clarena.cl_datasets.permuted_cub2002011

The submodule in cl_datasets for Permuted CUB-200-2011 dataset.

  1r"""
  2The submodule in `cl_datasets` for Permuted CUB-200-2011 dataset.
  3"""
  4
  5__all__ = ["PermutedCUB2002011"]
  6
  7import logging
  8from typing import Callable
  9
 10import torch
 11from torch.utils.data import Dataset, random_split
 12from torchvision.transforms import transforms
 13
 14from clarena.cl_datasets import CLPermutedDataset
 15from clarena.stl_datasets.raw import CUB2002011
 16
 17# always get logger for built-in logging in each module
 18pylogger = logging.getLogger(__name__)
 19
 20
 21class PermutedCUB2002011(CLPermutedDataset):
 22    r"""Permuted CUB-200-2011 dataset. The [CUB (Caltech-UCSD Birds)-200-2011)](https://www.vision.caltech.edu/datasets/cub_200_2011/) is a bird image dataset. It consists of 100,000 training, 10,000 validation, 10,000 test images of 200 bird species (classes), each 64x64 color image."""
 23
 24    original_dataset_python_class: type[Dataset] = CUB2002011
 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 CUB-200-2011 data 'CUB_200_2011/' 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
 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 CUB-200-2011 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        CUB2002011(root=self.root_t, train=True, download=True)
 93        CUB2002011(root=self.root_t, train=False, download=True)
 94
 95        pylogger.debug(
 96            "The original CUB-200-2011 dataset has been downloaded to %s.",
 97            self.root_t,
 98        )
 99
100    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
101        """Get the training and validation dataset of task `self.task_id`.
102
103        **Returns:**
104        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
105        """
106        dataset_train_and_val = CUB2002011(
107            root=self.root_t,
108            train=True,
109            transform=self.train_and_val_transforms(),
110            target_transform=self.target_transform(),
111            download=False,
112        )
113
114        return random_split(
115            dataset_train_and_val,
116            lengths=[1 - self.validation_percentage, self.validation_percentage],
117            generator=torch.Generator().manual_seed(
118                42
119            ),  # 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
120        )
121
122    def test_dataset(self) -> Dataset:
123        r"""Get the test dataset of task `self.task_id`.
124
125        **Returns:**
126        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
127        """
128        dataset_test = CUB2002011(
129            root=self.root_t,
130            train=False,
131            transform=self.test_transforms(),
132            target_transform=self.target_transform(),
133            download=False,
134        )
135
136        return dataset_test
class PermutedCUB2002011(clarena.cl_datasets.base.CLPermutedDataset): 
 22class PermutedCUB2002011(CLPermutedDataset):
 23    r"""Permuted CUB-200-2011 dataset. The [CUB (Caltech-UCSD Birds)-200-2011)](https://www.vision.caltech.edu/datasets/cub_200_2011/) is a bird image dataset. It consists of 100,000 training, 10,000 validation, 10,000 test images of 200 bird species (classes), each 64x64 color image."""
 24
 25    original_dataset_python_class: type[Dataset] = CUB2002011
 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 CUB-200-2011 data 'CUB_200_2011/' 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
 71        super().__init__(
 72            root=root,
 73            num_tasks=num_tasks,
 74            batch_size=batch_size,
 75            num_workers=num_workers,
 76            custom_transforms=custom_transforms,
 77            repeat_channels=repeat_channels,
 78            to_tensor=to_tensor,
 79            resize=resize,
 80            permutation_mode=permutation_mode,
 81            permutation_seeds=permutation_seeds,
 82        )
 83
 84        self.validation_percentage: float = validation_percentage
 85        r"""The percentage to randomly split some training data into validation data."""
 86
 87    def prepare_data(self) -> None:
 88        r"""Download the original CUB-200-2011 dataset if haven't."""
 89
 90        if self.task_id != 1:
 91            return  # download all original datasets only at the beginning of first task
 92
 93        CUB2002011(root=self.root_t, train=True, download=True)
 94        CUB2002011(root=self.root_t, train=False, download=True)
 95
 96        pylogger.debug(
 97            "The original CUB-200-2011 dataset has been downloaded to %s.",
 98            self.root_t,
 99        )
100
101    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
102        """Get the training and validation dataset of task `self.task_id`.
103
104        **Returns:**
105        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
106        """
107        dataset_train_and_val = CUB2002011(
108            root=self.root_t,
109            train=True,
110            transform=self.train_and_val_transforms(),
111            target_transform=self.target_transform(),
112            download=False,
113        )
114
115        return random_split(
116            dataset_train_and_val,
117            lengths=[1 - self.validation_percentage, self.validation_percentage],
118            generator=torch.Generator().manual_seed(
119                42
120            ),  # 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
121        )
122
123    def test_dataset(self) -> Dataset:
124        r"""Get the test dataset of task `self.task_id`.
125
126        **Returns:**
127        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
128        """
129        dataset_test = CUB2002011(
130            root=self.root_t,
131            train=False,
132            transform=self.test_transforms(),
133            target_transform=self.target_transform(),
134            download=False,
135        )
136
137        return dataset_test

Permuted CUB-200-2011 dataset. The CUB (Caltech-UCSD Birds)-200-2011) is a bird image dataset. It consists of 100,000 training, 10,000 validation, 10,000 test images of 200 bird species (classes), each 64x64 color image.

PermutedCUB2002011( 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 CUB-200-2011 data 'CUB_200_2011/' 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
71        super().__init__(
72            root=root,
73            num_tasks=num_tasks,
74            batch_size=batch_size,
75            num_workers=num_workers,
76            custom_transforms=custom_transforms,
77            repeat_channels=repeat_channels,
78            to_tensor=to_tensor,
79            resize=resize,
80            permutation_mode=permutation_mode,
81            permutation_seeds=permutation_seeds,
82        )
83
84        self.validation_percentage: float = validation_percentage
85        r"""The percentage to randomly split some training data into validation data."""

Args:

  • root (str): the root directory where the original CUB-200-2011 data 'CUB_200_2011/' 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 'clarena.stl_datasets.raw.cub2002011.CUB2002011'>

The original dataset class.

validation_percentage: float

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

def prepare_data(self) -> None: 
87    def prepare_data(self) -> None:
88        r"""Download the original CUB-200-2011 dataset if haven't."""
89
90        if self.task_id != 1:
91            return  # download all original datasets only at the beginning of first task
92
93        CUB2002011(root=self.root_t, train=True, download=True)
94        CUB2002011(root=self.root_t, train=False, download=True)
95
96        pylogger.debug(
97            "The original CUB-200-2011 dataset has been downloaded to %s.",
98            self.root_t,
99        )

Download the original CUB-200-2011 dataset if haven't.

def train_and_val_dataset( self) -> tuple[torch.utils.data.dataset.Dataset, torch.utils.data.dataset.Dataset]: 
101    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
102        """Get the training and validation dataset of task `self.task_id`.
103
104        **Returns:**
105        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
106        """
107        dataset_train_and_val = CUB2002011(
108            root=self.root_t,
109            train=True,
110            transform=self.train_and_val_transforms(),
111            target_transform=self.target_transform(),
112            download=False,
113        )
114
115        return random_split(
116            dataset_train_and_val,
117            lengths=[1 - self.validation_percentage, self.validation_percentage],
118            generator=torch.Generator().manual_seed(
119                42
120            ),  # 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
121        )

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: 
123    def test_dataset(self) -> Dataset:
124        r"""Get the test dataset of task `self.task_id`.
125
126        **Returns:**
127        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
128        """
129        dataset_test = CUB2002011(
130            root=self.root_t,
131            train=False,
132            transform=self.test_transforms(),
133            target_transform=self.target_transform(),
134            download=False,
135        )
136
137        return dataset_test

Get the test dataset of task self.task_id.

Returns:

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