feat(dataset): add HuggingFace image dataset

src/data/dataset/hf_image.py

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+from typing import Optional
+
+import torch
+from torch.utils.data import Dataset
+from torchvision.transforms import Normalize
+from torchvision.transforms.functional import to_tensor
+from PIL import Image
+from datasets import load_dataset
+
+from src.data.dataset.metric_dataset import CenterCrop
+
+
+class HuggingFaceImageDataset(Dataset):
+    def __init__(
+        self,
+        name: str,
+        split: str = "train",
+        image_column: str = "image",
+        label_column: Optional[str] = None,
+        resolution: int = 256,
+        cache_dir: Optional[str] = None,
+    ) -> None:
+        self.dataset = load_dataset(name, split=split, cache_dir=cache_dir)
+        self.image_column = image_column
+        self.label_column = label_column
+        self.transform = CenterCrop(resolution)
+        self.normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __getitem__(self, idx: int):
+        item = self.dataset[idx]
+        image = item[self.image_column]
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        image = image.convert("RGB")
+        image = self.transform(image)
+
+        raw_image = to_tensor(image)
+        normalized_image = self.normalize(raw_image)
+        label = 0 if self.label_column is None else int(item[self.label_column])
+        return raw_image, normalized_image, label