inference.py

"""

RadioUNet V3 推理脚本

使用训练好的模型对SoundMapDiff数据集进行推理

"""

import os
import sys
import argparse
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from pathlib import Path
from skimage.metrics import structural_similarity as ssim

sys.path.append(os.path.join(os.path.dirname(__file__), 'lib'))

from lib.modules import RadioWNet
from lib.soundmap_loader import SoundMapDataset
from torch.utils.data import DataLoader


def calculate_metrics(pred, target):
    """计算评估指标"""
    pred_np = pred.cpu().numpy().squeeze()
    target_np = target.cpu().numpy().squeeze()
    
    # MSE
    mse = np.mean((pred_np - target_np) ** 2)
    
    # MAE
    mae = np.mean(np.abs(pred_np - target_np))
    
    # RMSE
    rmse = np.sqrt(mse)
    
    # SSIM
    ssim_val = ssim(pred_np, target_np, data_range=1.0)
    
    # PSNR
    if mse > 0:
        psnr = 10 * np.log10(1.0 / mse)
    else:
        psnr = float('inf')
    
    return {
        'mse': mse,
        'mae': mae,
        'rmse': rmse,
        'ssim': ssim_val,
        'psnr': psnr
    }


def visualize_prediction(inputs, target, pred, metrics, save_path):
    """可视化预测结果"""
    fig, axes = plt.subplots(2, 2, figsize=(12, 12))
    
    # 建筑物布局
    axes[0, 0].imshow(inputs[0].cpu().numpy(), cmap='gray')
    axes[0, 0].set_title('Building Layout', fontsize=14)
    axes[0, 0].axis('off')
    
    # 声源位置
    axes[0, 1].imshow(inputs[1].cpu().numpy(), cmap='hot')
    axes[0, 1].set_title('Sound Source', fontsize=14)
    axes[0, 1].axis('off')
    
    # 真实热力图 - 使用viridis颜色方案（紫→蓝→绿→黄）
    im1 = axes[1, 0].imshow(target.cpu().numpy().squeeze(), cmap='viridis', vmin=0, vmax=1)
    axes[1, 0].set_title('Ground Truth', fontsize=14)
    axes[1, 0].axis('off')
    plt.colorbar(im1, ax=axes[1, 0], fraction=0.046, pad=0.04)
    
    # 预测热力图 - 使用viridis颜色方案（紫→蓝→绿→黄）
    im2 = axes[1, 1].imshow(pred.cpu().numpy().squeeze(), cmap='viridis', vmin=0, vmax=1)
    axes[1, 1].set_title(f"Prediction (SSIM: {metrics['ssim']:.4f})", fontsize=14)
    axes[1, 1].axis('off')
    plt.colorbar(im2, ax=axes[1, 1], fraction=0.046, pad=0.04)
    
    # 添加指标信息
    metrics_text = f"MSE: {metrics['mse']:.6f} | MAE: {metrics['mae']:.4f} | SSIM: {metrics['ssim']:.4f} | PSNR: {metrics['psnr']:.2f} dB"
    fig.suptitle(metrics_text, fontsize=12, y=0.02)
    
    plt.tight_layout()
    plt.savefig(save_path, dpi=150, bbox_inches='tight')
    plt.close()


def main():
    parser = argparse.ArgumentParser(description='RadioUNet V3 推理脚本')
    parser.add_argument('--checkpoint', type=str, 
                       default='outputs/radiounet_v3/checkpoints/best_model.pth',
                       help='模型检查点路径')
    parser.add_argument('--dataset_dir', type=str,
                       default='/home/djk/generate/dataset/SoundMapDiff',
                       help='数据集目录')
    parser.add_argument('--output_dir', type=str,
                       default='outputs/radiounet_v3/inference',
                       help='输出目录')
    parser.add_argument('--num_samples', type=int, default=20,
                       help='推理样本数量')
    parser.add_argument('--img_size', type=int, default=256,
                       help='图像尺寸')
    
    args = parser.parse_args()
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"使用设备: {device}")
    
    # 创建输出目录
    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # 加载模型
    print(f"加载模型: {args.checkpoint}")
    model = RadioWNet(inputs=2, phase="firstU").to(device)
    
    checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False)
    if 'model_state_dict' in checkpoint:
        model.load_state_dict(checkpoint['model_state_dict'])
        print(f"加载Epoch {checkpoint.get('epoch', 'unknown')}的模型")
    else:
        model.load_state_dict(checkpoint)
    
    model.eval()
    
    # 加载测试数据集
    print(f"加载数据集: {args.dataset_dir}")
    test_dataset = SoundMapDataset(
        dataset_dir=args.dataset_dir,
        phase="test",
        img_size=args.img_size
    )
    
    # 均匀采样
    total_samples = len(test_dataset)
    indices = np.linspace(0, total_samples - 1, args.num_samples, dtype=int)
    
    print(f"测试样本数: {total_samples}, 采样数: {args.num_samples}")
    print(f"\n{'='*60}")
    print("开始推理...")
    print(f"{'='*60}\n")
    
    all_metrics = []
    
    with torch.no_grad():
        for i, idx in enumerate(indices):
            inputs, target = test_dataset[idx]
            inputs = inputs.unsqueeze(0).to(device)
            target = target.unsqueeze(0).to(device)
            
            # 推理
            outputs = model(inputs)
            if isinstance(outputs, list):
                outputs = outputs[0]
            
            # 计算指标
            metrics = calculate_metrics(outputs.squeeze(0), target.squeeze(0))
            all_metrics.append(metrics)
            
            # 可视化
            save_path = output_dir / f'prediction_{i+1}_idx{idx}.png'
            visualize_prediction(inputs.squeeze(0), target.squeeze(0), 
                                outputs.squeeze(0), metrics, save_path)
            
            print(f"样本 {i+1}/{args.num_samples} (idx={idx}): "
                  f"SSIM={metrics['ssim']:.4f}, MSE={metrics['mse']:.6f}, PSNR={metrics['psnr']:.2f}dB")
    
    # 计算平均指标
    avg_metrics = {
        'mse': np.mean([m['mse'] for m in all_metrics]),
        'mae': np.mean([m['mae'] for m in all_metrics]),
        'rmse': np.mean([m['rmse'] for m in all_metrics]),
        'ssim': np.mean([m['ssim'] for m in all_metrics]),
        'psnr': np.mean([m['psnr'] for m in all_metrics])
    }
    
    print(f"\n{'='*60}")
    print("平均评估指标")
    print(f"{'='*60}")
    print(f"  平均 MSE:  {avg_metrics['mse']:.6f}")
    print(f"  平均 MAE:  {avg_metrics['mae']:.4f}")
    print(f"  平均 RMSE: {avg_metrics['rmse']:.4f}")
    print(f"  平均 SSIM: {avg_metrics['ssim']:.4f}")
    print(f"  平均 PSNR: {avg_metrics['psnr']:.2f} dB")
    print(f"{'='*60}")
    
    # 保存报告
    report_path = output_dir / 'evaluation_report.txt'
    with open(report_path, 'w', encoding='utf-8') as f:
        f.write("RadioUNet V3 评估报告\n")
        f.write("=" * 60 + "\n\n")
        f.write(f"模型: {args.checkpoint}\n")
        f.write(f"测试样本数: {args.num_samples}\n\n")
        
        for i, (idx, m) in enumerate(zip(indices, all_metrics)):
            f.write(f"样本 {i+1} (索引 {idx}):\n")
            f.write(f"  MSE:  {m['mse']:.6f}\n")
            f.write(f"  MAE:  {m['mae']:.4f}\n")
            f.write(f"  SSIM: {m['ssim']:.4f}\n")
            f.write(f"  PSNR: {m['psnr']:.2f} dB\n\n")
        
        f.write("=" * 60 + "\n")
        f.write("平均指标:\n")
        f.write("=" * 60 + "\n")
        f.write(f"  平均 MSE:  {avg_metrics['mse']:.6f}\n")
        f.write(f"  平均 MAE:  {avg_metrics['mae']:.4f}\n")
        f.write(f"  平均 RMSE: {avg_metrics['rmse']:.4f}\n")
        f.write(f"  平均 SSIM: {avg_metrics['ssim']:.4f}\n")
        f.write(f"  平均 PSNR: {avg_metrics['psnr']:.2f} dB\n")
    
    print(f"\n✅ 推理完成！结果保存在: {output_dir}")


if __name__ == '__main__':
    main()