387 lines
12 KiB
Python
387 lines
12 KiB
Python
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#!/usr/bin/env python3
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"""
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BEVFusion推理和可视化脚本
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基于epoch_19.pth在nuScenes验证集上进行推理并生成可视化结果
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"""
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import argparse
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import os
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import sys
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import torch
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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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from pathlib import Path
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from tqdm import tqdm
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# 添加项目路径
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sys.path.insert(0, '/workspace/bevfusion')
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from mmcv import Config
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from mmcv.parallel import MMDataParallel
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from mmdet3d.datasets import build_dataloader, build_dataset
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from mmdet3d.models import build_model
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from mmcv.runner import load_checkpoint
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def parse_args():
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parser = argparse.ArgumentParser(description='BEVFusion Inference and Visualization')
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parser.add_argument('--config',
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default='configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask.yaml',
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help='配置文件路径')
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parser.add_argument('--checkpoint',
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default='runs/run-326653dc-74184412/epoch_19.pth',
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help='checkpoint文件路径')
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parser.add_argument('--samples', type=int, default=10,
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help='推理样本数量')
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parser.add_argument('--output-dir', default='inference_results',
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help='输出目录')
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parser.add_argument('--show-score-thr', type=float, default=0.3,
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help='检测框显示的置信度阈值')
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parser.add_argument('--device', default='cuda:0',
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help='推理设备')
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return parser.parse_args()
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def setup_model(config_path, checkpoint_path, device='cuda:0'):
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"""加载模型和权重"""
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print(f"\n{'='*80}")
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print("加载模型配置和权重")
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print(f"{'='*80}")
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# 加载配置
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cfg = Config.fromfile(config_path)
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# 构建模型 - 使用完整的model配置
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print("\n1. 构建模型...")
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model = build_model(
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cfg.model,
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train_cfg=cfg.get('train_cfg'),
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test_cfg=cfg.get('test_cfg')
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)
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# 加载checkpoint
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print(f"\n2. 加载checkpoint: {checkpoint_path}")
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checkpoint = load_checkpoint(model, checkpoint_path, map_location='cpu')
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# 设置为评估模式
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model.eval()
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model = model.to(device)
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print(f"\n✅ 模型加载完成")
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print(f" - Device: {device}")
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print(f" - Checkpoint epoch: {checkpoint.get('meta', {}).get('epoch', 'N/A')}")
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return model, cfg
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def build_val_dataloader(cfg, samples=10):
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"""构建验证数据加载器"""
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print(f"\n{'='*80}")
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print("构建数据加载器")
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print(f"{'='*80}")
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# 构建验证数据集
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dataset = build_dataset(cfg.data.val)
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# 限制样本数量
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if samples < len(dataset):
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print(f"\n⚠️ 限制样本数量: {len(dataset)} → {samples}")
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# 均匀采样
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indices = np.linspace(0, len(dataset)-1, samples, dtype=int)
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dataset = torch.utils.data.Subset(dataset, indices)
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# 构建数据加载器
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=1,
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workers_per_gpu=0,
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dist=False,
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shuffle=False
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)
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print(f"\n✅ 数据加载器构建完成")
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print(f" - 样本数量: {len(dataset)}")
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print(f" - 数据集: nuScenes validation set")
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return data_loader, dataset
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def visualize_bev_segmentation(seg_pred, classes, save_path):
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"""可视化BEV分割结果"""
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# seg_pred: (C, H, W) - C个类别的预测
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seg_pred = torch.sigmoid(seg_pred)
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# 创建彩色分割图
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h, w = seg_pred.shape[1:]
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color_map = np.zeros((h, w, 3), dtype=np.uint8)
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# 定义每个类别的颜色
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colors = {
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'drivable_area': [128, 64, 128], # 紫色
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'ped_crossing': [244, 35, 232], # 粉色
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'walkway': [70, 70, 70], # 灰色
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'stop_line': [220, 20, 60], # 红色
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'carpark_area': [157, 234, 50], # 绿色
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'divider': [255, 255, 0], # 黄色
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}
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# 为每个类别着色
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for idx, class_name in enumerate(classes):
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mask = seg_pred[idx].cpu().numpy() > 0.5
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color = colors.get(class_name, [255, 255, 255])
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color_map[mask] = color
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# 保存可视化结果
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plt.figure(figsize=(10, 10))
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plt.imshow(color_map)
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plt.title('BEV Segmentation')
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plt.axis('off')
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# 添加图例
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from matplotlib.patches import Patch
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legend_elements = [
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Patch(facecolor=np.array(colors[name])/255, label=name)
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for name in classes if name in colors
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]
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plt.legend(handles=legend_elements, loc='upper right')
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plt.tight_layout()
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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plt.close()
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return color_map
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def visualize_3d_boxes(img, boxes_3d, labels, scores, save_path, score_thr=0.3):
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"""可视化3D检测框（投影到图像上）"""
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# 简化版：在BEV视图上显示框
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fig, ax = plt.subplots(figsize=(12, 12))
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# 绘制检测框
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if len(boxes_3d) > 0:
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# 提取中心点和尺寸
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centers = boxes_3d[:, :2] # x, y
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sizes = boxes_3d[:, 3:5] # length, width
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yaws = boxes_3d[:, 6] # yaw angle
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# 定义类别颜色
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class_colors = plt.cm.tab10(np.linspace(0, 1, 10))
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for i, (center, size, yaw, label, score) in enumerate(zip(centers, sizes, yaws, labels, scores)):
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if score < score_thr:
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continue
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# 绘制框
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color = class_colors[label % 10]
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# 计算框的四个角点
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l, w = size[0], size[1]
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corners = np.array([
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[-l/2, -w/2],
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[l/2, -w/2],
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[l/2, w/2],
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[-l/2, w/2],
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[-l/2, -w/2] # 闭合
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])
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# 旋转
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rot_mat = np.array([
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[np.cos(yaw), -np.sin(yaw)],
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[np.sin(yaw), np.cos(yaw)]
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])
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corners = corners @ rot_mat.T
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corners += center
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# 绘制
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ax.plot(corners[:, 0], corners[:, 1], color=color, linewidth=2)
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ax.scatter(center[0], center[1], color=color, s=50, zorder=10)
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# 添加标签
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ax.text(center[0], center[1], f'{score:.2f}',
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fontsize=8, color='white',
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bbox=dict(boxstyle='round', facecolor=color, alpha=0.7))
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ax.set_xlim(-50, 50)
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ax.set_ylim(-50, 50)
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ax.set_aspect('equal')
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ax.grid(True, alpha=0.3)
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ax.set_xlabel('X (meters)')
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ax.set_ylabel('Y (meters)')
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ax.set_title('3D Detection Results (BEV)')
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plt.tight_layout()
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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plt.close()
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def visualize_combined(img, seg_pred, boxes_3d, labels, scores, classes, save_path, score_thr=0.3):
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"""综合可视化：分割+检测"""
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fig = plt.figure(figsize=(20, 10))
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# 1. BEV分割
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ax1 = plt.subplot(1, 2, 1)
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seg_pred = torch.sigmoid(seg_pred)
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h, w = seg_pred.shape[1:]
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color_map = np.zeros((h, w, 3), dtype=np.uint8)
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colors = {
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'drivable_area': [128, 64, 128],
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'ped_crossing': [244, 35, 232],
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'walkway': [70, 70, 70],
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'stop_line': [220, 20, 60],
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'carpark_area': [157, 234, 50],
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'divider': [255, 255, 0],
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}
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for idx, class_name in enumerate(classes):
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mask = seg_pred[idx].cpu().numpy() > 0.5
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color = colors.get(class_name, [255, 255, 255])
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color_map[mask] = color
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ax1.imshow(color_map)
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ax1.set_title('BEV Segmentation', fontsize=16)
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ax1.axis('off')
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# 2. 3D检测
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ax2 = plt.subplot(1, 2, 2)
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if len(boxes_3d) > 0:
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centers = boxes_3d[:, :2]
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sizes = boxes_3d[:, 3:5]
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yaws = boxes_3d[:, 6]
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class_colors = plt.cm.tab10(np.linspace(0, 1, 10))
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for i, (center, size, yaw, label, score) in enumerate(zip(centers, sizes, yaws, labels, scores)):
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if score < score_thr:
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continue
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color = class_colors[label % 10]
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l, w = size[0], size[1]
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corners = np.array([
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[-l/2, -w/2], [l/2, -w/2], [l/2, w/2], [-l/2, w/2], [-l/2, -w/2]
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])
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rot_mat = np.array([
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[np.cos(yaw), -np.sin(yaw)],
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[np.sin(yaw), np.cos(yaw)]
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])
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corners = corners @ rot_mat.T + center
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ax2.plot(corners[:, 0], corners[:, 1], color=color, linewidth=2)
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ax2.scatter(center[0], center[1], color=color, s=50, zorder=10)
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ax2.set_xlim(-50, 50)
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ax2.set_ylim(-50, 50)
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ax2.set_aspect('equal')
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ax2.grid(True, alpha=0.3)
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ax2.set_xlabel('X (meters)', fontsize=12)
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ax2.set_ylabel('Y (meters)', fontsize=12)
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ax2.set_title('3D Object Detection (BEV)', fontsize=16)
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plt.tight_layout()
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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plt.close()
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print(f" ✅ 已保存: {save_path}")
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def inference(model, data_loader, output_dir, cfg, score_thr=0.3):
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"""执行推理并可视化"""
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print(f"\n{'='*80}")
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print("开始推理和可视化")
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print(f"{'='*80}\n")
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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# 获取类别信息
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map_classes = cfg.get('map_classes', [
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'drivable_area', 'ped_crossing', 'walkway',
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'stop_line', 'carpark_area', 'divider'
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])
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object_classes = cfg.get('object_classes', [
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'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
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'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
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])
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model.eval()
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with torch.no_grad():
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for idx, data in enumerate(tqdm(data_loader, desc="推理中")):
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# 前向传播
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result = model(return_loss=False, rescale=True, **data)
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# 提取结果
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if isinstance(result, dict):
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# 分割结果
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seg_pred = result.get('seg_pred', None)
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# 检测结果
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bbox_results = result.get('bbox_results', None)
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else:
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# 兼容性处理
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bbox_results = result[0] if len(result) > 0 else None
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seg_pred = None
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# 可视化
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sample_name = f"sample_{idx:04d}"
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# 如果有分割结果
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if seg_pred is not None:
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seg_path = os.path.join(output_dir, f"{sample_name}_segmentation.png")
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visualize_bev_segmentation(seg_pred[0], map_classes, seg_path)
|
|||
|
|
|
|||
|
|
# 如果有检测结果
|
|||
|
|
if bbox_results is not None and 'boxes_3d' in bbox_results:
|
|||
|
|
boxes_3d = bbox_results['boxes_3d'].tensor.cpu().numpy()
|
|||
|
|
labels = bbox_results['labels_3d'].cpu().numpy()
|
|||
|
|
scores = bbox_results['scores_3d'].cpu().numpy()
|
|||
|
|
|
|||
|
|
det_path = os.path.join(output_dir, f"{sample_name}_detection.png")
|
|||
|
|
visualize_3d_boxes(None, boxes_3d, labels, scores, det_path, score_thr)
|
|||
|
|
|
|||
|
|
# 综合可视化
|
|||
|
|
if seg_pred is not None and bbox_results is not None:
|
|||
|
|
combined_path = os.path.join(output_dir, f"{sample_name}_combined.png")
|
|||
|
|
visualize_combined(
|
|||
|
|
None, seg_pred[0],
|
|||
|
|
boxes_3d, labels, scores,
|
|||
|
|
map_classes, combined_path, score_thr
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
print(f"\n{'='*80}")
|
|||
|
|
print(f"✅ 推理完成！")
|
|||
|
|
print(f" - 结果保存在: {output_dir}")
|
|||
|
|
print(f" - 共处理样本: {len(data_loader)}")
|
|||
|
|
print(f"{'='*80}\n")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
args = parse_args()
|
|||
|
|
|
|||
|
|
print(f"\n{'='*80}")
|
|||
|
|
print("BEVFusion推理和可视化")
|
|||
|
|
print(f"{'='*80}")
|
|||
|
|
print(f"配置文件: {args.config}")
|
|||
|
|
print(f"Checkpoint: {args.checkpoint}")
|
|||
|
|
print(f"样本数量: {args.samples}")
|
|||
|
|
print(f"输出目录: {args.output_dir}")
|
|||
|
|
print(f"置信度阈值: {args.show_score_thr}")
|
|||
|
|
print(f"{'='*80}\n")
|
|||
|
|
|
|||
|
|
# 1. 加载模型
|
|||
|
|
model, cfg = setup_model(args.config, args.checkpoint, args.device)
|
|||
|
|
|
|||
|
|
# 2. 构建数据加载器
|
|||
|
|
data_loader, dataset = build_val_dataloader(cfg, args.samples)
|
|||
|
|
|
|||
|
|
# 3. 执行推理和可视化
|
|||
|
|
inference(model, data_loader, args.output_dir, cfg, args.show_score_thr)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
main()
|
|||
|
|
|