training code

src/dataset/preprocess.py

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+import torch
+from torchvision.transforms import v2
+
+
+# note that its LVD-1689M (not SAT)
+def make_transform(resize_size: int = 256):
+    to_tensor = v2.ToImage()
+    resize = v2.Resize((resize_size, resize_size), antialias=True)
+    to_float = v2.ToDtype(torch.float32, scale=True)
+    normalize = v2.Normalize(
+        mean=(0.485, 0.456, 0.406),
+        std=(0.229, 0.224, 0.225),
+    )
+    return v2.Compose([to_tensor, resize, to_float, normalize])

src/rf.py

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+import torch
+
+
+class RF:
+    def __init__(self, model, ln=True):
+        self.model = model
+        self.ln = ln
+
+    def forward(self, x0, x1):
+        b = x0.size(0)
+        if 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
+        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()
+        ttloss = [(tv, tloss) for tv, tloss in zip(t, tlist)]
+        return batchwise_mse.mean(), ttloss
+
+    @torch.no_grad()
+    def sample(self, x0, sample_steps=50):
+        b = x0.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):
+            t = i / sample_steps
+            t = torch.tensor([t] * b).to(z.device)
+
+            vc = self.model(z, t)
+
+            z = z + dt * vc
+            images.append(z)
+        return images