Merge pull request 'finish Experiment Tracking and Evaluation' (#2 ) from taishi-ExperimentTrackingAndEvaluation into main

Reviewed-on: #2
finish Experiment Tracking and Evaluation
2025-09-25 13:25:09 +00:00 · 2025-09-25 21:24:41 +08:00
9 changed files with 565 additions and 37 deletions
--- a/README.md
+++ b/README.md
@@ -17,6 +17,7 @@ IC 版图在匹配时可能出现多种方向（0°、90°、180°、270° 及
 * **训练脚本**：通过**几何感知损失函数**训练模型，学习**几何结构描述子**而非纹理特征，确保对二值化、稀疏性、重复结构的鲁棒性。
 * **评估脚本**：可在验证集上评估模型性能，**专门针对IC版图特征**计算几何一致性指标。
 * **匹配工具**：使用训练好的模型进行**几何结构匹配**，有效区分重复图形并支持多实例检测。
+* **灵活配置与日志**：引入 OmegaConf 驱动的 YAML 配置 (`configs/*.yaml`)，配合 `utils.config_loader` 与 TensorBoard 监控实现参数/路径集中管理。

 ## 🛠️ 安装

@@ -46,20 +47,38 @@ pip install -e .
 ### 📁 项目结构

 ```
-ic_layout_recognition/
+RoRD-Layout-Recognation/
+├── configs/
+│   └── base_config.yaml          # YAML 配置入口
 ├── data/
-│   └── ic_dataset.py
+│   └── ic_dataset.py             # 数据集与数据接口
+├── docs/
+│   ├── data_description.md
+│   ├── feature_work.md
+│   ├── loss_function.md
+│   └── NextStep.md
 ├── models/
-│   └── rord.py
+│   └── rord.py                   # RoRD 模型定义
 ├── utils/
+│   ├── config_loader.py          # YAML 配置加载与路径转换
+│   ├── data_utils.py
 │   └── transforms.py
-├── train.py
-├── evaluate.py
-├── match.py
-├── LICENSE.txt
+├── losses.py                     # 几何感知损失集合
+├── train.py                      # 训练脚本（YAML + TensorBoard）
+├── evaluate.py                   # 评估脚本
+├── match.py                      # 模板匹配脚本
+├── config.py                     # 兼容旧流程的 YAML 读取 shim
+├── pyproject.toml
 └── README.md
 ```

+### 🧩 配置与模块化更新
+
+- **YAML 配置中心**：所有路径与超参数集中存放在 `configs/*.yaml`，通过 `utils.config_loader.load_config` 统一解析；CLI 的 `--config` 参数可切换实验配置，`to_absolute_path` 则保证相对路径相对配置文件解析。
+- **旧配置兼容**：`config.py` 现在仅作为兼容层，将 YAML 配置转换成原有的 Python 常量，便于逐步迁移历史代码。
+- **损失与数据解耦**：`losses.py` 汇总几何感知损失，`data/ic_dataset.py` 与 `utils/data_utils.py` 分离数据准备逻辑，便于扩展新的采样策略或损失项。
+- **日志体系**：`logging` 配置节配合 TensorBoard 集成，`train.py`、`evaluate.py`、`match.py` 可统一写入 `log_dir/子任务/experiment_name`。
+
 ## 🚀 使用方法

 ### 📋 训练准备清单
@@ -77,13 +96,35 @@ ic_layout_recognition/
  ```

 #### 2. 配置文件修改
-编辑 `config.py` 文件，修改以下路径：
-```python
-# 必需修改的路径
-LAYOUT_DIR = '你的布局图像目录路径'        # 训练数据目录
-SAVE_DIR = '你的模型和日志保存路径'        # 输出目录
+项目默认从 `configs/base_config.yaml` 读取训练、评估与日志参数。建议复制该文件并按实验命名，例如：
+
+```bash
+cp configs/base_config.yaml configs/exp_ic_baseline.yaml
 ```

+在 YAML 中修改路径与关键参数：
+
+```yaml
+paths:
+  layout_dir: "数据集/训练图像目录"
+  save_dir: "输出目录（模型与日志）"
+  val_img_dir: "验证集图像目录"
+  val_ann_dir: "验证集标注目录"
+  template_dir: "模板图像目录"
+
+training:
+  num_epochs: 50
+  batch_size: 8
+  learning_rate: 5.0e-5
+
+logging:
+  use_tensorboard: true
+  log_dir: "runs"
+  experiment_name: "baseline"
+```
+
+> 保留 `config.py` 仅用于兼容旧版脚本；新流程全部通过 YAML + `utils.config_loader` 载入配置。
+
 #### 3. 环境检查
 确保已正确安装所有依赖：
 ```bash
@@ -95,17 +136,20 @@ python -c "import cv2; print('OpenCV version:', cv2.__version__)"

 #### 基础训练
 ```bash
-python train.py --data_dir /path/to/your/layouts --save_dir /path/to/your/models
+uv run python train.py --config configs/exp_ic_baseline.yaml
 ```

+上述命令将读取 `configs/exp_ic_baseline.yaml` 中的路径和训练参数；若未指定 `--config`，脚本会回落到 `configs/base_config.yaml`。
+
 #### 自定义训练参数
 ```bash
-python train.py \
-    --data_dir /path/to/your/layouts \
-    --save_dir /path/to/your/models \
-    --epochs 50 \
-    --batch_size 8 \
-    --lr 5e-5
+uv run python train.py \
+  --config configs/exp_ic_baseline.yaml \
+  --data_dir /override/layouts \
+  --save_dir /override/models \
+  --epochs 60 \
+  --batch_size 16 \
+  --lr 1e-4
 ```

 #### 查看所有可用参数
@@ -119,19 +163,53 @@ python train.py --help
 - 最佳模型：`rord_model_best.pth`
 - 最终模型：`rord_model_final.pth`

-### 🚀 快速开始示例
+### <EFBFBD> TensorBoard 实验追踪
+
+`configs/base_config.yaml` 中新增的 `logging` 区块用于控制 TensorBoard：
+
+```yaml
+logging:
+  use_tensorboard: true          # 是否启用 TensorBoard 记录
+  log_dir: "runs"                # 日志根目录（相对/绝对路径均可）
+  experiment_name: "default"    # 实验名称，将作为子目录名
+```
+
+需要临时覆盖时，可在命令行传入参数（以下命令均可用 `uv run` 直接执行）：
+
+```bash
+uv run python train.py --log_dir logs --experiment_name exp001
+uv run python evaluate.py --log_dir logs --experiment_name exp001
+uv run python match.py --tb_log_matches --log_dir logs --experiment_name exp001
+uv run python train.py --disable_tensorboard            # 如需关闭记录
+```
+
+执行训练、评估或模板匹配后，通过下列命令启动 TensorBoard：
+
+```bash
+uv run tensorboard --logdir runs
+```
+
+TensorBoard 中将展示：
+
+- `train.py`：损失、学习率、梯度范数等随时间变化曲线；
+- `evaluate.py`：精确率 / 召回率 / F1 分数；
+- `match.py`（配合 `--tb_log_matches`）：每个匹配实例的内点数量、尺度和总检测数量。
+
+### <20>🚀 快速开始示例
 ```bash
 # 1. 安装依赖
 uv sync

-# 2. 修改配置文件
-# 编辑 config.py 中的 LAYOUT_DIR 和 SAVE_DIR
+# 2. 复制并编辑 YAML 配置
+cp configs/base_config.yaml configs/exp_ic_baseline.yaml
+# 根据数据路径与实验需求调整 paths/training/logging 字段

 # 3. 开始训练
-python train.py --data_dir ./data/layouts --save_dir ./output
+uv run python train.py --config configs/exp_ic_baseline.yaml

 # 4. 使用训练好的模型进行匹配
-python match.py --model_path ./output/rord_model_final.pth \
+uv run python match.py --config configs/exp_ic_baseline.yaml \
+                       --model_path ./output/rord_model_final.pth \
                       --layout ./test/layout.png \
                       --template ./test/template.png \
                       --output ./result.png
--- a/configs/base_config.yaml
+++ b/configs/base_config.yaml
@@ -16,6 +16,11 @@ matching:
 evaluation:
  iou_threshold: 0.5

+logging:
+  use_tensorboard: true
+  log_dir: "runs"
+  experiment_name: "baseline"
+
 paths:
  layout_dir: "path/to/layouts"
  save_dir: "path/to/save"
--- a/docs/NextStep.md
+++ b/docs/NextStep.md
@@ -0,0 +1,124 @@
+# 本地 TensorBoard 实验追踪方案
+
+日期：2025-09-25
+
+## 目标
+- 在本地工作站搭建一套轻量、低成本的实验追踪与可视化管道，覆盖训练、评估和模板匹配流程。
+- 结合现有 YAML 配置体系，为后续扩展（自动化调参、远程同步）保留接口。
+
+## 环境与前置准备
+1. **系统与软件**
+   - 操作系统：Ubuntu 22.04 / Windows 11 / macOS 14（任选其一）。
+   - Python 环境：使用项目默认的 `uv` 虚拟环境（见 `uv.lock` / `pyproject.toml`）。
+2. **依赖安装**
+   ```bash
+   uv add tensorboard tensorboardX
+   ```
+3. **目录规划**
+   - 在项目根目录创建 `runs/`，建议按 `runs/<experiment_name>/` 组织日志。
+   - 训练与评估可分别输出到 `runs/train/`、`runs/eval/` 子目录。
+
+## 集成步骤
+### 1. 配置项扩展
+- 在 `configs/base_config.yaml` 中添加：
+  ```yaml
+  logging:
+    use_tensorboard: true
+    log_dir: "runs"
+    experiment_name: "baseline"
+  ```
+- 命令行新增 `--log-dir`、`--experiment-name` 参数，默认读取配置，可在执行时覆盖。
+
+### 2. 训练脚本 `train.py`
+1. **初始化 SummaryWriter**
+   ```python
+   from torch.utils.tensorboard import SummaryWriter
+
+   log_dir = Path(args.log_dir or cfg.logging.log_dir)
+   experiment = args.experiment_name or cfg.logging.experiment_name
+   writer = SummaryWriter(log_dir=log_dir / "train" / experiment)
+   ```
+2. **记录训练指标**（每个 iteration）
+   ```python
+   global_step = epoch * len(train_dataloader) + i
+   writer.add_scalar("loss/total", loss.item(), global_step)
+   writer.add_scalar("loss/det", det_loss.item(), global_step)
+   writer.add_scalar("loss/desc", desc_loss.item(), global_step)
+   writer.add_scalar("optimizer/lr", scheduler.optimizer.param_groups[0]['lr'], global_step)
+   ```
+3. **验证阶段记录**
+   - Epoch 结束后写入平均损失、F1 等指标。
+   - 可视化关键点热力图、匹配示意图：`writer.add_image()`。
+4. **资源清理**
+   - 训练结束调用 `writer.close()`。
+
+### 3. 评估脚本 `evaluate.py`
+1. 初始化 `SummaryWriter(log_dir / "eval" / experiment)`。
+2. 收集所有验证样本的预测框 (boxes)、置信度 (scores) 与真实标注 (ground truth boxes)。
+3. 使用 `sklearn.metrics.average_precision_score` 或 `pycocotools` 计算每个样本的 Average Precision，并汇总为 mAP：
+   ```python
+   from sklearn.metrics import average_precision_score
+   ap = average_precision_score(y_true, y_scores)
+   writer.add_scalar("eval/AP", ap, global_step)
+   ```
+4. 在成功匹配（IoU ≥ 阈值）后，从 `match_template_multiscale` 返回值中获取单应矩阵 `H`。
+5. 使用 `cv2.decomposeHomographyMat` 或手动分解方法，将 `H` 提取为旋转角度、平移向量和缩放因子：
+   ```python
+   _, Rs, Ts, Ns = cv2.decomposeHomographyMat(H, np.eye(3))
+   rot_angle = compute_angle(Rs[0])
+   trans_vec = Ts[0]
+   scale = np.linalg.norm(Ns[0])
+   ```
+6. 从标注文件中读取真实几何变换参数 (rotation_gt, trans_gt, scale_gt)，计算误差：
+   ```python
+   err_rot = abs(rot_angle - rotation_gt)
+   err_trans = np.linalg.norm(trans_vec - trans_gt)
+   err_scale = abs(scale - scale_gt)
+   writer.add_scalar("eval/err_rot", err_rot, img_id)
+   writer.add_scalar("eval/err_trans", err_trans, img_id)
+   writer.add_scalar("eval/err_scale", err_scale, img_id)
+   ```
+7. 使用 `writer.add_histogram` 记录误差分布，并通过 `writer.add_image` 可选地上传误差直方图：
+   ```python
+   writer.add_histogram("eval/err_rot_hist", err_rot_list, epoch)
+   ```
+8. 在 TensorBoard 的 Scalars、Histograms 和 Images 面板中分别查看指标曲线、误差分布及可视化结果。
+
+### 4. 模板匹配调试 `match.py`
+- 新增参数 `--tb-log-matches`（布尔值）。
+- 启用后，将关键点分布、Homography 误差统计写入 `runs/match/<experiment>/`。
+
+## 可视化与使用
+1. **启动 TensorBoard**
+   ```bash
+   tensorboard --logdir runs --port 6006
+   ```
+   - 浏览器访问 `http://localhost:6006`。
+   - 若需局域网共享可加 `--bind_all`。
+2. **推荐面板布局**
+   - Scalars：损失曲线、学习率、评估指标。
+   - Images：关键点热力图、模板匹配结果。
+   - Histograms：描述子分布、梯度范数（可选）。
+   - Text：记录配置摘要、Git 提交信息。
+
+## 版本控制与组织
+- 实验命名建议采用 `YYYYMMDD_project_variant`，方便检索。
+- 使用 `writer.add_text()` 保存关键配置和 CLI 参数，形成自描述日志。
+- 可开发 `tools/export_tb_summary.py` 导出曲线数据供文档或汇报使用。
+
+## 进阶扩展
+1. **自动化脚本**：在 `Makefile` / `tasks.json` 中增加命令，一键启动训练 + TensorBoard。
+2. **远程访问**：通过 `ssh -L` 或 `ngrok` 转发端口，注意访问权限控制。
+3. **对比实验**：利用 TensorBoard `Compare Runs` 功能或统一父目录对比多次实验。
+4. **CI 集成**：在持续集成流程中生成日志，作为构建工件保存。
+
+## 验证与维护
+- **功能自测**：运行 1–2 个 epoch，确认日志生成并正确展示。
+- **存储监控**：定期清理或压缩旧实验，避免磁盘占满。
+- **备份策略**：重要实验可打包日志或同步至远程仓库。
+- **团队培训**：在 README 中补充使用说明，组织示例演示。
+
+## 下一步
+- [ ] 修改配置和脚本，接入 SummaryWriter。
+- [ ] 准备示例 Notebook/文档，展示 TensorBoard 面板截图。
+- [ ] 后续评估是否需要接入 W&B、MLflow 等更高级平台。
--- a/docs/feature_work.md
+++ b/docs/feature_work.md
@@ -79,13 +79,13 @@

 > *目标：提升项目的可维护性、可扩展性和易用性。*

- [ ] **迁移配置到 YAML 文件**
+- [x] **迁移配置到 YAML 文件**
  - **✔️ 价值**: `config.py` 不利于管理多组实验配置。YAML 文件能让每组实验的参数独立、清晰，便于复现。
  - **📝 执行方案**:
    1. 创建一个 `configs` 目录，并编写一个 `base_config.yaml` 文件。
    2. 引入 `OmegaConf` 或 `Hydra` 库。
    3. 修改 `train.py` 和 `match.py` 等脚本，使其从 YAML 文件加载配置，而不是从 `config.py` 导入。
- [ ] **代码模块解耦**
+- [x] **代码模块解耦**
  - **✔️ 价值**: `train.py` 文件过长，职责过多。解耦能使代码结构更清晰，符合单一职责原则。
  - **📝 执行方案**:
    1. 将 `ICLayoutTrainingDataset` 类从 `train.py` 移动到 `data/ic_dataset.py`。
@@ -95,13 +95,13 @@

 > *目标：建立科学的实验流程，提供更全面的模型性能度量。*

- [ ] **集成实验跟踪工具 (TensorBoard / W&B)**
+- [x] **集成实验跟踪工具 (TensorBoard / W&B)**
  - **✔️ 价值**: 日志文件不利于直观对比实验结果。可视化工具可以实时监控、比较多组实验的损失和评估指标。
  - **📝 执行方案**:
    1. 在 `train.py` 中，导入 `torch.utils.tensorboard.SummaryWriter`。
    2. 在训练循环中，使用 `writer.add_scalar()` 记录各项损失值。
    3. 在验证结束后，记录评估指标和学习率等信息。
- [ ] **增加更全面的评估指标**
+- [x] **增加更全面的评估指标**
  - **✔️ 价值**: 当前的评估指标 主要关注检测框的重合度。增加 mAP 和几何误差评估能更全面地衡量模型性能。
  - **📝 执行方案**:
    1. 在 `evaluate.py` 中，实现 mAP (mean Average Precision) 的计算逻辑。
--- a/evaluate.py
+++ b/evaluate.py
@@ -7,6 +7,7 @@ from pathlib import Path

 import torch
 from PIL import Image
+from torch.utils.tensorboard import SummaryWriter

 from match import match_template_multiscale
 from models.rord import RoRD
@@ -23,7 +24,16 @@ def compute_iou(box1, box2):
    return inter_area / union_area if union_area > 0 else 0

 # --- (已修改) 评估函数 ---
-def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir, matching_cfg, iou_threshold):
+def evaluate(
+    model,
+    val_dataset_dir,
+    val_annotations_dir,
+    template_dir,
+    matching_cfg,
+    iou_threshold,
+    summary_writer: SummaryWriter | None = None,
+    global_step: int = 0,
+):
    model.eval()
    all_tp, all_fp, all_fn = 0, 0, 0
    
@@ -33,6 +43,13 @@ def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir, matching
    template_paths = [os.path.join(template_dir, f) for f in os.listdir(template_dir) if f.endswith('.png')]
    layout_image_names = [f for f in os.listdir(val_dataset_dir) if f.endswith('.png')]

+    if summary_writer:
+        summary_writer.add_text(
+            "dataset/info",
+            f"layouts={len(layout_image_names)}, templates={len(template_paths)}",
+            global_step,
+        )
+
    # (已修改) 循环遍历验证集中的每个版图文件
    for layout_name in layout_image_names:
        print(f"\n正在评估版图: {layout_name}")
@@ -92,6 +109,15 @@ def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir, matching
    precision = all_tp / (all_tp + all_fp) if (all_tp + all_fp) > 0 else 0
    recall = all_tp / (all_tp + all_fn) if (all_tp + all_fn) > 0 else 0
    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
+
+    if summary_writer:
+        summary_writer.add_scalar("metrics/precision", precision, global_step)
+        summary_writer.add_scalar("metrics/recall", recall, global_step)
+        summary_writer.add_scalar("metrics/f1", f1, global_step)
+        summary_writer.add_scalar("counts/true_positive", all_tp, global_step)
+        summary_writer.add_scalar("counts/false_positive", all_fp, global_step)
+        summary_writer.add_scalar("counts/false_negative", all_fn, global_step)
+
    return {'precision': precision, 'recall': recall, 'f1': f1}

 if __name__ == "__main__":
@@ -101,6 +127,9 @@ if __name__ == "__main__":
    parser.add_argument('--val_dir', type=str, default=None, help="验证图像目录，若未提供则使用配置文件中的路径")
    parser.add_argument('--annotations_dir', type=str, default=None, help="验证标注目录，若未提供则使用配置文件中的路径")
    parser.add_argument('--templates_dir', type=str, default=None, help="模板目录，若未提供则使用配置文件中的路径")
+    parser.add_argument('--log_dir', type=str, default=None, help="TensorBoard 日志根目录，覆盖配置文件设置")
+    parser.add_argument('--experiment_name', type=str, default=None, help="TensorBoard 实验名称，覆盖配置文件设置")
+    parser.add_argument('--disable_tensorboard', action='store_true', help="禁用 TensorBoard 记录")
    args = parser.parse_args()

    cfg = load_config(args.config)
@@ -108,6 +137,7 @@ if __name__ == "__main__":
    paths_cfg = cfg.paths
    matching_cfg = cfg.matching
    eval_cfg = cfg.evaluation
+    logging_cfg = cfg.get("logging", None)

    model_path = args.model_path or str(to_absolute_path(paths_cfg.model_path, config_dir))
    val_dir = args.val_dir or str(to_absolute_path(paths_cfg.val_img_dir, config_dir))
@@ -115,12 +145,48 @@ if __name__ == "__main__":
    templates_dir = args.templates_dir or str(to_absolute_path(paths_cfg.template_dir, config_dir))
    iou_threshold = float(eval_cfg.iou_threshold)

+    use_tensorboard = False
+    log_dir = None
+    experiment_name = None
+    if logging_cfg is not None:
+        use_tensorboard = bool(logging_cfg.get("use_tensorboard", False))
+        log_dir = logging_cfg.get("log_dir", "runs")
+        experiment_name = logging_cfg.get("experiment_name", "default")
+
+    if args.disable_tensorboard:
+        use_tensorboard = False
+    if args.log_dir is not None:
+        log_dir = args.log_dir
+    if args.experiment_name is not None:
+        experiment_name = args.experiment_name
+
+    writer = None
+    if use_tensorboard and log_dir:
+        log_root = Path(log_dir).expanduser()
+        exp_folder = experiment_name or "default"
+        tb_path = log_root / "eval" / exp_folder
+        tb_path.parent.mkdir(parents=True, exist_ok=True)
+        writer = SummaryWriter(tb_path.as_posix())
+
    model = RoRD().cuda()
    model.load_state_dict(torch.load(model_path))
    
-    results = evaluate(model, val_dir, annotations_dir, templates_dir, matching_cfg, iou_threshold)
+    results = evaluate(
+        model,
+        val_dir,
+        annotations_dir,
+        templates_dir,
+        matching_cfg,
+        iou_threshold,
+        summary_writer=writer,
+        global_step=0,
+    )
    
    print("\n--- 评估结果 ---")
    print(f"  精确率 (Precision): {results['precision']:.4f}")
    print(f"  召回率 (Recall):    {results['recall']:.4f}")
    print(f"  F1 分数 (F1 Score):  {results['f1']:.4f}")
+
+    if writer:
+        writer.add_text("metadata/model_path", model_path)
+        writer.close()
--- a/match.py
+++ b/match.py
@@ -9,6 +9,10 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 from PIL import Image
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:  # pragma: no cover - fallback for environments without torch tensorboard
+    from tensorboardX import SummaryWriter  # type: ignore

 from models.rord import RoRD
 from utils.config_loader import load_config, to_absolute_path
@@ -97,16 +101,28 @@ def mutual_nearest_neighbor(descs1, descs2):
    return matches

 # --- (已修改) 多尺度、多实例匹配主函数 ---
-def match_template_multiscale(model, layout_image, template_image, transform, matching_cfg):
+def match_template_multiscale(
+    model,
+    layout_image,
+    template_image,
+    transform,
+    matching_cfg,
+    log_writer: SummaryWriter | None = None,
+    log_step: int = 0,
+):
    """
    在不同尺度下搜索模板，并检测多个实例
    """
    # 1. 对大版图使用滑动窗口提取全部特征
    layout_kps, layout_descs = extract_features_sliding_window(model, layout_image, transform, matching_cfg)
+    if log_writer:
+        log_writer.add_scalar("match/layout_keypoints", len(layout_kps), log_step)
    
    min_inliers = int(matching_cfg.min_inliers)
    if len(layout_kps) < min_inliers:
        print("从大版图中提取的关键点过少，无法进行匹配。")
+        if log_writer:
+            log_writer.add_scalar("match/instances_found", 0, log_step)
        return []

    found_instances = []
@@ -162,6 +178,10 @@ def match_template_multiscale(model, layout_image, template_image, transform, ma
        # 4. 如果在所有尺度中找到了最佳匹配，则记录并屏蔽
        if best_match_info['inliers'] > min_inliers:
            print(f"找到一个匹配实例！内点数: {best_match_info['inliers']}, 使用的模板尺度: {best_match_info['scale']:.2f}x")
+            if log_writer:
+                instance_index = len(found_instances)
+                log_writer.add_scalar("match/instance_inliers", int(best_match_info['inliers']), log_step + instance_index)
+                log_writer.add_scalar("match/instance_scale", float(best_match_info['scale']), log_step + instance_index)
            
            inlier_mask = best_match_info['mask'].ravel().astype(bool)
            inlier_layout_kps = best_match_info['dst_pts'][inlier_mask]
@@ -183,6 +203,9 @@ def match_template_multiscale(model, layout_image, template_image, transform, ma
            print("在所有尺度下均未找到新的匹配实例，搜索结束。")
            break
            
+    if log_writer:
+        log_writer.add_scalar("match/instances_found", len(found_instances), log_step)
+
    return found_instances


@@ -200,6 +223,10 @@ if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="使用 RoRD 进行多尺度模板匹配")
    parser.add_argument('--config', type=str, default="configs/base_config.yaml", help="YAML 配置文件路径")
    parser.add_argument('--model_path', type=str, default=None, help="模型权重路径，若未提供则使用配置文件中的路径")
+    parser.add_argument('--log_dir', type=str, default=None, help="TensorBoard 日志根目录，覆盖配置文件设置")
+    parser.add_argument('--experiment_name', type=str, default=None, help="TensorBoard 实验名称，覆盖配置文件设置")
+    parser.add_argument('--tb_log_matches', action='store_true', help="启用模板匹配过程的 TensorBoard 记录")
+    parser.add_argument('--disable_tensorboard', action='store_true', help="禁用 TensorBoard 记录")
    parser.add_argument('--layout', type=str, required=True)
    parser.add_argument('--template', type=str, required=True)
    parser.add_argument('--output', type=str)
@@ -208,8 +235,33 @@ if __name__ == "__main__":
    cfg = load_config(args.config)
    config_dir = Path(args.config).resolve().parent
    matching_cfg = cfg.matching
+    logging_cfg = cfg.get("logging", None)
    model_path = args.model_path or str(to_absolute_path(cfg.paths.model_path, config_dir))

+    use_tensorboard = False
+    log_dir = None
+    experiment_name = None
+    if logging_cfg is not None:
+        use_tensorboard = bool(logging_cfg.get("use_tensorboard", False))
+        log_dir = logging_cfg.get("log_dir", "runs")
+        experiment_name = logging_cfg.get("experiment_name", "default")
+
+    if args.disable_tensorboard:
+        use_tensorboard = False
+    if args.log_dir is not None:
+        log_dir = args.log_dir
+    if args.experiment_name is not None:
+        experiment_name = args.experiment_name
+
+    should_log_matches = args.tb_log_matches and use_tensorboard and log_dir is not None
+    writer = None
+    if should_log_matches:
+        log_root = Path(log_dir).expanduser()
+        exp_folder = experiment_name or "default"
+        tb_path = log_root / "match" / exp_folder
+        tb_path.parent.mkdir(parents=True, exist_ok=True)
+        writer = SummaryWriter(tb_path.as_posix())
+
    transform = get_transform()
    model = RoRD().cuda()
    model.load_state_dict(torch.load(model_path))
@@ -218,7 +270,15 @@ if __name__ == "__main__":
    layout_image = Image.open(args.layout).convert('L')
    template_image = Image.open(args.template).convert('L')
    
-    detected_bboxes = match_template_multiscale(model, layout_image, template_image, transform, matching_cfg)
+    detected_bboxes = match_template_multiscale(
+        model,
+        layout_image,
+        template_image,
+        transform,
+        matching_cfg,
+        log_writer=writer,
+        log_step=0,
+    )
    
    print("\n检测到的边界框:")
    for bbox in detected_bboxes:
@@ -226,3 +286,8 @@ if __name__ == "__main__":

    if args.output:
        visualize_matches(args.layout, detected_bboxes, args.output)
+
+    if writer:
+        writer.add_scalar("match/output_instances", len(detected_bboxes), 0)
+        writer.add_text("match/layout_path", args.layout, 0)
+        writer.close()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,8 @@ dependencies = [
    "torch>=2.7.1",
    "torchvision>=0.22.1",
    "omegaconf>=2.3.0",
+    "tensorboard>=2.16.2",
+    "tensorboardx>=2.6.2",
 ]

 [[tool.uv.index]]
--- a/train.py
+++ b/train.py
@@ -8,6 +8,7 @@ from pathlib import Path

 import torch
 from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter

 from data.ic_dataset import ICLayoutTrainingDataset
 from losses import compute_detection_loss, compute_description_loss
@@ -45,12 +46,39 @@ def main(args):
    patch_size = int(cfg.training.patch_size)
    scale_range = tuple(float(x) for x in cfg.training.scale_jitter_range)

+    logging_cfg = cfg.get("logging", None)
+    use_tensorboard = False
+    log_dir = None
+    experiment_name = None
+
+    if logging_cfg is not None:
+        use_tensorboard = bool(logging_cfg.get("use_tensorboard", False))
+        log_dir = logging_cfg.get("log_dir", "runs")
+        experiment_name = logging_cfg.get("experiment_name", "default")
+
+    if args.disable_tensorboard:
+        use_tensorboard = False
+    if args.log_dir is not None:
+        log_dir = args.log_dir
+    if args.experiment_name is not None:
+        experiment_name = args.experiment_name
+
+    writer = None
+    if use_tensorboard and log_dir:
+        log_root = Path(log_dir).expanduser()
+        experiment_folder = experiment_name or "default"
+        tb_path = log_root / "train" / experiment_folder
+        tb_path.parent.mkdir(parents=True, exist_ok=True)
+        writer = SummaryWriter(tb_path.as_posix())
+
    logger = setup_logging(save_dir)

    logger.info("--- 开始训练 RoRD 模型 ---")
    logger.info(f"训练参数: Epochs={epochs}, Batch Size={batch_size}, LR={lr}")
    logger.info(f"数据目录: {data_dir}")
    logger.info(f"保存目录: {save_dir}")
+    if writer:
+        logger.info(f"TensorBoard 日志目录: {tb_path}")

    transform = get_transform()

@@ -69,6 +97,8 @@ def main(args):
    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
    
    logger.info(f"训练集大小: {len(train_dataset)}, 验证集大小: {len(val_dataset)}")
+    if writer:
+        writer.add_text("dataset/info", f"train={len(train_dataset)}, val={len(val_dataset)}")
    
    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
    val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
@@ -116,6 +146,14 @@ def main(args):
            total_det_loss += det_loss.item()
            total_desc_loss += desc_loss.item()

+            if writer:
+                num_batches = len(train_dataloader) if len(train_dataloader) > 0 else 1
+                global_step = epoch * num_batches + i
+                writer.add_scalar("train/loss_total", loss.item(), global_step)
+                writer.add_scalar("train/loss_det", det_loss.item(), global_step)
+                writer.add_scalar("train/loss_desc", desc_loss.item(), global_step)
+                writer.add_scalar("train/lr", optimizer.param_groups[0]['lr'], global_step)
+            
            if i % 10 == 0:
                logger.info(f"Epoch {epoch+1}, Batch {i}, Total Loss: {loss.item():.4f}, "
                          f"Det Loss: {det_loss.item():.4f}, Desc Loss: {desc_loss.item():.4f}")
@@ -123,6 +161,10 @@ def main(args):
        avg_train_loss = total_train_loss / len(train_dataloader)
        avg_det_loss = total_det_loss / len(train_dataloader)
        avg_desc_loss = total_desc_loss / len(train_dataloader)
+        if writer:
+            writer.add_scalar("epoch/train_loss_total", avg_train_loss, epoch)
+            writer.add_scalar("epoch/train_loss_det", avg_det_loss, epoch)
+            writer.add_scalar("epoch/train_loss_desc", avg_desc_loss, epoch)
        
        # 验证阶段
        model.eval()
@@ -156,6 +198,11 @@ def main(args):
        logger.info(f"训练 - Total: {avg_train_loss:.4f}, Det: {avg_det_loss:.4f}, Desc: {avg_desc_loss:.4f}")
        logger.info(f"验证 - Total: {avg_val_loss:.4f}, Det: {avg_val_det_loss:.4f}, Desc: {avg_val_desc_loss:.4f}")
        logger.info(f"学习率: {optimizer.param_groups[0]['lr']:.2e}")
+        if writer:
+            writer.add_scalar("epoch/val_loss_total", avg_val_loss, epoch)
+            writer.add_scalar("epoch/val_loss_det", avg_val_det_loss, epoch)
+            writer.add_scalar("epoch/val_loss_desc", avg_val_desc_loss, epoch)
+            writer.add_scalar("epoch/lr", optimizer.param_groups[0]['lr'], epoch)
        
        # 早停检查
        if avg_val_loss < best_val_loss:
@@ -179,6 +226,8 @@ def main(args):
                }
            }, save_path)
            logger.info(f"最佳模型已保存至: {save_path}")
+            if writer:
+                writer.add_scalar("checkpoint/best_val_loss", best_val_loss, epoch)
        else:
            patience_counter += 1
            if patience_counter >= patience:
@@ -202,6 +251,10 @@ def main(args):
    logger.info(f"最终模型已保存至: {save_path}")
    logger.info("训练完成！")

+    if writer:
+        writer.add_scalar("final/val_loss", avg_val_loss, epochs - 1)
+        writer.close()
+
 if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="训练 RoRD 模型")
    parser.add_argument('--config', type=str, default="configs/base_config.yaml", help="YAML 配置文件路径")
@@ -210,4 +263,7 @@ if __name__ == "__main__":
    parser.add_argument('--epochs', type=int, default=None, help="训练轮数，若未提供则使用配置文件中的值")
    parser.add_argument('--batch_size', type=int, default=None, help="批次大小，若未提供则使用配置文件中的值")
    parser.add_argument('--lr', type=float, default=None, help="学习率，若未提供则使用配置文件中的值")
+    parser.add_argument('--log_dir', type=str, default=None, help="TensorBoard 日志根目录，覆盖配置文件中的设置")
+    parser.add_argument('--experiment_name', type=str, default=None, help="TensorBoard 实验名称，覆盖配置文件中的设置")
+    parser.add_argument('--disable_tensorboard', action='store_true', help="禁用 TensorBoard 日志记录")
    main(parser.parse_args())
--- a/uv.lock
+++ b/uv.lock
@@ -7,6 +7,15 @@ resolution-markers = [
    "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
 ]

+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "antlr4-python3-runtime"
 version = "4.9.3"
@@ -150,6 +159,37 @@ wheels = [
    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/30/bc/70950ace7b5db1cbe5bdf2f9238cab885f51ad51d6befac2d0fb11d94fb9/gdstk-0.9.60-cp313-cp313-win_amd64.whl", hash = "sha256:9e41b38a719991b1e36ea0320c44688ac18e64ae53d9e5775fc7222fccfbb34a", size = 499802, upload-time = "2025-04-15T12:37:59.404Z" },
 ]

+[[package]]
+name = "grpcio"
+version = "1.75.0"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/91/88/fe2844eefd3d2188bc0d7a2768c6375b46dfd96469ea52d8aeee8587d7e0/grpcio-1.75.0.tar.gz", hash = "sha256:b989e8b09489478c2d19fecc744a298930f40d8b27c3638afbfe84d22f36ce4e", size = 12722485, upload-time = "2025-09-16T09:20:21.731Z" }
+wheels = [
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0d/93/a1b29c2452d15cecc4a39700fbf54721a3341f2ddbd1bd883f8ec0004e6e/grpcio-1.75.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:fa35ccd9501ffdd82b861809cbfc4b5b13f4b4c5dc3434d2d9170b9ed38a9054", size = 5661861, upload-time = "2025-09-16T09:18:58.748Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/b8/ce/7280df197e602d14594e61d1e60e89dfa734bb59a884ba86cdd39686aadb/grpcio-1.75.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:0fcb77f2d718c1e58cc04ef6d3b51e0fa3b26cf926446e86c7eba105727b6cd4", size = 11459982, upload-time = "2025-09-16T09:19:01.211Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/7c/9b/37e61349771f89b543a0a0bbc960741115ea8656a2414bfb24c4de6f3dd7/grpcio-1.75.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:36764a4ad9dc1eb891042fab51e8cdf7cc014ad82cee807c10796fb708455041", size = 6239680, upload-time = "2025-09-16T09:19:04.443Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a6/66/f645d9d5b22ca307f76e71abc83ab0e574b5dfef3ebde4ec8b865dd7e93e/grpcio-1.75.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:725e67c010f63ef17fc052b261004942763c0b18dcd84841e6578ddacf1f9d10", size = 6908511, upload-time = "2025-09-16T09:19:07.884Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/e6/9a/34b11cd62d03c01b99068e257595804c695c3c119596c7077f4923295e19/grpcio-1.75.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:91fbfc43f605c5ee015c9056d580a70dd35df78a7bad97e05426795ceacdb59f", size = 6429105, upload-time = "2025-09-16T09:19:10.085Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/1a/46/76eaceaad1f42c1e7e6a5b49a61aac40fc5c9bee4b14a1630f056ac3a57e/grpcio-1.75.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7a9337ac4ce61c388e02019d27fa837496c4b7837cbbcec71b05934337e51531", size = 7060578, upload-time = "2025-09-16T09:19:12.283Z" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/3d/82/181a0e3f1397b6d43239e95becbeb448563f236c0db11ce990f073b08d01/grpcio-1.75.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:ee16e232e3d0974750ab5f4da0ab92b59d6473872690b5e40dcec9a22927f22e", size = 8003283, upload-time = "2025-09-16T09:19:15.601Z" },
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    { name = "torch" },
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Author	SHA1	Message	Date
Jiao77	2ccfe7b07f	Merge pull request 'finish Experiment Tracking and Evaluation' (#2 ) from taishi-ExperimentTrackingAndEvaluation into main Reviewed-on: #2	2025-09-25 13:25:09 +00:00
Jiao77	17d3f419f6	finish Experiment Tracking and Evaluation	2025-09-25 21:24:41 +08:00