Post-Training Quantization with TensorFlow Classification Model

This Jupyter notebook can be launched after a local installation only.

Github

This example demonstrates how to quantize the OpenVINO model that was created in 301-tensorflow-training-openvino notebook, to improve inference speed. Quantization is performed with Post-training Quantization with NNCF. A custom dataloader and metric will be defined, and accuracy and performance will be computed for the original IR model and the quantized model.

Table of contents:

Preparation

The notebook requires that the training notebook has been run and that the Intermediate Representation (IR) models are created. If the IR models do not exist, running the next cell will run the training notebook. This will take a while.

%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}}]]
../_images/301-tensorflow-training-openvino-nncf-with-output_3_11.png
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}}]]
../_images/301-tensorflow-training-openvino-nncf-with-output_3_17.png
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 [==============================] - 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 [==============================] - 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 [==============================] - 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 [==============================] - 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 [==============================] - 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 [==============================] - 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 [==============================] - 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 [==============================] - 6s 63ms/step - loss: 0.6230 - accuracy: 0.7646 - val_loss: 0.7725 - val_accuracy: 0.7153

Epoch 11/15
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