from pathlib import Path
from typing import Literal
import numpy as np
import torch
from numpy.typing import ArrayLike
from timm.data.auto_augment import rand_augment_transform
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import DTD, SVHN
from torchvision.transforms import v2
from torch_uncertainty.datamodules import TUDataModule
from torch_uncertainty.datasets.classification import (
ImageNetO,
OpenImageO,
TinyImageNet,
TinyImageNetC,
)
from torch_uncertainty.datasets.utils import create_train_val_split
from torch_uncertainty.utils import (
interpolation_modes_from_str,
)
[docs]
class TinyImageNetDataModule(TUDataModule):
num_classes = 200
num_channels = 3
training_task = "classification"
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
def __init__(
self,
root: str | Path,
batch_size: int,
eval_batch_size: int | None = None,
eval_ood: bool = False,
eval_shift: bool = False,
shift_severity: int = 1,
val_split: float | None = None,
num_tta: int = 1,
postprocess_set: Literal["val", "test"] = "val",
train_transform: nn.Module | None = None,
test_transform: nn.Module | None = None,
ood_ds: str = "svhn",
interpolation: str = "bilinear",
basic_augment: bool = True,
rand_augment_opt: str | None = None,
num_workers: int = 1,
pin_memory: bool = True,
persistent_workers: bool = True,
) -> None:
"""DataModule for the Tiny-ImageNet dataset.
This datamodule uses Tiny-ImageNet as In-distribution dataset, OpenImage-O, ImageNet-0,
SVHN or DTD as Out-of-distribution dataset and Tiny-ImageNet-C as shifted dataset.
Args:
root (str): Root directory of the datasets.
batch_size (int): Number of samples per batch during training.
eval_batch_size (int | None) : Number of samples per batch during evaluation (val
and test). Set to :attr:`batch_size` if ``None``. Defaults to ``None``.
eval_ood (bool): Whether to evaluate out-of-distribution performance. Defaults to ``False``.
eval_shift (bool): Whether to evaluate on shifted data. Defaults to ``False``.
num_tta (int): Number of test-time augmentations (TTA). Defaults to ``1`` (no TTA).
shift_severity (int): Severity of the shift. Defaults to ``1``.
val_split (float or Path): Share of samples to use for validation
or path to a yaml file containing a list of validation images
ids. Defaults to ``0.0``.
postprocess_set (str, optional): The post-hoc calibration dataset to
use for the post-processing method. Defaults to ``val``.
train_transform (nn.Module | None): Custom training transform. Defaults
to ``None``. If not provided, a default transform is used.
test_transform (nn.Module | None): Custom test transform. Defaults to
``None``. If not provided, a default transform is used.
ood_ds (str): Which out-of-distribution dataset to use. Defaults to
``"openimage-o"``.
test_alt (str): Which test set to use. Defaults to ``None``.
procedure (str): Which procedure to use. Defaults to ``None``.
train_size (int): Size of training images. Defaults to ``224``.
interpolation (str): Interpolation method for the Resize Crops. Defaults to ``"bilinear"``.
basic_augment (bool): Whether to apply base augmentations. Defaults to ``True``.
rand_augment_opt (str): Which RandAugment to use. Defaults to ``None``.
num_workers (int): Number of workers to use for data loading. Defaults to ``1``.
pin_memory (bool): Whether to pin memory. Defaults to ``True``.
persistent_workers (bool): Whether to use persistent workers. Defaults to ``True``.
"""
super().__init__(
root=root,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
val_split=val_split,
num_tta=num_tta,
postprocess_set=postprocess_set,
num_workers=num_workers,
pin_memory=pin_memory,
persistent_workers=persistent_workers,
)
self.eval_ood = eval_ood
self.eval_shift = eval_shift
self.shift_severity = shift_severity
self.ood_ds = ood_ds
self.interpolation = interpolation_modes_from_str(interpolation)
self.dataset = TinyImageNet
if ood_ds == "imagenet-o":
self.ood_dataset = ImageNetO
elif ood_ds == "svhn":
self.ood_dataset = SVHN
elif ood_ds == "textures":
self.ood_dataset = DTD
elif ood_ds == "openimage-o":
self.ood_dataset = OpenImageO
else:
raise ValueError(f"OOD dataset {ood_ds} not supported for TinyImageNet.")
self.shift_dataset = TinyImageNetC
if train_transform is not None:
self.train_transform = train_transform
else:
if basic_augment:
basic_transform = v2.Compose(
[
v2.RandomCrop(64, padding=4),
v2.RandomHorizontalFlip(),
]
)
else:
basic_transform = nn.Identity()
if rand_augment_opt is not None:
main_transform = v2.Compose(
[
v2.ToPILImage(),
rand_augment_transform(rand_augment_opt, {}),
v2.ToImage(),
]
)
else:
main_transform = nn.Identity()
self.train_transform = v2.Compose(
[
v2.ToImage(),
basic_transform,
main_transform,
v2.ToDtype(dtype=torch.float32, scale=True),
v2.Normalize(mean=self.mean, std=self.std),
]
)
if num_tta != 1:
self.test_transform = train_transform
elif test_transform is not None:
self.test_transform = test_transform
else:
self.test_transform = v2.Compose(
[
v2.ToImage(),
v2.Resize(64, interpolation=self.interpolation),
v2.ToDtype(dtype=torch.float32, scale=True),
v2.Normalize(mean=self.mean, std=self.std),
]
)
def _verify_splits(self, split: str) -> None: # coverage: ignore
if split not in list(self.root.iterdir()):
raise FileNotFoundError(
f"a {split} TinyImagenet split was not found in {self.root},"
f" make sure the folder contains a subfolder named {split}"
)
def prepare_data(self) -> None: # coverage: ignore
if self.eval_ood:
self.ood_dataset(
self.root,
split="test",
download=True,
transform=self.test_transform,
)
if self.eval_shift:
self.shift_dataset(
self.root,
download=True,
transform=self.test_transform,
shift_severity=self.shift_severity,
)
def setup(self, stage: Literal["fit", "test"] | None = None) -> None:
if stage == "fit" or stage is None:
full = self.dataset(
self.root,
split="train",
transform=self.train_transform,
)
if self.val_split:
self.train, self.val = create_train_val_split(
full,
self.val_split,
self.test_transform,
)
else:
self.train = full
self.val = self.dataset(
self.root,
split="val",
transform=self.test_transform,
)
if stage == "test" or stage is None:
self.test = self.dataset(
self.root,
split="val",
transform=self.test_transform,
)
if stage not in ["fit", "test", None]:
raise ValueError(f"Stage {stage} is not supported.")
if self.eval_ood:
self.ood = self.ood_dataset(
self.root,
split="test",
transform=self.test_transform,
)
if self.eval_shift:
self.shift = self.shift_dataset(
self.root,
download=False,
shift_severity=self.shift_severity,
transform=self.test_transform,
)
[docs]
def test_dataloader(self) -> list[DataLoader]:
r"""Get test dataloaders for TinyImageNet.
Return:
list[DataLoader]: test set for in distribution data, OOD data, and/or
TinyImageNetC data.
"""
dataloader = [self._data_loader(self.get_test_set(), training=False, shuffle=False)]
if self.eval_ood:
dataloader.append(self._data_loader(self.get_ood_set(), training=False, shuffle=False))
if self.eval_shift:
dataloader.append(
self._data_loader(self.get_shift_set(), training=False, shuffle=False)
)
return dataloader
def _get_train_data(self) -> ArrayLike:
if self.val_split:
return self.train.dataset.samples[self.train.indices]
return self.train.samples
def _get_train_targets(self) -> ArrayLike:
if self.val_split:
return np.array(self.train.dataset.label_data)[self.train.indices]
return np.array(self.train.label_data)
