Shortcuts

Source code for torchvision.datasets.semeion

from PIL import Image
import os
import os.path
import numpy as np
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from .utils import download_url, check_integrity


[docs]class SEMEION(VisionDataset): r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset. Args: root (string): Root directory of dataset where directory ``semeion.py`` exists. 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. 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: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = True, ) -> None: super(SEMEION, self).__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]
[docs] 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)

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources