https://github.com/thangvubk/SoftGroup.git在H20-NVLink * 1卡调通

如若1232026-02-10 11:02

User: (softgroup) root@autodl-container-9af746813b-35181418:~/autodl-tmp/SoftGroup-main# ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth > train_continue.log

/root/miniconda3/envs/softgroup/lib/python3.7/site-packages/torch/cuda/init .py:145: UserWarning:

NVIDIA H20 with CUDA capability sm_90 is not compatible with the current PyTorch installation.

The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70 sm_75 sm_80 sm_86.

If you want to use the NVIDIA H20 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))

2026-02-09 18:21:20,839 - INFO - Config:

data:

test:

data_root: dataset/s3dis/preprocess

prefix:

  • Area_7

suffix: _inst_nostuff.pth

training: false

type: s3dis

voxel_cfg:

max_npoint: 250000

min_npoint: 5000

scale: 30

spatial_shape:

  • 128

  • 512

x4_split: true

train:

data_root: dataset/s3dis/preprocess

prefix:

  • Area_1

  • Area_2

  • Area_3

  • Area_4

  • Area_5

  • Area_6

repeat: 20

suffix: _inst_nostuff.pth

training: true

type: s3dis

voxel_cfg:

max_npoint: 250000

min_npoint: 5000

scale: 30

spatial_shape:

  • 128

  • 512

x4_split: true

dataloader:

test:

batch_size: 1

num_workers: 4

train:

batch_size: 4

num_workers: 8

persistent_workers: true

epochs: 50

fp16: false

model:

channels: 32

fixed_modules: []

grouping_cfg:

score_thr: 0.2

radius: 0.3

mean_active: 300

class_numpoint_mean:

  • 20000

  • 50000

npoint_thr: 0.02

ignore_classes: []

ignore_label: -100

instance_classes: 2

instance_voxel_cfg:

scale: 30

spatial_shape: 50

num_blocks: 7

sem2ins_classes: []

semantic_classes: 2

semantic_only: false

test_cfg:

cls_score_thr: 0.001

eval_tasks:

  • semantic

  • instance

mask_score_thr: -0.5

min_npoint: 200

x4_split: true

train_cfg:

max_proposal_num: 200

pos_iou_thr: 0.5

optimizer:

lr: 0.004

type: Adam

pretrain: ./work_dirs/softgroup_s3dis_backbone_fold5/latest.pth

save_freq: 4

step_epoch: 32

work_dir: work_dirs/softgroup_s3dis_fold5_v4

2026-02-09 18:21:20,839 - INFO - Distributed: True

2026-02-09 18:21:20,839 - INFO - Mix precision training: False

Traceback (most recent call last):

File "./tools/train.py", line 209, in

main()

File "./tools/train.py", line 177, in main

model = DistributedDataParallel(model, device_ids=[torch.cuda.current_device()])

File "/root/miniconda3/envs/softgroup/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 643, in init

self._sync_params_and_buffers(authoritative_rank=0)

File "/root/miniconda3/envs/softgroup/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 668, in _sync_params_and_buffers

module_states, self.broadcast_bucket_size, authoritative_rank

File "/root/miniconda3/envs/softgroup/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 1544, in _distributed_broadcast_coalesced

self.process_group, tensors, buffer_size, authoritative_rank

RuntimeError: CUDA error: no kernel image is available for execution on the device

CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.

GitHub Copilot:

问题很明确：NVIDIA H20 GPU 的 CUDA 计算能力是 sm_90，而当前安装的 PyTorch 版本最高只支持到 sm_86，所以无法在该 GPU 上运行。

让我先检查当前环境的具体版本：

Ran terminal command: conda activate softgroup && python -c "import torch; print('PyTorch:', torch.version); print('CUDA:', torch.version.cuda); print('cuDNN:', torch.backends.cudnn.version())"

Ran terminal command: python --version && nvidia-smi | head -5

Ran terminal command: python --version

Ran terminal command: nvidia-smi | head -5

读取 [](file:///root/autodl-tmp/SoftGroup-main/requirements.txt)

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/requirements.txt

Ran terminal command: pip list | grep -iE "spconv|torch|gorilla|pointgroup"

现在情况很清楚了：

项目 当前值 问题
GPU NVIDIA H20 (sm_90, Hopper架构) 需要 sm_90 支持
驱动 565.57.01 / CUDA 12.7 OK
Python 3.7.16 PyTorch 2.0+ 需要 ≥3.8
PyTorch 1.11.0+cu113 最高只支持 sm_86
spconv spconv-cu113 2.3.6 需要对应升级

根本原因 ：PyTorch 1.11 (CUDA 11.3) 不支持 H20 GPU 的 sm_90 计算能力。需要 PyTorch ≥ 2.0 + CUDA ≥ 11.8 才能支持 sm_90。

解决方案

需要重建整个 conda 环境。步骤如下：

bash 复制代码 
# 1. 创建新环境 (Python 3.8，因为 PyTorch 2.0 不支持 3.7)
conda create -n softgroup_new python=3.8 -y
conda activate softgroup_new

# 2. 安装支持 sm_90 的 PyTorch (CUDA 11.8 版)
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 \
    --index-url https://download.pytorch.org/whl/cu118

# 3. 安装匹配的 spconv
pip install spconv-cu118

# 4. 安装 SoftGroup 的其他依赖
cd ~/autodl-tmp/SoftGroup-main
pip install -r requirements.txt

# 5. 重新编译 SoftGroup 的自定义 CUDA 算子
python setup.py build_ext develop

# 6. 恢复训练
./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 \
    --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth > train_continue.log

关键点：

  • Python 3.7 → 3.8（PyTorch 2.0 的最低要求）
  • PyTorch 1.11+cu113 → 2.0.1+cu118（支持 sm_90）
  • spconv-cu113 → spconv-cu118
  • SoftGroup 的自定义 CUDA ops 必须在新环境中重新编译 （setup.py build_ext develop）
  • 之前保存的 checkpoint（epoch_4.pth）权重文件本身是兼容的，可以继续 --resume

如果 SoftGroup 代码有与 PyTorch API 变更不兼容的地方（如 torch.cuda.amp 接口变化等），编译或运行时可能还需要做少量代码适配。需要我帮你执行这些步骤吗？

User: ObjC but not for C++

ninja: build stopped: subcommand failed.

Traceback (most recent call last):

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1893, in _run_ninja_build

subprocess.run(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/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 6, in

setup(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/init .py", line 117, in setup

return distutils.core.setup(**attrs)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/core.py", line 183, in setup

return run_commands(dist)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/core.py", line 199, in run_commands

dist.run_commands()

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/dist.py", line 954, in run_commands

self.run_command(cmd)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/dist.py", line 950, in run_command

super().run_command(command)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/dist.py", line 973, in run_command

cmd_obj.run()

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/command/build_ext.py", line 98, in run

_build_ext.run(self)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/command/build_ext.py", line 359, in run

self.build_extensions()

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 843, in build_extensions

build_ext.build_extensions(self)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/command/build_ext.py", line 476, in build_extensions

self._build_extensions_serial()

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/command/build_ext.py", line 502, in _build_extensions_serial

self.build_extension(ext)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/command/build_ext.py", line 263, in build_extension

_build_ext.build_extension(self, ext)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/setuptools/_distutils/command/build_ext.py", line 557, in build_extension

objects = self.compiler.compile(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 658, in unix_wrap_ninja_compile

_write_ninja_file_and_compile_objects(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1574, in _write_ninja_file_and_compile_objects

_run_ninja_build(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1909, in _run_ninja_build

raise RuntimeError(message) from e

RuntimeError: Error compiling objects for extension

GitHub Copilot:

The compilation error提到 "ObjC but not for C++"，这通常是编译器标志不兼容导致的。让我查看更多细节。

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && cat setup.py

Ran terminal command: cat setup.py

Ran terminal command: python setup.py build_ext develop 2>&1 | tail -40

Ran terminal command: python setup.py build_ext develop 2>&1 | grep -iE "error|ObjC|warning.*not for" | head -20

需要看到 ninja 的完整输出：

Ran terminal command: rm -rf build/ && python setup.py build_ext develop 2>&1 | head -80

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01K9ZtvbxVFGRbPiNTzwC6Jo__vscode-1770632045874/content.txt)

找到了！关键错误是：

复制代码 
nvcc fatal   : Unsupported gpu architecture 'compute_90'

系统安装的 /usr/local/cuda 是 CUDA 11.3 ，不支持 compute_90 (sm_90)。PyTorch 2.0.1+cu118 自动生成 sm_90 的编译标志，但系统 nvcc 太旧不认识。

让我检查系统上可用的 CUDA 版本：

Ran terminal command: ls -la /usr/local/cuda* && /usr/local/cuda/bin/nvcc --version

系统只有 CUDA 11.3 toolkit。需要安装 CUDA 11.8 toolkit 来支持 sm_90 的编译。有两个方案：

方案 A（快速） ：通过 conda 安装 CUDA toolkit 11.8
方案 B（更快） ：设置 TORCH_CUDA_ARCH_LIST 绕过，但这里绕不过因为 nvcc 11.3 根本不认识 compute_90

让我用 conda 安装 CUDA 11.8 的开发工具包：

Ran terminal command: conda install -y -c nvidia cuda-toolkit=11.8 2>&1 | tail -20

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01R9gquTtBiQWArUW1wg86a2__vscode-1770632045877/content.txt#1-1)，行 1 到 30

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01R9gquTtBiQWArUW1wg86a2__vscode-1770632045877/content.txt#280-280)，行 280 到 318

看起来 conda install 没有输出结果，可能还在进行中或者失败了。让我检查：

Ran terminal command: conda install -y -c nvidia/label/cuda-11.8.0 cuda-toolkit 2>&1 | tail -30

User: (softgroup_new) root@autodl-container-9af746813b-35181418:~/autodl-tmp/SoftGroup-main# conda install -y -c nvidia cuda-toolkit=11.8 2>&1 | tail -20

cuda-opencl-13.1.115 | 27 KB | ########## | 100%

libgcc-devel_linux-6 | 2.6 MB | ########## | 100%

cuda-nvtx-13.1.115 | 28 KB | ########## | 100%

cuda-sanitizer-api-1 | 8.7 MB | ########## | 100%

cuda-nvprune-13.1.11 | 68 KB | ########## | 100%

libnvfatbin-13.1.115 | 811 KB | ########## | 100%

Preparing transaction: ...working... done

Verifying transaction: ...working... done

Executing transaction: ...working... done

> WARNING: A newer version of conda exists. <

current version: 4.10.3

latest version: 26.1.0

Please update conda by running

复制代码 
$ conda update -n base -c defaults conda

(softgroup_new) root@autodl-container-9af746813b-35181418:~/autodl-tmp/SoftGroup-main#

(softgroup_new) root@autodl-container-9af746813b-35181418:~/autodl-tmp/SoftGroup-main#

GitHub Copilot:

看起来 conda 安装了 CUDA 13.1 而不是 11.8。让我检查现在的 nvcc 情况：

Ran terminal command: which nvcc && nvcc --version

conda 装了 CUDA 13.1 的 nvcc，这个版本支持 compute_90。现在用这个新的 nvcc 重新编译 SoftGroup：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && rm -rf build/ && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -30

CUDA 13.1 的 nvcc 与 PyTorch 编译时用的 CUDA 11.8 不匹配。需要安装 CUDA 11.8 的 nvcc。让我先移除 13.1，改装 11.8：

Ran terminal command: conda install -y -c nvidia/label/cuda-11.8.0 cuda-nvcc=11.8 2>&1 | tail -20

Ran terminal command: nvcc --version

nvcc 11.8 已就位，支持 compute_90。现在重新编译：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && rm -rf build/ && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -30

还有错误，让我看完整输出：

Ran terminal command: rm -rf build/ && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | head -60

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01CkSqp4NwAgFdW9YhmTAxvY__vscode-1770632045886/content.txt#1-1)，行 1 到 60

现在 nvcc 版本对了，但缺少 CUDA runtime 头文件（cuda.h, cuda_runtime.h）。需要安装 CUDA 11.8 的开发头文件包：

Ran terminal command: conda install -y -c nvidia/label/cuda-11.8.0 cuda-cudart-dev cuda-libraries-dev 2>&1 | tail -20

Ran terminal command: find /root/miniconda3/envs/softgroup_new -name "cuda.h" 2>/dev/null | head -5

Ran terminal command: find /root/miniconda3/envs/softgroup_new -name "cuda_runtime.h" 2>/dev/null | head -5

头文件在 /root/miniconda3/envs/softgroup_new/targets/x86_64-linux/include/。需要设置 CUDA_HOME 让编译找到这些头文件。让我检查 conda 安装的 CUDA toolkit 目录结构：

Ran terminal command: ls /root/miniconda3/envs/softgroup_new/targets/x86_64-linux/include/cuda_runtime_api.h 2>/dev/null && ls /root/miniconda3/envs/softgroup_new/bin/nvcc

conda 安装的 CUDA toolkit 结构与标准 /usr/local/cuda 不同。需要设置 CUDA_HOME 和创建符号链接。最简单的方式是让 CUDA_HOME 指向 conda 环境根目录，因为 bin/nvcc 和 include/ 能对应上。但 include/ 中没有 cuda.h。让我创建符号链接：

Ran terminal command: ls /root/miniconda3/envs/softgroup_new/include/cuda.h 2>/dev/null; ls /root/miniconda3/envs/softgroup_new/include/cuda_runtime.h 2>/dev/null; echo "---"; ls /root/miniconda3/envs/softgroup_new/targets/x86_64-linux/include/ | head -20

targets/x86_64-linux/include/ 有全部 CUDA 头文件。需要设置 CUDA_HOME 指向正确路径，或者创建符号链接。最直接的方案是设置环境变量 CUDA_HOME 并确保 include 路径正确：

Ran terminal command: ln -sf /root/miniconda3/envs/softgroup_new/targets/x86_64-linux/include/* /root/miniconda3/envs/softgroup_new/include/ 2>/dev/null; ls /root/miniconda3/envs/softgroup_new/include/cuda.h

现在重新编译：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/miniconda3/envs/softgroup_new && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -20

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/miniconda3/envs/softgroup_new && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "FAILED|error|Error" | head -10

还差 surface_functions.h。这是因为 cuda-nvcc 包只包含了核心头文件，缺少完整的 CUDA runtime 头文件。让我安装完整的 CUDA 11.8 runtime 开发包：

Ran terminal command: conda install -y -c nvidia/label/cuda-11.8.0 cuda-cudart-dev cuda-nvcc cuda-profiler-api 2>&1 | tail -10

Ran terminal command: find /root/miniconda3/envs/softgroup_new -name "surface_functions.h" 2>/dev/null

这个文件根本不存在。conda 安装的只是部分 CUDA 开发包，缺少很多文件。最可靠的方案是直接使用系统的 CUDA 11.3 toolkit，但通过环境变量跳过 PyTorch 的版本检查，或者用 /usr/local/cuda-11.3 的头文件配合 conda 的 nvcc 11.8。

更好的方法：直接用 pip 安装 nvidia-cuda-runtime-cu11 等全套包：

Ran terminal command: pip install nvidia-cuda-runtime-cu11 nvidia-cuda-nvcc-cu11 2>&1 | tail -10

Ran terminal command: python -m pip install nvidia-cuda-runtime-cu11 nvidia-cuda-nvcc-cu11 2>&1 | tail -10

这些 pip 包也不包含完整的开发头文件。最靠谱的方案是创建一个组合式的 CUDA_HOME 目录：用 conda 的 nvcc 11.8（支持 sm_90）+ 系统 CUDA 11.3 的头文件（内容兼容）：

Ran terminal command: mkdir -p /root/cuda-11.8-fake/bin && ln -sf /root/miniconda3/envs/softgroup_new/bin/nvcc /root/cuda-11.8-fake/bin/nvcc && ln -sf /usr/local/cuda-11.3/include /root/cuda-11.8-fake/include && ln -sf /usr/local/cuda-11.3/lib64 /root/cuda-11.8-fake/lib64 && ls /root/cuda-11.8-fake/bin/nvcc /root/cuda-11.8-fake/include/cuda.h /root/cuda-11.8-fake/include/surface_functions.h /root/cuda-11.8-fake/lib64/

组合 CUDA 目录创建成功。现在用这个重新编译：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/cuda-11.8-fake && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "FAILED|fatal error|Error|Successfully|Installed" | head -15

exit code 127 表示命令未找到。nvcc 需要依赖一些共享库。让我看看具体什么找不到：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/cuda-11.8-fake && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -B2 "FAILED" | head -15

nvcc 是一个前端工具，需要同目录下的 cicc、ptxas 等辅助程序。改回用 conda env 作为 CUDA_HOME，把系统 CUDA 11.3 的头文件全部拷贝过来：

Ran terminal command: cp -rn /usr/local/cuda-11.3/include/* /root/miniconda3/envs/softgroup_new/include/ 2>/dev/null; ls /root/miniconda3/envs/softgroup_new/include/surface_functions.h /root/miniconda3/envs/softgroup_new/include/cuda_runtime.h

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/miniconda3/envs/softgroup_new && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "FAILED|fatal error|Error|Successfully|Installed|success" | head -15

Ran terminal command: rm -rf build/ && export CUDA_HOME=/root/miniconda3/envs/softgroup_new && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -A1 "FAILED" | head -15

需要看到 nvcc 的实际错误消息：

是否继续迭代？

object Object

User: @agent 继续: "是否继续迭代？"

GitHub Copilot: Ran terminal command: export CUDA_HOME=/root/miniconda3/envs/softgroup_new && /root/miniconda3/envs/softgroup_new/bin/nvcc -I/root/miniconda3/envs/softgroup_new/include -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/test_cuda.o --compiler-options '-fPIC' -O2 -gencode=arch=compute_90,code=sm_90 -std=c++17 2>&1 | head -20

Ran terminal command: export CUDA_HOME=/root/miniconda3/envs/softgroup_new && /root/miniconda3/envs/softgroup_new/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/root/miniconda3/envs/softgroup_new/include -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/test_cuda.o --compiler-options '-fPIC' -O2 -gencode=arch=compute_90,code=sm_90 -std=c++17 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -DTORCH_EXTENSION_NAME=ops -D_GLIBCXX_USE_CXX11_ABI=0 2>&1 | head -30

混合不同版本的 CUDA 头文件导致了冲突。让我用一个更干净的方式 --- 下载安装完整的 CUDA 11.8 toolkit 到系统：

Ran terminal command: # 先清理混入的头文件

用更合适的方法：直接下载 CUDA 11.8 runfile installer

wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O /tmp/cuda_11.8.run 2>&1 | tail -5

Ran terminal command: wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O /tmp/cuda_11.8.run

Completed with input: wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O /tmp/cuda_11.8.run

Ran terminal command: wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O /tmp/cuda_11.8.run

User: 检查环境

GitHub Copilot:

Ran terminal command: conda activate softgroup_new 2>/dev/null; echo "=== Python =" && python --version && echo "= PyTorch =" && python -c "import torch; print('PyTorch:', torch.version); print('CUDA (PyTorch):', torch.version.cuda); print('GPU available:', torch.cuda.is_available()); print('GPU:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A')" 2>&1 && echo "= nvcc =" && which nvcc && nvcc --version 2>&1 && echo "= CUDA_HOME =" && echo "KaTeX parse error: Expected 'EOF', got '&' at position 23: ...OME:-not set}" &̲& echo "=== /us...{CUDA_HOME:-not set}" && echo "= /usr/local/cuda =" && ls -la /usr/local/cuda 2>&1 && echo "= nvidia-smi ===" && nvidia-smi --query-gpu=name,driver_version,compute_cap --format=csv,noheader 2>&1

Ran terminal command: conda env list && echo "=== spconv =" && python -c "import spconv; print(spconv.version)" 2>&1 && echo "= softgroup =" && python -c "import softgroup" 2>&1 && echo "= /usr/local/cuda version ===" && /usr/local/cuda/bin/nvcc --version 2>&1

Ran terminal command: conda env list

Ran terminal command: python -c "import spconv; print('spconv:', spconv.version)" 2>&1

Ran terminal command: python -c "import softgroup; print('softgroup OK')" 2>&1

Ran terminal command: python -c "from softgroup.ops import ops; print('softgroup.ops.ops OK')" 2>&1

Completed with input: python -c "from softgroup.ops import ops; print('softgroup.ops.ops OK')" 2>&1

Ran terminal command: ls /root/autodl-tmp/SoftGroup-main/softgroup/ops/*.so 2>/dev/null; ls /root/autodl-tmp/SoftGroup-main/build/ 2>/dev/null; /usr/local/cuda/bin/nvcc --version 2>&1

现在情况很清楚了。汇总如下：

当前环境状态

项目 状态 说明
活跃环境 softgroup_new Python 3.8.20
PyTorch 2.0.1+cu118 CUDA 11.8 编译
nvcc (conda) 11.8 支持 sm_90
nvcc (系统) 11.3 /usr/local/cuda，不支持 sm_90
spconv 2.3.6 已安装
GPU 未检测到 nvidia-smi 报 "No devices were found"
SoftGroup CUDA ops 未编译成功 .so 文件是旧的 Python 3.7 版本 (cpython-37m)，不匹配当前 Python 3.8

关键问题

  1. GPU 不可用 --- nvidia-smi 找不到设备，torch.cuda.is_available() 返回 False。这说明：

    • 机器可能已关机/重启过，或者 GPU 实例已过期
    • AutoDL 容器可能需要重新启动才能重新挂载 GPU

  2. SoftGroup CUDA 扩展未编译 --- 现有的 .so 是旧环境（Python 3.7）编译的，在 Python 3.8 环境下无法加载。之前因为系统 CUDA 11.3 不支持 sm_90 导致编译失败。

下一步

需要先解决 GPU 不可用 的问题（在 AutoDL 控制台重启实例），然后再编译 CUDA 扩展。没有 GPU 的情况下即使编译成功也无法训练。

你需要在 AutoDL 控制台检查/重启实例，确保 GPU (H20) 正常挂载后，再运行以下命令编译：

bash 复制代码 
conda activate softgroup_new
export CUDA_HOME=/root/miniconda3/envs/softgroup_new
rm -rf build/ softgroup/ops/*.so
TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop

User: 先配环境，在使用gpu,租的

GitHub Copilot:

Created 5 todos

Ran terminal command: nvidia-smi 2>&1 | head -15

GPU 当前不可用。AutoDL 平台上需要先开机/启动实例 才能使用 GPU。请在 AutoDL 控制台上：

  1. 找到你的实例
  2. 点击 "开机" 或 "启动"
  3. 等待状态变为 "运行中"
  4. 重新连接终端

开机后告诉我，我会继续完成环境配置和 SoftGroup 编译。

User: 不是的，我先配好环境

GitHub Copilot:

Updated todo list

先检查当前状态，然后安装完整的 CUDA 11.8 toolkit：

Ran terminal command: conda activate softgroup_new && python --version && python -c "import torch; print('PyTorch:', torch.version, 'CUDA:', torch.version.cuda)" && nvcc --version 2>&1 | grep release && ls /usr/local/cuda-11.8 2>/dev/null || echo "CUDA 11.8 not installed at /usr/local"

conda 有 nvcc 11.8 但系统缺少完整 CUDA 11.8 toolkit（头文件/库不全）。直接下载安装完整版：

Ran terminal command: wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O /tmp/cuda_11.8.run && echo "Download OK"

Ran terminal command: sh /tmp/cuda_11.8.run --silent --toolkit --toolkitpath=/usr/local/cuda-11.8 --no-opengl-libs --no-man-page --no-drm 2>&1 | tail -10

Ran terminal command: df -h /tmp && df -h /root

/tmp 空间不够解压（需要约 5GB）。换个方式，用 conda 安装完整的 CUDA 11.8 toolkit：

Ran terminal command: rm -f /tmp/cuda_11.8.run && df -h /tmp

Ran terminal command: rm -f /tmp/cuda_11.8.run && TMPDIR=/root/autodl-tmp/tmp_install && mkdir -p TMPDIR \&\& wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run -O TMPDIR/cuda_11.8.run && echo "Download OK, size:" && ls -lh $TMPDIR/cuda_11.8.run

Ran terminal command: export TMPDIR=/root/autodl-tmp/tmp_install && sh T M P D I R / c u d a 1 1.8. r u n − − s i l e n t − − t o o l k i t − − t o o l k i t p a t h = / u s r / l o c a l / c u d a − 11.8 − − n o − o p e n g l − l i b s − − n o − m a n − p a g e − − n o − d r m − − t m p d i r = TMPDIR/cuda_11.8.run --silent --toolkit --toolkitpath=/usr/local/cuda-11.8 --no-opengl-libs --no-man-page --no-drm --tmpdir= TMPDIR/cuda11.8.run−−silent−−toolkit−−toolkitpath=/usr/local/cuda−11.8−−no−opengl−libs−−no−man−page−−no−drm−−tmpdir=TMPDIR 2>&1 | tail -10

Ran terminal command: ls /usr/local/cuda-11.8/bin/nvcc && /usr/local/cuda-11.8/bin/nvcc --version

CUDA 11.8 安装成功！清理安装包，然后编译 SoftGroup：

Starting: 配置环境变量并清理 (2/4)

Ran terminal command: rm -rf /root/autodl-tmp/tmp_install && echo "Cleanup done" && df -h /root/autodl-tmp

Starting: 编译 SoftGroup CUDA ops (3/4)

现在编译 SoftGroup CUDA 扩展：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && rm -rf build/ softgroup/ops/*.so && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -25

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED" | head -10

缺少 crypt.h。这是系统头文件的问题：

Ran terminal command: apt-get update -qq && apt-get install -y -qq libcrypt-dev 2>&1 | tail -5

Ran terminal command: find / -name "crypt.h" 2>/dev/null | head -5

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|Successfully|running install" | head -15

编译器找到的 Python.h 引用了 crypt.h，但 include 路径没有指向系统 /usr/include。让我检查一下：

Ran terminal command: cat /root/miniconda3/envs/softgroup_new/include/python3.8/Python.h | grep -n "crypt.h"

问题是前面我们把 CUDA 头文件 symlink/copy 到 conda env 的 include/ 目录时，可能覆盖了一些东西，或者之前安装的 CUDA 13.1 的包把 include 环境搞乱了。让我检查实际的编译命令 include 路径：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export CFLAGS="-I/usr/include" && export CXXFLAGS="-I/usr/include" && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|Successfully|running install" | head -15

系统和 conda 的头文件冲突了。之前安装 CUDA 13.1 包污染了 conda env 的 include 目录。让我用更干净的方式 --- 直接在 setup.py 的 extra_compile_args 中添加正确的 include 路径：

Ran terminal command: ls /root/miniconda3/envs/softgroup_new/include/crypt.h 2>/dev/null; ls /root/miniconda3/envs/softgroup_new/x86_64-conda-linux-gnu/sysroot/usr/include/crypt.h 2>/dev/null

Completed with input: ls /root/miniconda3/envs/softgroup_new/include/crypt.h 2>/dev/null; ls /root/miniconda3/envs/softgroup_new/x86_64-conda-linux-gnu/sysroot/usr/include/crypt.h 2>/dev/null

conda sysroot 缺少 crypt.h。这是 conda 的编译器工具链（compiler_compat）跟系统不匹配。让我直接把系统的 crypt.h 链接过去：

Ran terminal command: ln -sf /usr/include/crypt.h /root/miniconda3/envs/softgroup_new/x86_64-conda-linux-gnu/sysroot/usr/include/crypt.h && echo "OK"

Ran terminal command: unset CFLAGS CXXFLAGS && rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|Successfully|running install|Installed" | head -15

进展了！现在是缺少 google/dense_hash_map（来自 sparsehash 库）：

Ran terminal command: apt-get install -y -qq libsparsehash-dev 2>&1 | tail -3 && ls /usr/include/google/dense_hash_map

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|Successfully|running install|Installed" | head -15

编译器没有在 /usr/include 中搜索。这是因为 conda 的编译器配置用了 --sysroot，导致只搜索 conda sysroot 下的 include。让我把 sparsehash 也链接到 conda sysroot：

Ran terminal command: cp -r /usr/include/google /root/miniconda3/envs/softgroup_new/x86_64-conda-linux-gnu/sysroot/usr/include/ && echo "OK"

是否继续迭代？

object Object

User: @agent 继续: "是否继续迭代？"

GitHub Copilot: Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -10

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED" | head -10

Ran terminal command: cp -r /usr/include/sparsehash /root/miniconda3/envs/softgroup_new/x86_64-conda-linux-gnu/sysroot/usr/include/ && echo "OK"

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|Successfully|running install|Installed|copying" | head -15

被 OOM killed 了。内存不够。限制编译并发数：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -15

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|warning:" | head -15

只有 cuda.o 失败了。让我看 nvcc 编译的具体错误：

Ran terminal command: rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -A3 "FAILED.*cuda.o"

找到了！nvcc 还在尝试写入已删除的 /root/autodl-tmp/tmp_install/ 目录。这是因为 TMPDIR 环境变量还指向那个被删掉的目录：

Ran terminal command: unset TMPDIR && rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -15

Ran terminal command: unset TMPDIR && rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|fatal" | head -10

GCC 版本太高！CUDA 11.8 要求 GCC ≤ 11。检查一下并添加 --allow-unsupported-compiler 标志：

Ran terminal command: x86_64-conda-linux-gnu-cc --version 2>&1 | head -1 && gcc --version 2>&1 | head -1

conda 的 GCC 是 15.2.0（太新），但系统 GCC 9.4.0 完全兼容。让 nvcc 用系统 GCC 来编译：

Ran terminal command: unset TMPDIR && rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && export CC=/usr/bin/gcc && export CXX=/usr/bin/g++ && export CUDAHOSTCXX=/usr/bin/g++ && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -10

Ran terminal command: unset TMPDIR && rm -rf build/ && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export MAX_JOBS=1 && export CC=/usr/bin/gcc && export CXX=/usr/bin/g++ && export CUDAHOSTCXX=/usr/bin/g++ && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | grep -E "error:|FAILED|fatal|warning.*ccbin" | head -10

exit code 137 = OOM killed。即使 MAX_JOBS=1，nvcc 编译单个 .cu 文件也可能消耗大量内存。让我检查 swap 并增加：

Ran terminal command: free -h && swapon --show

1.2 TiB 内存，可用 81Gi，不应该 OOM。之前可能有其他进程占用了很多内存。看看当前占用：

Ran terminal command: ps aux --sort=-%mem | head -10

1.2Ti 内存，用了 78Gi，绰绰有余。code 137 不是 OOM。可能是容器的 cgroups 限制或者是 nvcc 编译 sm_90 时 ptxas 有问题。让我直接在命令行手动试编译：

Ran terminal command: unset TMPDIR && /usr/local/cuda-11.8/bin/nvcc --version && /usr/local/cuda-11.8/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.8/include -I/root/miniconda3/envs/softgroup_new/include/python3.8 -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/cuda_test.o --expt-relaxed-constexpr -fPIC -O2 -gencode=arch=compute_90,code=sm_90 -std=c++17 -ccbin /usr/bin/g++ -D_GLIBCXX_USE_CXX11_ABI=0 -DTORCH_EXTENSION_NAME=ops 2>&1

Completed with input: unset TMPDIR && /usr/local/cuda-11.8/bin/nvcc --version && /usr/local/cuda-11.8/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.8/include -I/root/miniconda3/envs/softgroup_new/include/python3.8 -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/cuda_test.o --expt-relaxed-constexpr -fPIC -O2 -gencode=arch=compute_90,code=sm_90 -std=c++17 -ccbin /usr/bin/g++ -D_GLIBCXX_USE_CXX11_ABI=0 -DTORCH_EXTENSION_NAME=ops 2>&1

Ran terminal command: /usr/local/cuda-11.8/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.8/include -I/root/miniconda3/envs/softgroup_new/include/python3.8 -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/cuda_test.o --expt-relaxed-constexpr --compiler-options '-fPIC' -O2 -gencode=arch=compute_90,code=sm_90 -std=c++17 -ccbin /usr/bin/g++ -D_GLIBCXX_USE_CXX11_ABI=0 -DTORCH_EXTENSION_NAME=ops 2>&1 | tail -10

编译中又被 killed。让我查看容器的 cgroup 内存限制：

Ran terminal command: cat /sys/fs/cgroup/memory/memory.limit_in_bytes 2>/dev/null || cat /sys/fs/cgroup/memory.max 2>/dev/null; echo "---"; cat /sys/fs/cgroup/memory/memory.usage_in_bytes 2>/dev/null || cat /sys/fs/cgroup/memory.current 2>/dev/null

容器 cgroup 内存限制是 2GB（2147483648 bytes），当前已用约 900MB。nvcc 编译 sm_90 的 .cu 文件需要大量内存超过了这个限制。

这是 AutoDL 容器在无 GPU 状态（关机/CPU 模式）下的内存限制。编译 CUDA 代码需要更多内存。

解决方案 --- 先减少编译开销，用 --ptxas-options=-v --maxrregcount=64 减少内存使用，或者分步编译。但最靠谱的方案是：开机（带 GPU 的模式）后再编译，那时容器会有更多内存。

不过让我先试试能不能通过减少 ptxas 内存使用来在 2GB 内编译成功：

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu | wc -l

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu

文件很小但包含了 6 个 .cu 头文件。让我创建一个 swap 文件来扩展可用内存：

Ran terminal command: fallocate -l 4G /root/autodl-tmp/swapfile && chmod 600 /root/autodl-tmp/swapfile && mkswap /root/autodl-tmp/swapfile && swapon /root/autodl-tmp/swapfile && free -h

Completed with input: fallocate -l 4G /root/autodl-tmp/swapfile && chmod 600 /root/autodl-tmp/swapfile && mkswap /root/autodl-tmp/swapfile && swapon /root/autodl-tmp/swapfile && free -h

容器内没有 swapon 权限。那直接在 2GB 限制内编译 --- 需要用 --no-host-device-move 之类来减少 nvcc 内存使用，或者用 compute_80 先编译（兼容 sm_90，runtime 会 JIT 编译 PTX）：

Ran terminal command: /usr/local/cuda-11.8/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.8/include -I/root/miniconda3/envs/softgroup_new/include/python3.8 -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/cuda_test.o --expt-relaxed-constexpr --compiler-options '-fPIC' -O2 -gencode=arch=compute_80,code=compute_80 -std=c++17 -ccbin /usr/bin/g++ -D_GLIBCXX_USE_CXX11_ABI=0 -DTORCH_EXTENSION_NAME=ops 2>&1 | tail -5

即使用 compute_80 也被 killed。2GB 的 cgroup 限制对 nvcc 编译太紧了。让我试用更低的优化等级或尝试 -O0：

Ran terminal command: /usr/local/cuda-11.8/bin/nvcc -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/TH -I/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.8/include -I/root/miniconda3/envs/softgroup_new/include/python3.8 -c /root/autodl-tmp/SoftGroup-main/softgroup/ops/src/cuda.cu -o /tmp/cuda_test.o --expt-relaxed-constexpr --compiler-options '-fPIC' -O0 -gencode=arch=compute_80,code=compute_80 -std=c++17 -ccbin /usr/bin/g++ -D_GLIBCXX_USE_CXX11_ABI=0 -DTORCH_EXTENSION_NAME=ops -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ 2>&1 | tail -5

2GB 实在太少了。让我看看能否用 --threads 0 并行化降低单线程内存峰值。不过核心问题是 AutoDL 容器在无 GPU 模式（CPU 模式）下只有 2GB 内存限制，而 nvcc 编译单个 CUDA 文件需要超过 2GB。

Updated todo list

当前状况

环境已经配好了大部分：

  • CUDA 11.8 toolkit ✅ 已安装到 /usr/local/cuda-11.8
  • nvcc 11.8 ✅ 支持 sm_90
  • Python 3.8 + PyTorch 2.0.1+cu118 + spconv-cu118 ✅
  • 系统依赖（libcrypt-dev, libsparsehash-dev）✅

唯一卡住的是编译 SoftGroup CUDA 扩展 ：容器在无 GPU 模式下 cgroup 内存限制只有 2GB，不够 nvcc 编译。

你需要做的

  1. 在 AutoDL 控制台开机（选择带 GPU 的模式）
  2. 开机后重新连接，运行以下命令即可一键编译完成：
bash 复制代码 
conda activate softgroup_new
cd /root/autodl-tmp/SoftGroup-main
rm -rf build/ softgroup/ops/*.so
export CUDA_HOME=/usr/local/cuda-11.8
export PATH=$CUDA_HOME/bin:$PATH
export CC=/usr/bin/gcc CXX=/usr/bin/g++ CUDAHOSTCXX=/usr/bin/g++
export MAX_JOBS=1
TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop

开机后告诉我，我直接帮你执行。

User: 开机了

GitHub Copilot:

Ran terminal command: nvidia-smi 2>&1 | head -10

GPU 可用了！开始编译：

Ran terminal command: conda activate softgroup_new && cd /root/autodl-tmp/SoftGroup-main && rm -rf build/ softgroup/ops/*.so && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export CC=/usr/bin/gcc && export CXX=/usr/bin/g++ && export CUDAHOSTCXX=/usr/bin/g++ && export MAX_JOBS=2 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -20

编译成功！验证一下：

Ran terminal command: python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU:', torch.cuda.get_device_name(0)); from softgroup.ops import ops; print('SoftGroup CUDA ops: OK')"

Completed: 编译 SoftGroup CUDA ops (3/4)

全部完成！环境已就绪：

项目 状态
GPU NVIDIA H20 ✅
PyTorch 2.0.1+cu118 ✅
CUDA toolkit 11.8 ✅
SoftGroup CUDA ops 编译成功 ✅

现在可以恢复训练了：

bash 复制代码 
export CUDA_HOME=/usr/local/cuda-11.8
./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 \
    --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth

User: iou_score_linear.weight, iou_score_linear.bias

2026-02-09 19:38:39,743 - INFO - Training

CUDA kernel failed : CUDA driver version is insufficient for CUDA runtime version

ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 255) local_rank: 0 (pid: 2695) of binary: /root/miniconda3/envs/softgroup_new/bin/python

Traceback (most recent call last):

File "/root/miniconda3/envs/softgroup_new/bin/torchrun", line 8, in

sys.exit(main())

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/init .py", line 346, in wrapper

return f(*args, **kwargs)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/distributed/run.py", line 794, in main

run(args)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/distributed/run.py", line 785, in run

elastic_launch(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 134, in call

return launch_agent(self._config, self._entrypoint, list(args))

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent

raise ChildFailedError(

torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

GitHub Copilot:

错误 "CUDA driver version is insufficient for CUDA runtime version" 说明加载了比驱动支持版本更高的 CUDA runtime 库。之前通过 conda 安装的 CUDA 13.1 包的库文件还在，被优先加载了。

Ran terminal command: python -c "import torch; print(torch.version.cuda)" && python -c "import ctypes; lib = ctypes.CDLL('libcudart.so'); print('loaded')" 2>&1 && ldconfig -p | grep libcudart | head -5

Ran terminal command: ls /root/miniconda3/envs/softgroup_new/lib/libcudart* 2>/dev/null && ls /root/miniconda3/envs/softgroup_new/targets/x86_64-linux/lib/libcudart* 2>/dev/null

找到了！conda 环境里有 CUDA 13.1 的 libcudart.so.13，驱动只支持到 CUDA 12.7。需要替换为 CUDA 11.8 的库：

Ran terminal command: cd /root/miniconda3/envs/softgroup_new/lib && rm -f libcudart.so libcudart.so.13 libcudart.so.13.1.80 && ln -sf /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudart.so.11.8.89 libcudart.so.11.8.89 && ln -sf libcudart.so.11.8.89 libcudart.so.11 && ln -sf libcudart.so.11 libcudart.so && ls -la libcudart*

同样处理 targets 目录下的，然后检查是否还有其他 CUDA 13.x 的库：

Ran terminal command: find /root/miniconda3/envs/softgroup_new/lib -name ".so.13" -o -name "*.so.13" 2>/dev/null | grep -i cuda | head -20

Ran terminal command: find /root/miniconda3/envs/softgroup_new -path "/lib " -name ".so" 2>/dev/null | xargs ls -la 2>/dev/null | grep "13." | head -20

大量 CUDA 13.x 库（cublas, npp 等）。最干净的方案是直接设置 LD_LIBRARY_PATH 优先使用 PyTorch 自带的和 CUDA 11.8 的库，覆盖 conda 环境的 13.x 库：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && python -c "import torch; print('CUDA:', torch.cuda.is_available()); t = torch.zeros(1).cuda(); print('GPU tensor OK')" 2>&1

现在重新运行训练：

Ran terminal command: export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth 2>&1 | head -40

编译出的 .so 文件链接了 libcudart.so.13。需要重新编译，确保链接到 CUDA 11.8 的库：

Ran terminal command: ldd /root/autodl-tmp/SoftGroup-main/softgroup/ops/ops.cpython-38-x86_64-linux-gnu.so | grep cuda

确认了，.so 硬链接了 libcudart.so.13。需要用正确的 LD_LIBRARY_PATH 重新编译：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && rm -rf build/ softgroup/ops/*.so && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && export CC=/usr/bin/gcc && export CXX=/usr/bin/g++ && export CUDAHOSTCXX=/usr/bin/g++ && export MAX_JOBS=2 && TORCH_CUDA_ARCH_LIST="9.0" python setup.py build_ext develop 2>&1 | tail -5

Ran terminal command: ldd /root/autodl-tmp/SoftGroup-main/softgroup/ops/ops.cpython-38-x86_64-linux-gnu.so | grep cuda

现在链接到 libcudart.so.11.0 了。libc10_cuda.so 是 PyTorch 的库，运行时会自动找到。再次启动训练：

Ran terminal command: export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth 2>&1 | head -30

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01PHJydaGQst8ivRD36KGhJD__vscode-1770636876598/content.txt#1-1)，行 1 到 50

读取 [](file:///c%3A/Users/wyj/AppData/Roaming/Code/User/workspaceStorage/6cef33e0f45cc9bb2f9a886d90f730fe/GitHub.copilot-chat/chat-session-resources/078c178e-6326-4927-8c7a-07ec6580d69f/toolu_vrtx_01PHJydaGQst8ivRD36KGhJD__vscode-1770636876598/content.txt#350-350)，行 350 到 396

看起来这次训练可能正在运行了（输出被 head -30 截断了）。让我用后台模式正确启动训练：

Ran terminal command: export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 --resume work_dirs/softgroup_s3dis_fold5_fix/epoch_4.pth > train_continue.log 2>&1 &

echo "PID: $!"

Ran terminal command: sleep 10 && tail -20 train_continue.log

Ran terminal command: sleep 30 && tail -5 train_continue.log

Completed with input: sleep 30 && tail -5 train_continue.log

Ran terminal command: tail -5 train_continue.log

Ran terminal command: ps aux | grep train.py | grep -v grep

训练已经正常运行了！多个 dataloader worker 进程在加载数据，CPU 使用率高说明正在处理数据。

训练已成功恢复。你可以通过 tail -f train_continue.log 实时查看训练日志。

User: Traceback (most recent call last):

File "./tools/train.py", line 209, in

main()

File "./tools/train.py", line 202, in main

train(epoch, model, optimizer, scaler, train_loader, cfg, logger, writer)

File "./tools/train.py", line 61, in train

scaler.step(optimizer)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py", line 315, in step

return optimizer.step(*args, **kwargs)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/optim/optimizer.py", line 280, in wrapper

out = func(*args, **kwargs)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/optim/optimizer.py", line 33, in _use_grad

ret = func(self, *args, **kwargs)

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/optim/adam.py", line 141, in step

adam(

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/optim/adam.py", line 281, in adam

func(params,

File "/root/miniconda3/envs/softgroup_new/lib/python3.8/site-packages/torch/optim/adam.py", line 446, in _multi_tensor_adam

torch.foreach_add (device_exp_avgs, device_grads, alpha=1 - beta1)

RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0

GitHub Copilot:

这个错误是因为 --resume 加载了旧 checkpoint 的优化器状态 ，而模型的部分层尺寸已经变了（之前日志显示 cls_linear, mask_linear, iou_score_linear 因为尺寸不匹配被跳过）。优化器的 exp_avg 等 buffer 还是旧维度，和新的参数维度冲突了。

让我查看 resume 逻辑并修复：

Ran terminal command: grep -n "resume" /root/autodl-tmp/SoftGroup-main/tools/train.py

Completed with input: grep -n "resume" /root/autodl-tmp/SoftGroup-main/tools/train.py

Ran terminal command: cat -n /root/autodl-tmp/SoftGroup-main/tools/train.py | head -210 | tail -60

Ran terminal command: grep -rn "def load_checkpoint" /root/autodl-tmp/SoftGroup-main/softgroup/

User: 重新开始训练也可以

GitHub Copilot:

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_fix.yaml 1 > train_fresh.log 2>&1 &

echo "PID: $!"

User: 37 0.717 0.858 0.704 0.833 1.000

average : 0.286 0.399 0.490 0.393 0.482 0.577

################################################################

为什么tree，指标不上升

Analyzed reasons for tree and ground AP differences

从结果看，ground AP 提升显著（0.205→0.537），但 tree AP 几乎不变（0.035→0.034）。关键问题：

  1. Offset MAE = 6.328m 太大

说明 offset 预测还没学好

树的 offset 学习更难（多实例、形状复杂）

ground 只有1个实例，offset 学习容易

  1. radius=0.3m 对树太小

之前分析过，你的树 GT offset 平均 2.253m，但聚类半径只有 0.3m，导致一棵树被分成多个碎片。

让我检查当前 grouping 参数并给出优化建议：

Analyzed tree clustering issues and proposed user suggestions

问题很明显了！

指标 值

树平均直径 14.64m

当前 radius 0.3m

建议 radius 4.9m ~ 7.3m

radius=0.3m 太小了，一棵14米直径的树会被分成几十个碎片，无法正确识别。

但增大 radius 会导致 OOM。让我分析当前是否可以尝试更大的 radius：

Executed command in terminal

当前显存: 21GB/32GB，还有空间。

有两个解决方案：

方案1: 继续训练，等 offset 学好

当前 Offset MAE = 6.328m 太大。offset 学好后（<1m），即使 radius 小，同一棵树的点也会聚到一起。

方案2: 适度增大 radius

尝试 radius=1.0 或 radius=2.0，配合减小 mean_active 避免 OOM。

你想选哪个？或者我可以先让训练继续，观察 Offset MAE 是否下降？

GitHub Copilot:

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_fix.yaml

Ran terminal command: tail -30 /root/autodl-tmp/SoftGroup-main/train_continue.log 2>/dev/null || tail -30 /root/autodl-tmp/SoftGroup-main/train_fresh.log 2>/dev/null

Ran terminal command: nvidia-smi | grep -E "MiB|H20"

GPU 空闲，之前训练已经停了。建议方案：两步走 ，先增大 radius 并从头训练。

你的核心问题：

  • 树平均直径 ~14m，radius=0.3m 在 offset 空间中太小，树被碎片化
  • Offset MAE=6.3m 说明 offset 还没学好，需要更多 epoch
  • 即使 offset 学好了，radius=0.3 仍然偏小

创建一个新配置，将 radius 增大到 1.5，同时降低 mean_active 防止 OOM，从 pretrain backbone 全新开始训练：

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_fix.yaml

已创建 [](file:///root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r1.5.yaml)

新配置相对旧配置的改动：

参数 旧值 新值 原因
radius 0.3 1.5 树太大，0.3 碎片化严重
mean_active 300 100 配合大 radius 防 OOM
epochs 50 100 offset 需要更多 epoch 学习
step_epoch 32 64 配合更长训练，LR 衰减推迟
work_dir fold5_v4 fold5_r1.5 区分实验

启动全新训练（不带 --resume，从 pretrain backbone 开始）：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:LD_LIBRARY_PATH \&\& nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r1.5.yaml 1 \> train_r1.5.log 2\>\&1 \& echo "PID: !"

Made changes.

User: radius 增大到 能够包括tree,你可以查看数据是不是设置这个？具体看看多少

GitHub Copilot:

Created 3 todos

先看看数据的实际结构和 grouping 代码中 radius 的含义：

Ran terminal command: ls /root/autodl-tmp/SoftGroup-main/dataset/s3dis/preprocess/ | head -20

Ran terminal command: grep -n "radius|grouping|ball_query|octree" /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py | head -20

Ran terminal command: sed -n '410,465p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

关键行：coords_ + pt_offsets_ --- radius 作用于 offset 偏移后的坐标空间。理论上如果 offset 学得完美，同一实例的所有点会聚到实例中心，距离接近 0。但 offset 没学好时，需要 radius 能覆盖残差。

同时注意 scale: 30 --- 坐标被缩放了 30 倍。让我分析实际数据来确定合适的 radius：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && python3 -c "

import torch

import numpy as np

import glob

files = glob.glob('dataset/s3dis/preprocess/Area_*_inst_nostuff.pth')

print(f'Found {len(files)} files')

all_tree_diameters = []

all_tree_offsets = []

all_ground_diameters = []

for f in files:

data = torch.load(f)

coords = data[0] # xyz

sem_labels = data[2] # semantic labels

inst_labels = data[3] # instance labels

复制代码 
print(f'\n=== {f.split(\"/\")[-1]} ===')
print(f'  coords shape: {coords.shape}, range: [{coords.min(0)}, {coords.max(0)}]')
print(f'  sem_labels unique: {np.unique(sem_labels)}')
print(f'  inst_labels unique: {np.unique(inst_labels)}')

# 分析每个类别的实例尺寸
for sem_id in np.unique(sem_labels):
    if sem_id < 0:
        continue
    mask = sem_labels == sem_id
    inst_ids = np.unique(inst_labels[mask])
    inst_ids = inst_ids[inst_ids >= 0]
    
    class_name = 'ground' if sem_id == 0 else 'tree'
    
    for iid in inst_ids:
        imask = inst_labels == iid
        pts = coords[imask]
        center = pts.mean(axis=0)
        # 到中心的距离（offset 的理想值）
        offsets = pts - center
        max_offset = np.linalg.norm(offsets, axis=1).max()
        mean_offset = np.linalg.norm(offsets, axis=1).mean()
        diameter = np.linalg.norm(pts.max(0) - pts.min(0))
        
        if sem_id == 1:  # tree
            all_tree_diameters.append(diameter)
            all_tree_offsets.append(mean_offset)
        else:
            all_ground_diameters.append(diameter)
        
        print(f'  {class_name} inst={iid}: npts={imask.sum()}, diameter={diameter:.2f}m, max_offset_to_center={max_offset:.2f}m, mean_offset={mean_offset:.2f}m')

print('\n' + '='*60)

print('SUMMARY (original coordinates, before scale):')

print(f'Tree instances: {len(all_tree_diameters)}')

if all_tree_diameters:

print(f' Diameter: min={min(all_tree_diameters):.2f}, max={max(all_tree_diameters):.2f}, mean={np.mean(all_tree_diameters):.2f}')

print(f' Mean offset to center: min={min(all_tree_offsets):.2f}, max={max(all_tree_offsets):.2f}, mean={np.mean(all_tree_offsets):.2f}')

print(f' After scale(30x): mean_offset -> {np.mean(all_tree_offsets)*30:.2f}')

print(f' Suggested radius (scaled) = mean_offset * 30 * 1.5 = {np.mean(all_tree_offsets)30 1.5:.2f}')

print(f'Ground instances: {len(all_ground_diameters)}')

if all_ground_diameters:

print(f' Diameter: min={min(all_ground_diameters):.2f}, max={max(all_ground_diameters):.2f}, mean={np.mean(all_ground_diameters):.2f}')

" 2>&1

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && python3 -c "

import torch

import numpy as np

import glob

files = glob.glob('dataset/s3dis/preprocess/Area_*_inst_nostuff.pth')

all_tree_diameters = []

all_tree_mean_offsets = []

all_tree_max_offsets = []

all_tree_npts = []

for f in files:

data = torch.load(f)

coords = data[0]

sem_labels = data[2]

inst_labels = data[3]

复制代码 
tree_mask = sem_labels == 1
tree_inst_ids = np.unique(inst_labels[tree_mask])
tree_inst_ids = tree_inst_ids[tree_inst_ids >= 0]

for iid in tree_inst_ids:
    imask = inst_labels == iid
    pts = coords[imask]
    center = pts.mean(axis=0)
    offsets = np.linalg.norm(pts - center, axis=1)
    diameter = np.linalg.norm(pts.max(0) - pts.min(0))
    all_tree_diameters.append(diameter)
    all_tree_mean_offsets.append(offsets.mean())
    all_tree_max_offsets.append(offsets.max())
    all_tree_npts.append(len(pts))

td = np.array(all_tree_diameters)

tmo = np.array(all_tree_mean_offsets)

tmx = np.array(all_tree_max_offsets)

tn = np.array(all_tree_npts)

print('=== TREE INSTANCE STATISTICS (original coords, meters) =')
print(f'Total tree instances: {len(td)}')
print(f'Diameter: min={td.min():.2f} median={np.median(td):.2f} mean={td.mean():.2f} max={td.max():.2f}')
print(f'Mean offset: min={tmo.min():.2f} median={np.median(tmo):.2f} mean={tmo.mean():.2f} max={tmo.max():.2f}')
print(f'Max offset: min={tmx.min():.2f} median={np.median(tmx):.2f} mean={tmx.mean():.2f} max={tmx.max():.2f}')
print(f'Num points: min={tn.min()} median={np.median(tn):.0f} mean={tn.mean():.0f} max={tn.max()}')
print()
print('= AFTER VOXELIZATION (scale=30, coords*30) =')
print(f'Mean offset (scaled): min={tmo.min()*30:.1f} median={np.median(tmo)*30:.1f} mean={tmo.mean()*30:.1f} max={tmo.max()*30:.1f}')
print(f'Max offset (scaled): min={tmx.min()*30:.1f} median={np.median(tmx)*30:.1f} mean={tmx.mean()*30:.1f} max={tmx.max()*30:.1f}')
print()
print('= RADIUS RECOMMENDATION (in voxel space, scale=30) ===')

print('If offset is perfectly learned (all points -> center):')

print(f' radius >= 0 would work')

print('If offset MAE ~ 50% of mean_offset:')

print(f' radius >= {np.median(tmo)30 0.5:.1f} (median tree)')

print('If offset barely learned (MAE ~ mean_offset):')

print(f' radius >= {np.median(tmo)*30:.1f} (median tree)')

print(f' radius >= {np.percentile(tmo,90)*30:.1f} (90th pct tree)')

" 2>&1

Starting: 确定合适的 radius (2/3)

现在来看看 ball_query 和 bfs_cluster 的 radius 到底在什么空间下操作：

Ran terminal command: grep -n "coords_float|voxelize|scale|coords" /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py | head -30

Ran terminal command: grep -n "coords_float" /root/autodl-tmp/SoftGroup-main/softgroup/data/custom.py | head -20

Ran terminal command: sed -n '180,230p' /root/autodl-tmp/SoftGroup-main/softgroup/data/custom.py

Ran terminal command: grep -n "xyz_middle|xyz|scale" /root/autodl-tmp/SoftGroup-main/softgroup/data/custom.py | head -30

关键发现！看看第 140-141 行：

python 复制代码 
xyz_middle = self.dataAugment(xyz, ...)  # 原始坐标（米）
xyz = xyz_middle * self.voxel_cfg.scale  # 缩放 30 倍用于体素化

而 coords_float = xyz_middle --- 即原始坐标（米），没有经过 scale 缩放！

所以 ball_query(coords_ + pt_offsets_, ...) 中的坐标是米为单位 的原始坐标，radius 也是以米为单位的。

同时 pt_offset_label = pt_mean - xyz，这里的 xyz 也是 xyz_middle（原始米）。

Starting: 更新配置并启动训练 (3/3)

现在数据清楚了，总结：

树实例统计（原始坐标，米）

指标 最小 中位 平均 最大
直径 33.8m 47.6m 59.5m 105.0m
到中心平均距离 6.2m 11.4m 13.9m 24.3m
到中心最大距离 14.3m 21.3m 27.7m 52.5m
点数 19116 169914 149406 188371

radius 是在原始坐标空间（米）上操作的，不是缩放后的。

  • radius=0.3m --- 只能聚合 30cm 范围内的点，远远不够
  • 理想情况（offset 完美学习）radius>=0 就行，但实际上 offset 需要很多 epoch 才能收敛
  • 建议 radius 取树的中位平均半径的一半 ：11.4 / 2 ≈ 5.7m，取整 6.0m

更新配置：

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r1.5.yaml

Completed with input: cat /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r1.5.yaml

已创建 [](file:///root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml)

Completed: 更新配置并启动训练 (3/3)

新配置已创建：softgroup_s3dis_fold5_r6.yaml

数据分析结论

坐标空间确认 ：coords_float 和 pt_offsets 都在原始坐标空间（米）下，radius 单位也是米。

参数 旧值 新值 依据
radius 0.3m 6.0m 树中位平均半径 11.4m 的一半，在 offset 未完全收敛时仍能覆盖
mean_active 300 100 大 radius 下邻域点数暴增，降低防 OOM
epochs 50 100 大尺度树的 offset 需要更多训练
step_epoch 32 64 配合更长训练

启动命令：

bash 复制代码 
export CUDA_HOME=/usr/local/cuda-11.8
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH
nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r6.yaml 1 > train_r6.log 2>&1 &

需要我帮你启动吗？

Made changes.

User: 需要

GitHub Copilot: Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:LD_LIBRARY_PATH \&\& nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r6.yaml 1 \> train_r6.log 2\>\&1 \& echo "PID: !"

Ran terminal command: sleep 20 && tail -10 /root/autodl-tmp/SoftGroup-main/train_r6.log

Ran terminal command: grep -E "Error|error|Traceback|RuntimeError" /root/autodl-tmp/SoftGroup-main/train_r6.log | head -10

已汇总会话历史记录配置文件路径找不到。可能是工作目录不对。让我检查一下： 正在优化工具选择...

Ran terminal command: ls /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml

Completed with input: ls /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml

搜索匹配 **/softgroup_s3dis_fold5_r6.yaml 的文件，无匹配项

配置文件不存在了，需要重新创建。让我先看看原有的配置作为参考：

搜索与 **/softgroup_s3dis_fold5*.yaml 匹配的文件，10 匹配项

读取 [](file:///root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_fix.yaml)

Ran terminal command: cat /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_fix.yaml

现在我来创建 radius=6.0 的新配置文件：

Ran terminal command: cat > /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml << 'EOF'

data:

test:

data_root: dataset/s3dis/preprocess

prefix:

  • Area_7

suffix: _inst_nostuff.pth

training: false

type: s3dis

voxel_cfg:

max_npoint: 250000

min_npoint: 5000

scale: 30

spatial_shape:

  • 128

  • 512

x4_split: true

train:

data_root: dataset/s3dis/preprocess

prefix:

  • Area_1

  • Area_2

  • Area_3

  • Area_4

  • Area_5

  • Area_6

repeat: 20

suffix: _inst_nostuff.pth

training: true

type: s3dis

voxel_cfg:

max_npoint: 250000

min_npoint: 5000

scale: 30

spatial_shape:

  • 128

  • 512

x4_split: true

dataloader:

test:

batch_size: 1

num_workers: 4

train:

batch_size: 4

num_workers: 8

persistent_workers: true

epochs: 100

fp16: false

model:

channels: 32

fixed_modules: []

grouping_cfg:

score_thr: 0.2

radius: 6.0

mean_active: 100

class_numpoint_mean:

  • 20000

  • 50000

npoint_thr: 0.02

ignore_classes: []

ignore_label: -100

instance_classes: 2

instance_voxel_cfg:

scale: 30

spatial_shape: 50

num_blocks: 7

sem2ins_classes: []

semantic_classes: 2

semantic_only: false

test_cfg:

cls_score_thr: 0.001

eval_tasks:

  • semantic

  • instance

mask_score_thr: -0.5

min_npoint: 200

x4_split: true

train_cfg:

max_proposal_num: 200

pos_iou_thr: 0.5

optimizer:

lr: 0.004

type: Adam

pretrain: ./work_dirs/softgroup_s3dis_backbone_fold5/latest.pth

save_freq: 4

step_epoch: 64

work_dir: work_dirs/softgroup_s3dis_fold5_r6

EOF

配置文件已创建。现在设置环境变量并启动训练：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r6.yaml 1 > train_r6.log 2>&1 &

echo "PID: $!"

Ran terminal command: sleep 30 && tail -20 /root/autodl-tmp/SoftGroup-main/train_r6.log

训练已成功启动！日志显示：

  • 加载了 120 个训练扫描、6 个测试扫描
  • 从 backbone pretrain 加载了权重（missing keys 是正常的，这些是 instance segmentation head 的参数，需要从头训练）
  • 正在进行训练

让我再等一会儿确认第一个 epoch 是否正常运行：

Ran terminal command: sleep 60 && tail -5 /root/autodl-tmp/SoftGroup-main/train_r6.log

Completed with input: sleep 60 && tail -5 /root/autodl-tmp/SoftGroup-main/train_r6.log

Ran terminal command: tail -5 /root/autodl-tmp/SoftGroup-main/train_r6.log

日志停在 "Training" 没更新，让我检查进程是否还在运行：

Ran terminal command: ps aux | grep train.py | grep -v grep

训练进程正在运行，有多个 dataloader worker 进程在加载数据（CPU 使用率 ~100%）。日志输出可能是缓冲延迟，等数据加载完成并完成第一个 iteration 后会输出。

让我再等一会儿检查：

Ran terminal command: sleep 120 && tail -10 /root/autodl-tmp/SoftGroup-main/train_r6.log

Completed with input: sleep 120 && tail -10 /root/autodl-tmp/SoftGroup-main/train_r6.log

Ran terminal command: tail -10 /root/autodl-tmp/SoftGroup-main/train_r6.log

日志仍然停在 "Training"，但进程还活着------这是因为 nohup 的输出缓冲。让我检查进程和 GPU 状态：

Ran terminal command: nvidia-smi | head -20

GPU 只占了 1088MiB，使用率 0%，看起来还在数据加载阶段（dataloader worker 在预加载数据）。进程仍然正常运行。

日志没有更新是因为 Python 的 stdout 缓冲 + nohup。让我用 stdbuf 或检查 work_dirs 来确认进度：

Ran terminal command: ls -la /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/ 2>/dev/null || echo "目录尚未创建"

工作目录已创建，有日志文件。让我看看这个内部日志（它的更新不受 nohup 缓冲影响）：

Ran terminal command: tail -5 /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/20260209_200710.log

数据仍在加载中。120 个扫描 × repeat=20 的数据集首次加载需要时间。进程和 GPU 进程(PID 7435)都正常。让我再等几分钟检查：

Ran terminal command: sleep 180 && nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader && echo "---" && tail -3 /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/20260209_200710.log

GPU 内存已增加到 20874 MiB，说明数据正在加载到 GPU。但 GPU 利用率仍然 0%，可能在处理第一个 batch 的 CPU 部分。让我检查进程状态和 strace：

Ran terminal command: ps aux | grep "train.py" | grep -v grep | head -3 && echo "---" && wc -l /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/20260209_200710.log

进程正在运行（状态 Rl），-u 标志已启用无缓冲输出。GPU 内存分配了 ~20GB。可能是第一个 batch 的数据加载和 voxelization 花费时间较长。再等几分钟：

Ran terminal command: sleep 300 && tail -5 /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/20260209_200710.log && echo "===" && nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader

训练完成了 Epoch 1 的 30 个迭代但在 Validation 阶段崩溃了（Exit 1）。让我查看错误详情：

Ran terminal command: grep -A 20 "Validation" /root/autodl-tmp/SoftGroup-main/train_r6.log | head -30

Ran terminal command: grep -A 40 "Validation" /root/autodl-tmp/SoftGroup-main/train_r6.log | tail -20

错误在 get_instances 函数中：proposals_idx 和 mask_inds 分别在 CPU 和 GPU 上，导致设备不匹配。让我查看并修复这个问题：

Ran terminal command: grep -n "cur_proposals_idx = proposals_idx[mask_inds]" /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

是否继续迭代？

object Object

User: @agent 继续: "是否继续迭代？"

GitHub Copilot: Ran terminal command: sed -n '555,585p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

问题是 proposals_idx 在 CPU 上，而 mask_inds 在 GPU 上。需要保持它们在同一设备。让我看看 proposals_idx 从哪来的：

Ran terminal command: sed -n '530,560p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

让我查看 proposals_idx 在 forward_test 中是如何传递的：

Ran terminal command: sed -n '310,360p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

Ran terminal command: grep -n "forward_grouping" /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py | head -5

Ran terminal command: sed -n '412,470p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

找到了！bfs_cluster 在 CPU 上运行（neighbor_inds.cpu(), start_len.cpu()），所以 proposals_idx 在 CPU 上，但 mask_inds(来自 GPU 模型输出) 在 GPU 上。修复方法是在 get_instances 开头将 proposals_idx 移到 GPU：

Ran terminal command: sed -n '548,552p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

Ran terminal command: sed -i '548a\ proposals_idx = proposals_idx.cuda()' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

验证修改：

Ran terminal command: sed -n '546,555p' /root/autodl-tmp/SoftGroup-main/softgroup/model/softgroup.py

修改正确。现在重新启动训练：

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r6.yaml 1 > train_r6.log 2>&1 &

echo "PID: $!"

User: 刚才是使用了多少显存

GitHub Copilot:

根据训练日志中的记录：

  • Epoch [1/100][10/30]: mem: 14846 MiB (~14.5 GB)
  • Epoch [1/100][20/30]: mem: 19160 MiB (~18.7 GB)
  • Epoch [1/100][30/30]: mem: 19160 MiB (~18.7 GB)

nvidia-smi 显示峰值分配约 20874 MiB (~20.4 GB)。

H20 有 97871 MiB (约 96 GB) 显存，当前只用了约 21% ，还有大量余量。如果需要加速训练可以考虑增大 batch_size。

User: 增加到90%

GitHub Copilot:

当前 batch_size=4 用了约 20 GB，要达到 ~88 GB（90%），估算 batch_size 约 16-20。考虑到点云大小不固定，先用 batch_size=16 较稳妥。

Ran terminal command: ps aux | grep train.py | grep -v grep | head -3

没有训练进程在运行。更新配置并启动训练：

Ran terminal command: sed -i 's/batch_size: 4/batch_size: 16/' /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml && grep -A2 "train:" /root/autodl-tmp/SoftGroup-main/configs/softgroup/softgroup_s3dis_fold5_r6.yaml | head -6

Ran terminal command: cd /root/autodl-tmp/SoftGroup-main && export CUDA_HOME=/usr/local/cuda-11.8 && export PATH= C U D A H O M E / b i n : CUDA_HOME/bin: CUDAHOME/bin:PATH && export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH && nohup ./tools/dist_train.sh configs/softgroup/softgroup_s3dis_fold5_r6.yaml 1 > train_r6.log 2>&1 &

echo "PID: $!"

Ran terminal command: sleep 420 && nvidia-smi --query-gpu=utilization.gpu,memory.used,memory.total --format=csv,noheader && echo "---" && tail -5 /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/$(ls -t /root/autodl-tmp/SoftGroup-main/work_dirs/softgroup_s3dis_fold5_r6/*.log 2>/dev/null | head -1 | xargs basename) 2>/dev/null || tail -5 /root/autodl-tmp/SoftGroup-main/train_r6.log

相关推荐
九.九9 小时前
ops-transformer：AI 处理器上的高性能 Transformer 算子库
人工智能·深度学习·transformer
春日见9 小时前
拉取与合并：如何让个人分支既包含你昨天的修改，也包含 develop 最新更新
大数据·人工智能·深度学习·elasticsearch·搜索引擎
恋猫de小郭9 小时前
AI 在提高你工作效率的同时，也一直在增加你的疲惫和焦虑
前端·人工智能·ai编程
YJlio10 小时前
1.7 通过 Sysinternals Live 在线运行工具：不下载也能用的“云端工具箱”
c语言·网络·python·数码相机·ios·django·iphone
deephub10 小时前
Agent Lightning：微软开源的框架无关 Agent 训练方案，LangChain/AutoGen 都能用
人工智能·microsoft·langchain·大语言模型·agent·强化学习
l1t10 小时前
在wsl的python 3.14.3容器中使用databend包
开发语言·数据库·python·databend
大模型RAG和Agent技术实践10 小时前
从零构建本地AI合同审查系统：架构设计与流式交互实战（完整源代码）
人工智能·交互·智能合同审核
老邋遢10 小时前
第三章-AI知识扫盲看这一篇就够了
人工智能
互联网江湖10 小时前
Seedance2.0炸场：长短视频们“修坝”十年，不如AI放水一天？
人工智能
PythonPioneer10 小时前
在AI技术迅猛发展的今天，传统职业该如何“踏浪前行”？
人工智能
热门推荐
01GitHub 镜像站点02Claude Code + GLM4.7 避坑指南：解决 Unable to connect to Anthropic services03UV安装并设置国内源04Linux下V2Ray安装配置指南05openclaw配置教程（linux+局域网ollama）06243 行 microGPT：把“训练 + 推理”拆到骨头里07AI 规范驱动开发“三剑客”深度对比：Spec-Kit、Kiro 与 OpenSpec 实战指南08Spring Flux方法总结09openclaw使用nginx反代部署过程 与disconnected (1008): pairing required解决10Vue-skills的中文文档