Delete SSL4EO-L-Benchmark.py

SSL4EO-L-Benchmark.py

@@ -1,177 +0,0 @@
-import os
-import json
-import shutil
-import datasets
-import tifffile
-
-import pandas as pd
-import numpy as np
-
-from torchgeo.datasets.cdl import CDL
-from torchgeo.datasets.nlcd import NLCD
-
-CMAPS = {
-    'nlcd': NLCD.cmap,
-    'cdl': CDL.cmap,
-}
-
-S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2581.64687018, 2368.51236873, 1805.06846033]
-
-S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1439.3086061, 1455.52084939, 1343.48379601]
-
-subset_names = ["etm_sr_cdl", "etm_sr_nlcd", "etm_toa_cdl", "etm_toa_nlcd", "oli_sr_cdl", "oli_sr_nlcd", "oli_tirs_toa_cdl", "oli_tirs_toa_nlcd"]
-
-num_classes = {
-    'etm_sr_cdl': 134,
-    'etm_sr_nlcd': 21,
-    'etm_toa_cdl': 134,
-    'etm_toa_nlcd': 21,
-    'oli_sr_cdl': 134,
-    'oli_sr_nlcd': 21,
-    'oli_tirs_toa_cdl': 134,
-    'oli_tirs_toa_nlcd': 21,
-}
-
-num_channels = {
-    'etm_sr_cdl': 6,
-    'etm_sr_nlcd': 6,
-    'etm_toa_cdl': 9,
-    'etm_toa_nlcd': 9,
-    'oli_sr_cdl': 7,
-    'oli_sr_nlcd': 7,
-    'oli_tirs_toa_cdl': 11,
-    'oli_tirs_toa_nlcd': 11,
-}
-
-MEAN = [0]
-STD = [0]
-
-metadata = { # TODO: check if info below is correct or not
-    'etm_sr_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B7"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 2220.0], "mean": MEAN * 6, 'std': STD * 6}, # B6 (Thermal Band) and B8 (Panchromatic Band) are excluded
-    'etm_sr_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B7"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 2220.0], "mean": MEAN * 6, 'std': STD * 6},
-    'etm_toa_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6L", "B6H", "B7", "B8"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0], "mean": MEAN * 9, 'std': STD * 9},
-    'etm_toa_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6L", "B6H", "B7", "B8"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0], "mean": MEAN * 9, 'std': STD * 9},
-    'oli_sr_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0], "mean": MEAN * 7, 'std': STD * 7},
-    'oli_sr_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0], "mean": MEAN * 7, 'std': STD * 7},
-    'oli_tirs_toa_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11},
-    'oli_tirs_toa_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11},
-}
-
-class SSL4EOLBenchmarkDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-    
-    DATA_URL = "https://huggingface.co/datasets/GFM-Bench/SSL4EO-L-Benchmark/resolve/main/SSL4EOLBenchmark.zip" 
-
-    SIZE = HEIGHT = WIDTH = 264
-
-    spatial_resolution = 30
-
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name=name) for name in subset_names]
-
-    DEFAULT_CONFIG_NAME = "etm_sr_cdl"
-
-    def __init__(self, *args, **kwargs):
-        name = kwargs.get('config_name', None)
-        print(f"config_name: {name}")
-        self.NUM_CLASSES = num_classes[name] if name else num_classes['etm_sr_cdl']
-        self.NUM_CHANNELS = num_channels[name] if name else num_channels['etm_sr_cdl']
-        self.metadata = metadata[name] if name else metadata['etm_sr_cdl']
-
-        product = name.split('_')[-1]
-        cmap = CMAPS[product]
-        classes = list(cmap.keys())
-        ordinal_map = np.zeros(max(cmap.keys()) + 1, dtype=np.int64)
-        for v, k in enumerate(classes):
-            ordinal_map[k] = v
-
-        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=(self.NUM_CHANNELS, self.HEIGHT, self.WIDTH), dtype="float32"),
-                "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"),
-                "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):
-        spatial_resolution = self.spatial_resolution
-
-        data_dir = os.path.join(data_dir, "SSL4EOLBenchmark")
-        metadata = pd.read_csv(os.path.join(data_dir, f"metadata_{self.config.name}.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_path = os.path.join(data_dir, row.label_path)
-            label = self._read_image(label_path).astype(np.int32)
-            label = self.ordinal_map[label]
-
-            sample = {
-                "optical": optical,
-                "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