alnn_rewrite/tasks/train_normal.py

import pathlib as pl
import time
import gc
from threading import Event
from typing import Any, Dict

import torch
from torch import nn, optim
from torch.utils.data import DataLoader

import model.dataset as ds
import model.training as tn
from model.cnn import CNN3D
from util.progress import ProgressTracker
from util.ui_logger import PipelineLogger


def _release_torch_memory(device: str) -> None:
    gc.collect()
    if device.startswith("cuda"):
        torch.cuda.empty_cache()
    elif device.startswith("mps") and hasattr(torch, "mps"):
        torch.mps.empty_cache()


def _check_control_events(
    stop_event: Event | None,
    pause_event: Event | None,
) -> None:
    if stop_event is not None and stop_event.is_set():
        raise InterruptedError("Pipeline execution stopped by user.")
    while pause_event is not None and pause_event.is_set():
        time.sleep(0.5)
        if stop_event is not None and stop_event.is_set():
            raise InterruptedError("Pipeline execution stopped by user while paused.")


def train_normal_task(
    track: ProgressTracker,
    log: PipelineLogger,
    config: Dict[str, Any],
    state: Dict[str, Any],
) -> None:
    log.info("Starting normal model training...")
    events = state.get("events")
    stop_event = events.get("stop") if isinstance(events, dict) else None
    pause_event = events.get("pause") if isinstance(events, dict) else None

    # Get the loaded datesets from the state
    train_loader: DataLoader[ds.ADNIDataset] = state["train_loader"]
    val_loader: DataLoader[ds.ADNIDataset] = state["val_loader"]
    test_loader: DataLoader[ds.ADNIDataset] = state["test_loader"]

    normal_models_path = pl.Path(config["work_dir"]) / "normal_models"

    # Set up intermediate model directory
    intermediate_model_dir = normal_models_path / "intermediate_models"
    if not intermediate_model_dir.exists():
        intermediate_model_dir.mkdir(parents=True, exist_ok=True)
    log.info(f"Intermediate models will be saved to {intermediate_model_dir}")

    models = []
    train_progress = track.get_sub_tracker("Training Progress")
    track.update(total=config["training"]["ensemble_size"], advance=0)
    for model_num in range(config["training"]["ensemble_size"]):
        _check_control_events(stop_event=stop_event, pause_event=pause_event)
        log.info(
            f"Training model {model_num + 1}/{config['training']['ensemble_size']}..."
        )
        # Train the model
        model = (
            CNN3D(
                image_channels=config["data"]["image_channels"],
                clin_data_channels=config["data"]["clin_data_channels"],
                num_classes=config["data"]["num_classes"],
                droprate=config["training"]["droprate"],
            )
            .float()
            .to(config["training"]["device"])
        )

        models.append(model)

        optimizer = optim.Adam(
            model.parameters(), lr=config["training"]["learning_rate"]
        )
        criterion = nn.BCELoss()

        # Train model -
        model, history = tn.train_model(
            log=log,
            progress=train_progress,
            model=model,
            train_loader=train_loader,
            val_loader=val_loader,
            optimizer=optimizer,
            criterion=criterion,
            num_epochs=config["training"]["num_epochs"],
            output_path=normal_models_path,
            stop_event=stop_event,
            pause_event=pause_event,
        )

        # Test model
        test_loss, test_acc = tn.test_model(
            model=model,
            test_loader=test_loader,
            criterion=criterion,
            progress=track.get_sub_tracker("Testing Progress"),
            log=log,
            stop_event=stop_event,
            pause_event=pause_event,
        )

        log.info(
            f"Run {model_num + 1}/{config['training']['ensemble_size']} - "
            f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_acc:.4f}"
        )

        # Save the model
        model_save_path = normal_models_path / f"model_{model_num + 1}.pt"
        torch.save(model.state_dict(), model_save_path)
        log.info(f"Model saved to {model_save_path}")
        del history
        del optimizer
        del criterion
        del model
        _release_torch_memory(config["training"]["device"])
        track.update(total=config["training"]["ensemble_size"], advance=1)

    log.info("All models trained and saved successfully.")