quantize_resnet18_cifar10()

inspect_resnet18_cifar10.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright © 2023 Advanced Micro Devices, Inc. All rights reserved.
# SPDX-License-Identifier: MIT

# Author: Daniele Bagni, Xilinx Inc
# date:  28 Apr. 2023

# ==========================================================================================
# import dependencies
# ==========================================================================================


from config import cifar10_config as cfg #DB

import argparse
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from keras.utils import np_utils

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,models,layers
from tensorflow.keras.utils import plot_model
from tensorflow.keras.models import Sequential, Model


# ==========================================================================================
# Get Input Arguments
# ==========================================================================================
def get_arguments():
    """Parse all the arguments.

    Returns:
      A list of parsed arguments.
    """
    parser = argparse.ArgumentParser(description="Vitis AI TF2 Quantization of ResNet18")

    # model config
    parser.add_argument("--float_file", type=str, default="./build/float/train2_resnet18_cifar10.h5",
                        help="h5 floating point file full path name")

    return parser.parse_args()

args = get_arguments()


# ==========================================================================================
# Global Variables
# ==========================================================================================
print(cfg.SCRIPT_DIR)

FLOAT_HDF5_FILE = os.path.join(cfg.SCRIPT_DIR,  args.float_file)

# ==========================================================================================
# Get the trained floating point model
# ==========================================================================================

model = keras.models.load_model(FLOAT_HDF5_FILE)

# ==========================================================================================
# Vitis AI Model Inspector
# ==========================================================================================
print("\n[DB INFO] Vitis AI Model Inspector...\n")

from tensorflow_model_optimization.quantization.keras import vitis_inspect
#inspector = vitis_inspect.VitisInspector(target="DPUCZDX8G")
inspector = vitis_inspect.VitisInspector(target="/opt/vitis_ai/compiler/arch/DPUCZDX8G/ZCU102/arch.json")
filename_dump = os.path.join(cfg.SCRIPT_DIR,  "build/log/inspect_results.txt")
filename_svg  = os.path.join(cfg.SCRIPT_DIR,  "build/log/model.svg")
inspector.inspect_model(model,
                        input_shape=[1,  32, 32, 3],
                        plot=True,
                        plot_file=filename_svg,
                        dump_results=True,
                        dump_results_file=filename_dump,
                        verbose=0)

def_arguments() : argument parsing
argparse.ArgumentParser : 명령행에 입력된 인자 파싱
.add_argument : --float_file 라는 명령행 인자 정의, string으로 입력 받음
스크립트를 실행할 때 --float_file과 같은 인자를 사용하여 값을 전달, 이렇게 전달된 값은 스크립트 내에서 args.float_file을 통해 접근 가능

FLOAT_HDF5_FILE : directory path와 파일을 합한 경로로 만듦

해당 경로로 keras model 호출하고

vitis_inspect 를 통해 tf2 모델을 quantizing 하는 과정

vai_q_resnet18_cifar10.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright © 2023 Advanced Micro Devices, Inc. All rights reserved.
# SPDX-License-Identifier: MIT

# Author: Daniele Bagni, Xilinx Inc
# date:  28 Apr. 2023

# ==========================================================================================
# import dependencies
# ==========================================================================================


from config import cifar10_config as cfg #DB

import argparse
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder

from keras.utils import np_utils

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,models,layers
from tensorflow.keras.utils import plot_model
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Conv2D,  MaxPool2D, Flatten, GlobalAveragePooling2D,  BatchNormalization, Layer, Add
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import SGD

from classification_models.keras import Classifiers



# ==========================================================================================
# Get Input Arguments
# ==========================================================================================
def get_arguments():
    """Parse all the arguments.

    Returns:
      A list of parsed arguments.
    """
    parser = argparse.ArgumentParser(description="Vitis AI TF2 Quantization of ResNet18 trained on CIFAR10")

    # model config
    parser.add_argument("--float_file", type=str, default="./build/float/train2_resnet18_cifar10.h5",
                        help="h5 floating point file full path name")
    # others
    parser.add_argument("--gpus", type=str, default="0",
                        help="choose gpu devices.")
    # quantization config
    parser.add_argument("--quant_file", type=str, default="./build/quantized/q_train2_resnet18_cifar10.h5",
                        help="quantized model file full path ename ")

    return parser.parse_args()


#def main():
args = get_arguments()

# ==========================================================================================
# Global Variables
# ==========================================================================================
print(cfg.SCRIPT_DIR)

os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus

FLOAT_HDF5_FILE = os.path.join(cfg.SCRIPT_DIR,  args.float_file)
QUANT_HDF5_FILE = os.path.join(cfg.SCRIPT_DIR,  args.quant_file)


# ==========================================================================================
# prepare your data
# ==========================================================================================
print("\n[DB INFO] Preparing Data ...\n")

(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
X_train.shape, X_test.shape, np.unique(Y_train).shape[0]
# one-hot encoding
n_classes = 10

# Normalize the data
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train = cfg.Normalize(X_train)
X_test  = cfg.Normalize(X_test)

X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.2,shuffle = True)

encoder = OneHotEncoder()
encoder.fit(Y_train)
Y_train = encoder.transform(Y_train).toarray()
Y_test = encoder.transform(Y_test).toarray()
Y_val =  encoder.transform(Y_val).toarray()

# ==========================================================================================
# Get the trained floating point model
# ==========================================================================================

model = keras.models.load_model(FLOAT_HDF5_FILE)

# ==========================================================================================
# Prediction
# ==========================================================================================
print("\n[DB INFO] Make Predictions with Float Model...\n")

## Evaluation on Training Dataset
ModelLoss, ModelAccuracy = model.evaluate(X_train, Y_train)
print("X_Train Model Loss     is {}".format(ModelLoss))
print("X_Train Model Accuracy is {}".format(ModelAccuracy))

## Evaluation on Test Dataset
t_ModelLoss, t_ModelAccuracy = model.evaluate(X_test, Y_test)
print("X_Test Model Loss     is {}".format(t_ModelLoss))
print("X_Test Model Accuracy is {}".format(t_ModelAccuracy))


# ==========================================================================================
# Vitis AI Quantization
# ==========================================================================================
print("\n[DB INFO] Vitis AI Quantization...\n")

from tensorflow_model_optimization.quantization.keras import vitis_quantize
quantizer = vitis_quantize.VitisQuantizer(model)
q_model = quantizer.quantize_model(calib_dataset=X_train[0:100])
#q_model.save(QUANT_HDF5_FILE)

print("\n[DB INFO] Evaluation of Quantized Model...\n")
with vitis_quantize.quantize_scope():
    #q_model = tf.keras.models.load_model("./quantized.h5", custom_objects=custom_objects)
    #q_model = tf.keras.models.load_model(QUANT_HDF5_FILE)
    q_model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
    q_eval_results = q_model.evaluate(X_test, Y_test)
    print("\n***************** Summary *****************")
    print("X_Test Quantized model accuracy: ", q_eval_results[1])

print("\n[DB INFO] Saving Quantized Model...\n")
q_model.save(QUANT_HDF5_FILE)
loaded_model = keras.models.load_model(QUANT_HDF5_FILE)
#eval_results = loaded_model.evaluate(X_test, Y_test)
#print("\n***************** Summary *****************")
#print("X_Test Quantized model accuracy: ", eval_results[1])
eval_results = loaded_model.evaluate(X_train, Y_train)
print("\n***************** Summary *****************")
print("X_Train Quantized model accuracy: ", eval_results[1])

get_arguments : 인자 받기
Global Variables : path 설정

cifar10 dataset 받아다가 train set, test set으로 나누고 train에서 test_size는 20%
one-hot-encoder 방법 사용, 클래스는 10가지

FLOAT_HDF5_FILE 로 floating point model 받고
train set 정확도 출력

vitis_quantize 에서 양자화 실시

with : 리소스 관리나 예외 처리를 편리하게 할 수 있도록 도와줌
vitis_quantize.quantize_scope() : with 문법 내에서 쓰는 부분들은 모두 양자화 되어있음