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Source code for torchgeo.datasets.advance

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""ADVANCE dataset."""

import glob
import os
from typing import Callable, Dict, List, Optional, cast

import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from torch import Tensor

from .geo import NonGeoDataset
from .utils import download_and_extract_archive


class ADVANCE(NonGeoDataset):
    """ADVANCE dataset.

    The `ADVANCE <https://akchen.github.io/ADVANCE-DATASET/>`__
    dataset is a dataset for audio visual scene recognition.

    Dataset features:

    * 5,075 pairs of geotagged audio recordings and images
    * three spectral bands - RGB (512x512 px)
    * 10-second audio recordings

    Dataset format:

    * images are three-channel jpgs
    * audio files are in wav format

    Dataset classes:

    0. airport
    1. beach
    2. bridge
    3. farmland
    4. forest
    5. grassland
    6. harbour
    7. lake
    8. orchard
    9. residential
    10. sparse shrub land
    11. sports land
    12. train station

    If you use this dataset in your research, please cite the following paper:

    * https://doi.org/10.1007/978-3-030-58586-0_5

    .. note::
        This dataset requires the following additional library to be installed:

        * `scipy <https://pypi.org/project/scipy/>`_ to load the audio files to tensors
    """

    urls = [
        "https://zenodo.org/record/3828124/files/ADVANCE_vision.zip?download=1",
        "https://zenodo.org/record/3828124/files/ADVANCE_sound.zip?download=1",
    ]
    filenames = ["ADVANCE_vision.zip", "ADVANCE_sound.zip"]
    md5s = ["a9e8748219ef5864d3b5a8979a67b471", "a2d12f2d2a64f5c3d3a9d8c09aaf1c31"]
    directories = ["vision", "sound"]
    classes = [
        "airport",
        "beach",
        "bridge",
        "farmland",
        "forest",
        "grassland",
        "harbour",
        "lake",
        "orchard",
        "residential",
        "sparse shrub land",
        "sports land",
        "train station",
    ]

[docs] def __init__( self, root: str = "data", transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new ADVANCE dataset instance. Args: root: root directory where dataset can be found transforms: a function/transform that takes input sample and its target as entry and returns a transformed version download: if True, download dataset and store it in the root directory checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ self.root = root self.transforms = transforms self.checksum = checksum if download: self._download() if not self._check_integrity(): raise RuntimeError( "Dataset not found or corrupted. " + "You can use download=True to download it" ) self.files = self._load_files(self.root) self.classes = sorted({f["cls"] for f in self.files}) self.class_to_idx: Dict[str, int] = {c: i for i, c in enumerate(self.classes)}
[docs] def __getitem__(self, index: int) -> Dict[str, Tensor]: """Return an index within the dataset. Args: index: index to return Returns: data and label at that index """ files = self.files[index] image = self._load_image(files["image"]) audio = self._load_target(files["audio"]) cls_label = self.class_to_idx[files["cls"]] label = torch.tensor(cls_label, dtype=torch.long) sample = {"image": image, "audio": audio, "label": label} if self.transforms is not None: sample = self.transforms(sample) return sample
[docs] def __len__(self) -> int: """Return the number of data points in the dataset. Returns: length of the dataset """ return len(self.files)
def _load_files(self, root: str) -> List[Dict[str, str]]: """Return the paths of the files in the dataset. Args: root: root dir of dataset Returns: list of dicts containing paths for each pair of image, audio, label """ images = sorted(glob.glob(os.path.join(root, "vision", "**", "*.jpg"))) wavs = sorted(glob.glob(os.path.join(root, "sound", "**", "*.wav"))) labels = [image.split(os.sep)[-2] for image in images] files = [ dict(image=image, audio=wav, cls=label) for image, wav, label in zip(images, wavs, labels) ] return files def _load_image(self, path: str) -> Tensor: """Load a single image. Args: path: path to the image Returns: the image """ with Image.open(path) as img: array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB")) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)) return tensor def _load_target(self, path: str) -> Tensor: """Load the target audio for a single image. Args: path: path to the target Returns: the target audio """ try: from scipy.io import wavfile except ImportError: raise ImportError( "scipy is not installed and is required to use this dataset" ) array = wavfile.read(path, mmap=True)[1] tensor = torch.from_numpy(array) tensor = tensor.unsqueeze(0) return tensor def _check_integrity(self) -> bool: """Checks the integrity of the dataset structure. Returns: True if the dataset directories are found, else False """ for directory in self.directories: filepath = os.path.join(self.root, directory) if not os.path.exists(filepath): return False return True def _download(self) -> None: """Download the dataset and extract it. Raises: AssertionError: if the checksum of split.py does not match """ if self._check_integrity(): print("Files already downloaded and verified") return for filename, url, md5 in zip(self.filenames, self.urls, self.md5s): download_and_extract_archive( url, self.root, filename=filename, md5=md5 if self.checksum else None )
[docs] def plot( self, sample: Dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> plt.Figure: """Plot a sample from the dataset. Args: sample: a sample returned by :meth:`__getitem__` show_titles: flag indicating whether to show titles above each panel suptitle: optional string to use as a suptitle Returns: a matplotlib Figure with the rendered sample .. versionadded:: 0.2 """ image = np.rollaxis(sample["image"].numpy(), 0, 3) label = cast(int, sample["label"].item()) label_class = self.classes[label] showing_predictions = "prediction" in sample if showing_predictions: prediction = cast(int, sample["prediction"].item()) prediction_class = self.classes[prediction] fig, ax = plt.subplots(figsize=(4, 4)) ax.imshow(image) ax.axis("off") if show_titles: title = f"Label: {label_class}" if showing_predictions: title += f"\nPrediction: {prediction_class}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig

© Copyright 2021, Microsoft Corporation. Revision 34680c94.

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