diff --git a/.gitignore b/.gitignore index 341af49..003ca72 100644 --- a/.gitignore +++ b/.gitignore @@ -9,3 +9,5 @@ wheels/ # Virtual environments .venv artifact +.zed +wandb diff --git a/main.py b/main.py index 8c88887..976084f 100644 --- a/main.py +++ b/main.py @@ -1,44 +1,25 @@ import os -from typing import cast import torch import torch.optim as optim -import wandb -from datasets import DatasetDict, load_dataset from tqdm import tqdm -from src.dataset.preprocess import make_transform +import wandb +from src.benchmark import benchmark +from src.dataset.cuhk_cr1 import get_dataset +from src.dataset.preprocess import denormalize from src.model.utransformer import UTransformer from src.rf import RF -transform = make_transform() +device = "cuda:3" + model = UTransformer.from_pretrained_backbone( "facebook/dinov3-vits16-pretrain-lvd1689m" -).to("cuda:3") +).to(device) rf = RF(model) optimizer = optim.AdamW(model.parameters(), lr=5e-4) -dataset = cast(DatasetDict, load_dataset("your-dataset-name")) -train_dataset = dataset["train"] - -def preprocess_function(examples): - x0_list = [] - x1_list = [] - for x0_img, x1_img in zip(examples["cloudy"], examples["clear"]): - x0_transformed = transform(x0_img) - x1_transformed = transform(x1_img) - x0_list.append(x0_transformed) - x1_list.append(x1_transformed) - return {"x0": x0_list, "x1": x1_list} - - -train_dataset = train_dataset.map( - preprocess_function, - batched=True, - batch_size=32, - remove_columns=train_dataset.column_names, -) -train_dataset.set_format(type="torch", columns=["x0", "x1"]) +train_dataset, test_dataset = get_dataset() wandb.init(project="cloud-removal-kmu") @@ -47,7 +28,7 @@ if not (wandb.run and wandb.run.name): os.makedirs(f"artifact/{wandb.run.name}", exist_ok=True) -batch_size = 16 +batch_size = 32 for epoch in range(100): lossbin = {i: 0 for i in range(10)} losscnt = {i: 1e-6 for i in range(10)} @@ -58,8 +39,8 @@ for epoch in range(100): range(0, len(train_dataset), batch_size), desc=f"Epoch {epoch + 1}/100" ): batch = train_dataset[i : i + batch_size] - x0 = torch.stack(batch["x0"]).to("cuda:3") - x1 = torch.stack(batch["x1"]).to("cuda:3") + x0 = batch["x0"].to(device) + x1 = batch["x1"].to(device) optimizer.zero_grad() loss, blsct = rf.forward(x0, x1) @@ -77,6 +58,43 @@ for epoch in range(100): wandb.log(epoch_metrics) if (epoch + 1) % 10 == 0: + # bench + rf.model.eval() + psnr_sum = 0 + ssim_sum = 0 + lpips_sum = 0 + count = 0 + + with torch.no_grad(): + for i in tqdm( + range(0, len(test_dataset), batch_size), + desc=f"Benchmark {epoch + 1}/100", + ): + batch = test_dataset[i : i + batch_size] + images = rf.sample(batch["x0"].to(device)) + image = denormalize(images[-1]).clamp(0, 1) * 255 + original = denormalize(batch["x1"]).clamp(0, 1) * 255 + + psnr, ssim, lpips = benchmark(image.cpu(), original.cpu()) + psnr_sum += psnr.sum().item() + ssim_sum += ssim.sum().item() + lpips_sum += lpips.sum().item() + count += image.shape[0] + + avg_psnr = psnr_sum / count + avg_ssim = ssim_sum / count + avg_lpips = lpips_sum / count + wandb.log( + { + "eval/psnr": avg_psnr, + "eval/ssim": avg_ssim, + "eval/lpips": avg_lpips, + "epoch": epoch + 1, + } + ) + + rf.model.train() + torch.save( { "epoch": epoch, diff --git a/pyproject.toml b/pyproject.toml index cd0c921..787fbf4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,9 +8,11 @@ dependencies = [ "datasets>=4.1.1", "einops>=0.8.1", "pyright>=1.1.405", + "python-lsp-server>=1.13.1", "ruff>=0.13.2", "safetensors>=0.6.2", "torch>=2.8.0", + "torchmetrics>=1.8.2", "torchvision>=0.23.0", "tqdm>=4.67.1", "transformers>=4.56.2", diff --git a/quick_eval.py b/quick_eval.py new file mode 100644 index 0000000..cf8dd60 --- /dev/null +++ b/quick_eval.py @@ -0,0 +1,69 @@ +import os + +import torch +from torchvision.utils import save_image +from tqdm import tqdm + +from src.benchmark import benchmark +from src.dataset.cuhk_cr1 import get_dataset +from src.dataset.preprocess import denormalize +from src.model.utransformer import UTransformer +from src.rf import RF + +checkpoint_path = "artifact/wild-wave-3/checkpoint_epoch_100.pt" +device = "cuda:0" +save_dir = "test_images" + +os.makedirs(save_dir, exist_ok=True) + +model = UTransformer.from_pretrained_backbone( + "facebook/dinov3-vits16-pretrain-lvd1689m" +).to(device) + +checkpoint = torch.load(checkpoint_path, map_location=device) +model.load_state_dict(checkpoint["model_state_dict"]) + +rf = RF(model) +rf.model.eval() + +_, test_dataset = get_dataset() + +batch_size = 32 +psnr_sum = 0 +ssim_sum = 0 +lpips_sum = 0 +count = 0 +saved_count = 0 +max_save = 10 + +with torch.no_grad(): + for i in tqdm(range(0, len(test_dataset), batch_size), desc="Evaluating"): + batch = test_dataset[i : i + batch_size] + images = rf.sample(batch["x0"].to(device)) + + image = denormalize(images[-1]).clamp(0, 1) * 255 + original = denormalize(batch["x1"]).clamp(0, 1) * 255 + + if saved_count < max_save: + for j in range(min(image.shape[0], max_save - saved_count)): + save_image(image[j] / 255, f"{save_dir}/pred_{saved_count}.png") + save_image(original[j] / 255, f"{save_dir}/gt_{saved_count}.png") + save_image( + denormalize(batch["x0"][j]).clamp(0, 1), + f"{save_dir}/input_{saved_count}.png", + ) + saved_count += 1 + + psnr, ssim, lpips = benchmark(image.cpu(), original.cpu()) + psnr_sum += psnr.sum().item() + ssim_sum += ssim.sum().item() + lpips_sum += lpips.sum().item() + count += image.shape[0] + +avg_psnr = psnr_sum / count +avg_ssim = ssim_sum / count +avg_lpips = lpips_sum / count + +print(f"PSNR: {avg_psnr:.4f}") +print(f"SSIM: {avg_ssim:.4f}") +print(f"LPIPS: {avg_lpips:.4f}") diff --git a/src/benchmark/__init__.py b/src/benchmark/__init__.py new file mode 100644 index 0000000..35ce9ee --- /dev/null +++ b/src/benchmark/__init__.py @@ -0,0 +1,13 @@ +from torchmetrics.image import ( + LearnedPerceptualImagePatchSimilarity, + PeakSignalNoiseRatio, + StructuralSimilarityIndexMeasure, +) + +psnr = PeakSignalNoiseRatio(255.0, reduction="none") +ssim = StructuralSimilarityIndexMeasure(reduction="none") +lpips = LearnedPerceptualImagePatchSimilarity(net_type="alex", reduction="none") + + +def benchmark(image1, image2): + return psnr(image1, image2), ssim(image1, image2), lpips(image1, image2) diff --git a/src/dataset/cuhk_cr1.py b/src/dataset/cuhk_cr1.py new file mode 100644 index 0000000..7e9168f --- /dev/null +++ b/src/dataset/cuhk_cr1.py @@ -0,0 +1,66 @@ +from pathlib import Path + +from datasets import Dataset, DatasetDict, Image + +from src.dataset.preprocess import make_transform + +transform = make_transform(512) + + +def get_dataset() -> tuple[Dataset, Dataset]: + data_dir = Path("/data2/C-CUHK/CUHK-CR1") + + train_cloud = sorted((data_dir / "train/cloud").glob("*.png")) + train_no_cloud = sorted((data_dir / "train/label").glob("*.png")) + test_cloud = sorted((data_dir / "test/cloud").glob("*.png")) + test_no_cloud = sorted((data_dir / "test/label").glob("*.png")) + + dataset = DatasetDict( + { + "train": Dataset.from_dict( + { + "cloud": [str(p) for p in train_cloud], + "label": [str(p) for p in train_no_cloud], + } + ) + .cast_column("cloud", Image()) + .cast_column("label", Image()), + "test": Dataset.from_dict( + { + "cloud": [str(p) for p in test_cloud], + "label": [str(p) for p in test_no_cloud], + } + ) + .cast_column("cloud", Image()) + .cast_column("label", Image()), + } + ) + train_dataset = dataset["train"] + train_dataset = train_dataset.map( + preprocess_function, + batched=True, + batch_size=32, + remove_columns=train_dataset.column_names, + ) + train_dataset.set_format(type="torch", columns=["x0", "x1"]) + test_dataset = dataset["test"] + test_dataset = test_dataset.map( + preprocess_function, + batched=True, + batch_size=32, + remove_columns=test_dataset.column_names, + ) + test_dataset.set_format(type="torch", columns=["x0", "x1"]) + + return train_dataset, test_dataset + + +def preprocess_function(examples): + x0_list = [] + x1_list = [] + for x0_img, x1_img in zip(examples["cloud"], examples["label"]): + x0_transformed = transform(x0_img) + x1_transformed = transform(x1_img) + x0_list.append(x0_transformed) + x1_list.append(x1_transformed) + return {"x0": x0_list, "x1": x1_list} diff --git a/src/dataset/preprocess.py b/src/dataset/preprocess.py index 2f121a8..781d98f 100644 --- a/src/dataset/preprocess.py +++ b/src/dataset/preprocess.py @@ -12,3 +12,9 @@ def make_transform(resize_size: int = 256): std=(0.229, 0.224, 0.225), ) return v2.Compose([to_tensor, resize, to_float, normalize]) + + +def denormalize(tensor): + mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1).to(tensor.device) + std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1).to(tensor.device) + return tensor * std + mean diff --git a/test_images/gt_0.png b/test_images/gt_0.png new file mode 100644 index 0000000..39ea38a Binary files /dev/null and b/test_images/gt_0.png differ diff --git a/test_images/gt_1.png b/test_images/gt_1.png 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