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f846a93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | from __future__ import annotations
from typing import Any
import pandas as pd
import torch
from torch.utils.data import Dataset, WeightedRandomSampler
from .data_discovery import LABEL_TO_ID
from .preprocessing import load_pil_image
class EggImageDataset(Dataset):
def __init__(self, dataframe: pd.DataFrame, transform: Any | None = None) -> None:
self.df = dataframe.reset_index(drop=True)
self.transform = transform
def __len__(self) -> int:
return len(self.df)
def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor, str]:
row = self.df.iloc[index]
image = load_pil_image(row["filepath"], mode="RGB")
if self.transform:
image_tensor = self.transform(image)
else:
from torchvision import transforms
image_tensor = transforms.ToTensor()(image)
label = int(row.get("label_id", LABEL_TO_ID[row["label"]]))
return image_tensor, torch.tensor(label, dtype=torch.long), str(row["filepath"])
def create_balanced_sampler(dataframe: pd.DataFrame, seed: int) -> WeightedRandomSampler:
labels = dataframe["label_id"].astype(int).tolist()
counts = dataframe["label_id"].value_counts().to_dict()
weights = torch.DoubleTensor([1.0 / counts[label] for label in labels])
num_samples = int(max(counts.values()) * len(counts))
generator = torch.Generator()
generator.manual_seed(seed)
return WeightedRandomSampler(weights, num_samples=num_samples, replacement=True, generator=generator)
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