From 29eb04d1a49fca93a70bdfe38cc771b2b697c936 Mon Sep 17 00:00:00 2001 From: neulus Date: Wed, 1 Oct 2025 18:44:26 +0900 Subject: [PATCH] improved rf --- main.py | 10 ++--- pyproject.toml | 1 + quick_eval.py | 4 +- src/dataset/cuhk_cr1.py | 2 +- src/dataset/preprocess.py | 2 +- src/model/utransformer.py | 4 +- src/rf.py | 75 +++++++++++++++++++++++++++++-------- uv.lock | 79 +++++++++++++++++++++++++++++++++++++++ 8 files changed, 150 insertions(+), 27 deletions(-) diff --git a/main.py b/main.py index 5b4d09e..c792122 100644 --- 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() diff --git a/pyproject.toml b/pyproject.toml index 8c7fc43..1591939 100644 --- 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", diff --git a/quick_eval.py b/quick_eval.py index 5c63891..220f433 100644 --- 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)): diff --git a/src/dataset/cuhk_cr1.py b/src/dataset/cuhk_cr1.py index 4058c19..6c7e075 100644 --- 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} diff --git a/src/dataset/preprocess.py b/src/dataset/preprocess.py index 42efec9..11d2be8 100644 --- 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 diff --git a/src/model/utransformer.py b/src/model/utransformer.py index f96b284..e3e2a57 100644 --- 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:]) diff --git a/src/rf.py b/src/rf.py index 6ff0666..1f65d44 100644 --- 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 diff --git a/uv.lock b/uv.lock index 6a27ba5..d5384fd 100644 --- a/uv.lock +++ b/uv.lock @@ -192,6 +192,7 @@ source = { virtual = "." } dependencies = 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