CV4A Kenya Crop Type: radiant mlhub -> source cooperative (#2090)

tests/data/cv4a_kenya_crop_type/FieldIds.csv

@@ -0,0 +1,5 @@
+train,test
+1,2
+3,4
+5
+6

tests/data/cv4a_kenya_crop_type/data.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import numpy as np
+from PIL import Image
+
+DTYPE = np.float32
+SIZE = 2
+
+np.random.seed(0)
+
+all_bands = (
+    'B01',
+    'B02',
+    'B03',
+    'B04',
+    'B05',
+    'B06',
+    'B07',
+    'B08',
+    'B8A',
+    'B09',
+    'B11',
+    'B12',
+    'CLD',
+)
+
+for tile in range(1):
+    directory = os.path.join('data', str(tile))
+    os.makedirs(directory, exist_ok=True)
+
+    arr = np.random.randint(np.iinfo(np.int32).max, size=(SIZE, SIZE), dtype=np.int32)
+    img = Image.fromarray(arr)
+    img.save(os.path.join(directory, f'{tile}_field_id.tif'))
+
+    arr = np.random.randint(np.iinfo(np.uint8).max, size=(SIZE, SIZE), dtype=np.uint8)
+    img = Image.fromarray(arr)
+    img.save(os.path.join(directory, f'{tile}_label.tif'))
+
+    for date in ['20190606']:
+        directory = os.path.join(directory, date)
+        os.makedirs(directory, exist_ok=True)
+
+        for band in all_bands:
+            arr = np.random.rand(SIZE, SIZE).astype(DTYPE) * np.finfo(DTYPE).max
+            img = Image.fromarray(arr)
+            img.save(os.path.join(directory, f'{tile}_{band}_{date}.tif'))

tests/datasets/test_cv4a_kenya_crop_type.py

@@ -1,9 +1,7 @@
 # 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
@@ -18,44 +16,23 @@
     DatasetNotFoundError,
     RGBBandsMissingError,
 )
-
-
-class Collection:
-    def download(self, output_dir: str, **kwargs: str) -> None:
-        glob_path = os.path.join(
-            'tests', 'data', 'ref_african_crops_kenya_02', '*.tar.gz'
-        )
-        for tarball in glob.iglob(glob_path):
-            shutil.copy(tarball, output_dir)
-
-
-def fetch(dataset_id: str, **kwargs: str) -> Collection:
-    return Collection()
+from torchgeo.datasets.utils import Executable
 
 
 class TestCV4AKenyaCropType:
     @pytest.fixture
-    def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> CV4AKenyaCropType:
-        radiant_mlhub = pytest.importorskip('radiant_mlhub', minversion='0.3')
-        monkeypatch.setattr(radiant_mlhub.Collection, 'fetch', fetch)
-        source_md5 = '7f4dcb3f33743dddd73f453176308bfb'
-        labels_md5 = '95fc59f1d94a85ec00931d4d1280bec9'
-        monkeypatch.setitem(CV4AKenyaCropType.image_meta, 'md5', source_md5)
-        monkeypatch.setitem(CV4AKenyaCropType.target_meta, 'md5', labels_md5)
-        monkeypatch.setattr(
-            CV4AKenyaCropType, 'tile_names', ['ref_african_crops_kenya_02_tile_00']
-        )
+    def dataset(
+        self, azcopy: Executable, monkeypatch: MonkeyPatch, tmp_path: Path
+    ) -> CV4AKenyaCropType:
+        url = os.path.join('tests', 'data', 'cv4a_kenya_crop_type')
+        monkeypatch.setattr(CV4AKenyaCropType, 'url', url)
+        monkeypatch.setattr(CV4AKenyaCropType, 'tiles', list(map(str, range(1))))
         monkeypatch.setattr(CV4AKenyaCropType, 'dates', ['20190606'])
+        monkeypatch.setattr(CV4AKenyaCropType, 'tile_height', 2)
+        monkeypatch.setattr(CV4AKenyaCropType, 'tile_width', 2)
         root = str(tmp_path)
         transforms = nn.Identity()
-        return CV4AKenyaCropType(
-            root,
-            transforms=transforms,
-            download=True,
-            api_key='',
-            checksum=True,
-            verbose=True,
-        )
+        return CV4AKenyaCropType(root, transforms=transforms, download=True)
 
     def test_getitem(self, dataset: CV4AKenyaCropType) -> None:
         x = dataset[0]
@@ -66,60 +43,34 @@ def test_getitem(self, dataset: CV4AKenyaCropType) -> None:
         assert isinstance(x['y'], torch.Tensor)
 
     def test_len(self, dataset: CV4AKenyaCropType) -> None:
-        assert len(dataset) == 345
+        assert len(dataset) == 1
 
     def test_add(self, dataset: CV4AKenyaCropType) -> None:
         ds = dataset + dataset
         assert isinstance(ds, ConcatDataset)
-        assert len(ds) == 690
-
-    def test_get_splits(self, dataset: CV4AKenyaCropType) -> None:
-        train_field_ids, test_field_ids = dataset.get_splits()
-        assert isinstance(train_field_ids, list)
-        assert isinstance(test_field_ids, list)
-        assert len(train_field_ids) == 18
-        assert len(test_field_ids) == 9
-        assert 336 in train_field_ids
-        assert 336 not in test_field_ids
-        assert 4793 in test_field_ids
-        assert 4793 not in train_field_ids
+        assert len(ds) == 2
 
     def test_already_downloaded(self, dataset: CV4AKenyaCropType) -> None:
-        CV4AKenyaCropType(root=dataset.root, download=True, api_key='')
+        CV4AKenyaCropType(root=dataset.root, download=True)
 
     def test_not_downloaded(self, tmp_path: Path) -> None:
         with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
             CV4AKenyaCropType(str(tmp_path))
 
-    def test_invalid_tile(self, dataset: CV4AKenyaCropType) -> None:
-        with pytest.raises(AssertionError):
-            dataset._load_label_tile('foo')
-
-        with pytest.raises(AssertionError):
-            dataset._load_all_image_tiles('foo', ('B01', 'B02'))
-
-        with pytest.raises(AssertionError):
-            dataset._load_single_image_tile('foo', '20190606', ('B01', 'B02'))
-
     def test_invalid_bands(self) -> None:
         with pytest.raises(AssertionError):
-            CV4AKenyaCropType(bands=['B01', 'B02'])  # type: ignore[arg-type]
-
-        with pytest.raises(ValueError, match='is an invalid band name.'):
             CV4AKenyaCropType(bands=('foo', 'bar'))
 
     def test_plot(self, dataset: CV4AKenyaCropType) -> None:
-        dataset.plot(dataset[0], time_step=0, suptitle='Test')
-        plt.close()
-
         sample = dataset[0]
+        dataset.plot(sample, time_step=0, suptitle='Test')
+        plt.close()
         sample['prediction'] = sample['mask'].clone()
         dataset.plot(sample, time_step=0, suptitle='Pred')
         plt.close()
 
     def test_plot_rgb(self, dataset: CV4AKenyaCropType) -> None:
         dataset = CV4AKenyaCropType(root=dataset.root, bands=tuple(['B01']))
-        with pytest.raises(
-            RGBBandsMissingError, match='Dataset does not contain some of the RGB bands'
-        ):
-            dataset.plot(dataset[0], time_step=0, suptitle='Single Band')
+        match = 'Dataset does not contain some of the RGB bands'
+        with pytest.raises(RGBBandsMissingError, match=match):
+            dataset.plot(dataset[0])