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158 lines
5.1 KiB
Python
158 lines
5.1 KiB
Python
"""Local dataset loader for benchmark evaluation."""
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from pathlib import Path
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from typing import Any, Optional
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from ..base import BaseDataset
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class LocalDataset(BaseDataset):
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"""Dataset loader for local datasets."""
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def __init__(
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self,
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local_path: str,
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img_column: str = "image_path",
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label_column: str = "label",
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):
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"""Initialize local dataset loader.
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Args:
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local_path: Path to local dataset directory or CSV file.
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img_column: Name of the image path column.
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label_column: Name of the label column.
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"""
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self.local_path = Path(local_path)
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self.img_column = img_column
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self.label_column = label_column
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self._train_dataset: Optional[Any] = None
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self._test_dataset: Optional[Any] = None
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def _load_csv_dataset(self) -> tuple[Any, Any]:
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"""Load dataset from CSV file.
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Expected CSV format:
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label,image_path,x1,y1,x2,y2
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"class_name","path/to/image.jpg",100,200,300,400
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Returns:
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Tuple of (train_dataset, test_dataset).
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"""
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import pandas as pd
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from torch.utils.data import Dataset as TorchDataset
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# Load CSV file
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df = pd.read_csv(self.local_path)
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# Create a simple dataset class
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class CSVDataset(TorchDataset):
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def __init__(self, dataframe: pd.DataFrame, img_col: str, label_col: str):
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self.df = dataframe.reset_index(drop=True)
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self.img_col = img_col
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self.label_col = label_col
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def __len__(self) -> int:
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return len(self.df)
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def __getitem__(self, idx: int) -> dict[str, Any]:
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row = self.df.iloc[idx]
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return {
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"img": row[self.img_col],
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"label": row[self.label_col],
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}
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# Split into train/test (80/20)
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split_idx = int(len(df) * 0.8)
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train_df = df.iloc[:split_idx]
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test_df = df.iloc[split_idx:]
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self._train_dataset = CSVDataset(train_df, self.img_column, self.label_column)
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self._test_dataset = CSVDataset(test_df, self.img_column, self.label_column)
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return self._train_dataset, self._test_dataset
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def _load_directory_dataset(self) -> tuple[Any, Any]:
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"""Load dataset from directory structure.
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Expected structure:
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local_path/
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train/
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class_name_1/
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image1.jpg
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image2.jpg
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class_name_2/
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image1.jpg
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test/
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class_name_1/
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image1.jpg
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Returns:
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Tuple of (train_dataset, test_dataset).
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"""
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from torch.utils.data import Dataset as TorchDataset
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from PIL import Image
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class DirectoryDataset(TorchDataset):
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def __init__(self, root_dir: Path, transform=None):
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self.root_dir = root_dir
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self.transform = transform
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self.samples = []
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self.label_map = {}
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# Build label map
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classes = sorted([d.name for d in root_dir.iterdir() if d.is_dir()])
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self.label_map = {cls: idx for idx, cls in enumerate(classes)}
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# Build sample list
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for cls_dir in root_dir.iterdir():
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if cls_dir.is_dir():
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label = self.label_map[cls_dir.name]
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for img_path in cls_dir.iterdir():
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if img_path.suffix.lower() in [".jpg", ".jpeg", ".png", ".bmp"]:
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self.samples.append((img_path, label))
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def __len__(self) -> int:
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return len(self.samples)
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def __getitem__(self, idx: int) -> dict[str, Any]:
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img_path, label = self.samples[idx]
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image = Image.open(img_path).convert("RGB")
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return {"img": image, "label": label}
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train_dir = self.local_path / "train"
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test_dir = self.local_path / "test"
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if train_dir.exists():
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self._train_dataset = DirectoryDataset(train_dir)
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if test_dir.exists():
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self._test_dataset = DirectoryDataset(test_dir)
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return self._train_dataset, self._test_dataset
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def get_train_split(self) -> Any:
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"""Get training split of the dataset.
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Returns:
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Training dataset.
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"""
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if self._train_dataset is None:
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if self.local_path.suffix.lower() == ".csv":
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self._load_csv_dataset()
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else:
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self._load_directory_dataset()
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return self._train_dataset
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def get_test_split(self) -> Any:
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"""Get test/evaluation split of the dataset.
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Returns:
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Test dataset.
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"""
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if self._test_dataset is None:
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if self.local_path.suffix.lower() == ".csv":
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self._load_csv_dataset()
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else:
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self._load_directory_dataset()
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return self._test_dataset
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