Upload satin_class.py

satin_class.py

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+"""
+
+import datasets
+import os
+import pyarrow.parquet as pq
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import pandas as pd
+
+
+def load_data(data_dir):
+    parquet_file = [file for file in os.listdir(data_dir) if file.endswith('.parquet')][0]
+    print(parquet_file)
+    parquet_path = os.path.join(data_dir, parquet_file)
+
+    parquet_path = data_dir
+    table = pq.read_table(parquet_path)
+
+    for row in table.iterrecords():
+        image_bytes = row['image']
+        image = Image.open(BytesIO(image_bytes))
+        label = row['label']
+        yield image, label
+
+
+
+class SATINConfig(datasets.BuilderConfig):
+
+
+    def __init__(self, name, description, data_url, class_names, **kwargs):
+
+        Args:
+          data_url: `string`, url to download the zip file from.
+          metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
+          **kwargs: keyword arguments forwarded to super.
+
+        super(SATINConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.name = name
+        self.data_url = data_url
+        self.description = description
+        self.class_names = class_names
+
+
+class SATIN(datasets.GeneratorBasedBuilder):
+    SATIN Images dataset
+
+    _SAT_4_NAMES = ['barren land', 'grassland', 'other', 'trees']
+    _SAT_6_NAMES = ['barren land', 'building', 'grassland', 'road', 'trees', 'water']
+
+    BUILDER_CONFIGS = [
+        SATINConfig(
+            name="SAT_4",
+            description="SAT_4.",
+            data_url="https://huggingface.co/datasets/jonathan-roberts1/SAT-4/tree/main/data/",#train-00000-of-00001-e2dcb38bc165dfb0.parquet",
+            class_names = _SAT_4_NAMES
+            #metadata_urls={
+            #    "train": "https://link-to-breakfast-foods-train.txt",
+         ),
+        SATINConfig(
+            name="SAT_6",
+            description="SAT_6.",
+            data_url="https://huggingface.co/datasets/jonathan-roberts1/SAT-6/tree/main/data/",#train-00000-of-00001-c47ada2c92f814d2.parquet",
+            class_names = _SAT_6_NAMES
+        )
+    ]
+
+    @property
+    def url_prefix(self):
+        return {
+            "SAT-4": "https://huggingface.co/datasets/jonathan-roberts1/SAT-4/tree/main/data/",#train-00000-of-00001-e2dcb38bc165dfb0.parquet",#train-00000-of-00001-e2dcb38bc165dfb0.parquet",
+            "SAT-6": "https://huggingface.co/datasets/jonathan-roberts1/SAT-6/tree/main/data/",
+        }
+
+    def _info(self):
+        return datasets.DatasetInfo(
+        description=self.config.description,
+        features=datasets.Features(
+            {
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=self.config.class_names),
+            }
+        ),
+        supervised_keys=("image", "label"),
+        #homepage=_HOMEPAGE,
+        #citation=_CITATION,
+        #license=_LICENSE,
+        #task_templates=[ImageClassification(image_column="image", label_column="label")],
+    )
+
+    def _split_generators(self, dl_manager):
+        url = self.config.data_url
+        data_dir = dl_manager.download_and_extract(url)#, use_auth_token=True)
+        print(data_dir)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        #base_url = self.url_prefix[self.config.name]
+        file_url = self.config.data_url
+        use_auth_token = os.environ.get("HUGGINGFACE_TOKEN")
+
+        with NamedTemporaryFile() as file:
+            download(file_url, file.name, use_auth_token=use_auth_token)
+            df = pd.read_parquet(file.name)
+
+        for idx, row in df.iterrows():
+            example = {
+                "image": row["image"],
+                "label": row["label"],
+            }
+            yield idx, example
+
+
+    #def _generate_examples(self, data_dir):
+    #    for idx, (image, label) in enumerate(load_data(data_dir)):
+    #        image_array = np.array(image)
+    #        yield idx, {"image": image_array, "label": label}
+"""
+
+
+from datasets.utils.download_manager import DownloadManager
+import tempfile
+import datasets
+import os
+import pyarrow.parquet as pq
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import pandas as pd
+
+
+class SATINConfig(datasets.BuilderConfig):
+
+
+    def __init__(self, name, description, data_url, class_names, **kwargs):
+
+        super(SATINConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.name = name
+        self.data_url = data_url
+        self.description = description
+        self.class_names = class_names
+
+
+
+class SATIN(datasets.GeneratorBasedBuilder):
+    """SATIN Images dataset"""
+
+    _SAT_4_NAMES = ['barren land', 'grassland', 'other', 'trees']
+    _SAT_6_NAMES = ['barren land', 'building', 'grassland', 'road', 'trees', 'water']
+
+    BUILDER_CONFIGS = [
+        SATINConfig(
+            name="SAT_4",
+            description="SAT_4.",
+            data_url="jonathan-roberts1/SAT-4",#https://huggingface.co/datasets/jonathan-roberts1/SAT-4/blob/main/data/train-00000-of-00001-e2dcb38bc165dfb0.parquet?raw=true",
+            class_names=_SAT_4_NAMES
+        ),
+        SATINConfig(
+            name="SAT_6",
+            description="SAT_6.",
+            data_url="jonathan-roberts1/SAT-6",#"https://huggingface.co/datasets/jonathan-roberts1/SAT-6/blob/main/data/train-00000-of-00001-c47ada2c92f814d2.parquet?raw=true",
+            class_names=_SAT_6_NAMES
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=self.config.class_names),
+                }
+            ),
+            supervised_keys=("image", "label"),
+        )
+
+    def _split_generators(self, dl_manager):
+        #data_path = dl_manager.download(self.config.data_url)
+        from datasets import load_dataset
+        dataset = load_dataset(self.config.data_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_path": dataset},
+            ),
+        ]
+
+    def _generate_examples(self, data_path):
+        # iterate over the Huggingface dataset and yield the idx, image and label
+        huggingface_dataset = data_path["train"]
+        for idx, row in enumerate(huggingface_dataset):
+            yield idx, {"image": row["image"], "label": row["label"]}
+
+
+