improved rf

2025-10-01 18:44:26 +09:00
parent 49025c4d87
commit 29eb04d1a4
8 changed files with 150 additions and 27 deletions
--- a/main.py
+++ b/main.py
@@ -50,10 +50,10 @@ for epoch in range(start_epoch, total_epoch):
        desc=f"Epoch {epoch + 1}/{total_epoch}",
    ):
        batch = train_dataset[i : i + batch_size]
-        x0 = batch["x0"].to(device)
-        x1 = batch["x1"].to(device)
+        cloud = batch["cloud"].to(device)
+        gt = batch["gt"].to(device)

-        loss, blsct = rf.forward(x0, x1)
+        loss, blsct = rf.forward(gt, cloud)
        loss = loss / accumulation_steps
        loss.backward()

@@ -89,9 +89,9 @@ 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["x0"].to(device))
+                images = rf.sample(batch["cloud"].to(device))
                image = denormalize(images[-1]).clamp(0, 1)
-                original = denormalize(batch["x1"]).clamp(0, 1)
+                original = denormalize(batch["gt"]).clamp(0, 1)

                psnr, ssim, lpips = benchmark(image.cpu(), original.cpu())
                psnr_sum += psnr.sum().item()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
    "datasets>=4.1.1",
    "einops>=0.8.1",
+    "lpips>=0.1.4",
    "pyright>=1.1.405",
    "python-lsp-server>=1.13.1",
    "ruff>=0.13.2",
--- a/quick_eval.py
+++ b/quick_eval.py
@@ -39,10 +39,10 @@ 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))
+        images = rf.sample(batch["cloud"].to(device))

        image = denormalize(images[-1]).clamp(0, 1)
-        original = denormalize(batch["x1"]).clamp(0, 1)
+        original = denormalize(batch["gt"]).clamp(0, 1)

        if saved_count < max_save:
            for j in range(min(image.shape[0], max_save - saved_count)):
--- a/src/dataset/cuhk_cr1.py
+++ b/src/dataset/cuhk_cr1.py
@@ -59,4 +59,4 @@ def preprocess_function(examples):
        x1_transformed = transform(x1_img)
        x0_list.append(x0_transformed)
        x1_list.append(x1_transformed)
-    return {"x0": x0_list, "x1": x1_list}
+    return {"cloud": x0_list, "gt": x1_list}
--- a/src/dataset/preprocess.py
+++ b/src/dataset/preprocess.py
@@ -14,7 +14,7 @@ def make_transform(resize_size: int = 256):
    return v2.Compose([to_tensor, resize, to_float, normalize])


-def denormalize(tensor):
+def denormalize(tensor: torch.Tensor) -> torch.Tensor:
    mean = torch.tensor([0.430, 0.411, 0.296]).view(3, 1, 1).to(tensor.device)
    std = torch.tensor([0.213, 0.156, 0.143]).view(3, 1, 1).to(tensor.device)
    return tensor * std + mean
--- a/src/model/utransformer.py
+++ b/src/model/utransformer.py
@@ -232,7 +232,7 @@ class UTransformer(nn.Module):
        self.decoder_layers = nn.ModuleList(
            [
                DinoConditionedLayer(config, False)
-                for _ in range(config.num_hidden_layers)
+                for _ in range(config.num_hidden_layers // 2)
            ]
        )
        self.decoder = DinoV3ViTDecoder(config)
@@ -271,13 +271,13 @@ class UTransformer(nn.Module):

        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 + residual.pop()

        return self.decoder(x, image_size=pixel_values.shape[-2:])

--- a/src/rf.py
+++ b/src/rf.py
@@ -1,41 +1,84 @@
+import math
+
+import lpips
 import torch

+from src.dataset.preprocess import denormalize
+
+
+def pseudo_huber_loss(x: torch.Tensor, c=0.00054):
+    """Loss = sqrt(||x||₂² + c²) - c"""
+    d = x.shape[1:].numel()
+    c = c * (d**0.5)
+    x = torch.linalg.vector_norm(x.flatten(1), ord=2, dim=1)
+    return torch.sqrt(x**2 + c**2) - c
+

 class RF:
-    def __init__(self, model, ln=True):
+    def __init__(self, model, ln=False, ushaped=True, loss_fn="lpips_huber"):
        self.model = model
        self.ln = ln
+        self.ushaped = ushaped
+        self.loss_fn = loss_fn

-    def forward(self, x0, x1):
+        self.lpips = (
+            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
        b = x0.size(0)
-        if self.ln:
+        if self.ushaped:
+            a = 4.0  # HYPERPARMS
+            u = torch.rand((b,)).to(x0.device)
+            t = torch.asinh((2 * u - 1) * math.sinh(a)) / a
+        elif self.ln:
            nt = torch.randn((b,)).to(x0.device)
            t = torch.sigmoid(nt)
        else:
            t = torch.rand((b,)).to(x0.device)
        texp = t.view([b, *([1] * len(x0.shape[1:]))])
-        zt = (1 - texp) * x0 + texp * x1
+        zt = (1 - texp) * x0 + texp * z1
+
        vtheta = self.model(zt, t)
-        batchwise_mse = ((x1 - x0 - vtheta) ** 2).mean(
-            dim=list(range(1, len(x0.shape)))
-        )
-        tlist = batchwise_mse.detach().cpu().reshape(-1).tolist()
+
+        if self.loss_fn == "lpips_huber":
+            # https://ar5iv.labs.arxiv.org/html/2405.20320v1 /  (z - x) - v_θ(x_t, t)
+            if not self.lpips:
+                raise Exception
+
+            huber = torch.nn.functional.huber_loss(
+                z1 - x0, vtheta, reduction="none"
+            ).mean(dim=list(range(1, len(x0.shape))))
+            lpips = self.lpips(
+                denormalize(x0) * 2 - 1, (denormalize(zt - texp * vtheta) * 2 - 1)
+            )
+            weight = t.view(-1)
+
+            loss = (1 - weight) * huber + lpips
+        else:
+            loss = ((z1 - x0 - vtheta) ** 2).mean(dim=list(range(1, len(x0.shape))))
+
+        tlist = loss.detach().cpu().reshape(-1).tolist()
        ttloss = [(tv, tloss) for tv, tloss in zip(t, tlist)]
-        return batchwise_mse.mean(), ttloss
+
+        return loss.mean(), ttloss

    @torch.no_grad()
-    def sample(self, x0, sample_steps=50):
-        b = x0.size(0)
+    def sample(self, z1, sample_steps=50):
+        b = z1.size(0)
        dt = 1.0 / sample_steps
-        dt = torch.tensor([dt] * b).to(x0.device).view([b, *([1] * len(x0.shape[1:]))])
-        images = [x0]
-        z = x0
-        for i in range(sample_steps):
+        dt = torch.tensor([dt] * b).to(z1.device).view([b, *([1] * len(z1.shape[1:]))])
+        images = [z1]
+        z = z1
+        for i in range(sample_steps, 0, -1):
            t = i / sample_steps
            t = torch.tensor([t] * b).to(z.device)

            vc = self.model(z, t)

-            z = z + dt * vc
+            z = z - dt * vc
            images.append(z)
        return images
--- a/uv.lock
+++ b/uv.lock
@@ -192,6 +192,7 @@ source = { virtual = "." }
 dependencies = [
    { name = "datasets" },
    { name = "einops" },
+    { name = "lpips" },
    { name = "pyright" },
    { name = "python-lsp-server" },
    { name = "ruff" },
@@ -209,6 +210,7 @@ dependencies = [
 requires-dist = [
    { name = "datasets", specifier = ">=4.1.1" },
    { name = "einops", specifier = ">=0.8.1" },
+    { name = "lpips", specifier = ">=0.1.4" },
    { name = "pyright", specifier = ">=1.1.405" },
    { name = "python-lsp-server", specifier = ">=1.13.1" },
    { name = "ruff", specifier = ">=0.13.2" },
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 ]

+[[package]]
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+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "torch" },
+    { name = "torchvision" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e8/2d/4b8148d32f5bd461eb7d5daa54fcc998f86eaa709a57f4ef6aa4c62f024f/lpips-0.1.4.tar.gz", hash = "sha256:3846331df6c69688aec3d300a5eeef6c529435bc8460bd58201c3d62e56188fa", size = 18029, upload-time = "2021-08-25T22:10:32.803Z" }
+wheels = [
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+
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 name = "markupsafe"
 version = "3.0.2"
@@ -1369,6 +1387,67 @@ wheels = [
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 ]

+[[package]]
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