File size: 3,335 Bytes
6ca8932 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | # Low-Light Restoration Dataset
Paired low-light / normal-light image patches for training and evaluation.
## Contents of `low-light.tar`
```
low-light/
├── train/ 30,000 paired patches (60,000 files)
├── val/ 1,000 paired patches (2,000 files)
├── test/ 1,000 input patches (no ground truth)
└── dataset.py reference PyTorch Dataset (Python 3.10+)
```
All images are **lossless WebP** (`.webp`).
### File naming
Every image is named `<id>-<role>.webp` where `role ∈ {in, gt}`:
| Split | Files |
|-------|-------|
| `train/` | `<id>-in.webp` paired with `<id>-gt.webp` (30,000 pairs) |
| `val/` | `<id>-in.webp` paired with `<id>-gt.webp` (1,000 pairs) |
| `test/` | `<id>-in.webp` only — **no GT is provided** |
`<id>` is opaque; do not parse it. Pairing is by exact stem match.
## Quick start
```python
from pathlib import Path
from torch.utils.data import DataLoader
from dataset import (
PairedLowLightDataset, TestLowLightDataset,
PairedCompose, PairedRandomCrop, PairedRandomFlip, PairedToTensor,
)
root = Path("low-light")
train_tf = PairedCompose([
PairedRandomCrop(256),
PairedRandomFlip(p_h=0.5),
PairedToTensor(),
])
train_set = PairedLowLightDataset(root / "train", transform=train_tf)
val_set = PairedLowLightDataset(root / "val", transform=None)
test_set = TestLowLightDataset(root / "test", transform=None)
train_loader = DataLoader(train_set, batch_size=16, shuffle=True, num_workers=4)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False, num_workers=2)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False, num_workers=2)
for x, y in train_loader: # x, y are float32 CHW tensors in [0, 1]
...
for x, stem in test_loader: # test yields (input, stem)
pred = model(x)
save_image(pred, f"submission/{stem[0]}-in.webp")
```
## Dataset classes (in `dataset.py`)
### `PairedLowLightDataset(root, transform=None)`
For `train/` and `val/`. Returns `(input_tensor, gt_tensor)`.
### `TestLowLightDataset(root, transform=None)`
For `test/`. Returns `(input_tensor, stem)` where `stem` lets you save predictions
under the original filename.
## Transform contract
A transform may be one of:
1. **`None`** — images are converted to `float32` CHW tensors in `[0, 1]`.
2. **A single-image torchvision-style callable** `fn(pil) -> tensor` — applied
independently to input and GT. **Use only for deterministic ops**
(`ToTensor`, `Normalize`). Random single-image transforms will desync the pair.
3. **A pair-aware callable** `fn(in_pil, gt_pil) -> (in_tensor, gt_tensor)`,
marked by setting `fn.paired = True`. The callable owns randomness and must
apply the same geometric augmentation to both images.
The provided `PairedCompose`, `PairedRandomCrop`, `PairedRandomFlip`,
`PairedToTensor` building blocks already follow contract #3.
## Submission format
For each `<id>-in.webp` in `test/`, produce a restored image and save it as
`<id>-in.webp` (or `.png` if preferred). Keep the original stem.
Evaluation pairs each prediction against the private ground truth held by the
organizers — do **not** attempt to obtain or infer test GTs.
## Requirements
- Python 3.10+
- `torch`, `torchvision`, `Pillow` (with WebP support; built into modern Pillow)
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