clarena.cl_datasets.permuted_tinyimagenet

The submodule in cl_datasets for Permuted TinyImageNet dataset.

  1r"""
  2The submodule in `cl_datasets` for Permuted TinyImageNet dataset.
  3"""
  4
  5__all__ = ["PermutedTinyImageNet"]
  6
  7import logging
  8from typing import Callable
  9
 10import torch
 11from tinyimagenet import TinyImageNet
 12from torch.utils.data import Dataset, random_split
 13from torchvision.transforms import transforms
 14
 15from clarena.cl_datasets import CLPermutedDataset
 16
 17# always get logger for built-in logging in each module
 18pylogger = logging.getLogger(__name__)
 19
 20
 21class PermutedTinyImageNet(CLPermutedDataset):
 22    r"""Permuted TinyImageNet dataset. The [TinyImageNet dataset](http://vision.stanford.edu/teaching/cs231n/reports/2015/pdfs/yle_project.pdf) is smaller, more manageable version of the [Imagenet dataset](https://www.image-net.org). It consists of 100,000 training, 10,000 validation and 10,000 test images of 200 classes, each 64x64 color image."""
 23
 24    original_dataset_python_class: type[Dataset] = TinyImageNet
 25    r"""The original dataset class."""
 26
 27    def __init__(
 28        self,
 29        root: str,
 30        num_tasks: int,
 31        validation_percentage: float,
 32        batch_size: int | dict[int, int] = 1,
 33        num_workers: int | dict[int, int] = 0,
 34        custom_transforms: (
 35            Callable
 36            | transforms.Compose
 37            | None
 38            | dict[int, Callable | transforms.Compose | None]
 39        ) = None,
 40        repeat_channels: int | None | dict[int, int | None] = None,
 41        to_tensor: bool | dict[int, bool] = True,
 42        resize: tuple[int, int] | None | dict[int, tuple[int, int] | None] = None,
 43        permutation_mode: str = "first_channel_only",
 44        permutation_seeds: dict[int, int] | None = None,
 45    ) -> None:
 46        r"""
 47        **Args:**
 48        - **root** (`str`): the root directory where the original TinyImageNet data 'tiny-imagenet-200/' live.
 49        - **num_tasks** (`int`): the maximum number of tasks supported by the CL dataset. This decides the valid task IDs from 1 to `num_tasks`.
 50        - **validation_percentage** (`float`): the percentage to randomly split some training data into validation data.
 51        - **batch_size** (`int` | `dict[int, int]`): the batch size for train, val, and test dataloaders.
 52        If it is a dict, the keys are task IDs and the values are the batch sizes for each task. If it is an `int`, it is the same batch size for all tasks.
 53        - **num_workers** (`int` | `dict[int, int]`): the number of workers for dataloaders.
 54        If it is a dict, the keys are task IDs and the values are the number of workers for each task. If it is an `int`, it is the same number of workers for all tasks.
 55        - **custom_transforms** (`transform` or `transforms.Compose` or `None` or dict of them): the custom transforms to apply ONLY to the TRAIN dataset. Can be a single transform, composed transforms, or no transform. `ToTensor()`, normalization, permute, and so on are not included.
 56        If it is a dict, the keys are task IDs and the values are the custom transforms for each task. If it is a single transform or composed transforms, it is applied to all tasks. If it is `None`, no custom transforms are applied.
 57        - **repeat_channels** (`int` | `None` | dict of them): the number of channels to repeat for each task. Default is `None`, which means no repeat.
 58        If it is a dict, the keys are task IDs and the values are the number of channels to repeat for each task. If it is an `int`, it is the same number of channels to repeat for all tasks. If it is `None`, no repeat is applied.
 59        - **to_tensor** (`bool` | `dict[int, bool]`): whether to include the `ToTensor()` transform. Default is `True`.
 60        If it is a dict, the keys are task IDs and the values are whether to include the `ToTensor()` transform for each task. If it is a single boolean value, it is applied to all tasks.
 61        - **resize** (`tuple[int, int]` | `None` or dict of them): the size to resize the images to. Default is `None`, which means no resize.
 62        If it is a dict, the keys are task IDs and the values are the sizes to resize for each task. If it is a single tuple of two integers, it is applied to all tasks. If it is `None`, no resize is applied.
 63        - **permutation_mode** (`str`): the mode of permutation; one of:
 64            1. 'all': permute all pixels.
 65            2. 'by_channel': permute channel by channel separately. All channels are applied the same permutation order.
 66            3. 'first_channel_only': permute only the first channel.
 67        - **permutation_seeds** (`dict[int, int]` | `None`): the dict of seeds for permutation operations used to construct each task. Keys are task IDs and the values are permutation seeds for each task. Default is `None`, which creates a dict of seeds from 0 to `num_tasks`-1.
 68        """
 69        super().__init__(
 70            root=root,
 71            num_tasks=num_tasks,
 72            batch_size=batch_size,
 73            num_workers=num_workers,
 74            custom_transforms=custom_transforms,
 75            repeat_channels=repeat_channels,
 76            to_tensor=to_tensor,
 77            resize=resize,
 78            permutation_mode=permutation_mode,
 79            permutation_seeds=permutation_seeds,
 80        )
 81
 82        self.validation_percentage: float = validation_percentage
 83        r"""The percentage to randomly split some training data into validation data."""
 84
 85    def prepare_data(self) -> None:
 86        r"""Download the original TinyImageNet dataset if haven't."""
 87
 88        if self.task_id != 1:
 89            return  # download all original datasets only at the beginning of first task
 90
 91        TinyImageNet(root=self.root_t)
 92
 93        pylogger.debug(
 94            "The original TinyImageNet dataset has been downloaded to %s.",
 95            self.root_t,
 96        )
 97
 98    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
 99        """Get the training and validation dataset of task `self.task_id`.
100
101        **Returns:**
102        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
103        """
104        dataset_train_and_val = TinyImageNet(
105            root=self.root_t,
106            split="train",
107            transform=self.train_and_val_transforms(),
108            target_transform=self.target_transform(),
109        )
110
111        return random_split(
112            dataset_train_and_val,
113            lengths=[1 - self.validation_percentage, self.validation_percentage],
114            generator=torch.Generator().manual_seed(
115                42
116            ),  # this must be set fixed to make sure the datasets across experiments are the same. Don't handle it to global seed as it might vary across experiments
117        )
118
119    def test_dataset(self) -> Dataset:
120        r"""Get the test dataset of task `self.task_id`.
121
122        **Returns:**
123        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
124        """
125        dataset_test = TinyImageNet(
126            root=self.root_t,
127            split="val",
128            transform=self.test_transforms(),
129            target_transform=self.target_transform(),
130        )
131
132        return dataset_test
class PermutedTinyImageNet(clarena.cl_datasets.base.CLPermutedDataset): 
 22class PermutedTinyImageNet(CLPermutedDataset):
 23    r"""Permuted TinyImageNet dataset. The [TinyImageNet dataset](http://vision.stanford.edu/teaching/cs231n/reports/2015/pdfs/yle_project.pdf) is smaller, more manageable version of the [Imagenet dataset](https://www.image-net.org). It consists of 100,000 training, 10,000 validation and 10,000 test images of 200 classes, each 64x64 color image."""
 24
 25    original_dataset_python_class: type[Dataset] = TinyImageNet
 26    r"""The original dataset class."""
 27
 28    def __init__(
 29        self,
 30        root: str,
 31        num_tasks: int,
 32        validation_percentage: float,
 33        batch_size: int | dict[int, int] = 1,
 34        num_workers: int | dict[int, int] = 0,
 35        custom_transforms: (
 36            Callable
 37            | transforms.Compose
 38            | None
 39            | dict[int, Callable | transforms.Compose | None]
 40        ) = None,
 41        repeat_channels: int | None | dict[int, int | None] = None,
 42        to_tensor: bool | dict[int, bool] = True,
 43        resize: tuple[int, int] | None | dict[int, tuple[int, int] | None] = None,
 44        permutation_mode: str = "first_channel_only",
 45        permutation_seeds: dict[int, int] | None = None,
 46    ) -> None:
 47        r"""
 48        **Args:**
 49        - **root** (`str`): the root directory where the original TinyImageNet data 'tiny-imagenet-200/' live.
 50        - **num_tasks** (`int`): the maximum number of tasks supported by the CL dataset. This decides the valid task IDs from 1 to `num_tasks`.
 51        - **validation_percentage** (`float`): the percentage to randomly split some training data into validation data.
 52        - **batch_size** (`int` | `dict[int, int]`): the batch size for train, val, and test dataloaders.
 53        If it is a dict, the keys are task IDs and the values are the batch sizes for each task. If it is an `int`, it is the same batch size for all tasks.
 54        - **num_workers** (`int` | `dict[int, int]`): the number of workers for dataloaders.
 55        If it is a dict, the keys are task IDs and the values are the number of workers for each task. If it is an `int`, it is the same number of workers for all tasks.
 56        - **custom_transforms** (`transform` or `transforms.Compose` or `None` or dict of them): the custom transforms to apply ONLY to the TRAIN dataset. Can be a single transform, composed transforms, or no transform. `ToTensor()`, normalization, permute, and so on are not included.
 57        If it is a dict, the keys are task IDs and the values are the custom transforms for each task. If it is a single transform or composed transforms, it is applied to all tasks. If it is `None`, no custom transforms are applied.
 58        - **repeat_channels** (`int` | `None` | dict of them): the number of channels to repeat for each task. Default is `None`, which means no repeat.
 59        If it is a dict, the keys are task IDs and the values are the number of channels to repeat for each task. If it is an `int`, it is the same number of channels to repeat for all tasks. If it is `None`, no repeat is applied.
 60        - **to_tensor** (`bool` | `dict[int, bool]`): whether to include the `ToTensor()` transform. Default is `True`.
 61        If it is a dict, the keys are task IDs and the values are whether to include the `ToTensor()` transform for each task. If it is a single boolean value, it is applied to all tasks.
 62        - **resize** (`tuple[int, int]` | `None` or dict of them): the size to resize the images to. Default is `None`, which means no resize.
 63        If it is a dict, the keys are task IDs and the values are the sizes to resize for each task. If it is a single tuple of two integers, it is applied to all tasks. If it is `None`, no resize is applied.
 64        - **permutation_mode** (`str`): the mode of permutation; one of:
 65            1. 'all': permute all pixels.
 66            2. 'by_channel': permute channel by channel separately. All channels are applied the same permutation order.
 67            3. 'first_channel_only': permute only the first channel.
 68        - **permutation_seeds** (`dict[int, int]` | `None`): the dict of seeds for permutation operations used to construct each task. Keys are task IDs and the values are permutation seeds for each task. Default is `None`, which creates a dict of seeds from 0 to `num_tasks`-1.
 69        """
 70        super().__init__(
 71            root=root,
 72            num_tasks=num_tasks,
 73            batch_size=batch_size,
 74            num_workers=num_workers,
 75            custom_transforms=custom_transforms,
 76            repeat_channels=repeat_channels,
 77            to_tensor=to_tensor,
 78            resize=resize,
 79            permutation_mode=permutation_mode,
 80            permutation_seeds=permutation_seeds,
 81        )
 82
 83        self.validation_percentage: float = validation_percentage
 84        r"""The percentage to randomly split some training data into validation data."""
 85
 86    def prepare_data(self) -> None:
 87        r"""Download the original TinyImageNet dataset if haven't."""
 88
 89        if self.task_id != 1:
 90            return  # download all original datasets only at the beginning of first task
 91
 92        TinyImageNet(root=self.root_t)
 93
 94        pylogger.debug(
 95            "The original TinyImageNet dataset has been downloaded to %s.",
 96            self.root_t,
 97        )
 98
 99    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
100        """Get the training and validation dataset of task `self.task_id`.
101
102        **Returns:**
103        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
104        """
105        dataset_train_and_val = TinyImageNet(
106            root=self.root_t,
107            split="train",
108            transform=self.train_and_val_transforms(),
109            target_transform=self.target_transform(),
110        )
111
112        return random_split(
113            dataset_train_and_val,
114            lengths=[1 - self.validation_percentage, self.validation_percentage],
115            generator=torch.Generator().manual_seed(
116                42
117            ),  # this must be set fixed to make sure the datasets across experiments are the same. Don't handle it to global seed as it might vary across experiments
118        )
119
120    def test_dataset(self) -> Dataset:
121        r"""Get the test dataset of task `self.task_id`.
122
123        **Returns:**
124        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
125        """
126        dataset_test = TinyImageNet(
127            root=self.root_t,
128            split="val",
129            transform=self.test_transforms(),
130            target_transform=self.target_transform(),
131        )
132
133        return dataset_test

Permuted TinyImageNet dataset. The TinyImageNet dataset is smaller, more manageable version of the Imagenet dataset. It consists of 100,000 training, 10,000 validation and 10,000 test images of 200 classes, each 64x64 color image.

PermutedTinyImageNet( root: str, num_tasks: int, validation_percentage: float, batch_size: int | dict[int, int] = 1, num_workers: int | dict[int, int] = 0, custom_transforms: Union[Callable, torchvision.transforms.transforms.Compose, NoneType, dict[int, Union[Callable, torchvision.transforms.transforms.Compose, NoneType]]] = None, repeat_channels: int | None | dict[int, int | None] = None, to_tensor: bool | dict[int, bool] = True, resize: tuple[int, int] | None | dict[int, tuple[int, int] | None] = None, permutation_mode: str = 'first_channel_only', permutation_seeds: dict[int, int] | None = None) 
28    def __init__(
29        self,
30        root: str,
31        num_tasks: int,
32        validation_percentage: float,
33        batch_size: int | dict[int, int] = 1,
34        num_workers: int | dict[int, int] = 0,
35        custom_transforms: (
36            Callable
37            | transforms.Compose
38            | None
39            | dict[int, Callable | transforms.Compose | None]
40        ) = None,
41        repeat_channels: int | None | dict[int, int | None] = None,
42        to_tensor: bool | dict[int, bool] = True,
43        resize: tuple[int, int] | None | dict[int, tuple[int, int] | None] = None,
44        permutation_mode: str = "first_channel_only",
45        permutation_seeds: dict[int, int] | None = None,
46    ) -> None:
47        r"""
48        **Args:**
49        - **root** (`str`): the root directory where the original TinyImageNet data 'tiny-imagenet-200/' live.
50        - **num_tasks** (`int`): the maximum number of tasks supported by the CL dataset. This decides the valid task IDs from 1 to `num_tasks`.
51        - **validation_percentage** (`float`): the percentage to randomly split some training data into validation data.
52        - **batch_size** (`int` | `dict[int, int]`): the batch size for train, val, and test dataloaders.
53        If it is a dict, the keys are task IDs and the values are the batch sizes for each task. If it is an `int`, it is the same batch size for all tasks.
54        - **num_workers** (`int` | `dict[int, int]`): the number of workers for dataloaders.
55        If it is a dict, the keys are task IDs and the values are the number of workers for each task. If it is an `int`, it is the same number of workers for all tasks.
56        - **custom_transforms** (`transform` or `transforms.Compose` or `None` or dict of them): the custom transforms to apply ONLY to the TRAIN dataset. Can be a single transform, composed transforms, or no transform. `ToTensor()`, normalization, permute, and so on are not included.
57        If it is a dict, the keys are task IDs and the values are the custom transforms for each task. If it is a single transform or composed transforms, it is applied to all tasks. If it is `None`, no custom transforms are applied.
58        - **repeat_channels** (`int` | `None` | dict of them): the number of channels to repeat for each task. Default is `None`, which means no repeat.
59        If it is a dict, the keys are task IDs and the values are the number of channels to repeat for each task. If it is an `int`, it is the same number of channels to repeat for all tasks. If it is `None`, no repeat is applied.
60        - **to_tensor** (`bool` | `dict[int, bool]`): whether to include the `ToTensor()` transform. Default is `True`.
61        If it is a dict, the keys are task IDs and the values are whether to include the `ToTensor()` transform for each task. If it is a single boolean value, it is applied to all tasks.
62        - **resize** (`tuple[int, int]` | `None` or dict of them): the size to resize the images to. Default is `None`, which means no resize.
63        If it is a dict, the keys are task IDs and the values are the sizes to resize for each task. If it is a single tuple of two integers, it is applied to all tasks. If it is `None`, no resize is applied.
64        - **permutation_mode** (`str`): the mode of permutation; one of:
65            1. 'all': permute all pixels.
66            2. 'by_channel': permute channel by channel separately. All channels are applied the same permutation order.
67            3. 'first_channel_only': permute only the first channel.
68        - **permutation_seeds** (`dict[int, int]` | `None`): the dict of seeds for permutation operations used to construct each task. Keys are task IDs and the values are permutation seeds for each task. Default is `None`, which creates a dict of seeds from 0 to `num_tasks`-1.
69        """
70        super().__init__(
71            root=root,
72            num_tasks=num_tasks,
73            batch_size=batch_size,
74            num_workers=num_workers,
75            custom_transforms=custom_transforms,
76            repeat_channels=repeat_channels,
77            to_tensor=to_tensor,
78            resize=resize,
79            permutation_mode=permutation_mode,
80            permutation_seeds=permutation_seeds,
81        )
82
83        self.validation_percentage: float = validation_percentage
84        r"""The percentage to randomly split some training data into validation data."""

Args:

  • root (str): the root directory where the original TinyImageNet data 'tiny-imagenet-200/' live.
  • num_tasks (int): the maximum number of tasks supported by the CL dataset. This decides the valid task IDs from 1 to num_tasks.
  • validation_percentage (float): the percentage to randomly split some training data into validation data.
  • batch_size (int | dict[int, int]): the batch size for train, val, and test dataloaders. If it is a dict, the keys are task IDs and the values are the batch sizes for each task. If it is an int, it is the same batch size for all tasks.
  • num_workers (int | dict[int, int]): the number of workers for dataloaders. If it is a dict, the keys are task IDs and the values are the number of workers for each task. If it is an int, it is the same number of workers for all tasks.
  • custom_transforms (transform or transforms.Compose or None or dict of them): the custom transforms to apply ONLY to the TRAIN dataset. Can be a single transform, composed transforms, or no transform. ToTensor(), normalization, permute, and so on are not included. If it is a dict, the keys are task IDs and the values are the custom transforms for each task. If it is a single transform or composed transforms, it is applied to all tasks. If it is None, no custom transforms are applied.
  • repeat_channels (int | None | dict of them): the number of channels to repeat for each task. Default is None, which means no repeat. If it is a dict, the keys are task IDs and the values are the number of channels to repeat for each task. If it is an int, it is the same number of channels to repeat for all tasks. If it is None, no repeat is applied.
  • to_tensor (bool | dict[int, bool]): whether to include the ToTensor() transform. Default is True. If it is a dict, the keys are task IDs and the values are whether to include the ToTensor() transform for each task. If it is a single boolean value, it is applied to all tasks.
  • resize (tuple[int, int] | None or dict of them): the size to resize the images to. Default is None, which means no resize. If it is a dict, the keys are task IDs and the values are the sizes to resize for each task. If it is a single tuple of two integers, it is applied to all tasks. If it is None, no resize is applied.
  • permutation_mode (str): the mode of permutation; one of:
    1. 'all': permute all pixels.
    2. 'by_channel': permute channel by channel separately. All channels are applied the same permutation order.
    3. 'first_channel_only': permute only the first channel.
  • permutation_seeds (dict[int, int] | None): the dict of seeds for permutation operations used to construct each task. Keys are task IDs and the values are permutation seeds for each task. Default is None, which creates a dict of seeds from 0 to num_tasks-1.
original_dataset_python_class: type[torch.utils.data.dataset.Dataset] = <class 'tinyimagenet.TinyImageNet'>

The original dataset class.

validation_percentage: float

The percentage to randomly split some training data into validation data.

def prepare_data(self) -> None: 
86    def prepare_data(self) -> None:
87        r"""Download the original TinyImageNet dataset if haven't."""
88
89        if self.task_id != 1:
90            return  # download all original datasets only at the beginning of first task
91
92        TinyImageNet(root=self.root_t)
93
94        pylogger.debug(
95            "The original TinyImageNet dataset has been downloaded to %s.",
96            self.root_t,
97        )

Download the original TinyImageNet dataset if haven't.

def train_and_val_dataset( self) -> tuple[torch.utils.data.dataset.Dataset, torch.utils.data.dataset.Dataset]: 
 99    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
100        """Get the training and validation dataset of task `self.task_id`.
101
102        **Returns:**
103        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
104        """
105        dataset_train_and_val = TinyImageNet(
106            root=self.root_t,
107            split="train",
108            transform=self.train_and_val_transforms(),
109            target_transform=self.target_transform(),
110        )
111
112        return random_split(
113            dataset_train_and_val,
114            lengths=[1 - self.validation_percentage, self.validation_percentage],
115            generator=torch.Generator().manual_seed(
116                42
117            ),  # this must be set fixed to make sure the datasets across experiments are the same. Don't handle it to global seed as it might vary across experiments
118        )

Get the training and validation dataset of task self.task_id.

Returns:

  • train_and_val_dataset (tuple[Dataset, Dataset]): the train and validation dataset of task self.task_id.
def test_dataset(self) -> torch.utils.data.dataset.Dataset: 
120    def test_dataset(self) -> Dataset:
121        r"""Get the test dataset of task `self.task_id`.
122
123        **Returns:**
124        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
125        """
126        dataset_test = TinyImageNet(
127            root=self.root_t,
128            split="val",
129            transform=self.test_transforms(),
130            target_transform=self.target_transform(),
131        )
132
133        return dataset_test

Get the test dataset of task self.task_id.

Returns:

  • test_dataset (Dataset): the test dataset of task self.task_id.