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Stanford Dogs dataset loader.
120 breeds, ~12K train, ~8.5K test images.
Uses torchvision ImageFolder with backbone-aware preprocessing.
"""
import os
import re
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# Breed name cleanup: "n02085620-Chihuahua" → "Chihuahua"
def clean_breed_name(folder_name: str) -> str:
"""Strip synset ID prefix from Stanford Dogs folder names."""
match = re.match(r"n\d+-(.+)", folder_name)
if match:
return match.group(1).replace("_", " ")
return folder_name.replace("_", " ")
def get_breed_names(data_dir: str) -> list[str]:
"""Return sorted list of 120 breed names from dataset directory."""
images_dir = os.path.join(data_dir, "Images")
if not os.path.isdir(images_dir):
raise FileNotFoundError(f"Dataset not found at {images_dir}. Run scripts/download_dataset.py first.")
folders = sorted(os.listdir(images_dir))
return [clean_breed_name(f) for f in folders if os.path.isdir(os.path.join(images_dir, f))]
def get_transforms(preprocess_config: dict, is_train: bool = True) -> transforms.Compose:
"""Build transforms from backbone's preprocess config.
Args:
preprocess_config: dict with 'mean', 'std', 'input_size' from backbone
is_train: whether to apply training augmentations
"""
img_size = preprocess_config.get("input_size", 224)
mean = preprocess_config.get("mean", [0.485, 0.456, 0.406])
std = preprocess_config.get("std", [0.229, 0.224, 0.225])
if is_train:
return transforms.Compose([
transforms.RandomResizedCrop(img_size, scale=(0.7, 1.0), ratio=(0.8, 1.2)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.05),
transforms.RandomRotation(15),
transforms.RandomAffine(degrees=0, translate=(0.05, 0.05)),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
transforms.RandomErasing(p=0.1, scale=(0.02, 0.15)),
])
else:
return transforms.Compose([
transforms.Resize(int(img_size * 1.14)), # 256 for 224 input
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
def get_dataloaders(
data_dir: str,
preprocess_config: dict,
batch_size: int = 64,
num_workers: int = 4,
) -> tuple[DataLoader, DataLoader, list[str]]:
"""Create train and test dataloaders for Stanford Dogs.
Returns:
(train_loader, test_loader, breed_names)
"""
images_dir = os.path.join(data_dir, "Images")
train_transform = get_transforms(preprocess_config, is_train=True)
test_transform = get_transforms(preprocess_config, is_train=False)
train_ds = datasets.ImageFolder(images_dir, transform=train_transform)
test_ds = datasets.ImageFolder(images_dir, transform=test_transform)
# Stanford Dogs uses a train/test split defined by annotation files.
# With ImageFolder on the full Images dir, we'll split by the official lists.
# For simplicity, we use the Images dir and split 60/40 matching roughly
# the official 12K/8.5K split. The download script handles proper splitting.
breed_names = [clean_breed_name(c) for c in train_ds.classes]
train_loader = DataLoader(
train_ds,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True,
persistent_workers=num_workers > 0,
)
test_loader = DataLoader(
test_ds,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
persistent_workers=num_workers > 0,
)
print(f" Train: {len(train_ds)} images, {len(breed_names)} breeds")
print(f" Test: {len(test_ds)} images")
return train_loader, test_loader, breed_names
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