177 lines
6 KiB
Python
177 lines
6 KiB
Python
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import glob
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import os
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from collections import defaultdict
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from html.parser import HTMLParser
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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from .folder import default_loader
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from .vision import VisionDataset
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class Flickr8kParser(HTMLParser):
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"""Parser for extracting captions from the Flickr8k dataset web page."""
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def __init__(self, root: Union[str, Path]) -> None:
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super().__init__()
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self.root = root
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# Data structure to store captions
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self.annotations: Dict[str, List[str]] = {}
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# State variables
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self.in_table = False
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self.current_tag: Optional[str] = None
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self.current_img: Optional[str] = None
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def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None:
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self.current_tag = tag
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if tag == "table":
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self.in_table = True
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def handle_endtag(self, tag: str) -> None:
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self.current_tag = None
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if tag == "table":
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self.in_table = False
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def handle_data(self, data: str) -> None:
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if self.in_table:
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if data == "Image Not Found":
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self.current_img = None
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elif self.current_tag == "a":
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img_id = data.split("/")[-2]
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img_id = os.path.join(self.root, img_id + "_*.jpg")
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img_id = glob.glob(img_id)[0]
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self.current_img = img_id
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self.annotations[img_id] = []
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elif self.current_tag == "li" and self.current_img:
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img_id = self.current_img
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self.annotations[img_id].append(data.strip())
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class Flickr8k(VisionDataset):
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"""`Flickr8k Entities <http://hockenmaier.cs.illinois.edu/8k-pictures.html>`_ Dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory where images are downloaded to.
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ann_file (string): Path to annotation file.
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transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader,
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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loader (callable, optional): A function to load an image given its path.
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By default, it uses PIL as its image loader, but users could also pass in
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``torchvision.io.decode_image`` for decoding image data into tensors directly.
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"""
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def __init__(
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self,
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root: Union[str, Path],
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ann_file: str,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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loader: Callable[[str], Any] = default_loader,
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) -> None:
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super().__init__(root, transform=transform, target_transform=target_transform)
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self.ann_file = os.path.expanduser(ann_file)
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# Read annotations and store in a dict
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parser = Flickr8kParser(self.root)
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with open(self.ann_file) as fh:
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parser.feed(fh.read())
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self.annotations = parser.annotations
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self.ids = list(sorted(self.annotations.keys()))
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self.loader = loader
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: Tuple (image, target). target is a list of captions for the image.
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"""
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img_id = self.ids[index]
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# Image
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img = self.loader(img_id)
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if self.transform is not None:
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img = self.transform(img)
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# Captions
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target = self.annotations[img_id]
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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 img, target
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def __len__(self) -> int:
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return len(self.ids)
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class Flickr30k(VisionDataset):
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"""`Flickr30k Entities <https://bryanplummer.com/Flickr30kEntities/>`_ Dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory where images are downloaded to.
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ann_file (string): Path to annotation file.
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transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader,
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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loader (callable, optional): A function to load an image given its path.
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By default, it uses PIL as its image loader, but users could also pass in
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``torchvision.io.decode_image`` for decoding image data into tensors directly.
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"""
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def __init__(
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self,
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root: str,
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ann_file: str,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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loader: Callable[[str], Any] = default_loader,
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) -> None:
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super().__init__(root, transform=transform, target_transform=target_transform)
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self.ann_file = os.path.expanduser(ann_file)
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# Read annotations and store in a dict
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self.annotations = defaultdict(list)
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with open(self.ann_file) as fh:
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for line in fh:
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img_id, caption = line.strip().split("\t")
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self.annotations[img_id[:-2]].append(caption)
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self.ids = list(sorted(self.annotations.keys()))
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self.loader = loader
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: Tuple (image, target). target is a list of captions for the image.
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"""
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img_id = self.ids[index]
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# Image
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filename = os.path.join(self.root, img_id)
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img = self.loader(filename)
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if self.transform is not None:
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img = self.transform(img)
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# Captions
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target = self.annotations[img_id]
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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 img, target
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def __len__(self) -> int:
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return len(self.ids)
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