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Zero
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from __future__ import annotations
import os
from collections import defaultdict
from collections.abc import Callable
import numpy as np
import pandas as pd
import torch
import wandb
from torch.cuda.amp import GradScaler, autocast
from tqdm import tqdm
from utmosv2.utils import calc_metrics, print_metrics
def _train_1epoch(
cfg,
model: torch.nn.Module,
train_dataloader: torch.utils.data.DataLoader,
criterion: torch.nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
device: torch.device,
) -> dict[str, float]:
model.train()
train_loss = defaultdict(float)
scaler = GradScaler()
print(f" (lr: {scheduler.get_last_lr()[0]:.6f})")
pbar = tqdm(train_dataloader, total=len(train_dataloader))
for i, t in enumerate(pbar):
x, y = t[:-1], t[-1]
x = [t.to(device, non_blocking=True) for t in x]
y = y.to(device, non_blocking=True)
if cfg.run.mixup:
lmd = np.random.beta(cfg.run.mixup_alpha, cfg.run.mixup_alpha)
perm = torch.randperm(x[0].shape[0]).to(device)
x2 = [t[perm, :] for t in x]
y2 = y[perm]
optimizer.zero_grad()
with autocast():
if cfg.run.mixup:
output = model(
*[lmd * t + (1 - lmd) * t2 for t, t2 in zip(x, x2)]
).squeeze(1)
if isinstance(cfg.loss, list):
loss = [
(w1, lmd * l1 + (1 - lmd) * l2)
for (w1, l1), (_, l2) in zip(
criterion(output, y), criterion(output, y2)
)
]
else:
loss = lmd * criterion(output, y) + (1 - lmd) * criterion(
output, y2
)
else:
output = model(*x).squeeze(1)
loss = criterion(output, y)
if isinstance(loss, list):
loss_total = sum(w * ls for w, ls in loss)
else:
loss_total = loss
scaler.scale(loss_total).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
train_loss["loss"] += loss_total.detach().float().cpu().item()
if isinstance(loss, list):
for (cl, _), (_, ls) in zip(cfg.loss, loss):
train_loss[cl.name] += ls.detach().float().cpu().item()
pbar.set_description(
f' loss: {train_loss["loss"] / (i + 1):.4f}'
+ (
f' ({", ".join([f"{cl.name}: {train_loss[cl.name] / (i + 1):.4f}" for cl, _ in cfg.loss])})'
if isinstance(loss, list)
else ""
)
)
return {name: v / len(train_dataloader) for name, v in train_loss.items()}
def _validate_1epoch(
cfg,
model: torch.nn.Module,
valid_dataloader: torch.utils.data.DataLoader,
criterion: torch.nn.Module,
metrics: dict[str, Callable[[np.ndarray, np.ndarray], float]],
device: torch.device,
) -> tuple[dict[str, float], dict[str, float], np.ndarray]:
model.eval()
valid_loss = defaultdict(float)
valid_metrics = {name: 0.0 for name in metrics}
valid_preds = []
pbar = tqdm(valid_dataloader, total=len(valid_dataloader))
with torch.no_grad():
for i, t in enumerate(pbar):
x, y = t[:-1], t[-1]
x = [t.to(device, non_blocking=True) for t in x]
y_cpu = y
y = y.to(device, non_blocking=True)
with autocast():
output = model(*x).squeeze(1)
loss = criterion(output, y)
if isinstance(loss, list):
loss_total = sum(w * ls for w, ls in loss)
else:
loss_total = loss
valid_loss["loss"] += loss_total.detach().float().cpu().item()
if isinstance(loss, list):
for (cl, _), (_, ls) in zip(cfg.loss, loss):
valid_loss[cl.name] += ls.detach().float().cpu().item()
output = output.cpu().numpy()
for name, metric in metrics.items():
valid_metrics[name] += metric(output, y_cpu.numpy())
pbar.set_description(
f' val_loss: {valid_loss["loss"] / (i + 1):.4f} '
+ (
f'({", ".join([f"{cl.name}: {valid_loss[cl.name] / (i + 1):.4f}" for cl, _ in cfg.loss])}) '
if isinstance(loss, list)
else ""
)
+ " - ".join(
[
f"val_{name}: {v / (i + 1):.4f}"
for name, v in valid_metrics.items()
]
)
)
valid_preds.append(output)
valid_loss = {name: v / len(valid_dataloader) for name, v in valid_loss.items()}
valid_metrics = {
name: v / len(valid_dataloader) for name, v in valid_metrics.items()
}
valid_preds = np.concatenate(valid_preds)
return valid_loss, valid_metrics, valid_preds
def run_train(
cfg,
model: torch.nn.Module,
train_dataloader: torch.utils.data.DataLoader,
valid_dataloader: torch.utils.data.DataLoader,
valid_data: pd.DataFrame,
oof_preds: np.ndarray,
now_fold: int,
criterion: torch.nn.Module,
metrics: dict[str, Callable[[np.ndarray, np.ndarray], float]],
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
device: torch.device,
) -> None:
best_metric = 0.0
os.makedirs(cfg.save_path, exist_ok=True)
for epoch in range(cfg.run.num_epochs):
print(f"[Epoch {epoch + 1}/{cfg.run.num_epochs}]")
train_loss = _train_1epoch(
cfg, model, train_dataloader, criterion, optimizer, scheduler, device
)
valid_loss, _, valid_preds = _validate_1epoch(
cfg, model, valid_dataloader, criterion, metrics, device
)
print(f"Validation dataset: {cfg.validation_dataset}")
if cfg.validation_dataset == "each":
dataset = valid_data["dataset"].unique()
val_metrics = [
calc_metrics(
valid_data[valid_data["dataset"] == ds],
valid_preds[valid_data["dataset"] == ds],
)
for ds in dataset
]
val_metrics = {
name: sum([m[name] for m in val_metrics]) / len(val_metrics)
for name in val_metrics[0].keys()
}
if cfg.validation_dataset == "all":
print("Validation dataset: ALL")
val_metrics = calc_metrics(valid_data, valid_preds)
else:
val_metrics = calc_metrics(
valid_data[valid_data["dataset"] == cfg.validation_dataset],
valid_preds[valid_data["dataset"] == cfg.validation_dataset],
)
print_metrics(val_metrics)
if val_metrics[cfg.main_metric] > best_metric:
new_metric = val_metrics[cfg.main_metric]
print(f"(Found best metric: {best_metric:.4f} -> {new_metric:.4f})")
best_metric = new_metric
save_path = (
cfg.save_path / f"fold{now_fold}_s{cfg.split.seed}_best_model.pth"
)
torch.save(model.state_dict(), save_path)
print(f"Save best model: {save_path}")
oof_preds[valid_data.index] = valid_preds
save_path = cfg.save_path / f"fold{now_fold}_s{cfg.split.seed}_last_model.pth"
torch.save(model.state_dict(), save_path)
print()
val_metrics["train_loss"] = train_loss["loss"]
val_metrics["val_loss"] = valid_loss["loss"]
for cl, _ in cfg.loss:
val_metrics[f"train_loss_{cl.name}"] = train_loss[cl.name]
val_metrics[f"val_loss_{cl.name}"] = valid_loss[cl.name]
if cfg.wandb:
wandb.log(val_metrics)
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