76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
|
import csv
|
||
|
|
import pathlib
|
||
|
|
from typing import Any, Callable, Optional, Tuple, Union
|
||
|
|
|
||
|
|
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 (str or ``pathlib.Path``): 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 a 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: Union[str, pathlib.Path],
|
||
|
|
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}"
|