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training code

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neulus
2025-09-29 16:13:18 +09:00
parent d3793890a7
commit 1b445444cf
7 changed files with 406 additions and 22 deletions
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1
.gitignore vendored
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@@ -8,3 +8,4 @@ wheels/
# Virtual environments
.venv
artifact

87
main.py
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@@ -1,7 +1,88 @@
from src.model.utransformer import UTransformer
import torch
import torch.optim as optim
import wandb
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
test_model = UTransformer.from_pretrained_backbone(
from src.dataset.preprocess import make_transform
from src.model.utransformer import UTransformer
from src.rf import RF
transform = make_transform()
model = UTransformer.from_pretrained_backbone(
"facebook/dinov3-vits16-pretrain-lvd1689m"
).to("cuda:3")
print(test_model)
rf = RF(model)
optimizer = optim.AdamW(model.parameters(), lr=5e-4)
dataset = load_dataset("your-dataset-name")
train_dataset = dataset["train"]
def preprocess_function(examples):
x0_list = []
x1_list = []
for x0_img, x1_img in zip(examples["cloudy_image"], examples["clear_image"]):
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"])
dataloader = DataLoader(
train_dataset, batch_size=16, shuffle=True, num_workers=4, pin_memory=True
)
wandb.init(project="cloud-removal-kmu")
for epoch in range(100):
lossbin = {i: 0 for i in range(10)}
losscnt = {i: 1e-6 for i in range(10)}
for batch in tqdm(dataloader, desc=f"Epoch {epoch + 1}/100"):
x0 = batch["x0"].to("cuda:3")
x1 = batch["x1"].to("cuda:3")
optimizer.zero_grad()
loss, blsct = rf.forward(x0, x1)
loss.backward()
optimizer.step()
wandb.log({"loss": loss.item()})
for t, l in blsct:
bin_idx = min(int(t * 10), 9)
lossbin[bin_idx] += l
losscnt[bin_idx] += 1
epoch_metrics = {f"lossbin_{i}": lossbin[i] / losscnt[i] for i in range(10)}
epoch_metrics["epoch"] = epoch
wandb.log(epoch_metrics)
if (epoch + 1) % 10 == 0:
torch.save(
{
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
},
f"checkpoint_epoch_{epoch + 1}.pt",
)
wandb.finish()

3
pyproject.toml
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@@ -10,5 +10,8 @@ dependencies = [
"ruff>=0.13.2",
"safetensors>=0.6.2",
"torch>=2.8.0",
"torchvision>=0.23.0",
"tqdm>=4.67.1",
"transformers>=4.56.2",
"wandb>=0.22.0",
]

0
src/dataset/__init__.py Normal file
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14
src/dataset/preprocess.py Normal file
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@@ -0,0 +1,14 @@
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])

22
rf.py → src/rf.py
View File

@@ -6,7 +6,7 @@ class RF:
self.model = model
self.ln = ln
def forward(self, x0, x1, cond):
def forward(self, x0, x1):
b = x0.size(0)
if self.ln:
nt = torch.randn((b,)).to(x0.device)
@@ -15,7 +15,7 @@ class RF:
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, cond)
vtheta = self.model(zt, t)
batchwise_mse = ((x1 - x0 - vtheta) ** 2).mean(
dim=list(range(1, len(x0.shape)))
)
@@ -24,20 +24,18 @@ class RF:
return batchwise_mse.mean(), ttloss
@torch.no_grad()
def sample(self, z, cond, null_cond=None, sample_steps=50, cfg=2.0):
b = z.size(0)
def sample(self, x0, sample_steps=50):
b = x0.size(0)
dt = 1.0 / sample_steps
dt = torch.tensor([dt] * b).to(z.device).view([b, *([1] * len(z.shape[1:]))])
images = [z]
for i in range(sample_steps, 0, -1):
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, cond)
if null_cond is not None:
vu = self.model(z, t, null_cond)
vc = vu + cfg * (vc - vu)
vc = self.model(z, t)
z = z - dt * vc
z = z + dt * vc
images.append(z)
return images

301
uv.lock generated
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@@ -1,6 +1,19 @@
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revision = 3
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version = "0.1.0"
@@ -63,7 +88,10 @@ dependencies = [
{ name = "ruff" },
{ name = "safetensors" },
{ name = "torch" },
{ name = "torchvision" },
{ name = "tqdm" },
{ name = "transformers" },
{ name = "wandb" },
]
[package.metadata]
@@ -73,7 +101,10 @@ requires-dist = [
{ name = "ruff", specifier = ">=0.13.2" },
{ name = "safetensors", specifier = ">=0.6.2" },
{ name = "torch", specifier = ">=2.8.0" },
{ name = "torchvision", specifier = ">=0.23.0" },
{ name = "tqdm", specifier = ">=4.67.1" },
{ name = "transformers", specifier = ">=4.56.2" },
{ name = "wandb", specifier = ">=0.22.0" },
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@@ -332,7 +387,7 @@ name = "nvidia-cudnn-cu12"
version = "9.10.2.21"
source = { registry = "https://pypi.org/simple" }
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@@ -343,7 +398,7 @@ name = "nvidia-cufft-cu12"
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source = { registry = "https://pypi.org/simple" }
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@@ -370,9 +425,9 @@ name = "nvidia-cusolver-cu12"
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