HySpecNet-11k: add new dataset

docs/api/datamodules.rst

@@ -124,6 +124,11 @@ GID-15
 
 .. autoclass:: GID15DataModule
 
+HySpecNet-11k
+^^^^^^^^^^^^^
+
+.. autoclass:: HySpecNet11kDataModule
+
 Inria Aerial Image Labeling
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/api/datasets.rst

@@ -312,6 +312,11 @@ GID-15
 
 .. autoclass:: GID15
 
+HySpecNet-11k
+^^^^^^^^^^^^^
+
+.. autoclass:: HySpecNet11k
+
 IDTReeS
 ^^^^^^^
 

docs/api/datasets/non_geo_datasets.csv

@@ -21,6 +21,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `Forest Damage`_,OD,Drone imagery,"CDLA-Permissive-1.0","1,543",4,"1,500x1,500",,RGB
 `GeoNRW`_,S,Aerial,"CC-BY-4.0","7,783",11,"1,000x1,000",1,"RGB, DEM"
 `GID-15`_,S,Gaofen-2,-,150,15,"6,800x7,200",3,RGB
+`HySpecNet-11k`_,-,EnMAP,CC0-1.0,11k,-,128,30,HSI
 `IDTReeS`_,"OD,C",Aerial,"CC-BY-4.0",591,33,200x200,0.1--1,RGB
 `Inria Aerial Image Labeling`_,S,Aerial,-,360,2,"5,000x5,000",0.3,RGB
 `LandCover.ai`_,S,Aerial,"CC-BY-NC-SA-4.0","10,674",5,512x512,0.25--0.5,RGB

tests/conf/hyspecnet_byol.yaml

@@ -0,0 +1,11 @@
+model:
+  class_path: BYOLTask
+  init_args:
+    model: 'resnet18'
+    in_channels: 202
+data:
+  class_path: HySpecNet11kDataModule
+  init_args:
+    batch_size: 2
+  dict_kwargs:
+    root: 'tests/data/hyspecnet'

tests/conf/hyspecnet_moco.yaml

@@ -0,0 +1,11 @@
+model:
+  class_path: MoCoTask
+  init_args:
+    model: 'resnet18'
+    in_channels: 202
+data:
+  class_path: HySpecNet11kDataModule
+  init_args:
+    batch_size: 2
+  dict_kwargs:
+    root: 'tests/data/hyspecnet'

tests/conf/hyspecnet_simclr.yaml

@@ -0,0 +1,11 @@
+model:
+  class_path: SimCLRTask
+  init_args:
+    model: 'resnet18'
+    in_channels: 202
+data:
+  class_path: HySpecNet11kDataModule
+  init_args:
+    batch_size: 2
+  dict_kwargs:
+    root: 'tests/data/hyspecnet'

tests/data/hyspecnet/data.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+
+import numpy as np
+import rasterio
+from rasterio import Affine
+from rasterio.crs import CRS
+
+SIZE = 32
+DTYPE = 'int16'
+
+np.random.seed(0)
+
+# Tile name purposefully shortened to avoid Windows git filename length limit.
+tiles = ['ENMAP01_20221103T162438Z']
+patches = ['Y01460273_X05670694', 'Y01460273_X06950822']
+
+profile = {
+    'driver': 'GTiff',
+    'dtype': DTYPE,
+    'nodata': -32768.0,
+    'width': SIZE,
+    'height': SIZE,
+    'count': 224,
+    'crs': CRS.from_epsg(32618),
+    'transform': Affine(30.0, 0.0, 691845.0, 0.0, -30.0, 4561935.0),
+    'blockysize': 3,
+    'tiled': False,
+    'compress': 'deflate',
+    'interleave': 'band',
+}
+
+root = 'hyspecnet-11k'
+path = os.path.join(root, 'splits', 'easy')
+os.makedirs(path, exist_ok=True)
+for tile in tiles:
+    for patch in patches:
+        # Split CSV
+        path = os.path.join(tile, f'{tile}-{patch}', f'{tile}-{patch}-DATA.npy')
+        for split in ['train', 'val', 'test']:
+            with open(os.path.join(root, 'splits', 'easy', f'{split}.csv'), 'a+') as f:
+                f.write(f'{path}\n')
+
+        # Spectral image
+        path = os.path.join(root, 'patches', path)
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        path = path.replace('DATA.npy', 'SPECTRAL_IMAGE.TIF')
+        Z = np.random.randint(
+            np.iinfo(DTYPE).min, np.iinfo(DTYPE).max, size=(SIZE, SIZE), dtype=DTYPE
+        )
+        with rasterio.open(path, 'w', **profile) as src:
+            for i in range(1, profile['count'] + 1):
+                src.write(Z, i)
+
+shutil.make_archive(f'{root}-01', 'gztar', '.', os.path.join(root, 'patches'))
+shutil.make_archive(f'{root}-splits', 'gztar', '.', os.path.join(root, 'splits'))

tests/data/hyspecnet/hyspecnet-11k/splits/easy/test.csv

@@ -0,0 +1,2 @@
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy

tests/data/hyspecnet/hyspecnet-11k/splits/easy/train.csv

@@ -0,0 +1,2 @@
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy

tests/data/hyspecnet/hyspecnet-11k/splits/easy/val.csv

@@ -0,0 +1,2 @@
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
+ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy

tests/datasets/test_hyspecnet.py

@@ -0,0 +1,58 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import glob
+import os
+import shutil
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch.nn as nn
+from pytest import MonkeyPatch
+from torch import Tensor
+
+from torchgeo.datasets import DatasetNotFoundError, HySpecNet11k, RGBBandsMissingError
+
+root = os.path.join('tests', 'data', 'hyspecnet')
+md5s = {'hyspecnet-11k-01.tar.gz': '', 'hyspecnet-11k-splits.tar.gz': ''}
+
+
+class TestHySpecNet11k:
+    @pytest.fixture
+    def dataset(self, monkeypatch: MonkeyPatch) -> HySpecNet11k:
+        monkeypatch.setattr(HySpecNet11k, 'url', root + os.sep)
+        monkeypatch.setattr(HySpecNet11k, 'md5s', md5s)
+        transforms = nn.Identity()
+        return HySpecNet11k(root, transforms=transforms)
+
+    def test_getitem(self, dataset: HySpecNet11k) -> None:
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x['image'], Tensor)
+
+    def test_len(self, dataset: HySpecNet11k) -> None:
+        assert len(dataset) == 2
+
+    def test_download(self, dataset: HySpecNet11k, tmp_path: Path) -> None:
+        HySpecNet11k(tmp_path, download=True)
+
+    def test_extract(self, dataset: HySpecNet11k, tmp_path: Path) -> None:
+        for file in glob.iglob(os.path.join(root, '*.tar.gz')):
+            shutil.copy(file, tmp_path)
+        HySpecNet11k(tmp_path)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            HySpecNet11k(tmp_path)
+
+    def test_plot(self, dataset: HySpecNet11k) -> None:
+        x = dataset[0]
+        dataset.plot(x, suptitle='Test')
+        plt.close()
+
+    def test_plot_rgb(self, dataset: HySpecNet11k) -> None:
+        dataset = HySpecNet11k(root=dataset.root, bands=(1, 2, 3))
+        match = 'Dataset does not contain some of the RGB bands'
+        with pytest.raises(RGBBandsMissingError, match=match):
+            dataset.plot(dataset[0])

tests/trainers/test_byol.py

@@ -41,6 +41,7 @@ class TestBYOLTask:
         'name',
         [
             'chesapeake_cvpr_prior_byol',
+            'hyspecnet_byol',
             'seco_byol_1',
             'seco_byol_2',
             'ssl4eo_l_byol_1',

tests/trainers/test_moco.py

@@ -29,6 +29,7 @@ class TestMoCoTask:
         'name',
         [
             'chesapeake_cvpr_prior_moco',
+            'hyspecnet_moco',
             'seco_moco_1',
             'seco_moco_2',
             'ssl4eo_l_moco_1',

tests/trainers/test_simclr.py

@@ -29,6 +29,7 @@ class TestSimCLRTask:
         'name',
         [
             'chesapeake_cvpr_prior_simclr',
+            'hyspecnet_simclr',
             'seco_simclr_1',
             'seco_simclr_2',
             'ssl4eo_l_simclr_1',

torchgeo/datamodules/__init__.py

@@ -21,6 +21,7 @@
 from .geo import BaseDataModule, GeoDataModule, NonGeoDataModule
 from .geonrw import GeoNRWDataModule
 from .gid15 import GID15DataModule
+from .hyspecnet import HySpecNet11kDataModule
 from .inria import InriaAerialImageLabelingDataModule
 from .iobench import IOBenchDataModule
 from .l7irish import L7IrishDataModule
@@ -84,6 +85,7 @@
     'FireRiskDataModule',
     'GeoNRWDataModule',
     'GID15DataModule',
+    'HySpecNet11kDataModule',
     'InriaAerialImageLabelingDataModule',
     'LandCoverAIDataModule',
     'LandCoverAI100DataModule',

torchgeo/datamodules/hyspecnet.py

@@ -0,0 +1,35 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""HySpecNet datamodule."""
+
+from typing import Any
+
+import torch
+
+from ..datasets import HySpecNet11k
+from .geo import NonGeoDataModule
+
+
+class HySpecNet11kDataModule(NonGeoDataModule):
+    """LightningDataModule implementation for the HySpecNet11k dataset.
+
+    .. versionadded:: 0.7
+    """
+
+    # https://git.tu-berlin.de/rsim/hyspecnet-tools/-/blob/main/tif_to_npy.ipynb
+    mean = torch.tensor(0)
+    std = torch.tensor(10000)
+
+    def __init__(
+        self, batch_size: int = 64, num_workers: int = 0, **kwargs: Any
+    ) -> None:
+        """Initialize a new HySpecNet11kDataModule instance.
+
+        Args:
+            batch_size: Size of each mini-batch.
+            num_workers: Number of workers for parallel data loading.
+            **kwargs: Additional keyword arguments passed to
+                :class:`~torchgeo.datasets.HySpecNet11k`.
+        """
+        super().__init__(HySpecNet11k, batch_size, num_workers, **kwargs)

torchgeo/datasets/__init__.py

@@ -61,6 +61,7 @@
 from .geonrw import GeoNRW
 from .gid15 import GID15
 from .globbiomass import GlobBiomass
+from .hyspecnet import HySpecNet11k
 from .idtrees import IDTReeS
 from .inaturalist import INaturalist
 from .inria import InriaAerialImageLabeling
@@ -225,6 +226,7 @@
     'ForestDamage',
     'GeoNRW',
     'GID15',
+    'HySpecNet11k',
     'IDTReeS',
     'InriaAerialImageLabeling',
     'LandCoverAI',