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data_preprocess.py
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data_preprocess.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
@author: Xiao Jin
In this file we load CIFAR-100 data
"""
from config import *
import gc
import pathlib
import random as r
import tensorflow as tf
import os
train_data_dir = 'train/'
test_data_dir = 'test/'
AUTOTUNE = tf.data.experimental.AUTOTUNE
def preprocess_image(image):
data_image = tf.cast(tf.image.decode_jpeg(image, channels=3), tf.float32)
# size = data_image.numpy().shape()
# data_image = tf.image.resize(data_image, [64, 64])
return data_image
def load_and_preprocess_image(path):
image = tf.io.read_file(path)
return preprocess_image(image)
def load_data(data_path):
'''
Load data
'''
(train_data, train_label), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
N_train = train_data.shape[0]
N_test = test_images.shape[0]
train_dataset = (
tf.data.Dataset.from_tensor_slices((train_data, train_label)).batch(N_train)
)
test_dataset = (
tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(N_test)
)
train_dataset = (
train_dataset.map(lambda x, y:
(tf.divide(tf.cast(x, tf.float32), 255.0),
tf.reshape(tf.one_hot(y, 10), (-1, 10))))
)
test_dataset = (
test_dataset.map(lambda x, y:
(tf.divide(tf.cast(x, tf.float32), 255.0),
tf.reshape(tf.one_hot(y, 10), (-1, 10))))
)
train_data, train_label = zip(*train_dataset)
test_data, test_label = zip(*test_dataset)
train_data = train_data[0]
train_label = train_label[0]
test_data = test_data[0]
test_label = test_label[0]
train_datasets = []
for worker_index in range(4):
i = worker_index // 2
j = worker_index % 2
slice = train_data[:, 14 * i: 14 * (i + 1), 14 * j: 14 * (j + 1)]
train_datasets.append(slice)
train_datasets.append(train_label)
train_datasets = tuple(train_datasets)
train_ds = tf.data.Dataset.from_tensor_slices(train_datasets).batch(data_number)
test_datasets = []
for worker_index in range(4):
i = worker_index // 2
j = worker_index % 2
slice = test_data[:, 14 * i: 14 * (i + 1), 14 * j: 14 * (j + 1)]
test_datasets.append(slice)
test_datasets.append(test_label)
test_datasets = tuple(test_datasets)
test_ds = tf.data.Dataset.from_tensor_slices(test_datasets).batch(data_number)
return train_ds, test_ds
train_datasets, test_datasets = load_data(train_data_dir)
if __name__ == "__main__":
train_datasets = load_data(train_data_dir)
print('Done')