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DatasetLoad.py
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DatasetLoad.py
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# ***************************************References*************************************************************
# [1] H. Chen, S. A. Asif, J. Park, C.-C. Shen, and M. Bennis, “Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus.” arXiv, Jan. 09, 2021. Accessed: Nov. 12, 2022. [Online]. Available: http://arxiv.org/abs/2101.03300
# Github Link: https://github.com/hanglearning/VBFL.git
# [1] H. Zhao, “Exact Decomposition of Quantum Channels for Non-IID Quantum Federated Learning.” arXiv, Sep. 01, 2022. Accessed: Nov. 06, 2022. [Online]. Available: http://arxiv.org/abs/2209.00768
# Github Link: https://github.com/JasonZHM/quantum-fed-infer.git (MIT Licence)
# ***************************************References*************************************************************
import numpy as np
import gzip
import os
class DatasetLoad(object):
def __init__(self, dataSetName, isIID):
self.name = dataSetName
self.train_data = None
self.train_label = None
self.train_data_size = None
self.test_data = None
self.test_label = None
self.test_data_size = None
self._index_in_train_epoch = 0
if self.name == 'mnist':
self.mnistDataSetConstruct(isIID)
else:
pass
def mnistDataSetConstruct(self, isIID):
data_dir = 'data/MNIST'
train_images_path = os.path.join(data_dir, 'train-images-idx3-ubyte.gz')
train_labels_path = os.path.join(data_dir, 'train-labels-idx1-ubyte.gz')
test_images_path = os.path.join(data_dir, 't10k-images-idx3-ubyte.gz')
test_labels_path = os.path.join(data_dir, 't10k-labels-idx1-ubyte.gz')
train_images = extract_images(train_images_path)
train_labels = extract_labels(train_labels_path)
test_images = extract_images(test_images_path)
test_labels = extract_labels(test_labels_path)
assert train_images.shape[0] == train_labels.shape[0]
assert test_images.shape[0] == test_labels.shape[0]
self.train_data_size = train_images.shape[0]
self.test_data_size = test_images.shape[0]
assert train_images.shape[3] == 1
assert test_images.shape[3] == 1
train_images = train_images.reshape(train_images.shape[0], train_images.shape[1] * train_images.shape[2])
test_images = test_images.reshape(test_images.shape[0], test_images.shape[1] * test_images.shape[2])
train_images = train_images.astype(np.float32)
train_images = np.multiply(train_images, 1.0 / 255.0)
test_images = test_images.astype(np.float32)
test_images = np.multiply(test_images, 1.0 / 255.0)
if isIID:
order = np.arange(self.train_data_size)
np.random.shuffle(order)
self.train_data = train_images[order]
self.train_label = train_labels[order]
else:
labels = np.argmax(train_labels, axis=1)
order = np.argsort(labels)
self.train_data = train_images[order]
self.train_label = train_labels[order]
self.test_data = test_images
self.test_label = test_labels
def _read32(bytestream):
dt = np.dtype(np.uint32).newbyteorder('>')
return np.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = np.frombuffer(buf, dtype=np.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
def dense_to_one_hot(labels_dense, num_classes=10):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = np.arange(num_labels) * num_classes
labels_one_hot = np.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(filename):
"""Extract the labels into a 1D uint8 numpy array [index]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' %
(magic, filename))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = np.frombuffer(buf, dtype=np.uint8)
return dense_to_one_hot(labels)
if __name__=="__main__":
'test data set'
mnistDataSet = GetDataSet('mnist', True)
if type(mnistDataSet.train_data) is np.ndarray and type(mnistDataSet.test_data) is np.ndarray and \
type(mnistDataSet.train_label) is np.ndarray and type(mnistDataSet.test_label) is np.ndarray:
print('the type of data is numpy ndarray')
else:
print('the type of data is not numpy ndarray')
print('the shape of the train data set is {}'.format(mnistDataSet.train_data.shape))
print('the shape of the test data set is {}'.format(mnistDataSet.test_data.shape))
print(mnistDataSet.train_label[0:100], mnistDataSet.train_label[11000:11100])
class AddGaussianNoise(object):
def __init__(self, mean=0., std=1.):
self.std = std
self.mean = mean
def __call__(self, tensor):
return tensor + torch.randn(tensor.size()) * self.std + self.mean
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)