Convert a TensorFlow Model to OpenVINO™#

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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.

Table of contents:#

import platform

# Install openvino package
%pip install -q "openvino>=2023.1.0" "opencv-python"
if platform.system() != "Windows":
    %pip install -q "matplotlib>=3.4"
else:
    %pip install -q "matplotlib>=3.4,<3.7"
%pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
%pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
%pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
%pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
%pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
%pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
%pip install -q tf_keras tensorflow_hub tqdm
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at pypa/pip#12063
Note: you may need to restart the kernel to use updated packages.

Imports#

import os
import time
from pathlib import Path

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["TF_USE_LEGACY_KERAS"] = "1"

import cv2
import matplotlib.pyplot as plt
import numpy as np
import openvino as ov
import tensorflow as tf

# Fetch `notebook_utils` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)

from notebook_utils import download_file

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.
2024-06-06 03:35:45.398097: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
2024-06-06 03:35:45.398280: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
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.
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 ov.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 ov.compile_model or can be saved on a disk using the ov.save_model 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 = ov.convert_model(model_path, input=[[1, 224, 224, 3]])
    ov.save_model(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 = ov.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.

# Download the image from the openvino_notebooks storage
image_filename = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
    directory="data",
)

# The MobileNet network expects images in RGB format.
image = cv2.cvtColor(cv2.imread(filename=str(image_filename)), 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);
data/coco.jpg:   0%|          | 0.00/202k [00:00<?, ?B/s]
../_images/tensorflow-classification-to-openvino-with-output_19_1.png

Do Inference#

result = compiled_model(input_image)[output_key]

result_index = np.argmax(result)
# Download the datasets from the openvino_notebooks storage
image_filename = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
    directory="data",
)

# Convert the inference result to a class name.
imagenet_classes = image_filename.read_text().splitlines()

imagenet_classes[result_index]
data/imagenet_2012.txt:   0%|          | 0.00/30.9k [00:00<?, ?B/s]
'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: 955.14