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PASTIS.py

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+import os
+import json
+import shutil
+import datasets
+import tifffile
+
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+
+from datetime import datetime
+
+S2_MEAN = [1180.2278549 , 1387.76882557, 1436.67627781, 1773.66437066, 2735.86417202, 3080.12530686, 3223.60015887, 3338.35639825, 2418.01390106, 1630.11250759]
+
+S2_STD = [1976.91493068, 1917.02121286, 1996.45123112, 1903.34296117, 1785.08356262, 1796.4477813 , 1811.90019014, 1793.47036145, 1474.46979658, 1309.88416505]
+
+class PASTISDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    
+    DATA_URL = "https://huggingface.co/datasets/yuxuanw8/PASTIS/resolve/main/PASTIS.zip" # TODO
+
+    metadata = {
+        "s2c": {
+            "bands": ["B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12"],
+            "channel_wv": [492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 1613.7, 2202.4],
+            "mean": S2_MEAN,
+            "std": S2_STD,
+        },
+        "s1": {
+            "bands": None,
+            "channel_wv": None,
+            "mean": None,
+            "std": None   
+        }
+    }
+
+    SIZE = HEIGHT = WIDTH = 128
+
+    spatial_resolution = 10 
+
+    NUM_CLASSES = 20 # 0 is background class, and 19 is the void label
+
+    def __init__(self, reference_date="2018-09-10", **kwargs):
+        super().__init__(**kwargs)
+        self.reference_date = datetime(*map(int, reference_date.split("-")))
+        print(f"reference_date: {reference_date} -> {self.reference_date}")
+    
+    def _split_generators(self, dl_manager):
+        if isinstance(self.DATA_URL, list):
+            downloaded_files = dl_manager.download(self.DATA_URL)
+            combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")        
+            with open(combined_file, 'wb') as outfile:
+                for part_file in downloaded_files:
+                    with open(part_file, 'rb') as infile:
+                        shutil.copyfileobj(infile, outfile)
+            data_dir = dl_manager.extract(combined_file)
+            os.remove(combined_file)
+        else:
+            data_dir = dl_manager.download_and_extract(self.DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "split": 'train',
+                    "data_dir": data_dir, 
+                },
+            ),
+            datasets.SplitGenerator(
+                name="val",
+                gen_kwargs={
+                    "split": 'val',
+                    "data_dir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "split": 'test',
+                    "data_dir": data_dir,
+                },
+            )
+        ]
+
+    def _info(self):
+        metadata = self.metadata
+        metadata['size'] = self.SIZE
+        metadata['num_classes'] = self.NUM_CLASSES
+        metadata['spatial_resolution'] = self.spatial_resolution
+        return datasets.DatasetInfo(
+            description=json.dumps(metadata),
+            features=datasets.Features({
+                "optical": datasets.Array4D(shape=(61, 10, self.HEIGHT, self.WIDTH), dtype="float32"),
+                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
+                "dates": datasets.Sequence(datasets.Value("int32")),
+                "sequence_len": datasets.Value("int32"),
+                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
+                "spatial_resolution": datasets.Value("int32"),
+            }),
+        )
+
+    def _generate_examples(self, split, data_dir):
+        optical_channel_wv = self.metadata["s2c"]["channel_wv"]
+        spatial_resolution = self.spatial_resolution
+
+        data_dir = os.path.join(data_dir, "PASTIS")
+        metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
+        metadata = metadata[metadata["split"] == split].reset_index(drop=True)
+
+        self._prepare_meta_patch(data_dir)
+        self._prepare_date_tables(data_dir)
+
+        for index, row in metadata.iterrows():
+            id_patch = row.optical_path.replace("DATA_S2/S2_", "").replace(".tif", "")
+            optical_path = os.path.join(data_dir, row.optical_path)
+            optical = self._read_image(optical_path).astype(np.float32) # TxCxHxW
+            sequence_len = optical.shape[0]
+            optical = self._pad_sequence(optical) # 61xCxHxW
+
+            label_path = os.path.join(data_dir, row.label_path) # 3xHxW
+            label = tifffile.imread(label_path)[0] # HxW
+
+            # Retrieve date sequences
+            dates = self._get_dates(id_patch=id_patch, sat="S2")
+
+            sample = {
+                "optical": optical,
+                "optical_channel_wv": optical_channel_wv,
+                "dates": dates,
+                "sequence_len": sequence_len,
+                "label": label,
+                "spatial_resolution": spatial_resolution,
+            }
+
+            yield f"{index}", sample
+
+    # util functions
+
+    def _prepare_meta_patch(self, data_dir):
+        self.meta_patch = gpd.read_file(os.path.join(data_dir, "metadata.geojson"))
+        self.meta_patch.index = self.meta_patch["ID_PATCH"].astype(int)
+        self.meta_patch.sort_index(inplace=True)
+
+    def _prepare_date_tables(self, data_dir):
+        self.date_tables = {"S2": None}
+        self.date_range = np.array(range(-200, 600))
+        for s in ["S2"]:
+            dates = self.meta_patch["dates-{}".format(s)]
+            date_table = pd.DataFrame(
+                index=self.meta_patch.index, columns=self.date_range, dtype=int
+            )
+            for pid, date_seq in dates.items():
+                if type(date_seq) == str:
+                    date_seq = json.loads(date_seq)
+                d = pd.DataFrame().from_dict(date_seq, orient="index")
+                d = d[0].apply(
+                    lambda x: (
+                        datetime(int(str(x)[:4]), int(str(x)[4:6]), int(str(x)[6:]))
+                        - self.reference_date
+                    ).days
+                )
+                date_table.loc[pid, d.values] = 1
+            date_table = date_table.fillna(0)
+            self.date_tables[s] = {
+                index: np.array(list(d.values()))
+                for index, d in date_table.to_dict(orient="index").items()
+            }
+    
+    def _get_dates(self, id_patch, sat="S2"):
+        id_patch = int(id_patch)
+        return self.date_range[np.where(self.date_tables[sat][id_patch] == 1)[0]]
+    
+    def _pad_sequence(self, optical):
+        padding_size = 61 - optical.shape[0]
+        if padding_size == 0:
+            return optical
+        
+        pad = np.zeros((padding_size, *optical.shape[1:]))
+        padded_optical = np.concatenate((optical, pad), axis=0)
+        return padded_optical
+
+    def _read_image(self, image_path):
+        """Read tiff image from image_path
+        Args:
+            image_path: 
+                Image path to read from
+
+        Return:
+            image: 
+                C, H, W numpy array image
+        """
+        image = tifffile.imread(image_path)
+        if len(image.shape) == 3:
+            image = np.transpose(image, (2, 0, 1))
+
+        return image