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62 lines
2.3 KiB
62 lines
2.3 KiB
import torch
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from torch.utils.data import Dataset, DataLoader
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from torchvision import transforms
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from PIL import Image
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import os
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import random
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from pathlib import Path
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# Define the transformations
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transform = transforms.Compose([
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transforms.RandomRotation(degrees=10),
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transforms.Resize((512, 512)),
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transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
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transforms.ToTensor(),
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])
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class CustomDataset(Dataset):
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def __init__(self, root_dir, transform=None):
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self.root_dir = root_dir
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self.transform = transform
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self.image_folders = [folder for folder in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, folder))]
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def __len__(self):
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return len(self.image_folders)
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def __getitem__(self, idx):
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folder_name = self.image_folders[idx]
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folder_path = os.path.join(self.root_dir, folder_name)
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# # Get the list of image filenames in the folder
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image_filenames = os.listdir(folder_path)
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# Pick two random assets from the folder
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source_image_name, target_image_name = random.sample(image_filenames, 2)
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source_age = int(Path(source_image_name).stem) / 100
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target_age = int(Path(target_image_name).stem) / 100
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# Randomly select two assets from the folder
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source_image_path = os.path.join(folder_path, source_image_name)
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target_image_path = os.path.join(folder_path, target_image_name)
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source_image = Image.open(source_image_path).convert('RGB')
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target_image = Image.open(target_image_path).convert('RGB')
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# Apply the same random crop and augmentations to both assets
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if self.transform:
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seed = torch.randint(0, 2 ** 32 - 1, (1,)).item()
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torch.manual_seed(seed)
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source_image = self.transform(source_image)
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torch.manual_seed(seed)
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target_image = self.transform(target_image)
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source_age_channel = torch.full_like(source_image[:1, :, :], source_age)
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target_age_channel = torch.full_like(source_image[:1, :, :], target_age)
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# Concatenate the age channels with the source_image
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source_image = torch.cat([source_image, source_age_channel, target_age_channel], dim=0)
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return source_image, target_image
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