Johnny Fernandes
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
import random
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from pathlib import Path
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from PIL import Image
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import torchvision.transforms as T
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from torch.utils.data import Dataset
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class GeneratorDataset(Dataset):
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"""Unlabeled image dataset for generative model training.
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Loads images from source subdirectories and returns tensors only —
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no labels, since generation is unsupervised.
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"""
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def __init__(self, data_dir, sources=None, subsample=1.0, transform=None, seed=42):
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self.transform = transform
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self.samples = []
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# Accept either a single root or a list of roots (used by 1D to mix
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# raw + aligned crops in one dataset).
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roots = [data_dir] if isinstance(data_dir, (str, Path)) else list(data_dir)
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if sources is None:
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sources = ["wiki"]
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for root in roots:
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root = Path(root)
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if not root.exists():
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raise FileNotFoundError(f"Dataset root not found: {root}")
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for source in sources:
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source_dir = root / source
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if not source_dir.exists():
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raise FileNotFoundError(f"Missing source directory: {source_dir}")
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for subdir in sorted(source_dir.iterdir()):
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if subdir.is_dir():
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for img_path in sorted(subdir.glob("*.jpg")):
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self.samples.append(img_path)
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if subsample < 1.0:
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rng = random.Random(seed)
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n = max(1, int(len(self.samples) * subsample))
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self.samples = rng.sample(self.samples, n)
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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img = Image.open(self.samples[idx]).convert("RGB")
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if self.transform:
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img = self.transform(img)
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return img
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def get_transform(image_size: int, augment: bool = False) -> T.Compose:
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"""Build transform for generator training. Output is in [-1, 1].
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augment=True adds horizontal flip + mild rotation + mild color jitter.
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Use augment=False for validation / FID real-image sets.
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"""
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ops = [
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T.Resize(image_size),
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T.CenterCrop(image_size),
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]
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if augment:
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ops += [
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T.RandomHorizontalFlip(p=0.5),
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T.RandomRotation(degrees=5, interpolation=T.InterpolationMode.BILINEAR),
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T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.05),
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]
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ops += [
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T.ToTensor(),
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T.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), # -> [-1, 1]
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]
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return T.Compose(ops)
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