clarena.cl_datasets.permuted_kannadamnist

  1r"""
  2The submodule in `cl_datasets` for Permuted Kannada-MNIST dataset.
  3"""
  4
  5__all__ = ["PermutedKannadaMNIST"]
  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 KannadaMNIST
 16
 17# always get logger for built-in logging in each module
 18pylogger = logging.getLogger(__name__)
 19
 20
 21class PermutedKannadaMNIST(CLPermutedDataset):
 22    r"""Permuted Kannada-MNIST dataset. The [Kannada-MNIST dataset](https://github.com/vinayprabhu/Kannada_MNIST) is a collection of handwritten Kannada digits (0-9). It consists of 60,000 training and 10,000 test images of handwritten Kannada digits (10 classes), each 28x28 grayscale image (similar to MNIST)."""
 23
 24    original_dataset_python_class: type[Dataset] = KannadaMNIST
 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 Kannada-MNIST data 'KannadaMNIST/' 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 Kannada-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 = KannadaMNIST(
 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        dataset_test = KannadaMNIST(
118            root=self.root_t,
119            train=False,
120            transform=self.test_transforms(),
121            target_transform=self.target_transform(),
122            download=False,
123        )
124
125        return dataset_test