modified: Figure_1.png

modified: checkpoints/forward/best_tcn_model.pt modified: checkpoints/forward/training_history.csv new file: checkpoints_mlp/task1_feature_mlp/best_feature_mlp.pt new file: checkpoints_mlp/task1_feature_mlp/training_history.csv new file: checkpoints_rms/forward_rms/best_rms_model.pt new file: checkpoints_rms/forward_rms/training_history.csv modified: evaluation_outputs/forward/evaluation_forward_test_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b3_s0.png new file: evaluation_outputs/forward_rms/evaluation_test_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_test_s0.png new file: evaluation_outputs/forward_rms/evaluation_train_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_s0.png new file: evaluation_outputs/forward_rms/evaluation_val_s0_waveform.png new file: evaluation_outputs/task1_feature_mlp/evaluation_train_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_train_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_val_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_s0.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_0.75Hz_prediction.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_1.55Hz_prediction.png new file: scripts/__pycache__/config.cpython-310.pyc new file: scripts/__pycache__/dataset.cpython-310.pyc new file: scripts/__pycache__/model.cpython-310.pyc new file: scripts/config.py new file: scripts/dataset.py new file: scripts/evaluate.py new file: scripts/model.py new file: scripts/predict_single.py new file: scripts/train.py modified: src/__pycache__/config.cpython-310.pyc modified: src/__pycache__/dataset.cpython-310.pyc modified: src/__pycache__/model.cpython-310.pyc modified: src/config.py modified: src/dataset.py modified: src/model.py new file: src_new/__pycache__/config.cpython-310.pyc new file: src_new/__pycache__/dataset.cpython-310.pyc new file: src_new/__pycache__/evaluate.cpython-310.pyc new file: src_new/__pycache__/model.cpython-310.pyc new file: src_new/__pycache__/train.cpython-310.pyc new file: src_new/config.py new file: src_new/dataset.py new file: src_new/evaluate.py new file: src_new/model.py new file: src_new/train.py
2026-05-06 12:19:55 +08:00
parent dcc023cc04
commit 484643409d
48 changed files with 2706 additions and 18 deletions
--- a/scripts/predict_single.py
+++ b/scripts/predict_single.py
@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+try:
+    from .config import CORE_FEATURE_NAMES, make_experiment_config
+    from .dataset import build_record_from_file, normalization_from_payload
+    from .model import build_model
+except ImportError:
+    from config import CORE_FEATURE_NAMES, make_experiment_config
+    from dataset import build_record_from_file, normalization_from_payload
+    from model import build_model
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Predict task1 RMS from a single waveform CSV.")
+    parser.add_argument("--file", type=str, required=True)
+    parser.add_argument("--device", type=str, default=None)
+    parser.add_argument("--checkpoint", type=str, default=None)
+    return parser.parse_args()
+
+
+def resolve_device(device_name: str | None, default_device: str) -> torch.device:
+    requested = device_name or default_device
+    if requested.startswith("cuda") and not torch.cuda.is_available():
+        return torch.device("cpu")
+    return torch.device(requested)
+
+
+def resolve_checkpoint_path(project_root: Path, checkpoint_dir: str, best_model_name: str, checkpoint_arg: str | None) -> Path:
+    if checkpoint_arg:
+        return Path(checkpoint_arg).resolve()
+    return project_root / checkpoint_dir / "task1_feature_mlp" / best_model_name
+
+
+def save_prediction_figure(
+    file_path: Path,
+    record,
+    pred_rms: float,
+    true_rms: float | None,
+    output_dir: Path,
+) -> Path:
+    time_middle = record.time_middle.detach().cpu().numpy().reshape(-1)
+    x_middle = record.x_middle.detach().cpu().numpy().reshape(-1)
+    y_middle = record.y_middle.detach().cpu().numpy().reshape(-1)
+    sampling_rate = record.sampling_rate
+    fft_values = np.fft.rfft(x_middle)
+    freqs = np.fft.rfftfreq(x_middle.size, d=1.0 / sampling_rate)
+    amplitudes = np.abs(fft_values)
+
+    fig, axes = plt.subplots(3, 1, figsize=(12, 10))
+    fig.suptitle(f"Task1 Single-File Prediction | {file_path.name}")
+
+    axes[0].plot(time_middle, x_middle, color="tab:blue")
+    axes[0].set_title("Input Base Excitation (Middle Segment)")
+    axes[0].set_xlabel("Time")
+    axes[0].set_ylabel("Acceleration")
+    axes[0].grid(True, alpha=0.3)
+
+    axes[1].plot(freqs, amplitudes, color="tab:purple")
+    axes[1].axvline(record.frequency_hz, color="tab:red", linestyle="--", label=f"Dominant freq = {record.frequency_hz:.4f} Hz")
+    axes[1].set_xlim(0.0, 5.0)
+    axes[1].set_title("Input Spectrum")
+    axes[1].set_xlabel("Frequency (Hz)")
+    axes[1].set_ylabel("Amplitude")
+    axes[1].grid(True, alpha=0.3)
+    axes[1].legend()
+
+    labels = ["Pred RMS"] if true_rms is None else ["True RMS", "Pred RMS"]
+    values = [pred_rms] if true_rms is None else [true_rms, pred_rms]
+    colors = ["tab:orange"] if true_rms is None else ["tab:green", "tab:orange"]
+    axes[2].bar(labels, values, color=colors)
+    title = f"Predicted RMS = {pred_rms:.6f}"
+    if true_rms is not None:
+        error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
+        title = f"True RMS = {true_rms:.6f} | Pred RMS = {pred_rms:.6f} | Error = {error_percent:.2f}%"
+    axes[2].set_title(title)
+    axes[2].set_ylabel("RMS")
+    axes[2].grid(True, axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    figure_path = output_dir / f"{file_path.stem}_prediction.png"
+    plt.savefig(figure_path, dpi=180, bbox_inches="tight")
+    plt.close(fig)
+    return figure_path
+
+
+def main() -> None:
+    args = parse_args()
+    config = make_experiment_config()
+    device = resolve_device(args.device, config.train.device)
+    checkpoint_path = resolve_checkpoint_path(
+        config.data.project_root,
+        config.train.checkpoint_dir,
+        config.train.best_model_name,
+        args.checkpoint,
+    )
+    if not checkpoint_path.exists():
+        raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
+
+    checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False)
+    normalization = normalization_from_payload(checkpoint["normalization"])
+    model = build_model(config).to(device)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.eval()
+
+    file_path = Path(args.file).resolve()
+    record = build_record_from_file(file_path, split="predict", config=config.data)
+    feature_norm = ((record.features - normalization.feature_mean) / normalization.feature_std).unsqueeze(0).to(device)
+    x_rms_raw = record.x_rms.unsqueeze(0).to(device)
+    target_mean = normalization.target_mean.to(device).view(1, 1)
+    target_std = normalization.target_std.to(device).view(1, 1)
+
+    with torch.no_grad():
+        pred_norm = model(feature_norm)
+        pred_tr = torch.clamp(pred_norm * target_std + target_mean, min=1e-6)
+        pred_rms = float((pred_tr * x_rms_raw).detach().cpu().numpy().reshape(-1)[0])
+
+    true_rms = float(record.y_rms.item()) if record.y_rms.numel() > 0 else None
+    output_dir = config.data.project_root / "evaluation_outputs" / "task1_feature_mlp"
+    figure_path = save_prediction_figure(file_path, record, pred_rms, true_rms, output_dir)
+
+    print(f"Checkpoint: {checkpoint_path}")
+    print(f"Input file: {file_path}")
+    print(f"Extracted frequency (Hz): {record.frequency_hz:.6f}")
+    for feature_name, feature_value in zip(CORE_FEATURE_NAMES, record.features.tolist()):
+        print(f"{feature_name}: {feature_value:.6f}")
+    print(f"Predicted RMS: {pred_rms:.6f}")
+    if true_rms is not None:
+        error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
+        print(f"True RMS: {true_rms:.6f}")
+        print(f"Relative RMS Error (%): {error_percent:.4f}")
+    print(f"Figure saved to: {figure_path}")
+
+
+if __name__ == "__main__":
+    main()