test

.gitignore

@@ -9,3 +9,5 @@ wheels/
 # Virtual environments
 .venv
 artifact
+.zed
+wandb

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"]
 
-
+train_dataset, test_dataset = get_dataset()
-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"])
 
 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")
+        x0 = batch["x0"].to(device)
-        x1 = torch.stack(batch["x1"]).to("cuda:3")
+        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,

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",

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}")

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)

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}

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

uv.lock

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 ]
 
+[[package]]
+name = "black"
+version = "25.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "mypy-extensions" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
+    { name = "pytokens" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4b/43/20b5c90612d7bdb2bdbcceeb53d588acca3bb8f0e4c5d5c751a2c8fdd55a/black-25.9.0.tar.gz", hash = "sha256:0474bca9a0dd1b51791fcc507a4e02078a1c63f6d4e4ae5544b9848c7adfb619", size = 648393, upload-time = "2025-09-19T00:27:37.758Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/94/cc/f562fe5d0a40cd2a4e6ae3f685e4c36e365b1f7e494af99c26ff7f28117f/black-25.9.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8e46eecf65a095fa62e53245ae2795c90bdecabd53b50c448d0a8bcd0d2e74c4", size = 1581421, upload-time = "2025-09-19T00:35:25.937Z" },
+    { url = "https://files.pythonhosted.org/packages/84/67/6db6dff1ebc8965fd7661498aea0da5d7301074b85bba8606a28f47ede4d/black-25.9.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9101ee58ddc2442199a25cb648d46ba22cd580b00ca4b44234a324e3ec7a0f7e", size = 1655619, upload-time = "2025-09-19T00:30:49.241Z" },
+    { url = "https://files.pythonhosted.org/packages/10/10/3faef9aa2a730306cf469d76f7f155a8cc1f66e74781298df0ba31f8b4c8/black-25.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:77e7060a00c5ec4b3367c55f39cf9b06e68965a4f2e61cecacd6d0d9b7ec945a", size = 1342481, upload-time = "2025-09-19T00:31:29.625Z" },
+    { url = "https://files.pythonhosted.org/packages/48/99/3acfea65f5e79f45472c45f87ec13037b506522719cd9d4ac86484ff51ac/black-25.9.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0172a012f725b792c358d57fe7b6b6e8e67375dd157f64fa7a3097b3ed3e2175", size = 1742165, upload-time = "2025-09-19T00:34:10.402Z" },
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+    { url = "https://files.pythonhosted.org/packages/f1/ce/883ec4b6303acdeca93ee06b7622f1fa383c6b3765294824165d49b1a86b/black-25.9.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b756fc75871cb1bcac5499552d771822fd9db5a2bb8db2a7247936ca48f39831", size = 1655583, upload-time = "2025-09-19T00:30:44.505Z" },
+    { url = "https://files.pythonhosted.org/packages/21/17/5c253aa80a0639ccc427a5c7144534b661505ae2b5a10b77ebe13fa25334/black-25.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:846d58e3ce7879ec1ffe816bb9df6d006cd9590515ed5d17db14e17666b2b357", size = 1343428, upload-time = "2025-09-19T00:32:13.839Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/46/863c90dcd3f9d41b109b7f19032ae0db021f0b2a81482ba0a1e28c84de86/black-25.9.0-py3-none-any.whl", hash = "sha256:474b34c1342cdc157d307b56c4c65bce916480c4a8f6551fdc6bf9b486a7c4ae", size = 203363, upload-time = "2025-09-19T00:27:35.724Z" },
+]
+
 [[package]]
 name = "certifi"
 version = "2025.8.3"
@@ -168,9 +193,11 @@ dependencies = [
     { name = "datasets" },
     { name = "einops" },
     { name = "pyright" },
+    { name = "python-lsp-server" },
     { name = "ruff" },
     { name = "safetensors" },
     { name = "torch" },
+    { name = "torchmetrics" },
     { name = "torchvision" },
     { name = "tqdm" },
     { name = "transformers" },
@@ -182,9 +209,11 @@ requires-dist = [
     { name = "datasets", specifier = ">=4.1.1" },
     { name = "einops", specifier = ">=0.8.1" },
     { name = "pyright", specifier = ">=1.1.405" },
+    { name = "python-lsp-server", specifier = ">=1.13.1" },
     { name = "ruff", specifier = ">=0.13.2" },
     { name = "safetensors", specifier = ">=0.6.2" },
     { name = "torch", specifier = ">=2.8.0" },
+    { name = "torchmetrics", specifier = ">=1.8.2" },
     { name = "torchvision", specifier = ">=0.23.0" },
     { name = "tqdm", specifier = ">=4.67.1" },
     { name = "transformers", specifier = ">=4.56.2" },
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 ]
 
+[[package]]
+name = "docstring-to-markdown"
+version = "0.17"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "importlib-metadata" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/52/d8/8abe80d62c5dce1075578031bcfde07e735bcf0afe2886dd48b470162ab4/docstring_to_markdown-0.17.tar.gz", hash = "sha256:df72a112294c7492487c9da2451cae0faeee06e86008245c188c5761c9590ca3", size = 32260, upload-time = "2025-05-02T15:09:07.932Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/56/7b/af3d0da15bed3a8665419bb3a630585756920f4ad67abfdfef26240ebcc0/docstring_to_markdown-0.17-py3-none-any.whl", hash = "sha256:fd7d5094aa83943bf5f9e1a13701866b7c452eac19765380dead666e36d3711c", size = 23479, upload-time = "2025-05-02T15:09:06.676Z" },
+]
+
 [[package]]
 name = "einops"
 version = "0.8.1"
@@ -392,6 +434,30 @@ wheels = [
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 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "8.7.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "zipp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641, upload-time = "2025-04-27T15:29:01.736Z" }
+wheels = [
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+]
+
+[[package]]
+name = "jedi"
+version = "0.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "parso" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" }
+wheels = [
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+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -404,6 +470,20 @@ wheels = [
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 ]
 
+[[package]]
+name = "lightning-utilities"
+version = "0.15.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+    { name = "setuptools" },
+    { name = "typing-extensions" },
+]
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