Source code for torchvision.datasets.flickr

import glob
import os
from collections import defaultdict
from html.parser import HTMLParser
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

from PIL import Image

from .vision import VisionDataset


class Flickr8kParser(HTMLParser):
    """Parser for extracting captions from the Flickr8k dataset web page."""

    def __init__(self, root: Union[str, Path]) -> None:
        super().__init__()

        self.root = root

        # Data structure to store captions
        self.annotations: Dict[str, List[str]] = {}

        # State variables
        self.in_table = False
        self.current_tag: Optional[str] = None
        self.current_img: Optional[str] = None

    def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None:
        self.current_tag = tag

        if tag == "table":
            self.in_table = True

    def handle_endtag(self, tag: str) -> None:
        self.current_tag = None

        if tag == "table":
            self.in_table = False

    def handle_data(self, data: str) -> None:
        if self.in_table:
            if data == "Image Not Found":
                self.current_img = None
            elif self.current_tag == "a":
                img_id = data.split("/")[-2]
                img_id = os.path.join(self.root, img_id + "_*.jpg")
                img_id = glob.glob(img_id)[0]
                self.current_img = img_id
                self.annotations[img_id] = []
            elif self.current_tag == "li" and self.current_img:
                img_id = self.current_img
                self.annotations[img_id].append(data.strip())


class Flickr8k(VisionDataset):
    """`Flickr8k Entities <http://hockenmaier.cs.illinois.edu/8k-pictures.html>`_ Dataset.

    Args:
        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
        ann_file (string): Path to annotation file.
        transform (callable, optional): A function/transform that takes in a PIL image
            and returns a transformed version. E.g, ``transforms.PILToTensor``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
    """

    def __init__(
        self,
        root: Union[str, Path],
        ann_file: str,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)
        self.ann_file = os.path.expanduser(ann_file)

        # Read annotations and store in a dict
        parser = Flickr8kParser(self.root)
        with open(self.ann_file) as fh:
            parser.feed(fh.read())
        self.annotations = parser.annotations

        self.ids = list(sorted(self.annotations.keys()))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: Tuple (image, target). target is a list of captions for the image.
        """
        img_id = self.ids[index]

        # Image
        img = Image.open(img_id).convert("RGB")
        if self.transform is not None:
            img = self.transform(img)

        # Captions
        target = self.annotations[img_id]
        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def __len__(self) -> int:
        return len(self.ids)


class Flickr30k(VisionDataset):
    """`Flickr30k Entities <https://bryanplummer.com/Flickr30kEntities/>`_ Dataset.

    Args:
        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
        ann_file (string): Path to annotation file.
        transform (callable, optional): A function/transform that takes in a PIL image
            and returns a transformed version. E.g, ``transforms.PILToTensor``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
    """

    def __init__(
        self,
        root: str,
        ann_file: str,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)
        self.ann_file = os.path.expanduser(ann_file)

        # Read annotations and store in a dict
        self.annotations = defaultdict(list)
        with open(self.ann_file) as fh:
            for line in fh:
                img_id, caption = line.strip().split("\t")
                self.annotations[img_id[:-2]].append(caption)

        self.ids = list(sorted(self.annotations.keys()))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: Tuple (image, target). target is a list of captions for the image.
        """
        img_id = self.ids[index]

        # Image
        filename = os.path.join(self.root, img_id)
        img = Image.open(filename).convert("RGB")
        if self.transform is not None:
            img = self.transform(img)

        # Captions
        target = self.annotations[img_id]
        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

    def __len__(self) -> int:
        return len(self.ids)