Delete BigEarthNet.py

BigEarthNet.py

@@ -1,272 +0,0 @@
-import os
-import json
-import shutil
-import string
-import tifffile
-import datasets
-
-import numpy as np
-import pandas as pd
-
-class_sets = {
-        19: [
-            'Urban fabric',
-            'Industrial or commercial units',
-            'Arable land',
-            'Permanent crops',
-            'Pastures',
-            'Complex cultivation patterns',
-            'Land principally occupied by agriculture, with significant areas of'
-            ' natural vegetation',
-            'Agro-forestry areas',
-            'Broad-leaved forest',
-            'Coniferous forest',
-            'Mixed forest',
-            'Natural grassland and sparsely vegetated areas',
-            'Moors, heathland and sclerophyllous vegetation',
-            'Transitional woodland, shrub',
-            'Beaches, dunes, sands',
-            'Inland wetlands',
-            'Coastal wetlands',
-            'Inland waters',
-            'Marine waters',
-        ],
-        43: [
-            'Continuous urban fabric',
-            'Discontinuous urban fabric',
-            'Industrial or commercial units',
-            'Road and rail networks and associated land',
-            'Port areas',
-            'Airports',
-            'Mineral extraction sites',
-            'Dump sites',
-            'Construction sites',
-            'Green urban areas',
-            'Sport and leisure facilities',
-            'Non-irrigated arable land',
-            'Permanently irrigated land',
-            'Rice fields',
-            'Vineyards',
-            'Fruit trees and berry plantations',
-            'Olive groves',
-            'Pastures',
-            'Annual crops associated with permanent crops',
-            'Complex cultivation patterns',
-            'Land principally occupied by agriculture, with significant areas of'
-            ' natural vegetation',
-            'Agro-forestry areas',
-            'Broad-leaved forest',
-            'Coniferous forest',
-            'Mixed forest',
-            'Natural grassland',
-            'Moors and heathland',
-            'Sclerophyllous vegetation',
-            'Transitional woodland/shrub',
-            'Beaches, dunes, sands',
-            'Bare rock',
-            'Sparsely vegetated areas',
-            'Burnt areas',
-            'Inland marshes',
-            'Peatbogs',
-            'Salt marshes',
-            'Salines',
-            'Intertidal flats',
-            'Water courses',
-            'Water bodies',
-            'Coastal lagoons',
-            'Estuaries',
-            'Sea and ocean',
-        ],
-    }
-
-label_converter = {
-        0: 0,
-        1: 0,
-        2: 1,
-        11: 2,
-        12: 2,
-        13: 2,
-        14: 3,
-        15: 3,
-        16: 3,
-        18: 3,
-        17: 4,
-        19: 5,
-        20: 6,
-        21: 7,
-        22: 8,
-        23: 9,
-        24: 10,
-        25: 11,
-        31: 11,
-        26: 12,
-        27: 12,
-        28: 13,
-        29: 14,
-        33: 15,
-        34: 15,
-        35: 16,
-        36: 16,
-        38: 17,
-        39: 17,
-        40: 18,
-        41: 18,
-        42: 18,
-    }
-
-S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033]
-S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601]
-
-S1_MEAN = [-12.54847273, -20.19237134]
-S1_STD = [5.25697717, 5.91150917]
-
-parts = [f"a{letter}" for letter in string.ascii_lowercase]
-parts.extend([f"b{letter}" for letter in string.ascii_lowercase[:8]]) 
-
-class BigEarthNetDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    DATA_URL = [
-        f"https://huggingface.co/datasets/GFM-Bench/BigEarthNet/resolve/main/data/bigearthnet_part_{part}"
-        for part in parts
-    ]
-
-    metadata = {
-        "s2c": {
-            "bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
-            "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4],
-            "mean": S2_MEAN,
-            "std": S2_STD
-        },
-        "s1": {
-            "bands": ["VV", "VH"],
-            "channel_wv": [5500, 5700],
-            "mean": S1_MEAN,
-            "std": S1_STD
-        }
-    }
-
-    SIZE = HEIGHT = WIDTH = 120
-
-    NUM_CLASSES = 19
-
-    spatial_resolution = 10
-
-    def __init__(self, *args, **kwargs):
-        self.class2idx = {c: i for i, c in enumerate(class_sets[43])}
-
-        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=(12, self.HEIGHT, self.WIDTH), dtype="float32"),
-                "radar": datasets.Array3D(shape=(2, self.HEIGHT, self.WIDTH), dtype="float32"),
-                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
-                "radar_channel_wv": datasets.Sequence(datasets.Value("float32")),
-                "label": datasets.Sequence(datasets.Value("float32"), length=self.NUM_CLASSES),
-                "spatial_resolution": datasets.Value("int32"),
-            }),
-        )
-
-    def _split_generators(self, dl_manager):
-        if isinstance(self.DATA_URL, list):
-            try:
-                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)
-            except Exception as e:
-                # Print the error message
-                print(f"An error occurred: {e}, setting data_dir to None")
-                data_dir = None
-        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"])
-        radar_channel_wv = np.array(self.metadata["s1"]["channel_wv"])
-        spatial_resolution = self.spatial_resolution
-
-        data_dir = os.path.join(data_dir, "BigEarthNet")
-        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
-
-            radar_path = os.path.join(data_dir, row.radar_path)
-            radar = self._read_image(radar_path).astype(np.float32)
-
-            label_path = os.path.join(data_dir, row.label_path)
-            label = self._load_label(label_path)
-
-            sample = {
-                "optical": optical,
-                "radar": radar,
-                "optical_channel_wv": optical_channel_wv,
-                "radar_channel_wv": radar_channel_wv,
-                "label": label,
-                "spatial_resolution": spatial_resolution,
-            }
-
-            yield f"{index}", sample
-    
-    def _load_label(self, label_path):
-        with open(label_path) as f:
-            labels = json.load(f)['labels']
-        indices =[self.class2idx[label] for label in labels]
-        indices_optional = [label_converter.get(idx) for idx in indices]
-        indices = [idx for idx in indices_optional if idx is not None]
-        label = np.zeros(19, dtype=np.int64)
-        label[indices] = 1
-        return label
-    
-    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)
-        image = np.transpose(image, (2, 0, 1))
-
-        return image