Delete DATA_FASHION_MNIST directory
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David Rotermund
GitHub
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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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from abc import ABC, abstractmethod
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import torch
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import numpy as np
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import torchvision as tv # type: ignore
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from Parameter import Config
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class DatasetMaster(torch.utils.data.Dataset, ABC):
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path_label: str
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label_storage: np.ndarray
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pattern_storage: np.ndarray
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number_of_pattern: int
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mean: list[float]
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# Initialize
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def __init__(
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self,
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train: bool = False,
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path_pattern: str = "./",
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path_label: str = "./",
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) -> None:
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super().__init__()
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if train is True:
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self.label_storage = np.load(path_label + "/TrainLabelStorage.npy")
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else:
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self.label_storage = np.load(path_label + "/TestLabelStorage.npy")
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if train is True:
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self.pattern_storage = np.load(path_pattern + "/TrainPatternStorage.npy")
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else:
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self.pattern_storage = np.load(path_pattern + "/TestPatternStorage.npy")
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self.number_of_pattern = self.label_storage.shape[0]
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self.mean = []
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def __len__(self) -> int:
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return self.number_of_pattern
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# Get one pattern at position index
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@abstractmethod
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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pass
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@abstractmethod
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def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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pass
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@abstractmethod
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def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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pass
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class DatasetMNIST(DatasetMaster):
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"""Contstructor"""
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# Initialize
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def __init__(
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self,
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train: bool = False,
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path_pattern: str = "./",
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path_label: str = "./",
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) -> None:
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super().__init__(train, path_pattern, path_label)
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self.pattern_storage = np.ascontiguousarray(
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self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32)
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)
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self.pattern_storage /= np.max(self.pattern_storage)
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mean = self.pattern_storage.mean(3).mean(2).mean(0)
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self.mean = [*mean]
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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image = self.pattern_storage[index, 0:1, :, :]
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target = int(self.label_storage[index])
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return torch.tensor(image), target
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def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The test image comes in
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1. is center cropped
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2. on/off filteres
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3. returned.
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This is a 1 channel version (e.g. one gray channel).
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"""
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assert len(cfg.image_statistics.mean) == 1
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.CenterCrop(size=cfg.image_statistics.the_size),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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gray: torch.Tensor = my_on_off_filter(
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pattern[:, 0:1, :, :],
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)
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return gray
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def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The training image comes in
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1. is cropped from a random position
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2. on/off filteres
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3. returned.
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This is a 1 channel version (e.g. one gray channel).
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"""
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assert len(cfg.image_statistics.mean) == 1
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.RandomCrop(size=cfg.image_statistics.the_size),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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gray: torch.Tensor = my_on_off_filter(
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pattern[:, 0:1, :, :],
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)
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return gray
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class DatasetFashionMNIST(DatasetMaster):
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"""Contstructor"""
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# Initialize
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def __init__(
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self,
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train: bool = False,
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path_pattern: str = "./",
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path_label: str = "./",
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) -> None:
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super().__init__(train, path_pattern, path_label)
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self.pattern_storage = np.ascontiguousarray(
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self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32)
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)
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self.pattern_storage /= np.max(self.pattern_storage)
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mean = self.pattern_storage.mean(3).mean(2).mean(0)
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self.mean = [*mean]
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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image = self.pattern_storage[index, 0:1, :, :]
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target = int(self.label_storage[index])
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return torch.tensor(image), target
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def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The test image comes in
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1. is center cropped
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2. on/off filteres
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3. returned.
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This is a 1 channel version (e.g. one gray channel).
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"""
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assert len(cfg.image_statistics.mean) == 1
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.CenterCrop(size=cfg.image_statistics.the_size),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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gray: torch.Tensor = my_on_off_filter(
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pattern[:, 0:1, :, :],
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)
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return gray
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def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The training image comes in
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1. is cropped from a random position
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2. on/off filteres
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3. returned.
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This is a 1 channel version (e.g. one gray channel).
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"""
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assert len(cfg.image_statistics.mean) == 1
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.RandomCrop(size=cfg.image_statistics.the_size),
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tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p),
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tv.transforms.ColorJitter(
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brightness=cfg.augmentation.jitter_brightness,
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contrast=cfg.augmentation.jitter_contrast,
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saturation=cfg.augmentation.jitter_saturation,
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hue=cfg.augmentation.jitter_hue,
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),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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gray: torch.Tensor = my_on_off_filter(
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pattern[:, 0:1, :, :],
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)
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return gray
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class DatasetCIFAR(DatasetMaster):
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"""Contstructor"""
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# Initialize
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def __init__(
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self,
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train: bool = False,
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path_pattern: str = "./",
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path_label: str = "./",
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) -> None:
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super().__init__(train, path_pattern, path_label)
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self.pattern_storage = np.ascontiguousarray(
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np.moveaxis(self.pattern_storage.astype(dtype=np.float32), 3, 1)
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)
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self.pattern_storage /= np.max(self.pattern_storage)
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mean = self.pattern_storage.mean(3).mean(2).mean(0)
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self.mean = [*mean]
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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image = self.pattern_storage[index, :, :, :]
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target = int(self.label_storage[index])
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return torch.tensor(image), target
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def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The test image comes in
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1. is center cropped
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2. on/off filteres
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3. returned.
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This is a 3 channel version (e.g. r,g,b channels).
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"""
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assert len(cfg.image_statistics.mean) == 3
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.CenterCrop(size=cfg.image_statistics.the_size),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
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my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
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r: torch.Tensor = my_on_off_filter_r(
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pattern[:, 0:1, :, :],
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)
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g: torch.Tensor = my_on_off_filter_g(
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pattern[:, 1:2, :, :],
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)
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b: torch.Tensor = my_on_off_filter_b(
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pattern[:, 2:3, :, :],
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)
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new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1)
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return new_tensor
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def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor:
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"""0. The training image comes in
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1. is cropped from a random position
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2. is randomly horizontally flipped
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3. is randomly color jitteres
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4. on/off filteres
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5. returned.
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This is a 3 channel version (e.g. r,g,b channels).
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"""
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assert len(cfg.image_statistics.mean) == 3
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assert len(cfg.image_statistics.the_size) == 2
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assert cfg.image_statistics.the_size[0] > 0
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assert cfg.image_statistics.the_size[1] > 0
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# Transformation chain
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my_transforms: torch.nn.Sequential = torch.nn.Sequential(
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tv.transforms.RandomCrop(size=cfg.image_statistics.the_size),
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tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p),
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tv.transforms.ColorJitter(
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brightness=cfg.augmentation.jitter_brightness,
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contrast=cfg.augmentation.jitter_contrast,
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saturation=cfg.augmentation.jitter_saturation,
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hue=cfg.augmentation.jitter_hue,
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),
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)
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scripted_transforms = torch.jit.script(my_transforms)
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# Preprocess the input data
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pattern = scripted_transforms(pattern)
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# => On/Off
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my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
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my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
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my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
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r: torch.Tensor = my_on_off_filter_r(
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pattern[:, 0:1, :, :],
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)
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g: torch.Tensor = my_on_off_filter_g(
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pattern[:, 1:2, :, :],
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)
|
|
||||||
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(
|
|
||||||
tensor_clone >= 0.0,
|
|
||||||
tensor_clone,
|
|
||||||
tensor_clone.new_zeros(tensor_clone.shape, dtype=tensor_clone.dtype),
|
|
||||||
)
|
|
||||||
|
|
||||||
new_tensor: torch.Tensor = torch.cat((temp_0, temp_1), dim=1)
|
|
||||||
|
|
||||||
return new_tensor
|
|
||||||
|
|
||||||
def __repr__(self) -> str:
|
|
||||||
return self.__class__.__name__ + "(p={0})".format(self.p)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
pass
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 33 KiB |
Binary file not shown.
|
|
@ -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
|
|
||||||
Binary file not shown.
|
|
@ -1 +0,0 @@
|
||||||
Performance reached (test data correct classifications): 89.82%
|
|
||||||
|
|
@ -1,31 +0,0 @@
|
||||||
import os
|
|
||||||
|
|
||||||
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
from tensorboard.backend.event_processing import event_accumulator
|
|
||||||
|
|
||||||
filename: str = "events.out.tfevents.1651328399.fedora.118340.0"
|
|
||||||
|
|
||||||
acc = event_accumulator.EventAccumulator(filename)
|
|
||||||
acc.Reload()
|
|
||||||
|
|
||||||
# What is available?
|
|
||||||
# available_scalar = acc.Tags()["scalars"]
|
|
||||||
# print("Available Scalars")
|
|
||||||
# print(available_scalar)
|
|
||||||
|
|
||||||
which_scalar: str = "Test Number Correct"
|
|
||||||
te = acc.Scalars(which_scalar)
|
|
||||||
|
|
||||||
temp: list = []
|
|
||||||
for te_item in te:
|
|
||||||
temp.append((te_item[1], te_item[2]))
|
|
||||||
temp_np = np.array(temp)
|
|
||||||
|
|
||||||
plt.semilogy(temp_np[:, 0], (1.0 - (temp_np[:, 1] / 10000)) * 100)
|
|
||||||
plt.xlabel("Epochs")
|
|
||||||
plt.ylabel("Error [%]")
|
|
||||||
plt.savefig("Error.png")
|
|
||||||
plt.show()
|
|
||||||
|
|
@ -1,203 +0,0 @@
|
||||||
# %%
|
|
||||||
import torch
|
|
||||||
from Dataset import DatasetFashionMNIST
|
|
||||||
from Parameter import Config
|
|
||||||
import torchvision as tv # type: ignore
|
|
||||||
|
|
||||||
# Some parameters
|
|
||||||
|
|
||||||
cfg = Config()
|
|
||||||
|
|
||||||
input_number_of_channel: int = 1
|
|
||||||
input_dim_x: int = 24
|
|
||||||
input_dim_y: int = 24
|
|
||||||
|
|
||||||
number_of_output_channels_conv1: int = 32
|
|
||||||
number_of_output_channels_conv2: int = 64
|
|
||||||
number_of_output_channels_flatten1: int = 576
|
|
||||||
number_of_output_channels_full1: int = 10
|
|
||||||
|
|
||||||
kernel_size_conv1: tuple[int, int] = (5, 5)
|
|
||||||
kernel_size_pool1: tuple[int, int] = (2, 2)
|
|
||||||
kernel_size_conv2: tuple[int, int] = (5, 5)
|
|
||||||
kernel_size_pool2: tuple[int, int] = (2, 2)
|
|
||||||
|
|
||||||
stride_conv1: tuple[int, int] = (1, 1)
|
|
||||||
stride_pool1: tuple[int, int] = (2, 2)
|
|
||||||
stride_conv2: tuple[int, int] = (1, 1)
|
|
||||||
stride_pool2: tuple[int, int] = (2, 2)
|
|
||||||
|
|
||||||
padding_conv1: int = 0
|
|
||||||
padding_pool1: int = 0
|
|
||||||
padding_conv2: int = 0
|
|
||||||
padding_pool2: int = 0
|
|
||||||
|
|
||||||
network = torch.nn.Sequential(
|
|
||||||
torch.nn.Conv2d(
|
|
||||||
in_channels=input_number_of_channel,
|
|
||||||
out_channels=number_of_output_channels_conv1,
|
|
||||||
kernel_size=kernel_size_conv1,
|
|
||||||
stride=stride_conv1,
|
|
||||||
padding=padding_conv1,
|
|
||||||
),
|
|
||||||
torch.nn.ReLU(),
|
|
||||||
torch.nn.MaxPool2d(
|
|
||||||
kernel_size=kernel_size_pool1, stride=stride_pool1, padding=padding_pool1
|
|
||||||
),
|
|
||||||
torch.nn.Conv2d(
|
|
||||||
in_channels=number_of_output_channels_conv1,
|
|
||||||
out_channels=number_of_output_channels_conv2,
|
|
||||||
kernel_size=kernel_size_conv2,
|
|
||||||
stride=stride_conv2,
|
|
||||||
padding=padding_conv2,
|
|
||||||
),
|
|
||||||
torch.nn.ReLU(),
|
|
||||||
torch.nn.MaxPool2d(
|
|
||||||
kernel_size=kernel_size_pool2, stride=stride_pool2, padding=padding_pool2
|
|
||||||
),
|
|
||||||
torch.nn.Flatten(
|
|
||||||
start_dim=1,
|
|
||||||
),
|
|
||||||
torch.nn.Linear(
|
|
||||||
in_features=number_of_output_channels_flatten1,
|
|
||||||
out_features=number_of_output_channels_full1,
|
|
||||||
bias=True,
|
|
||||||
),
|
|
||||||
torch.nn.Softmax(dim=1),
|
|
||||||
)
|
|
||||||
# %%
|
|
||||||
path_pattern: str = "./DATA_FASHION_MNIST/"
|
|
||||||
path_label: str = "./DATA_FASHION_MNIST/"
|
|
||||||
|
|
||||||
dataset_train = DatasetFashionMNIST(
|
|
||||||
train=True, path_pattern=path_pattern, path_label=path_label
|
|
||||||
)
|
|
||||||
dataset_test = DatasetFashionMNIST(
|
|
||||||
train=False, path_pattern=path_pattern, path_label=path_label
|
|
||||||
)
|
|
||||||
cfg.image_statistics.mean = dataset_train.mean
|
|
||||||
# The basic size
|
|
||||||
cfg.image_statistics.the_size = [
|
|
||||||
dataset_train.pattern_storage.shape[2],
|
|
||||||
dataset_train.pattern_storage.shape[3],
|
|
||||||
]
|
|
||||||
# Minus the stuff we cut away in the pattern filter
|
|
||||||
cfg.image_statistics.the_size[0] -= 2 * cfg.augmentation.crop_width_in_pixel
|
|
||||||
cfg.image_statistics.the_size[1] -= 2 * cfg.augmentation.crop_width_in_pixel
|
|
||||||
|
|
||||||
|
|
||||||
batch_size_train: int = 100
|
|
||||||
batch_size_test: int = 100
|
|
||||||
|
|
||||||
|
|
||||||
train_data_load = torch.utils.data.DataLoader(
|
|
||||||
dataset_train, batch_size=batch_size_train, shuffle=True
|
|
||||||
)
|
|
||||||
|
|
||||||
test_data_load = torch.utils.data.DataLoader(
|
|
||||||
dataset_test, batch_size=batch_size_test, shuffle=False
|
|
||||||
)
|
|
||||||
|
|
||||||
transforms_test: torch.nn.Sequential = torch.nn.Sequential(
|
|
||||||
tv.transforms.CenterCrop(size=cfg.image_statistics.the_size),
|
|
||||||
)
|
|
||||||
scripted_transforms_test = torch.jit.script(transforms_test)
|
|
||||||
|
|
||||||
transforms_train: 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_train = torch.jit.script(transforms_train)
|
|
||||||
# %%
|
|
||||||
# The optimizer
|
|
||||||
optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
|
|
||||||
# The LR Scheduler
|
|
||||||
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.75)
|
|
||||||
|
|
||||||
# %%
|
|
||||||
number_of_test_pattern: int = dataset_test.__len__()
|
|
||||||
number_of_train_pattern: int = dataset_train.__len__()
|
|
||||||
|
|
||||||
number_of_epoch: int = 200
|
|
||||||
|
|
||||||
# %%
|
|
||||||
import time
|
|
||||||
from torch.utils.tensorboard import SummaryWriter
|
|
||||||
|
|
||||||
tb = SummaryWriter()
|
|
||||||
|
|
||||||
# %%
|
|
||||||
loss_function = torch.nn.CrossEntropyLoss()
|
|
||||||
|
|
||||||
for epoch_id in range(0, number_of_epoch):
|
|
||||||
print(f"Epoch: {epoch_id}")
|
|
||||||
t_start: float = time.perf_counter()
|
|
||||||
|
|
||||||
train_loss: float = 0.0
|
|
||||||
train_correct: int = 0
|
|
||||||
train_number: int = 0
|
|
||||||
test_correct: int = 0
|
|
||||||
test_number: int = 0
|
|
||||||
|
|
||||||
# Switch the network into training mode
|
|
||||||
network.train()
|
|
||||||
|
|
||||||
# This runs in total for one epoch split up into mini-batches
|
|
||||||
for image, target in train_data_load:
|
|
||||||
|
|
||||||
# Clean the gradient
|
|
||||||
optimizer.zero_grad()
|
|
||||||
|
|
||||||
output = network(scripted_transforms_train(image))
|
|
||||||
|
|
||||||
loss = loss_function(output, target)
|
|
||||||
|
|
||||||
train_loss += loss.item()
|
|
||||||
train_correct += (output.argmax(dim=1) == target).sum().numpy()
|
|
||||||
train_number += target.shape[0]
|
|
||||||
# Calculate backprop
|
|
||||||
loss.backward()
|
|
||||||
|
|
||||||
# Update the parameter
|
|
||||||
optimizer.step()
|
|
||||||
|
|
||||||
# Update the learning rate
|
|
||||||
lr_scheduler.step(train_loss)
|
|
||||||
|
|
||||||
t_training: float = time.perf_counter()
|
|
||||||
|
|
||||||
# Switch the network into evalution mode
|
|
||||||
network.eval()
|
|
||||||
with torch.no_grad():
|
|
||||||
for image, target in test_data_load:
|
|
||||||
|
|
||||||
output = network(scripted_transforms_test(image))
|
|
||||||
|
|
||||||
test_correct += (output.argmax(dim=1) == target).sum().numpy()
|
|
||||||
test_number += target.shape[0]
|
|
||||||
|
|
||||||
t_testing = time.perf_counter()
|
|
||||||
|
|
||||||
perfomance_test_correct: float = 100.0 * test_correct / test_number
|
|
||||||
perfomance_train_correct: float = 100.0 * train_correct / train_number
|
|
||||||
|
|
||||||
tb.add_scalar("Train Loss", train_loss, epoch_id)
|
|
||||||
tb.add_scalar("Train Number Correct", train_correct, epoch_id)
|
|
||||||
tb.add_scalar("Test Number Correct", test_correct, epoch_id)
|
|
||||||
|
|
||||||
print(f"Training: Loss={train_loss:.5f} Correct={perfomance_train_correct:.2f}%")
|
|
||||||
print(f"Testing: Correct={perfomance_test_correct:.2f}%")
|
|
||||||
print(
|
|
||||||
f"Time: Training={(t_training-t_start):.1f}sec, Testing={(t_testing-t_training):.1f}sec"
|
|
||||||
)
|
|
||||||
torch.save(network, "Model_MNIST_A_" + str(epoch_id) + ".pt")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# %%
|
|
||||||
tb.close()
|
|
||||||
|
|
@ -1,161 +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
|
|
||||||
#
|
|
||||||
#
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
@ -1,8 +0,0 @@
|
||||||
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
|
|
||||||
Loading…
Reference in a new issue