TensorFlow フレームワークを使用した NNCF による量子化対応トレーニング¶
この Jupyter ノートブックはオンラインで起動でき、ブラウザーのウィンドウで対話型環境を開きます。ローカルにインストールすることもできます。次のオプションのいずれかを選択します。
のノートブックの目的は、ニューラル・ネットワーク圧縮フレームワーク NNCF 8 ビット量子化を使用して、OpenVINO™ ツールキットによる推論用に TensorFlow モデルを最適化する方法を示すことです。最適化プロセスには次の手順が含まれます。
オリジナルの
FP32モデルをINT8に変換します。微調整を使用して精度を復元します。
最適化されたオリジナルのモデルを Frozen Graph にエクスポートし、次に OpenVINO にエクスポートします。
モデルのパフォーマンスを測定および比較します。
さらに高度な使用法については、これらの例を参照してください。
このチュートリアルでは、Imagenette データセットを持つ ResNet-18 モデルを使用します。Imagenette は、ImageNet データセットから簡単に分類された 10 個のクラスのサブセットです。より小さいモデルとデータセットを使用すると、トレーニングとダウンロード時間が短縮されます。
目次¶
インポートと設定¶
NNCF とすべての補助パッケージを Python コードからインポートします。モデルの名前、入力画像サイズ、使用するバッチサイズ、学習率を設定します。また、モデルの Frozen Graph および OpenVINO IR バージョンが保存されるパスを定義します。
注: チュートリアルを簡略化するために、エラーレベル未満のすべての NNCF ログメッセージ (情報および警告) が無効になっています。運用環境で使用する場合は、
set_log_level(logging.ERROR)を削除してログを有効にすることを推奨します。
import sys
import importlib.util
%pip install -q "openvino>=2023.1.0" "nncf>=2.5.0"
if sys.platform == "win32":
if importlib.util.find_spec("tensorflow_datasets"):
%pip uninstall -q tensorflow-datasets
%pip install -q --upgrade "tfds-nightly"
else:
%pip install -q "tensorflow-datasets>=4.8.0"
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.
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
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 3.20.3 which is incompatible.
Note: you may need to restart the kernel to use updated packages.
from pathlib import Path
import logging
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import layers
from tensorflow.keras import models
from nncf import NNCFConfig
from nncf.tensorflow.helpers.model_creation import create_compressed_model
from nncf.tensorflow.initialization import register_default_init_args
from nncf.common.logging.logger import set_log_level
import openvino as ov
set_log_level(logging.ERROR)
MODEL_DIR = Path("model")
OUTPUT_DIR = Path("output")
MODEL_DIR.mkdir(exist_ok=True)
OUTPUT_DIR.mkdir(exist_ok=True)
BASE_MODEL_NAME = "ResNet-18"
fp32_h5_path = Path(MODEL_DIR / (BASE_MODEL_NAME + "_fp32")).with_suffix(".h5")
fp32_ir_path = Path(OUTPUT_DIR / "saved_model").with_suffix(".xml")
int8_pb_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_int8")).with_suffix(".pb")
int8_ir_path = int8_pb_path.with_suffix(".xml")
BATCH_SIZE = 128
IMG_SIZE = (64, 64) # Default Imagenet image size
NUM_CLASSES = 10 # For Imagenette dataset
LR = 1e-5
MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255) # From Imagenet dataset
STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255) # From Imagenet dataset
fp32_pth_url = "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/305_resnet18_imagenette_fp32_v1.h5"
_ = tf.keras.utils.get_file(fp32_h5_path.resolve(), fp32_pth_url)
print(f'Absolute path where the model weights are saved:\n {fp32_h5_path.resolve()}')
2024-02-10 01:20:39.681060: 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 01:20:39.715569: 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 01:20:40.314657: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
Downloading data from https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/305_resnet18_imagenette_fp32_v1.h5
8192/134604992 [..............................] - ETA: 0s
114688/134604992 [..............................] - ETA: 1:25
311296/134604992 [..............................] - ETA: 57s
507904/134604992 [..............................] - ETA: 48s
704512/134604992 [..............................] - ETA: 46s
901120/134604992 [..............................] - ETA: 43s
1089536/134604992 [..............................] - ETA: 43s
1294336/134604992 [..............................] - ETA: 41s
1490944/134604992 [..............................] - ETA: 42s
1744896/134604992 [..............................] - ETA: 40s
1933312/134604992 [..............................] - ETA: 40s
2138112/134604992 [..............................] - ETA: 40s
2342912/134604992 [..............................] - ETA: 39s
2531328/134604992 [..............................] - ETA: 39s
2785280/134604992 [..............................] - ETA: 39s
2932736/134604992 [..............................] - ETA: 39s
3178496/134604992 [..............................] - ETA: 38s
3325952/134604992 [..............................] - ETA: 39s
3571712/134604992 [..............................] - ETA: 38s
3727360/134604992 [..............................] - ETA: 38s
3956736/134604992 [..............................] - ETA: 38s
4112384/134604992 [..............................] - ETA: 38s
4358144/134604992 [..............................] - ETA: 38s
4603904/134604992 [>.............................] - ETA: 37s
4759552/134604992 [>.............................] - ETA: 37s
4997120/134604992 [>.............................] - ETA: 37s
5160960/134604992 [>.............................] - ETA: 37s
5390336/134604992 [>.............................] - ETA: 37s
5554176/134604992 [>.............................] - ETA: 37s
5783552/134604992 [>.............................] - ETA: 37s
5939200/134604992 [>.............................] - ETA: 37s
6176768/134604992 [>.............................] - ETA: 36s
6332416/134604992 [>.............................] - ETA: 37s
6569984/134604992 [>.............................] - ETA: 36s
6725632/134604992 [>.............................] - ETA: 36s
6955008/134604992 [>.............................] - ETA: 36s
7110656/134604992 [>.............................] - ETA: 36s
7356416/134604992 [>.............................] - ETA: 36s
7512064/134604992 [>.............................] - ETA: 36s
7749632/134604992 [>.............................] - ETA: 36s
7913472/134604992 [>.............................] - ETA: 36s
8142848/134604992 [>.............................] - ETA: 36s
8298496/134604992 [>.............................] - ETA: 36s
8527872/134604992 [>.............................] - ETA: 36s
8683520/134604992 [>.............................] - ETA: 36s
8912896/134604992 [>.............................] - ETA: 35s
9076736/134604992 [=>............................] - ETA: 35s
9306112/134604992 [=>............................] - ETA: 35s
9469952/134604992 [=>............................] - ETA: 35s
9699328/134604992 [=>............................] - ETA: 35s
9879552/134604992 [=>............................] - ETA: 35s
10100736/134604992 [=>............................] - ETA: 35s
10272768/134604992 [=>............................] - ETA: 35s
10485760/134604992 [=>............................] - ETA: 35s
10657792/134604992 [=>............................] - ETA: 35s
10870784/134604992 [=>............................] - ETA: 35s
11042816/134604992 [=>............................] - ETA: 35s
11272192/134604992 [=>............................] - ETA: 35s
11493376/134604992 [=>............................] - ETA: 35s
11657216/134604992 [=>............................] - ETA: 35s
11886592/134604992 [=>............................] - ETA: 34s
12050432/134604992 [=>............................] - ETA: 34s
12263424/134604992 [=>............................] - ETA: 34s
12443648/134604992 [=>............................] - ETA: 34s
12656640/134604992 [=>............................] - ETA: 34s
12828672/134604992 [=>............................] - ETA: 34s
13049856/134604992 [=>............................] - ETA: 34s
13230080/134604992 [=>............................] - ETA: 34s
13443072/134604992 [=>............................] - ETA: 34s
13623296/134604992 [==>...........................] - ETA: 34s
13828096/134604992 [==>...........................] - ETA: 34s
14016512/134604992 [==>...........................] - ETA: 34s
14229504/134604992 [==>...........................] - ETA: 34s
14409728/134604992 [==>...........................] - ETA: 34s
14614528/134604992 [==>...........................] - ETA: 34s
14802944/134604992 [==>...........................] - ETA: 34s
15007744/134604992 [==>...........................] - ETA: 33s
15187968/134604992 [==>...........................] - ETA: 33s
15400960/134604992 [==>...........................] - ETA: 33s
15589376/134604992 [==>...........................] - ETA: 33s
15785984/134604992 [==>...........................] - ETA: 33s
15982592/134604992 [==>...........................] - ETA: 33s
16171008/134604992 [==>...........................] - ETA: 33s
16351232/134604992 [==>...........................] - ETA: 33s
16572416/134604992 [==>...........................] - ETA: 33s
16752640/134604992 [==>...........................] - ETA: 33s
16965632/134604992 [==>...........................] - ETA: 33s
17154048/134604992 [==>...........................] - ETA: 33s
17350656/134604992 [==>...........................] - ETA: 33s
17530880/134604992 [==>...........................] - ETA: 33s
17743872/134604992 [==>...........................] - ETA: 33s
17932288/134604992 [==>...........................] - ETA: 33s
18128896/134604992 [===>..........................] - ETA: 33s
18309120/134604992 [===>..........................] - ETA: 32s
18522112/134604992 [===>..........................] - ETA: 32s
18718720/134604992 [===>..........................] - ETA: 32s
18907136/134604992 [===>..........................] - ETA: 32s
19095552/134604992 [===>..........................] - ETA: 32s
19300352/134604992 [===>..........................] - ETA: 32s
19496960/134604992 [===>..........................] - ETA: 32s
19701760/134604992 [===>..........................] - ETA: 32s
19898368/134604992 [===>..........................] - ETA: 32s
20094976/134604992 [===>..........................] - ETA: 32s
20291584/134604992 [===>..........................] - ETA: 32s
20488192/134604992 [===>..........................] - ETA: 32s
20684800/134604992 [===>..........................] - ETA: 32s
20873216/134604992 [===>..........................] - ETA: 32s
21069824/134604992 [===>..........................] - ETA: 32s
21266432/134604992 [===>..........................] - ETA: 32s
21454848/134604992 [===>..........................] - ETA: 32s
21659648/134604992 [===>..........................] - ETA: 31s
21848064/134604992 [===>..........................] - ETA: 31s
22052864/134604992 [===>..........................] - ETA: 31s
22249472/134604992 [===>..........................] - ETA: 31s
22437888/134604992 [====>.........................] - ETA: 31s
22626304/134604992 [====>.........................] - ETA: 31s
22822912/134604992 [====>.........................] - ETA: 31s
23019520/134604992 [====>.........................] - ETA: 31s
23216128/134604992 [====>.........................] - ETA: 31s
23412736/134604992 [====>.........................] - ETA: 31s
23609344/134604992 [====>.........................] - ETA: 31s
23797760/134604992 [====>.........................] - ETA: 31s
23994368/134604992 [====>.........................] - ETA: 31s
24182784/134604992 [====>.........................] - ETA: 31s
24395776/134604992 [====>.........................] - ETA: 31s
24592384/134604992 [====>.........................] - ETA: 31s
24788992/134604992 [====>.........................] - ETA: 31s
24977408/134604992 [====>.........................] - ETA: 30s
25165824/134604992 [====>.........................] - ETA: 30s
25362432/134604992 [====>.........................] - ETA: 30s
25559040/134604992 [====>.........................] - ETA: 30s
25755648/134604992 [====>.........................] - ETA: 30s
25952256/134604992 [====>.........................] - ETA: 30s
26148864/134604992 [====>.........................] - ETA: 30s
26345472/134604992 [====>.........................] - ETA: 30s
26533888/134604992 [====>.........................] - ETA: 30s
26722304/134604992 [====>.........................] - ETA: 30s
26918912/134604992 [====>.........................] - ETA: 30s
27115520/134604992 [=====>........................] - ETA: 30s
27262976/134604992 [=====>........................] - ETA: 30s
27451392/134604992 [=====>........................] - ETA: 30s
27639808/134604992 [=====>........................] - ETA: 30s
27844608/134604992 [=====>........................] - ETA: 30s
28033024/134604992 [=====>........................] - ETA: 30s
28229632/134604992 [=====>........................] - ETA: 30s
28426240/134604992 [=====>........................] - ETA: 30s
28622848/134604992 [=====>........................] - ETA: 29s
28835840/134604992 [=====>........................] - ETA: 29s
29016064/134604992 [=====>........................] - ETA: 29s
29220864/134604992 [=====>........................] - ETA: 29s
29417472/134604992 [=====>........................] - ETA: 29s
29605888/134604992 [=====>........................] - ETA: 29s
29786112/134604992 [=====>........................] - ETA: 29s
29999104/134604992 [=====>........................] - ETA: 29s
30195712/134604992 [=====>........................] - ETA: 29s
30384128/134604992 [=====>........................] - ETA: 29s
30580736/134604992 [=====>........................] - ETA: 29s
30785536/134604992 [=====>........................] - ETA: 29s
30973952/134604992 [=====>........................] - ETA: 29s
31170560/134604992 [=====>........................] - ETA: 29s
31367168/134604992 [=====>........................] - ETA: 29s
31563776/134604992 [======>.......................] - ETA: 29s
31752192/134604992 [======>.......................] - ETA: 29s
31965184/134604992 [======>.......................] - ETA: 28s
32153600/134604992 [======>.......................] - ETA: 28s
32350208/134604992 [======>.......................] - ETA: 28s
32522240/134604992 [======>.......................] - ETA: 28s
32735232/134604992 [======>.......................] - ETA: 28s
32923648/134604992 [======>.......................] - ETA: 28s
33120256/134604992 [======>.......................] - ETA: 28s
33308672/134604992 [======>.......................] - ETA: 28s
33521664/134604992 [======>.......................] - ETA: 28s
33710080/134604992 [======>.......................] - ETA: 28s
33906688/134604992 [======>.......................] - ETA: 28s
34086912/134604992 [======>.......................] - ETA: 28s
34299904/134604992 [======>.......................] - ETA: 28s
34480128/134604992 [======>.......................] - ETA: 28s
34693120/134604992 [======>.......................] - ETA: 28s
34881536/134604992 [======>.......................] - ETA: 28s
35078144/134604992 [======>.......................] - ETA: 28s
35266560/134604992 [======>.......................] - ETA: 28s
35463168/134604992 [======>.......................] - ETA: 27s
35659776/134604992 [======>.......................] - ETA: 27s
35864576/134604992 [======>.......................] - ETA: 27s
36061184/134604992 [=======>......................] - ETA: 27s
36249600/134604992 [=======>......................] - ETA: 27s
36446208/134604992 [=======>......................] - ETA: 27s
36642816/134604992 [=======>......................] - ETA: 27s
36839424/134604992 [=======>......................] - ETA: 27s
37036032/134604992 [=======>......................] - ETA: 27s
37216256/134604992 [=======>......................] - ETA: 27s
37412864/134604992 [=======>......................] - ETA: 27s
37601280/134604992 [=======>......................] - ETA: 27s
37814272/134604992 [=======>......................] - ETA: 27s
37994496/134604992 [=======>......................] - ETA: 27s
38207488/134604992 [=======>......................] - ETA: 27s
38395904/134604992 [=======>......................] - ETA: 27s
38600704/134604992 [=======>......................] - ETA: 27s
38789120/134604992 [=======>......................] - ETA: 27s
38985728/134604992 [=======>......................] - ETA: 26s
39174144/134604992 [=======>......................] - ETA: 26s
39370752/134604992 [=======>......................] - ETA: 26s
39567360/134604992 [=======>......................] - ETA: 26s
39763968/134604992 [=======>......................] - ETA: 26s
39960576/134604992 [=======>......................] - ETA: 26s
40157184/134604992 [=======>......................] - ETA: 26s
40345600/134604992 [=======>......................] - ETA: 26s
40542208/134604992 [========>.....................] - ETA: 26s
40730624/134604992 [========>.....................] - ETA: 26s
40943616/134604992 [========>.....................] - ETA: 26s
41140224/134604992 [========>.....................] - ETA: 26s
41328640/134604992 [========>.....................] - ETA: 26s
41517056/134604992 [========>.....................] - ETA: 26s
41713664/134604992 [========>.....................] - ETA: 26s
41902080/134604992 [========>.....................] - ETA: 26s
42098688/134604992 [========>.....................] - ETA: 26s
42295296/134604992 [========>.....................] - ETA: 26s
42491904/134604992 [========>.....................] - ETA: 25s
42688512/134604992 [========>.....................] - ETA: 25s
42885120/134604992 [========>.....................] - ETA: 25s
43081728/134604992 [========>.....................] - ETA: 25s
43270144/134604992 [========>.....................] - ETA: 25s
43466752/134604992 [========>.....................] - ETA: 25s
43671552/134604992 [========>.....................] - ETA: 25s
43859968/134604992 [========>.....................] - ETA: 25s
44056576/134604992 [========>.....................] - ETA: 25s
44253184/134604992 [========>.....................] - ETA: 25s
44449792/134604992 [========>.....................] - ETA: 25s
44638208/134604992 [========>.....................] - ETA: 25s
44843008/134604992 [========>.....................] - ETA: 25s
45039616/134604992 [=========>....................] - ETA: 25s
45236224/134604992 [=========>....................] - ETA: 25s
45432832/134604992 [=========>....................] - ETA: 25s
45629440/134604992 [=========>....................] - ETA: 25s
45826048/134604992 [=========>....................] - ETA: 25s
46022656/134604992 [=========>....................] - ETA: 24s
46211072/134604992 [=========>....................] - ETA: 24s
46415872/134604992 [=========>....................] - ETA: 24s
46604288/134604992 [=========>....................] - ETA: 24s
46792704/134604992 [=========>....................] - ETA: 24s
46981120/134604992 [=========>....................] - ETA: 24s
47194112/134604992 [=========>....................] - ETA: 24s
47390720/134604992 [=========>....................] - ETA: 24s
47595520/134604992 [=========>....................] - ETA: 24s
47775744/134604992 [=========>....................] - ETA: 24s
47988736/134604992 [=========>....................] - ETA: 24s
48185344/134604992 [=========>....................] - ETA: 24s
48381952/134604992 [=========>....................] - ETA: 24s
48562176/134604992 [=========>....................] - ETA: 24s
48766976/134604992 [=========>....................] - ETA: 24s
48963584/134604992 [=========>....................] - ETA: 24s
49168384/134604992 [=========>....................] - ETA: 24s
49348608/134604992 [=========>....................] - ETA: 24s
49553408/134604992 [==========>...................] - ETA: 23s
49733632/134604992 [==========>...................] - ETA: 23s
49946624/134604992 [==========>...................] - ETA: 23s
50135040/134604992 [==========>...................] - ETA: 23s
50339840/134604992 [==========>...................] - ETA: 23s
50520064/134604992 [==========>...................] - ETA: 23s
50733056/134604992 [==========>...................] - ETA: 23s
50913280/134604992 [==========>...................] - ETA: 23s
51126272/134604992 [==========>...................] - ETA: 23s
51298304/134604992 [==========>...................] - ETA: 23s
51511296/134604992 [==========>...................] - ETA: 23s
51691520/134604992 [==========>...................] - ETA: 23s
51904512/134604992 [==========>...................] - ETA: 23s
52076544/134604992 [==========>...................] - ETA: 23s
52281344/134604992 [==========>...................] - ETA: 23s
52461568/134604992 [==========>...................] - ETA: 23s
52682752/134604992 [==========>...................] - ETA: 23s
52862976/134604992 [==========>...................] - ETA: 23s
53067776/134604992 [==========>...................] - ETA: 22s
53248000/134604992 [==========>...................] - ETA: 22s
53460992/134604992 [==========>...................] - ETA: 22s
53641216/134604992 [==========>...................] - ETA: 22s
53862400/134604992 [===========>..................] - ETA: 22s
54042624/134604992 [===========>..................] - ETA: 22s
54255616/134604992 [===========>..................] - ETA: 22s
54435840/134604992 [===========>..................] - ETA: 22s
54648832/134604992 [===========>..................] - ETA: 22s
54820864/134604992 [===========>..................] - ETA: 22s
55025664/134604992 [===========>..................] - ETA: 22s
55205888/134604992 [===========>..................] - ETA: 22s
55410688/134604992 [===========>..................] - ETA: 22s
55574528/134604992 [===========>..................] - ETA: 22s
55738368/134604992 [===========>..................] - ETA: 22s
55951360/134604992 [===========>..................] - ETA: 22s
56123392/134604992 [===========>..................] - ETA: 22s
56336384/134604992 [===========>..................] - ETA: 22s
56500224/134604992 [===========>..................] - ETA: 22s
56721408/134604992 [===========>..................] - ETA: 21s
56893440/134604992 [===========>..................] - ETA: 21s
57122816/134604992 [===========>..................] - ETA: 21s
57286656/134604992 [===========>..................] - ETA: 21s
57507840/134604992 [===========>..................] - ETA: 21s
57688064/134604992 [===========>..................] - ETA: 21s
57909248/134604992 [===========>..................] - ETA: 21s
58064896/134604992 [===========>..................] - ETA: 21s
58294272/134604992 [===========>..................] - ETA: 21s
58474496/134604992 [============>.................] - ETA: 21s
58695680/134604992 [============>.................] - ETA: 21s
58851328/134604992 [============>.................] - ETA: 21s
59064320/134604992 [============>.................] - ETA: 21s
59244544/134604992 [============>.................] - ETA: 21s
59457536/134604992 [============>.................] - ETA: 21s
59637760/134604992 [============>.................] - ETA: 21s
59858944/134604992 [============>.................] - ETA: 21s
60022784/134604992 [============>.................] - ETA: 21s
60252160/134604992 [============>.................] - ETA: 20s
60416000/134604992 [============>.................] - ETA: 20s
60645376/134604992 [============>.................] - ETA: 20s
60792832/134604992 [============>.................] - ETA: 20s
60915712/134604992 [============>.................] - ETA: 20s
61177856/134604992 [============>.................] - ETA: 20s
61440000/134604992 [============>.................] - ETA: 20s
61702144/134604992 [============>.................] - ETA: 20s
61947904/134604992 [============>.................] - ETA: 20s
62210048/134604992 [============>.................] - ETA: 20s
62472192/134604992 [============>.................] - ETA: 20s
62734336/134604992 [============>.................] - ETA: 20s
62996480/134604992 [=============>................] - ETA: 20s
63258624/134604992 [=============>................] - ETA: 20s
63520768/134604992 [=============>................] - ETA: 20s
63782912/134604992 [=============>................] - ETA: 19s
64028672/134604992 [=============>................] - ETA: 19s
64290816/134604992 [=============>................] - ETA: 19s
64561152/134604992 [=============>................] - ETA: 19s
64823296/134604992 [=============>................] - ETA: 19s
65069056/134604992 [=============>................] - ETA: 19s
65331200/134604992 [=============>................] - ETA: 19s
65593344/134604992 [=============>................] - ETA: 19s
65855488/134604992 [=============>................] - ETA: 19s
66117632/134604992 [=============>................] - ETA: 19s
66371584/134604992 [=============>................] - ETA: 19s
66633728/134604992 [=============>................] - ETA: 19s
66904064/134604992 [=============>................] - ETA: 19s
67158016/134604992 [=============>................] - ETA: 19s
67420160/134604992 [==============>...............] - ETA: 18s
67665920/134604992 [==============>...............] - ETA: 18s
67928064/134604992 [==============>...............] - ETA: 18s
68198400/134604992 [==============>...............] - ETA: 18s
68452352/134604992 [==============>...............] - ETA: 18s
68722688/134604992 [==============>...............] - ETA: 18s
68984832/134604992 [==============>...............] - ETA: 18s
69230592/134604992 [==============>...............] - ETA: 18s
69500928/134604992 [==============>...............] - ETA: 18s
69763072/134604992 [==============>...............] - ETA: 18s
70017024/134604992 [==============>...............] - ETA: 18s
70279168/134604992 [==============>...............] - ETA: 18s
70541312/134604992 [==============>...............] - ETA: 18s
70803456/134604992 [==============>...............] - ETA: 18s
71065600/134604992 [==============>...............] - ETA: 17s
71319552/134604992 [==============>...............] - ETA: 17s
71581696/134604992 [==============>...............] - ETA: 17s
71843840/134604992 [===============>..............] - ETA: 17s
72105984/134604992 [===============>..............] - ETA: 17s
72368128/134604992 [===============>..............] - ETA: 17s
72622080/134604992 [===============>..............] - ETA: 17s
72884224/134604992 [===============>..............] - ETA: 17s
73146368/134604992 [===============>..............] - ETA: 17s
73408512/134604992 [===============>..............] - ETA: 17s
73670656/134604992 [===============>..............] - ETA: 17s
73924608/134604992 [===============>..............] - ETA: 17s
74072064/134604992 [===============>..............] - ETA: 17s
74309632/134604992 [===============>..............] - ETA: 17s
74457088/134604992 [===============>..............] - ETA: 16s
74702848/134604992 [===============>..............] - ETA: 16s
74850304/134604992 [===============>..............] - ETA: 16s
75096064/134604992 [===============>..............] - ETA: 16s
75350016/134604992 [===============>..............] - ETA: 16s
75497472/134604992 [===============>..............] - ETA: 16s
75743232/134604992 [===============>..............] - ETA: 16s
75890688/134604992 [===============>..............] - ETA: 16s
76136448/134604992 [===============>..............] - ETA: 16s
76283904/134604992 [================>.............] - ETA: 16s
76529664/134604992 [================>.............] - ETA: 16s
76668928/134604992 [================>.............] - ETA: 16s
76931072/134604992 [================>.............] - ETA: 16s
77185024/134604992 [================>.............] - ETA: 16s
77438976/134604992 [================>.............] - ETA: 16s
77701120/134604992 [================>.............] - ETA: 16s
77840384/134604992 [================>.............] - ETA: 16s
78086144/134604992 [================>.............] - ETA: 15s
78225408/134604992 [================>.............] - ETA: 15s
78487552/134604992 [================>.............] - ETA: 15s
78733312/134604992 [================>.............] - ETA: 15s
78880768/134604992 [================>.............] - ETA: 15s
79126528/134604992 [================>.............] - ETA: 15s
79282176/134604992 [================>.............] - ETA: 15s
79527936/134604992 [================>.............] - ETA: 15s
79790080/134604992 [================>.............] - ETA: 15s
79929344/134604992 [================>.............] - ETA: 15s
80175104/134604992 [================>.............] - ETA: 15s
80322560/134604992 [================>.............] - ETA: 15s
80576512/134604992 [================>.............] - ETA: 15s
80830464/134604992 [=================>............] - ETA: 15s
81092608/134604992 [=================>............] - ETA: 15s
81354752/134604992 [=================>............] - ETA: 15s
81616896/134604992 [=================>............] - ETA: 14s
81879040/134604992 [=================>............] - ETA: 14s
82132992/134604992 [=================>............] - ETA: 14s
82280448/134604992 [=================>............] - ETA: 14s
82526208/134604992 [=================>............] - ETA: 14s
82780160/134604992 [=================>............] - ETA: 14s
82919424/134604992 [=================>............] - ETA: 14s
83181568/134604992 [=================>............] - ETA: 14s
83435520/134604992 [=================>............] - ETA: 14s
83689472/134604992 [=================>............] - ETA: 14s
83869696/134604992 [=================>............] - ETA: 14s
84082688/134604992 [=================>............] - ETA: 14s
84344832/134604992 [=================>............] - ETA: 14s
84606976/134604992 [=================>............] - ETA: 14s
84860928/134604992 [=================>............] - ETA: 14s
85123072/134604992 [=================>............] - ETA: 13s
85377024/134604992 [==================>...........] - ETA: 13s
85524480/134604992 [==================>...........] - ETA: 13s
85778432/134604992 [==================>...........] - ETA: 13s
86040576/134604992 [==================>...........] - ETA: 13s
86310912/134604992 [==================>...........] - ETA: 13s
86564864/134604992 [==================>...........] - ETA: 13s
86827008/134604992 [==================>...........] - ETA: 13s
87089152/134604992 [==================>...........] - ETA: 13s
87343104/134604992 [==================>...........] - ETA: 13s
87490560/134604992 [==================>...........] - ETA: 13s
87736320/134604992 [==================>...........] - ETA: 13s
87998464/134604992 [==================>...........] - ETA: 13s
88260608/134604992 [==================>...........] - ETA: 13s
88514560/134604992 [==================>...........] - ETA: 12s
88653824/134604992 [==================>...........] - ETA: 12s
88915968/134604992 [==================>...........] - ETA: 12s
89169920/134604992 [==================>...........] - ETA: 12s
89432064/134604992 [==================>...........] - ETA: 12s
89694208/134604992 [==================>...........] - ETA: 12s
89956352/134604992 [===================>..........] - ETA: 12s
90218496/134604992 [===================>..........] - ETA: 12s
90472448/134604992 [===================>..........] - ETA: 12s
90734592/134604992 [===================>..........] - ETA: 12s
90996736/134604992 [===================>..........] - ETA: 12s
91250688/134604992 [===================>..........] - ETA: 12s
91512832/134604992 [===================>..........] - ETA: 12s
91774976/134604992 [===================>..........] - ETA: 12s
92037120/134604992 [===================>..........] - ETA: 12s
92299264/134604992 [===================>..........] - ETA: 11s
92561408/134604992 [===================>..........] - ETA: 11s
92815360/134604992 [===================>..........] - ETA: 11s
93069312/134604992 [===================>..........] - ETA: 11s
93331456/134604992 [===================>..........] - ETA: 11s
93593600/134604992 [===================>..........] - ETA: 11s
93863936/134604992 [===================>..........] - ETA: 11s
94117888/134604992 [===================>..........] - ETA: 11s
94380032/134604992 [====================>.........] - ETA: 11s
94642176/134604992 [====================>.........] - ETA: 11s
94887936/134604992 [====================>.........] - ETA: 11s
95150080/134604992 [====================>.........] - ETA: 11s
95412224/134604992 [====================>.........] - ETA: 11s
95674368/134604992 [====================>.........] - ETA: 10s
95936512/134604992 [====================>.........] - ETA: 10s
96206848/134604992 [====================>.........] - ETA: 10s
96460800/134604992 [====================>.........] - ETA: 10s
96706560/134604992 [====================>.........] - ETA: 10s
96976896/134604992 [====================>.........] - ETA: 10s
97239040/134604992 [====================>.........] - ETA: 10s
97501184/134604992 [====================>.........] - ETA: 10s
97746944/134604992 [====================>.........] - ETA: 10s
98009088/134604992 [====================>.........] - ETA: 10s
98271232/134604992 [====================>.........] - ETA: 10s
98533376/134604992 [====================>.........] - ETA: 10s
98787328/134604992 [=====================>........] - ETA: 10s
99000320/134604992 [=====================>........] - ETA: 10s
99196928/134604992 [=====================>........] - ETA: 9s
99459072/134604992 [=====================>........] - ETA: 9s
99713024/134604992 [=====================>........] - ETA: 9s
99966976/134604992 [=====================>........] - ETA: 9s
100237312/134604992 [=====================>……..] - ETA: 9s
100499456/134604992 [=====================>……..]- ETA: 9s
100761600/134604992 [=====================>……..]- ETA: 9s
101023744/134604992 [=====================>……..]- ETA: 9s
101269504/134604992 [=====================>……..]- ETA: 9s
101433344/134604992 [=====================>……..]- ETA: 9s
101662720/134604992 [=====================>……..]- ETA: 9s
101924864/134604992 [=====================>……..]- ETA: 9s
102187008/134604992 [=====================>……..]- ETA: 9s
102440960/134604992 [=====================>……..]- ETA: 9s
102604800/134604992 [=====================>……..]- ETA: 9s
102842368/134604992 [=====================>……..]- ETA: 8s
103096320/134604992 [=====================>……..]- ETA: 8s
103358464/134604992 [======================>…….] - ETA: 8s
103620608/134604992 [======================>…….]- ETA: 8s
103882752/134604992 [======================>…….]- ETA: 8s
104144896/134604992 [======================>…….]- ETA: 8s
104398848/134604992 [======================>…….]- ETA: 8s
104546304/134604992 [======================>…….]- ETA: 8s
104792064/134604992 [======================>…….]- ETA: 8s
105054208/134604992 [======================>…….]- ETA: 8s
105316352/134604992 [======================>…….]- ETA: 8s
105570304/134604992 [======================>…….]- ETA: 8s
105832448/134604992 [======================>…….]- ETA: 8s
105979904/134604992 [======================>…….]- ETA: 8s
106225664/134604992 [======================>…….]- ETA: 7s
106389504/134604992 [======================>…….]- ETA: 7s
106618880/134604992 [======================>…….]- ETA: 7s
106872832/134604992 [======================>…….]- ETA: 7s
107020288/134604992 [======================>…….]- ETA: 7s
107266048/134604992 [======================>…….]- ETA: 7s
107413504/134604992 [======================>…….]- ETA: 7s
107651072/134604992 [======================>…….]- ETA: 7s
107798528/134604992 [=======================>……] - ETA: 7s
108044288/134604992 [=======================>……] - ETA: 7s
108191744/134604992 [=======================>……] - ETA: 7s
108437504/134604992 [=======================>……] - ETA: 7s
108584960/134604992 [=======================>……] - ETA: 7s
108830720/134604992 [=======================>……] - ETA: 7s
108994560/134604992 [=======================>……] - ETA: 7s
109223936/134604992 [=======================>……] - ETA: 7s
109477888/134604992 [=======================>……] - ETA: 7s
109625344/134604992 [=======================>……] - ETA: 7s
109862912/134604992 [=======================>……] - ETA: 6s
110010368/134604992 [=======================>……] - ETA: 6s
110264320/134604992 [=======================>……] - ETA: 6s
110518272/134604992 [=======================>……] - ETA: 6s
110673920/134604992 [=======================>……] - ETA: 6s
110903296/134604992 [=======================>……] - ETA: 6s
111050752/134604992 [=======================>……] - ETA: 6s
111304704/134604992 [=======================>……] - ETA: 6s
111550464/134604992 [=======================>……] - ETA: 6s
111697920/134604992 [=======================>……] - ETA: 6s
111943680/134604992 [=======================>……] - ETA: 6s
112091136/134604992 [=======================>……] - ETA: 6s
112336896/134604992 [========================>…..] - ETA: 6s
112484352/134604992 [========================>…..]- ETA: 6s
112738304/134604992 [========================>…..]- ETA: 6s
112893952/134604992 [========================>…..]- ETA: 6s
113123328/134604992 [========================>…..]- ETA: 6s
113270784/134604992 [========================>…..]- ETA: 6s
113516544/134604992 [========================>…..]- ETA: 5s
113655808/134604992 [========================>…..]- ETA: 5s
113901568/134604992 [========================>…..]- ETA: 5s
114057216/134604992 [========================>…..]- ETA: 5s
114302976/134604992 [========================>…..]- ETA: 5s
114450432/134604992 [========================>…..]- ETA: 5s
114696192/134604992 [========================>…..]- ETA: 5s
114843648/134604992 [========================>…..]- ETA: 5s
115089408/134604992 [========================>…..]- ETA: 5s
115228672/134604992 [========================>…..]- ETA: 5s
115466240/134604992 [========================>…..]- ETA: 5s
115613696/134604992 [========================>…..]- ETA: 5s
115859456/134604992 [========================>…..]- ETA: 5s
116006912/134604992 [========================>…..]- ETA: 5s
116244480/134604992 [========================>…..]- ETA: 5s
116383744/134604992 [========================>…..]- ETA: 5s
116629504/134604992 [========================>…..]- ETA: 5s
116768768/134604992 [=========================>….] - ETA: 5s
116981760/134604992 [=========================>….]- ETA: 4s
117161984/134604992 [=========================>….]- ETA: 4s
117407744/134604992 [=========================>….]- ETA: 4s
117555200/134604992 [=========================>….]- ETA: 4s
117800960/134604992 [=========================>….]- ETA: 4s
117948416/134604992 [=========================>….]- ETA: 4s
118194176/134604992 [=========================>….]- ETA: 4s
118333440/134604992 [=========================>….]- ETA: 4s
118579200/134604992 [=========================>….]- ETA: 4s
118726656/134604992 [=========================>….]- ETA: 4s
118972416/134604992 [=========================>….]- ETA: 4s
119119872/134604992 [=========================>….]- ETA: 4s
119365632/134604992 [=========================>….]- ETA: 4s
119513088/134604992 [=========================>….]- ETA: 4s
119758848/134604992 [=========================>….]- ETA: 4s
119898112/134604992 [=========================>….]- ETA: 4s
120143872/134604992 [=========================>….]- ETA: 4s
120291328/134604992 [=========================>….]- ETA: 4s
120545280/134604992 [=========================>….]- ETA: 3s
120692736/134604992 [=========================>….]- ETA: 3s
120938496/134604992 [=========================>….]- ETA: 3s
121077760/134604992 [=========================>….]- ETA: 3s
121323520/134604992 [==========================>…] - ETA: 3s
121470976/134604992 [==========================>…] - ETA: 3s
121716736/134604992 [==========================>…] - ETA: 3s
121970688/134604992 [==========================>…] - ETA: 3s
122118144/134604992 [==========================>…] - ETA: 3s
122363904/134604992 [==========================>…] - ETA: 3s
122511360/134604992 [==========================>…] - ETA: 3s
122757120/134604992 [==========================>…] - ETA: 3s
122904576/134604992 [==========================>…] - ETA: 3s
123150336/134604992 [==========================>…] - ETA: 3s
123289600/134604992 [==========================>…] - ETA: 3s
123535360/134604992 [==========================>…] - ETA: 3s
123674624/134604992 [==========================>…] - ETA: 3s
123936768/134604992 [==========================>…] - ETA: 3s
124190720/134604992 [==========================>…] - ETA: 2s
124338176/134604992 [==========================>…] - ETA: 2s
124575744/134604992 [==========================>…] - ETA: 2s
124723200/134604992 [==========================>…] - ETA: 2s
124977152/134604992 [==========================>…] - ETA: 2s
125239296/134604992 [==========================>…] - ETA: 2s
125501440/134604992 [==========================>…] - ETA: 2s
125747200/134604992 [===========================>..] - ETA: 2s
125894656/134604992 [===========================>..]- ETA: 2s
126148608/134604992 [===========================>..]- ETA: 2s
126296064/134604992 [===========================>..]- ETA: 2s
126533632/134604992 [===========================>..]- ETA: 2s
126795776/134604992 [===========================>..]- ETA: 2s
127057920/134604992 [===========================>..]- ETA: 2s
127197184/134604992 [===========================>..]- ETA: 2s
127451136/134604992 [===========================>..]- ETA: 2s
127713280/134604992 [===========================>..]- ETA: 1s
127967232/134604992 [===========================>..]- ETA: 1s
128114688/134604992 [===========================>..]- ETA: 1s
128360448/134604992 [===========================>..]- ETA: 1s
128507904/134604992 [===========================>..]- ETA: 1s
128761856/134604992 [===========================>..]- ETA: 1s
128901120/134604992 [===========================>..]- ETA: 1s
129146880/134604992 [===========================>..]- ETA: 1s
129286144/134604992 [===========================>..]- ETA: 1s
129531904/134604992 [===========================>..]- ETA: 1s
129671168/134604992 [===========================>..]- ETA: 1s
129925120/134604992 [===========================>..]- ETA: 1s
130064384/134604992 [===========================>..]- ETA: 1s
130318336/134604992 [============================>.]- ETA: 1s
130572288/134604992 [============================>.]- ETA: 1s
130703360/134604992 [============================>.]- ETA: 1s
130965504/134604992 [============================>.]- ETA: 1s
131227648/134604992 [============================>.]- ETA: 0s
131481600/134604992 [============================>.]- ETA: 0s
131743744/134604992 [============================>.]- ETA: 0s
132005888/134604992 [============================>.]- ETA: 0s
132268032/134604992 [============================>.]- ETA: 0s
132521984/134604992 [============================>.]- ETA: 0s
132784128/134604992 [============================>.]- ETA: 0s
133046272/134604992 [============================>.]- ETA: 0s
133300224/134604992 [============================>.]- ETA: 0s
133570560/134604992 [============================>.]- ETA: 0s
133832704/134604992 [============================>.]- ETA: 0s
134094848/134604992 [============================>.]- ETA: 0s
134348800/134604992 [============================>.]- ETA: 0s
134604992/134604992 [==============================] - 38s 0us/step
Absolute path where the model weights are saved:
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/305-tensorflow-quantization-aware-training/model/ResNet-18_fp32.h5
データセットの前処理¶
Imagenette 160px データセットをダウンロードして準備します。
- クラスの数: 10
- ダウンロード・サイズ: 94.18 MiB
| Split | Examples |
|--------------|----------|
| 'train' | 12,894 |
| 'validation' | 500 |
datasets, datasets_info = tfds.load('imagenette/160px', shuffle_files=True, as_supervised=True, with_info=True,
read_config=tfds.ReadConfig(shuffle_seed=0))
train_dataset, validation_dataset = datasets['train'], datasets['validation']
fig = tfds.show_examples(train_dataset, datasets_info)
2024-02-10 01:21:23.377534: 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 01:21:23.377573: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
2024-02-10 01:21:23.377577: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
2024-02-10 01:21:23.377743: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
2024-02-10 01:21:23.377761: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
2024-02-10 01:21:23.377764: 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
2024-02-10 01:21:23.497343: 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/_1' with dtype string and shape [1]
[[{{node Placeholder/_1}}]]
2024-02-10 01:21:23.497696: 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/_2' with dtype string and shape [1]
[[{{node Placeholder/_2}}]]
2024-02-10 01:21:23.570158: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
def preprocessing(image, label):
image = tf.image.resize(image, IMG_SIZE)
image = image - MEAN_RGB
image = image / STDDEV_RGB
label = tf.one_hot(label, NUM_CLASSES)
return image, label
train_dataset = (train_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE)
.batch(BATCH_SIZE)
.prefetch(tf.data.experimental.AUTOTUNE))
validation_dataset = (validation_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE)
.batch(BATCH_SIZE)
.prefetch(tf.data.experimental.AUTOTUNE))
浮動小数点モデルを定義¶
def residual_conv_block(filters, stage, block, strides=(1, 1), cut='pre'):
def layer(input_tensor):
x = layers.BatchNormalization(epsilon=2e-5)(input_tensor)
x = layers.Activation('relu')(x)
# Defining shortcut connection.
if cut == 'pre':
shortcut = input_tensor
elif cut == 'post':
shortcut = layers.Conv2D(filters, (1, 1), strides=strides, kernel_initializer='he_uniform',
use_bias=False)(x)
# Continue with convolution layers.
x = layers.ZeroPadding2D(padding=(1, 1))(x)
x = layers.Conv2D(filters, (3, 3), strides=strides, kernel_initializer='he_uniform', use_bias=False)(x)
x = layers.BatchNormalization(epsilon=2e-5)(x)
x = layers.Activation('relu')(x)
x = layers.ZeroPadding2D(padding=(1, 1))(x)
x = layers.Conv2D(filters, (3, 3), kernel_initializer='he_uniform', use_bias=False)(x)
# Add residual connection.
x = layers.Add()([x, shortcut])
return x
return layer
def ResNet18(input_shape=None):
"""Instantiates the ResNet18 architecture."""
img_input = layers.Input(shape=input_shape, name='data')
# ResNet18 bottom
x = layers.BatchNormalization(epsilon=2e-5, scale=False)(img_input)
x = layers.ZeroPadding2D(padding=(3, 3))(x)
x = layers.Conv2D(64, (7, 7), strides=(2, 2), kernel_initializer='he_uniform', use_bias=False)(x)
x = layers.BatchNormalization(epsilon=2e-5)(x)
x = layers.Activation('relu')(x)
x = layers.ZeroPadding2D(padding=(1, 1))(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(x)
# ResNet18 body
repetitions = (2, 2, 2, 2)
for stage, rep in enumerate(repetitions):
for block in range(rep):
filters = 64 * (2 ** stage)
if block == 0 and stage == 0:
x = residual_conv_block(filters, stage, block, strides=(1, 1), cut='post')(x)
elif block == 0:
x = residual_conv_block(filters, stage, block, strides=(2, 2), cut='post')(x)
else:
x = residual_conv_block(filters, stage, block, strides=(1, 1), cut='pre')(x)
x = layers.BatchNormalization(epsilon=2e-5)(x)
x = layers.Activation('relu')(x)
# ResNet18 top
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(NUM_CLASSES)(x)
x = layers.Activation('softmax')(x)
# Create the model.
model = models.Model(img_input, x)
return model
IMG_SHAPE = IMG_SIZE + (3,)
fp32_model = ResNet18(input_shape=IMG_SHAPE)
浮動小数点モデルの事前トレーニング¶
Using NNCF for model compression assumes that the user has a pre-trained model and a training pipeline.
注: チュートリアルを簡単にするため、
FP32モデルのトレーニングをスキップして、提供されている重みをロードすることをお勧めします。
# Load the floating-point weights.
fp32_model.load_weights(fp32_h5_path)
# Compile the floating-point model.
fp32_model.compile(
loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
metrics=[tf.keras.metrics.CategoricalAccuracy(name='acc@1')]
)
# Validate the floating-point model.
test_loss, acc_fp32 = fp32_model.evaluate(
validation_dataset,
callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1'])
)
print(f"\nAccuracy of FP32 model: {acc_fp32:.3f}")
2024-02-10 01:21:24.470744: 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/_3' with dtype int64 and shape [1]
[[{{node Placeholder/_3}}]]
2024-02-10 01:21:24.471113: 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 int64 and shape [1]
[[{{node Placeholder/_4}}]]
0/Unknown - 1s 0s/sample - loss: 1.0472 - acc@1: 0.7891
0/Unknown - 1s 0s/sample - loss: 0.9818 - acc@1: 0.8203
0/Unknown - 1s 0s/sample - loss: 0.9774 - acc@1: 0.8203
0/Unknown - 1s 0s/sample - loss: 0.9807 - acc@1: 0.8220
4/4 [==============================] - 1s 292ms/sample - loss: 0.9807 - acc@1: 0.8220
Accuracy of FP32 model: 0.822
量子化の作成と初期化¶
NNCF は、通常のトレーニング・パイプラインに統合することで、圧縮を意識したトレーニングを可能にします。このフレームワークは、元のトレーニング・コードへの変更が最小限になるように設計されています。量子化は最も単純なシナリオであり、わずか 3 つの変更で済みます。
NNCFパラメーターを設定して圧縮を指定します
nncf_config_dict = {
"input_info": {"sample_size": [1, 3] + list(IMG_SIZE)},
"log_dir": str(OUTPUT_DIR), # The log directory for NNCF-specific logging outputs.
"compression": {
"algorithm": "quantization", # Specify the algorithm here.
},
}
nncf_config = NNCFConfig.from_dict(nncf_config_dict)
量子化範囲の値を初期化し、指定された数のサンプルを使用して、収集された統計からどのアクティベーションを署名するか署名しないかを決定するデータローダーを提供します。
nncf_config = register_default_init_args(nncf_config=nncf_config,
data_loader=train_dataset,
batch_size=BATCH_SIZE)
事前トレーニングされた
FP32モデルと構成オブジェクトから、圧縮の微調整が可能なラップされたモデルを作成します。
compression_ctrl, int8_model = create_compressed_model(fp32_model, nncf_config)
2024-02-10 01:21:27.245500: 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 int64 and shape [1]
[[{{node Placeholder/_4}}]]
2024-02-10 01:21:27.245887: 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/_2' with dtype string and shape [1]
[[{{node Placeholder/_2}}]]
2024-02-10 01:21:28.182472: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
2024-02-10 01:21:28.866589: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
2024-02-10 01:21:37.201501: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to dataset.cache().take(k).repeat(). You should use dataset.take(k).cache().repeat() instead.
量子化の初期化後、検証セットで新しいモデルを評価します。ここで実証されているような単純なケースでは、精度は浮動小数点 FP32 モデルの精度とそれほど変わらないはずです。
# Compile the INT8 model.
int8_model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=LR),
loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
metrics=[tf.keras.metrics.CategoricalAccuracy(name='acc@1')]
)
# Validate the INT8 model.
test_loss, test_acc = int8_model.evaluate(
validation_dataset,
callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1'])
)
0/Unknown - 1s 0s/sample - loss: 1.0468 - acc@1: 0.7656
0/Unknown - 1s 0s/sample - loss: 0.9804 - acc@1: 0.8008
0/Unknown - 1s 0s/sample - loss: 0.9769 - acc@1: 0.8099
0/Unknown - 1s 0s/sample - loss: 0.9766 - acc@1: 0.8120
4/4 [==============================] - 1s 296ms/sample - loss: 0.9766 - acc@1: 0.8120
圧縮モデルを微調整¶
このステップでは、量子化モデルの精度をさらに向上させるため定期的な微調整プロセスが適用されます。通常、元のモデルのトレーニングの最後に使用されるのと同じ学習率を小さくして、数エポックの調整が必要になります。トレーニング・パイプラインにその他の変更は必要ありません。以下に簡単な例を示します。
print(f"\nAccuracy of INT8 model after initialization: {test_acc:.3f}")
# Train the INT8 model.
int8_model.fit(train_dataset, epochs=2)
# Validate the INT8 model.
test_loss, acc_int8 = int8_model.evaluate(
validation_dataset, callbacks=tf.keras.callbacks.ProgbarLogger(stateful_metrics=['acc@1']))
print(f"\nAccuracy of INT8 model after fine-tuning: {acc_int8:.3f}")
print(
f"\nAccuracy drop of tuned INT8 model over pre-trained FP32 model: {acc_fp32 - acc_int8:.3f}")
Accuracy of INT8 model after initialization: 0.812
Epoch 1/2
1/101 [..............................] - ETA: 11:46 - loss: 0.6168 - acc@1: 0.9844
2/101 [..............................] - ETA: 41s - loss: 0.6303 - acc@1: 0.9766
3/101 [..............................] - ETA: 41s - loss: 0.6613 - acc@1: 0.9609
4/101 [>.............................] - ETA: 41s - loss: 0.6650 - acc@1: 0.9551
5/101 [>.............................] - ETA: 40s - loss: 0.6783 - acc@1: 0.9469
6/101 [>.............................] - ETA: 40s - loss: 0.6805 - acc@1: 0.9466
7/101 [=>............................] - ETA: 39s - loss: 0.6796 - acc@1: 0.9442
8/101 [=>............................] - ETA: 39s - loss: 0.6790 - acc@1: 0.9463
9/101 [=>............................] - ETA: 38s - loss: 0.6828 - acc@1: 0.9462
10/101 [=>............................] - ETA: 38s - loss: 0.6908 - acc@1: 0.9422
11/101 [==>...........................] - ETA: 38s - loss: 0.6899 - acc@1: 0.9425
12/101 [==>...........................] - ETA: 37s - loss: 0.6930 - acc@1: 0.9421
13/101 [==>...........................] - ETA: 37s - loss: 0.6923 - acc@1: 0.9417
14/101 [===>..........................] - ETA: 36s - loss: 0.6960 - acc@1: 0.9386
15/101 [===>..........................] - ETA: 36s - loss: 0.6956 - acc@1: 0.9385
16/101 [===>..........................] - ETA: 36s - loss: 0.6946 - acc@1: 0.9395
17/101 [====>.........................] - ETA: 35s - loss: 0.6948 - acc@1: 0.9393
18/101 [====>.........................] - ETA: 35s - loss: 0.6941 - acc@1: 0.9405
19/101 [====>.........................] - ETA: 34s - loss: 0.6955 - acc@1: 0.9400
20/101 [====>.........................] - ETA: 34s - loss: 0.6931 - acc@1: 0.9402
21/101 [=====>........................] - ETA: 33s - loss: 0.6944 - acc@1: 0.9394
22/101 [=====>........................] - ETA: 33s - loss: 0.6953 - acc@1: 0.9382
23/101 [=====>........................] - ETA: 32s - loss: 0.6966 - acc@1: 0.9375
24/101 [======>.......................] - ETA: 32s - loss: 0.6971 - acc@1: 0.9368
25/101 [======>.......................] - ETA: 32s - loss: 0.6973 - acc@1: 0.9366
26/101 [======>.......................] - ETA: 31s - loss: 0.6975 - acc@1: 0.9369
27/101 [=======>......................] - ETA: 31s - loss: 0.6963 - acc@1: 0.9372
28/101 [=======>......................] - ETA: 30s - loss: 0.6960 - acc@1: 0.9378
29/101 [=======>......................] - ETA: 30s - loss: 0.6967 - acc@1: 0.9375
30/101 [=======>......................] - ETA: 29s - loss: 0.6982 - acc@1: 0.9365
31/101 [========>.....................] - ETA: 29s - loss: 0.6974 - acc@1: 0.9367
32/101 [========>.....................] - ETA: 29s - loss: 0.6966 - acc@1: 0.9373
33/101 [========>.....................] - ETA: 28s - loss: 0.6965 - acc@1: 0.9375
34/101 [=========>....................] - ETA: 28s - loss: 0.6978 - acc@1: 0.9370
35/101 [=========>....................] - ETA: 27s - loss: 0.6981 - acc@1: 0.9375
36/101 [=========>....................] - ETA: 27s - loss: 0.6992 - acc@1: 0.9382
37/101 [=========>....................] - ETA: 26s - loss: 0.7001 - acc@1: 0.9375
38/101 [==========>...................] - ETA: 26s - loss: 0.7023 - acc@1: 0.9369
39/101 [==========>...................] - ETA: 26s - loss: 0.7019 - acc@1: 0.9371
40/101 [==========>...................] - ETA: 25s - loss: 0.7016 - acc@1: 0.9373
41/101 [===========>..................] - ETA: 25s - loss: 0.7021 - acc@1: 0.9371
42/101 [===========>..................] - ETA: 24s - loss: 0.7018 - acc@1: 0.9371
43/101 [===========>..................] - ETA: 24s - loss: 0.7014 - acc@1: 0.9375
44/101 [============>.................] - ETA: 23s - loss: 0.7016 - acc@1: 0.9373
45/101 [============>.................] - ETA: 23s - loss: 0.7025 - acc@1: 0.9373
46/101 [============>.................] - ETA: 23s - loss: 0.7028 - acc@1: 0.9372
47/101 [============>.................] - ETA: 22s - loss: 0.7044 - acc@1: 0.9362
48/101 [=============>................] - ETA: 22s - loss: 0.7045 - acc@1: 0.9357
49/101 [=============>................] - ETA: 21s - loss: 0.7052 - acc@1: 0.9361
50/101 [=============>................] - ETA: 21s - loss: 0.7052 - acc@1: 0.9359
51/101 [==============>...............] - ETA: 21s - loss: 0.7061 - acc@1: 0.9357
52/101 [==============>...............] - ETA: 20s - loss: 0.7057 - acc@1: 0.9358
53/101 [==============>...............] - ETA: 20s - loss: 0.7061 - acc@1: 0.9350
54/101 [===============>..............] - ETA: 19s - loss: 0.7055 - acc@1: 0.9355
55/101 [===============>..............] - ETA: 19s - loss: 0.7052 - acc@1: 0.9357
56/101 [===============>..............] - ETA: 18s - loss: 0.7050 - acc@1: 0.9357
57/101 [===============>..............] - ETA: 18s - loss: 0.7053 - acc@1: 0.9352
58/101 [================>.............] - ETA: 18s - loss: 0.7057 - acc@1: 0.9351
59/101 [================>.............] - ETA: 17s - loss: 0.7062 - acc@1: 0.9345
60/101 [================>.............] - ETA: 17s - loss: 0.7064 - acc@1: 0.9345
61/101 [=================>............] - ETA: 16s - loss: 0.7064 - acc@1: 0.9343
62/101 [=================>............] - ETA: 16s - loss: 0.7056 - acc@1: 0.9347
63/101 [=================>............] - ETA: 15s - loss: 0.7060 - acc@1: 0.9345
64/101 [==================>...........] - ETA: 15s - loss: 0.7063 - acc@1: 0.9342
65/101 [==================>...........] - ETA: 15s - loss: 0.7073 - acc@1: 0.9337
66/101 [==================>...........] - ETA: 14s - loss: 0.7077 - acc@1: 0.9332
67/101 [==================>...........] - ETA: 14s - loss: 0.7083 - acc@1: 0.9327
68/101 [===================>..........] - ETA: 13s - loss: 0.7081 - acc@1: 0.9330
69/101 [===================>..........] - ETA: 13s - loss: 0.7087 - acc@1: 0.9330
70/101 [===================>..........] - ETA: 13s - loss: 0.7091 - acc@1: 0.9326
71/101 [====================>.........] - ETA: 12s - loss: 0.7081 - acc@1: 0.9330
72/101 [====================>.........] - ETA: 12s - loss: 0.7083 - acc@1: 0.9329
73/101 [====================>.........] - ETA: 11s - loss: 0.7075 - acc@1: 0.9334
74/101 [====================>.........] - ETA: 11s - loss: 0.7079 - acc@1: 0.9334
75/101 [=====================>........] - ETA: 10s - loss: 0.7085 - acc@1: 0.9329
76/101 [=====================>........] - ETA: 10s - loss: 0.7082 - acc@1: 0.9332
77/101 [=====================>........] - ETA: 10s - loss: 0.7078 - acc@1: 0.9333
78/101 [======================>.......] - ETA: 9s - loss: 0.7080 - acc@1: 0.9334
79/101 [======================>.......] - ETA: 9s - loss: 0.7079 - acc@1: 0.9332
80/101 [======================>.......] - ETA: 8s - loss: 0.7081 - acc@1: 0.9330
81/101 [=======================>......] - ETA: 8s - loss: 0.7078 - acc@1: 0.9333
82/101 [=======================>......] - ETA: 7s - loss: 0.7081 - acc@1: 0.9332
83/101 [=======================>......] - ETA: 7s - loss: 0.7080 - acc@1: 0.9332
84/101 [=======================>......] - ETA: 7s - loss: 0.7075 - acc@1: 0.9332
85/101 [========================>.....] - ETA: 6s - loss: 0.7080 - acc@1: 0.9332
86/101 [========================>.....] - ETA: 6s - loss: 0.7073 - acc@1: 0.9337
87/101 [========================>.....] - ETA: 5s - loss: 0.7079 - acc@1: 0.9330
88/101 [=========================>....] - ETA: 5s - loss: 0.7084 - acc@1: 0.9330
89/101 [=========================>....] - ETA: 5s - loss: 0.7087 - acc@1: 0.9331
90/101 [=========================>....] - ETA: 4s - loss: 0.7091 - acc@1: 0.9330
91/101 [==========================>...] - ETA: 4s - loss: 0.7096 - acc@1: 0.9327
92/101 [==========================>...] - ETA: 3s - loss: 0.7095 - acc@1: 0.9325
93/101 [==========================>...] - ETA: 3s - loss: 0.7099 - acc@1: 0.9320
94/101 [==========================>...] - ETA: 2s - loss: 0.7105 - acc@1: 0.9317
95/101 [===========================>..] - ETA: 2s - loss: 0.7107 - acc@1: 0.9312
96/101 [===========================>..] - ETA: 2s - loss: 0.7107 - acc@1: 0.9313
97/101 [===========================>..] - ETA: 1s - loss: 0.7109 - acc@1: 0.9312
98/101 [============================>.] - ETA: 1s - loss: 0.7111 - acc@1: 0.9311
99/101 [============================>.] - ETA: 0s - loss: 0.7123 - acc@1: 0.9305
100/101 [============================>.] - ETA: 0s - loss: 0.7123 - acc@1: 0.9305
101/101 [==============================] - ETA: 0s - loss: 0.7134 - acc@1: 0.9299
101/101 [==============================] - 49s 418ms/step - loss: 0.7134 - acc@1: 0.9299
Epoch 2/2
1/101 [..............................] - ETA: 41s - loss: 0.5798 - acc@1: 1.0000
2/101 [..............................] - ETA: 41s - loss: 0.5917 - acc@1: 1.0000
3/101 [..............................] - ETA: 41s - loss: 0.6191 - acc@1: 0.9896
4/101 [>.............................] - ETA: 40s - loss: 0.6225 - acc@1: 0.9844
5/101 [>.............................] - ETA: 40s - loss: 0.6332 - acc@1: 0.9781
6/101 [>.............................] - ETA: 39s - loss: 0.6378 - acc@1: 0.9753
7/101 [=>............................] - ETA: 39s - loss: 0.6392 - acc@1: 0.9732
8/101 [=>............................] - ETA: 38s - loss: 0.6395 - acc@1: 0.9736
9/101 [=>............................] - ETA: 38s - loss: 0.6435 - acc@1: 0.9740
10/101 [=>............................] - ETA: 37s - loss: 0.6508 - acc@1: 0.9688
11/101 [==>...........................] - ETA: 37s - loss: 0.6517 - acc@1: 0.9695
12/101 [==>...........................] - ETA: 37s - loss: 0.6548 - acc@1: 0.9681
13/101 [==>...........................] - ETA: 36s - loss: 0.6551 - acc@1: 0.9681
14/101 [===>..........................] - ETA: 36s - loss: 0.6592 - acc@1: 0.9660
15/101 [===>..........................] - ETA: 35s - loss: 0.6590 - acc@1: 0.9656
16/101 [===>..........................] - ETA: 35s - loss: 0.6580 - acc@1: 0.9673
17/101 [====>.........................] - ETA: 34s - loss: 0.6583 - acc@1: 0.9665
18/101 [====>.........................] - ETA: 34s - loss: 0.6584 - acc@1: 0.9666
19/101 [====>.........................] - ETA: 34s - loss: 0.6601 - acc@1: 0.9659
20/101 [====>.........................] - ETA: 33s - loss: 0.6586 - acc@1: 0.9656
21/101 [=====>........................] - ETA: 33s - loss: 0.6599 - acc@1: 0.9639
22/101 [=====>........................] - ETA: 32s - loss: 0.6610 - acc@1: 0.9634
23/101 [=====>........................] - ETA: 32s - loss: 0.6623 - acc@1: 0.9620
24/101 [======>.......................] - ETA: 31s - loss: 0.6630 - acc@1: 0.9609
25/101 [======>.......................] - ETA: 31s - loss: 0.6632 - acc@1: 0.9606
26/101 [======>.......................] - ETA: 31s - loss: 0.6638 - acc@1: 0.9603
27/101 [=======>......................] - ETA: 30s - loss: 0.6631 - acc@1: 0.9604
28/101 [=======>......................] - ETA: 30s - loss: 0.6629 - acc@1: 0.9609
29/101 [=======>......................] - ETA: 30s - loss: 0.6636 - acc@1: 0.9604
30/101 [=======>......................] - ETA: 29s - loss: 0.6652 - acc@1: 0.9594
31/101 [========>.....................] - ETA: 29s - loss: 0.6645 - acc@1: 0.9592
32/101 [========>.....................] - ETA: 28s - loss: 0.6641 - acc@1: 0.9592
33/101 [========>.....................] - ETA: 28s - loss: 0.6641 - acc@1: 0.9593
34/101 [=========>....................] - ETA: 27s - loss: 0.6655 - acc@1: 0.9586
35/101 [=========>....................] - ETA: 27s - loss: 0.6657 - acc@1: 0.9587
36/101 [=========>....................] - ETA: 27s - loss: 0.6665 - acc@1: 0.9588
37/101 [=========>....................] - ETA: 26s - loss: 0.6674 - acc@1: 0.9578
38/101 [==========>...................] - ETA: 26s - loss: 0.6695 - acc@1: 0.9570
39/101 [==========>...................] - ETA: 25s - loss: 0.6692 - acc@1: 0.9569
40/101 [==========>...................] - ETA: 25s - loss: 0.6689 - acc@1: 0.9574
41/101 [===========>..................] - ETA: 24s - loss: 0.6692 - acc@1: 0.9571
42/101 [===========>..................] - ETA: 24s - loss: 0.6692 - acc@1: 0.9568
43/101 [===========>..................] - ETA: 24s - loss: 0.6689 - acc@1: 0.9571
44/101 [============>.................] - ETA: 23s - loss: 0.6692 - acc@1: 0.9569
45/101 [============>.................] - ETA: 23s - loss: 0.6700 - acc@1: 0.9564
46/101 [============>.................] - ETA: 22s - loss: 0.6702 - acc@1: 0.9562
47/101 [============>.................] - ETA: 22s - loss: 0.6715 - acc@1: 0.9551
48/101 [=============>................] - ETA: 22s - loss: 0.6715 - acc@1: 0.9552
49/101 [=============>................] - ETA: 21s - loss: 0.6722 - acc@1: 0.9554
50/101 [=============>................] - ETA: 21s - loss: 0.6723 - acc@1: 0.9552
51/101 [==============>...............] - ETA: 20s - loss: 0.6732 - acc@1: 0.9547
52/101 [==============>...............] - ETA: 20s - loss: 0.6729 - acc@1: 0.9548
53/101 [==============>...............] - ETA: 19s - loss: 0.6734 - acc@1: 0.9542
54/101 [===============>..............] - ETA: 19s - loss: 0.6730 - acc@1: 0.9546
55/101 [===============>..............] - ETA: 19s - loss: 0.6728 - acc@1: 0.9544
56/101 [===============>..............] - ETA: 18s - loss: 0.6727 - acc@1: 0.9544
57/101 [===============>..............] - ETA: 18s - loss: 0.6732 - acc@1: 0.9538
58/101 [================>.............] - ETA: 17s - loss: 0.6735 - acc@1: 0.9537
59/101 [================>.............] - ETA: 17s - loss: 0.6739 - acc@1: 0.9531
60/101 [================>.............] - ETA: 17s - loss: 0.6741 - acc@1: 0.9530
61/101 [=================>............] - ETA: 16s - loss: 0.6741 - acc@1: 0.9530
62/101 [=================>............] - ETA: 16s - loss: 0.6735 - acc@1: 0.9533
63/101 [=================>............] - ETA: 15s - loss: 0.6738 - acc@1: 0.9531
64/101 [==================>...........] - ETA: 15s - loss: 0.6741 - acc@1: 0.9529
65/101 [==================>...........] - ETA: 14s - loss: 0.6750 - acc@1: 0.9523
66/101 [==================>...........] - ETA: 14s - loss: 0.6754 - acc@1: 0.9522
67/101 [==================>...........] - ETA: 14s - loss: 0.6758 - acc@1: 0.9518
68/101 [===================>..........] - ETA: 13s - loss: 0.6758 - acc@1: 0.9520
69/101 [===================>..........] - ETA: 13s - loss: 0.6763 - acc@1: 0.9520
70/101 [===================>..........] - ETA: 12s - loss: 0.6768 - acc@1: 0.9516
71/101 [====================>.........] - ETA: 12s - loss: 0.6760 - acc@1: 0.9518
72/101 [====================>.........] - ETA: 12s - loss: 0.6761 - acc@1: 0.9516
73/101 [====================>.........] - ETA: 11s - loss: 0.6755 - acc@1: 0.9518
74/101 [====================>.........] - ETA: 11s - loss: 0.6759 - acc@1: 0.9516
75/101 [=====================>........] - ETA: 10s - loss: 0.6765 - acc@1: 0.9515
76/101 [=====================>........] - ETA: 10s - loss: 0.6762 - acc@1: 0.9517
77/101 [=====================>........] - ETA: 9s - loss: 0.6759 - acc@1: 0.9520
78/101 [======================>.......] - ETA: 9s - loss: 0.6761 - acc@1: 0.9521
79/101 [======================>.......] - ETA: 9s - loss: 0.6760 - acc@1: 0.9518
80/101 [======================>.......] - ETA: 8s - loss: 0.6762 - acc@1: 0.9514
81/101 [=======================>......] - ETA: 8s - loss: 0.6759 - acc@1: 0.9516
82/101 [=======================>......] - ETA: 7s - loss: 0.6762 - acc@1: 0.9516
83/101 [=======================>......] - ETA: 7s - loss: 0.6761 - acc@1: 0.9515
84/101 [=======================>......] - ETA: 7s - loss: 0.6757 - acc@1: 0.9517
85/101 [========================>.....] - ETA: 6s - loss: 0.6762 - acc@1: 0.9517
86/101 [========================>.....] - ETA: 6s - loss: 0.6756 - acc@1: 0.9521
87/101 [========================>.....] - ETA: 5s - loss: 0.6762 - acc@1: 0.9516
88/101 [=========================>....] - ETA: 5s - loss: 0.6766 - acc@1: 0.9513
89/101 [=========================>....] - ETA: 4s - loss: 0.6768 - acc@1: 0.9515
90/101 [=========================>....] - ETA: 4s - loss: 0.6771 - acc@1: 0.9515
91/101 [==========================>...] - ETA: 4s - loss: 0.6775 - acc@1: 0.9512
92/101 [==========================>...] - ETA: 3s - loss: 0.6775 - acc@1: 0.9511
93/101 [==========================>...] - ETA: 3s - loss: 0.6778 - acc@1: 0.9509
94/101 [==========================>...] - ETA: 2s - loss: 0.6783 - acc@1: 0.9507
95/101 [===========================>..] - ETA: 2s - loss: 0.6785 - acc@1: 0.9502
96/101 [===========================>..] - ETA: 2s - loss: 0.6785 - acc@1: 0.9504
97/101 [===========================>..] - ETA: 1s - loss: 0.6787 - acc@1: 0.9501
98/101 [============================>.] - ETA: 1s - loss: 0.6790 - acc@1: 0.9499
99/101 [============================>.] - ETA: 0s - loss: 0.6800 - acc@1: 0.9493
100/101 [============================>.]- ETA: 0s - loss: 0.6800 - acc@1: 0.9493
101/101 [==============================] - ETA: 0s - loss: 0.6807 - acc@1: 0.9489
101/101 [==============================] - 42s 415ms/step - loss: 0.6807 - acc@1: 0.9489
0/Unknown - 0s 0s/sample - loss: 1.0568 - acc@1: 0.7812
0/Unknown - 0s 0s/sample - loss: 0.9848 - acc@1: 0.8086
0/Unknown - 0s 0s/sample - loss: 0.9768 - acc@1: 0.8177
0/Unknown - 1s 0s/sample - loss: 0.9760 - acc@1: 0.8160
4/4 [==============================] - 1s 143ms/sample - loss: 0.9760 - acc@1: 0.8160
Accuracy of INT8 model after fine-tuning: 0.816
Accuracy drop of tuned INT8 model over pre-trained FP32 model: 0.006
OpenVINO 中間表現 (IR) へのモデルのエクスポート¶
モデル変換 Python API を使用して、モデルを OpenVINO IR に変換します。
モデル変換の詳細については、このページを参照してください。
このコマンドの実行には時間がかかる場合があります。
model_ir_fp32 = ov.convert_model(fp32_model)
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
model_ir_int8 = ov.convert_model(int8_model)
推論時間の計算によるベンチマーク・モデルのパフォーマンス¶
最後に、ベンチマーク・ツールを使用して、FP32 モデルと INT8 モデルの推論パフォーマンスを測定します。
- OpenVINO の推論パフォーマンス測定ツール。デフォルトでは、ベンチマーク・ツールは CPU 上の非同期モードで推論を 60 秒間実行します。推論速度をレイテンシー (画像あたりのミリ秒) およびスループット (1 秒あたりのフレーム数) の値として返します。
注: このノートブックは、
benchmark_appを 15 秒間実行して、パフォーマンスを簡単に示します。より正確なパフォーマンスを得るには、他のアプリケーションを閉じて、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。benchmark_app -m model.xml -d CPUを実行して、CPU で非同期推論のベンチマークを 1 分間実行します。GPU でベンチマークを行うには、CPU を GPU に変更します。benchmark_app --helpを実行すると、すべてのコマンドライン・オプションの概要が表示されます。
ov.save_model(model_ir_fp32, fp32_ir_path, compress_to_fp16=False)
ov.save_model(model_ir_int8, int8_ir_path, compress_to_fp16=False)
def parse_benchmark_output(benchmark_output):
parsed_output = [line for line in benchmark_output if 'FPS' in line]
print(*parsed_output, sep='\n')
print('Benchmark FP32 model (IR)')
benchmark_output = ! benchmark_app -m $fp32_ir_path -d CPU -api async -t 15 -shape [1,64,64,3]
parse_benchmark_output(benchmark_output)
print('\nBenchmark INT8 model (IR)')
benchmark_output = ! benchmark_app -m $int8_ir_path -d CPU -api async -t 15 -shape [1,64,64,3]
parse_benchmark_output(benchmark_output)
Benchmark FP32 model (IR)
[ INFO ] Throughput: 2822.70 FPS
Benchmark INT8 model (IR)
[ INFO ] Throughput: 11084.28 FPS
参考として CPU 情報を表示します。
core = ov.Core()
core.get_property('CPU', "FULL_DEVICE_NAME")
'Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz'