Add ETCI2021 Dataset

docs/api/datasets.rst

@@ -87,6 +87,11 @@ CV4A Kenya Crop Type Competition
 
 .. autoclass:: CV4AKenyaCropType
 
+ETCI2021 Flood Detection
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: ETCI2021
+
 LandCover.ai (Land Cover from Aerial Imagery)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

tests/datasets/test_etci2021.py

@@ -0,0 +1,86 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from pathlib import Path
+from typing import Generator
+
+import pytest
+import torch
+from _pytest.fixtures import SubRequest
+from _pytest.monkeypatch import MonkeyPatch
+
+import torchgeo.datasets.utils
+from torchgeo.datasets import ETCI2021
+from torchgeo.transforms import Identity
+
+
+def download_url(url: str, root: str, *args: str) -> None:
+    shutil.copy(url, root)
+
+
+class TestETCI2021:
+    @pytest.fixture(params=["train", "val", "test"])
+    def dataset(
+        self,
+        monkeypatch: Generator[MonkeyPatch, None, None],
+        tmp_path: Path,
+        request: SubRequest,
+    ) -> ETCI2021:
+        monkeypatch.setattr(  # type: ignore[attr-defined]
+            torchgeo.datasets.utils, "download_url", download_url
+        )
+        data_dir = os.path.join("tests", "data", "etci2021")
+        metadata = {
+            "train": {
+                "filename": "train.zip",
+                "md5": "50c10eb07d6db9aee3ba36401e4a2c45",
+                "directory": "train",
+                "url": os.path.join(data_dir, "train.zip"),
+            },
+            "val": {
+                "filename": "val_with_ref_labels.zip",
+                "md5": "3e8b5a3cb95e6029e0e2c2d4b4ec6fba",
+                "directory": "test",
+                "url": os.path.join(data_dir, "val_with_ref_labels.zip"),
+            },
+            "test": {
+                "filename": "test_without_ref_labels.zip",
+                "md5": "c8ee1e5d3e478761cd00ebc6f28b0ae7",
+                "directory": "test_internal",
+                "url": os.path.join(data_dir, "test_without_ref_labels.zip"),
+            },
+        }
+        monkeypatch.setattr(ETCI2021, "metadata", metadata)  # type: ignore[attr-defined]   # noqa: E501
+        root = str(tmp_path)
+        split = request.param
+        transforms = Identity()
+        return ETCI2021(root, split, transforms, download=True, checksum=True)
+
+    def test_getitem(self, dataset: ETCI2021) -> None:
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x["image"], torch.Tensor)
+        assert isinstance(x["mask"], torch.Tensor)
+        assert x["image"].shape[0] == 6
+        assert x["image"].shape[-2:] == x["mask"].shape[-2:]
+
+        if dataset.split != "test":
+            assert x["mask"].shape[0] == 2
+        else:
+            assert x["mask"].shape[0] == 1
+
+    def test_len(self, dataset: ETCI2021) -> None:
+        assert len(dataset) == 2
+
+    def test_already_downloaded(self, dataset: ETCI2021) -> None:
+        ETCI2021(root=dataset.root, download=True)
+
+    def test_invalid_split(self) -> None:
+        with pytest.raises(AssertionError):
+            ETCI2021(split="foo")
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(RuntimeError, match="Dataset not found or corrupted."):
+            ETCI2021(str(tmp_path))

torchgeo/datasets/__init__.py

@@ -22,6 +22,7 @@
 from .cowc import COWC, COWCCounting, COWCDetection
 from .cv4a_kenya_crop_type import CV4AKenyaCropType
 from .cyclone import TropicalCycloneWindEstimation
+from .etci2021 import ETCI2021
 from .geo import GeoDataset, RasterDataset, VectorDataset, VisionDataset, ZipDataset
 from .landcoverai import LandCoverAI
 from .landsat import (
@@ -81,6 +82,7 @@
     "COWCCounting",
     "COWCDetection",
     "CV4AKenyaCropType",
+    "ETCI2021",
     "LandCoverAI",
     "LEVIRCDPlus",
     "PatternNet",

torchgeo/datasets/etci2021.py

@@ -0,0 +1,249 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""ETCI 2021 dataset."""
+
+import glob
+import os
+import shutil
+from typing import Callable, Dict, List, Optional
+
+import numpy as np
+import torch
+from PIL import Image
+from torch import Tensor
+
+from .geo import VisionDataset
+from .utils import download_and_extract_archive
+
+
+class ETCI2021(VisionDataset):
+    """ETCI 2021 Flood Detection dataset.
+
+    The `ETCI2021 <https://nasa-impact.github.io/etci2021/>`_
+    dataset is a dataset for flood detection
+
+    Dataset features:
+    * 33,405 VV & VH Sentinel-1 Synthetic Aperture Radar (SAR) images
+    * 2 binary masks per image representing water body & flood, respectively
+    * 2 polarization band images (VV, VH) of 3 RGB channels per band
+    * 3 RGB channels per band generated by the Hybrid Pluggable Processing Pipeline 'hyp3'
+    * Images with 5x20m per pixel resolution (256x256) px) taken in Interferometric Wide Swath acquisition mode
+    * Flood events from 5 different regions
+
+    Dataset format:
+    * VV band three-channel png
+    * VH band three-channel png
+    * water body mask single-channel png where no water body = 0, water body = 255
+    * flood mask single-channel png where no flood = 0, flood = 255
+
+    Dataset classes:
+    1. no flood/water
+    2. flood/water
+
+    If you use this dataset in your research, please add the following to your
+    acknowledgements section:
+
+    'The authors would like to thank the NASA Earth Science Data Systems Program,
+    NASA Digital Transformation AI/ML thrust, and IEEE GRSS for organizing the ETCI competition'.
+    """  # noqa: E501
+
+    splits = ["train", "val", "test"]
+    bands = ["VV", "VH"]
+    masks = ["flood", "water_body"]
+    metadata = {
+        "train": {
+            "filename": "train.zip",
+            "md5": "1e95792fe0f6e3c9000abdeab2a8ab0f",
+            "directory": "train",
+            "url": "https://drive.google.com/file/d/14HqNW5uWLS92n7KrxKgDwUTsSEST6LCr",
+        },
+        "val": {
+            "filename": "val_with_ref_labels.zip",
+            "md5": "fd18cecb318efc69f8319f90c3771bdf",
+            "directory": "test",
+            "url": "https://drive.google.com/file/d/19sriKPHCZLfJn_Jmk3Z_0b3VaCBVRVyn",
+        },
+        "test": {
+            "filename": "test_without_ref_labels.zip",
+            "md5": "da9fa69e1498bd49d5c766338c6dac3d",
+            "directory": "test_internal",
+            "url": "https://drive.google.com/file/d/1rpMVluASnSHBfm2FhpPDio0GyCPOqg7E",
+        },
+    }
+
+    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 ETCI 2021 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)
+
+        Raises:
+            AssertionError: if ``split`` argument is invalid
+            RuntimeError: if ``download=False`` and data is not found, or checksums
+                don't match
+        """
+        assert split in self.splits
+
+        self.root = root
+        self.split = split
+        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.split)
+
+    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]
+        vv = self._load_image(files["vv"])
+        vh = self._load_image(files["vh"])
+        water_mask = self._load_target(files["water_mask"])
+
+        if self.split != "test":
+            flood_mask = self._load_target(files["flood_mask"])
+            mask = torch.stack(tensors=[water_mask, flood_mask], dim=0)
+        else:
+            mask = water_mask.unsqueeze(0)
+
+        image = torch.cat(tensors=[vv, vh], dim=0)  # type: ignore[attr-defined]
+        sample = {"image": image, "mask": mask}
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    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, split: str) -> List[Dict[str, str]]:
+        """Return the paths of the files in the dataset.
+
+        Args:
+            root: root dir of dataset
+            split: subset of dataset, one of [train, val, test]
+
+        Returns:
+            list of dicts containing paths for each pair of vv, vh,
+            water body mask, flood mask (train/val only)
+        """
+        files = []
+        directory = self.metadata[split]["directory"]
+        folders = sorted(glob.glob(os.path.join(root, directory, "*")))
+        folders = [os.path.join(folder, "tiles") for folder in folders]
+        for folder in folders:
+            vvs = glob.glob(os.path.join(folder, "vv", "*.png"))
+            vhs = glob.glob(os.path.join(folder, "vh", "*.png"))
+            water_masks = glob.glob(os.path.join(folder, "water_body_label", "*.png"))
+
+            if split == "test":
+                flood_masks = [""] * len(water_masks)
+            else:
+                flood_masks = glob.glob(os.path.join(folder, "flood_label", "*.png"))
+
+            for vv, vh, flood_mask, water_mask in zip(
+                vvs, vhs, flood_masks, water_masks
+            ):
+                files.append(
+                    dict(vv=vv, vh=vh, flood_mask=flood_mask, water_mask=water_mask)
+                )
+        return files
+
+    def _load_image(self, path: str) -> Tensor:
+        """Load a single image.
+
+        Args:
+            path: path to the image
+
+        Returns:
+            the image
+        """
+        filename = os.path.join(path)
+        with Image.open(filename) as img:
+            array = np.array(img.convert("RGB"))
+            tensor: Tensor = torch.from_numpy(array)  # type: ignore[attr-defined]
+            # Convert from HxWxC to CxHxW
+            tensor = tensor.permute((2, 0, 1))
+            return tensor
+
+    def _load_target(self, path: str) -> Tensor:
+        """Load the target mask for a single image.
+
+        Args:
+            path: path to the image
+
+        Returns:
+            the target mask
+        """
+        filename = os.path.join(path)
+        with Image.open(filename) as img:
+            array = np.array(img.convert("L"))
+            tensor: Tensor = torch.from_numpy(array)  # type: ignore[attr-defined]
+            tensor = torch.clip(tensor, min=0, max=1)  # type: ignore[attr-defined]
+            tensor = tensor.to(torch.long)  # type: ignore[attr-defined]
+            return tensor
+
+    def _check_integrity(self) -> bool:
+        """Checks the integrity of the dataset structure.
+
+        Returns:
+            True if the dataset directories and split files are found, else False
+        """
+        directory = self.metadata[self.split]["directory"]
+        dirpath = os.path.join(self.root, directory)
+        if not os.path.exists(dirpath):
+            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
+
+        download_and_extract_archive(
+            self.metadata[self.split]["url"],
+            self.root,
+            filename=self.metadata[self.split]["filename"],
+            md5=self.metadata[self.split]["md5"] if self.checksum else None,
+        )
+
+        if os.path.exists(os.path.join(self.root, "__MACOSX")):
+            shutil.rmtree(os.path.join(self.root, "__MACOSX"))