TensorFlow 分類モデルを使用したトレーニング後の量子化¶
この Jupyter ノートブックは、ローカルへのインストール後にのみ起動できます。
この例では、301-tensorflow-training-openvino ノートブックで作成された OpenVINO モデルを量子化して推論速度を向上させる方法を示します。量子化は、NNCF を使用したトレーニング後の量子化によって実行されます。カスタム・データローダーとメトリックが定義され、元の IR モデルと量子化されたモデルの精度とパフォーマンスが計算されます。
目次¶
準備¶
ノートブックでは、トレーニング・ノートブックが実行され、中間表現 (IR) モデルが作成されている必要があります。IR モデルが存在しない場合、次のセルを実行するとトレーニング・ノートブックが実行されます。これにはしばらく時間がかかります。
%pip install -q tensorflow Pillow matplotlib numpy tqdm nncf
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 https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
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
2024-02-10 01:09:00.730910: 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. 2024-02-10 01:09:00.766002: 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.
2024-02-10 01:09:01.406366: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Executing training notebook. This will take a while...
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 https://github.com/pypa/pip/issues/12063
Note: you may need to restart the kernel to use updated packages.
3670
Found 3670 files belonging to 5 classes.
Using 2936 files for training.
2024-02-10 01:09:08.525687: 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
2024-02-10 01:09:08.525725: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
2024-02-10 01:09:08.525729: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
2024-02-10 01:09:08.525856: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
2024-02-10 01:09:08.525872: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
2024-02-10 01:09:08.525876: 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
Found 3670 files belonging to 5 classes.
Using 734 files for validation.
['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
2024-02-10 01:09:08.855253: 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}}]]
2024-02-10 01:09:08.855534: 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}}]]
2024-02-10 01:09:09.711519: 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}}]]
2024-02-10 01:09:09.711766: 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}}]]
(32, 180, 180, 3)
(32,)
2024-02-10 01:09:10.063734: 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}}]]
2024-02-10 01:09:10.064340: 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}}]]
0.0 0.9970461
2024-02-10 01:09:10.875056: 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}}]]
2024-02-10 01:09:10.875365: 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}}]]
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential_1 (Sequential) (None, 180, 180, 3) 0
rescaling_2 (Rescaling) (None, 180, 180, 3) 0
conv2d_3 (Conv2D) (None, 180, 180, 16) 448
max_pooling2d_3 (MaxPooling (None, 90, 90, 16) 0
2D)
conv2d_4 (Conv2D) (None, 90, 90, 32) 4640
max_pooling2d_4 (MaxPooling (None, 45, 45, 32) 0
2D)
conv2d_5 (Conv2D) (None, 45, 45, 64) 18496
max_pooling2d_5 (MaxPooling (None, 22, 22, 64) 0
2D)
dropout (Dropout) (None, 22, 22, 64) 0
flatten_1 (Flatten) (None, 30976) 0
dense_2 (Dense) (None, 128) 3965056
outputs (Dense) (None, 5) 645
=================================================================
Total params: 3,989,285
Trainable params: 3,989,285
Non-trainable params: 0
_________________________________________________________________
Epoch 1/15
2024-02-10 01:09:11.882327: 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}}]]
2024-02-10 01:09:11.882802: 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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2024-02-10 01:09:18.229567: 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}}]]
2024-02-10 01:09:18.229847: 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}}]]
92/92 [==============================] - 7s 66ms/step - loss: 1.2400 - accuracy: 0.4741 - val_loss: 1.3762 - val_accuracy: 0.5014
Epoch 2/15
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92/92 [==============================] - ETA: 0s - loss: 0.9956 - accuracy: 0.5974
92/92 [==============================] - 6s 64ms/step - loss: 0.9956 - accuracy: 0.5974 - val_loss: 0.9920 - val_accuracy: 0.6090
Epoch 3/15
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92/92 [==============================] - ETA: 0s - loss: 0.9155 - accuracy: 0.6298
92/92 [==============================] - 6s 64ms/step - loss: 0.9155 - accuracy: 0.6298 - val_loss: 0.8959 - val_accuracy: 0.6621
Epoch 4/15
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92/92 [==============================] - 6s 64ms/step - loss: 0.8158 - accuracy: 0.6945 - val_loss: 0.8530 - val_accuracy: 0.6757
Epoch 5/15
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92/92 [==============================] - ETA: 0s - loss: 0.7896 - accuracy: 0.6931
92/92 [==============================] - 6s 63ms/step - loss: 0.7896 - accuracy: 0.6931 - val_loss: 0.8867 - val_accuracy: 0.6798
Epoch 6/15
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92/92 [==============================] - ETA: 0s - loss: 0.7647 - accuracy: 0.7115
92/92 [==============================] - 6s 64ms/step - loss: 0.7647 - accuracy: 0.7115 - val_loss: 0.7599 - val_accuracy: 0.7016
Epoch 7/15
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92/92 [==============================] - ETA: 0s - loss: 0.6932 - accuracy: 0.7360
92/92 [==============================] - 6s 64ms/step - loss: 0.6932 - accuracy: 0.7360 - val_loss: 0.7731 - val_accuracy: 0.6853
Epoch 8/15
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92/92 [==============================] - ETA: 0s - loss: 0.6821 - accuracy: 0.7398
92/92 [==============================] - 6s 64ms/step - loss: 0.6821 - accuracy: 0.7398 - val_loss: 0.7942 - val_accuracy: 0.6812
Epoch 9/15
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92/92 [==============================] - ETA: 0s - loss: 0.6469 - accuracy: 0.7510
92/92 [==============================] - 6s 63ms/step - loss: 0.6469 - accuracy: 0.7510 - val_loss: 0.7705 - val_accuracy: 0.6921
Epoch 10/15
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92/92 [==============================] - ETA: 0s - loss: 0.6230 - accuracy: 0.7646
92/92 [==============================] - 6s 63ms/step - loss: 0.6230 - accuracy: 0.7646 - val_loss: 0.7725 - val_accuracy: 0.7153
Epoch 11/15
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92/92 [==============================] - ETA: 0s - loss: 0.5883 - accuracy: 0.7725
92/92 [==============================] - 6s 64ms/step - loss: 0.5883 - accuracy: 0.7725 - val_loss: 0.7175 - val_accuracy: 0.7234
Epoch 12/15
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92/92 [==============================] - ETA: 0s - loss: 0.5609 - accuracy: 0.7827
92/92 [==============================] - 6s 64ms/step - loss: 0.5609 - accuracy: 0.7827 - val_loss: 0.6652 - val_accuracy: 0.7357
Epoch 13/15
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92/92 [==============================] - ETA: 0s - loss: 0.5255 - accuracy: 0.8072
92/92 [==============================] - 6s 64ms/step - loss: 0.5255 - accuracy: 0.8072 - val_loss: 0.7346 - val_accuracy: 0.7384
Epoch 14/15
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92/92 [==============================] - ETA: 0s - loss: 0.5438 - accuracy: 0.7895
92/92 [==============================] - 6s 64ms/step - loss: 0.5438 - accuracy: 0.7895 - val_loss: 0.7761 - val_accuracy: 0.7275
Epoch 15/15
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92/92 [==============================] - ETA: 0s - loss: 0.5165 - accuracy: 0.8004
92/92 [==============================] - 6s 64ms/step - loss: 0.5165 - accuracy: 0.8004 - val_loss: 0.7822 - val_accuracy: 0.7289
1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 74ms/step
This image most likely belongs to sunflowers with a 99.24 percent confidence.
2024-02-10 01:10:41.607321: 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}}]]
2024-02-10 01:10:41.692936: 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}}]]
2024-02-10 01:10:41.703478: 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}}]]
2024-02-10 01:10:41.714441: 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}}]]
2024-02-10 01:10:41.722136: 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}}]]
2024-02-10 01:10:41.728943: 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}}]]
2024-02-10 01:10:41.739850: 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}}]]
2024-02-10 01:10:41.778944: 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}}]]
2024-02-10 01:10:41.847949: 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}}]]
2024-02-10 01:10:41.868730: 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}}]]
2024-02-10 01:10:41.907533: 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}}]]
2024-02-10 01:10:41.933427: 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}}]]
2024-02-10 01:10:42.007195: 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}}]]
2024-02-10 01:10:42.149634: 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}}]]
2024-02-10 01:10:42.286901: 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}}]]
2024-02-10 01:10:42.489392: 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}}]]
2024-02-10 01:10:42.517820: 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}}]]
2024-02-10 01:10:42.563861: 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.
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
INFO:tensorflow:Assets written to: model/flower/saved_model/assets
output/A_Close_Up_Photo_of_a_Dandelion.jpg: 0%| | 0.00/21.7k [00:00<?, ?B/s]
(1, 180, 180, 3)
[1,180,180,3]
This image most likely belongs to dandelion with a 97.96 percent confidence.
インポート¶
トレーニング後の量子化 API は nncf ライブラリーに実装されています。
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
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
NNCF によるトレーニング後の量子化¶
NNCF は、精度の低下を最小限に抑えながら、OpenVINO でニューラル・ネットワーク推論を最適化する一連の高度なアルゴリズムを提供します。
事前トレーニングされた FP32 モデルとキャリブレーション・データセットから量子化モデルを作成します。最適化プロセスには次の手順が含まれます。
量子化用のデータセットを作成します。
最適化されたモデルを取得するには、nncf.quantize を実行します。
検証データセットはトレーニング・ノートブックですでに定義されています。
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
Found 3670 files belonging to 5 classes.
Using 734 files for validation.
<class 'tensorflow.python.framework.ops.EagerTensor'> <class 'tensorflow.python.framework.ops.EagerTensor'>
2024-02-10 01:10:45.668839: 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}}]]
2024-02-10 01:10:45.669302: 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}}]]
また、検証データセットは量子化プロセスで再利用できます。ただし、calibration_dataset は画像のみを返すのに対し、ここではタプル (画像、ラベル) を返します。変換関数は、ユーザー検証データセットをキャリブレーション・データセットに変換するのに役立ちます。
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)
中間表現 (IR) モデルをダウンロードします。
core = Core()
ir_model = core.read_model(model_xml)
基本的な量子化フローを使用します。精度制御を備えたモデルに 8 ビットの量子化を適用する最も高度な量子化フローを使用するには、精度制御による量子化を参照してください。
quantized_model = nncf.quantize(
ir_model,
calibration_dataset,
subset_size=1000
)
Output()
Exception in thread Thread-88:
Traceback (most recent call last):
File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 32, in run
self.live.refresh()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 223, in refresh
self._live_render.set_renderable(self.renderable)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 203, in renderable
renderable = self.get_renderable()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/live.py", line 98, in get_renderable
self._get_renderable()
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1537, in get_renderable
renderable = Group(*self.get_renderables())
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1542, in get_renderables
table = self.make_tasks_table(self.tasks)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1566, in make_tasks_table
table.add_row(
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1571, in <genexpr>
else column(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 528, in __call__
renderable = self.render(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/nncf/common/logging/track_progress.py", line 58, in render
text = super().render(task)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 787, in render
task_time = task.time_remaining
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/rich/progress.py", line 1039, in time_remaining
estimate = ceil(remaining / speed)
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/si te-packages/tensorflow/python/ops/math_ops.py", line 1569, in _truediv_python3
raise TypeError(f"`x` and `y` must have the same dtype, "
TypeError: `x` and `y` must have the same dtype, got tf.int64 != tf.float32.
Output()
量子化されたモデルをベンチマークに保存します。
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))
推論デバイスの選択¶
OpenVINO を使用して推論を実行するためにドロップダウン・リストからデバイスを選択します。
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')
メトリックを比較¶
モデルのパフォーマンスを判断するメトリックを定義します。
このデモでは、精度メトリックを計算する検証関数を定義します。
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
元のモデルと量子化されたモデルの精度を計算します。
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}")
Accuracy of the original model: 0.729
Accuracy of the quantized model: 0.729
モデルのファイルサイズを比較します。
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")
Original model size: 7791.65 KB
Quantized model size: 3897.08 KB
したがって、元のモデルと量子化されたモデルでは、量子化されたモデルのサイズがはるかに小さいにもかかわらず、同様の精度を持っていることが分かります。
量子化モデルで推論を実行¶
トレーニング・ノートブックから前処理関数をコピーし、OpenVINO を使用して量子化モデルで推論を実行します。OpenVINO Python API を使用した推論の実行の詳細については、OpenVINO API チュートリアルを参照してください。
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
# 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)
)
)
'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 98.03 percent confidence.
推論速度の比較¶
OpenVINO ベンチマーク・アプリで推論速度を測定します。
ベンチマーク・アプリは、指定された OpenVINO IR モデルの生の推論パフォーマンスを測定するコマンドライン・ツールです。使用可能なパラメーターのリストを表示するには、benchmark_app --help を実行します。デフォルトでは、ベンチマーク・アプリは、CPU 上で非同期推論を使用して、-m パラメーターで指定されたモデルのパフォーマンスを 1 分間テストします。-d パラメーターを使用して、インテル® 統合グラフィックス (iGPU) などのデバイスでパフォーマンスをテストし、-t を使用して推論を実行する秒数を設定します。詳細についてはドキュメントを参照してください。
このチュートリアルでは、ノートブック・ユーティリティーのラッパー関数を使用します。選択したパラメーターを使用して benchmark_app コマンドを出力します。
次のセルでは、CPU 上で元のモデルと量子化されたモデルの推論速度を測定します。iGPU が利用可能な場合、CPU + GPU の推論速度も測定されます。秒数は 15 に設定されています。
注: 最も正確なパフォーマンス推定を行うには、他のアプリケーションを閉じた後、ターミナル/コマンドプロンプトで
benchmark_appを実行することを推奨します。
# 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"))
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
# Original model - CPU
! benchmark_app -m $model_xml -d CPU -t 15 -api async
[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
[ INFO ] Read model took 12.98 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
[ INFO ] Compile model took 71.95 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 7.48 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 57660 iterations
[ INFO ] Duration: 15004.59 ms
[ INFO ] Latency:
[ INFO ] Median: 2.95 ms
[ INFO ] Average: 2.95 ms
[ INFO ] Min: 1.69 ms
[ INFO ] Max: 12.80 ms
[ INFO ] Throughput: 3842.82 FPS
# Quantized model - CPU
! benchmark_app -m $compressed_model_xml -d CPU -t 15 -api async
[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
[ INFO ] Read model took 15.15 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
[ INFO ] Compile model took 67.57 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 1.99 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count: 178152 iterations
[ INFO ] Duration: 15001.85 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.98 ms
[ INFO ] Min: 0.55 ms
[ INFO ] Max: 11.77 ms
[ INFO ] Throughput: 11875.34 FPS
MULTI のベンチマーク: CPU、GPU
最近のインテル CPU では、OpenVINO のマルチデバイス・プラグインを使用して、CPU と iGPU の両方で推論を実行することで、最高のパフォーマンスを実現できる場合があります。GPU でモデルをロードするには CPU よりも少し時間がかかるため、このベンチマークを初めて実行する場合、完了するまでに CPU ベンチマークよりも少し時間がかかります。ベンチマーク・アプリは、--cdir パラメーターを指定することでキャッシュをサポートします。下のセルでは、モデルは model_cache ディレクトリーにキャッシュされます。
# 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.")
A supported integrated GPU is not available on this system.
# 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.")
A supported integrated GPU is not available on this system.
# 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"))
Device information:
Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
元の IR モデル - CPU
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))
[ INFO ] Count: 57840 iterations
[ INFO ] Duration: 15004.24 ms
[ INFO ] Latency:
[ INFO ] Median: 2.94 ms
[ INFO ] Average: 2.94 ms
[ INFO ] Min: 1.98 ms
[ INFO ] Max: 12.12 ms
[ INFO ] Throughput: 3854.91 FPS
量子化された IR モデル - CPU
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))
[ INFO ] Count: 178836 iterations
[ INFO ] Duration: 15001.19 ms
[ INFO ] Latency:
[ INFO ] Median: 0.94 ms
[ INFO ] Average: 0.97 ms
[ INFO ] Min: 0.58 ms
[ INFO ] Max: 6.85 ms
[ INFO ] Throughput: 11921.45 FPS
元の IR モデル - MULTI: CPU、GPU
最近のインテル CPU では、OpenVINO のマルチデバイス・プラグインを使用して、CPU と iGPU の両方で推論を実行することで、最高のパフォーマンスを実現できる場合があります。GPU でモデルをロードするには CPU よりも少し時間がかかるため、このベンチマークは CPU ベンチマークよりも完了するまでに少し時間を要します。
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("GPU is not available on this system.")
GPU is not available on this system.
量子化された IR モデル - MULTI: CPU、GPU
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("GPU is not available on this system.")
GPU is not available on this system.