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import os
import logging
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from src import config
def get_transforms(image_size=config.IMAGE_SIZE):
train_transforms = transforms.Compose([
transforms.Resize((image_size, image_size)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(config.DATA_AUG_ROTATION),
transforms.ColorJitter(
brightness=config.DATA_AUG_COLOR_JITTER,
contrast=config.DATA_AUG_COLOR_JITTER,
saturation=config.DATA_AUG_COLOR_JITTER,
hue=config.DATA_AUG_COLOR_JITTER
),
transforms.RandomAffine(degrees=0, translate=(config.DATA_AUG_TRANSLATE, config.DATA_AUG_TRANSLATE)),
transforms.RandomResizedCrop(image_size, scale=config.DATA_AUG_SCALE),
transforms.ToTensor(),
transforms.Normalize([0.5]*3, [0.5]*3)
])
val_test_transforms = transforms.Compose([
transforms.Resize((image_size, image_size)),
transforms.ToTensor(),
transforms.Normalize([0.5]*3, [0.5]*3)
])
return train_transforms, val_test_transforms
def get_dataloaders(data_dir, batch_size=config.BATCH_SIZE, image_size=config.IMAGE_SIZE, num_workers=config.NUM_WORKERS):
train_transforms, val_test_transforms = get_transforms(image_size)
train_dir = os.path.join(data_dir, 'train')
val_dir = os.path.join(data_dir, 'val')
test_dir = os.path.join(data_dir, 'test')
logging.info(f"Loading datasets from: {data_dir}")
logging.info(f"Train directory: {train_dir}")
logging.info(f"Validation directory: {val_dir}")
logging.info(f"Test directory: {test_dir}")
train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms)
val_dataset = datasets.ImageFolder(val_dir, transform=val_test_transforms)
test_dataset = datasets.ImageFolder(test_dir, transform=val_test_transforms)
logging.info(f"Train dataset size: {len(train_dataset)}")
logging.info(f"Validation dataset size: {len(val_dataset)}")
logging.info(f"Test dataset size: {len(test_dataset)}")
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
class_names = train_dataset.classes
logging.info(f"Classes: {class_names}")
return train_loader, val_loader, test_loader, class_names
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