Delete dataset.py

dataset.py

@@ -1,136 +0,0 @@
-from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value, ClassLabel, Image, Sequence
-import csv
-import datasets
-import ast
-
-class CAFOSatConfig(datasets.BuilderConfig):
-    def __init__(self, split_column=None, data_filter=None, **kwargs):
-        super().__init__(**kwargs)
-        self.split_column = split_column  # e.g., 'cafosat_set1_training_train'
-        self.data_filter = data_filter    # e.g., 'verified', 'augmented', 'neg', 'cafo'
-
-class CAFOSat(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = CAFOSatConfig
-    BUILDER_CONFIGS = [
-        # Train/test/val configs
-        CAFOSatConfig(name="verified_train", split_column="cafosat_verified_training_train", description="Verified training split"),
-        CAFOSatConfig(name="verified_val", split_column="cafosat_verified_training_val", description="Verified validation split"),
-        CAFOSatConfig(name="verified_test", split_column="cafosat_verified_training_test", description="Verified test split"),
-
-        CAFOSatConfig(name="all_train", split_column="cafosat_all_training_train", description="All training split"),
-        CAFOSatConfig(name="all_val", split_column="cafosat_all_training_val", description="All validation split"),
-        CAFOSatConfig(name="all_test", split_column="cafosat_all_training_test", description="All test split"),
-
-        CAFOSatConfig(name="set1_train", split_column="cafosat_training_set1_train", description="Set1 training split"),
-        CAFOSatConfig(name="set1_val", split_column="cafosat_training_set1_val", description="Set1 validation split"),
-        CAFOSatConfig(name="set1_test", split_column="cafosat_training_set1_test", description="Set1 test split"),
-
-        CAFOSatConfig(name="set2_train", split_column="cafosat_training_set2_train", description="Set2 training split"),
-        CAFOSatConfig(name="set2_val", split_column="cafosat_training_set2_val", description="Set2 validation split"),
-        CAFOSatConfig(name="set2_test", split_column="cafosat_training_set2_test", description="Set2 test split"),
-
-        CAFOSatConfig(name="merged_train", split_column="cafosat_merged_training_train", description="Merged training split"),
-        CAFOSatConfig(name="merged_val", split_column="cafosat_merged_training_val", description="Merged validation split"),
-        CAFOSatConfig(name="merged_test", split_column="cafosat_merged_training_test", description="Merged test split"),
-
-        CAFOSatConfig(name="augmented_train", split_column="cafosat_augmented_training_train", description="Augmented training split"),
-        CAFOSatConfig(name="augmented_val", split_column="cafosat_augmented_training_val", description="Augmented validation split"),
-        CAFOSatConfig(name="augmented_test", split_column="cafosat_augmented_training_test", description="Augmented test split"),
-
-        
-        # Data type filters
-        CAFOSatConfig(name="verified_only", data_filter="verified", description="Only verified patches"),
-        CAFOSatConfig(name="verified_cafo_only", data_filter="verified_cafo", description="Only verified cafo patches"),
-        CAFOSatConfig(name="augmented_only", data_filter="augmented", description="Only augmented patches"),
-        CAFOSatConfig(name="negatives_only", data_filter="neg", description="Only negative examples"),
-        CAFOSatConfig(name="cafo_only", data_filter="cafo", description="Only positive CAFOs (label > 0)"),
-        CAFOSatConfig(name="all", data_filter="all", description="All Patches"),
-    ]
-    # DEFAULT_CONFIG_NAME = "all_train"
-
-    def _info(self):
-        return DatasetInfo(
-            description="CAFOSat: Remote sensing CAFO dataset with bounding boxes and infrastructure tags.",
-            features=Features({
-                "patch_file": Image(),
-                "label": ClassLabel(
-                    names=["Negative", "Swine", "Dairy", "Beef", "Poultry", "Horses", "Sheep/Goats"]
-                ),
-                "barn": Value("float32"),
-                "manure_pond": Value("float32"),
-                "grazing_area": Value("float32"),
-                "others": Value("float32"),
-                "geom_bbox": Sequence(Value("float32")),
-                "category": Value("string"),
-                "state": Value("string"),
-                "image_type": Value("string"),
-                "CAFO_UNIQUE_ID": Value("string"),
-                "verified_label": Value("string"),
-                "patch_res": Value("string"),
-                "refine_x": Value("string"),
-                "refine_y": Value("string")
-            }),
-            supervised_keys=None,
-            homepage="https://huggingface.co/datasets/oishee3003/CAFOSat",
-            license="cc-by-4.0"
-        )
-
-    def _split_generators(self, dl_manager):
-        csv_path = dl_manager.download_and_extract("cafosat.csv")
-        return [
-            SplitGenerator(name=Split.TRAIN, gen_kwargs={"csv_path": csv_path})
-        ]
-
-    def _generate_examples(self, csv_path):
-        split_col = self.config.split_column
-        data_filter = self.config.data_filter
-
-        with open(csv_path, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for idx, row in enumerate(reader):
-                include = True
-
-                # Apply split filtering
-                if split_col:
-                    if row.get(split_col, "0") != "1":
-                        continue
-
-                # Apply type-based filtering
-                if data_filter == "augmented":
-                    include = "augmented" in row.get("image_type", "").lower()
-                elif data_filter == "verified":
-                    include = bool(row.get("verified_label", "").strip())
-                elif data_filter == "verified_cafo_only":
-                    include = bool(row.get("verified_label", "CAFO").strip())
-                elif data_filter == "neg":
-                    include = int(row.get("label", 0)) < 0
-                elif data_filter == "cafo":
-                    include = int(row.get("label", 0)) > 0
-                elif data_filter == "all":
-                    include = True
-
-                if not include:
-                    continue
-
-                try:
-                    bbox = ast.literal_eval(row.get("geom_bbox", "[5.0, 5.0, 700.0, 700.0]"))
-                except:
-                    bbox = [5.0, 5.0, 700.0, 700.0]
-
-                yield idx, {
-                    "patch_file": row["patch_file"],
-                    "label": int(row["label"]),
-                    "barn": float(row.get("barn", 0)),
-                    "manure_pond": float(row.get("manure_pond", 0)),
-                    "grazing_area": float(row.get("grazing_area", 0)),
-                    "others": float(row.get("others", 0)),
-                    "geom_bbox": bbox,
-                    "category": row.get("category", ""),
-                    "state": row.get("state", ""),
-                    "image_type": row.get("image_type", ""),
-                    "CAFO_UNIQUE_ID": row.get("CAFO_UNIQUE_ID", ""),
-                    "verified_label": row.get("verified_label", ""),
-                    "patch_res": row.get("patch_res", ""),
-                    "refine_x": row.get("refine_x", ""),
-                    "refine_y": row.get("refine_y", "")
-                }