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Example data converter

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# MIT License
# Copyright 2022 University of Bremen
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# David Rotermund ( davrot@uni-bremen.de )
#
#
# Release history:
# ================
# 1.0.0 -- 01.05.2022: first release
#
#
import numpy as np
import pickle
def give_filenames(id: int) -> tuple[str, str, int]:
if id == 0:
start_id: int = 0
prefix: str = "Test"
filename: str = "cifar-10-batches-py/test_batch"
if id == 1:
start_id = 0
prefix = "Train"
filename = "cifar-10-batches-py/data_batch_1"
if id == 2:
start_id = 10000
prefix = "Train"
filename = "cifar-10-batches-py/data_batch_2"
if id == 3:
start_id = 20000
prefix = "Train"
filename = "cifar-10-batches-py/data_batch_3"
if id == 4:
start_id = 30000
prefix = "Train"
filename = "cifar-10-batches-py/data_batch_4"
if id == 5:
start_id = 40000
prefix = "Train"
filename = "cifar-10-batches-py/data_batch_5"
return filename, prefix, start_id
def load_data(filename: str) -> tuple[np.ndarray, np.ndarray]:
fo = open(filename, "rb")
dict_data = pickle.load(fo, encoding="bytes")
_, labels_temp, data_temp, _ = dict_data.items()
data: np.ndarray = np.array(data_temp[1])
labels: np.ndarray = np.array(labels_temp[1])
return data, labels
def split_into_three_color_channels(
image: np.ndarray,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
channel_r = image[0:1024].astype(np.float32)
channel_r = channel_r.reshape(32, 32)
channel_g = image[1024:2048].astype(np.float32)
channel_g = channel_g.reshape(32, 32)
channel_b = image[2048:3072].astype(np.float32)
channel_b = channel_b.reshape(32, 32)
return channel_r, channel_g, channel_b
def process_data_set(test_data_mode: bool) -> None:
if test_data_mode is True:
filename_out_pattern: str = "TestPatternStorage.npy"
filename_out_label: str = "TestLabelStorage.npy"
number_of_pictures: int = 10000
start_id: int = 0
end_id: int = 0
else:
filename_out_pattern = "TrainPatternStorage.npy"
filename_out_label = "TrainLabelStorage.npy"
number_of_pictures = 50000
start_id = 1
end_id = 5
np_data: np.ndarray = np.zeros((number_of_pictures, 32, 32, 3), dtype=np.float32)
np_label: np.ndarray = np.zeros((number_of_pictures), dtype=np.uint64)
for id in range(start_id, end_id + 1):
filename, _, start_id_pattern = give_filenames(id)
pictures, labels = load_data(filename)
for i in range(0, pictures.shape[0]):
channel_r, channel_g, channel_b = split_into_three_color_channels(
pictures[i, :]
)
np_data[i + start_id_pattern, :, :, 0] = channel_r
np_data[i + start_id_pattern, :, :, 1] = channel_g
np_data[i + start_id_pattern, :, :, 2] = channel_b
np_label[i + start_id_pattern] = labels[i]
np_data /= np.max(np_data)
label_storage: np.ndarray = np_label.astype(dtype=np.uint64)
pattern_storage: np.ndarray = np_data.astype(dtype=np.float32)
np.save(filename_out_pattern, pattern_storage)
np.save(filename_out_label, label_storage)
process_data_set(True)
process_data_set(False)

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https://www.cs.toronto.edu/~kriz/cifar.html
Download the CIFAR-10 python version
https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
Then
tar -xvzf cifar-10-python.tar.gz
python convert.py

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# MIT License
# Copyright 2022 University of Bremen
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# David Rotermund ( davrot@uni-bremen.de )
#
#
# Release history:
# ================
# 1.0.0 -- 01.05.2022: first release
#
#
import numpy as np
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
# 0004 32 bit integer 60000 number of items
# 0008 unsigned byte ?? label
# 0009 unsigned byte ?? label
# ........
# xxxx unsigned byte ?? label
# The labels values are 0 to 9.
class ReadLabel:
"""Class for reading the labels from an MNIST label file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2049:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
else:
data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)
file.close()
return data
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000803(2051) magic number
# 0004 32 bit integer 60000 number of images
# 0008 32 bit integer 28 number of rows
# 0012 32 bit integer 28 number of columns
# 0016 unsigned byte ?? pixel
# 0017 unsigned byte ?? pixel
# ........
# xxxx unsigned byte ?? pixel
# Pixels are organized row-wise.
# Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
class ReadPicture:
"""Class for reading the images from an MNIST image file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2051:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
number_of_rows = 0
else:
number_of_rows = np.frombuffer(file.read(4), int32_data)[0]
if number_of_rows != 28:
data = np.zeros(0)
number_of_columns = 0
else:
number_of_columns = np.frombuffer(file.read(4), int32_data)[0]
if number_of_columns != 28:
data = np.zeros(0)
else:
data = np.frombuffer(
file.read(number_of_elements * number_of_rows * number_of_columns),
dtype=np.uint8,
)
data = data.reshape(number_of_elements, number_of_columns, number_of_rows)
file.close()
return data
def proprocess_data_set(test_mode):
if test_mode is True:
filename_out_pattern: str = "TestPatternStorage.npy"
filename_out_label: str = "TestLabelStorage.npy"
filename_in_image: str = "t10k-images-idx3-ubyte"
filename_in_label = "t10k-labels-idx1-ubyte"
else:
filename_out_pattern = "TrainPatternStorage.npy"
filename_out_label = "TrainLabelStorage.npy"
filename_in_image = "train-images-idx3-ubyte"
filename_in_label = "train-labels-idx1-ubyte"
pictures = ReadPicture(filename_in_image)
labels = ReadLabel(filename_in_label)
# Down to 0 ... 1.0
max_value = np.max(pictures.data.astype(np.float32))
d = np.float32(pictures.data.astype(np.float32) / max_value)
label_storage = np.uint64(labels.data)
pattern_storage = d.astype(np.float32)
np.save(filename_out_pattern, pattern_storage)
np.save(filename_out_label, label_storage)
proprocess_data_set(True)
proprocess_data_set(False)

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DATA_FASHION_MNIST/data_url.txt Normal file
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https://github.com/zalandoresearch/fashion-mnist
We need:
t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
Then
gzip -d *.gz
python convert.py

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# MIT License
# Copyright 2022 University of Bremen
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# David Rotermund ( davrot@uni-bremen.de )
#
#
# Release history:
# ================
# 1.0.0 -- 01.05.2022: first release
#
#
import numpy as np
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
# 0004 32 bit integer 60000 number of items
# 0008 unsigned byte ?? label
# 0009 unsigned byte ?? label
# ........
# xxxx unsigned byte ?? label
# The labels values are 0 to 9.
class ReadLabel:
"""Class for reading the labels from an MNIST label file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2049:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
else:
data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)
file.close()
return data
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000803(2051) magic number
# 0004 32 bit integer 60000 number of images
# 0008 32 bit integer 28 number of rows
# 0012 32 bit integer 28 number of columns
# 0016 unsigned byte ?? pixel
# 0017 unsigned byte ?? pixel
# ........
# xxxx unsigned byte ?? pixel
# Pixels are organized row-wise.
# Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
class ReadPicture:
"""Class for reading the images from an MNIST image file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2051:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
number_of_rows = 0
else:
number_of_rows = np.frombuffer(file.read(4), int32_data)[0]
if number_of_rows != 28:
data = np.zeros(0)
number_of_columns = 0
else:
number_of_columns = np.frombuffer(file.read(4), int32_data)[0]
if number_of_columns != 28:
data = np.zeros(0)
else:
data = np.frombuffer(
file.read(number_of_elements * number_of_rows * number_of_columns),
dtype=np.uint8,
)
data = data.reshape(number_of_elements, number_of_columns, number_of_rows)
file.close()
return data
def proprocess_data_set(test_mode):
if test_mode is True:
filename_out_pattern: str = "TestPatternStorage.npy"
filename_out_label: str = "TestLabelStorage.npy"
filename_in_image: str = "t10k-images-idx3-ubyte"
filename_in_label = "t10k-labels-idx1-ubyte"
else:
filename_out_pattern = "TrainPatternStorage.npy"
filename_out_label = "TrainLabelStorage.npy"
filename_in_image = "train-images-idx3-ubyte"
filename_in_label = "train-labels-idx1-ubyte"
pictures = ReadPicture(filename_in_image)
labels = ReadLabel(filename_in_label)
# Down to 0 ... 1.0
max_value = np.max(pictures.data.astype(np.float32))
d = np.float32(pictures.data.astype(np.float32) / max_value)
label_storage = np.uint64(labels.data)
pattern_storage = d.astype(np.float32)
np.save(filename_out_pattern, pattern_storage)
np.save(filename_out_label, label_storage)
proprocess_data_set(True)
proprocess_data_set(False)

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http://yann.lecun.com/exdb/mnist/
We need:
t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
Then
gzip -d *.gz
python convert.py