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Build error on cpp/custom-dataset #1255

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dribllerrad opened this issue May 9, 2024 · 1 comment · May be fixed by #1293
Open

Build error on cpp/custom-dataset #1255

dribllerrad opened this issue May 9, 2024 · 1 comment · May be fixed by #1293

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@dribllerrad
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I'm getting a build error on a data type conversion with this example:
https://github.com/pytorch/examples/tree/main/cpp/custom-dataset

Error message:

error: conversion from 'const long' to 'detail::TensorDataContainer' is ambiguous
[build]     auto tlabel = torch::tensor(data[index].second, torch::kLong);
[build]                                 ^~~~~~~~~~~~~~~~~~

I believe the paths to libtorch and opencv are good because other sample apps using them compile fine.

I've tried playing around with some casts (both with c++ casts and torch method conversions like .to(torch::kFloat) ) but I don't really know the library enough to troubleshoot. Removing the const-ness of the data also didn't help (revert of b2832cc).

torch env

PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 14.4.1 (arm64)
GCC version: Could not collect
Clang version: 15.0.0 (clang-1500.3.9.4)
CMake version: version 3.29.3
Libc version: N/A

Python version: 3.12.3 (main, Apr  9 2024, 08:09:14) [Clang 15.0.0 (clang-1500.3.9.4)] (64-bit runtime)
Python platform: macOS-14.4.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Apple M1 Max

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchvision==0.18.0
[conda] Could not collect
@nccrrv
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nccrrv commented Oct 16, 2024

I encountered the same issue.
After checking, I found that there is no long type in ScalarType.h.

#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(_) \
  _(uint8_t, Byte) /* 0 */                               \
  _(int8_t, Char) /* 1 */                                \
  _(int16_t, Short) /* 2 */                              \
  _(int, Int) /* 3 */                                    \
  _(int64_t, Long) /* 4 */                               \
  _(at::Half, Half) /* 5 */                              \
  _(float, Float) /* 6 */                                \
  _(double, Double) /* 7 */                              \
  ...

The closest type is int64_t.
So, this seems to be an issue caused by platform differences.
On Linux, std::is_same_v<int64_t, long> == true,
while on my VS and your platform, std::is_same_v<int64_t, long> == false.

We can fix this by changing long to int64_t in the example.
(Translated by AI)

我遇到了同样的问题
经过检查发现,在ScalarType.h里并没有long类型
与之最近的是int64_t
所以这应该是一个平台差异所导致的问题
在linux上std::is_same_v<int64_t,long>==true
而在我的VS和你的平台上std::is_same_v<int64_t,long>==false

可以把例子里的long改为int64_t来修复

@nccrrv nccrrv linked a pull request Oct 16, 2024 that will close this issue
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Fix platform type inconsistency custom-dataset.cpp nccrrv/pytorch_examples
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@dribllerrad @nccrrv