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