Add plot method and data.py to Esri2020 dataset

tests/data/esri2020/data.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import os
+import random
+import shutil
+
+import numpy as np
+import rasterio
+
+np.random.seed(0)
+random.seed(0)
+
+SIZE = 64
+
+
+files = [{"image": "N00E020_agb.tif"}, {"image": "N00E020_agb_err.tif"}]
+
+
+def create_file(path: str, dtype: str, num_channels: int) -> None:
+    profile = {}
+    profile["driver"] = "GTiff"
+    profile["dtype"] = dtype
+    profile["count"] = num_channels
+    profile["crs"] = "epsg:4326"
+    profile["transform"] = rasterio.transform.from_bounds(0, 0, 1, 1, 1, 1)
+    profile["height"] = SIZE
+    profile["width"] = SIZE
+    profile["compress"] = "lzw"
+    profile["predictor"] = 2
+
+    Z = np.random.randint(
+        np.iinfo(profile["dtype"]).max, size=(1, SIZE, SIZE), dtype=profile["dtype"]
+    )
+    src = rasterio.open(path, "w", **profile)
+    src.write(Z)
+
+
+if __name__ == "__main__":
+    dir = "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01"
+    tif_name = "00A_20200101-20210101.tif"
+
+    if os.path.exists(dir):
+        shutil.rmtree(dir)
+
+    os.makedirs(dir)
+
+    # Create mask file
+    create_file(os.path.join(dir, tif_name), dtype="int8", num_channels=1)
+
+    shutil.make_archive(dir, "zip", base_dir=dir)
+
+    # Compute checksums
+    zipfilename = dir + ".zip"
+    with open(zipfilename, "rb") as f:
+        md5 = hashlib.md5(f.read()).hexdigest()
+        print(f"{zipfilename}: {md5}")

tests/datasets/test_esri2020.py

@@ -32,7 +32,7 @@ def dataset(
         zipfile = "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip"
         monkeypatch.setattr(Esri2020, "zipfile", zipfile)  # type: ignore[attr-defined]
 
-        md5 = "4932855fcd00735a34b74b1f87db3df0"
+        md5 = "34aec55538694171c7b605b0cc0d0138"
         monkeypatch.setattr(Esri2020, "md5", md5)  # type: ignore[attr-defined]
         url = os.path.join(
             "tests",
@@ -45,17 +45,6 @@ def dataset(
         transforms = nn.Identity()  # type: ignore[attr-defined]
         return Esri2020(root, transforms=transforms, download=True, checksum=True)
 
-    def test_already_downloaded(self, tmp_path: Path) -> None:
-        url = os.path.join(
-            "tests",
-            "data",
-            "esri2020",
-            "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip",
-        )
-        root = str(tmp_path)
-        shutil.copy(url, root)
-        Esri2020(root)
-
     def test_getitem(self, dataset: Esri2020) -> None:
         x = dataset[dataset.bounds]
         assert isinstance(x, dict)
@@ -65,6 +54,16 @@ def test_getitem(self, dataset: Esri2020) -> None:
     def test_already_extracted(self, dataset: Esri2020) -> None:
         Esri2020(root=dataset.root, download=True)
 
+    def test_not_extracted(self, tmp_path: Path) -> None:
+        url = os.path.join(
+            "tests",
+            "data",
+            "esri2020",
+            "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip",
+        )
+        shutil.copy(url, tmp_path)
+        Esri2020(root=str(tmp_path))
+
     def test_not_downloaded(self, tmp_path: Path) -> None:
         with pytest.raises(RuntimeError, match="Dataset not found"):
             Esri2020(str(tmp_path), checksum=True)
@@ -80,7 +79,14 @@ def test_or(self, dataset: Esri2020) -> None:
     def test_plot(self, dataset: Esri2020) -> None:
         query = dataset.bounds
         x = dataset[query]
-        dataset.plot(x["mask"])
+        dataset.plot(x, suptitle="Test")
+        plt.close()
+
+    def test_plot_prediction(self, dataset: Esri2020) -> None:
+        query = dataset.bounds
+        x = dataset[query]
+        x["prediction"] = x["mask"].clone()
+        dataset.plot(x, suptitle="Prediction")
         plt.close()
 
     def test_url(self) -> None:

torchgeo/datasets/esri2020.py

@@ -3,18 +3,18 @@
 
 """Esri 2020 Land Cover Dataset."""
 
-import abc
 import glob
 import os
 from typing import Any, Callable, Dict, Optional
 
+import matplotlib.pyplot as plt
 from rasterio.crs import CRS
 
 from .geo import RasterDataset
 from .utils import download_url, extract_archive
 
 
-class Esri2020(RasterDataset, abc.ABC):
+class Esri2020(RasterDataset):
     """Esri 2020 Land Cover Dataset.
 
     The `Esri 2020 Land Cover dataset
@@ -136,3 +136,48 @@ def _download(self) -> None:
     def _extract(self) -> None:
         """Extract the dataset."""
         extract_archive(os.path.join(self.root, self.zipfile))
+
+    def plot(  # type: ignore[override]
+        self,
+        sample: Dict[str, Any],
+        show_titles: bool = True,
+        suptitle: Optional[str] = None,
+    ) -> plt.Figure:
+        """Plot a sample from the dataset.
+
+        Args:
+            sample: a sample returned by :meth:`RasterDataset.__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
+        """
+        mask = sample["mask"].squeeze()
+        ncols = 1
+
+        showing_predictions = "prediction" in sample
+        if showing_predictions:
+            prediction = sample["prediction"].squeeze()
+            ncols = 2
+
+        fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(4 * ncols, 4))
+
+        if showing_predictions:
+            axs[0].imshow(mask)
+            axs[0].axis("off")
+            axs[1].imshow(prediction)
+            axs[1].axis("off")
+            if show_titles:
+                axs[0].set_title("Mask")
+                axs[1].set_title("Prediction")
+        else:
+            axs.imshow(mask)
+            axs.axis("off")
+            if show_titles:
+                axs.set_title("Mask")
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        return fig