Delete fer2013.py

fer2013.py

@@ -1,75 +0,0 @@
-import csv
-import pathlib
-from typing import Any, Callable, Optional, Tuple
-
-import torch
-from PIL import Image
-
-from .utils import check_integrity, verify_str_arg
-from .vision import VisionDataset
-
-
-class FER2013(VisionDataset):
-    """`FER2013
-    <https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge>`_ Dataset.
-
-    Args:
-        root (string): Root directory of dataset where directory
-            ``root/fer2013`` exists.
-        split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
-        transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
-            version. E.g, ``transforms.RandomCrop``
-        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
-    """
-
-    _RESOURCES = {
-        "train": ("train.csv", "3f0dfb3d3fd99c811a1299cb947e3131"),
-        "test": ("test.csv", "b02c2298636a634e8c2faabbf3ea9a23"),
-    }
-
-    def __init__(
-        self,
-        root: str,
-        split: str = "train",
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-    ) -> None:
-        self._split = verify_str_arg(split, "split", self._RESOURCES.keys())
-        super().__init__(root, transform=transform, target_transform=target_transform)
-
-        base_folder = pathlib.Path(self.root) / "fer2013"
-        file_name, md5 = self._RESOURCES[self._split]
-        data_file = base_folder / file_name
-        if not check_integrity(str(data_file), md5=md5):
-            raise RuntimeError(
-                f"{file_name} not found in {base_folder} or corrupted. "
-                f"You can download it from "
-                f"https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge"
-            )
-
-        with open(data_file, "r", newline="") as file:
-            self._samples = [
-                (
-                    torch.tensor([int(idx) for idx in row["pixels"].split()], dtype=torch.uint8).reshape(48, 48),
-                    int(row["emotion"]) if "emotion" in row else None,
-                )
-                for row in csv.DictReader(file)
-            ]
-
-    def __len__(self) -> int:
-        return len(self._samples)
-
-    def __getitem__(self, idx: int) -> Tuple[Any, Any]:
-        image_tensor, target = self._samples[idx]
-        image = Image.fromarray(image_tensor.numpy())
-
-        if self.transform is not None:
-            image = self.transform(image)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return image, target
-
-    def extra_repr(self) -> str:
-        return f"split={self._split}"