Source code for torchgeo.datasets.resisc45

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

"""RESISC45 dataset."""

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

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

from .geo import NonGeoClassificationDataset
from .utils import download_url, extract_archive

class RESISC45(NonGeoClassificationDataset):
    """RESISC45 dataset.

    The `RESISC45 <>`__
    dataset is a dataset for remote sensing image scene classification.

    Dataset features:

    * 31,500 images with 0.2-30 m per pixel resolution (256x256 px)
    * three spectral bands - RGB
    * 45 scene classes, 700 images per class
    * images extracted from Google Earth from over 100 countries
    * images conditions with high variability (resolution, weather, illumination)

    Dataset format:

    * images are three-channel jpgs

    Dataset classes:

    0. airplane
    1. airport
    2. baseball_diamond
    3. basketball_court
    4. beach
    5. bridge
    6. chaparral
    7. church
    8. circular_farmland
    9. cloud
    10. commercial_area
    11. dense_residential
    12. desert
    13. forest
    14. freeway
    15. golf_course
    16. ground_track_field
    17. harbor
    18. industrial_area
    19. intersection
    20. island
    21. lake
    22. meadow
    23. medium_residential
    24. mobile_home_park
    25. mountain
    26. overpass
    27. palace
    28. parking_lot
    29. railway
    30. railway_station
    31. rectangular_farmland
    32. river
    33. roundabout
    34. runway
    35. sea_ice
    36. ship
    37. snowberg
    38. sparse_residential
    39. stadium
    40. storage_tank
    41. tennis_court
    42. terrace
    43. thermal_power_station
    44. wetland

    This dataset uses the train/val/test splits defined in the "In-domain representation
    learning for remote sensing" paper:


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



    url = ""
    md5 = "d824acb73957502b00efd559fc6cfbbb"
    filename = "NWPU-RESISC45.rar"
    directory = "NWPU-RESISC45"

    splits = ["train", "val", "test"]
    split_urls = {
        "train": "",  # noqa: E501
        "val": "",  # noqa: E501
        "test": "",  # noqa: E501
    split_md5s = {
        "train": "b5a4c05a37de15e4ca886696a85c403e",
        "val": "a0770cee4c5ca20b8c32bbd61e114805",
        "test": "3dda9e4988b47eb1de9f07993653eb08",
    classes = [

[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new RESISC45 dataset instance. Args: root: root directory where dataset can be found split: one of "train", "val", or "test" 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) """ assert split in self.splits self.root = root = download self.checksum = checksum self._verify() valid_fns = set() with open(os.path.join(self.root, f"resisc45-{split}.txt")) as f: for fn in f: valid_fns.add(fn.strip()) is_in_split: Callable[[str], bool] = lambda x: os.path.basename(x) in valid_fns super().__init__( root=os.path.join(root,, transforms=transforms, is_valid_file=is_in_split, )
def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ # Check if the files already exist filepath = os.path.join(self.root, if os.path.exists(filepath): return # Check if zip file already exists (if so then extract) filepath = os.path.join(self.root, self.filename) if os.path.exists(filepath): self._extract() return # Check if the user requested to download the dataset if not raise RuntimeError( "Dataset not found in `root` directory and `download=False`, " "either specify a different `root` directory or use `download=True` " "to automatically download the dataset." ) # Download and extract the dataset self._download() self._extract() def _download(self) -> None: """Download the dataset.""" download_url( self.url, self.root, filename=self.filename, md5=self.md5 if self.checksum else None, ) for split in self.splits: download_url( self.split_urls[split], self.root, filename=f"resisc45-{split}.txt", md5=self.split_md5s[split] if self.checksum else None, ) def _extract(self) -> None: """Extract the dataset.""" filepath = os.path.join(self.root, self.filename) extract_archive(filepath)
[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:`NonGeoClassificationDataset.__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 44fa4132.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: stable
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources