d5ffba099e
Permanently integrates the Atomizer-Field GNN surrogate system: - neural_models/: Graph Neural Network for FEA field prediction - batch_parser.py: Parse training data from FEA exports - train.py: Neural network training pipeline - predict.py: Inference engine for fast predictions This enables 600x-2200x speedup over traditional FEA by replacing expensive simulations with millisecond neural network predictions. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
374 lines
12 KiB
Python
374 lines
12 KiB
Python
"""
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predict.py
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Inference script for AtomizerField trained models
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AtomizerField Inference v2.0
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Uses trained GNN to predict FEA fields 1000x faster than traditional simulation.
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Usage:
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python predict.py --model checkpoint_best.pt --input case_001
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This enables:
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- Rapid design exploration (milliseconds vs hours per analysis)
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- Real-time optimization
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- Interactive design feedback
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"""
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import argparse
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import json
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from pathlib import Path
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import time
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import torch
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import numpy as np
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import h5py
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from neural_models.field_predictor import AtomizerFieldModel
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from neural_models.data_loader import FEAMeshDataset
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class FieldPredictor:
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"""
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Inference engine for trained field prediction models
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"""
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def __init__(self, checkpoint_path, device=None):
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"""
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Initialize predictor
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Args:
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checkpoint_path (str): Path to trained model checkpoint
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device (str): Device to run on ('cuda' or 'cpu')
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"""
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if device is None:
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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else:
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self.device = torch.device(device)
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print(f"\nAtomizerField Inference Engine v2.0")
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print(f"Device: {self.device}")
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# Load checkpoint
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print(f"Loading model from {checkpoint_path}...")
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checkpoint = torch.load(checkpoint_path, map_location=self.device)
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# Create model
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model_config = checkpoint['config']['model']
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self.model = AtomizerFieldModel(**model_config)
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self.model.load_state_dict(checkpoint['model_state_dict'])
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self.model = self.model.to(self.device)
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self.model.eval()
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self.config = checkpoint['config']
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print(f"Model loaded (epoch {checkpoint['epoch']}, val_loss={checkpoint['best_val_loss']:.6f})")
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def predict(self, case_directory):
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"""
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Predict displacement and stress fields for a case
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Args:
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case_directory (str): Path to parsed FEA case
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Returns:
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dict: Predictions with displacement, stress, von_mises fields
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"""
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print(f"\nPredicting fields for {Path(case_directory).name}...")
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# Load data
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dataset = FEAMeshDataset(
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[case_directory],
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normalize=True,
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include_stress=False # Don't need ground truth for prediction
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)
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if len(dataset) == 0:
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raise ValueError(f"Could not load case from {case_directory}")
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data = dataset[0].to(self.device)
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# Predict
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start_time = time.time()
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with torch.no_grad():
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predictions = self.model(data, return_stress=True)
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inference_time = time.time() - start_time
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print(f"Prediction complete in {inference_time*1000:.1f} ms")
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# Convert to numpy
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results = {
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'displacement': predictions['displacement'].cpu().numpy(),
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'stress': predictions['stress'].cpu().numpy(),
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'von_mises': predictions['von_mises'].cpu().numpy(),
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'inference_time_ms': inference_time * 1000
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}
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# Compute max values
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max_disp = np.max(np.linalg.norm(results['displacement'][:, :3], axis=1))
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max_stress = np.max(results['von_mises'])
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results['max_displacement'] = float(max_disp)
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results['max_stress'] = float(max_stress)
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print(f"\nResults:")
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print(f" Max displacement: {max_disp:.6f} mm")
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print(f" Max von Mises stress: {max_stress:.2f} MPa")
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return results
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def save_predictions(self, predictions, case_directory, output_name='predicted'):
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"""
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Save predictions in same format as ground truth
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Args:
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predictions (dict): Prediction results
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case_directory (str): Case directory
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output_name (str): Output file name prefix
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"""
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case_dir = Path(case_directory)
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output_file = case_dir / f"{output_name}_fields.h5"
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print(f"\nSaving predictions to {output_file}...")
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with h5py.File(output_file, 'w') as f:
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# Save displacement
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f.create_dataset('displacement',
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data=predictions['displacement'],
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compression='gzip')
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# Save stress
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f.create_dataset('stress',
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data=predictions['stress'],
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compression='gzip')
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# Save von Mises
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f.create_dataset('von_mises',
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data=predictions['von_mises'],
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compression='gzip')
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# Save metadata
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f.attrs['max_displacement'] = predictions['max_displacement']
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f.attrs['max_stress'] = predictions['max_stress']
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f.attrs['inference_time_ms'] = predictions['inference_time_ms']
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print(f"Predictions saved!")
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# Also save JSON summary
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summary_file = case_dir / f"{output_name}_summary.json"
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summary = {
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'max_displacement': predictions['max_displacement'],
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'max_stress': predictions['max_stress'],
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'inference_time_ms': predictions['inference_time_ms'],
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'num_nodes': len(predictions['displacement'])
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}
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with open(summary_file, 'w') as f:
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json.dump(summary, f, indent=2)
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print(f"Summary saved to {summary_file}")
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def compare_with_ground_truth(self, predictions, case_directory):
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"""
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Compare predictions with FEA ground truth
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Args:
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predictions (dict): Model predictions
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case_directory (str): Case directory with ground truth
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Returns:
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dict: Comparison metrics
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"""
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case_dir = Path(case_directory)
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h5_file = case_dir / "neural_field_data.h5"
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if not h5_file.exists():
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print("No ground truth available for comparison")
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return None
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print("\nComparing with FEA ground truth...")
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# Load ground truth
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with h5py.File(h5_file, 'r') as f:
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gt_displacement = f['results/displacement'][:]
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# Try to load stress
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gt_stress = None
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if 'results/stress' in f:
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stress_group = f['results/stress']
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for stress_type in stress_group.keys():
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gt_stress = stress_group[stress_type]['data'][:]
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break
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# Compute errors
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pred_disp = predictions['displacement']
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disp_error = np.linalg.norm(pred_disp - gt_displacement, axis=1)
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disp_magnitude = np.linalg.norm(gt_displacement, axis=1)
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rel_disp_error = disp_error / (disp_magnitude + 1e-8)
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metrics = {
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'displacement': {
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'mae': float(np.mean(disp_error)),
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'rmse': float(np.sqrt(np.mean(disp_error**2))),
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'relative_error': float(np.mean(rel_disp_error)),
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'max_error': float(np.max(disp_error))
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}
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}
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# Compare max values
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pred_max_disp = predictions['max_displacement']
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gt_max_disp = float(np.max(disp_magnitude))
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metrics['max_displacement_error'] = abs(pred_max_disp - gt_max_disp)
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metrics['max_displacement_relative_error'] = metrics['max_displacement_error'] / (gt_max_disp + 1e-8)
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if gt_stress is not None:
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pred_stress = predictions['stress']
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stress_error = np.linalg.norm(pred_stress - gt_stress, axis=1)
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metrics['stress'] = {
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'mae': float(np.mean(stress_error)),
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'rmse': float(np.sqrt(np.mean(stress_error**2))),
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}
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# Print comparison
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print("\nComparison Results:")
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print(f" Displacement MAE: {metrics['displacement']['mae']:.6f} mm")
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print(f" Displacement RMSE: {metrics['displacement']['rmse']:.6f} mm")
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print(f" Displacement Relative Error: {metrics['displacement']['relative_error']*100:.2f}%")
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print(f" Max Displacement Error: {metrics['max_displacement_error']:.6f} mm ({metrics['max_displacement_relative_error']*100:.2f}%)")
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if 'stress' in metrics:
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print(f" Stress MAE: {metrics['stress']['mae']:.2f} MPa")
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print(f" Stress RMSE: {metrics['stress']['rmse']:.2f} MPa")
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return metrics
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def batch_predict(predictor, case_directories, output_dir=None):
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"""
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Run predictions on multiple cases
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Args:
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predictor (FieldPredictor): Initialized predictor
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case_directories (list): List of case directories
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output_dir (str): Optional output directory for results
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Returns:
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list: List of prediction results
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"""
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print(f"\n{'='*60}")
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print(f"Batch Prediction: {len(case_directories)} cases")
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print(f"{'='*60}")
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results = []
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for i, case_dir in enumerate(case_directories, 1):
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print(f"\n[{i}/{len(case_directories)}] Processing {Path(case_dir).name}...")
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try:
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# Predict
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predictions = predictor.predict(case_dir)
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# Save predictions
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predictor.save_predictions(predictions, case_dir)
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# Compare with ground truth
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comparison = predictor.compare_with_ground_truth(predictions, case_dir)
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result = {
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'case': str(case_dir),
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'status': 'success',
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'predictions': {
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'max_displacement': predictions['max_displacement'],
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'max_stress': predictions['max_stress'],
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'inference_time_ms': predictions['inference_time_ms']
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},
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'comparison': comparison
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}
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results.append(result)
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except Exception as e:
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print(f"ERROR: {e}")
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results.append({
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'case': str(case_dir),
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'status': 'failed',
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'error': str(e)
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})
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# Save batch results
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if output_dir:
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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results_file = output_path / 'batch_predictions.json'
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with open(results_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nBatch results saved to {results_file}")
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# Print summary
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print(f"\n{'='*60}")
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print("Batch Prediction Summary")
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print(f"{'='*60}")
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successful = sum(1 for r in results if r['status'] == 'success')
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print(f"Successful: {successful}/{len(results)}")
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if successful > 0:
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avg_time = np.mean([r['predictions']['inference_time_ms']
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for r in results if r['status'] == 'success'])
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print(f"Average inference time: {avg_time:.1f} ms")
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return results
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def main():
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"""
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Main inference entry point
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"""
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parser = argparse.ArgumentParser(description='Predict FEA fields using trained model')
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parser.add_argument('--model', type=str, required=True,
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help='Path to model checkpoint')
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parser.add_argument('--input', type=str, required=True,
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help='Input case directory or directory containing multiple cases')
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parser.add_argument('--output_dir', type=str, default=None,
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help='Output directory for batch results')
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parser.add_argument('--batch', action='store_true',
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help='Process all subdirectories as separate cases')
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parser.add_argument('--device', type=str, default=None,
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choices=['cuda', 'cpu'],
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help='Device to run on')
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parser.add_argument('--compare', action='store_true',
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help='Compare predictions with ground truth')
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args = parser.parse_args()
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# Create predictor
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predictor = FieldPredictor(args.model, device=args.device)
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input_path = Path(args.input)
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if args.batch:
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# Batch prediction
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case_dirs = [d for d in input_path.iterdir() if d.is_dir()]
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batch_predict(predictor, case_dirs, args.output_dir)
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else:
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# Single prediction
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predictions = predictor.predict(args.input)
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# Save predictions
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predictor.save_predictions(predictions, args.input)
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# Compare with ground truth if requested
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if args.compare:
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predictor.compare_with_ground_truth(predictions, args.input)
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print("\nInference complete!")
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if __name__ == "__main__":
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main()
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