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Post-Training Quantization with TensorFlow Classification Model
===============================================================
This example demonstrates how to quantize the OpenVINO model that was
created in `301-tensorflow-training-openvino
notebook <301-tensorflow-training-openvino-with-output.html>`__, to improve
inference speed. Quantization is performed with `Post-training
Quantization with
NNCF <https://docs.openvino.ai/nightly/basic_quantization_flow.html>`__.
A custom dataloader and metric will be defined, and accuracy and
performance will be computed for the original IR model and the quantized
model.
Table of contents:
^^^^^^^^^^^^^^^^^^
- `Preparation <#preparation>`__
- `Imports <#imports>`__
- `Post-training Quantization with
NNCF <#post-training-quantization-with-nncf>`__
- `Select inference device <#select-inference-device>`__
- `Compare Metrics <#compare-metrics>`__
- `Run Inference on Quantized
Model <#run-inference-on-quantized-model>`__
- `Compare Inference Speed <#compare-inference-speed>`__
Preparation
-----------
The notebook requires that the training notebook has been run and that
the Intermediate Representation (IR) models are created. If the IR
models do not exist, running the next cell will run the training
notebook. This will take a while.
.. code:: ipython3
%pip install -q tensorflow Pillow matplotlib numpy tqdm nncf
.. parsed-literal::
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 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 https://github.com/pypa/pip/issues/12063
.. parsed-literal::
Note: you may need to restart the kernel to use updated packages.
.. code:: ipython3
from pathlib import Path
import tensorflow as tf
model_xml = Path("model/flower/flower_ir.xml")
dataset_url = (
"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
)
data_dir = Path(tf.keras.utils.get_file("flower_photos", origin=dataset_url, untar=True))
if not model_xml.exists():
print("Executing training notebook. This will take a while...")
%run 301-tensorflow-training-openvino.ipynb
.. parsed-literal::
2024-01-26 00:38:58.168511: 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`.
2024-01-26 00:38:58.203263: 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.
.. parsed-literal::
2024-01-26 00:38:58.795644: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
.. parsed-literal::
Executing training notebook. This will take a while...
.. parsed-literal::
DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.0 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 https://github.com/pypa/pip/issues/12063
.. parsed-literal::
Note: you may need to restart the kernel to use updated packages.
.. parsed-literal::
3670
.. parsed-literal::
Found 3670 files belonging to 5 classes.
.. parsed-literal::
Using 2936 files for training.
.. parsed-literal::
2024-01-26 00:39:04.673372: 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
2024-01-26 00:39:04.673408: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
2024-01-26 00:39:04.673412: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
2024-01-26 00:39:04.673543: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
2024-01-26 00:39:04.673559: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
2024-01-26 00:39:04.673562: 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
.. parsed-literal::
Found 3670 files belonging to 5 classes.
.. parsed-literal::
Using 734 files for validation.
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
.. parsed-literal::
2024-01-26 00:39:04.952983: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-01-26 00:39:04.953258: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_12.png
.. parsed-literal::
2024-01-26 00:39:05.819322: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-01-26 00:39:05.819561: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
.. parsed-literal::
(32, 180, 180, 3)
(32,)
.. parsed-literal::
2024-01-26 00:39:06.138784: 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 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
2024-01-26 00:39:06.139071: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
.. parsed-literal::
0.005936881 0.9981924
.. parsed-literal::
2024-01-26 00:39:06.854372: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
2024-01-26 00:39:06.854685: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_18.png
.. parsed-literal::
Model: "sequential_2"
.. parsed-literal::
_________________________________________________________________
.. parsed-literal::
Layer (type) Output Shape Param #
.. parsed-literal::
=================================================================
.. parsed-literal::
sequential_1 (Sequential) (None, 180, 180, 3) 0
.. parsed-literal::
rescaling_2 (Rescaling) (None, 180, 180, 3) 0
.. parsed-literal::
conv2d_3 (Conv2D) (None, 180, 180, 16) 448
.. parsed-literal::
max_pooling2d_3 (MaxPooling (None, 90, 90, 16) 0
.. parsed-literal::
2D)
.. parsed-literal::
conv2d_4 (Conv2D) (None, 90, 90, 32) 4640
.. parsed-literal::
max_pooling2d_4 (MaxPooling (None, 45, 45, 32) 0
.. parsed-literal::
2D)
.. parsed-literal::
conv2d_5 (Conv2D) (None, 45, 45, 64) 18496
.. parsed-literal::
max_pooling2d_5 (MaxPooling (None, 22, 22, 64) 0
.. parsed-literal::
2D)
.. parsed-literal::
dropout (Dropout) (None, 22, 22, 64) 0
.. parsed-literal::
flatten_1 (Flatten) (None, 30976) 0
.. parsed-literal::
dense_2 (Dense) (None, 128) 3965056
.. parsed-literal::
outputs (Dense) (None, 5) 645
.. parsed-literal::
=================================================================
.. parsed-literal::
Total params: 3,989,285
.. parsed-literal::
Trainable params: 3,989,285
.. parsed-literal::
Non-trainable params: 0
.. parsed-literal::
_________________________________________________________________
.. parsed-literal::
Epoch 1/15
.. parsed-literal::
2024-01-26 00:39:07.843867: 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 'Placeholder/_0' with dtype string and shape [2936]
[[{{node Placeholder/_0}}]]
2024-01-26 00:39:07.844357: 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 'Placeholder/_4' with dtype int32 and shape [2936]
[[{{node Placeholder/_4}}]]
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2024-01-26 00:39:14.155330: 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 'Placeholder/_4' with dtype int32 and shape [734]
[[{{node Placeholder/_4}}]]
2024-01-26 00:39:14.155580: 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 'Placeholder/_0' with dtype string and shape [734]
[[{{node Placeholder/_0}}]]
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92/92 [==============================] - 7s 66ms/step - loss: 1.3202 - accuracy: 0.4230 - val_loss: 1.1764 - val_accuracy: 0.5014
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92/92 [==============================] - 6s 64ms/step - loss: 0.5345 - accuracy: 0.7936 - val_loss: 0.7319 - val_accuracy: 0.7044
.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_1452.png
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2024-01-26 00:40:37.537773: 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 'random_flip_input' with dtype float and shape [?,180,180,3]
[[{{node random_flip_input}}]]
2024-01-26 00:40:37.623389: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.633313: 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 'random_flip_input' with dtype float and shape [?,180,180,3]
[[{{node random_flip_input}}]]
2024-01-26 00:40:37.644579: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.651808: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.658580: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.669430: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.709186: 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 'sequential_1_input' with dtype float and shape [?,180,180,3]
[[{{node sequential_1_input}}]]
.. parsed-literal::
2024-01-26 00:40:37.808931: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.829365: 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 'sequential_1_input' with dtype float and shape [?,180,180,3]
[[{{node sequential_1_input}}]]
2024-01-26 00:40:37.868335: 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 [?,22,22,64]
[[{{node inputs}}]]
2024-01-26 00:40:37.893339: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:37.967640: 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 [?,180,180,3]
[[{{node inputs}}]]
.. parsed-literal::
2024-01-26 00:40:38.111119: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:38.248703: 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 [?,22,22,64]
[[{{node inputs}}]]
2024-01-26 00:40:38.283618: 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 [?,180,180,3]
[[{{node inputs}}]]
2024-01-26 00:40:38.311335: 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 [?,180,180,3]
[[{{node inputs}}]]
.. parsed-literal::
2024-01-26 00:40:38.358384: 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 [?,180,180,3]
[[{{node inputs}}]]
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.
.. parsed-literal::
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
.. parsed-literal::
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
.. parsed-literal::
output/A_Close_Up_Photo_of_a_Dandelion.jpg: 0%| | 0.00/21.7k [00:00<?, ?B/s]
.. parsed-literal::
(1, 180, 180, 3)
[1,180,180,3]
This image most likely belongs to dandelion with a 99.60 percent confidence.
.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_3_1464.png
Imports
~~~~~~~
The Post Training Quantization API is implemented in the ``nncf``
library.
.. code:: ipython3
import sys
import matplotlib.pyplot as plt
import numpy as np
import nncf
from openvino.runtime import Core
from openvino.runtime import serialize
from PIL import Image
from sklearn.metrics import accuracy_score
sys.path.append("../utils")
from notebook_utils import download_file
.. parsed-literal::
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
Post-training Quantization with NNCF
------------------------------------
`NNCF <https://github.com/openvinotoolkit/nncf>`__ provides a suite of
advanced algorithms for Neural Networks inference optimization in
OpenVINO with minimal accuracy drop.
Create a quantized model from the pre-trained FP32 model and the
calibration dataset. The optimization process contains the following
steps:
1. Create a Dataset for quantization.
2. Run nncf.quantize for getting an optimized model.
The validation dataset already defined in the training notebook.
.. code:: ipython3
img_height = 180
img_width = 180
val_dataset = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=1
)
for a, b in val_dataset:
print(type(a), type(b))
break
.. parsed-literal::
Found 3670 files belonging to 5 classes.
.. parsed-literal::
Using 734 files for validation.
<class 'tensorflow.python.framework.ops.EagerTensor'> <class 'tensorflow.python.framework.ops.EagerTensor'>
.. parsed-literal::
2024-01-26 00:40:41.533104: 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 'Placeholder/_0' with dtype string and shape [734]
[[{{node Placeholder/_0}}]]
2024-01-26 00:40:41.533348: 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 'Placeholder/_4' with dtype int32 and shape [734]
[[{{node Placeholder/_4}}]]
The validation dataset can be reused in quantization process. But it
returns a tuple (images, labels), whereas calibration_dataset should
only return images. The transformation function helps to transform a
user validation dataset to the calibration dataset.
.. code:: ipython3
def transform_fn(data_item):
"""
The transformation function transforms a data item into model input data.
This function should be passed when the data item cannot be used as model's input.
"""
images, _ = data_item
return images.numpy()
calibration_dataset = nncf.Dataset(val_dataset, transform_fn)
Download Intermediate Representation (IR) model.
.. code:: ipython3
core = Core()
ir_model = core.read_model(model_xml)
Use `Basic Quantization
Flow <https://docs.openvino.ai/2023.3/basic_quantization_flow.html>`__.
To use the most advanced quantization flow that allows to apply 8-bit
quantization to the model with accuracy control see `Quantizing with
accuracy
control <https://docs.openvino.ai/2023.3/quantization_w_accuracy_control.html>`__.
.. code:: ipython3
quantized_model = nncf.quantize(
ir_model,
calibration_dataset,
subset_size=1000
)
.. parsed-literal::
Output()
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Exception in thread Thread-88:
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">Traceback (most recent call last):
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> self.run()
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/live.py", line 32, in run
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> self.live.refresh()
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/live.py", line 223, in refresh
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> self._live_render.set_renderable(self.renderable)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/live.py", line 203, in renderable
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> renderable = self.get_renderable()
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/live.py", line 98, in get_renderable
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> self._get_renderable()
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 1537, in get_renderable
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> renderable = Group(*self.get_renderables())
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 1542, in get_renderables
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> table = self.make_tasks_table(self.tasks)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 1566, in make_tasks_table
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> table.add_row(
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 1571, in &lt;genexpr&gt;
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> else column(task)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 528, in __call__
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> renderable = self.render(task)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/nncf/common/logging/track_progress.py", line 58, in render
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> text = super().render(task)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 787, in render
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> task_time = task.time_remaining
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/rich/progress.py", line 1039, in time_remaining
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> estimate = ceil(remaining / speed)
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> raise e.with_traceback(filtered_tb) from None
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> File
"/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-598/.workspace/scm/ov-notebook/.venv/lib/python3.8/si
te-packages/tensorflow/python/ops/math_ops.py", line 1569, in _truediv_python3
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"> raise TypeError(f"`x` and `y` must have the same dtype, "
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">TypeError: `x` and `y` must have the same dtype, got tf.int64 != tf.float32.
</pre>
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
.. parsed-literal::
Output()
.. raw:: html
<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
Save quantized model to benchmark.
.. code:: ipython3
compressed_model_dir = Path("model/optimized")
compressed_model_dir.mkdir(parents=True, exist_ok=True)
compressed_model_xml = compressed_model_dir / "flower_ir.xml"
serialize(quantized_model, str(compressed_model_xml))
Select inference device
~~~~~~~~~~~~~~~~~~~~~~~
select device from dropdown list for running inference using OpenVINO
.. code:: ipython3
import ipywidgets as widgets
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value='AUTO',
description='Device:',
disabled=False,
)
device
.. parsed-literal::
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
Compare Metrics
---------------
Define a metric to determine the performance of the model.
For this demo we define validate function to compute accuracy metrics.
.. code:: ipython3
def validate(model, validation_loader):
"""
Evaluate model and compute accuracy metrics.
:param model: Model to validate
:param validation_loader: Validation dataset
:returns: Accuracy scores
"""
predictions = []
references = []
output = model.outputs[0]
for images, target in validation_loader:
pred = model(images.numpy())[output]
predictions.append(np.argmax(pred, axis=1))
references.append(target)
predictions = np.concatenate(predictions, axis=0)
references = np.concatenate(references, axis=0)
scores = accuracy_score(references, predictions)
return scores
Calculate accuracy for the original model and the quantized model.
.. code:: ipython3
original_compiled_model = core.compile_model(model=ir_model, device_name=device.value)
quantized_compiled_model = core.compile_model(model=quantized_model, device_name=device.value)
original_accuracy = validate(original_compiled_model, val_dataset)
quantized_accuracy = validate(quantized_compiled_model, val_dataset)
print(f"Accuracy of the original model: {original_accuracy:.3f}")
print(f"Accuracy of the quantized model: {quantized_accuracy:.3f}")
.. parsed-literal::
Accuracy of the original model: 0.703
Accuracy of the quantized model: 0.711
Compare file size of the models.
.. code:: ipython3
original_model_size = model_xml.with_suffix(".bin").stat().st_size / 1024
quantized_model_size = compressed_model_xml.with_suffix(".bin").stat().st_size / 1024
print(f"Original model size: {original_model_size:.2f} KB")
print(f"Quantized model size: {quantized_model_size:.2f} KB")
.. parsed-literal::
Original model size: 7791.65 KB
Quantized model size: 3897.08 KB
So, we can see that the original and quantized models have similar
accuracy with a much smaller size of the quantized model.
Run Inference on Quantized Model
--------------------------------
Copy the preprocess function from the training notebook and run
inference on the quantized model with Inference Engine. See the
`OpenVINO API tutorial <002-openvino-api-with-output.html>`__
for more information about running inference with Inference Engine
Python API.
.. code:: ipython3
def pre_process_image(imagePath, img_height=180):
# Model input format
n, c, h, w = [1, 3, img_height, img_height]
image = Image.open(imagePath)
image = image.resize((h, w), resample=Image.BILINEAR)
# Convert to array and change data layout from HWC to CHW
image = np.array(image)
input_image = image.reshape((n, h, w, c))
return input_image
.. code:: ipython3
# Get the names of the input and output layer
# model_pot = ie.read_model(model="model/optimized/flower_ir.xml")
input_layer = quantized_compiled_model.input(0)
output_layer = quantized_compiled_model.output(0)
# Get the class names: a list of directory names in alphabetical order
class_names = sorted([item.name for item in Path(data_dir).iterdir() if item.is_dir()])
# Run inference on an input image...
inp_img_url = (
"https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
)
directory = "output"
inp_file_name = "A_Close_Up_Photo_of_a_Dandelion.jpg"
file_path = Path(directory)/Path(inp_file_name)
# Download the image if it does not exist yet
if not Path(inp_file_name).exists():
download_file(inp_img_url, inp_file_name, directory=directory)
# Pre-process the image and get it ready for inference.
input_image = pre_process_image(imagePath=file_path)
print(f'input image shape: {input_image.shape}')
print(f'input layer shape: {input_layer.shape}')
res = quantized_compiled_model([input_image])[output_layer]
score = tf.nn.softmax(res[0])
# Show the results
image = Image.open(file_path)
plt.imshow(image)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence.".format(
class_names[np.argmax(score)], 100 * np.max(score)
)
)
.. parsed-literal::
'output/A_Close_Up_Photo_of_a_Dandelion.jpg' already exists.
input image shape: (1, 180, 180, 3)
input layer shape: [1,180,180,3]
This image most likely belongs to dandelion with a 99.63 percent confidence.
.. image:: 301-tensorflow-training-openvino-nncf-with-output_files/301-tensorflow-training-openvino-nncf-with-output_27_1.png
Compare Inference Speed
-----------------------
Measure inference speed with the `OpenVINO Benchmark
App <https://docs.openvino.ai/2023.3/openvino_sample_benchmark_tool.html>`__.
Benchmark App is a command line tool that measures raw inference
performance for a specified OpenVINO IR model. Run
``benchmark_app --help`` to see a list of available parameters. By
default, Benchmark App tests the performance of the model specified with
the ``-m`` parameter with asynchronous inference on CPU, for one minute.
Use the ``-d`` parameter to test performance on a different device, for
example an Intel integrated Graphics (iGPU), and ``-t`` to set the
number of seconds to run inference. See the
`documentation <https://docs.openvino.ai/2023.3/openvino_sample_benchmark_tool.html>`__
for more information.
This tutorial uses a wrapper function from `Notebook
Utils <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/utils/notebook_utils.ipynb>`__.
It prints the ``benchmark_app`` command with the chosen parameters.
In the next cells, inference speed will be measured for the original and
quantized model on CPU. If an iGPU is available, inference speed will be
measured for CPU+GPU as well. The number of seconds is set to 15.
**NOTE**: For the most accurate performance estimation, it is
recommended to run ``benchmark_app`` in a terminal/command prompt
after closing other applications.
.. code:: ipython3
# print the available devices on this system
print("Device information:")
print(core.get_property("CPU", "FULL_DEVICE_NAME"))
if "GPU" in core.available_devices:
print(core.get_property("GPU", "FULL_DEVICE_NAME"))
.. parsed-literal::
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
.. code:: ipython3
# Original model - CPU
! benchmark_app -m $model_xml -d CPU -t 15 -api async
.. parsed-literal::
[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
.. parsed-literal::
[ INFO ] Read model took 12.29 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : u8 / [N,H,W,C] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 7/11] Loading the model to the device
.. parsed-literal::
[ INFO ] Compile model took 61.58 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: TensorFlow_Frontend_IR
[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ] NUM_STREAMS: 12
[ INFO ] AFFINITY: Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS: 24
[ INFO ] PERF_COUNT: NO
[ INFO ] INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ] PERFORMANCE_HINT: THROUGHPUT
[ INFO ] EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ] ENABLE_CPU_PINNING: True
[ INFO ] SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ] ENABLE_HYPER_THREADING: True
[ INFO ] EXECUTION_DEVICES: ['CPU']
[ INFO ] CPU_DENORMALS_OPTIMIZATION: False
[ INFO ] CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'sequential_1_input'!. This input will be filled with random values!
[ INFO ] Fill input 'sequential_1_input' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 8.29 ms
.. parsed-literal::
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 57588 iterations
[ INFO ] Duration: 15001.83 ms
[ INFO ] Latency:
[ INFO ] Median: 2.95 ms
[ INFO ] Average: 2.96 ms
[ INFO ] Min: 1.42 ms
[ INFO ] Max: 11.85 ms
[ INFO ] Throughput: 3838.73 FPS
.. code:: ipython3
# Quantized model - CPU
! benchmark_app -m $compressed_model_xml -d CPU -t 15 -api async
.. parsed-literal::
[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ] Device info:
.. parsed-literal::
[ INFO ] CPU
[ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 14.03 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : f32 / [...] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ] sequential_1_input (node: sequential_1_input) : u8 / [N,H,W,C] / [1,180,180,3]
[ INFO ] Model outputs:
[ INFO ] outputs (node: sequential_2/outputs/BiasAdd) : f32 / [...] / [1,5]
[Step 7/11] Loading the model to the device
.. parsed-literal::
[ INFO ] Compile model took 61.77 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ] NETWORK_NAME: TensorFlow_Frontend_IR
[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ] NUM_STREAMS: 12
[ INFO ] AFFINITY: Affinity.CORE
[ INFO ] INFERENCE_NUM_THREADS: 24
[ INFO ] PERF_COUNT: NO
[ INFO ] INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ] PERFORMANCE_HINT: THROUGHPUT
[ INFO ] EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ] ENABLE_CPU_PINNING: True
[ INFO ] SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ] ENABLE_HYPER_THREADING: True
[ INFO ] EXECUTION_DEVICES: ['CPU']
[ INFO ] CPU_DENORMALS_OPTIMIZATION: False
[ INFO ] CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'sequential_1_input'!. This input will be filled with random values!
[ INFO ] Fill input 'sequential_1_input' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
.. parsed-literal::
[ INFO ] First inference took 2.37 ms
.. parsed-literal::
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 178980 iterations
[ INFO ] Duration: 15001.64 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.97 ms
[ INFO ] Min: 0.59 ms
[ INFO ] Max: 6.96 ms
[ INFO ] Throughput: 11930.70 FPS
**Benchmark on MULTI:CPU,GPU**
With a recent Intel CPU, the best performance can often be achieved by
doing inference on both the CPU and the iGPU, with OpenVINO’s `Multi
Device
Plugin <https://docs.openvino.ai/2021.4/openvino_docs_IE_DG_supported_plugins_MULTI.html>`__.
It takes a bit longer to load a model on GPU than on CPU, so this
benchmark will take a bit longer to complete than the CPU benchmark,
when run for the first time. Benchmark App supports caching, by
specifying the ``--cdir`` parameter. In the cells below, the model will
cached to the ``model_cache`` directory.
.. code:: ipython3
# Original model - MULTI:CPU,GPU
if "GPU" in core.available_devices:
! benchmark_app -m $model_xml -d MULTI:CPU,GPU -t 15 -api async
else:
print("A supported integrated GPU is not available on this system.")
.. parsed-literal::
A supported integrated GPU is not available on this system.
.. code:: ipython3
# Quantized model - MULTI:CPU,GPU
if "GPU" in core.available_devices:
! benchmark_app -m $compressed_model_xml -d MULTI:CPU,GPU -t 15 -api async
else:
print("A supported integrated GPU is not available on this system.")
.. parsed-literal::
A supported integrated GPU is not available on this system.
.. code:: ipython3
# print the available devices on this system
print("Device information:")
print(core.get_property("CPU", "FULL_DEVICE_NAME"))
if "GPU" in core.available_devices:
print(core.get_property("GPU", "FULL_DEVICE_NAME"))
.. parsed-literal::
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
**Original IR model - CPU**
.. code:: ipython3
benchmark_output = %sx benchmark_app -m $model_xml -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
.. parsed-literal::
[ INFO ] Count: 57720 iterations
[ INFO ] Duration: 15002.10 ms
[ INFO ] Latency:
[ INFO ] Median: 2.95 ms
[ INFO ] Average: 2.95 ms
[ INFO ] Min: 1.82 ms
[ INFO ] Max: 13.28 ms
[ INFO ] Throughput: 3847.46 FPS
**Quantized IR model - CPU**
.. code:: ipython3
benchmark_output = %sx benchmark_app -m $compressed_model_xml -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
.. parsed-literal::
[ INFO ] Count: 178680 iterations
[ INFO ] Duration: 15001.15 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.97 ms
[ INFO ] Min: 0.58 ms
[ INFO ] Max: 6.79 ms
[ INFO ] Throughput: 11911.08 FPS
**Original IR model - MULTI:CPU,GPU**
With a recent Intel CPU, the best performance can often be achieved by
doing inference on both the CPU and the iGPU, with OpenVINO’s `Multi
Device
Plugin <https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Running_on_multiple_devices.html>`__.
It takes a bit longer to load a model on GPU than on CPU, so this
benchmark will take a bit longer to complete than the CPU benchmark.
.. code:: ipython3
if "GPU" in core.available_devices:
benchmark_output = %sx benchmark_app -m $model_xml -d MULTI:CPU,GPU -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
else:
print("An GPU is not available on this system.")
.. parsed-literal::
An GPU is not available on this system.
**Quantized IR model - MULTI:CPU,GPU**
.. code:: ipython3
if "GPU" in core.available_devices:
benchmark_output = %sx benchmark_app -m $compressed_model_xml -d MULTI:CPU,GPU -t 15 -api async
# Remove logging info from benchmark_app output and show only the results
benchmark_result = benchmark_output[-8:]
print("\n".join(benchmark_result))
else:
print("An GPU is not available on this system.")
.. parsed-literal::
An GPU is not available on this system.
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