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training code done

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neulus
2025-09-29 16:16:12 +09:00
parent 1b445444cf
commit 95cc43d69e
3 changed files with 540 additions and 22 deletions
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38
main.py
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@@ -1,8 +1,9 @@
from typing import cast
import torch import torch
import torch.optim as optim import torch.optim as optim
import wandb import wandb
from datasets import load_dataset from datasets import DatasetDict, load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm from tqdm import tqdm
from src.dataset.preprocess import make_transform from src.dataset.preprocess import make_transform
@@ -10,29 +11,23 @@ from src.model.utransformer import UTransformer
from src.rf import RF from src.rf import RF
transform = make_transform() transform = make_transform()
model = UTransformer.from_pretrained_backbone( model = UTransformer.from_pretrained_backbone(
"facebook/dinov3-vits16-pretrain-lvd1689m" "facebook/dinov3-vits16-pretrain-lvd1689m"
).to("cuda:3") ).to("cuda:3")
rf = RF(model) rf = RF(model)
optimizer = optim.AdamW(model.parameters(), lr=5e-4) optimizer = optim.AdamW(model.parameters(), lr=5e-4)
dataset = cast(DatasetDict, load_dataset("your-dataset-name"))
dataset = load_dataset("your-dataset-name")
train_dataset = dataset["train"] train_dataset = dataset["train"]
def preprocess_function(examples): def preprocess_function(examples):
x0_list = [] x0_list = []
x1_list = [] x1_list = []
for x0_img, x1_img in zip(examples["cloudy"], examples["clear"]):
for x0_img, x1_img in zip(examples["cloudy_image"], examples["clear_image"]):
x0_transformed = transform(x0_img) x0_transformed = transform(x0_img)
x1_transformed = transform(x1_img) x1_transformed = transform(x1_img)
x0_list.append(x0_transformed) x0_list.append(x0_transformed)
x1_list.append(x1_transformed) x1_list.append(x1_transformed)
return {"x0": x0_list, "x1": x1_list} return {"x0": x0_list, "x1": x1_list}
@@ -42,33 +37,33 @@ train_dataset = train_dataset.map(
batch_size=32, batch_size=32,
remove_columns=train_dataset.column_names, remove_columns=train_dataset.column_names,
) )
train_dataset.set_format(type="torch", columns=["x0", "x1"]) 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") wandb.init(project="cloud-removal-kmu")
batch_size = 16
for epoch in range(100): for epoch in range(100):
lossbin = {i: 0 for i in range(10)} lossbin = {i: 0 for i in range(10)}
losscnt = {i: 1e-6 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"): train_dataset = train_dataset.shuffle(seed=epoch)
x0 = batch["x0"].to("cuda:3")
x1 = batch["x1"].to("cuda:3") for i in tqdm(
range(0, len(train_dataset), batch_size), desc=f"Epoch {epoch + 1}/100"
):
batch = train_dataset[i : i + batch_size]
x0 = torch.stack(batch["x0"]).to("cuda:3")
x1 = torch.stack(batch["x1"]).to("cuda:3")
optimizer.zero_grad() optimizer.zero_grad()
loss, blsct = rf.forward(x0, x1) loss, blsct = rf.forward(x0, x1)
loss.backward() loss.backward()
optimizer.step() optimizer.step()
wandb.log({"loss": loss.item()}) wandb.log({"loss": loss.item()})
for t, l in blsct: for t, lss in blsct:
bin_idx = min(int(t * 10), 9) bin_idx = min(int(t * 10), 9)
lossbin[bin_idx] += l lossbin[bin_idx] += lss
losscnt[bin_idx] += 1 losscnt[bin_idx] += 1
epoch_metrics = {f"lossbin_{i}": lossbin[i] / losscnt[i] for i in range(10)} epoch_metrics = {f"lossbin_{i}": lossbin[i] / losscnt[i] for i in range(10)}
@@ -84,5 +79,4 @@ for epoch in range(100):
}, },
f"checkpoint_epoch_{epoch + 1}.pt", f"checkpoint_epoch_{epoch + 1}.pt",
) )
wandb.finish() wandb.finish()

1
pyproject.toml
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@@ -5,6 +5,7 @@ description = "Add your description here"
readme = "README.md" readme = "README.md"
requires-python = ">=3.12" requires-python = ">=3.12"
dependencies = [ dependencies = [
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"pyright>=1.1.405", "pyright>=1.1.405",
"ruff>=0.13.2", "ruff>=0.13.2",

523
uv.lock generated
View File

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