50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from typing import Optional, Dict, Any
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from ..utils.config_model import TrainingConfig
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class Trainer:
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def __init__(
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self,
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model: nn.Module,
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train_loader: DataLoader,
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val_loader: DataLoader,
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loss_fn: nn.Module,
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device: torch.device,
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config: TrainingConfig,
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scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
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target_scaler: Optional[Any] = None
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):
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self.model = model
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self.train_loader = train_loader
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self.val_loader = val_loader
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self.loss_fn = loss_fn
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self.device = device
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self.config = config
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self.scheduler = scheduler
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self.target_scaler = target_scaler
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# TODO: Initialize optimizer (Adam)
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# TODO: Initialize early stopping if configured
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def train_epoch(self) -> Dict[str, float]:
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"""
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Train for one epoch.
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"""
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# TODO: Implement training loop for one epoch
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pass
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def evaluate(self, loader: DataLoader) -> Dict[str, float]:
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"""
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Evaluate model on given data loader.
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"""
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# TODO: Implement evaluation with metrics on original scale
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pass
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def train(self) -> Dict[str, Any]:
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"""
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Main training loop with validation and early stopping.
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"""
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# TODO: Implement full training loop with validation
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pass |