CrbnsCat10n
dcc023cc04
deleted: best_model.pth new file: checkpoints/forward/best_tcn_model.pt new file: checkpoints/forward/training_history.csv new file: evaluation_outputs/forward/evaluation_forward_test_b0_s0.png new file: sanity_check_alignment_forward.png modified: src/__pycache__/config.cpython-310.pyc modified: src/__pycache__/config.cpython-314.pyc modified: src/__pycache__/dataset.cpython-310.pyc modified: src/__pycache__/dataset.cpython-314.pyc modified: src/__pycache__/model.cpython-310.pyc modified: src/__pycache__/model.cpython-314.pyc modified: src/config.py modified: src/dataset.py modified: src/evaluate.py modified: src/model.py new file: src/sanity_check.py modified: src/train.py renamed: src/__init__.py -> src_old/__init__.py new file: src_old/__pycache__/config.cpython-310.pyc new file: src_old/__pycache__/config.cpython-314.pyc new file: src_old/__pycache__/dataset.cpython-310.pyc new file: src_old/__pycache__/dataset.cpython-314.pyc new file: src_old/__pycache__/evaluate.cpython-314.pyc new file: src_old/__pycache__/model.cpython-310.pyc new file: src_old/__pycache__/model.cpython-314.pyc new file: src_old/__pycache__/train.cpython-310.pyc new file: src_old/__pycache__/train.cpython-314.pyc new file: src_old/config.py new file: src_old/dataset.py new file: src_old/evaluate.py new file: src_old/model.py new file: src_old/train.py deleted: test_results.png
74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from config import *
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from dataset import get_dataloaders
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from model import BuildingTCN
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def evaluate_model():
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_, _, test_loader, scaler_X, scaler_Y = get_dataloaders()
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if test_loader is None:
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raise ValueError("当前数据配置下没有可用的测试集。")
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model = BuildingTCN(input_size=1, output_size=5, num_channels=CHANNELS,
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kernel_size=KERNEL_SIZE, dropout=DROPOUT).to(DEVICE)
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model.load_state_dict(torch.load('best_model.pth', map_location=DEVICE))
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model.eval()
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all_preds = []
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all_targets = []
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with torch.no_grad():
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for inputs, targets, masks in test_loader:
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inputs = inputs.to(DEVICE)
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outputs = model(inputs)
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all_preds.append(outputs.cpu().numpy())
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all_targets.append(targets.numpy())
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# Concatenate results
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all_preds = np.concatenate(all_preds, axis=0)
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all_targets = np.concatenate(all_targets, axis=0)
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# Inverse transform to physical scale for metrics
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all_preds_inv = scaler_Y.inverse_transform(all_preds.reshape(-1, len(OUTPUT_SENSORS))).reshape(all_preds.shape)
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all_targets_inv = scaler_Y.inverse_transform(all_targets.reshape(-1, len(OUTPUT_SENSORS))).reshape(all_targets.shape)
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print("\n=== Test Metrics (All Windows) ===")
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for i, sens in enumerate(OUTPUT_SENSORS):
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pred_i = all_preds_inv[:, :, i].reshape(-1)
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target_i = all_targets_inv[:, :, i].reshape(-1)
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mae = float(np.mean(np.abs(pred_i - target_i)))
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pred_std = float(np.std(pred_i))
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target_std = float(np.std(target_i))
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amp_ratio = pred_std / (target_std + 1e-12)
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pred_peak = float(np.max(np.abs(pred_i)))
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target_peak = float(np.max(np.abs(target_i)))
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peak_ratio = pred_peak / (target_peak + 1e-12)
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corr = float(np.corrcoef(pred_i, target_i)[0, 1]) if len(pred_i) > 1 else float("nan")
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print(
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f"{sens}: MAE={mae:.4f}, Corr={corr:.4f}, "
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f"AmpRatio(std_pred/std_true)={amp_ratio:.4f}, "
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f"PeakRatio(max|pred|/max|true|)={peak_ratio:.4f}"
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)
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# 取一个 batch 的第一条序列进行可视化
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sample_pred = all_preds_inv[0]
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sample_target = all_targets_inv[0]
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plt.figure(figsize=(15, 10))
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for i, sens in enumerate(OUTPUT_SENSORS):
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plt.subplot(5, 1, i+1)
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plt.plot(sample_target[:, i], label='True', alpha=0.7)
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plt.plot(sample_pred[:, i], label='Pred', alpha=0.7, linestyle='--')
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plt.title(f'Sensor {sens} Z-axis Response (Test: Earthquake)')
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plt.legend()
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plt.tight_layout()
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plt.savefig('test_results.png')
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print("Evaluation done. Result saved to test_results.png")
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if __name__ == '__main__':
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evaluate_model()
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