X_SSL_Net/lib/trainers/supervised.py

from __future__ import annotations

import time
from typing import Any

import torch
from torch.utils.data import DataLoader

from lib.modules import XNet2d
from lib.tools import build_loss, build_optimizer, build_scheduler
from .base import BaseTrainer


class SupervisedSegmentationTrainer(BaseTrainer):
    def __init__(self, cfg: dict[str, Any], args: Any | None = None) -> None:
        super().__init__(cfg=cfg, args=args)
        self.model: XNet2d | None = None
        self.optimizer = None
        self.scheduler = None
        self.loader: DataLoader | None = None
        self.val_loader: DataLoader | None = None
        self.seg_loss = None

    def build(self) -> None:
        dataset_cfg = self.cfg["dataset"]
        model_cfg = self.cfg["model"]
        train_cfg = self.cfg["train"]

        self.model = XNet2d(
            in_channels=int(model_cfg.get("in_channels", dataset_cfg.get("in_channels", 3))),
            num_classes=int(dataset_cfg["num_classes"]),
            encoder_channels=tuple(model_cfg.get("encoder_channels", (32, 64, 128, 192))),
            encoder_depths=tuple(model_cfg.get("encoder_depths", (2, 2, 2, 2))),
            decoder_channels=tuple(model_cfg.get("decoder_channels", (128, 64, 32))),
            stem_channels=int(model_cfg.get("stem_channels", 24)),
            bottleneck_depth=int(model_cfg.get("bottleneck_depth", 1)),
            global_ratio=float(model_cfg.get("global_ratio", 2.0)),
            wavelet_type=str(model_cfg.get("wavelet_type", "haar")),
            wavelet_level=int(model_cfg.get("wavelet_level", 1)),
            use_wavelet_branch=bool(model_cfg.get("use_wavelet_branch", True)),
            use_global_branch_stage1=bool(model_cfg.get("use_global_branch_stage1", False)),
            ssm_d_state=int(model_cfg.get("ssm_d_state", 16)),
            ssm_forward_type=str(model_cfg.get("ssm_forward_type", "v3")),
            ssm_backend=str(model_cfg.get("ssm_backend", "auto")),
            use_frequency_refine=bool(model_cfg.get("use_frequency_refine", True)),
            guide_mode=str(model_cfg.get("guide_mode", "affine")),
            out_channels=model_cfg.get("out_channels"),
        ).to(self.device)

        self.optimizer = build_optimizer(self.model, self.cfg["optimizer"])
        self.scheduler = build_scheduler(self.optimizer, self.cfg.get("scheduler"))
        loss_cfg = self.cfg.get("loss")
        if loss_cfg is not None:
            self.seg_loss = build_loss(loss_cfg)
        self.loader = self._build_segmentation_loader(
            split=str(dataset_cfg.get("split", "train")),
            split_file=dataset_cfg.get("split_file"),
            batch_size=self._resolve_batch_size("batch_size", 4),
            shuffle=bool(train_cfg.get("shuffle", True)),
            augmentation_config=self.cfg.get("augmentation", {}).get("train"),
        )
        self.val_loader = self._build_val_loader(
            batch_size=self._resolve_batch_size(
                "val_batch_size",
                int(train_cfg.get("batch_size", 4)),
            ),
            shuffle=False,
        )
        self._maybe_resume(
            module_map={"model": self.model},
            optimizer=self.optimizer,
            scheduler=self.scheduler,
        )
        self._init_swanlab()

    def _compute_losses(
            self,
            image: torch.Tensor,
            mask: torch.Tensor,
    ) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
        if self.model is None:
            raise RuntimeError("Model is not initialized.")
        with torch.autocast(device_type=self.device.type, enabled=self._amp_enabled()):
            outputs = self.model(image)
            seg_logits = outputs["seg_logits"]

            if self.seg_loss is None:
                seg_loss = torch.nn.functional.binary_cross_entropy_with_logits(seg_logits, mask)
            else:
                seg_loss = self.seg_loss(seg_logits, mask)

            total_loss = seg_loss

        losses = {
            "total": total_loss,
            "seg": seg_loss,
        }
        return outputs, losses

    @staticmethod
    def _detach_metrics(losses: dict[str, torch.Tensor]) -> dict[str, float]:
        return {key: float(value.detach().cpu()) for key, value in losses.items()}

    def _validate(self) -> dict[str, float] | None:
        if self.model is None or self.val_loader is None:
            return None

        self.model.eval()
        metrics = self._build_validation_metrics()
        total = 0.0
        seg = 0.0
        steps = 0
        with torch.no_grad():
            for batch in self.val_loader:
                image = batch["image"].to(self.device)
                mask = batch["mask"].to(self.device)
                outputs, losses = self._compute_losses(image, mask)
                total += float(losses["total"].detach().cpu())
                seg += float(losses["seg"].detach().cpu())
                self._update_validation_metrics(
                    metrics,
                    logits=outputs["seg_logits"],
                    target=mask,
                )
                steps += 1

        if steps == 0:
            return None
        val_metrics = {
            "total": total / steps,
            "seg": seg / steps,
        }
        val_metrics.update(self._compute_validation_metric_values(metrics))
        return val_metrics

    def train(self) -> None:
        if self.model is None or self.loader is None or self.optimizer is None:
            raise RuntimeError("Trainer.build() must be called before train().")

        epochs = int(self.cfg["train"].get("epochs", 1))
        accum_steps = self._accum_steps()
        try:
            self._print_training_setup(
                model_map={"model": self.model},
                loader_map={"train": self.loader, "val": self.val_loader},
                optimizer=self.optimizer,
                scheduler=self.scheduler,
            )
            for epoch in range(self.start_epoch, epochs):
                self.model.train()
                self.optimizer.zero_grad()
                train_metric_sums = {
                    "total": 0.0,
                    "seg": 0.0,
                }
                train_metrics: dict[str, float] | None = None
                end_time = time.perf_counter()
                num_steps = len(self.loader)
                for step, batch in enumerate(self.loader, start=1):
                    data_time = time.perf_counter() - end_time
                    iter_start = time.perf_counter()
                    image = batch["image"].to(self.device)
                    mask = batch["mask"].to(self.device)
                    _, losses = self._compute_losses(image, mask)
                    scaled_total_loss = losses["total"] / accum_steps
                    self.grad_scaler.scale(scaled_total_loss).backward()
                    grad_norm = None
                    should_step = (step % accum_steps == 0) or (step == num_steps)
                    if should_step:
                        if self._grad_clip_enabled():
                            self.grad_scaler.unscale_(self.optimizer)
                            grad_norm = self._clip_gradients(self.model)
                        self.grad_scaler.step(self.optimizer)
                        self.grad_scaler.update()
                        self.optimizer.zero_grad()
                    train_metrics = self._detach_metrics(losses)
                    if grad_norm is not None:
                        train_metrics["grad_norm"] = grad_norm
                    for key, value in train_metrics.items():
                        train_metric_sums.setdefault(key, 0.0)
                        train_metric_sums[key] += value
                    iter_time = time.perf_counter() - iter_start
                    self._maybe_log_step(
                        epoch=epoch,
                        step=step,
                        num_steps=num_steps,
                        data_time=data_time,
                        iter_time=iter_time,
                        metrics=train_metrics,
                        prefix="train",
                    )
                    end_time = time.perf_counter()

                if self.scheduler is not None:
                    self.scheduler.step()

                if train_metrics is None:
                    raise RuntimeError("Training loader is empty.")
                train_metrics = self._average_metric_sums(train_metric_sums, num_steps)
                val_metrics = self._validate() if self._should_validate(epoch) else None
                summary, should_stop = self._finalize_epoch(
                    epoch=epoch,
                    train_metrics=train_metrics,
                    val_metrics=val_metrics,
                    checkpoint_state={
                        "model": self.model.state_dict(),
                        "optimizer": self.optimizer.state_dict(),
                        "scheduler": self.scheduler.state_dict() if self.scheduler is not None else None,
                    },
                )
                print(summary)
                if should_stop:
                    print({"epoch": epoch, "message": "early stopping triggered"})
                    break
        finally:
            self._close_loggers()