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import torch |
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import pandas as pd |
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from torch.utils.data import Dataset, DataLoader |
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from PIL import Image |
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import io |
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from pathlib import Path |
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from torchvision import transforms |
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from typing import Optional, Callable, Tuple, Union |
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class ImageNet100Parquet(Dataset): |
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def __init__(self, data_dir: str, split: str = "train", |
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transform: Optional[Callable] = None) -> None: |
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""" |
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Initialize ImageNet-100 dataset from parquet files. |
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Args: |
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data_dir: Path to directory containing parquet files. |
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split: Dataset split - either "train" or "validation". |
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transform: Optional torchvision transforms to apply to images. |
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Raises: |
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FileNotFoundError: If data directory doesn't exist. |
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ValueError: If no parquet files found for the specified split. |
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""" |
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self.data_dir = Path(data_dir) |
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self.transform = transform |
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if not self.data_dir.exists(): |
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raise FileNotFoundError( |
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f"Data directory not found: {self.data_dir}") |
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valid_splits = ["train", "validation"] |
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if split not in valid_splits: |
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raise ValueError( |
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f"Invalid split '{split}'. Must be one of: {valid_splits}") |
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parquet_files = sorted(self.data_dir.glob(f"{split}-*.parquet")) |
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if not parquet_files: |
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raise ValueError( |
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f"No parquet files found for split '{split}' in {self.data_dir}") |
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try: |
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self.data = pd.concat([pd.read_parquet(f) |
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for f in parquet_files], ignore_index=True) |
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except Exception as e: |
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raise RuntimeError(f"Failed to load parquet files: {e}") |
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def __len__(self) -> int: |
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"""Return the total number of samples in the dataset.""" |
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return len(self.data) |
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def __getitem__(self, idx: int) -> Tuple[Union[Image.Image, torch.Tensor], torch.Tensor]: |
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""" |
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Get a sample from the dataset at the given index. |
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Args: |
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idx: Index of the sample to retrieve. |
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Returns: |
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Tuple of (image, label) where image is a PIL Image or transformed tensor, |
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and label is a torch tensor with dtype long. |
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Raises: |
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IndexError: If idx is out of range. |
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RuntimeError: If image decoding fails. |
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""" |
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if idx >= len(self.data): |
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raise IndexError( |
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f"Index {idx} out of range for dataset of size {len(self.data)}") |
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try: |
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row = self.data.iloc[idx] |
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image_bytes = row['image']['bytes'] |
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image = Image.open(io.BytesIO(image_bytes)).convert('RGB') |
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label = torch.tensor(row['label'], dtype=torch.long) |
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if self.transform: |
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image = self.transform(image) |
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return image, label |
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except Exception as e: |
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raise RuntimeError(f"Failed to load sample at index {idx}: {e}") |
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def main() -> None: |
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data_dir = "../data" |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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]) |
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train_dataset = ImageNet100Parquet(data_dir, "train", transform) |
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test_dataset = ImageNet100Parquet(data_dir, "validation", transform) |
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print(f"Train dataset size: {len(train_dataset)}") |
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print(f"Test dataset size: {len(test_dataset)}") |
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train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True) |
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test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False) |
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print("\nTesting train loader iteration...") |
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stop_idx = 0 |
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for x, y_true in train_loader: |
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print(f"Batch shape: {x.shape}, Labels shape: {y_true.shape}") |
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stop_idx += 1 |
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if stop_idx > 10: |
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break |
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stop_idx = 0 |
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print("\nTesting test loader iteration...") |
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for x, y_true in test_loader: |
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print(f"Batch shape: {x.shape}, Labels shape: {y_true.shape}") |
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stop_idx += 1 |
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if stop_idx > 10: |
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break |
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if __name__ == "__main__": |
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main() |
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