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MARIDA.py

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+import os
+import json
+import shutil
+import datasets
+import tifffile
+
+import pandas as pd
+import numpy as np
+
+from GFMBench.datasets.base_dataset import GFMBenchDataset
+
+S2_MEAN = [0.05197577, 0.04783991, 0.04056812, 0.03163572, 0.02972606, 0.03457443, 0.03875053, 0.03436435, 0.0392113,  0.02358126, 0.01588816]
+
+S2_STD = [0.04725893, 0.04743808, 0.04699043, 0.04967381, 0.04946782, 0.06458357, 0.07594915, 0.07120246, 0.08251058, 0.05111466, 0.03524419]
+
+class MARIDADataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    
+    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/MARIDA/resolve/main/MARIDA.zip"
+
+    metadata = {
+        "s2c": {
+            "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12"],
+            "channel_wv": [442.7, 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 = 96
+
+    spatial_resolution = 10 
+
+    NUM_CLASSES = 11
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        mean = np.array(S2_MEAN).astype(np.float32)
+        self.impute_nan = np.tile(mean, (self.SIZE, self.SIZE, 1))
+
+    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.Array3D(shape=(11, self.HEIGHT, self.WIDTH), dtype="float32"),
+                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
+                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
+                "spatial_resolution": datasets.Value("int32"),
+            }),
+        )
+
+    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 _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, "MARIDA")
+        metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
+        metadata = metadata[metadata["split"] == split].reset_index(drop=True)
+
+        for index, row in metadata.iterrows():
+            optical_path = os.path.join(data_dir, row.optical_path)
+            optical = self._read_image(optical_path).astype(np.float32) # CxHxW
+            optical = np.transpose(optical, (1, 2, 0))
+            nan_mask = np.isnan(optical)
+            optical[nan_mask] = self.impute_nan[nan_mask]
+            optical = np.transpose(optical, (2, 0, 1))
+
+            label_path = os.path.join(data_dir, row.label_path)
+            label = self._read_image(label_path).astype(np.int32)
+            label[label==15] = 7
+            label[label==14] = 7
+            label[label==13] = 7
+            label[label==12] = 7
+            label -= 1
+            label[label==-1] = 255
+
+            sample = {
+                "optical": optical,
+                "optical_channel_wv": optical_channel_wv,
+                "label": label,
+                "spatial_resolution": spatial_resolution,
+            }
+
+            yield f"{index}", sample
+    
+    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