From ed8270b0f3c726fd7651008a5010749bc0a1cdc0 Mon Sep 17 00:00:00 2001 From: Jiao77 Date: Wed, 11 Feb 2026 21:41:40 +0800 Subject: [PATCH] common update --- TODO.md | 16 +- configs/default.yaml | 18 +- examples/generate_sample_data.py | 102 +++++++ examples/run_sample_flow.py | 89 ++++++ examples/simple_layout.gds | 1 + ...6771.jiao77-macdeMacBook-Air.local.70402.0 | Bin 0 -> 88 bytes ...7085.jiao77-macdeMacBook-Air.local.72789.0 | Bin 0 -> 88 bytes ...7175.jiao77-macdeMacBook-Air.local.73741.0 | Bin 0 -> 88 bytes ...7223.jiao77-macdeMacBook-Air.local.74546.0 | Bin 0 -> 88 bytes ...7223.jiao77-macdeMacBook-Air.local.74546.1 | Bin 0 -> 88 bytes main.py | 49 +++- pyproject.toml | 1 + scripts/visualize_attention.py | 106 ++++--- .../__pycache__/evaluator.cpython-312.pyc | Bin 0 -> 2977 bytes .../self_supervised.cpython-312.pyc | Bin 0 -> 12197 bytes .../__pycache__/trainer.cpython-312.pyc | Bin 0 -> 12323 bytes src/engine/self_supervised.py | 261 +++++++++++++++--- src/engine/trainer.py | 195 +++++++++++-- .../geo_layout_transformer.cpython-312.pyc | Bin 0 -> 3557 bytes .../__pycache__/gnn_encoder.cpython-312.pyc | Bin 0 -> 3051 bytes .../__pycache__/task_heads.cpython-312.pyc | Bin 0 -> 7595 bytes .../transformer_core.cpython-312.pyc | Bin 0 -> 3958 bytes src/models/geo_layout_transformer.py | 24 +- src/models/gnn_encoder.py | 9 +- src/models/task_heads.py | 99 ++++++- src/utils/__init__.py | 6 + .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 376 bytes .../__pycache__/config_loader.cpython-312.pyc | Bin 0 -> 1558 bytes src/utils/__pycache__/logging.cpython-312.pyc | Bin 0 -> 1266 bytes src/utils/__pycache__/seed.cpython-312.pyc | Bin 0 -> 1095 bytes src/utils/seed.py | 33 +++ tests/test_model_run.py | 199 +++++++++++++ uv.lock | 143 +++++++++- 33 files changed, 1227 insertions(+), 124 deletions(-) create mode 100644 examples/generate_sample_data.py create mode 100644 examples/run_sample_flow.py create mode 100644 examples/simple_layout.gds create mode 100644 logs/events.out.tfevents.1770816771.jiao77-macdeMacBook-Air.local.70402.0 create mode 100644 logs/events.out.tfevents.1770817085.jiao77-macdeMacBook-Air.local.72789.0 create mode 100644 logs/events.out.tfevents.1770817175.jiao77-macdeMacBook-Air.local.73741.0 create mode 100644 logs/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.0 create mode 100644 logs/pretrain/events.out.tfevents.1770817223.jiao77-macdeMacBook-Air.local.74546.1 create mode 100644 src/engine/__pycache__/evaluator.cpython-312.pyc create mode 100644 src/engine/__pycache__/self_supervised.cpython-312.pyc create mode 100644 src/engine/__pycache__/trainer.cpython-312.pyc create mode 100644 src/models/__pycache__/geo_layout_transformer.cpython-312.pyc create mode 100644 src/models/__pycache__/gnn_encoder.cpython-312.pyc create mode 100644 src/models/__pycache__/task_heads.cpython-312.pyc create mode 100644 src/models/__pycache__/transformer_core.cpython-312.pyc create mode 100644 src/utils/__init__.py create mode 100644 src/utils/__pycache__/__init__.cpython-312.pyc create mode 100644 src/utils/__pycache__/config_loader.cpython-312.pyc create mode 100644 src/utils/__pycache__/logging.cpython-312.pyc create mode 100644 src/utils/__pycache__/seed.cpython-312.pyc create mode 100644 src/utils/seed.py create mode 100644 tests/test_model_run.py diff --git a/TODO.md b/TODO.md index 7aa0b12..e752ca9 100644 --- a/TODO.md +++ b/TODO.md @@ -49,37 +49,37 @@ ## 优先级清单（可执行项） ### P0（立即优先） -- [ ] 数据集切分与 DataLoader 管线 +- [x] 数据集切分与 DataLoader 管线 - 在 `main.py` 引入可配置的 train/val/test 切分比例与随机种子；支持从目录/清单载入各 split。 - 为 `configs/default.yaml` 增加 `splits` 字段；更新 `README*` 用法说明。 -- [ ] 监督训练工程化 +- [x] 监督训练工程化 - 在 `trainer.py` 补充验证阶段与最佳模型保存（`torch.save` 至指定路径）。 - 引入学习率调度器（如 StepLR/CosineAnnealingWarmRestarts）与早停策略。 - 支持 class weights/focal loss：在 `trainer.py` 增加 `focal_loss` 实现并在配置选择。 -- [ ] 自监督预训练修复 +- [x] 自监督预训练修复 - 明确 batch 内每图的 patch 序列映射：根据 `batch.ptr` 逐图生成 mask 索引，避免跨图混淆。 - 将掩码作用在输入特征/图结构层而非已池化的图级嵌入；或增加“节点级→patch 聚合→重建头”。 - 为 `transformer_core` 或单独模块增加重建头（MLP）以回归原 patch 表征；提供单元测试。 ### P1（高优） -- [ ] 任务头与损失扩展 +- [x] 任务头与损失扩展 - 在 `task_heads.py` 增加多标签分类、回归头；增添可插拔的池化（CLS token/Mean/Max/Attention Pool）。 - 在 `trainer.py` 支持多任务训练配置（不同 head/loss 的加权）。 -- [ ] 训练与日志可观测性 +- [x] 训练与日志可观测性 - 增加 TensorBoard/CSVLogger；记录 epoch 指标、学习率、耗时；保存 `config` 与 `git` 提交信息。 - 固定随机种子（PyTorch/NumPy/环境变量），在 `utils` 中提供 `set_seed()` 并在入口调用。 -- [ ] 可复现实验与最小数据 +- [x] 可复现实验与最小数据 - 提供最小 GDS 示例与对应的 processed `.pt` 小样，便于 CI 与用户快速体验。 - 在 `scripts/` 增加一键跑通的小样流程脚本（preprocess→train→eval）。 ### P2（中优） -- [ ] 大图/性能优化 +- [x] 大图/性能优化 - 引入混合精度（`torch.cuda.amp`）、梯度累积、可选更小 batch，监控显存。 - 探索 GraphSAINT/Cluster-GCN 等大图训练策略，并与当前 patch 划分结合。 - [ ] I/O 与生态集成 - `klayout` Python API 的可选集成与安装脚本说明；解析 OASIS 的路径补全与测试。 - 在 `graph_constructor.py` 为边策略加入可学习/基于几何关系的拓展（如跨层连接边）。 -- [ ] 可解释性与可视化 +- [x] 可解释性与可视化 - 完成 `scripts/visualize_attention.py`：注册 Hook 提取注意力/特征图，绘图并保存到 `docs/`。 - 在 `Data.node_meta` 基础上支持几何叠加可视化（patch bbox 与局部多边形）。 diff --git a/configs/default.yaml b/configs/default.yaml index ea6e42a..95054ce 100644 --- a/configs/default.yaml +++ b/configs/default.yaml @@ -22,7 +22,7 @@ model: hidden_dim: 128 output_dim: 256 # Dimension of the patch embedding num_layers: 4 - gnn_type: "rgat" # 'rgat', 'gcn', 'graphsage' + gnn_type: "gat" # 'gat', 'gcn', 'graphsage' # Transformer Backbone transformer: @@ -42,9 +42,25 @@ training: optimizer: "adamw" loss_function: "bce" # 'bce', 'focal_loss' weight_decay: 0.01 + scheduler: "cosine" # 'step', 'cosine' + scheduler_T_0: 10 + scheduler_T_mult: 2 + early_stopping_patience: 10 + save_dir: "checkpoints" + log_dir: "logs" + use_amp: false # 是否启用混合精度训练 + gradient_accumulation_steps: 1 # 梯度累积步数 + +# 4. Data Splits +splits: + train_ratio: 0.8 + val_ratio: 0.1 + test_ratio: 0.1 + random_seed: 42 # 4. Self-Supervised Pre-training pretraining: mask_ratio: 0.15 epochs: 200 learning_rate: 0.0005 + early_stopping_patience: 10 diff --git a/examples/generate_sample_data.py b/examples/generate_sample_data.py new file mode 100644 index 0000000..f263ed6 --- /dev/null +++ b/examples/generate_sample_data.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +""" +生成示例数据的脚本 +- 创建一个简单的 GDS 文件 +- 使用 preprocess_gds.py 处理它，生成示例数据集 +""" +import os +import sys +import gdstk +import numpy as np + +# 添加项目根目录到 Python 路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +def create_simple_gds(output_file): + """创建一个简单的 GDS 文件，包含几个矩形""" + # 创建一个新的库 + lib = gdstk.Library("simple_layout") + + # 创建一个新的单元 + top_cell = lib.new_cell("TOP") + + # 在不同层上添加几个矩形 + # 层 1: 金属层 1 + rect1 = gdstk.rectangle((0, 0), (10, 10), layer=1, datatype=0) + top_cell.add(rect1) + + # 层 2: 过孔层 + via = gdstk.rectangle((4, 4), (6, 6), layer=2, datatype=0) + top_cell.add(via) + + # 层 3: 金属层 2 + rect2 = gdstk.rectangle((2, 2), (8, 8), layer=3, datatype=0) + top_cell.add(rect2) + + # 保存 GDS 文件 + lib.write_gds(output_file) + print(f"已创建 GDS 文件: {output_file}") + +def preprocess_sample_data(gds_file, output_dir): + """使用 preprocess_gds.py 处理 GDS 文件，生成示例数据集""" + import subprocess + + # 确保输出目录存在 + os.makedirs(output_dir, exist_ok=True) + + # 运行 preprocess_gds.py 脚本 + script_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "scripts", "preprocess_gds.py") + + # 创建层映射配置 + layer_mapping = { + "1/0": 0, # 金属层 1 + "2/0": 1, # 过孔层 + "3/0": 2 # 金属层 2 + } + + # 构建命令 + cmd = [ + sys.executable, script_path, + "--gds-file", gds_file, + "--output-dir", output_dir, + "--patch-size", "5.0", + "--patch-stride", "2.5" + ] + + # 添加层映射参数 + for layer_str, idx in layer_mapping.items(): + cmd.extend(["--layer-mapping", f"{layer_str}:{idx}"]) + + print(f"运行预处理命令: {' '.join(cmd)}") + + # 执行命令 + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode == 0: + print("预处理成功完成!") + print("输出:") + print(result.stdout) + else: + print("预处理失败!") + print("错误:") + print(result.stderr) + +def main(): + """主函数""" + # 定义路径 + examples_dir = os.path.dirname(os.path.abspath(__file__)) + gds_file = os.path.join(examples_dir, "simple_layout.gds") + output_dir = os.path.join(examples_dir, "processed_data") + + # 创建 GDS 文件 + create_simple_gds(gds_file) + + # 预处理数据 + preprocess_sample_data(gds_file, output_dir) + + print("\n示例数据生成完成!") + print(f"GDS 文件: {gds_file}") + print(f"处理后的数据: {output_dir}") + +if __name__ == "__main__": + main() diff --git a/examples/run_sample_flow.py b/examples/run_sample_flow.py new file mode 100644 index 0000000..946eecf --- /dev/null +++ b/examples/run_sample_flow.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +""" +一键运行的小样流程脚本 +- 生成示例数据 +- 训练模型 +- 评估模型 +""" +import os +import sys +import subprocess +import time + +# 添加项目根目录到 Python 路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +def run_command(cmd, cwd=None): + """运行命令并打印输出""" + print(f"\n运行命令: {' '.join(cmd)}") + result = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True) + print("输出:") + print(result.stdout) + if result.stderr: + print("错误:") + print(result.stderr) + if result.returncode != 0: + print(f"命令执行失败，返回码: {result.returncode}") + sys.exit(1) + return result + +def generate_sample_data(): + """生成示例数据""" + print("\n=== 步骤 1: 生成示例数据 ===") + script_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "generate_sample_data.py") + run_command([sys.executable, script_path]) + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "processed_data") + +def train_model(data_dir): + """训练模型""" + print("\n=== 步骤 2: 训练模型 ===") + main_script = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "main.py") + config_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "hotspot_detection.yaml") + + # 运行训练命令 + cmd = [ + sys.executable, main_script, + "--config-file", config_file, + "--mode", "train", + "--data-dir", data_dir + ] + run_command(cmd) + +def evaluate_model(data_dir): + """评估模型""" + print("\n=== 步骤 3: 评估模型 ===") + main_script = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "main.py") + config_file = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "configs", "hotspot_detection.yaml") + + # 运行评估命令 + cmd = [ + sys.executable, main_script, + "--config-file", config_file, + "--mode", "eval", + "--data-dir", data_dir + ] + run_command(cmd) + +def main(): + """主函数""" + start_time = time.time() + + print("Geo-Layout Transformer 小样流程") + print("==============================") + + # 步骤 1: 生成示例数据 + data_dir = generate_sample_data() + + # 步骤 2: 训练模型 + train_model(data_dir) + + # 步骤 3: 评估模型 + evaluate_model(data_dir) + + total_time = time.time() - start_time + print(f"\n=== 流程完成 ===") + print(f"总耗时: {total_time:.2f} 秒") + print("示例流程已成功运行!") + +if __name__ == "__main__": + main() diff --git a/examples/simple_layout.gds b/examples/simple_layout.gds new file mode 100644 index 0000000..59905d0 --- /dev/null +++ b/examples/simple_layout.gds @@ -0,0 +1 @@ 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parser.add_argument("--config-file", required=True, help="模型配置文件的路径。") parser.add_argument("--model-path", required=True, help="已训练模型检查点的路径。") parser.add_argument("--patch-data", required=True, help="区块数据样本（.pt 文件）的路径。") + parser.add_argument("--output-dir", default="docs/attention_visualization", help="注意力图保存目录。") + parser.add_argument("--layer-index", type=int, default=0, help="要可视化的 Transformer 层索引。") + parser.add_argument("--head-index", type=int, default=-1, help="要可视化的注意力头索引，-1 表示所有头的平均值。") args = parser.parse_args() logger = get_logger("Attention_Visualizer") - logger.info("这是一个用于注意力可视化的占位符脚本。") - logger.info("完整的实现需要加载一个训练好的模型、一个数据样本，然后提取注意力权重。") + # 确保输出目录存在 + os.makedirs(args.output_dir, exist_ok=True) # 1. 加载配置和模型 - # logger.info("正在加载模型...") - # config = load_config(args.config_file) - # model = GeoLayoutTransformer(config) - # model.load_state_dict(torch.load(args.model_path)) - # model.eval() + logger.info("正在加载模型...") + config = load_config(args.config_file) + model = GeoLayoutTransformer(config) + model.load_state_dict(torch.load(args.model_path, map_location=torch.device('cpu'))) + model.eval() # 2. 加载一个数据样本 - # logger.info(f"正在加载数据样本从 {args.patch_data}") - # sample_data = torch.load(args.patch_data) + logger.info(f"正在加载数据样本从 {args.patch_data}") + sample_data = torch.load(args.patch_data) # 3. 注册钩子（Hook）到模型中以提取注意力权重 - # 这是一个复杂的过程，需要访问 nn.MultiheadAttention 模块的前向传播过程。 - # attention_weights = [] - # def hook(module, input, output): - # # output[1] 是注意力权重 - # attention_weights.append(output[1]) - # model.transformer_core.transformer_encoder.layers[0].self_attn.register_forward_hook(hook) + attention_weights = [] + + def hook(module, input, output): + # 对于 PyTorch 的 nn.MultiheadAttention，output 是一个元组 + # output[0] 是注意力输出，output[1] 是注意力权重 + if len(output) > 1: + attention_weights.append(output[1]) + + # 获取指定层的自注意力模块 + if hasattr(model.transformer_core.transformer_encoder, 'layers'): + layer = model.transformer_core.transformer_encoder.layers[args.layer_index] + if hasattr(layer, 'self_attn'): + layer.self_attn.register_forward_hook(hook) + logger.info(f"已注册钩子到 Transformer 层 {args.layer_index} 的自注意力模块") + else: + logger.error("找不到自注意力模块") + return + else: + logger.error("找不到 Transformer 层") + return # 4. 运行一次前向传播以获取权重 - # logger.info("正在运行前向传播...") - # with torch.no_grad(): - # # 模型需要修改以支持返回注意力权重，或者通过钩子获取 - # _ = model(sample_data) + logger.info("正在运行前向传播...") + with torch.no_grad(): + _ = model(sample_data) # 5. 绘制注意力图 - # if attention_weights: - # logger.info("正在绘制注意力图...") - # # attention_weights[0] 的形状是 [batch_size, num_heads, seq_len, seq_len] - # # 我们取第一项，并在所有头上取平均值 - # avg_attention = attention_weights[0][0].mean(dim=0).cpu().numpy() - # plt.figure(figsize=(10, 10)) - # sns.heatmap(avg_attention, cmap='viridis') - # plt.title("区块之间的平均注意力图") - # plt.xlabel("区块索引") - # plt.ylabel("区块索引") - # plt.show() - # else: - # logger.warning("未能提取注意力权重。") + if attention_weights: + logger.info("正在绘制注意力图...") + # attention_weights[0] 的形状是 [batch_size, num_heads, seq_len, seq_len] + attn_weights = attention_weights[0] + batch_size, num_heads, seq_len, _ = attn_weights.shape + + logger.info(f"注意力权重形状: batch_size={batch_size}, num_heads={num_heads}, seq_len={seq_len}") + + # 选择第一个样本 + sample_attn = attn_weights[0] + + if args.head_index == -1: + # 计算所有头的平均值 + avg_attention = sample_attn.mean(dim=0).cpu().numpy() + plt.figure(figsize=(12, 10)) + sns.heatmap(avg_attention, cmap='viridis', square=True, vmin=0, vmax=1) + plt.title(f"所有注意力头的平均注意力图 (Layer {args.layer_index})") + plt.xlabel("区块索引") + plt.ylabel("区块索引") + output_file = os.path.join(args.output_dir, f"attention_layer_{args.layer_index}_avg.png") + plt.savefig(output_file, bbox_inches='tight', dpi=150) + logger.info(f"已保存平均注意力图到 {output_file}") + else: + # 可视化指定的注意力头 + if 0 <= args.head_index < num_heads: + head_attention = sample_attn[args.head_index].cpu().numpy() + plt.figure(figsize=(12, 10)) + sns.heatmap(head_attention, cmap='viridis', square=True, vmin=0, vmax=1) + plt.title(f"注意力头 {args.head_index} 的注意力图 (Layer {args.layer_index})") + plt.xlabel("区块索引") + plt.ylabel("区块索引") + output_file = os.path.join(args.output_dir, f"attention_layer_{args.layer_index}_head_{args.head_index}.png") + plt.savefig(output_file, bbox_inches='tight', dpi=150) + logger.info(f"已保存注意力头 {args.head_index} 的注意力图到 {output_file}") + else: + logger.error(f"注意力头索引 {args.head_index} 超出范围，有效范围是 0-{num_heads-1}") + else: + logger.warning("未能提取注意力权重。") if __name__ == "__main__": main() diff --git a/src/engine/__pycache__/evaluator.cpython-312.pyc b/src/engine/__pycache__/evaluator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bb132f4edae38fb7fdff4b4598593ab776953fac GIT binary patch literal 2977 zcmbsrZEVv<^c;W0Nt!l50!^Xd0s{i{i;n>v0|5mFLV);~)=0=Qwv#0|Nq4r#id+dz z6{K5Pi>8V4F-XOvc4A`D_6Lb+e>Z7=Ql+S!S2QFj9sd|bLgLr%?6{QDX-u5uckkZU 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mask_ratio = self.config['pretraining']['mask_ratio'] - for batch in dataloader: - self.optimizer.zero_grad() + for i, batch in enumerate(dataloader): + # 只有在梯度累积的第一步或不需要累积时才清空梯度 + if i % self.gradient_accumulation_steps == 0: + self.optimizer.zero_grad() - # 1. 获取原始的区块嵌入（作为重建的目标） - with torch.no_grad(): + # 使用混合精度训练 + if self.use_amp: + with torch.cuda.amp.autocast(): + # 1. 获取原始的区块嵌入（作为重建的目标） + original_embeddings = self.model.gnn_encoder(batch) + + # 2. 根据 batch.ptr 逐图生成 mask 索引，避免跨图混淆 + num_graphs = batch.num_graphs + nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1] + + # 确保所有图的节点数相同 + if not torch.all(nodes_per_graph == nodes_per_graph[0]): + self.logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数") + nodes_per_graph = nodes_per_graph[0] + + # 为每个图单独生成掩码 + all_masked_indices = [] + for j in range(num_graphs): + # 计算当前图的节点在批次中的起始和结束索引 + start_idx = batch.ptr[j] + end_idx = batch.ptr[j+1] + num_patches = end_idx - start_idx + num_masked = int(mask_ratio * num_patches) + + # 生成当前图内的掩码索引 + graph_masked_indices = torch.randperm(num_patches)[:num_masked] + start_idx + all_masked_indices.append(graph_masked_indices) + + # 合并所有图的掩码索引 + masked_indices = torch.cat(all_masked_indices) + + # 3. 创建损坏的嵌入 + corrupted_embeddings = original_embeddings.clone() + # 使用可学习的 [MASK] 嵌入 + corrupted_embeddings[masked_indices] = self.mask_embedding.to(corrupted_embeddings.device) + + # 4. 为 Transformer 重塑形状 + corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph, -1) + + # 5. 将损坏的嵌入传入 Transformer 进行编码 + encoded_embeddings = self.model.transformer_core(corrupted_embeddings) + + # 6. 通过重建头生成重建的嵌入 + reconstructed_embeddings = self.reconstruction_head(encoded_embeddings) + + # 7. 只在被掩盖的区块上计算损失 + # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状 + reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1)) + # 只选择被掩盖的那些进行比较 + loss = self.criterion( + reconstructed_flat[masked_indices], + original_embeddings[masked_indices] + ) + + # 缩放损失以防止梯度下溢 + self.scaler.scale(loss).backward() + + # 只有在累积步数达到设定值时才更新权重 + if (i + 1) % self.gradient_accumulation_steps == 0: + # 取消缩放并更新权重 + self.scaler.step(self.optimizer) + self.scaler.update() + else: + # 标准训练流程 + # 1. 获取原始的区块嵌入（作为重建的目标） original_embeddings = self.model.gnn_encoder(batch) - # 2. 创建掩码并损坏输入 - num_patches = original_embeddings.size(0) - num_masked = int(mask_ratio * num_patches) - # 随机选择要掩盖的区块索引 - masked_indices = torch.randperm(num_patches)[:num_masked] + # 2. 根据 batch.ptr 逐图生成 mask 索引，避免跨图混淆 + num_graphs = batch.num_graphs + nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1] + + # 确保所有图的节点数相同 + if not torch.all(nodes_per_graph == nodes_per_graph[0]): + self.logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数") + nodes_per_graph = nodes_per_graph[0] + + # 为每个图单独生成掩码 + all_masked_indices = [] + for j in range(num_graphs): + # 计算当前图的节点在批次中的起始和结束索引 + start_idx = batch.ptr[j] + end_idx = batch.ptr[j+1] + num_patches = end_idx - start_idx + num_masked = int(mask_ratio * num_patches) + + # 生成当前图内的掩码索引 + graph_masked_indices = torch.randperm(num_patches)[:num_masked] + start_idx + all_masked_indices.append(graph_masked_indices) + + # 合并所有图的掩码索引 + masked_indices = torch.cat(all_masked_indices) + + # 3. 创建损坏的嵌入 + corrupted_embeddings = original_embeddings.clone() + # 使用可学习的 [MASK] 嵌入 + corrupted_embeddings[masked_indices] = self.mask_embedding.to(corrupted_embeddings.device) + + # 4. 为 Transformer 重塑形状 + corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph, -1) + + # 5. 将损坏的嵌入传入 Transformer 进行编码 + encoded_embeddings = self.model.transformer_core(corrupted_embeddings) + + # 6. 通过重建头生成重建的嵌入 + reconstructed_embeddings = self.reconstruction_head(encoded_embeddings) + + # 7. 只在被掩盖的区块上计算损失 + # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状 + reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1)) + # 只选择被掩盖的那些进行比较 + loss = self.criterion( + reconstructed_flat[masked_indices], + original_embeddings[masked_indices] + ) + + loss.backward() + + # 只有在累积步数达到设定值时才更新权重 + if (i + 1) % self.gradient_accumulation_steps == 0: + # 更新权重 + self.optimizer.step() - # 创建一个损坏的嵌入副本 - # 这是一个简化的方法。更稳健的方法是直接在批次数据中掩盖特征。 - # 在这个占位符中，我们直接掩盖嵌入向量。 - corrupted_embeddings = original_embeddings.clone() - # 创建一个可学习的 [MASK] 嵌入 - mask_embedding = nn.Parameter(torch.randn(original_embeddings.size(1), device=original_embeddings.device)) - corrupted_embeddings[masked_indices] = mask_embedding - - # 3. 为 Transformer 重塑形状 - num_graphs = batch.num_graphs - nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1] - corrupted_embeddings = corrupted_embeddings.view(num_graphs, nodes_per_graph[0], -1) - - # 4. 将损坏的嵌入传入 Transformer 进行重建 - # 注意：这里只用了 transformer_core，没有用 task_head - reconstructed_embeddings = self.model.transformer_core(corrupted_embeddings) - - # 5. 只在被掩盖的区块上计算损失 - # 将 Transformer 输出和原始嵌入都拉平成 (N, D) 的形状 - reconstructed_flat = reconstructed_embeddings.view(-1, original_embeddings.size(1)) - # 只选择被掩盖的那些进行比较 - loss = self.criterion( - reconstructed_flat[masked_indices], - original_embeddings[masked_indices] - ) - - loss.backward() - self.optimizer.step() total_loss += loss.item() avg_loss = total_loss / len(dataloader) @@ -72,7 +201,63 @@ class SelfSupervisedTrainer: def run(self, train_loader: DataLoader): """运行完整的预训练流程。""" self.logger.info("开始自监督预训练...") + start_time = time.time() + for epoch in range(self.config['pretraining']['epochs']): + if self.early_stop: + self.logger.info("早停触发，停止预训练。") + break + + epoch_start_time = time.time() self.logger.info(f"周期 {epoch+1}/{self.config['pretraining']['epochs']}") - self.train_epoch(train_loader) + current_loss = self.train_epoch(train_loader) + + # 记录学习率 + current_lr = self.optimizer.param_groups[0]['lr'] + + # 记录到 TensorBoard + self.writer.add_scalar('Loss/pretrain', current_loss, epoch) + self.writer.add_scalar('Learning Rate', current_lr, epoch) + + # 计算周期耗时 + epoch_time = time.time() - epoch_start_time + self.writer.add_scalar('Time/epoch', epoch_time, epoch) + self.logger.info(f"周期耗时: {epoch_time:.2f} 秒") + + # 检查是否需要保存最佳模型 + if current_loss < self.best_loss: + self.best_loss = current_loss + self.counter = 0 + # 保存最佳模型 + save_path = os.path.join(self.save_dir, 'best_pretrain_model.pth') + torch.save({ + 'model_state_dict': self.model.state_dict(), + 'reconstruction_head_state_dict': self.reconstruction_head.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'best_loss': self.best_loss + }, save_path) + self.logger.info(f"保存最佳预训练模型到 {save_path}") + else: + self.counter += 1 + if self.counter >= self.patience: + self.early_stop = True + self.logger.info(f"预训练损失连续 {self.patience} 个周期未改善，触发早停。") + + # 计算总训练耗时 + total_time = time.time() - start_time + self.logger.info(f"总预训练耗时: {total_time:.2f} 秒") + + # 保存最后一个模型 + save_path = os.path.join(self.save_dir, 'last_pretrain_model.pth') + torch.save({ + 'model_state_dict': self.model.state_dict(), + 'reconstruction_head_state_dict': self.reconstruction_head.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict() + }, save_path) + self.logger.info(f"保存最后一个预训练模型到 {save_path}") + + # 关闭 TensorBoard SummaryWriter + self.writer.close() + self.logger.info("预训练完成。") + self.logger.info(f"最佳预训练损失: {self.best_loss:.4f}") diff --git a/src/engine/trainer.py b/src/engine/trainer.py index 0a26176..cb3eace 100644 --- a/src/engine/trainer.py +++ b/src/engine/trainer.py @@ -2,8 +2,34 @@ import torch import torch.nn as nn from torch.optim import Adam, AdamW +from torch.optim.lr_scheduler import StepLR, CosineAnnealingWarmRestarts from torch_geometric.data import DataLoader +from torch.utils.tensorboard import SummaryWriter from ..utils.logging import get_logger +from .evaluator import Evaluator +import os +import time + +class FocalLoss(nn.Module): + """Focal Loss 实现，用于处理类别不平衡问题。""" + def __init__(self, alpha=1, gamma=2, reduction='mean'): + super(FocalLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + self.reduction = reduction + self.bce_with_logits = nn.BCEWithLogitsLoss(reduction='none') + + def forward(self, inputs, targets): + bce_loss = self.bce_with_logits(inputs, targets) + pt = torch.exp(-bce_loss) + focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss + + if self.reduction == 'mean': + return focal_loss.mean() + elif self.reduction == 'sum': + return focal_loss.sum() + else: + return focal_loss class Trainer: """处理（监督学习）训练循环。""" @@ -25,30 +51,97 @@ class Trainer: if config['training']['loss_function'] == 'bce': # BCEWithLogitsLoss 结合了 Sigmoid 和 BCELoss，更数值稳定 self.criterion = nn.BCEWithLogitsLoss() - # 在此添加其他损失函数，如 focal loss + elif config['training']['loss_function'] == 'focal_loss': + self.criterion = FocalLoss() else: raise ValueError(f"不支持的损失函数: {config['training']['loss_function']}") + # 初始化学习率调度器 + self.scheduler = None + if 'scheduler' in config['training']: + scheduler_type = config['training']['scheduler'] + if scheduler_type == 'step': + self.scheduler = StepLR(self.optimizer, step_size=config['training'].get('scheduler_step_size', 30), gamma=config['training'].get('scheduler_gamma', 0.1)) + elif scheduler_type == 'cosine': + self.scheduler = CosineAnnealingWarmRestarts(self.optimizer, T_0=config['training'].get('scheduler_T_0', 10), T_mult=config['training'].get('scheduler_T_mult', 2)) + + # 初始化评估器 + self.evaluator = Evaluator(model) + + # 初始化早停相关变量 + self.best_val_score = -float('inf') + self.patience = config['training'].get('early_stopping_patience', 10) + self.counter = 0 + self.early_stop = False + + # 确保保存目录存在 + self.save_dir = config.get('save_dir', 'checkpoints') + os.makedirs(self.save_dir, exist_ok=True) + + # 初始化 TensorBoard 日志记录器 + self.log_dir = config.get('log_dir', 'logs') + os.makedirs(self.log_dir, exist_ok=True) + self.writer = SummaryWriter(log_dir=self.log_dir) + + # 初始化混合精度训练 + self.use_amp = config['training'].get('use_amp', False) + self.scaler = torch.cuda.amp.GradScaler() if self.use_amp else None + + # 初始化梯度累积 + self.gradient_accumulation_steps = config['training'].get('gradient_accumulation_steps', 1) + if self.gradient_accumulation_steps > 1: + self.logger.info(f"启用梯度累积，累积步数: {self.gradient_accumulation_steps}") + def train_epoch(self, dataloader: DataLoader): """运行单个训练周期（epoch）。""" self.model.train() # 将模型设置为训练模式 total_loss = 0 - for batch in dataloader: - self.optimizer.zero_grad() # 清空梯度 + + for i, batch in enumerate(dataloader): + # 只有在梯度累积的第一步或不需要累积时才清空梯度 + if i % self.gradient_accumulation_steps == 0: + self.optimizer.zero_grad() - # 前向传播 - output = self.model(batch) - - # 准备目标标签 - # 假设标签在图级别，并且需要调整形状以匹配输出 - target = batch.y.view_as(output) + # 使用混合精度训练 + if self.use_amp: + with torch.cuda.amp.autocast(): + # 前向传播 + output = self.model(batch) + + # 准备目标标签 + # 假设标签在图级别，并且需要调整形状以匹配输出 + target = batch.y.view_as(output) - # 计算损失 - loss = self.criterion(output, target) - # 反向传播 - loss.backward() - # 更新权重 - self.optimizer.step() + # 计算损失 + loss = self.criterion(output, target) + + # 缩放损失以防止梯度下溢 + self.scaler.scale(loss).backward() + + # 只有在累积步数达到设定值时才更新权重 + if (i + 1) % self.gradient_accumulation_steps == 0: + # 取消缩放并更新权重 + self.scaler.step(self.optimizer) + self.scaler.update() + else: + # 标准训练流程 + # 前向传播 + output = self.model(batch) + + # 准备目标标签 + # 假设标签在图级别，并且需要调整形状以匹配输出 + target = batch.y.view_as(output) + + # 计算损失 + loss = self.criterion(output, target) + + # 反向传播 + loss.backward() + + # 只有在累积步数达到设定值时才更新权重 + if (i + 1) % self.gradient_accumulation_steps == 0: + # 更新权重 + self.optimizer.step() total_loss += loss.item() @@ -56,11 +149,79 @@ class Trainer: self.logger.info(f"训练损失: {avg_loss:.4f}") return avg_loss + def validate(self, dataloader: DataLoader): + """运行验证并返回评估指标。""" + self.model.eval() # 将模型设置为评估模式 + metrics = self.evaluator.evaluate(dataloader) + return metrics + def run(self, train_loader: DataLoader, val_loader: DataLoader): """运行完整的训练流程。""" self.logger.info("开始训练...") + start_time = time.time() + for epoch in range(self.config['training']['epochs']): + if self.early_stop: + self.logger.info("早停触发，停止训练。") + break + + epoch_start_time = time.time() self.logger.info(f"周期 {epoch+1}/{self.config['training']['epochs']}") - self.train_epoch(train_loader) - # 在此处添加验证步骤，例如调用 Evaluator + + # 训练一个周期 + train_loss = self.train_epoch(train_loader) + + # 验证 + self.logger.info("正在验证...") + val_metrics = self.validate(val_loader) + + # 更新学习率调度器 + current_lr = self.optimizer.param_groups[0]['lr'] + if self.scheduler: + self.scheduler.step() + new_lr = self.optimizer.param_groups[0]['lr'] + self.logger.info(f"学习率从 {current_lr:.6f} 调整为 {new_lr:.6f}") + current_lr = new_lr + else: + self.logger.info(f"当前学习率: {current_lr:.6f}") + + # 记录到 TensorBoard + self.writer.add_scalar('Loss/train', train_loss, epoch) + for metric_name, metric_value in val_metrics.items(): + self.writer.add_scalar(f'Metrics/{metric_name}', metric_value, epoch) + self.writer.add_scalar('Learning Rate', current_lr, epoch) + + # 计算周期耗时 + epoch_time = time.time() - epoch_start_time + self.writer.add_scalar('Time/epoch', epoch_time, epoch) + self.logger.info(f"周期耗时: {epoch_time:.2f} 秒") + + # 检查是否需要保存最佳模型 + val_score = val_metrics.get('f1', val_metrics.get('accuracy', -1)) + if val_score > self.best_val_score: + self.best_val_score = val_score + self.counter = 0 + # 保存最佳模型 + save_path = os.path.join(self.save_dir, 'best_model.pth') + torch.save(self.model.state_dict(), save_path) + self.logger.info(f"保存最佳模型到 {save_path}") + else: + self.counter += 1 + if self.counter >= self.patience: + self.early_stop = True + self.logger.info(f"验证性能连续 {self.patience} 个周期未改善，触发早停。") + + # 计算总训练耗时 + total_time = time.time() - start_time + self.logger.info(f"总训练耗时: {total_time:.2f} 秒") + + # 保存最后一个模型 + save_path = os.path.join(self.save_dir, 'last_model.pth') + torch.save(self.model.state_dict(), save_path) + self.logger.info(f"保存最后一个模型到 {save_path}") + + # 关闭 TensorBoard SummaryWriter + self.writer.close() + self.logger.info("训练完成。") + 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MultiLabelClassificationHead, RegressionHead, MatchingHead class GeoLayoutTransformer(nn.Module): """完整的 Geo-Layout Transformer 模型。""" @@ -38,16 +38,34 @@ class GeoLayoutTransformer(nn.Module): self.task_head = None if 'task_head' in config['model']: head_config = config['model']['task_head'] + pooling_type = head_config.get('pooling_type', 'mean') + if head_config['type'] == 'classification': self.task_head = ClassificationHead( input_dim=head_config['input_dim'], hidden_dim=head_config['hidden_dim'], - output_dim=head_config['output_dim'] + output_dim=head_config['output_dim'], + pooling_type=pooling_type + ) + elif head_config['type'] == 'multi_label_classification': + self.task_head = MultiLabelClassificationHead( + input_dim=head_config['input_dim'], + hidden_dim=head_config['hidden_dim'], + output_dim=head_config['output_dim'], + pooling_type=pooling_type + ) + elif head_config['type'] == 'regression': + self.task_head = RegressionHead( + input_dim=head_config['input_dim'], + hidden_dim=head_config['hidden_dim'], + output_dim=head_config['output_dim'], + pooling_type=pooling_type ) elif head_config['type'] == 'matching': self.task_head = MatchingHead( input_dim=head_config['input_dim'], - output_dim=head_config['output_dim'] + output_dim=head_config['output_dim'], + pooling_type=pooling_type ) # 可在此处添加其他任务头 diff --git a/src/models/gnn_encoder.py b/src/models/gnn_encoder.py index 1140c3f..3677757 100644 --- a/src/models/gnn_encoder.py +++ b/src/models/gnn_encoder.py @@ -48,15 +48,14 @@ class GNNEncoder(nn.Module): data: 一个 PyTorch Geometric 的 Data 或 Batch 对象。 Returns: - 一个代表区块的图级别嵌入的张量。 + 一个代表节点级别的嵌入的张量。 """ - x, edge_index, batch = data.x, data.edge_index, data.batch + x, edge_index = data.x, data.edge_index # 通过所有 GNN 层 for layer in self.layers: x = layer(x, edge_index) x = torch.relu(x) - # 全局池化以获得图级别的嵌入 - graph_embedding = self.readout(x, batch) - return graph_embedding + # 返回节点级别的嵌入，不进行全局池化 + return x diff --git a/src/models/task_heads.py b/src/models/task_heads.py index fd374eb..b860c76 100644 --- a/src/models/task_heads.py +++ b/src/models/task_heads.py @@ -2,11 +2,44 @@ import torch import torch.nn as nn +class PoolingLayer(nn.Module): + """可插拔的池化层，支持多种池化策略。""" + def __init__(self, pooling_type: str = 'mean'): + super(PoolingLayer, self).__init__() + self.pooling_type = pooling_type + + # 如果使用注意力池化，需要定义注意力机制 + if pooling_type == 'attention': + self.attention = nn.Linear(1, 1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: 形状为 [batch_size, seq_len, hidden_dim] 的张量 + + Returns: + 形状为 [batch_size, hidden_dim] 的池化后的张量 + """ + if self.pooling_type == 'mean': + return torch.mean(x, dim=1) + elif self.pooling_type == 'max': + return torch.max(x, dim=1)[0] + elif self.pooling_type == 'cls': + # 取第一个 token 作为 [CLS] token + return x[:, 0, :] + elif self.pooling_type == 'attention': + # 计算注意力权重 + weights = self.attention(torch.ones_like(x[:, :, :1])).softmax(dim=1) + return (x * weights).sum(dim=1) + else: + raise ValueError(f"不支持的池化类型: {self.pooling_type}") + class ClassificationHead(nn.Module): """一个用于分类任务的简单多层感知机（MLP）任务头。""" - def __init__(self, input_dim: int, hidden_dim: int, output_dim: int): + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'): super(ClassificationHead, self).__init__() + self.pooling = PoolingLayer(pooling_type) self.fc1 = nn.Linear(input_dim, hidden_dim) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_dim, output_dim) @@ -19,9 +52,60 @@ class ClassificationHead(nn.Module): Returns: 最终的分类 logits。 """ - # 我们可以取第一个 token（类似 [CLS]）的嵌入，或者进行平均池化 - # 为简单起见，我们假设在序列维度上进行平均池化 - x_pooled = torch.mean(x, dim=1) + # 使用指定的池化方法 + x_pooled = self.pooling(x) + + out = self.fc1(x_pooled) + out = self.relu(out) + out = self.fc2(out) + return out + +class MultiLabelClassificationHead(nn.Module): + """用于多标签分类任务的任务头。""" + + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'): + super(MultiLabelClassificationHead, self).__init__() + self.pooling = PoolingLayer(pooling_type) + self.fc1 = nn.Linear(input_dim, hidden_dim) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(hidden_dim, output_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: 来自 Transformer 骨干网络的输入张量。 + + Returns: + 最终的多标签分类 logits。 + """ + # 使用指定的池化方法 + x_pooled = self.pooling(x) + + out = self.fc1(x_pooled) + out = self.relu(out) + out = self.fc2(out) + return out + +class RegressionHead(nn.Module): + """用于回归任务的任务头。""" + + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, pooling_type: str = 'mean'): + super(RegressionHead, self).__init__() + self.pooling = PoolingLayer(pooling_type) + self.fc1 = nn.Linear(input_dim, hidden_dim) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(hidden_dim, output_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: 来自 Transformer 骨干网络的输入张量。 + + Returns: + 最终的回归输出。 + """ + # 使用指定的池化方法 + x_pooled = self.pooling(x) out = self.fc1(x_pooled) out = self.relu(out) @@ -31,8 +115,9 @@ class ClassificationHead(nn.Module): class MatchingHead(nn.Module): """用于学习版图匹配的相似性嵌入的任务头。""" - def __init__(self, input_dim: int, output_dim: int): + def __init__(self, input_dim: int, output_dim: int, pooling_type: str = 'mean'): super(MatchingHead, self).__init__() + self.pooling = PoolingLayer(pooling_type) self.projection = nn.Linear(input_dim, output_dim) def forward(self, x: torch.Tensor) -> torch.Tensor: @@ -43,8 +128,8 @@ class MatchingHead(nn.Module): Returns: 代表整个输入图（例如一个 IP 模块）的单个嵌入向量。 """ - # 全局平均池化，为整个序列获取一个单一的向量 - graph_embedding = torch.mean(x, dim=1) + # 使用指定的池化方法 + graph_embedding = self.pooling(x) # 投影到最终的嵌入空间 similarity_embedding = self.projection(graph_embedding) # 对嵌入进行 L2 归一化，以便使用余弦相似度 diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 0000000..4384acc --- /dev/null +++ b/src/utils/__init__.py @@ -0,0 +1,6 @@ +# src/utils/__init__.py +from 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--git a/src/utils/seed.py b/src/utils/seed.py new file mode 100644 index 0000000..d650785 --- /dev/null +++ b/src/utils/seed.py @@ -0,0 +1,33 @@ +# src/utils/seed.py +import random +import numpy as np +import torch +import os + + +def set_seed(seed: int = 42): + """ + 设置随机种子，确保实验的可重复性。 + + Args: + seed: 随机种子值 + """ + # 设置 Python 内置随机种子 + random.seed(seed) + + # 设置 NumPy 随机种子 + np.random.seed(seed) + + # 设置 PyTorch 随机种子 + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # 对于多 GPU 环境 + + # 禁用 CUDA 中的确定性算法，以提高性能（可选） + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + + # 设置环境变量中的随机种子 + os.environ['PYTHONHASHSEED'] = str(seed) + + print(f"随机种子已设置为: {seed}") diff --git a/tests/test_model_run.py b/tests/test_model_run.py new file mode 100644 index 0000000..47d8e8b --- /dev/null +++ b/tests/test_model_run.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +测试脚本，用于验证模型是否可以正常跑通，不需要真实数据 +- 生成随机图数据 +- 加载模型配置 +- 初始化模型 +- 运行前向传播和反向传播 +- 验证模型是否可以正常工作 +""" +import os +import sys +import torch +from torch_geometric.data import Data, Batch + +# 添加项目根目录到 Python 路径 +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from src.utils.config_loader import load_config +from src.models.geo_layout_transformer import GeoLayoutTransformer +from src.engine.trainer import Trainer +from src.engine.self_supervised import SelfSupervisedTrainer +from src.utils.logging import get_logger + +def generate_random_graph_data(num_graphs=4, num_nodes_per_graph=8, node_feature_dim=5, edge_feature_dim=0): + """ + 生成随机的图数据 + + Args: + num_graphs: 图的数量 + num_nodes_per_graph: 每个图的节点数量 + node_feature_dim: 节点特征维度 + edge_feature_dim: 边特征维度 + + Returns: + 一个 Batch 对象，包含多个随机生成的图 + """ + graphs = [] + + for _ in range(num_graphs): + # 生成随机节点特征 + x = torch.randn(num_nodes_per_graph, node_feature_dim) + + # 生成随机边（完全连接） + edge_index = [] + for i in range(num_nodes_per_graph): + for j in range(num_nodes_per_graph): + if i != j: + edge_index.append([i, j]) + edge_index = torch.tensor(edge_index, dtype=torch.long).t() + + # 生成随机标签 + y = torch.randn(1, 1) # 假设是图级别的标签 + + # 创建图数据 + graph = Data(x=x, edge_index=edge_index, y=y) + graphs.append(graph) + + # 构建批次 + batch = Batch.from_data_list(graphs) + return batch + +def test_supervised_training(): + """测试监督训练""" + logger = get_logger("Test_Supervised_Training") + logger.info("=== 测试监督训练 ===") + + # 加载配置 + config = load_config('configs/default.yaml') + + # 生成随机数据 + batch = generate_random_graph_data() + logger.info(f"生成的批次数据: {batch}") + logger.info(f"批次大小: {batch.num_graphs}") + logger.info(f"总节点数: {batch.num_nodes}") + logger.info(f"总边数: {batch.num_edges}") + + # 初始化模型 + logger.info("初始化模型...") + model = GeoLayoutTransformer(config) + logger.info("模型初始化成功") + + # 初始化训练器 + logger.info("初始化训练器...") + trainer = Trainer(model, config) + logger.info("训练器初始化成功") + + # 测试前向传播 + logger.info("测试前向传播...") + with torch.no_grad(): + # 先测试 GNN 编码器 + gnn_output = model.gnn_encoder(batch) + logger.info(f"GNN 编码器输出形状: {gnn_output.shape}") + + # 测试形状重塑 + num_graphs = batch.num_graphs + nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1] + logger.info(f"每个图的节点数: {nodes_per_graph}") + reshaped_embeddings = gnn_output.view(num_graphs, nodes_per_graph[0], -1) + logger.info(f"重塑后的嵌入形状: {reshaped_embeddings.shape}") + + # 测试 Transformer 核心 + transformer_output = model.transformer_core(reshaped_embeddings) + logger.info(f"Transformer 输出形状: {transformer_output.shape}") + + # 测试完整模型 + output = model(batch) + logger.info(f"前向传播成功，输出形状: {output.shape}") + + # 测试反向传播 + logger.info("测试反向传播...") + optimizer = torch.optim.Adam(model.parameters(), lr=0.0001) + optimizer.zero_grad() + output = model(batch) + + # 对输出进行全局池化，得到图级别的表示 + # 从 [batch_size, seq_len, hidden_dim] 变为 [batch_size, hidden_dim] + graph_output = output.mean(dim=1) + + # 使用 MSE 损失，只比较前 1 个维度（与 batch.y 形状匹配） + loss = torch.nn.MSELoss()(graph_output[:, :1], batch.y) + loss.backward() + optimizer.step() + logger.info(f"反向传播成功，损失值: {loss.item()}") + + logger.info("监督训练测试完成，模型可以正常工作！") + +def test_self_supervised_training(): + """测试自监督训练""" + logger = get_logger("Test_Self_Supervised_Training") + logger.info("\n=== 测试自监督训练 ===") + + # 加载配置 + config = load_config('configs/default.yaml') + + # 生成随机数据 + batch = generate_random_graph_data() + logger.info(f"生成的批次数据: {batch}") + logger.info(f"批次大小: {batch.num_graphs}") + logger.info(f"总节点数: {batch.num_nodes}") + logger.info(f"总边数: {batch.num_edges}") + + # 初始化模型 + logger.info("初始化模型...") + model = GeoLayoutTransformer(config) + logger.info("模型初始化成功") + + # 初始化自监督训练器 + logger.info("初始化自监督训练器...") + trainer = SelfSupervisedTrainer(model, config) + logger.info("自监督训练器初始化成功") + + # 测试前向传播 + logger.info("测试前向传播...") + with torch.no_grad(): + # 测试 GNN 编码器 + gnn_output = model.gnn_encoder(batch) + logger.info(f"GNN 编码器输出形状: {gnn_output.shape}") + + # 测试 Transformer 核心 + num_graphs = batch.num_graphs + nodes_per_graph = batch.ptr[1:] - batch.ptr[:-1] + if not torch.all(nodes_per_graph == nodes_per_graph[0]): + logger.warning("批次中图形的节点数不一致，使用第一个图形的节点数") + nodes_per_graph = nodes_per_graph[0] + + gnn_output_reshaped = gnn_output.view(num_graphs, nodes_per_graph, -1) + transformer_output = model.transformer_core(gnn_output_reshaped) + logger.info(f"Transformer 核心输出形状: {transformer_output.shape}") + + # 测试完整模型前向传播 + logger.info("测试完整模型前向传播...") + with torch.no_grad(): + output = model(batch) + logger.info(f"完整模型前向传播成功，输出形状: {output.shape}") + + logger.info("自监督训练测试完成，模型可以正常工作！") + +def main(): + """主函数""" + logger = get_logger("Test_Model_Run") + logger.info("开始测试模型是否可以正常跑通...") + + try: + # 测试监督训练 + test_supervised_training() + + # 测试自监督训练 + test_self_supervised_training() + + logger.info("\n✅ 所有测试通过，模型可以正常跑通！") + logger.info("模型已准备就绪，可以使用真实数据进行训练。") + except Exception as e: + logger.error(f"❌ 测试失败: {e}") + import traceback + traceback.print_exc() + sys.exit(1) + +if __name__ == 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