Add CaFFe Dataset

docs/api/datamodules.rst

@@ -62,6 +62,11 @@ CaBuAr
 
 .. autoclass:: CaBuArDataModule
 
+CaFFe
+^^^^^
+
+.. autoclass:: CaFFeDataModule
+
 ChaBuD
 ^^^^^^
 

docs/api/datasets.rst

@@ -222,6 +222,11 @@ CaBuAr
 
 .. autoclass:: CaBuAr
 
+CaFFe
+^^^^^
+
+.. autoclass:: CaFFe
+
 ChaBuD
 ^^^^^^
 

docs/api/datasets/non_geo_datasets.csv

@@ -4,6 +4,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `BigEarthNet`_,C,Sentinel-1/2,"CDLA-Permissive-1.0","590,326",19--43,120x120,10,"SAR, MSI"
 `BioMassters`_,R,Sentinel-1/2 and Lidar,"CC-BY-4.0",,,256x256, 10, "SAR, MSI"
 `CaBuAr`_,CD,Sentinel-2,"OpenRAIL",424,2,512x512,20,MSI
+`CaFFe`_,S,"Sentinel-1, TerraSAR-X, TanDEM-X, ENVISAT, European Remote Sensing Satellite 1&2, ALOS PALSAR, and RADARSAT-1","CC-BY-4.0","19092",4,"512x512",6-20,"SAR"
 `ChaBuD`_,CD,Sentinel-2,"OpenRAIL",356,2,512x512,10,MSI
 `Cloud Cover Detection`_,S,Sentinel-2,"CC-BY-4.0","22,728",2,512x512,10,MSI
 `COWC`_,"C, R","CSUAV AFRL, ISPRS, LINZ, AGRC","AGPL-3.0-only","388,435",2,256x256,0.15,RGB

tests/data/caffe/data.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import os
+import shutil
+
+import numpy as np
+from PIL import Image
+
+# Define the root directory and subdirectories
+root_dir = 'caffe'
+sub_dirs = ['zones', 'sar_images', 'fronts']
+splits = ['train', 'val', 'test']
+
+zone_file_names = [
+    'Crane_2002-11-09_ERS_20_2_061_zones__93_102_0_0_0.png',
+    'Crane_2007-09-22_ENVISAT_20_1_467_zones__93_102_8_1024_0.png',
+    'JAC_2015-12-23_TSX_6_1_005_zones__57_49_195_384_1024.png',
+]
+
+IMG_SIZE = 32
+
+
+# Function to create dummy images
+def create_dummy_image(path: str, shape: tuple[int], pixel_values: list[int]) -> None:
+    data = np.random.choice(pixel_values, size=shape, replace=True).astype(np.uint8)
+    img = Image.fromarray(data)
+    img.save(path)
+
+
+def create_zone_images(split: str, filename: str) -> None:
+    zone_pixel_values = [0, 64, 127, 255]
+    path = os.path.join(root_dir, 'zones', split, filename)
+    create_dummy_image(path, (IMG_SIZE, IMG_SIZE), zone_pixel_values)
+
+
+def create_sar_images(split: str, filename: str) -> None:
+    sar_pixel_values = range(256)
+    path = os.path.join(root_dir, 'sar_images', split, filename)
+    create_dummy_image(path, (IMG_SIZE, IMG_SIZE), sar_pixel_values)
+
+
+def create_front_images(split: str, filename: str) -> None:
+    sar_pixel_values = range(256)
+    path = os.path.join(root_dir, 'fronts', split, filename)
+    create_dummy_image(path, (IMG_SIZE, IMG_SIZE), sar_pixel_values)
+
+
+if os.path.exists(root_dir):
+    shutil.rmtree(root_dir)
+
+# Create the directory structure
+for sub_dir in sub_dirs:
+    for split in splits:
+        os.makedirs(os.path.join(root_dir, sub_dir, split), exist_ok=True)
+
+# Create dummy data for all splits and filenames
+for split in splits:
+    for filename in zone_file_names:
+        create_zone_images(split, filename)
+        create_sar_images(split, filename.replace('_zones_', '_'))
+        create_front_images(split, filename.replace('_zones_', '_front_'))
+
+# zip and compute md5
+shutil.make_archive(root_dir, 'zip', '.', root_dir)
+
+
+def md5(fname: str) -> str:
+    hash_md5 = hashlib.md5()
+    with open(fname, 'rb') as f:
+        for chunk in iter(lambda: f.read(4096), b''):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
+md5sum = md5('caffe.zip')
+print(f'MD5 checksum: {md5sum}')

tests/datamodules/test_caffe.py

@@ -0,0 +1,42 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import matplotlib.pyplot as plt
+import pytest
+
+from torchgeo.datamodules import CaFFeDataModule
+
+
+class TestCaFFeDataModule:
+    @pytest.fixture
+    def datamodule(self) -> CaFFeDataModule:
+        root = os.path.join('tests', 'data', 'caffe')
+        batch_size = 2
+        num_workers = 0
+        dm = CaFFeDataModule(root=root, batch_size=batch_size, num_workers=num_workers)
+        return dm
+
+    def test_train_dataloader(self, datamodule: CaFFeDataModule) -> None:
+        datamodule.setup('fit')
+        next(iter(datamodule.train_dataloader()))
+
+    def test_val_dataloader(self, datamodule: CaFFeDataModule) -> None:
+        datamodule.setup('validate')
+        next(iter(datamodule.val_dataloader()))
+
+    def test_test_dataloader(self, datamodule: CaFFeDataModule) -> None:
+        datamodule.setup('test')
+        next(iter(datamodule.test_dataloader()))
+
+    def test_plot(self, datamodule: CaFFeDataModule) -> None:
+        datamodule.setup('validate')
+        batch = next(iter(datamodule.val_dataloader()))
+        sample = {
+            'image': batch['image'][0],
+            'mask_zones': batch['mask_zones'][0],
+            'mask_front': batch['mask_front'][0],
+        }
+        datamodule.plot(sample)
+        plt.close()

tests/datasets/test_caffe.py

@@ -0,0 +1,72 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+import torch.nn as nn
+from _pytest.fixtures import SubRequest
+from pytest import MonkeyPatch
+
+from torchgeo.datasets import CaFFe, DatasetNotFoundError
+
+
+class TestCaFFe:
+    @pytest.fixture(params=['train', 'val', 'test'])
+    def dataset(
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
+    ) -> CaFFe:
+        md5 = 'f06c155a3fea372e884c234115c169e1'
+        monkeypatch.setattr(CaFFe, 'md5', md5)
+        url = os.path.join('tests', 'data', 'caffe', 'caffe.zip')
+        monkeypatch.setattr(CaFFe, 'url', url)
+        root = tmp_path
+        split = request.param
+        transforms = nn.Identity()
+        return CaFFe(root, split, transforms, download=True, checksum=True)
+
+    def test_getitem(self, dataset: CaFFe) -> None:
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x['image'], torch.Tensor)
+        assert x['image'].shape[0] == 1
+        assert isinstance(x['mask_zones'], torch.Tensor)
+        assert x['image'].shape[-2:] == x['mask_zones'].shape[-2:]
+
+    def test_len(self, dataset: CaFFe) -> None:
+        if dataset.split == 'train':
+            assert len(dataset) == 3
+        else:
+            assert len(dataset) == 3
+
+    def test_already_downloaded(self, dataset: CaFFe) -> None:
+        CaFFe(root=dataset.root)
+
+    def test_not_yet_extracted(self, tmp_path: Path) -> None:
+        filename = 'caffe.zip'
+        dir = os.path.join('tests', 'data', 'caffe')
+        shutil.copyfile(
+            os.path.join(dir, filename), os.path.join(str(tmp_path), filename)
+        )
+        CaFFe(root=str(tmp_path))
+
+    def test_invalid_split(self) -> None:
+        with pytest.raises(AssertionError):
+            CaFFe(split='foo')
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            CaFFe(tmp_path)
+
+    def test_plot(self, dataset: CaFFe) -> None:
+        dataset.plot(dataset[0], suptitle='Test')
+        plt.close()
+
+        sample = dataset[0]
+        sample['prediction'] = torch.clone(sample['mask_zones'])
+        dataset.plot(sample, suptitle='Prediction')
+        plt.close()

torchgeo/datamodules/__init__.py

@@ -6,6 +6,7 @@
 from .agrifieldnet import AgriFieldNetDataModule
 from .bigearthnet import BigEarthNetDataModule
 from .cabuar import CaBuArDataModule
+from .caffe import CaFFeDataModule
 from .chabud import ChaBuDDataModule
 from .chesapeake import ChesapeakeCVPRDataModule
 from .cowc import COWCCountingDataModule
@@ -67,6 +68,7 @@
     'SouthAfricaCropTypeDataModule',
     # NonGeoDataset
     'BigEarthNetDataModule',
+    'CaFFeDataModule',
     'CaBuArDataModule',
     'ChaBuDDataModule',
     'COWCCountingDataModule',

torchgeo/datamodules/caffe.py

@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""CaFFe datamodule."""
+
+from typing import Any
+
+import kornia.augmentation as K
+import torch
+
+from ..datasets import CaFFe
+from ..transforms import AugmentationSequential
+from .geo import NonGeoDataModule
+
+
+class CaFFeDataModule(NonGeoDataModule):
+    """LightningDataModule implementation for the CaFFe dataset.
+
+    Implements the default splits that come with the dataset.
+
+    .. versionadded:: 0.7
+    """
+
+    mean = torch.Tensor([0.5517])
+    std = torch.Tensor([11.8478])
+
+    def __init__(
+        self, batch_size: int = 64, num_workers: int = 0, size: int = 256, **kwargs: Any
+    ) -> None:
+        """Initialize a new CaFFeDataModule instance.
+
+        Args:
+            batch_size: Size of each mini-batch.
+            num_workers: Number of workers for parallel data loading.
+            size: resize images of input size 512x512 to size x size
+            **kwargs: Additional keyword arguments passed to
+                :class:`~torchgeo.datasets.CaFFe`.
+        """
+        super().__init__(CaFFe, batch_size, num_workers, **kwargs)
+
+        self.train_aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std),
+            K.Resize(size),
+            K.RandomHorizontalFlip(p=0.5),
+            K.RandomVerticalFlip(p=0.5),
+            data_keys=['image', 'mask'],
+        )
+
+        self.aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std),
+            K.Resize(size),
+            data_keys=['image', 'mask'],
+        )
+
+        self.size = size

torchgeo/datasets/__init__.py

@@ -12,6 +12,7 @@
 from .bigearthnet import BigEarthNet
 from .biomassters import BioMassters
 from .cabuar import CaBuAr
+from .caffe import CaFFe
 from .cbf import CanadianBuildingFootprints
 from .cdl import CDL
 from .chabud import ChaBuD
@@ -205,6 +206,7 @@
     'BigEarthNet',
     'BioMassters',
     'CaBuAr',
+    'CaFFe',
     'ChaBuD',
     'CloudCoverDetection',
     'COWC',