Comments (8)
@XuMicoder CMake的 时候记得添加 -DENABLE_UNIT_TESTS=ON
,然后可以直接打开 build 目录下的 Forward.sln 打开解决方案,然后点击 生成 -> 生成解决方案。然后可以直接右键 unit_test 项目 -> 设置为启动项目。然后开始 调试 -> 执行。
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谢谢解答,但是生成解决方案的时候报了很多错误,错误计数超过100:正在停止编译 环境的话都是推荐环境,看了一下基本上都是plugin文件夹里的文件报错@yuanzexi
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@XuMicoder 看起来像是未正确配置 CUDA 相关环境变量,导致 Visual Studio 找不到 nvcc。你可以先试试找一个简单的 CUDA 程序来测试系统环境是否能正常跑 CUDA 程序。建议检查一下 CUDA 相关的环境变量,以及检查 CUDA 安装时是否有安装 Visual Studio Extensions.
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谢谢解答,我的环境变量暂时没啥问题,我检查过一切正常,新建cuda项目测试也没问题,cuda安装时将解压对应的文件我复制到了vs对应的目录里面去了,cmake的时候是可以找得到nvcc的,一开始我也以为是环境问题,然后我在另外一台设备上重新配置了环境全部按照推荐配置来,出现的问题是一样的。这个我暂时没能找到合理的解决办法,在Ubuntu上是正常的,win10上确实有出现些bug。@yuanzexi
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@XuMicoder 你可以提供一下,你的系统环境:
TensorRT Version:
NVIDIA GPU:
NVIDIA Driver Version:
CUDA Version:
CUDNN Version:
Operating System:
Python Version (if applicable):
Tensorflow Version (if applicable):
PyTorch Version (if applicable):
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笔记本编译失败,配置如下:
TensorRT Version:7.2.1.6
NVIDIA GPU:NvidiaGTX2060
NVIDIA Driver Version: 27.21.14.6542
CUDA Version:10.2
CUDNN Version:8.0.4
Operating System:win10企业版Insider Preview
Python Version (if applicable):False
Tensorflow Version (if applicable):1.15.0
PyTorch Version (if applicable):False
vs版本:vs2017
同一台电脑对应的wsl2环境是:
TensorRT Version:7.2.1.6
NVIDIA GPU:NvidiaGTX2060
NVIDIA Driver Version: 27.21.14.6542
CUDA Version:10.2
CUDNN Version:8.0.2
Operating System:Ubuntu1804
Python Version (if applicable):False
Tensorflow Version (if applicable):1.15.0
PyTorch Version (if applicable):False
这个系统是可以正常编译与test的
另外一台电脑的配置如下:
TensorRT Version:7.2.1.6
NVIDIA GPU:NvidiaGTX3080
CUDA Version:11.0
CUDNN Version:8.0.5
Operating System:win10专业版
Python Version (if applicable):False
Tensorflow Version (if applicable):1.15.0
PyTorch Version (if applicable):False
vs版本:vs2019
这台电脑系统是我重装的,与第一次一样的报错
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This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.
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This issue was closed because it has been stalled for 5 days with no activity.
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Related Issues (20)
- 使用这个Forward框架进行推断的时候会有精度损失吗? HOT 6
- 关于fwd-torch的几个路径的问题 HOT 2
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- Help to support tensorflow slim "Flatten" pattern to Tensorrt. HOT 10
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- 编译出错 找不到cublas_device库 HOT 7
- keras.layer里Embedding的trt实现 HOT 8
- Keras中Flatten层的支持 HOT 2
- 对‘fwd::TrtForwardEngine::Load(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)’未定义的引用 HOT 9
- 是否考虑重新写一个模型序列化?? HOT 3
- 与trtorch等项目的优劣对比 HOT 3
- make报错 HOT 22
- reflectPad存在两个问题 HOT 9
- 编译fwd-pytroch时提示缺少头文件 HOT 1
- 优化模型时报错 HOT 2
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