clarena.cl_datasets.permuted_sign_language_mnist

The submodule in cl_datasets for Permuted Sign Language MNIST dataset.

  1r"""
  2The submodule in `cl_datasets` for Permuted Sign Language MNIST dataset.
  3"""
  4
  5__all__ = ["PermutedSignLanguageMNIST"]
  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 SignLanguageMNIST
 16
 17# always get logger for built-in logging in each module
 18pylogger = logging.getLogger(__name__)
 19
 20
 21class PermutedSignLanguageMNIST(CLPermutedDataset):
 22    r"""Permuted Sign Language MNIST dataset. The [Sign Language MNIST dataset](https://www.kaggle.com/datasets/datamunge/sign-language-mnist) is a collection of hand gesture images representing ASL letters (A-Y, excluding J). It consists of 34,627 images of 24 classes, each 28x28 grayscale image."""
 23
 24    original_dataset_python_class: type[Dataset] = SignLanguageMNIST
 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 Sign Language MNIST data 'SignLanguageMNIST/' 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 Sign Language MNIST dataset if haven't. Because the original dataset is published on Kaggle, we need to download it manually. This function will not download the original dataset automatically."""
 87        pass
 88
 89    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
 90        """Get the training and validation dataset of task `self.task_id`.
 91
 92        **Returns:**
 93        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
 94        """
 95        dataset_train_and_val = SignLanguageMNIST(
 96            root=self.root_t,
 97            train=True,
 98            transform=self.train_and_val_transforms(),
 99            target_transform=self.target_transform(),
100            download=False,
101        )
102
103        return random_split(
104            dataset_train_and_val,
105            lengths=[1 - self.validation_percentage, self.validation_percentage],
106            generator=torch.Generator().manual_seed(
107                42
108            ),  # 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
109        )
110
111    def test_dataset(self) -> Dataset:
112        r"""Get the test dataset of task `self.task_id`.
113
114        **Returns:**
115        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
116        """
117
118        dataset_test = SignLanguageMNIST(
119            root=self.root_t,
120            train=False,
121            transform=self.test_transforms(),
122            target_transform=self.target_transform(),
123            download=False,
124        )
125
126        return dataset_test
class PermutedSignLanguageMNIST(clarena.cl_datasets.base.CLPermutedDataset): 
 22class PermutedSignLanguageMNIST(CLPermutedDataset):
 23    r"""Permuted Sign Language MNIST dataset. The [Sign Language MNIST dataset](https://www.kaggle.com/datasets/datamunge/sign-language-mnist) is a collection of hand gesture images representing ASL letters (A-Y, excluding J). It consists of 34,627 images of 24 classes, each 28x28 grayscale image."""
 24
 25    original_dataset_python_class: type[Dataset] = SignLanguageMNIST
 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 Sign Language MNIST data 'SignLanguageMNIST/' 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 Sign Language MNIST dataset if haven't. Because the original dataset is published on Kaggle, we need to download it manually. This function will not download the original dataset automatically."""
 88        pass
 89
 90    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
 91        """Get the training and validation dataset of task `self.task_id`.
 92
 93        **Returns:**
 94        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
 95        """
 96        dataset_train_and_val = SignLanguageMNIST(
 97            root=self.root_t,
 98            train=True,
 99            transform=self.train_and_val_transforms(),
100            target_transform=self.target_transform(),
101            download=False,
102        )
103
104        return random_split(
105            dataset_train_and_val,
106            lengths=[1 - self.validation_percentage, self.validation_percentage],
107            generator=torch.Generator().manual_seed(
108                42
109            ),  # 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
110        )
111
112    def test_dataset(self) -> Dataset:
113        r"""Get the test dataset of task `self.task_id`.
114
115        **Returns:**
116        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
117        """
118
119        dataset_test = SignLanguageMNIST(
120            root=self.root_t,
121            train=False,
122            transform=self.test_transforms(),
123            target_transform=self.target_transform(),
124            download=False,
125        )
126
127        return dataset_test

Permuted Sign Language MNIST dataset. The Sign Language MNIST dataset is a collection of hand gesture images representing ASL letters (A-Y, excluding J). It consists of 34,627 images of 24 classes, each 28x28 grayscale image.

PermutedSignLanguageMNIST( 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 Sign Language MNIST data 'SignLanguageMNIST/' 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 Sign Language MNIST data 'SignLanguageMNIST/' 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.sign_language_mnist.SignLanguageMNIST'>

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 Sign Language MNIST dataset if haven't. Because the original dataset is published on Kaggle, we need to download it manually. This function will not download the original dataset automatically."""
88        pass

Download the original Sign Language MNIST dataset if haven't. Because the original dataset is published on Kaggle, we need to download it manually. This function will not download the original dataset automatically.

def train_and_val_dataset( self) -> tuple[torch.utils.data.dataset.Dataset, torch.utils.data.dataset.Dataset]: 
 90    def train_and_val_dataset(self) -> tuple[Dataset, Dataset]:
 91        """Get the training and validation dataset of task `self.task_id`.
 92
 93        **Returns:**
 94        - **train_and_val_dataset** (`tuple[Dataset, Dataset]`): the train and validation dataset of task `self.task_id`.
 95        """
 96        dataset_train_and_val = SignLanguageMNIST(
 97            root=self.root_t,
 98            train=True,
 99            transform=self.train_and_val_transforms(),
100            target_transform=self.target_transform(),
101            download=False,
102        )
103
104        return random_split(
105            dataset_train_and_val,
106            lengths=[1 - self.validation_percentage, self.validation_percentage],
107            generator=torch.Generator().manual_seed(
108                42
109            ),  # 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
110        )

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: 
112    def test_dataset(self) -> Dataset:
113        r"""Get the test dataset of task `self.task_id`.
114
115        **Returns:**
116        - **test_dataset** (`Dataset`): the test dataset of task `self.task_id`.
117        """
118
119        dataset_test = SignLanguageMNIST(
120            root=self.root_t,
121            train=False,
122            transform=self.test_transforms(),
123            target_transform=self.target_transform(),
124            download=False,
125        )
126
127        return dataset_test

Get the test dataset of task self.task_id.

Returns:

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