活动介绍

(torch) E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops>python setup.py build_ext --inplace running build_ext D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py:380: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。 warnings.warn(f'Error checking compiler version for {compiler}: {error}') building 'MultiScaleDeformableAttention' extension creating E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\build\temp.win-amd64-cpython-38\Release\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\src\cpu creating E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\build\temp.win-amd64-cpython-38\Release\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\src\cuda D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST']. warnings.warn( Emitting ninja build file E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\build\temp.win-amd64-cpython-38\Release\build.ninja... Compiling objects... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) ninja: error: 'E:/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/src/cpu/ms_deform_attn_cpu.cpp', needed by 'E:/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/build/temp.win-amd64-cpython-38/Release/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/src/cpu/ms_deform_attn_cpu.obj', missing and no known rule to make it Traceback (most recent call last): File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 2105, in _run_ninja_build subprocess.run( File "D:\anaconda3\envs\torch\lib\subprocess.py", line 516, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "setup.py", line 79, in <module> setup( File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\__init__.py", line 117, in setup return distutils.core.setup(**attrs) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\core.py", line 183, in setup return run_commands(dist) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\core.py", line 199, in run_commands dist.run_commands() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\dist.py", line 954, in run_commands self.run_command(cmd) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\dist.py", line 950, in run_command super().run_command(command) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\dist.py", line 973, in run_command cmd_obj.run() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\command\build_ext.py", line 98, in run _build_ext.run(self) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 359, in run self.build_extensions() File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 866, in build_extensions build_ext.build_extensions(self) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 476, in build_extensions self._build_extensions_serial() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 502, in _build_extensions_serial self.build_extension(ext) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\command\build_ext.py", line 263, in build_extension _build_ext.build_extension(self, ext) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 557, in build_extension objects = self.compiler.compile( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 838, in win_wrap_ninja_compile _write_ninja_file_and_compile_objects( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 1785, in _write_ninja_file_and_compile_objects _run_ninja_build( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 2121, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error compiling objects for extension (torch) E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops>python setup.py build_ext --inplace running build_ext D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py:380: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。 warnings.warn(f'Error checking compiler version for {compiler}: {error}') building 'MultiScaleDeformableAttention' extension D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST']. warnings.warn( Emitting ninja build file E:\2-三维点云课程\PersFormer_3DLane-main (1)\PersFormer_3DLane-main\models\ops\build\temp.win-amd64-cpython-38\Release\build.ninja... Compiling objects... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) ninja: error: 'E:/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/src/cpu/ms_deform_attn_cpu.cpp', needed by 'E:/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/build/temp.win-amd64-cpython-38/Release/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/src/cpu/ms_deform_attn_cpu.obj', missing and no known rule to make it Traceback (most recent call last): File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 2105, in _run_ninja_build subprocess.run( File "D:\anaconda3\envs\torch\lib\subprocess.py", line 516, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "setup.py", line 79, in <module> setup( File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\__init__.py", line 117, in setup return distutils.core.setup(**attrs) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\core.py", line 183, in setup return run_commands(dist) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\core.py", line 199, in run_commands dist.run_commands() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\dist.py", line 954, in run_commands self.run_command(cmd) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\dist.py", line 950, in run_command super().run_command(command) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\dist.py", line 973, in run_command cmd_obj.run() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\command\build_ext.py", line 98, in run _build_ext.run(self) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 359, in run self.build_extensions() File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 866, in build_extensions build_ext.build_extensions(self) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 476, in build_extensions self._build_extensions_serial() File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 502, in _build_extensions_serial self.build_extension(ext) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\command\build_ext.py", line 263, in build_extension _build_ext.build_extension(self, ext) File "D:\anaconda3\envs\torch\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 557, in build_extension objects = self.compiler.compile( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 838, in win_wrap_ninja_compile _write_ninja_file_and_compile_objects( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 1785, in _write_ninja_file_and_compile_objects _run_ninja_build( File "D:\anaconda3\envs\torch\lib\site-packages\torch\utils\cpp_extension.py", line 2121, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error compiling objects for extension

时间: 2025-07-26 22:33:32 浏览: 13
<think>我们遇到了一个编译错误，主要问题是ninja在尝试编译 MultiScaleDeformableAttention扩展时找不到源文件。错误信息：`ninja:error:'E:/2-三维点云课程/PersFormer_3DLane-main (1)/PersFormer_3DLane-main/models/ops/src/cpu/ms_deform_attn_cpu.cpp',needed by... missingand noknown ruleto makeit`原因分析：1.路径问题：路径中包含空格和括号，可能导致ninja或编译工具解析路径时出错。特别是路径中的括号`(1)`和空格。2.文件确实不存在：可能是文件被移动或删除，或者原始代码库中该文件缺失。解决方案：1.尝试将整个项目目录移动到没有空格和特殊字符（如括号）的路径中。例如，将 `E:\2-三维点云课程\PersFormer_3DLane-main(1)`改为一个简单的路径，如`E:\project\PersFormer_3DLane`。2.检查`src/cpu/`目录下是否存在 `ms_deform_attn_cpu.cpp`文件。如果不存在，可能需要从原始仓库重新获取该文件。另外，错误中还有关于编译器 cl找不到的警告（WinError2），这表明系统可能没有安装 VisualStudio的 C++编译器。在Windows上编译PyTorch C++扩展需要安装Visual Studio（建议2019版）并选择“使用C++的桌面开发”工作负载。步骤：1.安装 VisualStudio2019并确保安装了C++构建工具。2.将项目移动到简单路径（无空格和特殊字符）。3.如果问题仍然存在，尝试设置环境变量TORCH_CUDA_ARCH_LIST为当前 GPU的计算能力（如8.6对应RTX30系列），或者根据错误提示设置环境变量以避免编译所有架构。例如：`setTORCH_CUDA_ARCH_LIST=8.6`（在命令提示符中执行，或者通过系统环境变量设置）4.确保 ninja已安装（通常通过 pip安装PyTorch时会自动安装）。如果上述方法无效，可以尝试手动指定编译器路径或检查环境变量 PATH是否包含 VisualStudio的 cl.exe所在目录（通常在`VC\Tools\MSVC\版本号\bin\Hostx64\x64`下）。注意：在Anaconda环境中，有时需要激活环境并设置环境变量，例如：call"C:\Program Files(x86)\Microsoft VisualStudio\2019\Community\VC\Auxiliary\Build\vcvars64.bat"然后再运行编译命令。但是，由于错误直接指向文件缺失，首先解决路径问题。根据错误信息，我们也可以看到路径被拆分了，可能是因为路径中的空格和括号导致构建系统无法正确识别。因此，最直接的建议是：将项目移动到不含空格和特殊字符的路径，例如：`E:\Projects\PersFormer_3DLane`。如果移动后仍然报错，请检查源文件是否确实存在于新路径的对应位置。如果问题仍然存在，可能需要检查setup.py文件中的路径配置是否正确。由于用户提供了两次相同的错误，我们假设用户已经尝试了两次，所以重点解决路径问题。回答将按照要求，先给出解决方案，然后提出相关问题。</think>根据错误日志分析，您在Windows系统上编译PyTorch自定义算子（MultiScaleDeformableAttention）时遇到两个关键问题： ### 主要错误原因 1. **编译器路径问题** `Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件` → 缺少Visual Studio C++编译环境（MSVC） 2. **文件路径解析失败** `ninja: error: 'E:/2-三维点云课程/.../ms_deform_attn_cpu.cpp' ... missing` → 路径包含**中文目录名**和**特殊字符(括号)**导致ninja编译系统解析失败 ### 解决方案 #### 第一步：安装编译环境 1. 安装 **Visual Studio 2019/2022** 勾选工作负载： - `使用C++的桌面开发` - `Windows 10/11 SDK` - 确保安装 **MSVC v142** 编译器 2. 验证编译器（CMD执行）： ```bash where cl # 应返回类似：C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.36.32532\bin\Hostx64\x64\cl.exe ``` #### 第二步：修正项目路径 1. **移除路径中的中文和特殊字符** 将项目移动到纯英文路径，例如： `E:\Projects\PersFormer_3DLane` （原路径`E:\2-三维点云课程\PersFormer_3DLane-main (1)\`含中文和括号） 2. **缩短路径层级** 避免过深目录嵌套（建议不超过3级） #### 第三步：设置环境变量 ```bash # 设置CUDA架构（根据你的GPU） set TORCH_CUDA_ARCH_LIST=8.6 # RTX 30系列设为8.6 # 激活VS编译环境 call "C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat" ``` #### 第四步：重新编译 ```bash # 清除旧编译文件 python setup.py clean # 重新编译 python setup.py build_ext --inplace ``` ### 注意事项 - 确保`src/cpu/ms_deform_attn_cpu.cpp`文件实际存在 - 检查PyTorch与CUDA版本匹配性（`torch.__version__`和`nvcc -V`） - 中文Windows用户需确认系统区域设置未限制文件路径（控制面板 → 区域 → 管理 → 非Unicode程序语言 → 更改为"中文(简体)"）
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position_ids = torch.arange(max_position_embeddings).expand((1, -1)) state_dict["cond_stage_model.transformer.text_model.embeddings.position_ids"] = position_ids print("已添加 position_ids 到检查点...

import os import sys import argparse import datetime import logging import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm from pathlib import Path import numpy as np from sklearn.metrics import accuracy_score, balanced_accuracy_score # 添加项目根目录到系统路径 BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(ROOT_DIR) # 导入自定义模块 from data_utils import get_scanobjectnn_dataloader from model import PointEfficientNetForClassification # 使用分类模型 from utils import PointNetSetAbstraction # 用于模型 def parse_args(): parser = argparse.ArgumentParser('Point-EfficientNet Training for ScanObjectNN') parser.add_argument('--batch_size', type=int, default=32, help='Batch size during training') parser.add_argument('--epoch', type=int, default=300, help='Number of epochs to run') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate') parser.add_argument('--gpu', type=str, default='0', help='Specify GPU devices') parser.add_argument('--optimizer', type=str, default='Adam', choices=['Adam', 'SGD'], help='Optimizer') parser.add_argument('--log_dir', type=str, default=None, help='Log directory path') parser.add_argument('--decay_rate', type=float, default=0.0001, help='Weight decay rate') parser.add_argument('--npoint', type=int, default=2048, help='Number of points per point cloud') parser.add_argument('--normal', action='store_true', default=False, help='Use normal vectors') parser.add_argument('--step_size', type=int, default=20, help='Learning rate decay step') parser.add_argument('--lr_decay', type=float, default=0.5, help='Learning rate decay rate') parser.add_argument('--num_workers', type=int, default=8, help='Number of data loading workers') parser.add_argument('--root', type=str, default='/Users/zhaoboyang/Model_Introduction/Classification/PENet_cls/data', help='ScanObjectNN dataset root directory') return parser.parse_args() def main(): args = parse_args() # 设置GPU设备 os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 创建日志目录 timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')) if args.log_dir is None: experiment_dir = Path(f'./log/scanobjectnn/point_efficientnet_{timestr}') else: experiment_dir = Path(f'./log/scanobjectnn/{args.log_dir}') experiment_dir.mkdir(parents=True, exist_ok=True) checkpoints_dir = experiment_dir / 'checkpoints' checkpoints_dir.mkdir(exist_ok=True) log_dir = experiment_dir / 'logs' log_dir.mkdir(exist_ok=True) # 配置日志 logger = logging.getLogger("PointEfficientNet") logger.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler(str(log_dir / 'train.log')) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) def log_string(message): logger.info(message) print(message) log_string(f'Using device: {device}') log_string('PARAMETERS:') log_string(str(args)) # 创建数据加载器 log_string('Loading ScanObjectNN dataset...') train_loader = get_scanobjectnn_dataloader( root=args.root, batch_size=args.batch_size, npoints=args.npoint, split='train', normal_channel=args.normal, num_workers=args.num_workers, augment=True ) test_loader = get_scanobjectnn_dataloader( root=args.root, batch_size=args.batch_size, npoints=args.npoint, split='test', normal_channel=args.normal, num_workers=args.num_workers, augment=False, shuffle=False ) log_string(f"Number of training samples: {len(train_loader.dataset)}") log_string(f"Number of test samples: {len(test_loader.dataset)}") # 创建模型 num_classes = 15 # ScanObjectNN有15个类别 model = PointEfficientNetForClassification( num_classes=num_classes, normal_channel=args.normal ).to(device) # 初始化权重 model.init_weights() # 损失函数 criterion = nn.CrossEntropyLoss().to(device) # 优化器 if args.optimizer == 'Adam': optimizer = optim.Adam( model.parameters(), lr=args.learning_rate, weight_decay=args.decay_rate ) else: # SGD optimizer = optim.SGD( model.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.decay_rate ) # 学习率调度器 scheduler = optim.lr_scheduler.StepLR( optimizer, step_size=args.step_size, gamma=args.lr_decay ) # 训练状态变量 best_test_acc = 0.0 best_test_acc_avg = 0.0 start_epoch = 0 # 尝试加载检查点 checkpoint_path = checkpoints_dir / 'best_model.pth' if checkpoint_path.exists(): log_string('Loading checkpoint...') checkpoint = torch.load(str(checkpoint_path)) model.load_state_dict(checkpoint['model_state_dict']) optimizer.load_state_dict(checkpoint['optimizer_state_dict']) scheduler.load_state_dict(checkpoint['scheduler_state_dict']) start_epoch = checkpoint['epoch'] + 1 best_test_acc = checkpoint['best_test_acc'] best_test_acc_avg = checkpoint['best_test_acc_avg'] log_string(f'Resuming training from epoch {start_epoch}') # 训练循环 for epoch in range(start_epoch, args.epoch): log_string(f'Epoch {epoch + 1}/{args.epoch}') model.train() train_loss = 0.0 correct = 0 total = 0 # 训练一个epoch for i, (points, labels) in enumerate(tqdm(train_loader, desc='Training')): points = points.float().to(device) labels = labels.long().to(device) # 前向传播 outputs = model(points) # 计算损失 loss = criterion(outputs, labels) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step() # 统计指标 train_loss += loss.item() * points.size(0) # 计算准确率 _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += predicted.eq(labels).sum().item() # 更新学习率 scheduler.step() # 计算训练指标 train_loss /= len(train_loader.dataset) train_acc = 100. * correct / total log_string(f'Train Loss: {train_loss:.4f}, Accuracy: {train_acc:.2f}%') log_string(f'Current LR: {scheduler.get_last_lr()[0]:.6f}') # 评估模型 model.eval() test_loss = 0.0 test_correct = 0 test_total = 0 all_preds = [] all_labels = [] with torch.no_grad(): for points, labels in tqdm(test_loader, desc='Testing'): points = points.float().to(device) labels = labels.long().to(device) # 前向传播 outputs = model(points) # 计算损失 loss = criterion(outputs, labels) test_loss += loss.item() * points.size(0) # 计算准确率 _, predicted = torch.max(outputs.data, 1) test_total += labels.size(0) test_correct += predicted.eq(labels).sum().item() # 收集预测和真实标签 all_preds.append(predicted.cpu().numpy()) all_labels.append(labels.cpu().numpy()) # 计算测试指标 test_loss /= len(test_loader.dataset) test_acc = 100. * test_correct / test_total # 计算平均准确率 all_preds = np.concatenate(all_preds) all_labels = np.concatenate(all_labels) test_acc_avg = 100. * balanced_accuracy_score(all_labels, all_preds) log_string('Test Results:') log_string(f'Loss: {test_loss:.4f}, Accuracy: {test_acc:.2f}%, Avg Accuracy: {test_acc_avg:.2f}%') # 保存最佳模型 if test_acc > best_test_acc: best_test_acc = test_acc best_test_acc_avg = test_acc_avg is_best = True else: is_best = False if is_best: state = { 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'scheduler_state_dict': scheduler.state_dict(), 'train_loss': train_loss, 'train_acc': train_acc, 'test_loss': test_loss, 'test_acc': test_acc, 'test_acc_avg': test_acc_avg, 'best_test_acc': best_test_acc, 'best_test_acc_avg': best_test_acc_avg } torch.save(state, str(checkpoint_path)) log_string(f'Saved best model at epoch {epoch + 1} with Test Acc: {best_test_acc:.2f}%') # 定期保存检查点 if (epoch + 1) % 10 == 0: checkpoint = checkpoints_dir / f'model_epoch_{epoch + 1}.pth' torch.save(state, str(checkpoint)) log_string(f'Saved checkpoint at epoch {epoch + 1}') # 训练结束 log_string('Training completed!') log_string(f'Best Test Accuracy: {best_test_acc:.2f}%') log_string(f'Best Test Avg Accuracy: {best_test_acc_avg:.2f}%') if __name__ == '__main__': main() # *_*coding:utf-8 *_* import os import json import warnings import numpy as np import torch import h5py from torch.utils.data import Dataset from torchvision import transforms from utils import ( random_scale_point_cloud, shift_point_cloud, rotate_point_cloud, jitter_point_cloud, pc_normalize # 导入归一化函数 ) warnings.filterwarnings('ignore') os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" # 定义可序列化的转换函数 def jitter_transform(x): return jitter_point_cloud(x) def rotate_transform(x): return rotate_point_cloud(x) def scale_transform(x): return random_scale_point_cloud(x) def shift_transform(x): return shift_point_cloud(x) def normalize_transform(x): return pc_normalize(x) # 加载ScanObjectNN数据 def load_scanobjectnn_data(split, root_dir): # 映射分割名称到文件名 split_mapping = { 'train': 'training', 'val': 'training', # ScanObjectNN没有验证集，使用部分训练数据 'test': 'test' } if split not in split_mapping: raise ValueError(f"无效的分割类型: {split}. 可选: {list(split_mapping.keys())}") # 构建绝对路径 h5_name = os.path.join( root_dir, 'h5_files', 'main_split', f'{split_mapping[split]}_objectdataset_augmentedrot_scale75.h5' ) if not os.path.exists(h5_name): raise FileNotFoundError(f"ScanObjectNN数据文件不存在: {h5_name}") with h5py.File(h5_name, 'r') as f: data = f['data'][:].astype('float32') labels = f['label'][:].astype('int64') # 如果是验证集，取前10%作为验证 if split == 'val': num_val = int(len(data) * 0.1) data = data[:num_val] labels = labels[:num_val] return data, labels class ScanObjectNNDataset(Dataset): def __init__(self, root, npoints=2048, split='train', normal_channel=False, augment=True): """ 参数: root: ScanObjectNN数据集根目录 (包含h5_files目录) npoints: 每个点云采样的点数 (默认2048) split: 数据集分割 (train/val/test) (默认train) normal_channel: 是否使用法线信息 (默认False) augment: 是否使用数据增强 (默认True) """ self.root = root self.npoints = npoints self.normal_channel = normal_channel self.split = split self.augment = augment and (split == 'train') # 仅在训练时增强 # 加载数据 self.data, self.labels = load_scanobjectnn_data(split, root) # 类别数量 self.num_classes = 15 # 设置数据增强 if self.augment: self.augment_transforms = transforms.Compose([ transforms.Lambda(scale_transform), transforms.Lambda(shift_transform), transforms.Lambda(rotate_transform), transforms.Lambda(jitter_transform), transforms.Lambda(normalize_transform) ]) else: self.augment_transforms = transforms.Compose([ transforms.Lambda(normalize_transform) ]) print(f"成功初始化ScanObjectNN数据集: {split}分割, 样本数: {len(self.data)}") print(f"数据集根目录: {self.root}") print(f"使用法线信息: {'是' if self.normal_channel else '否'}") print(f"数据增强: {'启用' if self.augment else '禁用'}") def __len__(self): return len(self.data) def __getitem__(self, index): # 获取点云和标签 point_set = self.data[index].copy() label = self.labels[index] # 选择通道 if not self.normal_channel: point_set = point_set[:, :3] else: point_set = point_set[:, :6] # 采样点 if point_set.shape[0] < self.npoints: # 点不够时重复采样 indices = np.random.choice(point_set.shape[0], self.npoints, replace=True) else: indices = np.random.choice(point_set.shape[0], self.npoints, replace=False) point_set = point_set[indices] # 应用数据增强 point_set = self.augment_transforms(point_set) # 转换为PyTorch需要的格式 (C, N) point_set = point_set.transpose() return point_set, label # 定义可序列化的collate函数 def custom_collate_fn(batch): points, labels = zip(*batch) # 直接堆叠所有元素 points = torch.tensor(np.stack(points)).float() labels = torch.tensor(np.stack(labels)).long() return points, labels # 数据加载器函数 def get_scanobjectnn_dataloader(root, batch_size=32, npoints=2048, split='train', normal_channel=False, num_workers=4, augment=True, shuffle=None): """ 创建ScanObjectNN的数据加载器 参数: root: ScanObjectNN数据集根目录 (包含h5_files目录) batch_size: 批大小 (默认32) npoints: 采样点数 (默认2048) split: 数据集分割 (train/val/test) (默认train) normal_channel: 是否包含法线信息 (默认False) num_workers: 数据加载线程数 (默认4) augment: 是否数据增强 (默认True) shuffle: 是否打乱数据 (默认None, 自动根据split设置) """ if shuffle is None: shuffle = (split == 'train') # 训练集默认打乱 dataset = ScanObjectNNDataset( root=root, npoints=npoints, split=split, normal_channel=normal_channel, augment=augment ) dataloader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, collate_fn=custom_collate_fn, # 使用可序列化的函数 pin_memory=True, drop_last=(split == 'train') # 训练时丢弃最后不完整的批次 ) return dataloader # 示例用法 if __name__ == "__main__": # 设置ScanObjectNN数据集根目录 (包含h5_files目录) SCANOBJECTNN_ROOT = '/Users/zhaoboyang/Model_Introduction/Classification/PENet_cls/data' # 创建训练数据加载器 train_loader = get_scanobjectnn_dataloader( root=SCANOBJECTNN_ROOT, batch_size=16, npoints=1024, split='train', normal_channel=False ) # 创建测试数据加载器 test_loader = get_scanobjectnn_dataloader( root=SCANOBJECTNN_ROOT, batch_size=16, npoints=1024, split='test', normal_channel=False, augment=False, shuffle=False ) # 测试训练加载器 print("\n训练集示例:") for i, (points, labels) in enumerate(train_loader): print(f"批次 {i + 1}:") print(f" 点云形状: {points.shape}") # (B, C, N) print(f" 标签形状: {labels.shape}") # (B,) print(f" 示例标签: {labels[:4].tolist()}") if i == 0: # 仅显示第一批次 break # 测试测试加载器 print("\n测试集示例:") for i, (points, labels) in enumerate(test_loader): print(f"批次 {i + 1}:") print(f" 点云形状: {points.shape}") print(f" 标签形状: {labels.shape}") print(f" 示例标签: {labels[:4].tolist()}") if i == 0: # 仅显示第一批次 break import torch import torch.nn as nn import torch.nn.functional as F import numpy as np def pc_normalize(pc): """归一化点云数据""" centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc ** 2, axis=1))) pc = pc / m return pc def square_distance(src, dst): """ 计算点对之间的欧氏距离平方 参数: src: 源点, [B, N, C] dst: 目标点, [B, M, C] 返回: dist: 每点的平方距离, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def index_points(points, idx): """ 索引点云数据 参数: points: 输入点云, [B, N, C] idx: 采样索引, [B, S] 返回: new_points: 索引后的点, [B, S, C] """ device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def farthest_point_sample(xyz, npoint): """ 最远点采样(FPS) 参数: xyz: 点云数据, [B, N, 3] npoint: 采样点数 返回: centroids: 采样点索引, [B, npoint] """ device = xyz.device B, N, C = xyz.shape centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) distance = torch.ones(B, N).to(device) * 1e10 farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) batch_indices = torch.arange(B, dtype=torch.long).to(device) for i in range(npoint): centroids[:, i] = farthest centroid = xyz[batch_indices, farthest, :].view(B, 1, 3) dist = torch.sum((xyz - centroid) ** 2, -1) mask = dist < distance distance[mask] = dist[mask] farthest = torch.max(distance, -1)[1] return centroids def query_ball_point(radius, nsample, xyz, new_xyz): """ 球查询 参数: radius: 搜索半径 nsample: 最大采样数 xyz: 所有点, [B, N, 3] new_xyz: 查询点, [B, S, 3] 返回: group_idx: 分组点索引, [B, S, nsample] """ device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False): """ 采样和分组 参数: npoint: 采样点数 radius: 搜索半径 nsample: 每组点数 xyz: 点云位置, [B, N, 3] points: 点云特征, [B, N, D] 返回: new_xyz: 采样点位置, [B, npoint, nsample, 3] new_points: 采样点特征, [B, npoint, nsample, 3+D] """ B, N, C = xyz.shape S = npoint fps_idx = farthest_point_sample(xyz, npoint) new_xyz = index_points(xyz, fps_idx) idx = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = index_points(xyz, idx) grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) if points is not None: grouped_points = index_points(points, idx) new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) else: new_points = grouped_xyz_norm if returnfps: return new_xyz, new_points, grouped_xyz, fps_idx else: return new_xyz, new_points def sample_and_group_all(xyz, points): """ 全局采样和分组 参数: xyz: 点云位置, [B, N, 3] points: 点云特征, [B, N, D] 返回: new_xyz: 采样点位置, [B, 1, 3] new_points: 采样点特征, [B, 1, N, 3+D] """ device = xyz.device B, N, C = xyz.shape new_xyz = torch.zeros(B, 1, C).to(device) grouped_xyz = xyz.view(B, 1, N, C) if points is not None: new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1) else: new_points = grouped_xyz return new_xyz, new_points # 在utils.py中修改PointNetSetAbstraction类 class PointNetSetAbstraction(nn.Module): def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all): super(PointNetSetAbstraction, self).__init__() self.npoint = npoint self.radius = radius self.nsample = nsample # 关键修改：添加3个坐标差值通道 actual_in_channel = in_channel + 3 # 3个坐标差值 self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() # 使用实际输入通道数 last_channel = actual_in_channel for out_channel in mlp: self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm2d(out_channel)) last_channel = out_channel self.group_all = group_all def forward(self, xyz, points): xyz = xyz.permute(0, 2, 1) if points is not None: points = points.permute(0, 2, 1) if self.group_all: new_xyz, new_points = sample_and_group_all(xyz, points) else: new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points) new_points = new_points.permute(0, 3, 2, 1) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) new_points = torch.max(new_points, 2)[0] new_xyz = new_xyz.permute(0, 2, 1) return new_xyz, new_points # 数据增强函数 def random_scale_point_cloud(point_set, scale_low=0.8, scale_high=1.2): """随机缩放点云 (默认缩放范围0.8-1.2)""" scale = np.random.uniform(scale_low, scale_high) point_set[:, :3] *= scale return point_set def shift_point_cloud(point_set, shift_range=0.1): """随机平移点云 (默认平移范围±0.1)""" shifts = np.random.uniform(-shift_range, shift_range, 3) point_set[:, :3] += shifts return point_set def rotate_point_cloud(point_set, normal_start_idx=3): """ 随机旋转点云 (绕z轴旋转)，支持任意向量信息 :param point_set: 点云数组 [N, C] :param normal_start_idx: 法线/向量起始维度索引 """ angle = np.random.uniform(0, 2 * np.pi) cosval = np.cos(angle) sinval = np.sin(angle) rotation_matrix = np.array([ [cosval, sinval, 0], [-sinval, cosval, 0], [0, 0, 1] ]) # 旋转所有三维向量（坐标和法线） for i in range(0, point_set.shape[1], 3): if i + 3 > point_set.shape[1]: break point_set[:, i:i + 3] = np.dot(point_set[:, i:i + 3], rotation_matrix) return point_set def jitter_point_cloud(point_set, sigma=0.01, clip=0.05): """添加噪声扰动 (默认σ=0.01, 裁剪±0.05)""" noise = np.clip(sigma * np.random.randn(*point_set[:, :3].shape), -clip, clip) point_set[:, :3] += noise return point_set # 包装函数用于数据增强 def random_scale_wrapper(x): return random_scale_point_cloud(x, scale_low=0.85, scale_high=1.15) def shift_wrapper(x): return shift_point_cloud(x, shift_range=0.1) def rotate_wrapper(x): return rotate_point_cloud(x) def jitter_wrapper(x): return jitter_point_cloud(x, sigma=0.005, clip=0.02) def normalize_wrapper(x): return pc_normalize(x) 上述的三个代码分别是之前提到的分类模型的训练代码，数据加载代码以及工具函数代码（utils），看一下是否是这些代码出了错误才导致训练指标及其低的不正常的

在训练中，我们使用CrossEntropyLoss，它要求标签是0到num_classes-1的整数，所以应该没有问题。 2. **数据归一化**：在数据增强中，我们使用了归一化（pc_normalize），它将点云中心移到原点并缩放到单位球内...

(.venv) C:\yang>python -m utils.main --mode evaluate --model_path checkpoints/best_model.pth [Transformer] 输入形状: torch.Size([204, 1, 64]), 期望特征数: 204 Traceback (most recent call last): File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3

input_data = input_data.expand(-1, -1, 204) # 扩展特征维度 [1, 204, 204] # 或者直接重塑形状 input_data = input_data.view(1, 204, 204) # 新形状 [1, 204, 204] ##### 方案二：修改模型第一层 ...

F:\PythonProject\BNN\metrics.py:59: UserWarning: Using a target size (torch.Size([512])) that is different to the input size (torch.Size([512, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. mse = F.mse_loss(mean_adjusted, target_adjusted) Traceback (most recent call last): File "F:\PythonProject\BNN\main_bayesian.py", line 279, in <module> run( File "F:\PythonProject\BNN\main_bayesian.py", line 215, in run train_result = train_model( ^^^^^^^^^^^^ File "F:\PythonProject\BNN\main_bayesian.py", line 65, in train_model mean_original = mean_norm * label_std + label_mean ~~~~~~~~~~^~~~~~~~~~~ File "C:\Users\32303\anaconda3\envs\ml\Lib\site-packages\torch\_tensor.py", line 1030, in __array__ return self.numpy() ^^^^^^^^^^^^ TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

target = target.view(-1) # 确保目标是一维的（形状[512]） mean = mean.view(-1) # 确保mean也是一维的（形状[512]） 这样，在计算MSE时，两个张量都是[512]，就不会有警告了。 问题3：CUDA张量与numpy数组...

default.yaml，data: data_path: "data/raw/SalinasA_corrected.mat" label_path: "data/raw/SalinasA_gt.mat" normalize: true test_size: 0.2 random_state: 42 patch_size: 1 n_bands: 204 # ← 新增此行 n_classes: 6 training: batch_size: 16 learning_rate: 0.001 weight_decay: 0.0001 epochs: 100 save_dir: "checkpoints" model: d_model: 128 # 特征维度 nhead: 8 # 注意力头数 nhid: 256 # 前馈网络隐藏层维度 nlayers: 4 # Transformer编码器层数 dropout: 0.2 # Dropout率 activation: 'gelu' # 激活函数 max_len: 100 # 位置编码最大长度 pos_encoding: 'fixed' # 位置编码类型 ('fixed' 或 'learned') # 预测配置 prediction: batch_size: 64 # 预测时的批次大小 rgb_bands: [40, 20, 10] # 用于可视化的RGB波段索引 # 评估配置 evaluation: batch_size: 64 # 评估时的批次大小 metrics: ['OA', 'AA', 'Kappa']，transformer.py，import torch import torch.nn as nn import torch.nn.functional as F import math class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe) def forward(self, x): """ 应用位置编码 x shape: [batch_size, seq_len, features] """ x = x + self.pe[:x.size(1), :].unsqueeze(0) return self.dropout(x) class TransformerModel(nn.Module): def __init__(self, ntoken, ninp, nhead, nhid, nlayers, nclasses, dropout=0.5): super(TransformerModel, self).__init__() self.model_type = 'Transformer' self.ninp = ninp # 输入投影层 self.encoder = nn.Linear(ntoken, ninp) self.pos_encoder = PositionalEncoding(ninp, dropout) # 使用batch_first=True确保输入格式为[batch_size, seq_len, features] encoder_layers = nn.TransformerEncoderLayer( ninp, nhead, nhid, dropout, batch_first=True ) self.transformer_encoder = nn.TransformerEncoder(encoder_layers, nlayers) self.decoder = nn.Linear(ninp, nclasses) self.init_weights() def init_weights(self): """初始化模型权重""" initrange = 0.1 self.encoder.weight.data.uniform_(-initrange, initrange) if self.decoder.bias is not None: self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange) def forward(self, src): # 临时注释断言，打印详细信息 print(f"[Transformer] 输入形状: {src.shape}, 期望特征数: {self.encoder.in_features}") batch_size, seq_len, features = src.size() # 确保输入特征数与模型定义一致 assert features == self.encoder.in_features, \ f"特征数不匹配: 输入{features} vs 模型期望{self.encoder.in_features}" # 重塑为二维张量 [batch_size*seq_len, features] src_2d = src.view(batch_size * seq_len, features) print(f"[Transformer] 重塑后形状: {src_2d.shape}") # 应用投影层并缩放 src_proj = self.encoder(src_2d) * math.sqrt(self.ninp) print(f"[Transformer] 投影后形状: {src_proj.shape}") # 恢复三维形状 [batch_size, seq_len, ninp] src_3d = src_proj.view(batch_size, seq_len, self.ninp) # 应用位置编码 src_pos = self.pos_encoder(src_3d) # 通过Transformer编码器 output = self.transformer_encoder(src_pos) print(f"[Transformer] 编码器输出形状: {output.shape}") # 处理输出维度 if seq_len == 1: # 单像素输入，直接挤压seq_len维度 output = output.squeeze(1) # [batch_size, ninp] else: # 多像素patch，取序列平均作为特征表示 output = torch.mean(output, dim=1) # [batch_size, ninp] # 解码为类别预测 output = self.decoder(output) print(f"[Transformer] 最终输出形状: {output.shape}") return F.log_softmax(output, dim=-1) def create_transformer_model(config): """根据配置创建Transformer模型""" n_bands = config['data']['n_bands'] n_classes = config['data']['n_classes'] d_model = config['model']['d_model'] nhead = config['model']['nhead'] nhid = config['model']['nhid'] nlayers = config['model']['nlayers'] dropout = config['model']['dropout'] model = TransformerModel( n_bands, d_model, nhead, nhid, nlayers, n_classes, dropout ) return model，predict.py，import torch import numpy as np from model.transformer import create_transformer_model from tqdm import tqdm import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler import os import scipy.io as sio from typing import Tuple, Optional, Union def generate_prediction_map( model_path: str, config: dict, hsi_image: Optional[np.ndarray] = None ) -> Tuple[np.ndarray, plt.Figure]: """生成高光谱图像的预测图（最终修正版）""" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 加载模型 try: model = create_transformer_model(config) state_dict = torch.load(model_path, map_location=device) model.load_state_dict(state_dict) model = model.to(device) model.eval() print(f"成功加载模型: {model_path}") except Exception as e: raise RuntimeError(f"模型加载失败: {e}") from e # 加载图像数据 if hsi_image is None: try: data_path = config['data']['data_path'] if not os.path.exists(data_path): raise FileNotFoundError(f"数据文件不存在: {data_path}") data = sio.loadmat(data_path) data_key = next(key for key in data.keys() if not key.startswith('__')) hsi_image = data[data_key].astype(np.float32) # 验证数据形状和波段数 print(f"加载的数据形状: {hsi_image.shape}") n_bands = config['data']['n_bands'] assert hsi_image.shape[2] == n_bands, \ f"数据波段数错误: {hsi_image.shape[2]} vs 配置{n_bands}" print(f"成功加载数据: {data_path}, 波段数: {n_bands}") except Exception as e: raise RuntimeError(f"数据加载失败: {e}") from e # 数据预处理 try: if config['data'].get('normalize', False): scaler = StandardScaler() h, w, c = hsi_image.shape data_2d = hsi_image.reshape(h * w, c) data_2d = scaler.fit_transform(data_2d) hsi_image = data_2d.reshape(h, w, c).astype(np.float32) print(f"数据归一化完成，形状: {hsi_image.shape}") except Exception as e: raise RuntimeError(f"数据预处理失败: {e}") from e patch_size = config['data'].get('patch_size', 1) pad_size = patch_size // 2 n_classes = config['data']['n_classes'] n_bands = config['data']['n_bands'] # 图像零填充 h, w, c = hsi_image.shape padded_image = np.zeros((h + 2 * pad_size, w + 2 * pad_size, c), dtype=np.float32) padded_image[pad_size:-pad_size, pad_size:-pad_size, :] = hsi_image # 初始化预测图 prediction_map = np.zeros((h, w), dtype=np.int64) batch_size = config['prediction'].get('batch_size', 64) rgb_bands = config['prediction'].get('rgb_bands', [40, 20, 10]) # 逐行批量预测（最终维度修正） with torch.no_grad(): for i in tqdm(range(h), desc="生成预测图"): for j in range(0, w, batch_size): end_j = min(j + batch_size, w) batch_size_actual = end_j - j patches = np.zeros((batch_size_actual, c, patch_size, patch_size), dtype=np.float32) # 提取patch并转换为CHW格式 [C, H, W] for k in range(batch_size_actual): patch = padded_image[i:i + patch_size, j + k:j + k + patch_size, :] patches[k] = np.transpose(patch, (2, 0, 1)) # [C, H, W] # 转换为tensor并调整形状（关键修正） patches_tensor = torch.from_numpy(patches).to(device) # [B, C, H, W] b, c, h_p, w_p = patches_tensor.shape assert c == n_bands, f"特征数错误: {c} vs {n_bands}" # 调整为 [B, seq_len, C] 并强制转换维度顺序 seq_len = h_p * w_p inputs = patches_tensor.view(b, c, seq_len) # [B, C, seq_len] # 核心修正：交换C和seq_len维度 inputs = inputs.transpose(1, 2) # [B, seq_len, C] print(f"输入模型形状: {inputs.shape}") # 模型预测 outputs = model(inputs) print(f"模型输出形状: {outputs.shape}") # 获取预测结果 _, predicted = torch.max(outputs, 1) prediction_map[i, j:end_j] = predicted.cpu().numpy() # 可视化结果（略，与之前版本一致） fig = plt.figure(figsize=(12, 5)) try: # 显示RGB合成图像 ax1 = fig.add_subplot(1, 2, 1) if len(rgb_bands) == 3 and rgb_bands[0] < c and rgb_bands[1] < c and rgb_bands[2] < c: rgb_image = hsi_image[:, :, rgb_bands] rgb_image = rgb_image / np.max(rgb_image) if np.max(rgb_image) > 0 else rgb_image ax1.imshow(rgb_image) ax1.set_title('RGB合成图像') else: ax1.imshow(np.zeros((h, w, 3))) ax1.set_title('RGB合成图像 (波段配置错误)') ax1.axis('off') # 显示预测结果 ax2 = fig.add_subplot(1, 2, 2) cmap = plt.cm.get_cmap('viridis', n_classes) im = ax2.imshow(prediction_map, cmap=cmap) ax2.set_title('预测结果') ax2.axis('off') # 添加颜色条 cbar = fig.colorbar(im, ax=[ax1, ax2], ticks=range(n_classes)) cbar.ax.set_yticklabels([str(i + 1) for i in range(n_classes)]) plt.tight_layout() except Exception as e: print(f"可视化生成失败: {e}") # 确保此行缩进4个空格 return prediction_map, fig if __name__ == "__main__": import yaml with open('config/default.yaml', 'r') as f: config = yaml.safe_load(f) model_path = 'checkpoints/best_model.pth' pred_map, fig = generate_prediction_map(model_path, config) plt.savefig('prediction_visualization.png', dpi=300, bbox_inches='tight') plt.close(fig) print(f"预测完成，预测图形状: {pred_map.shape}")，出现这个错误AssertionError: 特征数不匹配: 输入64 vs 模型期望204，该怎么改

### 解决方案3：检查模型和数据流，确保一致性 - **检查数据预处理**：确保数据在进入模型之前已经过正确的变换。例如，如果模型是在特征维度为204的数据上训练的，那么评估时也应使用相同维度的数据。 - **检查模型...

import os import time import itertools import math import numpy as np import scipy as sp import scipy.sparse as sps from scipy.sparse.linalg import splu import torch import torch.nn as nn import pyamg from scipy.sparse import csr_matrix, isspmatrix_csr, diags from pyamg.multilevel import multilevel_solver from warnings import warn from scipy.sparse import csr_matrix, isspmatrix_csr, SparseEfficiencyWarning from pyamg.relaxation.smoothing import change_smoothers device = 'cpu' # ========== 辅助函数 ========== def prolongation_fn(grid_size): res_stencil = np.zeros((3,3), dtype=np.double) k=16 res_stencil[0,0] = 1/k res_stencil[0,1] = 2/k res_stencil[0,2] = 1/k res_stencil[1,0] = 2/k res_stencil[1,1] = 4/k res_stencil[1,2] = 2/k res_stencil[2,0] = 1/k res_stencil[2,1] = 2/k res_stencil[2,2] = 1/k P_stencils = np.zeros((grid_size//2, grid_size//2, 3, 3)) for i in range(grid_size//2): for j in range(grid_size//2): P_stencils[i,j,:,:] = res_stencil return compute_p2(P_stencils, grid_size).astype(np.double) def compute_p2(P_stencil, grid_size): indexes = get_p_matrix_indices_one(grid_size) P = csr_matrix((P_stencil.reshape(-1), (indexes[:, 1], indexes[:, 0])), shape=((grid_size//2) ** 2, (grid_size) ** 2)) return P def get_p_matrix_indices_one(grid_size): K = map_2_to_1(grid_size=grid_size) indices = [] for ic in range(grid_size // 2): i = 2 * ic + 1 for jc in range(grid_size // 2): j = 2 * jc + 1 J = int(grid_size // 2 * jc + ic) for k in range(3): for m in range(3): I = int(K[i, j, k, m]) indices.append([I, J]) return np.array(indices) def map_2_to_1(grid_size=8): k = np.zeros((grid_size, grid_size, 3, 3)) M = np.reshape(np.arange(grid_size ** 2), (grid_size, grid_size)).T M = np.concatenate([M, M], axis=0) M = np.concatenate([M, M], axis=1) for i in range(3): I = (i - 1) % grid_size for j in range(3): J = (j - 1) % grid_size k[:, :, i, j] = M[I:I + grid_size, J:J + grid_size] return k def diffusion_stencil_2d(epsilon=1.0, theta=0.0, type='FD'): eps = float(epsilon) theta = float(theta) C = np.cos(theta) S = np.sin(theta) CS = C*S CC = C**2 SS = S**2 if type == 'FE': a = (-1*eps - 1)*CC + (-1*eps - 1)*SS + (3*eps - 3)*CS b = (2*eps - 4)*CC + (-4*eps + 2)*SS c = (-1*eps - 1)*CC + (-1*eps - 1)*SS + (-3*eps + 3)*CS d = (-4*eps + 2)*CC + (2*eps - 4)*SS e = (8*eps + 8)*CC + (8*eps + 8)*SS stencil = np.array([[a, b, c],[d, e, d],[c, b, a]]) / 6.0 elif type == 'FD': a = -0.5*(eps - 1)*CS b = -(eps*SS + CC) c = -a d = -(eps*CC + SS) e = 2.0*(eps + 1) stencil = np.array([[a, d, c],[b, e, b],[c, d, a]]) return stencil def coo_to_tensor(coo): values = coo.data.astype(np.float64) indices = np.vstack((coo.row, coo.col)) i = torch.LongTensor(indices) v = torch.DoubleTensor(values) shape = coo.shape return torch.sparse_coo_tensor(i, v, torch.Size(shape)).to(device) # ========== 光滑算子 ========== def neural_smoother(net, size, mixed=0): # 返回PyTorch张量而不是SciPy矩阵 if mixed == 1: I = torch.eye(size*size, dtype=torch.double, device=device) x0 = I for conv_layer in net.convLayers1: kernel = conv_layer.weight.detach().view(3, 3) M = toeplitz_conv(kernel, size) x0 = torch.mm(M, x0) return x0 else: I = torch.eye(size*size, dtype=torch.double, device=device) x0 = I for conv_layer in net.convLayers1: kernel = conv_layer.weight.detach().view(3, 3) M = toeplitz_conv(kernel, size) x0 = torch.mm(M, x0) kernel2 = net.convLayers2[0].weight.detach().view(3, 3) M2 = toeplitz_conv(kernel2, size) y = x0 + (2/3) * M2 return y def toeplitz_conv(kernel, size): # 将3x3卷积核转换为Toeplitz矩阵 full_size = size * size M = torch.zeros(full_size, full_size, dtype=torch.double, device=device) for i in range(size): for j in range(size): idx = i * size + j for di in [-1, 0, 1]: for dj in [-1, 0, 1]: ni, nj = i + di, j + dj if 0 <= ni < size and 0 <= nj < size: nidx = ni * size + nj k_val = kernel[di+1, dj+1] M[idx, nidx] = k_val return M # ========== Level 创建 ========== def create_levels(eps, theta, n): mxl = 5 # max levels levels = [] # 创建最细层 s = diffusion_stencil_2d(eps, theta * np.pi / 180, 'FD') * 2 A = pyamg.gallery.stencil_grid(s, (n, n)).tocsr() # 创建第一层 - 使用PyAMG的level类而不是字典 level0 = multilevel_solver.level() level0.A = A level0.N = n level0.l = A.shape[0] levels.append(level0) current_n = n for i in range(1, mxl): # 因为已经有一层，所以从1开始 # 获取当前最细层（最后一层） fine_level = levels[-1] current_n = fine_level.N # 创建限制算子 R = prolongation_fn(current_n) # 插值算子是限制算子的转置 P = R.T * 4 # 存储到当前层（细层） fine_level.R = R fine_level.P = P # 为下一层准备：计算粗网格矩阵 A_coarse = R @ fine_level.A @ P # 创建粗网格层 coarse_level = multilevel_solver.level() coarse_level.A = A_coarse coarse_level.N = current_n // 2 # 网格大小减半 coarse_level.l = A_coarse.shape[0] levels.append(coarse_level) # 检查是否达到最小网格 if coarse_level.N < 8: break return levels # ========== Problem Class ========== class Problem: def __init__(self, eps, theta, grid_size, k=20, initial_ground_truth=None, initial_u=None, levels=None, net_trained=None, mxl=0): self.eps = eps self.theta = theta self.grid_size = grid_size if levels is None: levels = create_levels(eps, theta, grid_size) self.levels = levels N = levels[0].N l = levels[0].l # 初始化真实解 if initial_ground_truth is None: self.ground_truth = torch.rand(l, 1, dtype=torch.double, device=device, requires_grad=False) else: self.ground_truth = initial_ground_truth.detach().requires_grad_(False) # 初始解 if initial_u is None: self.initial_u = torch.rand(l, 1, dtype=torch.double, device=device, requires_grad=False) else: self.initial_u = initial_u.detach().requires_grad_(False) self.k = k self.N = N self.levels = levels self.mxl = mxl self.net_trained = net_trained or [] # 冻结预训练网络的参数 for net in self.net_trained: for param in net.parameters(): param.requires_grad = False # 使用SciPy稀疏矩阵计算右端项 A_sparse = self.levels[0].A gt_numpy = self.ground_truth.detach().cpu().numpy().flatten() f_numpy = A_sparse @ gt_numpy self.f = torch.tensor(f_numpy, dtype=torch.double, device=device).view(-1, 1).requires_grad_(False) def compute_solution(self, net): with torch.no_grad(): # 禁用梯度计算 A_sparse = self.levels[0].A # SciPy稀疏矩阵 b = self.f.detach().cpu().numpy().flatten() # 创建多重网格求解器 solver_a_CNN = multigrid_solver(A_sparse, self.grid_size, {'smoother': 'a-CNN', 'eps': self.eps, 'theta': self.theta}, net, self.net_trained, self.mxl) u_solution = solver_a_CNN.solve(b, maxiter=10, tol=1e-6) return torch.tensor(u_solution, dtype=torch.double, device=device).view(-1, 1) # ========== 求解器 ========== def multigrid_solver(A, size, args, net, net_trained, mxl): solver = geometric_solver(A, prolongation_fn, max_levels=5, coarse_solver='splu') if net_trained!=0: nets = [net]+net_trained else: nets = [net] if args['smoother'] == 'a-CNN': # mxl最大是5 i in range(4) 0 1 2 3 for i in range(mxl-1): # 创建当前层的光滑算子 M = neural_smoother(nets[i], size// (2 ** i )) # 定义光滑函数 - 修改后版本 def relax(A, x, b, M_new=M): # 计算残差 (使用NumPy的稀疏矩阵操作) r = b - A.dot(x) # 转换为PyTorch张量进行矩阵乘法 r_tensor = torch.tensor(r, dtype=torch.double, device='cpu').view(-1, 1) correction = M_new @ r_tensor # 转回NumPy并更新解 x += correction.view(-1).cpu().numpy() # 设置光滑器 solver.levels[i].presmoother = relax solver.levels[i].postsmoother = relax return solver def geometric_solver(A, prolongation_function, presmoother=('gauss_seidel', {'sweep': 'forward'}), postsmoother=('gauss_seidel', {'sweep': 'forward'}), max_levels=5, max_coarse=10, coarse_solver='splu', **kwargs): levels = [multilevel_solver.level()] # convert A to csr if not isspmatrix_csr(A): try: A = csr_matrix(A) warn("Implicit conversion of A to CSR", SparseEfficiencyWarning) except BaseException: raise TypeError('Argument A must have type csr_matrix, or be convertible to csr_matrix') # preprocess A A = A.asfptype() if A.shape[0] != A.shape[1]: raise ValueError('expected square matrix') levels[-1].A = A while len(levels) < max_levels and levels[-1].A.shape[0] > max_coarse: extend_hierarchy(levels, prolongation_function) # 使用MultilevelSolver代替弃用的multilevel_solver ml = pyamg.multilevel.MultilevelSolver(levels, **kwargs) change_smoothers(ml, presmoother, postsmoother) return ml # internal function def extend_hierarchy(levels, prolongation_fn): """Extend the multigrid hierarchy.""" A = levels[-1].A N = A.shape[0] n = int(math.sqrt(N)) R = prolongation_fn(n) P = R.T.tocsr() * 4 levels[-1].P = P # prolongation operator levels[-1].R = R # restriction operator levels.append(multilevel_solver.level()) # Form next level through Galerkin product A = R * A * P A = A.astype(np.float64) # convert from complex numbers, should have A.imag==0 levels[-1].A = A # ========== 神经网络模型 ========== class _ConvNet_(nn.Module): def __init__(self, initial=5, kernel_size=3, initial_kernel=0.1): super(_ConvNet_, self).__init__() self.convLayers1 = nn.ModuleList([ nn.Conv2d(1, 1, kernel_size, padding=kernel_size//2, bias=False).double() for _ in range(5) ]) self.convLayers2 = nn.ModuleList([ nn.Conv2d(1, 1, kernel_size, padding=kernel_size//2, bias=False).double() for _ in range(2) ]) # 初始化权重 initial_weights = torch.zeros(1, 1, kernel_size, kernel_size, dtype=torch.double) initial_weights[0, 0, kernel_size//2, kernel_size//2] = initial_kernel for net in self.convLayers1: net.weight = nn.Parameter(initial_weights.clone()) for net in self.convLayers2: net.weight = nn.Parameter(initial_weights.clone()) def forward(self, x): y1 = x y2 = x for net in self.convLayers1: y1 = torch.tanh(net(y1)) for net in self.convLayers2: y2 = torch.tanh(net(y2)) return y1 + (2/3) * y2 def compute_loss(net, problem_instances): loss = torch.zeros(1, device=device, requires_grad=True) for problem in problem_instances: # 确保计算图连接 with torch.set_grad_enabled(True): u_pred = problem.compute_solution(net) u_true = problem.ground_truth # 确保梯度可以回传 u_pred.requires_grad_(True) u_true.requires_grad_(False) # 计算损失 diff = u_pred - u_true norm_diff = torch.norm(diff) norm_true = torch.norm(u_true) loss = loss + norm_diff / norm_true return loss def chunks(l, n): for i in range(0, len(l), n): yield l[i:i + n] def set_seed(seed): torch.manual_seed(seed) np.random.seed(seed) # ========== AlphaCNN ========== class alphaCNN: def __init__(self, net=None, batch_size=1, learning_rate=1e-6, max_epochs=1000, nb_layers=5, tol=1e-6, stable_count=50, optimizer='SGD', check_spectral_radius=False, random_seed=None, kernel_size=3, initial_kernel=0.1): if random_seed is not None: set_seed(random_seed) if net is None: self.net = _ConvNet_(initial=5, kernel_size=kernel_size, initial_kernel=initial_kernel).to(device) else: self.net = net # 确保网络参数需要梯度 for param in self.net.parameters(): param.requires_grad = True self.learning_rate = learning_rate if optimizer == 'Adadelta': self.optim = torch.optim.Adadelta(self.net.parameters(), lr=learning_rate) elif optimizer == 'Adam': self.optim = torch.optim.Adam(self.net.parameters(), lr=learning_rate) else: self.optim = torch.optim.SGD(self.net.parameters(), lr=learning_rate) self.batch_size = batch_size self.max_epochs = max_epochs self.tol = tol self.stable_count = stable_count def _optimization_step_(self, problem_instances): shuffled_problem_instances = np.random.permutation(problem_instances) for problem_chunk in chunks(shuffled_problem_instances, self.batch_size): self.optim.zero_grad() loss = compute_loss(self.net, problem_chunk) # 检查梯度是否存在 if loss.grad_fn is None: raise RuntimeError("Loss has no gradient. Check the computation graph.") loss.backward() self.optim.step() # 确保梯度被应用 with torch.no_grad(): for param in self.net.parameters(): if param.grad is not None: param -= self.learning_rate * param.grad def fit(self, problem_instances): losses = [] prev_total_loss = compute_loss(self.net, problem_instances).item() convergence_counter = 0 problem_number = len(problem_instances) for n_epoch in range(self.max_epochs): start_time = time.time() self._optimization_step_(problem_instances) total_loss = compute_loss(self.net, problem_instances).item() losses.append(total_loss) if np.abs(total_loss - prev_total_loss) < self.tol * problem_number: convergence_counter += 1 if convergence_counter >= self.stable_count: print(f"Converged after {n_epoch} epochs") break else: convergence_counter = 0 prev_total_loss = total_loss epoch_time = time.time() - start_time if n_epoch % 10 == 0: print(f"Epoch: {n_epoch:>3} Loss: {total_loss:>10.6f} Time: {epoch_time:.2f}s") self.losses = losses print(f"Training completed. Final loss: {total_loss:.6f}") return self # ========== 模型训练 ========== def train_and_save_model(eps, theta, coarsening='full'): n = 33 # 网格大小 # 创建模型目录 model_dir = f'./models/theta_{theta}_eps_{eps}' if not os.path.isdir(model_dir): os.makedirs(model_dir) # 创建层级结构 levels = create_levels(eps, theta, n) # 第一层训练 (最粗层) problem_instances1 = [ Problem(eps, theta, n, k=k, levels=levels, mxl=1) for k in range(1, 13) ] model1 = alphaCNN( batch_size=8, learning_rate=1e-8, max_epochs=1000, nb_layers=5, tol=1e-6, stable_count=10, optimizer='Adam', random_seed=9, initial_kernel=0.1 ) model1.fit(problem_instances1) torch.save(model1.net.state_dict(), os.path.join(model_dir, f'theta_{theta}_eps_{eps}_level1.pth')) # 第二层训练 problem_instances2 = [ Problem(eps, theta, n, k=k, levels=levels, mxl=2, net_trained=[model1.net]) for k in range(1, 15) ] model2 = alphaCNN( batch_size=8, learning_rate=1e-8, max_epochs=1000, nb_layers=5, tol=1e-6, stable_count=10, optimizer='Adam', random_seed=9, initial_kernel=0.02/3 ) model2.fit(problem_instances2) torch.save(model2.net.state_dict(), os.path.join(model_dir, f'theta_{theta}_eps_{eps}_level2.pth')) # 第三层训练 problem_instances3 = [ Problem(eps, theta, n, k=k, levels=levels, mxl=3, net_trained=[model1.net, model2.net]) for k in range(1, 17) ] model3 = alphaCNN( batch_size=8, learning_rate=1e-8, max_epochs=1000, nb_layers=5, tol=1e-6, stable_count=10, optimizer='Adam', random_seed=9, initial_kernel=0.002/3 ) model3.fit(problem_instances3) torch.save(model3.net.state_dict(), os.path.join(model_dir, f'theta_{theta}_eps_{eps}_level3.pth')) # 第四层训练 (最细层) problem_instances4 = [ Problem(eps, theta, n, k=k, levels=levels, mxl=4, net_trained=[model1.net, model2.net, model3.net]) for k in range(1, 20) ] model4 = alphaCNN( batch_size=8, learning_rate=1e-8, max_epochs=1000, nb_layers=5, tol=1e-6, stable_count=10, optimizer='Adam', random_seed=9, initial_kernel=0.002/3 ) model4.fit(problem_instances4) torch.save(model4.net.state_dict(), os.path.join(model_dir, f'theta_{theta}_eps_{eps}_level4.pth')) # 训练模型 if __name__ == "__main__": train_and_save_model(100, 75) 这是代码，帮我修改

r_tensor = torch.tensor(r, dtype=torch.double, device='cpu').view(-1, 1) correction = M_new @ r_tensor # M_new是神经网络学到的矩阵（张量） x += correction.view(-1).cpu().numpy() 这里存在一个...

torch from torchvision import transforms from PIL import Image import numpy as np import random class DataAugmentation: def __init__(self, img_size=224): self.transform = transforms.Compose([ transforms.Resize((img_size, img_size)), transforms.RandomHorizontalFlip(p=0.5), transforms.RandomRotation(30), transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def add_gaussian_noise(self, img_tensor, mean=0, std=0.1): noise = torch.randn(img_tensor.size()) * std + mean return torch.clamp(img_tensor + noise, 0, 1) def add_salt_pepper_noise(self, img_tensor, prob=0.05): noise_tensor = torch.rand(img_tensor.size()) salt = torch.max(img_tensor) pepper = torch.min(img_tensor) img_tensor[noise_tensor < prob/2] = salt img_tensor[noise_tensor > 1-prob/2] = pepper return img_tensor def __call__(self, img_path): img = Image.open(img_path).convert('RGB') img_tensor = self.transform(img) if random.random() < 0.3: img_tensor = self.add_gaussian_noise(img_tensor) if random.random() < 0.2: img_tensor = self.add_salt_pepper_noise(img_tensor) return img_tensor在不需要改的情况下如何让它有输出结果

然后，将tensor的值裁剪到0到1之间（因为添加噪声后可能超出0-1，但我们在添加高斯噪声后使用了clamp(0,1)确保在0-1之间，所以反归一化后可能超出0-1，因为归一化操作是可逆线性变换，但原始数据范围是0-1，反归一...

import os import re import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader from torchvision import transforms, models import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm from PIL import Image import json from torch.cuda.amp import GradScaler, autocast # ------------------- # 环境配置 # ------------------- device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # ------------------- # 数据集定义 # ------------------- class COCOCaptionDataset(Dataset): def __init__(self, root_dir, captions_file, vocab=None, transform=None, max_len=20): self.root_dir = root_dir self.transform = transform self.max_len = max_len with open(captions_file, 'r') as f: self.captions_data = json.load(f) self.image_id_to_filename = {img['id']: img['file_name'] for img in self.captions_data['images']} self.image_id_to_captions = {} for ann in self.captions_data['annotations']: img_id = ann['image_id'] if img_id not in self.image_id_to_captions: self.image_id_to_captions[img_id] = [] self.image_id_to_captions[img_id].append(ann['caption']) self.image_ids = list(self.image_id_to_captions.keys()) if vocab is None: self.build_vocab() else: self.vocab = vocab self.idx_to_word = {idx: word for word, idx in self.vocab.items()} def simple_tokenize(self, text): text = text.lower() text = re.sub(r'[^\w\s]', ' ', text) return text.split() def build_vocab(self): all_captions = [] for img_id in self.image_ids: all_captions.extend(self.image_id_to_captions[img_id]) word_freq = {} for caption in all_captions: tokens = self.simple_tokenize(caption) for token in tokens: if token not in word_freq: word_freq[token] = 0 word_freq[token] += 1 min_freq = 5 self.vocab = { '': 0, '<start>': 1, '<end>': 2, '<unk>': 3 } idx = 4 for word, freq in word_freq.items(): if freq >= min_freq: self.vocab[word] = idx idx += 1 print(f"词汇表大小: {len(self.vocab)}") def __len__(self): return len(self.image_ids) def __getitem__(self, idx): img_id = self.image_ids[idx] filename = self.image_id_to_filename[img_id] image_path = os.path.join(self.root_dir, filename) image = Image.open(image_path).convert('RGB') if self.transform: image = self.transform(image) captions = self.image_id_to_captions[img_id] caption = np.random.choice(captions) tokens = self.simple_tokenize(caption) tokens = ['<start>'] + tokens + ['<end>'] caption_indices = [self.vocab.get(token, self.vocab['<unk>']) for token in tokens] if len(caption_indices) < self.max_len: caption_indices = caption_indices + [self.vocab['']] * (self.max_len - len(caption_indices)) else: caption_indices = caption_indices[:self.max_len] return image, torch.tensor(caption_indices) class COCOTestDataset(Dataset): def __init__(self, root_dir, image_info_file, transform=None): self.root_dir = root_dir self.transform = transform with open(image_info_file, 'r') as f: self.image_data = json.load(f) self.image_id_to_filename = {img['id']: img['file_name'] for img in self.image_data['images']} self.image_ids = self.image_data['images'] def __len__(self): return len(self.image_ids) def __getitem__(self, idx): img = self.image_ids[idx] img_id = img['id'] filename = self.image_id_to_filename[img_id] image_path = os.path.join(self.root_dir, filename) image = Image.open(image_path).convert('RGB') if self.transform: image = self.transform(image) return image, img_id # ------------------- # 模型定义 - 修复BatchNorm维度问题 # ------------------- class EncoderCNN(nn.Module): def __init__(self, embed_size): super(EncoderCNN, self).__init__() resnet = models.resnet50(pretrained=True) modules = list(resnet.children())[:-2] self.resnet = nn.Sequential(*modules) self.adaptive_pool = nn.AdaptiveAvgPool2d((14, 14)) self.embed = nn.Linear(2048, embed_size) # 修改：移除不正确的BatchNorm层 self.dropout = nn.Dropout(0.5) def forward(self, images): features = self.resnet(images) features = self.adaptive_pool(features) features = features.permute(0, 2, 3, 1) # [batch, H, W, channels] features = features.view(features.size(0), -1, features.size(3)) # [batch, H*W, channels] # 修改：调整BatchNorm的应用方式 batch_size, num_pixels, channels = features.size() features_reshaped = features.contiguous().view(batch_size * num_pixels, channels) features_reshaped = self.embed(features_reshaped) features = features_reshaped.view(batch_size, num_pixels, -1) return self.dropout(features) class Attention(nn.Module): def __init__(self, encoder_dim, decoder_dim, attention_dim): super(Attention, self).__init__() self.encoder_att = nn.Linear(encoder_dim, attention_dim) self.decoder_att = nn.Linear(decoder_dim, attention_dim) self.full_att = nn.Linear(attention_dim, 1) self.relu = nn.ReLU() self.softmax = nn.Softmax(dim=1) def forward(self, encoder_out, decoder_hidden): att1 = self.encoder_att(encoder_out) att2 = self.decoder_att(decoder_hidden) att2 = att2.unsqueeze(1) att = self.full_att(self.relu(att1 + att2)).squeeze(2) alpha = self.softmax(att) context = (encoder_out * alpha.unsqueeze(2)).sum(dim=1) return context, alpha class DecoderRNN(nn.Module): def __init__(self, embed_size, hidden_size, vocab_size, encoder_dim=2048, attention_dim=128, num_layers=1): super(DecoderRNN, self).__init__() self.embed_size = embed_size self.hidden_size = hidden_size self.vocab_size = vocab_size self.attention_dim = attention_dim self.encoder_dim = encoder_dim self.embedding = nn.Embedding(vocab_size, embed_size) self.attention = Attention(encoder_dim, hidden_size, attention_dim) self.lstm = nn.LSTM(embed_size + encoder_dim, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, vocab_size) self.init_weights() # 添加：保存词汇表引用，用于生成描述 self.vocab = vocab_size def init_weights(self): self.embedding.weight.data.uniform_(-0.1, 0.1) self.fc.weight.data.uniform_(-0.1, 0.1) self.fc.bias.data.fill_(0) def forward(self, encoder_out, captions, lengths): batch_size = encoder_out.size(0) encoder_dim = encoder_out.size(-1) vocab_size = self.vocab_size embeddings = self.embedding(captions) h, c = self.init_hidden_state(encoder_out) outputs = torch.zeros(batch_size, max(lengths)-1, vocab_size).to(device) for t in range(max(lengths)-1): batch_size_t = sum([l > t for l in lengths]) context, alpha = self.attention( encoder_out[:batch_size_t], h[:batch_size_t, 0] ) lstm_input = torch.cat( [embeddings[:batch_size_t, t], context], dim=1 ).unsqueeze(1) _, (h, c) = self.lstm(lstm_input, (h[:batch_size_t], c[:batch_size_t])) preds = self.fc(h.view(-1, self.hidden_size)) outputs[:batch_size_t, t] = preds return outputs def init_hidden_state(self, encoder_out): batch_size = encoder_out.size(0) h = torch.zeros(1, batch_size, self.hidden_size).to(device) c = torch.zeros(1, batch_size, self.hidden_size).to(device) return h, c def sample(self, encoder_out, max_len=20): batch_size = encoder_out.size(0) h, c = self.init_hidden_state(encoder_out) inputs = torch.tensor([[1]] * batch_size).to(device) # 使用<start>的索引 sampled_ids = [] for i in range(max_len): embeddings = self.embedding(inputs) context, alpha = self.attention(encoder_out, h[0]) lstm_input = torch.cat([embeddings.squeeze(1), context], dim=1).unsqueeze(1) _, (h, c) = self.lstm(lstm_input, (h, c)) outputs = self.fc(h.view(-1, self.hidden_size)) _, predicted = outputs.max(1) sampled_ids.append(predicted) inputs = predicted.unsqueeze(1) # 检查是否生成了<end>标记 if predicted.item() == 2: # <end>的索引 break sampled_ids = torch.stack(sampled_ids, 1) return sampled_ids # ------------------- # 训练与评估函数 # ------------------- def train(encoder, decoder, train_loader, criterion, optimizer, scheduler, device, epochs=10): encoder.train() decoder.train() scaler = GradScaler() for epoch in range(epochs): total_loss = 0 progress_bar = tqdm(enumerate(train_loader), total=len(train_loader)) for i, (images, captions) in progress_bar: images = images.to(device) captions = captions.to(device) lengths = [] for cap in captions: pad_pos = (cap == 0).nonzero() if len(pad_pos) > 0: lengths.append(pad_pos[0].item()) else: lengths.append(len(cap)) with autocast(): features = encoder(images) outputs = decoder(features, captions, lengths) targets = captions[:, 1:].contiguous().view(-1) outputs = outputs.view(-1, outputs.size(-1)) loss = criterion(outputs, targets) optimizer.zero_grad() scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() total_loss += loss.item() progress_bar.set_description(f'Epoch {epoch+1}/{epochs}, Loss: {total_loss/(i+1):.4f}') del images, captions, features, outputs, targets torch.cuda.empty_cache() scheduler.step() print(f'Epoch {epoch+1}/{epochs}, 平均损失: {total_loss/len(train_loader):.4f}') torch.save({ 'encoder': encoder.state_dict(), 'decoder': decoder.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), 'epoch': epoch }, f'model_epoch_{epoch+1}.pth') print(f'模型已保存至 model_epoch_{epoch+1}.pth') # ------------------- # 主函数 # ------------------- def main(): data_dir = 'data' image_dirs = { 'train': os.path.join(data_dir, 'images', 'train2014'), 'val': os.path.join(data_dir, 'images', 'val2014'), 'test': os.path.join(data_dir, 'images', 'test2014') } ann_files = { 'train': os.path.join(data_dir, 'annotations', 'captions_train2014.json'), 'val': os.path.join(data_dir, 'annotations', 'captions_val2014.json'), 'test': os.path.join(data_dir, 'annotations', 'image_info_test2014.json') } for key, path in image_dirs.items(): if not os.path.exists(path): print(f"错误: 图像目录不存在 - {path}") return for key, path in ann_files.items(): if not os.path.exists(path): print(f"错误: 标注文件不存在 - {path}") return train_transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.RandomCrop((224, 224)), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) ]) val_test_transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) ]) print("加载训练集...") train_dataset = COCOCaptionDataset( image_dirs['train'], ann_files['train'], transform=train_transform ) print(f"训练集大小: {len(train_dataset)}") print("加载验证集...") val_dataset = COCOCaptionDataset( image_dirs['val'], ann_files['val'], vocab=train_dataset.vocab, transform=val_test_transform ) print(f"验证集大小: {len(val_dataset)}") print("加载测试集...") test_dataset = COCOTestDataset( image_dirs['test'], ann_files['test'], transform=val_test_transform ) print(f"测试集大小: {len(test_dataset)}") batch_size = 16 if torch.cuda.is_available() else 4 train_loader = DataLoader( train_dataset, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True ) val_loader = DataLoader( val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True ) test_loader = DataLoader( test_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True ) embed_size = 256 hidden_size = 256 vocab_size = len(train_dataset.vocab) print("初始化模型...") encoder = EncoderCNN(embed_size).to(device) decoder = DecoderRNN( embed_size=embed_size, hidden_size=hidden_size, vocab_size=vocab_size, encoder_dim=2048, attention_dim=128 ).to(device) criterion = nn.CrossEntropyLoss(ignore_index=0) params = list(decoder.parameters()) + list(encoder.parameters()) optimizer = optim.Adam(params, lr=0.0001) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1) print("\n开始训练...") train(encoder, decoder, train_loader, criterion, optimizer, scheduler, device, epochs=10) sample_image = os.path.join(image_dirs['val'], 'COCO_val2014_000000000042.jpg') if os.path.exists(sample_image): print(f"\n为示例图像生成描述: {sample_image}") test_transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) ]) image = Image.open(sample_image).convert('RGB') image_tensor = test_transform(image).unsqueeze(0).to(device) with torch.no_grad(): features = encoder(image_tensor) sampled_ids = decoder.sample(features) sampled_ids = sampled_ids[0].cpu().numpy() caption_words = [] for word_id in sampled_ids: word = train_dataset.idx_to_word.get(word_id, '<unk>') if word == '<end>': break if word not in ['', '<start>']: caption_words.append(word) caption = ' '.join(caption_words) print(f"生成的描述: {caption}") plt.imshow(image) plt.title(caption) plt.axis('off') plt.savefig('generated_caption.png') plt.show() else: print("\n示例图像不存在，请替换为实际存在的图像路径") if __name__ == "__main__": main()报错：C:\Users\wb159\anaconda3\envs\torch_gpu\python.exe D:\code\pycode\pythonProject1\test1.py 使用设备: cuda 加载训练集... 词汇表大小: 8769 训练集大小: 82783 加载验证集... 验证集大小: 40504 加载测试集... 测试集大小: 40775 初始化模型... 开始训练... 使用设备: cuda 使用设备: cuda 0%| | 0/5174 [00:04<?, ?it/s] Traceback (most recent call last): File "D:\code\pycode\pythonProject1\test1.py", line 467, in <module> main() File "D:\code\pycode\pythonProject1\test1.py", line 428, in main train(encoder, decoder, train_loader, criterion, optimizer, scheduler, device, epochs=10) File "D:\code\pycode\pythonProject1\test1.py", line 291, in train outputs = decoder(features, captions, lengths) File "C:\Users\wb159\anaconda3\envs\torch_gpu\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "D:\code\pycode\pythonProject1\test1.py", line 220, in forward context, alpha = self.attention( File "C:\Users\wb159\anaconda3\envs\torch_gpu\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "D:\code\pycode\pythonProject1\test1.py", line 177, in forward att1 = self.encoder_att(encoder_out) File "C:\Users\wb159\anaconda3\envs\torch_gpu\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "C:\Users\wb159\anaconda3\envs\torch_gpu\lib\site-packages\torch\nn\modules\linear.py", line 103, in forward return F.linear(input, self.weight, self.bias) File "C:\Users\wb159\anaconda3\envs\torch_gpu\lib\site-packages\torch\nn\functional.py", line 1848, in linear return torch._C._nn.linear(input, weight, bias) RuntimeError: mat1 and mat2 shapes cannot be multiplied (3136x256 and 2048x128) 进程已结束，退出代码为 1

##### 方法3：检查特征图展平操作 python # 假设卷积层输出特征图形状 [batch, channels, H, W] = [b, 256, 14, 14] flattened = torch.flatten(conv_output, start_dim=1) # 正确形状 [b, 256*14*14]=[b, 50176]...

# data/dataset.py import os import numpy as np import torch from torch.utils.data import Dataset from plyfile import PlyData import open3d as o3d def read_ply_points(file_path): """ 使用 plyfile 解析 .ply 文件，提取 x, y, z 字段 返回一个 numpy 数组 (N, 3) """ ply = PlyData.read(file_path) # 提取 x, y, z 字段 x = ply['vertex']['x'] y = ply['vertex']['y'] z = ply['vertex']['z'] # 拼接为点云数组 points = np.vstack([x, y, z]).T points = points.astype(np.float32) return points class UnifiedPointCloudDataset(Dataset): def __init__(self, root_dirs, file_exts=['.ply', '.stl', '.obj'], num_points=1024): """ :param root_dirs: 包含所有数据文件夹的列表 :param file_exts: 支持的点云格式 :param num_points: 每个点云采样点数 """ self.file_list = [] self.num_points = num_points # 收集所有点云文件路径 for root_dir in root_dirs: if not os.path.exists(root_dir): raise FileNotFoundError(f"❌ 数据目录不存在: {root_dir}") for root, _, files in os.walk(root_dir): for file in files: if any(file.lower().endswith(ext) for ext in file_exts): full_path = os.path.join(root, file) if os.path.exists(full_path): # 确保文件真实存在 self.file_list.append(full_path) print(f"✅ 共发现 {len(self.file_list)} 个点云文件，用于训练") if len(self.file_list) == 0: raise ValueError("⚠️ 没有发现任何点云文件，请检查路径和文件格式") def __len__(self): return len(self.file_list) def __getitem__(self, idx): path = self.file_list[idx] ext = os.path.splitext(path)[1].lower() try: if ext == '.ply': # 使用 plyfile 读取 .ply 文件 points = read_ply_points(path) elif ext == '.stl': # 使用 open3d 读取 STL 文件并采样成点云 mesh = o3d.io.read_triangle_mesh(path) pcd = mesh.sample_points_uniformly(number_of_points=100000) points = np.asarray(pcd.points) elif ext == '.obj': # 使用 open3d 读取 OBJ 文件 pcd = o3d.io.read_point_cloud(path) if not pcd.has_points(): raise ValueError(f"点云为空或损坏: {path}") points = np.asarray(pcd.points) else: raise ValueError(f"不支持的格式: {ext}") # 检查点云是否为空 if len(points) < 10: raise ValueError(f"点云为空或损坏: {path}") # 固定点数采样 if len(points) < self.num_points: indices = np.random.choice(len(points), self.num_points, replace=True) else: indices = np.random.choice(len(points), self.num_points, replace=False) points = points[indices] return torch.tensor(points, dtype=torch.float32) except Exception as e: print(f"❌ 读取失败: {path}，错误: {str(e)}") return self.__getitem__((idx + 1) % len(self.file_list)) # models/ballmamba.py import torch import torch.nn as nn from torch_geometric.nn import knn_graph, radius_graph from models.mamba_block import MambaBlock from utils.pointcloud_utils import farthest_point_sample class FPS_Causal(nn.Module): def __init__(self, in_channels, hidden_channels, k=512): super(FPS_Causal, self).__init__() self.k = k self.downsample = nn.Linear(in_channels, hidden_channels) self.mamba = MambaBlock(hidden_channels) def forward(self, x, pos): batch_size, num_points, _ = pos.shape idxs = [] for i in range(batch_size): idx = farthest_point_sample(pos[i], self.k) # 现在支持 (N, 3) idxs.append(idx) idx = torch.stack(idxs, dim=0) # (B, k) # 使用 gather 正确采样 x_sampled = torch.gather(pos, 1, idx.unsqueeze(-1).expand(-1, -1, 3)) # (B, k, 3) x_sampled = self.downsample(x_sampled) # (B, k, hidden_channels) x_sampled = self.mamba(x_sampled) # (B, k, hidden_channels) return x_sampled class BallQuery_Sort(nn.Module): def __init__(self, radius=0.3, k=64): super(BallQuery_Sort, self).__init__() self.radius = radius self.k = k def forward(self, pos, x): edge_index = radius_graph(pos, r=self.radius) row, col = edge_index neighbor_pos = pos[col] neighbor_x = x[col] dist = torch.norm(neighbor_pos - pos[row], dim=1) sorted_indices = torch.argsort(dist.view(-1, self.k), dim=1) neighbors = neighbor_x.view(-1, self.k, neighbor_x.shape[1]) return neighbors.gather(1, sorted_indices.unsqueeze(-1).expand(-1, -1, neighbor_x.shape[1])) class BallMambaModel(nn.Module): def __init__(self, in_channels=3, num_keypoints=1024): super(BallMambaModel, self).__init__() self.fps = FPS_Causal(in_channels, 64, k=num_keypoints) self.ball_query = BallQuery_Sort(radius=0.1, k=64) self.mamba = MambaBlock(64) self.decoder = nn.Linear(64, 3) def forward(self, pos): print("pos shape:", pos.shape) # 添加此行，查看 pos 的形状 x = self.fps(None, pos) x = self.ball_query(pos, x) x = x.mean(dim=1) x = self.mamba(x.unsqueeze(0)).squeeze(0) out = self.decoder(x) return out import torch import torch.nn as nn import torch.nn.functional as F class MambaBlock(nn.Module): def __init__(self, d_model: int, d_state: int = 16, d_conv: int = 4, expand: int = 2, dt_rank: int or str = "auto"): """ 完整的 MambaBlock 实现（无 LSTM，基于状态空间模型 SSM） :param d_model: 输入特征维度（通道数） :param d_state: 状态维度（SSM 中的 N） :param d_conv: 卷积核大小（用于局部依赖建模） :param expand: 扩展因子，中间维度 = d_model * expand :param dt_rank: 离散时间步秩，控制参数量，若为 "auto" 则自动计算 """ super(MambaBlock, self).__init__() self.d_model = d_model self.d_state = d_state self.d_inner = int(expand * d_model) self.dt_rank = dt_rank if dt_rank != "auto" else max(1, self.d_model // 16) # 输入投影：将输入特征升维 self.in_proj = nn.Linear(d_model, self.d_inner * 2, bias=False) # 卷积分支（局部建模） self.conv = nn.Conv1d( in_channels=self.d_inner, out_channels=self.d_inner, kernel_size=d_conv, bias=True, groups=self.d_inner, padding=d_conv - 1, # 保证 causal ) # x_proj 将 x 映射到 dt、B、C self.x_proj = nn.Linear(self.d_inner, self.dt_rank + 2 * d_state, bias=False) # dt_proj 将 dt 映射到 d_inner self.dt_proj = nn.Linear(self.dt_rank, self.d_inner, bias=True) # A 和 D 参数（状态矩阵和跳跃连接） A = torch.arange(1, d_state + 1, dtype=torch.float32)[None, :].repeat(self.d_inner, 1) self.A_log = nn.Parameter(torch.log(A)) self.D = nn.Parameter(torch.ones(self.d_inner)) # (d_inner, ) # 输出投影 self.out_proj = nn.Linear(self.d_inner, d_model, bias=False) def forward(self, x: torch.Tensor) -> torch.Tensor: """ 前向传播 :param x: 输入张量，形状 (B, L, d_model) :return: 输出张量，形状 (B, L, d_model) """ batch, seqlen, dim = x.shape # 1. 输入投影 + 拆分 xz = self.in_proj(x) # (B, L, 2 * d_inner) x, z = torch.split(xz, [self.d_inner, self.d_inner], dim=-1) # (B, L, d_inner) # 2. 卷积处理 x = x.transpose(1, 2) # (B, d_inner, L) x = self.conv(x)[:, :, :seqlen] # (B, d_inner, L) x = x.transpose(1, 2) # (B, L, d_inner) # 3. x_proj 分割出 dt、B、C x_dbl = self.x_proj(x) # (B, L, dt_rank + 2 * d_state) dt, B, C = torch.split( x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=-1 ) # 4. dt_proj 映射 dt dt = F.softplus(self.dt_proj(dt)) # (B, L, d_inner) # 5. 获取 A A = -torch.exp(self.A_log) # (d_inner, d_state) A = A.unsqueeze(0).unsqueeze(0) # (1, 1, d_inner, d_state) B = B.unsqueeze(dim=2) # (B, L, 1, d_state) C = C.unsqueeze(dim=2) # (B, L, 1, d_state) # 调试信息 print(f"[MambaBlock] A.shape = {A.shape}") print(f"[MambaBlock] B.shape = {B.shape}") print(f"[MambaBlock] C.shape = {C.shape}") print(f"[MambaBlock] dt.shape = {dt.shape}") states = torch.zeros(batch, self.d_inner, self.d_state, device=x.device) outputs = [] for t in range(seqlen): # 更新状态 states = states + x[:, t:t+1, :, None] * B[:, t:t+1, :, :] states = states * torch.exp(A * dt[:, t:t+1, :, None]) # 添加 dt 到状态更新 # 获取当前时间步的 C 并进行 einsum current_C = C[:, t] # (B, 1, d_state) current_C = current_C.squeeze(1) # (B, d_state) # 使用广播机制 y = torch.einsum("binc,bc->bin", states, current_C) # bc 会广播为 binc outputs.append(y) y = torch.stack(outputs, dim=1) # (B, L, d_inner) # ✅ 修复：self.D 扩展为 (1, 1, d_inner) 以便广播 y = y + x * self.D.view(1, 1, -1) # 加上跳跃连接 # 激活 + 输出 y = y * F.silu(z) out = self.out_proj(y) # 调试信息 print(f"[MambaBlock] y.shape = {y.shape}") print(f"[MambaBlock] out.shape = {out.shape}") return out # train.py import os import torch import torch.nn as nn from torch.utils.data import DataLoader from data.dataset import UnifiedPointCloudDataset from models.ballmamba import BallMambaModel from utils.loss import ChamferLoss # 👇 Windows 多进程训练必须放在 if __name__ == '__main__' 里面 if __name__ == '__main__': # 设置多进程启动方式（Windows 下推荐 spawn） torch.multiprocessing.set_start_method('spawn') # ✅ 修改为你自己的路径 ROOT_DIRS = [ r"D:\桌面\point\data1\part1", r"D:\桌面\point\data1\part2", r"D:\桌面\point\data1\part3", r"D:\桌面\point\data1\part4", r"D:\桌面\point\data1\part5", r"D:\桌面\point\data1\part6", r"D:\桌面\point\data1\part7", r"D:\桌面\point\data1\part8", r"D:\桌面\point\data1\part9", r"D:\桌面\point\data1\part10", r"D:\桌面\point\data1\part11", r"D:\桌面\point\data1\part12", r"D:\桌面\point\data1\part13", r"D:\桌面\point\data1\part14", r"D:\桌面\point\data1\part15", r"D:\桌面\point\data1\part16", r"D:\桌面\point\data1\part17", r"D:\桌面\point\data1\part18", r"D:\桌面\point\data1\part19", r"D:\桌面\point\data1\part20", ] # ✅ 创建 Dataset dataset = UnifiedPointCloudDataset( root_dirs=ROOT_DIRS, file_exts=['.ply', '.stl'], num_points=1024 ) print(f"✅ 共发现 {len(dataset)} 个点云文件，用于训练") if len(dataset) == 0: raise ValueError("⚠️ 没有发现任何点云文件，请检查路径和文件格式") # ✅ 创建 DataLoader（num_workers=0 可临时绕过问题） loader = DataLoader( dataset, batch_size=16, shuffle=True, num_workers=0, # 👈 Windows 下训练先设置为 0，后续再尝试 4 pin_memory=True ) # ✅ 模型初始化 device = torch.device("cpu") model = BallMambaModel(in_channels=3, num_keypoints=512).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) criterion = ChamferLoss().to(device) # ✅ 训练循环 for epoch in range(50): model.train() total_loss = 0 for i, points in enumerate(loader): points = points.to(device) # 输入输出一致（重构任务） recon_points = model(points) loss = criterion(recon_points, points) optimizer.zero_grad() loss.backward() optimizer.step() total_loss += loss.item() if i % 10 == 0: print(f"Epoch [{epoch+1}/50], Batch [{i+1}/{len(loader)}], Loss: {loss.item():.4f}") print(f"Epoch [{epoch+1}/50] 完成，平均 Loss: {total_loss / len(loader):.4f}") torch.save(model.state_dict(), f"models/ballmamba_epoch_{epoch+1}.pth") 上述是我的项目模型训练的代码，现在运行后出现问题C:\ProgramData\miniconda3\envs\torch\python.exe D:\桌面\point\scripts\train_model.py ✅ 共发现 907 个点云文件，用于训练 ✅ 共发现 907 个点云文件，用于训练 pos shape: torch.Size([16, 1024, 3]) [MambaBlock] A.shape = torch.Size([1, 1, 128, 16]) [MambaBlock] B.shape = torch.Size([16, 512, 1, 16]) [MambaBlock] C.shape = torch.Size([16, 512, 1, 16]) [MambaBlock] dt.shape = torch.Size([16, 512, 128]) Traceback (most recent call last): File "D:\桌面\point\scripts\train_model.py", line 76, in <module> recon_points = model(points) File "C:\ProgramData\miniconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "D:\桌面\point\models\ballmamba.py", line 63, in forward x = self.fps(None, pos) File "C:\ProgramData\miniconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "D:\桌面\point\models\ballmamba.py", line 30, in forward x_sampled = self.mamba(x_sampled) # (B, k, hidden_channels) File "C:\ProgramData\miniconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "D:\桌面\point\models\mamba_block.py", line 111, in forward y = y + x * self.D.view(1, 1, -1) # 加上跳跃连接 RuntimeError: The size of tensor a (16) must match the size of tensor b (512) at non-singleton dimension 2 进程已结束，退出代码为 1 应该如何修改代码？给我修改后的完整代码

**原因3：跳跃连接特征融合问题** - **场景**：编码器输出（16维）与解码器输入（512维）拼接时维度不匹配 - **解决**： python # 使用上采样或投影调整维度 skip_feat = F.interpolate(skip_feat, size=512, ...

给注释：import torch from torch import nn class ImageToPatches(nn.Module): def __init__(self, image_size=224, patch_size=16): super(ImageToPatches, self).__init__() self.patch_size = patch_size self.num_patches = (image_size // patch_size) ** 2 def forward(self, x): batch_size, channels, height, width = x.shape # 将图像分割为小块 x = x.unfold(2, self.patch_size, self.patch_size) x = x.unfold(3, self.patch_size, self.patch_size) x = x.contiguous().view(batch_size, channels, -1, self.patch_size * self.patch_size) x = x.permute(0, 2, 1, 3).contiguous() return x.view(batch_size, -1, channels * self.patch_size * self.patch_size) # 示例：将图像转换为块并展平 image_input = torch.randn(32, 3, 224, 224) # 批量大小为32，3通道，224x224分辨率 patch_converter = ImageToPatches() patches = patch_converter(image_input) print(patches.shape) # 输出形状为 [32, 196, 768]

展平每个块：实际上，当我们把图像分割成块时，每个块已经是一个三维张量（channels, patch_size, patch_size），然后我们将每个块展平成一个向量，即channels * patch_size * patch_size。 下面是一个示例函数，...

import numpy as np from tqdm import tqdm import tensorflow as tf import pandas as pd import torch import re from sklearn.model_selection import train_test_split CHEM_FORMULA_SIZE = "([A-Z][a-z]*)([0-9]*)" VALID_ELEMENTS = [ "C", "N", "P", "O", "S", "Si", "I", "H", "Cl", "F", "Br", "B", "Se", "Fe", "Co", "As", "K", "Na", ] ELEMENT_VECTORS = np.eye(len(VALID_ELEMENTS)) element_to_position = dict(zip(VALID_ELEMENTS, ELEMENT_VECTORS)) def formula_to_dense(chem_formula: str) -> np.ndarray: total_onehot = [] for (chem_symbol, num) in re.findall(CHEM_FORMULA_SIZE, chem_formula): num = 1 if num == "" else int(num) one_hot = element_to_position[chem_symbol].reshape(1, -1) one_hot_repeats = np.repeat(one_hot, repeats=num, axis=0) total_onehot.append(one_hot_repeats) if len(total_onehot) == 0: dense_vec = np.zeros(len(element_to_position)) else: dense_vec = np.vstack(total_onehot).sum(0) return dense_vec def sine_embed(v, max_count=256): num_freqs = int(np.ceil(np.log2(max_count))) freqs = 0.5 ** torch.arange(num_freqs, dtype=torch.float32) * np.pi v_tensor = torch.tensor(v, dtype=torch.float32)[:, None] embedded = torch.sin(v_tensor * freqs[None, :]) return torch.abs(embedded).numpy() def encode_formula(formula: str): candidate_features = formula_to_dense(formula) # 将单个化学式转为特征向量 sine_embeddings = sine_embed(candidate_features) return sine_embeddings.flatten() def positional_encoding(max_position, d_model, min_freq=1e-6): position = np.arange(max_position) freqs = min_freq**(2*(np.arange(d_model)//2)/d_model) pos_enc = position.reshape(-1,1)*freqs.reshape(1,-1) pos_enc[:, ::2] = np.cos(pos_enc[:, ::2]) pos_enc[:, 1::2] = np.sin(pos_enc[:, 1::2]) return pos_enc P=positional_encoding(2000000,256, min_freq=1e2) dimn=256 def encoding(rag_tensor,P,dimn): to_pad=[] for sample in rag_tensor: all_dim=[sample[0].numpy().tolist()] pos_enc=[P[int(i)-1] for i in sample[1].numpy().tolist()] for dim in range(dimn): dim_n=[i[dim] for i in pos_enc] all_dim.append(dim_n) to_pad.append(all_dim) to_pad=[tf.keras.preprocessing.sequence.pad_sequences(i,maxlen=501,dtype='float32',padding='post',truncating='post',value=10) for i in to_pad] to_pad=np.stack((to_pad)) to_pad=np.swapaxes(to_pad, 1, -1) return to_pad def trun_n_d(n,d): return ( n if not n.find('.')+1 else n[:n.find('.')+d+1] ) def prepro_specs_train(df): df = df.reset_index(drop=True) valid = [] mz_intensity = df['Spectrum'].to_list() def process_line(line): pairs = line.split() mz_list = [] intensity_list = [] for pair in pairs: mz, intensity = pair.split(':') mz_list.append(float(mz)) intensity_list.append(float(intensity)) return mz_list, intensity_list for idx, intensities in tqdm(enumerate(mz_intensity)): mz_list, intensity_list = process_line(intensities) mz_list.append(float(df.at[idx, 'Total Exact Mass'])) round_mz_list = [round(float(mz), 2) for mz in mz_list] round_intensity_list = [round(float(intensity), 2) for intensity in intensity_list] valid.append([round_mz_list, round_intensity_list]) return tf.ragged.constant(valid) import json import torch from typing import Dict, List from torch.utils.data import Dataset import transformers from peft import LoraConfig, TaskType, get_peft_model from torch.utils.data import DataLoader, SequentialSampler from transformers import Trainer, TrainingArguments from lora_plus import LoraPlusTrainer from torch.utils.data import RandomSampler def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> tuple[int, int]: if target_len * 2 < cutoff_len: # truncate source max_target_len = cutoff_len elif source_len * 2 < cutoff_len: # truncate target max_target_len = cutoff_len - source_len else: # truncate both max_target_len = int(cutoff_len * (target_len / (source_len + target_len))) new_target_len = min(max_target_len , target_len) max_source_len = max(cutoff_len - new_target_len, 0) new_source_len = min(max_source_len, source_len) return new_source_len, new_target_len class SupervisedDataset(Dataset): """Dataset for supervised fine-tuning.""" def __init__( self, data_path, tokenizer, model_max_length, user_tokens=[151644], assistant_tokens=[151645], ): super(SupervisedDataset, self).__init__() self.data = json.load(open(data_path)) self.tokenizer = tokenizer self.model_max_length = model_max_length self.user_tokens = user_tokens self.assistant_tokens = assistant_tokens self.ignore_index = -100 # 测试第一条数据是否正确处理 item = self.preprocessing(self.data[0]) print("input:", self.tokenizer.decode(item["input_ids"])) labels = [id_ for id_ in item["labels"] if id_ != -100] # 过滤 -100 的标签 def __len__(self): return len(self.data) def preprocessing(self, example): input_ids = [] labels = [] # 将用户和助手的内容配对 messages = example["conversations"] pairs = [] current_user_encoded = None # 将 user 和 assistant 配对，并将其打包成编码后的 pairs for message in messages: if message["role"] == "user": # 编码用户消息 current_user_encoded = [self.tokenizer.bos_token_id] + self.user_tokens + self.tokenizer.encode( message["content"], add_special_tokens=False ) elif message["role"] == "assistant" and current_user_encoded is not None: # 编码助手消息 assistant_encoded = self.assistant_tokens + self.tokenizer.encode( message["content"], add_special_tokens=False ) # 配对形成一个 (source_ids, target_ids) pairs.append((current_user_encoded, assistant_encoded)) current_user_encoded = None total_length = 0 # 初始化总长度 # 逐对处理编码后的 (source_ids, target_ids) for turn_idx, (source_ids, target_ids) in enumerate(pairs): # 检查是否超出最大长度，若超出则停止处理 if total_length >= self.model_max_length: print("Exceeded max length, stopping processing further turns.") break # 动态截断长度 source_len, target_len = infer_seqlen( len(source_ids), len(target_ids), self.model_max_length - total_length ) source_ids = source_ids[:source_len] target_ids = target_ids[:target_len] # 更新总长度 total_length += source_len + target_len source_label = [self.tokenizer.bos_token_id] + [self.ignore_index] * (source_len-1) target_label = target_ids # 数据拼接 input_ids += source_ids + target_ids labels += source_label + target_label # 添加 EOS 标记 input_ids += [self.tokenizer.eos_token_id] labels += [self.tokenizer.eos_token_id] input_ids += [self.tokenizer.pad_token_id] * ( self.model_max_length - len(input_ids) ) labels += [self.ignore_index] * (self.model_max_length - len(labels)) # 转换为 Tensor input_ids = torch.LongTensor(input_ids) labels = torch.LongTensor(labels) # 构造 attention_mask attention_mask = attention_mask = input_ids.ne(self.tokenizer.pad_token_id) return { "input_ids": input_ids, "labels": labels, "attention_mask": attention_mask, } def __getitem__(self, idx) -> Dict[str, torch.Tensor]: return self.preprocessing(self.data[idx])修改代码，改为读取csv文件，提取csv文件的Molecular Formula、Total Exact Mass、Spectrum以及SELFIES列数据，对Molecular Formula使用formula_to_dense函数获取形状为（batch，18）的输入，对Total Exact Mass和Spectrum列使用prepro_specs_train和encoding函数获取形状为（batch，501，257）的输入，然后SELFIES使用tokenrizer编码，使用cls_token和sep_token作为开始和结束标记，填充标记为pad_token

# 然后，pos_enc = [P[int(i)-1] for i in sample[1].numpy().tolist()] # 这里i是sample[1]中的元素（即intensity_list中的每个强度值），取整后减1作为索引去取P中的行。这显然不对，因为强度值可能很大，而且P...

import scipy.io import numpy as np import torch import torch.nn as nn from torch.utils.data import DataLoader, TensorDataset from sklearn.model_selection import train_test_split # 1. 加载MAT文件 def load_matlab_data(file_path): data = scipy.io.loadmat(file_path) csi = np.squeeze(data['csi']) # [num_samples, num_subcarriers, num_users] allocations = np.squeeze(data['allocations']) # [num_samples, num_subcarriers] symbols = np.squeeze(data['symbols_with_channel']) snr = np.squeeze(data['snr']) return csi, allocations, symbols, snr # 2. 数据预处理 def preprocess_data(csi, allocations, snr): X = np.concatenate([ np.abs(csi).reshape(csi.shape[0], -1), snr.reshape(-1, 1) ], axis=1) y = allocations return X, y # 3. 定义LSTM模型 class LSTMModel(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers=2): super().__init__() self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): out, _ = self.lstm(x) # [batch_size, seq_length=1, hidden_dim] out = self.fc(out) # [batch_size, seq_length=1, output_dim] return out.squeeze(1) # [batch_size, output_dim] # 4. 训练与验证 def train_model(model, X_train, y_train, num_epochs=50, batch_size=32, lr=1e-3): dataset = TensorDataset( torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.long) ) dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=lr) for epoch in range(num_epochs): model.train() total_loss = 0 for batch_X, batch_y in dataloader: optimizer.zero_grad() outputs = model(batch_X.unsqueeze(1)) # [batch_size, output_dim] outputs_flat = outputs.view(-1, outputs.shape[-1]) targets_flat = batch_y.view(-1) loss = criterion(outputs_flat, targets_flat) loss.backward() optimizer.step() total_loss += loss.item() if (epoch + 1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(dataloader):.4f}') def evaluate_model(model, X_test, y_test): model.eval() with torch.no_grad(): outputs = model(torch.tensor(X_test, dtype=torch.float32).unsqueeze(1)) outputs_flat = outputs.view(-1, outputs.shape[-1]) targets_flat = torch.tensor(y_test, dtype=torch.long).view(-1) accuracy = (outputs_flat.argmax(1) == targets_flat).float().mean() print(f'Test Accuracy: {accuracy.item():.4f}') # 主函数 def main(): csi, allocations, _, snr = load_matlab_data('ofdm_dataset_with_channel.mat') X, y = preprocess_data(csi, allocations, snr) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LSTMModel( input_dim=X_train.shape[1], hidden_dim=128, output_dim=np.max(allocations) + 1 ) train_model(model, X_train, y_train) evaluate_model(model, X_test, y_test) if __name__ == '__main__': main()有Expected input batch_size (32) to match target batch_size (2048).修改一下

然后，模型将batch_X.unsqueeze(1)输入，这里unsqueeze(1)会在第二维增加一个维度，变成(batch_size, 1, input_dim)，因为LSTM的输入需要是三维的（batch, seq_len, features）。模型输出的形状应该是(batch_size, ...

import datasets import transformers import modelscope from itertools import chain import glob import torch import evaluate from swanlab.integration.transformers import SwanLabCallback import swanlab import numpy as np from sklearn.metrics import accuracy_score from rdkit import Chem from rdkit import DataStructs from rdkit.Chem import AllChem import evaluate from rdkit import Chem def main(): swanlab.init("PreTrain-llama-SELFIES") swanlab_callback = SwanLabCallback( project="PreTrain-llama-SELFIES", experiment_name="PreTrain-llama-SELFIES" ) raw_datasets = datasets.load_dataset( "json", data_files="/root/workspace/d1na3k4p420c73ccafcg/txt/selfies.json" ) # split dataset raw_datasets = raw_datasets["train"].train_test_split(test_size=0.05, seed=2333) print("dataset info") print(raw_datasets) tokenizer = transformers.AutoTokenizer.from_pretrained( "/root/workspace/d1na3k4p420c73ccafcg/DeepSeek-R1-Distill-Llama-8B",use_fast=True, trust_remote_code=True, model_max_length=1024 ) context_length = 1024 # use a small context length) # preprocess dataset def tokenize(element): # 对数据集进行预处理，将文本转换为模型可以处理的输入格式 # 这里使用的是Qwen2-0.5B的Tokenizer，将文本转换为模型可以处理的输入格式 # truncation=True表示如果文本长度超过了context_length，就截断 # max_length=context_length表示文本的最大长度为context_length # return_overflowing_tokens=True表示返回溢出的tokens outputs = tokenizer( element["text"], truncation=True, max_length=context_length, return_overflowing_tokens=True, return_length=True, ) input_batch = [] # 作用是将溢出的tokens转换为模型可以处理的输入格式 # 这里使用的是Qwen2-0.5B的Tokenizer，将文本转换为模型可以处理的输入格式 # 这里的input_ids是一个二维数组，每一行表示一个文本的输入格式 # 这里的length是一个一维数组，每一个元素表示一个文本的长度 # 这里的input_batch是一个二维数组，每一行表示一个文本的输入格式 # 这里的context_length是一个整数，表示文本的最大长度 for length, input_ids in zip(outputs["length"], outputs["input_ids"]): if length == context_length: input_batch.append(input_ids) return {"input_ids": input_batch} # map函数的作用是将tokenize函数应用到数据集的每一个元素上 # batched=True表示将数据集分成batch进行处理 # remove_columns=raw_datasets["train"].column_names表示删除原始数据集的列名 tokenized_datasets = raw_datasets.map( tokenize, batched=True,num_proc=20, remove_columns=raw_datasets["train"].column_names ) print("tokenize dataset info") # print(tokenized_datasets) # eos_token的作用是表示文本的结束 # pad_token的作用是表示填充的token tokenizer.pad_token = tokenizer.eos_token # DataCollatorForLanguageModeling的作用是将数据集转换为模型可以处理的输入格式 # 这里使用的是Qwen2-0.5B的Tokenizer，将文本转换为模型可以处理的输入格式 # mlm=False表示不进行masked language modeling data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False) # prepare a model from scratch config = transformers.AutoConfig.from_pretrained( "/root/workspace/d1na3k4p420c73ccafcg/DeepSeek-R1-Distill-Llama-8B", trust_remote_code=True) model = transformers.AutoModelForCausalLM.from_config(config) model_size = sum(t.numel() for t in model.parameters()) print("Model Config:") print(config) print(f"Model Size: {model_size/1000**2:.1f}M parameters") # 加载各个所需的指标 accuracy_metric = evaluate.load('./metrics/accuracy') def compute_metrics(eval_preds): logits, labels = eval_preds # 获取预测结果（取logits中概率最大的索引） preds = np.argmax(logits, axis=-1) # 形状: [batch_size, sequence_length] labels = labels[:, 1:].reshape(-1) preds = preds[:, :-1].reshape(-1) # 计算每个标记的准确度 accuracy = accuracy_metric.compute(predictions=preds, references=labels) return accuracy import random def compute_metrics_partial(eval_preds, subset_ratio=0.5): # 这里假设 eval_preds 是一个 list，包含 logits 和 labels logits, labels = eval_preds # 随机选择部分批次进行计算 batch_size = logits.shape[0] subset_size = int(batch_size * subset_ratio) # 计算子集的大小 selected_indices = random.sample(range(batch_size), subset_size) # 获取预测结果（只对选定的批次进行计算） selected_logits = logits[selected_indices] selected_labels = labels[selected_indices] preds = np.argmax(selected_logits, axis=-1) # shape: [subset_size, sequence_length] selected_labels = selected_labels[:, 1:].reshape(-1) selected_preds = preds[:, :-1].reshape(-1) # 计算准确度 accuracy = accuracy_metric.compute(predictions=selected_preds, references=selected_labels) return accuracy # train args = transformers.TrainingArguments( output_dir="./WikiLLM-llama-SELFIES", per_device_train_batch_size=3, # 每个GPU的训练batch数 per_device_eval_batch_size=3, # 每个GPU的测试batch数 eval_strategy="steps", eval_steps=2, logging_steps=5, gradient_accumulation_steps=8, # 梯度累计总数 num_train_epochs=100, # 训练epoch数 lr_scheduler_type="cosine", # 学习率衰减策略 learning_rate=1e-5, # 基础学习率， save_steps=500, save_total_limit=10, bf16=True, # 开启bf16训练, 对于Amper架构以下的显卡建议替换为fp16=True ) print("Train Args:") print(args) # enjoy training trainer = transformers.Trainer( model=model, tokenizer=tokenizer, args=args, data_collator=data_collator, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], compute_metrics=compute_metrics, callbacks=[swanlab_callback], ) trainer.train() # save model trainer.save_model("./WikiLLM-llama-SELFIES/Weight") # 保存模型的路径 # generate pipe = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer) print("GENERATE:", pipe("人工智能", num_return_sequences=1)[0]["generated_text"]) prompts = ["牛顿", "北京市", "亚洲历史"] examples = [] for i in range(3): # 根据提示词生成数据 text = pipe(prompts[i], num_return_sequences=1)[0]["generated_text"] text = swanlab.Text(text) examples.append(text) swanlab.log({"Generate": examples}) if __name__ == "__main__": main() 修改代码，预训练模型修改为gpt2-large，而且要将gpt2-large的位置编码转换为旋转位置编码

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