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import torch
import torch.nn as nn
import os
import random
import math
from PIL import Image
import torchvision.transforms.functional as TF
import torch.nn.functional as F
from config import (
patch_size, scale, dir_HR, dir_LR, dir_valid_HR, dir_valid_LR,
_project_root, device, gt_size
)
from realesrgan import RealESRGANDegrader
from autoencoder import get_vqgan
# Initialize degradation pipeline and VQGAN (lazy loading)
_degrader = None
_vqgan = None
def get_degrader():
"""Get or create degradation pipeline."""
global _degrader
if _degrader is None:
_degrader = RealESRGANDegrader(scale=scale)
return _degrader
def get_vqgan_model():
"""Get or create VQGAN model."""
global _vqgan
if _vqgan is None:
_vqgan = get_vqgan(device=device)
return _vqgan
class SRDatasetOnTheFly(torch.utils.data.Dataset):
"""
PyTorch Dataset for on-the-fly degradation and VQGAN encoding.
This dataset:
1. Loads full HR images
2. Crops 256x256 patches on-the-fly
3. Applies RealESRGAN degradation to generate LR
4. Upsamples LR to 256x256 using bicubic
5. Encodes both HR and LR through VQGAN to get 64x64 latents
Args:
dir_HR (str): Directory path containing high-resolution images.
scale (int, optional): Super-resolution scale factor. Defaults to config.scale (4).
patch_size (int, optional): Size of patches. Defaults to config.patch_size (256).
max_samples (int, optional): Maximum number of images to load. If None, loads all.
Returns:
tuple: (hr_latent, lr_latent) where both are torch.Tensor of shape (C, 64, 64)
representing VQGAN-encoded latents.
"""
def __init__(self, dir_HR, scale=scale, patch_size=patch_size, max_samples=None):
super().__init__()
self.dir_HR = dir_HR
self.scale = scale
self.patch_size = patch_size
# Get all image files
self.filenames = sorted([
f for f in os.listdir(self.dir_HR)
if f.lower().endswith(('.png', '.jpg', '.jpeg'))
])
# Limit to max_samples if specified
if max_samples is not None:
self.filenames = self.filenames[:max_samples]
# Initialize degradation and VQGAN (will be loaded on first use)
self.degrader = None
self.vqgan = None
def __len__(self):
return len(self.filenames)
def _load_image(self, img_path):
"""Load and validate image."""
img = Image.open(img_path).convert("RGB")
img_tensor = TF.to_tensor(img) # (C, H, W) in range [0, 1]
return img_tensor
def _crop_patch(self, img_tensor, patch_size):
"""
Crop a random patch from image.
Args:
img_tensor: (C, H, W) tensor
patch_size: Size of patch to crop
Returns:
patch: (C, patch_size, patch_size) tensor
"""
C, H, W = img_tensor.shape
# Pad if image is smaller than patch_size
if H < patch_size or W < patch_size:
pad_h = max(0, patch_size - H)
pad_w = max(0, patch_size - W)
img_tensor = F.pad(img_tensor, (0, pad_w, 0, pad_h), mode='reflect')
H, W = img_tensor.shape[1], img_tensor.shape[2]
# Random crop
top = random.randint(0, max(0, H - patch_size))
left = random.randint(0, max(0, W - patch_size))
patch = img_tensor[:, top:top+patch_size, left:left+patch_size]
return patch
def _apply_augmentations(self, hr, lr):
"""
Apply synchronized augmentations to HR and LR.
Args:
hr: (C, H, W) HR tensor
lr: (C, H, W) LR tensor
Returns:
hr_aug, lr_aug: Augmented tensors
"""
# Horizontal flip
if random.random() < 0.5:
hr = torch.flip(hr, dims=[2])
lr = torch.flip(lr, dims=[2])
# Vertical flip
if random.random() < 0.5:
hr = torch.flip(hr, dims=[1])
lr = torch.flip(lr, dims=[1])
# 180° rotation
if random.random() < 0.5:
hr = torch.rot90(hr, k=2, dims=[1, 2])
lr = torch.rot90(lr, k=2, dims=[1, 2])
return hr, lr
def __getitem__(self, idx):
# Load HR image
hr_path = os.path.join(self.dir_HR, self.filenames[idx])
hr_full = self._load_image(hr_path) # (C, H, W) in [0, 1]
# Crop 256x256 patch from HR
hr_patch = self._crop_patch(hr_full, self.patch_size) # (C, 256, 256)
# Initialize degrader and VQGAN on first use
if self.degrader is None:
self.degrader = get_degrader()
if self.vqgan is None:
self.vqgan = get_vqgan_model()
# Apply degradation on-the-fly to generate LR
# Degrader expects (C, H, W) and returns (C, H//scale, W//scale)
hr_patch_gpu = hr_patch.to(device) # (C, 256, 256)
with torch.no_grad():
lr_patch = self.degrader.degrade(hr_patch_gpu) # (C, 64, 64) in pixel space
# Upsample LR to 256x256 using bicubic interpolation
lr_patch_upsampled = F.interpolate(
lr_patch.unsqueeze(0), # (1, C, 64, 64)
size=(self.patch_size, self.patch_size),
mode='bicubic',
align_corners=False
).squeeze(0) # (C, 256, 256)
# Apply augmentations (synchronized)
hr_patch, lr_patch_upsampled = self._apply_augmentations(
hr_patch.cpu(),
lr_patch_upsampled.cpu()
)
# Encode through VQGAN to get latents (64x64)
# Move to device for encoding
hr_patch_gpu = hr_patch.to(device).unsqueeze(0) # (1, C, 256, 256)
lr_patch_gpu = lr_patch_upsampled.to(device).unsqueeze(0) # (1, C, 256, 256)
with torch.no_grad():
# Encode HR: 256x256 -> 64x64 latent
hr_latent = self.vqgan.encode(hr_patch_gpu) # (1, C, 64, 64)
# Encode LR: 256x256 -> 64x64 latent
lr_latent = self.vqgan.encode(lr_patch_gpu) # (1, C, 64, 64)
# Remove batch dimension and move to CPU
hr_latent = hr_latent.squeeze(0).cpu() # (C, 64, 64)
lr_latent = lr_latent.squeeze(0).cpu() # (C, 64, 64)
return hr_latent, lr_latent
# Create datasets using on-the-fly processing
train_dataset = SRDatasetOnTheFly(
dir_HR=dir_HR,
scale=scale,
patch_size=patch_size
)
valid_dataset = SRDatasetOnTheFly(
dir_HR=dir_valid_HR,
scale=scale,
patch_size=patch_size
)
# Mini dataset with 8 images for testing
mini_dataset = SRDatasetOnTheFly(
dir_HR=dir_HR,
scale=scale,
patch_size=patch_size,
max_samples=8
)
print(f"\nFull training dataset size: {len(train_dataset)}")
print(f"Full validation dataset size: {len(valid_dataset)}")
print(f"Mini dataset size: {len(mini_dataset)}")
print(f"Using on-the-fly degradation and VQGAN encoding")
print(f"Output: 64x64 latents (from 256x256 patches)")
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