Source code for torchvision.datasets.sbd

import os
import shutil
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import numpy as np
from PIL import Image

from .utils import download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset

[docs]class SBDataset(VisionDataset): """`Semantic Boundaries Dataset <>`_ The SBD currently contains annotations from 11355 images taken from the PASCAL VOC 2011 dataset. .. note :: Please note that the train and val splits included with this dataset are different from the splits in the PASCAL VOC dataset. In particular some "train" images might be part of VOC2012 val. If you are interested in testing on VOC 2012 val, then use `image_set='train_noval'`, which excludes all val images. .. warning:: This class needs `scipy <>`_ to load target files from `.mat` format. Args: root (str or ``pathlib.Path``): Root directory of the Semantic Boundaries Dataset image_set (string, optional): Select the image_set to use, ``train``, ``val`` or ``train_noval``. Image set ``train_noval`` excludes VOC 2012 val images. mode (string, optional): Select target type. Possible values 'boundaries' or 'segmentation'. In case of 'boundaries', the target is an array of shape `[num_classes, H, W]`, where `num_classes=20`. 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. transforms (callable, optional): A function/transform that takes input sample and its target as entry and returns a transformed version. Input sample is PIL image and target is a numpy array if `mode='boundaries'` or PIL image if `mode='segmentation'`. """ url = "" md5 = "82b4d87ceb2ed10f6038a1cba92111cb" filename = "benchmark.tgz" voc_train_url = "" voc_split_filename = "train_noval.txt" voc_split_md5 = "79bff800c5f0b1ec6b21080a3c066722" def __init__( self, root: Union[str, Path], image_set: str = "train", mode: str = "boundaries", download: bool = False, transforms: Optional[Callable] = None, ) -> None: try: from import loadmat self._loadmat = loadmat except ImportError: raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy") super().__init__(root, transforms) self.image_set = verify_str_arg(image_set, "image_set", ("train", "val", "train_noval")) self.mode = verify_str_arg(mode, "mode", ("segmentation", "boundaries")) self.num_classes = 20 sbd_root = self.root image_dir = os.path.join(sbd_root, "img") mask_dir = os.path.join(sbd_root, "cls") if download: download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.md5) extracted_ds_root = os.path.join(self.root, "benchmark_RELEASE", "dataset") for f in ["cls", "img", "inst", "train.txt", "val.txt"]: old_path = os.path.join(extracted_ds_root, f) shutil.move(old_path, sbd_root) if self.image_set == "train_noval": # Note: this is failing as of June 2024 download_url(self.voc_train_url, sbd_root, self.voc_split_filename, self.voc_split_md5) if not os.path.isdir(sbd_root): raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") split_f = os.path.join(sbd_root, image_set.rstrip("\n") + ".txt") with open(os.path.join(split_f)) as fh: file_names = [x.strip() for x in fh.readlines()] self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names] self.masks = [os.path.join(mask_dir, x + ".mat") for x in file_names] self._get_target = self._get_segmentation_target if self.mode == "segmentation" else self._get_boundaries_target def _get_segmentation_target(self, filepath: str) -> Image.Image: mat = self._loadmat(filepath) return Image.fromarray(mat["GTcls"][0]["Segmentation"][0]) def _get_boundaries_target(self, filepath: str) -> np.ndarray: mat = self._loadmat(filepath) return np.concatenate( [np.expand_dims(mat["GTcls"][0]["Boundaries"][0][i][0].toarray(), axis=0) for i in range(self.num_classes)], axis=0, )
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]: img =[index]).convert("RGB") target = self._get_target(self.masks[index]) if self.transforms is not None: img, target = self.transforms(img, target) return img, target
def __len__(self) -> int: return len(self.images) def extra_repr(self) -> str: lines = ["Image set: {image_set}", "Mode: {mode}"] return "\n".join(lines).format(**self.__dict__)


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