adding seasonet dataset + tests + doc

docs/api/datasets.rst

@@ -302,6 +302,11 @@ Seasonal Contrast
 
 .. autoclass:: SeasonalContrastS2
 
+SeasoNet
+^^^^^^^^
+
+.. autoclass:: SeasoNet
+
 SEN12MS
 ^^^^^^^
 

docs/api/non_geo_datasets.csv

@@ -27,6 +27,7 @@ Dataset,Task,Source,# Samples,# Classes,Size (px),Resolution (m),Bands
 `ReforesTree`_,"OD, R",Aerial,100,6,"4,000x4,000",0.02,RGB
 `RESISC45`_,C,Google Earth,"31,500",45,256x256,0.2--30,RGB
 `Seasonal Contrast`_,T,Sentinel-2,100K--1M,-,264x264,10,MSI
+`SeasoNet`_,S,Sentinel-2,"1,759,830",33,120x120,10,MSI
 `SEN12MS`_,S,"Sentinel-1/2, MODIS","180,662",33,256x256,10,"SAR, MSI"
 `SKIPP'D`_,R,"Fish-eye","363,375",-,64x64,-,RGB
 `So2Sat`_,C,Sentinel-1/2,"400,673",17,32x32,10,"SAR, MSI"

tests/data/seasonet/data.py

@@ -0,0 +1,141 @@
+#!/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
+import rasterio
+from rasterio.crs import CRS
+from rasterio.transform import Affine
+
+np.random.seed(0)
+
+meta = {
+    "driver": "GTiff",
+    "nodata": None,
+    "crs": CRS.from_epsg(32632),
+    "transform": Affine(10.0, 0.0, 664800.0, 0.0, -10.0, 5342400.0),
+    "compress": "zstd",
+}
+bands = ["10m_RGB", "10m_IR", "20m", "60m", "labels"]
+count = {"10m_RGB": 3, "10m_IR": 1, "20m": 6, "60m": 2, "labels": 1}
+dtype = {
+    "10m_RGB": np.uint16,
+    "10m_IR": np.uint16,
+    "20m": np.uint16,
+    "60m": np.uint16,
+    "labels": np.uint8,
+}
+size = {"10m_RGB": 120, "10m_IR": 120, "20m": 60, "60m": 20, "labels": 120}
+start = {"10m_RGB": 0, "10m_IR": 0, "20m": 0, "60m": 0, "labels": 1}
+stop = {
+    "10m_RGB": np.iinfo(np.uint16).max,
+    "10m_IR": np.iinfo(np.uint16).max,
+    "20m": np.iinfo(np.uint16).max,
+    "60m": np.iinfo(np.uint16).max,
+    "labels": 34,
+}
+
+meta_lines = [
+    "Index,Season,Grid,Latitude,Longitude,Satellite,Year,Month,Day,"
+    "Hour,Minute,Second,Clouds,Snow,Classes,SLRAUM,RTYP3,KTYP4,Path\n"
+]
+seasons = ["spring", "summer", "fall", "winter", "snow"]
+grids = [1, 2]
+name_comps = [
+    ["32UME", "2018", "04", "18", "T", "10", "40", "21", "53", "928425", "7", "503876"],
+    ["32TMT", "2019", "02", "14", "T", "10", "31", "29", "47", "793488", "7", "808487"],
+]
+index = 0
+for season in seasons:
+    # Remove old data
+    if os.path.exists(season):
+        shutil.rmtree(season)
+
+    archive = f"{season}.zip"
+
+    # Remove old data
+    if os.path.exists(archive):
+        os.remove(archive)
+
+    for grid, comp in zip(grids, name_comps):
+        file_name = f"{comp[0]}_{''.join(comp[1:8])}_{'_'.join(comp[8:])}"
+        dir = os.path.join(season, f"grid{grid}", file_name)
+        os.makedirs(dir)
+
+        # Random images
+        for band in bands:
+            meta["count"] = count[band]
+            meta["dtype"] = dtype[band]
+            meta["width"] = meta["height"] = size[band]
+            with rasterio.open(
+                os.path.join(dir, f"{file_name}_{band}.tif"), "w", **meta
+            ) as f:
+                for j in range(1, count[band] + 1):
+                    data = np.random.randint(
+                        start[band], stop[band], size=(size[band], size[band])
+                    ).astype(dtype[band])
+                    f.write(data, j)
+
+        # Generate meta.csv lines
+        meta_entries = [
+            index,
+            season.capitalize(),
+            grid,
+            f"{comp[8]}.{comp[9]}",
+            f"{comp[10]}.{comp[11]}",
+            "A",
+            comp[1],
+            comp[2],
+            comp[3],
+            comp[5],
+            comp[6],
+            comp[7],
+            0.0,
+            0.0,
+            "'2,3,12,15,17'",
+            1,
+            1,
+            1,
+            dir,
+        ]
+        meta_lines.append(",".join(map(str, meta_entries)) + "\n")
+        index += 1
+
+    # Create archives
+    shutil.make_archive(season, "zip", ".", season)
+
+    # Compute checksums
+    with open(archive, "rb") as f:
+        md5 = hashlib.md5(f.read()).hexdigest()
+        print(f"{season}: {repr(md5)}")
+
+# Write meta.csv
+with open("meta.csv", "w") as f:
+    f.writelines(meta_lines)
+
+# Compute checksums
+with open("meta.csv", "rb") as f:
+    md5 = hashlib.md5(f.read()).hexdigest()
+    print(f"meta.csv: {repr(md5)}")
+
+os.makedirs("splits", exist_ok=True)
+
+for split in ["train", "val", "test"]:
+    filename = f"{split}.csv"
+
+    # Create file list
+    with open(os.path.join("splits", filename), "w") as f:
+        for i in range(index):
+            f.write(str(i) + "\n")
+
+shutil.make_archive("splits", "zip", ".", "splits")
+
+# Compute checksums
+with open("splits.zip", "rb") as f:
+    md5 = hashlib.md5(f.read()).hexdigest()
+    print(f"splits: {repr(md5)}")

tests/data/seasonet/meta.csv

@@ -0,0 +1,11 @@
+Index,Season,Grid,Latitude,Longitude,Satellite,Year,Month,Day,Hour,Minute,Second,Clouds,Snow,Classes,SLRAUM,RTYP3,KTYP4,Path
+0,Spring,1,53.928425,7.503876,A,2018,04,18,10,40,21,0.0,0.0,"2,3,12,15,17",1,1,1,spring/grid1/32UME_20180418T104021_53_928425_7_503876
+1,Spring,2,47.793488,7.808487,A,2019,02,14,10,31,29,0.0,0.0,"2,3,12,15,17",1,1,1,spring/grid2/32TMT_20190214T103129_47_793488_7_808487
+2,Summer,1,53.928425,7.503876,A,2018,04,18,10,40,21,0.0,0.0,"2,3,12,15,17",1,1,1,summer/grid1/32UME_20180418T104021_53_928425_7_503876
+3,Summer,2,47.793488,7.808487,A,2019,02,14,10,31,29,0.0,0.0,"2,3,12,15,17",1,1,1,summer/grid2/32TMT_20190214T103129_47_793488_7_808487
+4,Fall,1,53.928425,7.503876,A,2018,04,18,10,40,21,0.0,0.0,"2,3,12,15,17",1,1,1,fall/grid1/32UME_20180418T104021_53_928425_7_503876
+5,Fall,2,47.793488,7.808487,A,2019,02,14,10,31,29,0.0,0.0,"2,3,12,15,17",1,1,1,fall/grid2/32TMT_20190214T103129_47_793488_7_808487
+6,Winter,1,53.928425,7.503876,A,2018,04,18,10,40,21,0.0,0.0,"2,3,12,15,17",1,1,1,winter/grid1/32UME_20180418T104021_53_928425_7_503876
+7,Winter,2,47.793488,7.808487,A,2019,02,14,10,31,29,0.0,0.0,"2,3,12,15,17",1,1,1,winter/grid2/32TMT_20190214T103129_47_793488_7_808487
+8,Snow,1,53.928425,7.503876,A,2018,04,18,10,40,21,0.0,0.0,"2,3,12,15,17",1,1,1,snow/grid1/32UME_20180418T104021_53_928425_7_503876
+9,Snow,2,47.793488,7.808487,A,2019,02,14,10,31,29,0.0,0.0,"2,3,12,15,17",1,1,1,snow/grid2/32TMT_20190214T103129_47_793488_7_808487

tests/data/seasonet/splits/test.csv

@@ -0,0 +1,10 @@
+0
+1
+2
+3
+4
+5
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+8
+9

tests/data/seasonet/splits/train.csv

@@ -0,0 +1,10 @@
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9

tests/data/seasonet/splits/val.csv

@@ -0,0 +1,10 @@
+0
+1
+2
+3
+4
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+6
+7
+8
+9