Delete network/dataset_collection directory
This commit is contained in:
David Rotermund
GitHub
parent
fa79f18d36
commit
982ff849b8
9 changed files with 0 additions and 484 deletions
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# MIT License
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# Copyright 2022 University of Bremen
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
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# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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#
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#
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# David Rotermund ( davrot@uni-bremen.de )
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#
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#
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# Release history:
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# ================
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# 1.0.0 -- 01.05.2022: first release
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#
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#
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import numpy as np
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import pickle
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def give_filenames(id: int) -> tuple[str, str, int]:
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if id == 0:
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start_id: int = 0
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prefix: str = "Test"
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filename: str = "cifar-10-batches-py/test_batch"
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if id == 1:
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start_id = 0
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prefix = "Train"
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filename = "cifar-10-batches-py/data_batch_1"
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if id == 2:
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start_id = 10000
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prefix = "Train"
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filename = "cifar-10-batches-py/data_batch_2"
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if id == 3:
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start_id = 20000
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prefix = "Train"
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filename = "cifar-10-batches-py/data_batch_3"
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if id == 4:
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start_id = 30000
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prefix = "Train"
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filename = "cifar-10-batches-py/data_batch_4"
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if id == 5:
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start_id = 40000
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prefix = "Train"
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filename = "cifar-10-batches-py/data_batch_5"
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return filename, prefix, start_id
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def load_data(filename: str) -> tuple[np.ndarray, np.ndarray]:
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fo = open(filename, "rb")
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dict_data = pickle.load(fo, encoding="bytes")
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_, labels_temp, data_temp, _ = dict_data.items()
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data: np.ndarray = np.array(data_temp[1])
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labels: np.ndarray = np.array(labels_temp[1])
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return data, labels
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def split_into_three_color_channels(
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image: np.ndarray,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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channel_r = image[0:1024].astype(np.float32)
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channel_r = channel_r.reshape(32, 32)
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channel_g = image[1024:2048].astype(np.float32)
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channel_g = channel_g.reshape(32, 32)
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channel_b = image[2048:3072].astype(np.float32)
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channel_b = channel_b.reshape(32, 32)
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return channel_r, channel_g, channel_b
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def process_data_set(test_data_mode: bool) -> None:
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if test_data_mode is True:
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filename_out_pattern: str = "TestPatternStorage.npy"
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filename_out_label: str = "TestLabelStorage.npy"
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number_of_pictures: int = 10000
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start_id: int = 0
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end_id: int = 0
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else:
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filename_out_pattern = "TrainPatternStorage.npy"
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filename_out_label = "TrainLabelStorage.npy"
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number_of_pictures = 50000
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start_id = 1
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end_id = 5
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np_data: np.ndarray = np.zeros((number_of_pictures, 32, 32, 3), dtype=np.float32)
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np_label: np.ndarray = np.zeros((number_of_pictures), dtype=np.uint64)
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for id in range(start_id, end_id + 1):
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filename, _, start_id_pattern = give_filenames(id)
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pictures, labels = load_data(filename)
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for i in range(0, pictures.shape[0]):
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channel_r, channel_g, channel_b = split_into_three_color_channels(
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pictures[i, :]
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)
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np_data[i + start_id_pattern, :, :, 0] = channel_r
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np_data[i + start_id_pattern, :, :, 1] = channel_g
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np_data[i + start_id_pattern, :, :, 2] = channel_b
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np_label[i + start_id_pattern] = labels[i]
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np_data /= np.max(np_data)
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label_storage: np.ndarray = np_label.astype(dtype=np.uint64)
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pattern_storage: np.ndarray = np_data.astype(dtype=np.float32)
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np.save(filename_out_pattern, pattern_storage)
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np.save(filename_out_label, label_storage)
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process_data_set(True)
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process_data_set(False)
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@ -1,8 +0,0 @@
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https://www.cs.toronto.edu/~kriz/cifar.html
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Download the CIFAR-10 python version
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https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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Then
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tar -xvzf cifar-10-python.tar.gz
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python convert.py
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{
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"data_path": "./DATA_CIFAR10/",
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"data_mode": "CIFAR10"
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}
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@ -1,161 +0,0 @@
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# MIT License
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# Copyright 2022 University of Bremen
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#
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# 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
|
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||||||
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
|
||||||
# and/or sell copies of the Software, and to permit persons to whom the
|
|
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# Software is furnished to do so, subject to the following conditions:
|
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#
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# The above copyright notice and this permission notice shall be included
|
|
||||||
# in all copies or substantial portions of the Software.
|
|
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#
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|
||||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
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||||||
# 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
|
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# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
|
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# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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#
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#
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# David Rotermund ( davrot@uni-bremen.de )
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#
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#
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# Release history:
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# ================
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# 1.0.0 -- 01.05.2022: first release
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#
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#
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import numpy as np
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# [offset] [type] [value] [description]
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# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
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# 0004 32 bit integer 60000 number of items
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# 0008 unsigned byte ?? label
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# 0009 unsigned byte ?? label
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# ........
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# xxxx unsigned byte ?? label
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# The labels values are 0 to 9.
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class ReadLabel:
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"""Class for reading the labels from an MNIST label file"""
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def __init__(self, filename):
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self.filename: str = filename
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self.data = self.read_from_file(filename)
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def read_from_file(self, filename):
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int32_data = np.dtype(np.uint32)
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int32_data = int32_data.newbyteorder(">")
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file = open(filename, "rb")
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magic_flag = np.frombuffer(file.read(4), int32_data)[0]
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if magic_flag != 2049:
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data = np.zeros(0)
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number_of_elements = 0
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else:
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number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_elements < 1:
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data = np.zeros(0)
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else:
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data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)
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file.close()
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return data
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# [offset] [type] [value] [description]
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# 0000 32 bit integer 0x00000803(2051) magic number
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# 0004 32 bit integer 60000 number of images
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# 0008 32 bit integer 28 number of rows
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# 0012 32 bit integer 28 number of columns
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# 0016 unsigned byte ?? pixel
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# 0017 unsigned byte ?? pixel
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# ........
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# xxxx unsigned byte ?? pixel
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# Pixels are organized row-wise.
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# Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
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class ReadPicture:
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"""Class for reading the images from an MNIST image file"""
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def __init__(self, filename):
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self.filename: str = filename
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self.data = self.read_from_file(filename)
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def read_from_file(self, filename):
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int32_data = np.dtype(np.uint32)
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int32_data = int32_data.newbyteorder(">")
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file = open(filename, "rb")
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magic_flag = np.frombuffer(file.read(4), int32_data)[0]
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if magic_flag != 2051:
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data = np.zeros(0)
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number_of_elements = 0
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else:
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number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_elements < 1:
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data = np.zeros(0)
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number_of_rows = 0
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else:
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number_of_rows = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_rows != 28:
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data = np.zeros(0)
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number_of_columns = 0
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else:
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number_of_columns = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_columns != 28:
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data = np.zeros(0)
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else:
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data = np.frombuffer(
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file.read(number_of_elements * number_of_rows * number_of_columns),
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dtype=np.uint8,
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)
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data = data.reshape(number_of_elements, number_of_columns, number_of_rows)
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file.close()
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return data
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def proprocess_data_set(test_mode):
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if test_mode is True:
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filename_out_pattern: str = "TestPatternStorage.npy"
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filename_out_label: str = "TestLabelStorage.npy"
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filename_in_image: str = "t10k-images-idx3-ubyte"
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filename_in_label = "t10k-labels-idx1-ubyte"
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else:
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filename_out_pattern = "TrainPatternStorage.npy"
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filename_out_label = "TrainLabelStorage.npy"
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filename_in_image = "train-images-idx3-ubyte"
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filename_in_label = "train-labels-idx1-ubyte"
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pictures = ReadPicture(filename_in_image)
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labels = ReadLabel(filename_in_label)
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# Down to 0 ... 1.0
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max_value = np.max(pictures.data.astype(np.float32))
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d = np.float32(pictures.data.astype(np.float32) / max_value)
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label_storage = np.uint64(labels.data)
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pattern_storage = d.astype(np.float32)
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np.save(filename_out_pattern, pattern_storage)
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np.save(filename_out_label, label_storage)
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proprocess_data_set(True)
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proprocess_data_set(False)
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@ -1,8 +0,0 @@
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https://github.com/zalandoresearch/fashion-mnist
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We need:
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t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
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Then
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gzip -d *.gz
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python convert.py
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{
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"data_path": "./DATA_FASHION_MNIST/",
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"data_mode": "MNIST_FASHION"
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}
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@ -1,161 +0,0 @@
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# MIT License
|
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# Copyright 2022 University of Bremen
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#
|
|
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# 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 )
|
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#
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#
|
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# Release history:
|
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||||||
# ================
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# 1.0.0 -- 01.05.2022: first release
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#
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#
|
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|
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import numpy as np
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|
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# [offset] [type] [value] [description]
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# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
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# 0004 32 bit integer 60000 number of items
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||||||
# 0008 unsigned byte ?? label
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# 0009 unsigned byte ?? label
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# ........
|
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# xxxx unsigned byte ?? label
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# The labels values are 0 to 9.
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class ReadLabel:
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"""Class for reading the labels from an MNIST label file"""
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def __init__(self, filename):
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self.filename: str = filename
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self.data = self.read_from_file(filename)
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def read_from_file(self, filename):
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int32_data = np.dtype(np.uint32)
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int32_data = int32_data.newbyteorder(">")
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file = open(filename, "rb")
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magic_flag = np.frombuffer(file.read(4), int32_data)[0]
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|
|
||||||
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)
|
|
||||||
|
|
@ -1,8 +0,0 @@
|
||||||
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
|
|
||||||
|
|
@ -1,4 +0,0 @@
|
||||||
{
|
|
||||||
"data_path": "./DATA_MNIST/",
|
|
||||||
"data_mode": "MNIST"
|
|
||||||
}
|
|
||||||
Loading…
Reference in a new issue