93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
|
import os.path
|
||
|
|
from pathlib import Path
|
||
|
|
from typing import Any, Callable, Optional, Tuple, Union
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
from .utils import check_integrity, download_url
|
||
|
|
from .vision import VisionDataset
|
||
|
|
|
||
|
|
|
||
|
|
class SEMEION(VisionDataset):
|
||
|
|
r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
root (str or ``pathlib.Path``): Root directory of dataset where directory
|
||
|
|
``semeion.py`` exists.
|
||
|
|
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.
|
||
|
|
download (bool, optional): If true, downloads the dataset from the internet and
|
||
|
|
puts it in root directory. If dataset is already downloaded, it is not
|
||
|
|
downloaded again.
|
||
|
|
|
||
|
|
"""
|
||
|
|
url = "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data"
|
||
|
|
filename = "semeion.data"
|
||
|
|
md5_checksum = "cb545d371d2ce14ec121470795a77432"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
root: Union[str, Path],
|
||
|
|
transform: Optional[Callable] = None,
|
||
|
|
target_transform: Optional[Callable] = None,
|
||
|
|
download: bool = True,
|
||
|
|
) -> None:
|
||
|
|
super().__init__(root, transform=transform, target_transform=target_transform)
|
||
|
|
|
||
|
|
if download:
|
||
|
|
self.download()
|
||
|
|
|
||
|
|
if not self._check_integrity():
|
||
|
|
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
|
||
|
|
|
||
|
|
fp = os.path.join(self.root, self.filename)
|
||
|
|
data = np.loadtxt(fp)
|
||
|
|
# convert value to 8 bit unsigned integer
|
||
|
|
# color (white #255) the pixels
|
||
|
|
self.data = (data[:, :256] * 255).astype("uint8")
|
||
|
|
self.data = np.reshape(self.data, (-1, 16, 16))
|
||
|
|
self.labels = np.nonzero(data[:, 256:])[1]
|
||
|
|
|
||
|
|
def __getitem__(self, index: int) -> Tuple[Any, Any]:
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
index (int): Index
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
tuple: (image, target) where target is index of the target class.
|
||
|
|
"""
|
||
|
|
img, target = self.data[index], int(self.labels[index])
|
||
|
|
|
||
|
|
# doing this so that it is consistent with all other datasets
|
||
|
|
# to return a PIL Image
|
||
|
|
img = Image.fromarray(img, mode="L")
|
||
|
|
|
||
|
|
if self.transform is not None:
|
||
|
|
img = self.transform(img)
|
||
|
|
|
||
|
|
if self.target_transform is not None:
|
||
|
|
target = self.target_transform(target)
|
||
|
|
|
||
|
|
return img, target
|
||
|
|
|
||
|
|
def __len__(self) -> int:
|
||
|
|
return len(self.data)
|
||
|
|
|
||
|
|
def _check_integrity(self) -> bool:
|
||
|
|
root = self.root
|
||
|
|
fpath = os.path.join(root, self.filename)
|
||
|
|
if not check_integrity(fpath, self.md5_checksum):
|
||
|
|
return False
|
||
|
|
return True
|
||
|
|
|
||
|
|
def download(self) -> None:
|
||
|
|
if self._check_integrity():
|
||
|
|
print("Files already downloaded and verified")
|
||
|
|
return
|
||
|
|
|
||
|
|
root = self.root
|
||
|
|
download_url(self.url, root, self.filename, self.md5_checksum)
|