From 17d3f419f687e678bcb7a10743309766057677d5 Mon Sep 17 00:00:00 2001 From: Jiao77 Date: Thu, 25 Sep 2025 21:24:41 +0800 Subject: [PATCH] finish Experiment Tracking and Evaluation --- README.md | 132 +++++++++++++++++++++++++++++++-------- configs/base_config.yaml | 5 ++ docs/NextStep.md | 124 ++++++++++++++++++++++++++++++++++++ docs/feature_work.md | 8 +-- evaluate.py | 72 ++++++++++++++++++++- match.py | 71 ++++++++++++++++++++- pyproject.toml | 2 + train.py | 56 +++++++++++++++++ uv.lock | 132 +++++++++++++++++++++++++++++++++++++++ 9 files changed, 565 insertions(+), 37 deletions(-) create mode 100644 docs/NextStep.md diff --git a/README.md b/README.md index 8e5d9ef..b19d798 100644 --- 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 @@ -119,22 +163,56 @@ 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 +### � 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。 + +### �🚀 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 \ - --layout ./test/layout.png \ - --template ./test/template.png \ - --output ./result.png +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 ``` ### 4. 模板匹配 diff --git a/configs/base_config.yaml b/configs/base_config.yaml index 70a6512..ee03075 100644 --- 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" diff --git a/docs/NextStep.md b/docs/NextStep.md new file mode 100644 index 0000000..5a5ddbb --- /dev/null +++ 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//` 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//`。 + +## 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。 diff --git a/docs/feature_work.md b/docs/feature_work.md index ce3a5e9..ed7b36b 100644 --- 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。 diff --git a/evaluate.py b/evaluate.py index 842c958..6075c9f 100644 --- 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}") \ No newline at end of file + print(f" F1 分数 (F1 Score): {results['f1']:.4f}") + + if writer: + writer.add_text("metadata/model_path", model_path) + writer.close() \ No newline at end of file diff --git a/match.py b/match.py index 88dd7ad..1368410 100644 --- 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)) @@ -218,11 +270,24 @@ 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: print(bbox) if args.output: - visualize_matches(args.layout, detected_bboxes, args.output) \ No newline at end of file + 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() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index d19550e..d600c3a 100644 --- 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]] diff --git a/train.py b/train.py index fb750fe..47858b5 100644 --- 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) @@ -115,6 +145,14 @@ def main(args): total_train_loss += loss.item() 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}, " @@ -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, 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