fix wrong average of psnr

main.py

@@ -2,16 +2,17 @@ import os
 
 import torch
 import torch.optim as optim
+from torchvision.utils import make_grid
 from tqdm import tqdm
 
 import wandb
 from src.benchmark import benchmark
-from src.dataset.cuhk_cr1 import get_dataset
+from src.dataset.cuhk_cr2 import get_dataset
 from src.dataset.preprocess import denormalize
 from src.model.utransformer import UTransformer
 from src.rf import RF
 
-device = "cuda:2"
+device = "cuda:1"
 
 model = UTransformer.from_pretrained_backbone(
     "facebook/dinov3-vitl16-pretrain-sat493m"
@@ -21,7 +22,7 @@ optimizer = optim.AdamW(model.parameters(), lr=1e-4)
 
 train_dataset, test_dataset = get_dataset()
 
-wandb.init(project="cloud-removal-kmu", id="icy-field-12", resume="allow")
+wandb.init(project="cloud-removal-kmu", id="dashing-moon-31", resume="allow")
 
 if not (wandb.run and wandb.run.name):
     raise Exception("nope")
@@ -36,7 +37,7 @@ if os.path.exists(checkpoint_path):
     optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
     start_epoch = checkpoint["epoch"] + 1
 
-batch_size = 4
+batch_size = 8
 accumulation_steps = 8
 total_epoch = 1000
 for epoch in range(start_epoch, total_epoch):
@@ -89,10 +90,20 @@ for epoch in range(start_epoch, total_epoch):
                 desc=f"Benchmark {epoch + 1}/{total_epoch}",
             ):
                 batch = test_dataset[i : i + batch_size]
-                images = rf.sample(batch["cloud"].to(device))
+                images = rf.sample_heun(batch["cloud"].to(device))
                 image = denormalize(images[-1]).clamp(0, 1)
                 original = denormalize(batch["gt"]).clamp(0, 1)
 
+                if i == 0:
+                    for step, demo in enumerate([images[0], images[-1]]):
+                        images = wandb.Image(
+                            make_grid(
+                                denormalize(demo).clamp(0, 1).float()[:4], nrow=2
+                            ),
+                            caption=f"step {step}",
+                        )
+                        wandb.log({"viz/decoded": images})
+
                 psnr, ssim, lpips = benchmark(image.cpu(), original.cpu())
                 psnr_sum += psnr.sum().item()
                 ssim_sum += ssim.sum().item()

quick_eval.py

@@ -10,8 +10,8 @@ from src.dataset.preprocess import denormalize
 from src.model.utransformer import UTransformer
 from src.rf import RF
 
-checkpoint_path = "artifact/icy-field-12/checkpoint_epoch_260.pt"
+checkpoint_path = "artifact/daily-forest-25/checkpoint_final.pt"
-device = "cuda:2"
+device = "cuda:1"
 save_dir = "test_images"
 
 os.makedirs(save_dir, exist_ok=True)
@@ -28,7 +28,7 @@ rf.model.eval()
 
 _, test_dataset = get_dataset()
 
-batch_size = 1
+batch_size = 8
 psnr_sum = 0
 ssim_sum = 0
 lpips_sum = 0
@@ -39,7 +39,7 @@ 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["cloud"].to(device))
+        images = rf.sample_heun(batch["cloud"].to(device), 1)
 
         image = denormalize(images[-1]).clamp(0, 1)
         original = denormalize(batch["gt"]).clamp(0, 1)
@@ -49,12 +49,13 @@ with torch.no_grad():
                 save_image(image[j], f"{save_dir}/pred_{saved_count}.png")
                 save_image(original[j], f"{save_dir}/gt_{saved_count}.png")
                 save_image(
-                    denormalize(batch["x0"][j]).clamp(0, 1),
+                    denormalize(batch["cloud"][j]).clamp(0, 1),
                     f"{save_dir}/input_{saved_count}.png",
                 )
                 saved_count += 1
 
         psnr, ssim, lpips = benchmark(image.cpu(), original.cpu())
+        print(psnr, ssim, lpips)
         psnr_sum += psnr.sum().item()
         ssim_sum += ssim.sum().item()
         lpips_sum += lpips.sum().item()

src/benchmark/__init__.py

@@ -4,6 +4,7 @@ from torchmetrics.image import (
     StructuralSimilarityIndexMeasure,
 )
 
+psnr = PeakSignalNoiseRatio(1.0, reduction="none", dim=(1, 2, 3))
 ssim = StructuralSimilarityIndexMeasure(data_range=1.0, reduction="none")
 lpips = LearnedPerceptualImagePatchSimilarity(
     net_type="alex", reduction="none", normalize=True
@@ -11,5 +12,4 @@ lpips = LearnedPerceptualImagePatchSimilarity(
 
 
 def benchmark(image1, image2):
-    psnr = PeakSignalNoiseRatio(1.0, reduction="none")
     return psnr(image1, image2), ssim(image1, image2), lpips(image1, image2)

src/dataset/cuhk_cr1.py

@@ -45,7 +45,7 @@ def get_dataset() -> tuple[Dataset, Dataset]:
         batch_size=32,
         remove_columns=dataset["train"].column_names,
     )
-    dataset.set_format(type="torch", columns=["x0", "x1"])
+    dataset.set_format(type="torch", columns=["cloud", "gt"])
     dataset.save_to_disk("datasets/CUHK-CR1")
 
     return dataset["train"], dataset["test"]

src/dataset/cuhk_cr2.py

@@ -0,0 +1,62 @@
+import os
+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]:
+    if os.path.exists("datasets/CUHK-CR2"):
+        dataset = DatasetDict.load_from_disk("datasets/CUHK-CR2")
+        return dataset["train"], dataset["test"]
+
+    data_dir = Path("/data2/C-CUHK/CUHK-CR2")
+
+    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()),
+        }
+    )
+    dataset = dataset.map(
+        preprocess_function,
+        batched=True,
+        batch_size=32,
+        remove_columns=dataset["train"].column_names,
+    )
+    dataset.set_format(type="torch", columns=["cloud", "gt"])
+    dataset.save_to_disk("datasets/CUHK-CR2")
+
+    return dataset["train"], dataset["test"]
+
+
+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 {"cloud": x0_list, "gt": x1_list}

src/model/utransformer.py

@@ -105,10 +105,11 @@ class DinoConditionedLayer(DINOv3ViTLayer):
         conditioning_input: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        do_condition: bool = True,
         **kwargs,
     ) -> torch.Tensor:
         assert position_embeddings is not None
-        assert conditioning_input is not None
+        assert conditioning_input is not None or not do_condition
 
         residual = hidden_states
         hidden_states = self.norm1(hidden_states)
@@ -120,6 +121,7 @@ class DinoConditionedLayer(DINOv3ViTLayer):
         hidden_states = self.layer_scale1(hidden_states)
         hidden_states = self.drop_path(hidden_states) + residual
 
+        if do_condition:
             residual = hidden_states
             hidden_states = self.norm_cond(hidden_states)
             hidden_states, _ = self.cond(
@@ -191,6 +193,8 @@ class DinoV3ViTDecoder(nn.Module):
         )
 
         self.pixel_shuffle = nn.PixelShuffle(self.patch_size)
+        nn.init.zeros_(self.projection.weight)
+        nn.init.zeros_(self.projection.bias)
 
     def forward(self, x: torch.Tensor, image_size: tuple[int, int]) -> torch.Tensor:
         batch_size = x.shape[0]
@@ -211,9 +215,14 @@ class DinoV3ViTDecoder(nn.Module):
 
 
 class UTransformer(nn.Module):
-    def __init__(self, config: DINOv3ViTConfig, num_classes: int):
+    def __init__(
+        self, config: DINOv3ViTConfig, num_classes: int, scale_factor: int = 4
+    ):
         super().__init__()
         self.config = config
+        self.scale_factor = scale_factor
+
+        assert config.num_hidden_layers % scale_factor == 0
 
         self.embeddings = DINOv3ViTEmbeddings(config)
         self.rope_embeddings = DINOv3ViTRopePositionEmbedding(config)
@@ -228,18 +237,21 @@ class UTransformer(nn.Module):
                 for _ in range(config.num_hidden_layers)
             ]
         )
+        self.encoder_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
         self.decoder_layers = nn.ModuleList(
             [
                 DinoConditionedLayer(config, False)
-                for _ in range(config.num_hidden_layers // 2)
+                for _ in range(config.num_hidden_layers // scale_factor)
             ]
         )
+        self.decoder_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.decoder = DinoV3ViTDecoder(config)
 
         # freeze pretrained
         self.embeddings.requires_grad_(False)
         self.rope_embeddings.requires_grad_(False)
+        self.encoder_norm.requires_grad_(False)
 
     def forward(
         self,
@@ -260,6 +272,7 @@ class UTransformer(nn.Module):
 
         residual = []
         for i, layer_module in enumerate(self.encoder_layers):
+            if i % self.scale_factor == 0:
                 residual.append(x)
             layer_head_mask = head_mask[i] if head_mask is not None else None
             x = layer_module(
@@ -269,35 +282,71 @@ class UTransformer(nn.Module):
                 position_embeddings=position_embeddings,
             )
 
+        x = self.encoder_norm(x)
+
+        reversed_residual = residual[::-1]
         for i, layer_module in enumerate(self.decoder_layers):
             layer_head_mask = head_mask[i] if head_mask is not None else None
-            x = x + residual.pop() + residual.pop()
             x = layer_module(
                 x,
                 conditioning_input=conditioning_input,
                 attention_mask=layer_head_mask,
                 position_embeddings=position_embeddings,
             )
+            x = x + reversed_residual[i]
 
-        return self.decoder(x, image_size=pixel_values.shape[-2:])
+        x = self.decoder_norm(x)
+
+        return self.decoder(x, image_size=pixel_values.shape[-2:]), residual
+
+    def get_residual(
+        self,
+        pixel_values: torch.Tensor,
+        time: Optional[torch.Tensor],
+        do_condition: bool,
+    ):
+        pixel_values = pixel_values.to(self.embeddings.patch_embeddings.weight.dtype)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        x = self.embeddings(pixel_values, bool_masked_pos=None)
+
+        if do_condition:
+            t = self.t_embedder(time).unsqueeze(1)
+            # y = self.y_embedder(cond, self.training).unsqueeze(1)
+            # conditioning_input = t.to(x.dtype) + y.to(x.dtype)
+            conditioning_input = t.to(x.dtype)
+        else:
+            conditioning_input = None
+
+        residual = []
+        for i, layer_module in enumerate(self.encoder_layers):
+            if i % self.scale_factor == 0:
+                residual.append(x)
+
+            x = layer_module(
+                x,
+                conditioning_input=conditioning_input,
+                attention_mask=None,
+                position_embeddings=position_embeddings,
+                do_condition=do_condition,
+            )
+
+        return residual
 
     @staticmethod
     def from_pretrained_backbone(name: str):
         config = DINOv3ViTConfig.from_pretrained(name)
-        instance = UTransformer(config, 0).to("cuda:2")
+        instance = UTransformer(config, 0).to("cuda:1")
 
         weight_dict = {}
         with safe_open(
-            hf_hub_download(name, "model.safetensors"), framework="pt", device="cuda:2"
+            hf_hub_download(name, "model.safetensors"), framework="pt", device="cuda:1"
         ) as f:
             for key in f.keys():
                 new_key = key.replace("layer.", "encoder_layers.").replace(
                     "norm.", "encoder_norm."
                 )
 
-                if key.startswith("norm."):
-                    continue
-
                 weight_dict[new_key] = f.get_tensor(key)
 
         instance.load_state_dict(weight_dict, strict=False)

src/rf.py

@@ -13,17 +13,13 @@ def pseudo_huber_loss(x: torch.Tensor, c=0.00054):
 
 
 class RF:
-    def __init__(self, model, ln=False, ushaped=True, loss_fn="lpips_huber"):
+    def __init__(self, model, ln=False, ushaped=True, loss_fn="lpips_mse_enhanced"):
         self.model = model
         self.ln = ln
         self.ushaped = ushaped
         self.loss_fn = loss_fn
 
-        self.lpips = (
+        self.lpips = lpips.LPIPS(net="vgg").to("cuda:1") if "lpips" in loss_fn else None
-            lpips.LPIPS(net="vgg").to(model.device)
-            if loss_fn == "lpips_huber"
-            else None
-        )
 
     def forward(self, x0, z1):
         # x0 is gt / z is noise
@@ -41,7 +37,7 @@ class RF:
         texp = t.view([b, *([1] * len(x0.shape[1:]))])
         zt = (1 - texp) * x0 + texp * z1
 
-        vtheta = self.model(zt, t)
+        vtheta, residual = self.model(zt, t)
 
         if self.loss_fn == "lpips_huber":
             # https://ar5iv.labs.arxiv.org/html/2405.20320v1 /  (z - x) - v_θ(x_t, t)
@@ -57,6 +53,41 @@ class RF:
             weight = t.view(-1)
 
             loss = (1 - weight) * huber + lpips
+        elif self.loss_fn == "lpips_mse":
+            if not self.lpips:
+                raise Exception
+
+            lpips = self.lpips(
+                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
+            )
+            loss = ((z1 - x0 - vtheta) ** 2).mean(
+                dim=list(range(1, len(x0.shape)))
+            ) + 2.0 * lpips
+        elif self.loss_fn == "lpips_mse_enhanced":
+            if not self.lpips:
+                raise Exception
+
+            lpips = self.lpips(
+                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
+            )
+            dino_loss = torch.stack(
+                [
+                    (
+                        1
+                        - torch.nn.functional.cosine_similarity(
+                            v_residual, x0_residual, dim=-1
+                        )
+                    ).mean(dim=-1)
+                    for v_residual, x0_residual in zip(
+                        residual, self.model.get_residual(x0, None, False)
+                    )
+                ]
+            ).mean(dim=0) * (2 - t.view(-1))
+            loss = (
+                ((z1 - x0 - vtheta) ** 2).mean(dim=list(range(1, len(x0.shape))))
+                + 2.0 * lpips
+                + dino_loss
+            )
         else:
             loss = ((z1 - x0 - vtheta) ** 2).mean(dim=list(range(1, len(x0.shape))))
 
@@ -66,7 +97,7 @@ class RF:
         return loss.mean(), ttloss
 
     @torch.no_grad()
-    def sample(self, z1, sample_steps=50):
+    def sample(self, z1, sample_steps=5):
         b = z1.size(0)
         dt = 1.0 / sample_steps
         dt = torch.tensor([dt] * b).to(z1.device).view([b, *([1] * len(z1.shape[1:]))])
@@ -76,8 +107,36 @@ class RF:
             t = i / sample_steps
             t = torch.tensor([t] * b).to(z.device)
 
-            vc = self.model(z, t)
+            vc, _ = self.model(z, t)
 
             z = z - dt * vc
             images.append(z)
         return images
+
+    @torch.no_grad()
+    def sample_heun(self, z1, sample_steps=50):
+        b = z1.size(0)
+        dt = 1.0 / sample_steps
+
+        images = [z1]
+        z = z1
+
+        for i in range(sample_steps, 0, -1):
+            t_current = i / sample_steps
+            t_next = (i - 1) / sample_steps
+
+            t_current_tensor = torch.tensor([t_current] * b, device=z.device)
+
+            v_current, _ = self.model(z, t_current_tensor)
+            z_pred = z - dt * v_current
+
+            t_next_tensor = torch.tensor([t_next] * b, device=z.device)
+            v_next, _ = self.model(z_pred, t_next_tensor)
+
+            v_avg = 0.5 * (v_current + v_next)
+
+            z = z - dt * v_avg
+
+            images.append(z)
+
+        return images