=cv2.COLOR_BGR2RGB)

# Resize the image to the network input shape.
resized_image = cv2.resize(src=image, dsize=(224, 224))

# Transpose the image to the network input shape.
input_image = np.expand_dims(resized_image, 0)

plt.imshow(image);

Do Inference ⇑

result = compiled_model(input_image)[output_key]

result_index = np.argmax(result)

# Convert the inference result to a class name.
imagenet_classes = open("../data/datasets/imagenet/imagenet_2012.txt").read().splitlines()

imagenet_classes[result_index]

'n02099267 flat-coated retriever'

Timing ⇑

Measure the time it takes to do inference on thousand images. This gives an indication of performance. For more accurate benchmarking, use the Benchmark Tool in OpenVINO. Note that many optimizations are possible to improve the performance.

num_images = 1000

start = time.perf_counter()

for _ in range(num_images):
    compiled_model([input_image])

end = time.perf_counter()
time_ir = end - start

print(
    f"IR model in OpenVINO Runtime/CPU: {time_ir/num_images:.4f} "
    f"seconds per image, FPS: {num_images/time_ir:.2f}"
)

IR model in OpenVINO Runtime/CPU: 0.0010 seconds per image, FPS: 988.20