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# MIT License
# Copyright 2022 University of Bremen
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# David Rotermund ( davrot@uni-bremen.de )
#
#
# Release history:
# ================
# 1.0.0 -- 01.05.2022: first release
#
#
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
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
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
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
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
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
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
if cfg.augmentation.use_on_off_filter is True:
my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
gray: torch.Tensor = my_on_off_filter(
pattern[:, 0:1, :, :],
)
else:
gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
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
if cfg.augmentation.use_on_off_filter is True:
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, :, :],
)
else:
r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
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
if cfg.augmentation.use_on_off_filter is True:
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, :, :],
)
else:
r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
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