OpenVINO™ Runtime API Tutorial¶
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 notebook explains the basics of the OpenVINO Runtime API. It covers:
The notebook is divided into sections with headers. Each section is standalone and does not depend on previous sections. A segmentation and classification OpenVINO IR model and a segmentation ONNX model are provided as examples. These model files can be replaced with your own models. The exact outputs will be different, but the process is the same.
Loading OpenVINO Runtime and Showing Info¶
Initialize OpenVINO Runtime with Core()
from openvino.runtime import Core
ie = Core()
OpenVINO Runtime can load a network on a device. A device in this
context means a CPU, an Intel GPU, a Neural Compute Stick 2, etc. The
available_devices
property shows the available devices in your
system. The “FULL_DEVICE_NAME” option to ie.get_property()
shows the
name of the device.
In this notebook, the CPU device is used. To use an integrated GPU, use
device_name="GPU"
instead. Be aware that loading a network on GPU
will be slower than loading a network on CPU, but inference will likely
be faster.
devices = ie.available_devices
for device in devices:
device_name = ie.get_property(device, "FULL_DEVICE_NAME")
print(f"{device}: {device_name}")
CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
Loading a Model¶
After initializing OpenVINO Runtime, first read the model file with
read_model()
, then compile it to the specified device with the
compile_model()
method.
OpenVINO IR Model¶
An OpenVINO IR (Intermediate Representation) model consists of an
.xml
file, containing information about network topology, and a
.bin
file, containing the weights and biases binary data. The
read_model()
function expects the .bin
weights file to have the
same filename and be located in the same directory as the .xml
file:
model_weights_file == Path(model_xml).with_suffix(".bin")
. If this
is the case, specifying the weights file is optional. If the weights
file has a different filename, it can be specified with the weights
parameter to read_model()
.
For information on how to convert your existing TensorFlow, PyTorch or
ONNX model to OpenVINO IR format with Model Optimizer, refer to the
tensorflow-to-openvino
and
pytorch-onnx-to-openvino
notebooks. For exporting ONNX models to OpenVINO IR with default
settings, the .serialize()
method can also be used.
from openvino.runtime import Core
ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
compiled_model = ie.compile_model(model=model, device_name="CPU")
ONNX Model¶
Reading and loading an ONNX model, which is a single file, works the
same way as with an OpenVINO IR model. The model
argument points to
the filename of an ONNX model.
from openvino.runtime import Core
ie = Core()
onnx_model_path = "model/segmentation.onnx"
model_onnx = ie.read_model(model=onnx_model_path)
compiled_model_onnx = ie.compile_model(model=model_onnx, device_name="CPU")
The ONNX model can be exported to OpenVINO IR with serialize():
from openvino.runtime import serialize
serialize(model=model, xml_path="model/exported_onnx_model.xml", bin_path="model/exported_onnx_model.bin")
Getting Information about a Model¶
The OpenVINO IENetwork instance stores information about the model.
Information about the inputs and outputs of the model are in
model.inputs
and model.outputs
. These are also properties of the
ExecutableNetwork instance. While using model.inputs
and
model.outputs
in the cells below, you can also use
compiled_model.inputs
and compiled_model.outputs
.
Model Inputs¶
from openvino.runtime import Core
ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
model.input(0).any_name
'input'
The cell above shows that the model loaded expects one input, with the name input. If you loaded a different model, you may see a different input layer name, and you may see more inputs.
It is often useful to have a reference to the name of the first input
layer. For a model with one input, model.input(0)
gets this name.
input_layer = model.input(0)
Information for this input layer is stored in inputs
. The next cell
prints the input layout, precision and a shape.
print(f"input precision: {input_layer.element_type}")
print(f"input shape: {input_layer.shape}")
input precision: <Type: 'float32'>
input shape: {1, 3, 224, 224}
This cell shows that the model expects inputs with a shape of
[1,3,224,224], and that this is in the NCHW
layout. This means that
the model expects input data with the batch size of 1 (N
), 3
channels (C
) , and images with a height (H
) and width (W
)
equal to 224. The input data is expected to be of FP32
(floating
point) precision.
Model Outputs¶
from openvino.runtime import Core
ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
model.output(0).any_name
'MobilenetV3/Predictions/Softmax'
Model output info is stored in model.outputs
. The cell above shows
that the model returns one output, with the
MobilenetV3/Predictions/Softmax
name. Loading a different model will
result in different output layer name, and more outputs might be
returned.
Since this model has one output, follow the same method as for the input layer to get its name.
output_layer = model.output(0)
output_layer
<Output: names[MobilenetV3/Predictions/Softmax] shape{1,1001} type: f32>
Getting the output precision and shape is similar to getting the input precision and shape.
print(f"output precision: {output_layer.element_type}")
print(f"output shape: {output_layer.shape}")
output precision: <Type: 'float32'>
output shape: {1, 1001}
This cell shows that the model returns outputs with a shape of [1,
1001], where 1 is the batch size (N
) and 1001 is the number of
classes (C
). The output is returned as 32-bit floating point.
Doing Inference on a Model¶
To do inference on a model, first create an inference request by calling
the create_infer_request()
method of ExecutableNetwork
,
exec_net
that was loaded with compile_model()
. Then, call the
infer()
method of InferRequest
. It expects one argument:
inputs
. This is a dictionary that maps input layer names to input
data.
Load the network
from openvino.runtime import Core
ie = Core()
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
compiled_model = ie.compile_model(model=model, device_name="CPU")
input_layer = compiled_model.input(0)
output_layer = compiled_model.output(0)
Load an image and convert to the input shape
To propagate an image through the network, it needs to be loaded into an array, resized to the shape that the network expects, and converted to the input layout of the network.
import cv2
image_filename = "data/coco_hollywood.jpg"
image = cv2.imread(image_filename)
image.shape
(663, 994, 3)
The image has a shape of (663,994,3). It is 663 pixels in height, 994 pixels in width, and has 3 color channels. A reference to the height and width expected by the network is obtained and the image is resized to these dimensions.
# N,C,H,W = batch size, number of channels, height, width.
N, C, H, W = input_layer.shape
# OpenCV resize expects the destination size as (width, height).
resized_image = cv2.resize(src=image, dsize=(W, H))
resized_image.shape
(224, 224, 3)
Now, the image has the width and height that the network expects. This
is still in HWC
format and must be changed to NCHW
format.
First, call the np.transpose()
method to change to CHW
and then
add the N
dimension (where N
= 1) by calling the
np.expand_dims()
method. Next, convert the data to FP32
with
np.astype()
method.
import numpy as np
input_data = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0).astype(np.float32)
input_data.shape
(1, 3, 224, 224)
Do inference
Now that the input data is in the right shape, do the inference.
result = compiled_model([input_data])[output_layer]
You can also create InferRequest
and run infer
method on
request.
request = compiled_model.create_infer_request()
request.infer(inputs={input_layer.any_name: input_data})
result = request.get_output_tensor(output_layer.index).data
The .infer()
function sets output tensor, that can be reached, using
get_output_tensor()
. Since this network returns one output, and the
reference to the output layer is in the output_layer.index
parameter, you can get the data with
request.get_output_tensor(output_layer.index)
. To get a numpy array
from the output, use the .data
parameter.
result.shape
(1, 1001)
The output shape is (1,1001), which is the expected output shape. This shape indicates that the network returns probabilities for 1001 classes. To learn more about this notion, refer to the hello world notebook.
Reshaping and Resizing¶
Change Image Size¶
Instead of reshaping the image to fit the model, it is also possible to reshape the model to fit the image. Be aware that not all models support reshaping, and models that do, may not support all input shapes. The model accuracy may also suffer if you reshape the model input shape.
First check the input shape of the model, then reshape it to the new input shape.
from openvino.runtime import Core, PartialShape
ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)
print("~~~~ ORIGINAL MODEL ~~~~")
print(f"input shape: {segmentation_input_layer.shape}")
print(f"output shape: {segmentation_output_layer.shape}")
new_shape = PartialShape([1, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")
# help(segmentation_compiled_model)
print("~~~~ RESHAPED MODEL ~~~~")
print(f"model input shape: {segmentation_input_layer.shape}")
print(
f"compiled_model input shape: "
f"{segmentation_compiled_model.input(index=0).shape}"
)
print(f"compiled_model output shape: {segmentation_output_layer.shape}")
~~~~ ORIGINAL MODEL ~~~~
input shape: {1, 3, 512, 512}
output shape: {1, 1, 512, 512}
~~~~ RESHAPED MODEL ~~~~
model input shape: {1, 3, 544, 544}
compiled_model input shape: {1, 3, 544, 544}
compiled_model output shape: {1, 1, 544, 544}
The input shape for the segmentation network is [1,3,512,512], with the
NCHW
layout: the network expects 3-channel images with a width and
height of 512 and a batch size of 1. Reshape the network with the
.reshape()
method of IENetwork
to make it accept input images
with a width and height of 544. This segmentation network always returns
arrays with the input width and height of equal value. Therefore,
setting the input dimensions to 544x544 also modifies the output
dimensions. After reshaping, compile the network once again.
Change Batch Size¶
Use the .reshape()
method to set the batch size, by increasing the
first element of new_shape
. For example, to set a batch size of two,
set new_shape = (2,3,544,544)
in the cell above.
from openvino.runtime import Core, PartialShape
ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)
new_shape = PartialShape([2, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")
print(f"input shape: {segmentation_input_layer.shape}")
print(f"output shape: {segmentation_output_layer.shape}")
input shape: {2, 3, 544, 544}
output shape: {2, 1, 544, 544}
The output shows that by setting the batch size to 2, the first element
(N
) of the input and output shape has a value of 2. Propagate the
input image through the network to see the result:
import numpy as np
from openvino.runtime import Core, PartialShape
ie = Core()
segmentation_model_xml = "model/segmentation.xml"
segmentation_model = ie.read_model(model=segmentation_model_xml)
segmentation_input_layer = segmentation_model.input(0)
segmentation_output_layer = segmentation_model.output(0)
new_shape = PartialShape([2, 3, 544, 544])
segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
segmentation_compiled_model = ie.compile_model(model=segmentation_model, device_name="CPU")
input_data = np.random.rand(2, 3, 544, 544)
output = segmentation_compiled_model([input_data])
print(f"input data shape: {input_data.shape}")
print(f"result data data shape: {segmentation_output_layer.shape}")
input data shape: (2, 3, 544, 544)
result data data shape: {2, 1, 544, 544}
Caching a Model¶
For some devices, like GPU, loading a model can take some time. Model
Caching solves this issue by caching the model in a cache directory. If
ie.compile_model(model=net, device_name=device_name, config=config_dict)
is set, caching will be used. This option checks if a model exists in
the cache. If so, it loads it from the cache. If not, it loads the model
regularly, and stores it in the cache, so that the next time the model
is loaded when this option is set, the model will be loaded from the
cache.
In the cell below, we create a model_cache directory as a subdirectory of model, where the model will be cached for the specified device. The model will be loaded to the GPU. After running this cell once, the model will be cached, so subsequent runs of this cell will load the model from the cache.
Note: Model Caching is also available on CPU devices
import time
from pathlib import Path
from openvino.runtime import Core
ie = Core()
device_name = "GPU"
if device_name in ie.available_devices:
cache_path = Path("model/model_cache")
cache_path.mkdir(exist_ok=True)
# Enable caching for OpenVINO Runtime. To disable caching set enable_caching = False
enable_caching = True
config_dict = {"CACHE_DIR": str(cache_path)} if enable_caching else {}
classification_model_xml = "model/classification.xml"
model = ie.read_model(model=classification_model_xml)
start_time = time.perf_counter()
compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict)
end_time = time.perf_counter()
print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.")
After running the previous cell, we know the model exists in the cache directory. We delete the compiled model and load it again. We measure the time it takes now.
if device_name in ie.available_devices:
del compiled_model
start_time = time.perf_counter()
compiled_model = ie.compile_model(model=model, device_name=device_name, config=config_dict)
end_time = time.perf_counter()
print(f"Loading the network to the {device_name} device took {end_time-start_time:.2f} seconds.")