So2Sat.py

import os
import json
import shutil
import datasets
import tifffile

import numpy as np
import pandas as pd

S2_MEAN = [0.12375696117681, 0.10927746363683, 0.10108552032678, 0.11423986161140, 0.15926566920230, 0.18147236008771, 
           0.17457403122913, 0.19501607349635, 0.15428468872076, 0.10905050699570]
S2_STD = [0.03958795, 0.04777826, 0.06636616, 0.06358874, 0.07744387, 0.09101635, 0.09218466, 0.10164581, 0.09991773, 0.08780632]

class So2SatDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/So2Sat/resolve/main/So2Sat.zip"

    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 = 32

    NUM_CLASSES = 17

    spatial_resolution = 10

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    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=(10, self.HEIGHT, self.WIDTH), dtype="float32"),
                "label": datasets.Value("int32"),
                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
                "spatial_resolution": datasets.Value("float32"),
            }),
        )
    
    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 = np.array(self.metadata["s2c"]["channel_wv"])
        spatial_resolution = self.spatial_resolution

        data_dir = os.path.join(data_dir, "So2Sat")
        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

            label = int(row.label)

            sample = {
                "optical": optical,
                "label": label,
                "optical_channel_wv": optical_channel_wv,
                "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