From dcc2a4f059a79421ff7ae76bec646d2e82e70e86 Mon Sep 17 00:00:00 2001 From: David Rotermund <54365609+davrot@users.noreply.github.com> Date: Thu, 5 Jan 2023 13:22:14 +0100 Subject: [PATCH] Delete DATA_CIFAR10 directory --- .../PyTorch_Non_Spike_Network/Dataset.py | 422 ------------------ .../PyTorch_Non_Spike_Network/Error.png | Bin 25623 -> 0 bytes .../Model_MNIST_A_499.pt.gz | Bin 1868145 -> 0 bytes .../PyTorch_Non_Spike_Network/Parameter.py | 164 ------- ...ts.out.tfevents.1651334099.fedora.121264.0 | Bin 82650 -> 0 bytes .../PyTorch_Non_Spike_Network/info.md | 1 - .../PyTorch_Non_Spike_Network/plot.py | 31 -- DATA_CIFAR10/PyTorch_Non_Spike_Network/run.py | 203 --------- DATA_CIFAR10/convert.py | 126 ------ DATA_CIFAR10/data_url.txt | 8 - 10 files changed, 955 deletions(-) delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/Dataset.py delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/Error.png delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/Model_MNIST_A_499.pt.gz delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/Parameter.py delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/events.out.tfevents.1651334099.fedora.121264.0 delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/info.md delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/plot.py delete mode 100644 DATA_CIFAR10/PyTorch_Non_Spike_Network/run.py delete mode 100644 DATA_CIFAR10/convert.py delete mode 100644 DATA_CIFAR10/data_url.txt diff --git a/DATA_CIFAR10/PyTorch_Non_Spike_Network/Dataset.py b/DATA_CIFAR10/PyTorch_Non_Spike_Network/Dataset.py deleted file mode 100644 index 11f9854..0000000 --- a/DATA_CIFAR10/PyTorch_Non_Spike_Network/Dataset.py +++ /dev/null @@ -1,422 +0,0 @@ -# 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 -# -# - -from abc import ABC, abstractmethod -import torch -import numpy as np -import torchvision as tv # type: ignore -from Parameter import Config - - -class DatasetMaster(torch.utils.data.Dataset, ABC): - - path_label: str - label_storage: np.ndarray - pattern_storage: np.ndarray - number_of_pattern: int - mean: list[float] - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__() - - if train is True: - self.label_storage = np.load(path_label + "/TrainLabelStorage.npy") - else: - self.label_storage = np.load(path_label + "/TestLabelStorage.npy") - - if train is True: - self.pattern_storage = np.load(path_pattern + "/TrainPatternStorage.npy") - else: - self.pattern_storage = np.load(path_pattern + "/TestPatternStorage.npy") - - self.number_of_pattern = self.label_storage.shape[0] - - self.mean = [] - - def __len__(self) -> int: - return self.number_of_pattern - - # Get one pattern at position index - @abstractmethod - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - pass - - @abstractmethod - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - pass - - @abstractmethod - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - pass - - -class DatasetMNIST(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32) - ) - - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, 0:1, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - -class DatasetFashionMNIST(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32) - ) - - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, 0:1, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p), - tv.transforms.ColorJitter( - brightness=cfg.augmentation.jitter_brightness, - contrast=cfg.augmentation.jitter_contrast, - saturation=cfg.augmentation.jitter_saturation, - hue=cfg.augmentation.jitter_hue, - ), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - -class DatasetCIFAR(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - np.moveaxis(self.pattern_storage.astype(dtype=np.float32), 3, 1) - ) - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, :, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 3 channel version (e.g. r,g,b channels). - """ - - assert len(cfg.image_statistics.mean) == 3 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - - my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1]) - my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2]) - r: torch.Tensor = my_on_off_filter_r( - pattern[:, 0:1, :, :], - ) - g: torch.Tensor = my_on_off_filter_g( - pattern[:, 1:2, :, :], - ) - b: torch.Tensor = my_on_off_filter_b( - pattern[:, 2:3, :, :], - ) - - new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1) - return new_tensor - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. is randomly horizontally flipped - 3. is randomly color jitteres - 4. on/off filteres - 5. returned. - - This is a 3 channel version (e.g. r,g,b channels). - """ - assert len(cfg.image_statistics.mean) == 3 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p), - tv.transforms.ColorJitter( - brightness=cfg.augmentation.jitter_brightness, - contrast=cfg.augmentation.jitter_contrast, - saturation=cfg.augmentation.jitter_saturation, - hue=cfg.augmentation.jitter_hue, - ), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1]) - my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2]) - r: torch.Tensor = my_on_off_filter_r( - pattern[:, 0:1, :, :], - ) - g: torch.Tensor = my_on_off_filter_g( - pattern[:, 1:2, :, :], - ) - b: torch.Tensor = my_on_off_filter_b( - pattern[:, 2:3, :, :], - ) - - new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1) - return new_tensor - - -class OnOffFilter(torch.nn.Module): - def __init__(self, p: float = 0.5) -> None: - super(OnOffFilter, self).__init__() - self.p: float = p - - def forward(self, tensor: torch.Tensor) -> torch.Tensor: - - assert tensor.shape[1] == 1 - - tensor_clone = 2.0 * (tensor - self.p) - - temp_0: torch.Tensor = torch.where( - tensor_clone < 0.0, - -tensor_clone, - tensor_clone.new_zeros(tensor_clone.shape, dtype=tensor_clone.dtype), - ) - - temp_1: torch.Tensor = torch.where( 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--- a/DATA_CIFAR10/PyTorch_Non_Spike_Network/Parameter.py +++ /dev/null @@ -1,164 +0,0 @@ -# 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 -# -# - -# %% -from dataclasses import dataclass, field -import numpy as np -import torch -import os - - -@dataclass -class Network: - """Parameters of the network. The details about - its layers and the number of output neurons.""" - - number_of_output_neurons: int = field(default=0) - forward_kernel_size: list[list[int]] = field(default_factory=list) - forward_neuron_numbers: list[list[int]] = field(default_factory=list) - strides: list[list[int]] = field(default_factory=list) - dilation: list[list[int]] = field(default_factory=list) - padding: list[list[int]] = field(default_factory=list) - is_pooling_layer: list[bool] = field(default_factory=list) - w_trainable: list[bool] = field(default_factory=list) - eps_xy_trainable: list[bool] = field(default_factory=list) - eps_xy_mean: list[bool] = field(default_factory=list) - - -@dataclass -class LearningParameters: - """Parameter required for training""" - - loss_coeffs_mse: float = field(default=0.5) - loss_coeffs_kldiv: float = field(default=1.0) - learning_rate_gamma_w: float = field(default=-1.0) - learning_rate_gamma_eps_xy: float = field(default=-1.0) - learning_rate_threshold_w: float = field(default=0.00001) - learning_rate_threshold_eps_xy: float = field(default=0.00001) - learning_active: bool = field(default=True) - weight_noise_amplitude: float = field(default=0.01) - eps_xy_intitial: float = field(default=0.1) - test_every_x_learning_steps: int = field(default=50) - test_during_learning: bool = field(default=True) - lr_scheduler_factor: float = field(default=0.75) - lr_scheduler_patience: int = field(default=10) - optimizer_name: str = field(default="Adam") - lr_schedule_name: str = field(default="ReduceLROnPlateau") - number_of_batches_for_one_update: int = field(default=1) - alpha_number_of_iterations: int = field(default=0) - overload_path: str = field(default="./Previous") - - -@dataclass -class Augmentation: - """Parameters used for data augmentation.""" - - crop_width_in_pixel: int = field(default=2) - flip_p: float = field(default=0.5) - jitter_brightness: float = field(default=0.5) - jitter_contrast: float = field(default=0.1) - jitter_saturation: float = field(default=0.1) - jitter_hue: float = field(default=0.15) - - -@dataclass -class ImageStatistics: - """(Statistical) information about the input. i.e. - mean values and the x and y size of the input""" - - mean: list[float] = field(default_factory=list) - the_size: list[int] = field(default_factory=list) - - -@dataclass -class Config: - """Master config class.""" - - # Sub classes - network_structure: Network = field(default_factory=Network) - learning_parameters: LearningParameters = field(default_factory=LearningParameters) - augmentation: Augmentation = field(default_factory=Augmentation) - image_statistics: ImageStatistics = field(default_factory=ImageStatistics) - - batch_size: int = field(default=500) - data_mode: str = field(default="") - - learning_step: int = field(default=0) - learning_step_max: int = field(default=10000) - - number_of_cpu_processes: int = field(default=-1) - - number_of_spikes: int = field(default=0) - cooldown_after_number_of_spikes: int = field(default=0) - - weight_path: str = field(default="./Weights/") - eps_xy_path: str = field(default="./EpsXY/") - data_path: str = field(default="./") - - reduction_cooldown: float = field(default=25.0) - epsilon_0: float = field(default=1.0) - - update_after_x_batch: float = field(default=1.0) - - def __post_init__(self) -> None: - """Post init determines the number of cores. - Creates the required directory and gives us an optimized - (for the amount of cores) batch size.""" - number_of_cpu_processes_temp = os.cpu_count() - - if self.number_of_cpu_processes < 1: - if number_of_cpu_processes_temp is None: - self.number_of_cpu_processes = 1 - else: - self.number_of_cpu_processes = number_of_cpu_processes_temp - - os.makedirs(self.weight_path, exist_ok=True) - os.makedirs(self.eps_xy_path, exist_ok=True) - os.makedirs(self.data_path, exist_ok=True) - - self.batch_size = ( - self.batch_size // self.number_of_cpu_processes - ) * self.number_of_cpu_processes - - self.batch_size = np.max((self.batch_size, self.number_of_cpu_processes)) - self.batch_size = int(self.batch_size) - - def get_epsilon_t(self): - """Generates the time series of the basic epsilon.""" - np_epsilon_t: np.ndarray = np.ones((self.number_of_spikes), dtype=np.float32) - np_epsilon_t[ - self.cooldown_after_number_of_spikes : self.number_of_spikes - ] /= self.reduction_cooldown - return torch.tensor(np_epsilon_t) - - def get_update_after_x_pattern(self): - """Tells us after how many pattern we need to update the weights.""" - return self.batch_size * self.update_after_x_batch diff --git a/DATA_CIFAR10/PyTorch_Non_Spike_Network/events.out.tfevents.1651334099.fedora.121264.0 b/DATA_CIFAR10/PyTorch_Non_Spike_Network/events.out.tfevents.1651334099.fedora.121264.0 deleted file mode 100644 index 3e5aae120224e7efed96a607a0e1ff9997dcf3fd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 82650 zcmaIfcU;fk|M>rCiuNXZRkoLr9nR?>tDQ6vvXzmYl_-&!y=Nj>+1c62N<>LR%O?Bx 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zbtzwo&z_fs_n(2WJdtGzBFZ{eDWzyit^Ilnpfo>21A%g>j4ppWYBNx7(4~B3wn@z` zaf8>8k0ZJ-C(6gHatr>2%=_JEFi?*Ekp=>#3m(fUo}+*=PnYtQcq|`$weB<+%Yr<& uEks$*Dw{NF%9nXV;q`crd>ROpukrrN52H5%W!sB1 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) diff --git a/DATA_CIFAR10/data_url.txt b/DATA_CIFAR10/data_url.txt deleted file mode 100644 index bcc1a82..0000000 --- a/DATA_CIFAR10/data_url.txt +++ /dev/null @@ -1,8 +0,0 @@ -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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