sentinel2_classification_finetuning/script/train_simulated_s2.py

import argparse
import logging
import csv
import random
import warnings
import time
from pathlib import Path
from typing import Dict, List, Tuple, Any, Optional

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import albumentations as A
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
    accuracy_score, recall_score, f1_score, matthews_corrcoef, confusion_matrix
)
from rasterio.errors import NotGeoreferencedWarning

# --- CRITICAL IMPORTS ---
import terramind
from terratorch.tasks import ClassificationTask

# Local Imports
from methane_simulated_datamodule import MethaneSimulatedDataModule

# --- Configuration & Setup ---

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)

logging.getLogger("rasterio._env").setLevel(logging.ERROR)
warnings.simplefilter("ignore", NotGeoreferencedWarning)
warnings.filterwarnings("ignore", category=FutureWarning)

def set_seed(seed: int = 42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

def get_training_transforms() -> A.Compose:
    return A.Compose([
        A.ElasticTransform(p=0.25),
        A.RandomRotate90(p=0.5),
        A.Flip(p=0.5),
        A.ShiftScaleRotate(rotate_limit=90, shift_limit_x=0.05, shift_limit_y=0.05, p=0.5)
    ])

# --- Path Utilities ---

def get_simulated_paths(paths: List[str], tag: str = "toarefl") -> List[str]:
    """
    Modifies filenames to match the I1/TOA naming convention.
    Converts 'ang2015..._S2_...' -> 'ang2015..._{tag}_...'
    """
    simulated_paths = []
    for path in paths:
        try:
            tokens = path.split('_')
            # Logic: {ID}_{tag}_{Coord1}_{Coord2}
            # Adjusts original filename tokens to target format
            if len(tokens) >= 5:
                simulated_path = f"{tokens[0]}_{tag}_{tokens[3]}_{tokens[4]}"
                simulated_paths.append(simulated_path)
            else:
                simulated_paths.append(path)
        except Exception as e:
            logger.warning(f"Could not parse path {path}: {e}")
            simulated_paths.append(path)
    return simulated_paths

def get_paths_for_fold(excel_file: str, folds: List[int]) -> List[str]:
    try:
        df = pd.read_excel(excel_file)
        df_filtered = df[df['Fold'].isin(folds)]
        return df_filtered['Filename'].tolist()
    except Exception as e:
        logger.error(f"Error reading Excel file: {e}")
        raise

# --- Helper Classes ---

class MetricTracker:
    def __init__(self):
        self.reset()

    def reset(self):
        self.all_targets = []
        self.all_predictions = []
        self.total_loss = 0.0
        self.steps = 0

    def update(self, loss: float, targets: torch.Tensor, probabilities: torch.Tensor):
        self.total_loss += loss
        self.steps += 1
        self.all_targets.extend(torch.argmax(targets, dim=1).detach().cpu().numpy())
        self.all_predictions.extend(torch.argmax(probabilities, dim=1).detach().cpu().numpy())

    def compute(self) -> Dict[str, float]:
        if not self.all_targets:
            return {}
        
        tn, fp, fn, tp = confusion_matrix(self.all_targets, self.all_predictions, labels=[0, 1]).ravel()
        
        return {
            "Loss": self.total_loss / max(self.steps, 1),
            "Accuracy": accuracy_score(self.all_targets, self.all_predictions),
            "Specificity": tn / (tn + fp) if (tn + fp) != 0 else 0.0,
            "Sensitivity": recall_score(self.all_targets, self.all_predictions, average='binary', pos_label=1, zero_division=0),
            "F1": f1_score(self.all_targets, self.all_predictions, average='binary', pos_label=1, zero_division=0),
            "MCC": matthews_corrcoef(self.all_targets, self.all_predictions),
            "TP": int(tp), "TN": int(tn), "FP": int(fp), "FN": int(fn)
        }

class TrainerI1:
    def __init__(self, args: argparse.Namespace):
        self.args = args
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.save_dir = Path(args.save_dir) / f'fold{args.test_fold}'
        self.save_dir.mkdir(parents=True, exist_ok=True)
        
        self.model = self._init_model()
        self.optimizer, self.scheduler = self._init_optimizer()
        self.criterion = self.task.criterion
        self.best_val_loss = float('inf')
        
        logger.info(f"Trainer initialized on device: {self.device}")

    def _init_model(self) -> nn.Module:
        model_args = dict(
            backbone="terramind_v1_base",
            backbone_pretrained=True,
            backbone_modalities=["S2L2A"], 
            backbone_merge_method="mean",
            decoder="UperNetDecoder",
            decoder_scale_modules=True,
            decoder_channels=256,
            num_classes=2,
            head_dropout=0.3,
            necks=[
                {"name": "ReshapeTokensToImage", "remove_cls_token": False},
                {"name": "SelectIndices", "indices": [2, 5, 8, 11]},
                {"name": "LearnedInterpolateToPyramidal"},
            ],
        )

        self.task = ClassificationTask(
            model_args=model_args,
            model_factory="EncoderDecoderFactory",
            loss="ce",
            lr=self.args.lr,
            ignore_index=-1,
            optimizer="AdamW",
            optimizer_hparams={"weight_decay": self.args.weight_decay},
        )
        self.task.configure_models()
        self.task.configure_losses()
        return self.task.model.to(self.device)

    def _init_optimizer(self):
        optimizer = optim.AdamW(self.model.parameters(), lr=self.args.lr, weight_decay=self.args.weight_decay)
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=5, verbose=True)
        return optimizer, scheduler

    def run_epoch(self, dataloader: DataLoader, stage: str = "train") -> Dict[str, float]:
        is_train = stage == "train"
        self.model.train() if is_train else self.model.eval()
        tracker = MetricTracker()
        
        with torch.set_grad_enabled(is_train):
            pbar = tqdm(dataloader, desc=f"  {stage.capitalize()}", leave=False)
            for batch in pbar:
                inputs = batch['S2L2A'].to(self.device)
                targets = batch['label'].to(self.device)

                outputs = self.model(x={"S2L2A": inputs})
                probabilities = torch.softmax(outputs.output, dim=1)
                loss = self.criterion(probabilities, targets)

                if is_train:
                    self.optimizer.zero_grad()
                    loss.backward()
                    self.optimizer.step()

                tracker.update(loss.item(), targets, probabilities)
                pbar.set_postfix(loss=f"{loss.item():.4f}")

        return tracker.compute()

    def fit(self, train_loader: DataLoader, val_loader: DataLoader):
        logger.info(f"Starting training for {self.args.epochs} epochs...")
        start_time = time.time()
        
        # Initialize CSV logging
        csv_path = self.save_dir / 'train_val_metrics.csv'
        with open(csv_path, 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow([
                'Epoch', 'Train_Loss', 'Train_F1', 'Train_Acc', 
                'Val_Loss', 'Val_F1', 'Val_Acc', 'Val_Spec', 'Val_Sens'
            ])

        for epoch in range(1, self.args.epochs + 1):
            logger.info(f"Epoch {epoch}/{self.args.epochs}")
            
            train_metrics = self.run_epoch(train_loader, stage="train")
            val_metrics = self.run_epoch(val_loader, stage="validate")
            
            self.scheduler.step(val_metrics['Loss'])
            
            # Log to CSV
            with open(csv_path, 'a', newline='') as f:
                writer = csv.writer(f)
                writer.writerow([
                    epoch, 
                    train_metrics.get('Loss'), train_metrics.get('F1'), train_metrics.get('Accuracy'),
                    val_metrics.get('Loss'), val_metrics.get('F1'), val_metrics.get('Accuracy'),
                    val_metrics.get('Specificity'), val_metrics.get('Sensitivity')
                ])

            logger.info(f"Train Loss: {train_metrics['Loss']:.4f} | Val Loss: {val_metrics['Loss']:.4f} | Val F1: {val_metrics['F1']:.4f}")

            # Save Best Model
            if val_metrics['Loss'] < self.best_val_loss:
                self.best_val_loss = val_metrics['Loss']
                torch.save(self.model.state_dict(), self.save_dir / "best_model.pth")
                logger.info(f"--> New best model saved")

        # Save Final Model
        torch.save(self.model.state_dict(), self.save_dir / "final_model.pth")
        logger.info(f"Training finished in {time.time() - start_time:.2f}s")

# --- Data Utilities ---

def get_data_loaders(args) -> Tuple[DataLoader, DataLoader]:
    # 1. Determine Folds
    all_folds = list(range(1, args.num_folds + 1))
    train_pool_folds = [f for f in all_folds if f != args.test_fold]
    
    # 2. Get Paths
    paths = get_paths_for_fold(args.excel_file, train_pool_folds)
    
    # 3. Apply Tag (Dynamic Tagging)
    paths = get_simulated_paths(paths, tag=args.sim_tag)
    
    # 4. Train/Val Split (80/20)
    train_paths, val_paths = train_test_split(paths, test_size=0.2, random_state=args.seed)
    
    logger.info(f"Data Split - Train: {len(train_paths)}, Val: {len(val_paths)} (Test Fold: {args.test_fold})")

    # 5. Initialize DataModule
    datamodule = MethaneSimulatedDataModule(
        data_root=args.root_dir,
        excel_file=args.excel_file,
        batch_size=args.batch_size,
        paths=paths, # Initial dummy
        train_transform=get_training_transforms(),
        val_transform=None,
    )
    
    # 6. Create Loaders
    datamodule.paths = train_paths
    datamodule.setup(stage="fit")
    train_loader = datamodule.train_dataloader()
    
    datamodule.paths = val_paths
    datamodule.setup(stage="validate")
    val_loader = datamodule.val_dataloader()
    
    return train_loader, val_loader

# --- Main Execution ---

def parse_args():
    parser = argparse.ArgumentParser(description="Methane I1 (Simulated) Training")
    
    # Paths
    parser.add_argument('--root_dir', type=str, required=True, help='Root directory for I1/TOA/BOA data')
    parser.add_argument('--excel_file', type=str, required=True, help='Path to Summary Excel')
    parser.add_argument('--save_dir', type=str, default='./checkpoints_i1', help='Output directory')
    
    # Simulation Tag Config
    parser.add_argument('--sim_tag', type=str, default='toarefl', 
                        help='String identifier in filename (e.g. "toarefl" or "boarefl")')

    # Hyperparameters
    parser.add_argument('--epochs', type=int, default=100)
    parser.add_argument('--batch_size', type=int, default=2, help='Batch size (must be >1 for BatchNorm)')
    parser.add_argument('--lr', type=float, default=1e-5)
    parser.add_argument('--weight_decay', type=float, default=0.05)
    parser.add_argument('--num_folds', type=int, default=5)
    parser.add_argument('--test_fold', type=int, default=4, help='Fold ID to hold out')
    parser.add_argument('--seed', type=int, default=42)
    
    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()
    set_seed(args.seed)
    
    train_loader, val_loader = get_data_loaders(args)
    
    trainer = TrainerI1(args)
    trainer.fit(train_loader, val_loader)