feat: Add parametric predictor and training script for AtomizerField
Rebuilds missing neural network components based on documentation: - neural_models/parametric_predictor.py: Design-conditioned GNN that predicts all 4 optimization objectives (mass, frequency, displacement, stress) directly from design parameters. ~500K trainable parameters. - train_parametric.py: Training script with multi-objective loss, checkpoint saving with normalization stats, and TensorBoard logging. - Updated __init__.py to export ParametricFieldPredictor and create_parametric_model for use by optimization_engine/neural_surrogate.py These files enable the neural acceleration workflow: 1. Collect FEA training data (189 trials already collected) 2. Train parametric model: python train_parametric.py --train_dir ... 3. Run neural-accelerated optimization with --enable-nn flag 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Antoine
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atomizer-field/train_parametric.py
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773
atomizer-field/train_parametric.py
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"""
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train_parametric.py
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Training script for AtomizerField parametric predictor
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AtomizerField Parametric Training Pipeline v2.0
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Trains design-conditioned GNN to predict optimization objectives directly.
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Usage:
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python train_parametric.py --train_dir ./training_data --val_dir ./validation_data
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Key Differences from train.py (field predictor):
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- Predicts scalar objectives (mass, frequency, displacement, stress) instead of fields
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- Uses design parameters as conditioning input
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- Multi-objective loss function for all 4 outputs
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- Faster training due to simpler output structure
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Output:
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checkpoint_best.pt containing:
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- model_state_dict: Trained weights
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- config: Model configuration
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- normalization: Normalization statistics for inference
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- design_var_names: Names of design variables
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- best_val_loss: Best validation loss achieved
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"""
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import argparse
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import json
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import sys
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from pathlib import Path
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import time
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from datetime import datetime
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from typing import Dict, List, Any, Optional, Tuple
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import Dataset, DataLoader
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import h5py
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# Try to import tensorboard, but make it optional
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try:
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from torch.utils.tensorboard import SummaryWriter
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TENSORBOARD_AVAILABLE = True
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except ImportError:
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TENSORBOARD_AVAILABLE = False
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from neural_models.parametric_predictor import create_parametric_model, ParametricFieldPredictor
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class ParametricDataset(Dataset):
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"""
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PyTorch Dataset for parametric training.
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Loads training data exported by Atomizer's TrainingDataExporter
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and prepares it for the parametric predictor.
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Expected directory structure:
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training_data/
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├── trial_0000/
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│ ├── metadata.json (design params + results)
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│ └── input/
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│ └── neural_field_data.h5 (mesh data)
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├── trial_0001/
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│ └── ...
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"""
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def __init__(
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self,
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data_dir: Path,
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normalize: bool = True,
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cache_in_memory: bool = False
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):
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"""
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Initialize dataset.
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Args:
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data_dir: Directory containing trial_* subdirectories
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normalize: Whether to normalize inputs/outputs
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cache_in_memory: Cache all data in RAM (faster but memory-intensive)
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"""
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self.data_dir = Path(data_dir)
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self.normalize = normalize
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self.cache_in_memory = cache_in_memory
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# Find all valid trial directories
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self.trial_dirs = sorted([
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d for d in self.data_dir.glob("trial_*")
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if d.is_dir() and self._is_valid_trial(d)
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])
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print(f"Found {len(self.trial_dirs)} valid trials in {data_dir}")
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if len(self.trial_dirs) == 0:
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raise ValueError(f"No valid trial directories found in {data_dir}")
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# Extract design variable names from first trial
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self.design_var_names = self._get_design_var_names()
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print(f"Design variables: {self.design_var_names}")
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# Compute normalization statistics
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if normalize:
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self._compute_normalization_stats()
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# Cache data if requested
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self.cache = {}
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if cache_in_memory:
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print("Caching data in memory...")
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for idx in range(len(self.trial_dirs)):
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self.cache[idx] = self._load_trial(idx)
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print("Cache complete!")
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def _is_valid_trial(self, trial_dir: Path) -> bool:
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"""Check if trial directory has required files."""
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metadata_file = trial_dir / "metadata.json"
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# Check for metadata
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if not metadata_file.exists():
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return False
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# Check metadata has required fields
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try:
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with open(metadata_file, 'r') as f:
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metadata = json.load(f)
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has_design = 'design_parameters' in metadata
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has_results = 'results' in metadata
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return has_design and has_results
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except:
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return False
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def _get_design_var_names(self) -> List[str]:
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"""Extract design variable names from first trial."""
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metadata_file = self.trial_dirs[0] / "metadata.json"
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with open(metadata_file, 'r') as f:
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metadata = json.load(f)
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return list(metadata['design_parameters'].keys())
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def _compute_normalization_stats(self):
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"""Compute normalization statistics across all trials."""
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print("Computing normalization statistics...")
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all_design_params = []
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all_mass = []
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all_disp = []
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all_stiffness = []
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for trial_dir in self.trial_dirs:
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with open(trial_dir / "metadata.json", 'r') as f:
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metadata = json.load(f)
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# Design parameters
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design_params = [metadata['design_parameters'][name]
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for name in self.design_var_names]
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all_design_params.append(design_params)
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# Results
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results = metadata.get('results', {})
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objectives = results.get('objectives', results)
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if 'mass' in objectives:
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all_mass.append(objectives['mass'])
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if 'max_displacement' in results:
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all_disp.append(results['max_displacement'])
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elif 'max_displacement' in objectives:
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all_disp.append(objectives['max_displacement'])
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if 'stiffness' in objectives:
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all_stiffness.append(objectives['stiffness'])
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# Convert to numpy arrays
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all_design_params = np.array(all_design_params)
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# Compute statistics
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self.design_mean = torch.from_numpy(all_design_params.mean(axis=0)).float()
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self.design_std = torch.from_numpy(all_design_params.std(axis=0)).float()
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self.design_std = torch.clamp(self.design_std, min=1e-6) # Prevent division by zero
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# Output statistics
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self.mass_mean = np.mean(all_mass) if all_mass else 0.1
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self.mass_std = np.std(all_mass) if all_mass else 0.05
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self.mass_std = max(self.mass_std, 1e-6)
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self.disp_mean = np.mean(all_disp) if all_disp else 0.01
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self.disp_std = np.std(all_disp) if all_disp else 0.005
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self.disp_std = max(self.disp_std, 1e-6)
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self.stiffness_mean = np.mean(all_stiffness) if all_stiffness else 20000.0
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self.stiffness_std = np.std(all_stiffness) if all_stiffness else 5000.0
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self.stiffness_std = max(self.stiffness_std, 1e-6)
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# Frequency and stress defaults (if not available in data)
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self.freq_mean = 18.0
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self.freq_std = 5.0
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self.stress_mean = 200.0
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self.stress_std = 50.0
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print(f" Design mean: {self.design_mean.numpy()}")
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print(f" Design std: {self.design_std.numpy()}")
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print(f" Mass: {self.mass_mean:.4f} +/- {self.mass_std:.4f}")
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print(f" Displacement: {self.disp_mean:.6f} +/- {self.disp_std:.6f}")
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print(f" Stiffness: {self.stiffness_mean:.2f} +/- {self.stiffness_std:.2f}")
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def __len__(self) -> int:
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return len(self.trial_dirs)
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def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
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if self.cache_in_memory and idx in self.cache:
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return self.cache[idx]
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return self._load_trial(idx)
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def _load_trial(self, idx: int) -> Dict[str, torch.Tensor]:
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"""Load and process a single trial."""
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trial_dir = self.trial_dirs[idx]
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# Load metadata
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with open(trial_dir / "metadata.json", 'r') as f:
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metadata = json.load(f)
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# Extract design parameters
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design_params = [metadata['design_parameters'][name]
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for name in self.design_var_names]
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design_tensor = torch.tensor(design_params, dtype=torch.float32)
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# Normalize design parameters
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if self.normalize:
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design_tensor = (design_tensor - self.design_mean) / self.design_std
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# Extract results
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results = metadata.get('results', {})
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objectives = results.get('objectives', results)
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# Get targets (with fallbacks)
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mass = objectives.get('mass', 0.1)
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stiffness = objectives.get('stiffness', 20000.0)
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max_displacement = results.get('max_displacement',
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objectives.get('max_displacement', 0.01))
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# Frequency and stress might not be available
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frequency = objectives.get('frequency', self.freq_mean)
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max_stress = objectives.get('max_stress', self.stress_mean)
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# Create target tensor
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targets = torch.tensor([mass, frequency, max_displacement, max_stress],
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dtype=torch.float32)
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# Normalize targets
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if self.normalize:
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targets[0] = (targets[0] - self.mass_mean) / self.mass_std
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targets[1] = (targets[1] - self.freq_mean) / self.freq_std
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targets[2] = (targets[2] - self.disp_mean) / self.disp_std
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targets[3] = (targets[3] - self.stress_mean) / self.stress_std
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# Try to load mesh data if available
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mesh_data = self._load_mesh_data(trial_dir)
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return {
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'design_params': design_tensor,
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'targets': targets,
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'mesh_data': mesh_data,
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'trial_dir': str(trial_dir)
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}
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def _load_mesh_data(self, trial_dir: Path) -> Optional[Dict[str, torch.Tensor]]:
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"""Load mesh data from H5 file if available."""
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h5_paths = [
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trial_dir / "input" / "neural_field_data.h5",
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trial_dir / "neural_field_data.h5",
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]
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for h5_path in h5_paths:
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if h5_path.exists():
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try:
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with h5py.File(h5_path, 'r') as f:
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node_coords = torch.from_numpy(f['mesh/node_coordinates'][:]).float()
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# Build simple edge index from connectivity if available
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# For now, return just coordinates
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return {
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'node_coords': node_coords,
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'num_nodes': node_coords.shape[0]
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}
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except Exception as e:
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print(f"Warning: Could not load mesh from {h5_path}: {e}")
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return None
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def get_normalization_stats(self) -> Dict[str, Any]:
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"""Return normalization statistics for saving with model."""
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return {
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'design_mean': self.design_mean.numpy().tolist(),
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'design_std': self.design_std.numpy().tolist(),
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'mass_mean': float(self.mass_mean),
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'mass_std': float(self.mass_std),
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'freq_mean': float(self.freq_mean),
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'freq_std': float(self.freq_std),
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'max_disp_mean': float(self.disp_mean),
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'max_disp_std': float(self.disp_std),
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'max_stress_mean': float(self.stress_mean),
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'max_stress_std': float(self.stress_std),
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}
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def create_reference_graph(num_nodes: int = 500, device: torch.device = None):
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"""
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Create a reference graph structure for the GNN.
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In production, this would come from the actual mesh.
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For now, create a simple grid-like structure.
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"""
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if device is None:
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device = torch.device('cpu')
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# Create simple node features (placeholder)
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x = torch.randn(num_nodes, 12, device=device)
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# Create grid-like connectivity
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edges = []
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grid_size = int(np.sqrt(num_nodes))
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for i in range(num_nodes):
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# Connect to neighbors
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if i % grid_size < grid_size - 1: # Right neighbor
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edges.append([i, i + 1])
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edges.append([i + 1, i])
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if i + grid_size < num_nodes: # Bottom neighbor
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edges.append([i, i + grid_size])
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edges.append([i + grid_size, i])
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edge_index = torch.tensor(edges, dtype=torch.long, device=device).t().contiguous()
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edge_attr = torch.randn(edge_index.shape[1], 5, device=device)
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from torch_geometric.data import Data
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return Data(x=x, edge_index=edge_index, edge_attr=edge_attr)
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class ParametricTrainer:
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"""
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Training manager for parametric predictor models.
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"""
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def __init__(self, config: Dict[str, Any]):
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"""
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Initialize trainer.
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Args:
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config: Training configuration
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"""
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self.config = config
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"\n{'='*60}")
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print("AtomizerField Parametric Training Pipeline v2.0")
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print(f"{'='*60}")
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print(f"Device: {self.device}")
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# Create model
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print("\nCreating parametric model...")
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model_config = config.get('model', {})
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self.model = create_parametric_model(model_config)
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self.model = self.model.to(self.device)
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num_params = self.model.get_num_parameters()
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print(f"Model created: {num_params:,} parameters")
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# Create optimizer
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self.optimizer = optim.AdamW(
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self.model.parameters(),
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lr=config.get('learning_rate', 1e-3),
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weight_decay=config.get('weight_decay', 1e-5)
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)
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# Learning rate scheduler
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self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer,
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mode='min',
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factor=0.5,
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patience=10,
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verbose=True
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)
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# Multi-objective loss weights
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self.loss_weights = config.get('loss_weights', {
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'mass': 1.0,
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'frequency': 1.0,
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'displacement': 1.0,
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'stress': 1.0
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})
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# Training state
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self.start_epoch = 0
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self.best_val_loss = float('inf')
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self.epochs_without_improvement = 0
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# Create output directories
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self.output_dir = Path(config.get('output_dir', './runs/parametric'))
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self.output_dir.mkdir(parents=True, exist_ok=True)
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# TensorBoard logging (optional)
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self.writer = None
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if TENSORBOARD_AVAILABLE:
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self.writer = SummaryWriter(log_dir=self.output_dir / 'tensorboard')
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# Create reference graph for inference
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self.reference_graph = create_reference_graph(
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num_nodes=config.get('reference_nodes', 500),
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device=self.device
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)
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# Save config
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with open(self.output_dir / 'config.json', 'w') as f:
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json.dump(config, f, indent=2)
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def compute_loss(
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self,
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predictions: Dict[str, torch.Tensor],
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targets: torch.Tensor
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) -> Tuple[torch.Tensor, Dict[str, float]]:
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"""
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Compute multi-objective loss.
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Args:
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predictions: Model outputs (mass, frequency, max_displacement, max_stress)
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targets: Target values [batch, 4]
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Returns:
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total_loss: Combined loss tensor
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loss_dict: Individual losses for logging
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"""
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# MSE losses for each objective
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mass_loss = nn.functional.mse_loss(predictions['mass'], targets[:, 0])
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freq_loss = nn.functional.mse_loss(predictions['frequency'], targets[:, 1])
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disp_loss = nn.functional.mse_loss(predictions['max_displacement'], targets[:, 2])
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stress_loss = nn.functional.mse_loss(predictions['max_stress'], targets[:, 3])
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# Weighted combination
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total_loss = (
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self.loss_weights['mass'] * mass_loss +
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self.loss_weights['frequency'] * freq_loss +
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self.loss_weights['displacement'] * disp_loss +
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self.loss_weights['stress'] * stress_loss
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)
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loss_dict = {
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'total': total_loss.item(),
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'mass': mass_loss.item(),
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'frequency': freq_loss.item(),
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'displacement': disp_loss.item(),
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'stress': stress_loss.item()
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}
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return total_loss, loss_dict
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def train_epoch(self, train_loader: DataLoader, epoch: int) -> Dict[str, float]:
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"""Train for one epoch."""
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self.model.train()
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total_losses = {'total': 0, 'mass': 0, 'frequency': 0, 'displacement': 0, 'stress': 0}
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num_batches = 0
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for batch_idx, batch in enumerate(train_loader):
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# Get data
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design_params = batch['design_params'].to(self.device)
|
||||
targets = batch['targets'].to(self.device)
|
||||
|
||||
# Zero gradients
|
||||
self.optimizer.zero_grad()
|
||||
|
||||
# Forward pass (using reference graph)
|
||||
predictions = self.model(self.reference_graph, design_params)
|
||||
|
||||
# Compute loss
|
||||
loss, loss_dict = self.compute_loss(predictions, targets)
|
||||
|
||||
# Backward pass
|
||||
loss.backward()
|
||||
|
||||
# Gradient clipping
|
||||
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
|
||||
|
||||
# Update weights
|
||||
self.optimizer.step()
|
||||
|
||||
# Accumulate losses
|
||||
for key in total_losses:
|
||||
total_losses[key] += loss_dict[key]
|
||||
num_batches += 1
|
||||
|
||||
# Print progress
|
||||
if batch_idx % 10 == 0:
|
||||
print(f" Batch {batch_idx}/{len(train_loader)}: Loss={loss_dict['total']:.6f}")
|
||||
|
||||
# Average losses
|
||||
return {k: v / num_batches for k, v in total_losses.items()}
|
||||
|
||||
def validate(self, val_loader: DataLoader) -> Dict[str, float]:
|
||||
"""Validate model."""
|
||||
self.model.eval()
|
||||
|
||||
total_losses = {'total': 0, 'mass': 0, 'frequency': 0, 'displacement': 0, 'stress': 0}
|
||||
num_batches = 0
|
||||
|
||||
with torch.no_grad():
|
||||
for batch in val_loader:
|
||||
design_params = batch['design_params'].to(self.device)
|
||||
targets = batch['targets'].to(self.device)
|
||||
|
||||
predictions = self.model(self.reference_graph, design_params)
|
||||
_, loss_dict = self.compute_loss(predictions, targets)
|
||||
|
||||
for key in total_losses:
|
||||
total_losses[key] += loss_dict[key]
|
||||
num_batches += 1
|
||||
|
||||
return {k: v / num_batches for k, v in total_losses.items()}
|
||||
|
||||
def train(
|
||||
self,
|
||||
train_loader: DataLoader,
|
||||
val_loader: DataLoader,
|
||||
num_epochs: int,
|
||||
train_dataset: ParametricDataset
|
||||
):
|
||||
"""
|
||||
Main training loop.
|
||||
|
||||
Args:
|
||||
train_loader: Training data loader
|
||||
val_loader: Validation data loader
|
||||
num_epochs: Number of epochs
|
||||
train_dataset: Training dataset (for normalization stats)
|
||||
"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Starting training for {num_epochs} epochs")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
for epoch in range(self.start_epoch, num_epochs):
|
||||
epoch_start = time.time()
|
||||
|
||||
print(f"Epoch {epoch + 1}/{num_epochs}")
|
||||
print("-" * 60)
|
||||
|
||||
# Train
|
||||
train_metrics = self.train_epoch(train_loader, epoch)
|
||||
|
||||
# Validate
|
||||
val_metrics = self.validate(val_loader)
|
||||
|
||||
epoch_time = time.time() - epoch_start
|
||||
|
||||
# Print metrics
|
||||
print(f"\nEpoch {epoch + 1} Results:")
|
||||
print(f" Training Loss: {train_metrics['total']:.6f}")
|
||||
print(f" Mass: {train_metrics['mass']:.6f}, Freq: {train_metrics['frequency']:.6f}")
|
||||
print(f" Disp: {train_metrics['displacement']:.6f}, Stress: {train_metrics['stress']:.6f}")
|
||||
print(f" Validation Loss: {val_metrics['total']:.6f}")
|
||||
print(f" Mass: {val_metrics['mass']:.6f}, Freq: {val_metrics['frequency']:.6f}")
|
||||
print(f" Disp: {val_metrics['displacement']:.6f}, Stress: {val_metrics['stress']:.6f}")
|
||||
print(f" Time: {epoch_time:.1f}s")
|
||||
|
||||
# Log to TensorBoard
|
||||
if self.writer:
|
||||
self.writer.add_scalar('Loss/train', train_metrics['total'], epoch)
|
||||
self.writer.add_scalar('Loss/val', val_metrics['total'], epoch)
|
||||
for key in ['mass', 'frequency', 'displacement', 'stress']:
|
||||
self.writer.add_scalar(f'{key}/train', train_metrics[key], epoch)
|
||||
self.writer.add_scalar(f'{key}/val', val_metrics[key], epoch)
|
||||
|
||||
# Learning rate scheduling
|
||||
self.scheduler.step(val_metrics['total'])
|
||||
|
||||
# Save checkpoint
|
||||
is_best = val_metrics['total'] < self.best_val_loss
|
||||
if is_best:
|
||||
self.best_val_loss = val_metrics['total']
|
||||
self.epochs_without_improvement = 0
|
||||
print(f" New best validation loss: {self.best_val_loss:.6f}")
|
||||
else:
|
||||
self.epochs_without_improvement += 1
|
||||
|
||||
self.save_checkpoint(epoch, val_metrics, train_dataset, is_best)
|
||||
|
||||
# Early stopping
|
||||
patience = self.config.get('early_stopping_patience', 50)
|
||||
if self.epochs_without_improvement >= patience:
|
||||
print(f"\nEarly stopping after {patience} epochs without improvement")
|
||||
break
|
||||
|
||||
print()
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("Training complete!")
|
||||
print(f"Best validation loss: {self.best_val_loss:.6f}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
if self.writer:
|
||||
self.writer.close()
|
||||
|
||||
def save_checkpoint(
|
||||
self,
|
||||
epoch: int,
|
||||
metrics: Dict[str, float],
|
||||
dataset: ParametricDataset,
|
||||
is_best: bool = False
|
||||
):
|
||||
"""Save model checkpoint with all required metadata."""
|
||||
checkpoint = {
|
||||
'epoch': epoch,
|
||||
'model_state_dict': self.model.state_dict(),
|
||||
'optimizer_state_dict': self.optimizer.state_dict(),
|
||||
'scheduler_state_dict': self.scheduler.state_dict(),
|
||||
'best_val_loss': self.best_val_loss,
|
||||
'config': self.model.config,
|
||||
'normalization': dataset.get_normalization_stats(),
|
||||
'design_var_names': dataset.design_var_names,
|
||||
'metrics': metrics
|
||||
}
|
||||
|
||||
# Save latest
|
||||
torch.save(checkpoint, self.output_dir / 'checkpoint_latest.pt')
|
||||
|
||||
# Save best
|
||||
if is_best:
|
||||
best_path = self.output_dir / 'checkpoint_best.pt'
|
||||
torch.save(checkpoint, best_path)
|
||||
print(f" Saved best model to {best_path}")
|
||||
|
||||
# Periodic checkpoint
|
||||
if (epoch + 1) % 10 == 0:
|
||||
torch.save(checkpoint, self.output_dir / f'checkpoint_epoch_{epoch + 1}.pt')
|
||||
|
||||
|
||||
def collate_fn(batch: List[Dict]) -> Dict[str, torch.Tensor]:
|
||||
"""Custom collate function for DataLoader."""
|
||||
design_params = torch.stack([item['design_params'] for item in batch])
|
||||
targets = torch.stack([item['targets'] for item in batch])
|
||||
|
||||
return {
|
||||
'design_params': design_params,
|
||||
'targets': targets
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main training entry point."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Train AtomizerField parametric predictor'
|
||||
)
|
||||
|
||||
# Data arguments
|
||||
parser.add_argument('--train_dir', type=str, required=True,
|
||||
help='Directory containing training trial_* subdirs')
|
||||
parser.add_argument('--val_dir', type=str, default=None,
|
||||
help='Directory containing validation data (uses split if not provided)')
|
||||
parser.add_argument('--val_split', type=float, default=0.2,
|
||||
help='Validation split ratio if val_dir not provided')
|
||||
|
||||
# Training arguments
|
||||
parser.add_argument('--epochs', type=int, default=200,
|
||||
help='Number of training epochs')
|
||||
parser.add_argument('--batch_size', type=int, default=16,
|
||||
help='Batch size')
|
||||
parser.add_argument('--learning_rate', type=float, default=1e-3,
|
||||
help='Learning rate')
|
||||
parser.add_argument('--weight_decay', type=float, default=1e-5,
|
||||
help='Weight decay')
|
||||
|
||||
# Model arguments
|
||||
parser.add_argument('--hidden_channels', type=int, default=128,
|
||||
help='Hidden dimension')
|
||||
parser.add_argument('--num_layers', type=int, default=4,
|
||||
help='Number of GNN layers')
|
||||
parser.add_argument('--dropout', type=float, default=0.1,
|
||||
help='Dropout rate')
|
||||
|
||||
# Output arguments
|
||||
parser.add_argument('--output_dir', type=str, default='./runs/parametric',
|
||||
help='Output directory')
|
||||
parser.add_argument('--resume', type=str, default=None,
|
||||
help='Path to checkpoint to resume from')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Create datasets
|
||||
print("\nLoading training data...")
|
||||
train_dataset = ParametricDataset(args.train_dir, normalize=True)
|
||||
|
||||
if args.val_dir:
|
||||
val_dataset = ParametricDataset(args.val_dir, normalize=True)
|
||||
# Share normalization stats
|
||||
val_dataset.design_mean = train_dataset.design_mean
|
||||
val_dataset.design_std = train_dataset.design_std
|
||||
else:
|
||||
# Split training data
|
||||
n_total = len(train_dataset)
|
||||
n_val = int(n_total * args.val_split)
|
||||
n_train = n_total - n_val
|
||||
|
||||
train_dataset, val_dataset = torch.utils.data.random_split(
|
||||
train_dataset, [n_train, n_val]
|
||||
)
|
||||
print(f"Split: {n_train} train, {n_val} validation")
|
||||
|
||||
# Create data loaders
|
||||
train_loader = DataLoader(
|
||||
train_dataset,
|
||||
batch_size=args.batch_size,
|
||||
shuffle=True,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=0
|
||||
)
|
||||
|
||||
val_loader = DataLoader(
|
||||
val_dataset,
|
||||
batch_size=args.batch_size,
|
||||
shuffle=False,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=0
|
||||
)
|
||||
|
||||
# Get design dimension from dataset
|
||||
if hasattr(train_dataset, 'design_var_names'):
|
||||
design_dim = len(train_dataset.design_var_names)
|
||||
else:
|
||||
# For split dataset, access underlying dataset
|
||||
design_dim = len(train_dataset.dataset.design_var_names)
|
||||
|
||||
# Build configuration
|
||||
config = {
|
||||
'model': {
|
||||
'input_channels': 12,
|
||||
'edge_dim': 5,
|
||||
'hidden_channels': args.hidden_channels,
|
||||
'num_layers': args.num_layers,
|
||||
'design_dim': design_dim,
|
||||
'dropout': args.dropout
|
||||
},
|
||||
'learning_rate': args.learning_rate,
|
||||
'weight_decay': args.weight_decay,
|
||||
'batch_size': args.batch_size,
|
||||
'num_epochs': args.epochs,
|
||||
'output_dir': args.output_dir,
|
||||
'early_stopping_patience': 50,
|
||||
'loss_weights': {
|
||||
'mass': 1.0,
|
||||
'frequency': 1.0,
|
||||
'displacement': 1.0,
|
||||
'stress': 1.0
|
||||
}
|
||||
}
|
||||
|
||||
# Create trainer
|
||||
trainer = ParametricTrainer(config)
|
||||
|
||||
# Resume if specified
|
||||
if args.resume:
|
||||
checkpoint = torch.load(args.resume, map_location=trainer.device)
|
||||
trainer.model.load_state_dict(checkpoint['model_state_dict'])
|
||||
trainer.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
|
||||
trainer.start_epoch = checkpoint['epoch'] + 1
|
||||
trainer.best_val_loss = checkpoint['best_val_loss']
|
||||
print(f"Resumed from epoch {checkpoint['epoch']}")
|
||||
|
||||
# Get base dataset for normalization stats
|
||||
base_dataset = train_dataset
|
||||
if hasattr(train_dataset, 'dataset'):
|
||||
base_dataset = train_dataset.dataset
|
||||
|
||||
# Train
|
||||
trainer.train(train_loader, val_loader, args.epochs, base_dataset)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user