Bug in datasets.kitti.Kitti: expanduser operation not performed for root parameter

[fang-d]

🐛 Describe the bug

When initializing the torchvision.datasets.kitti.Kitti class from the torchvision project, the root parameter is directly assigned to self.root without performing the expanduser operation. This behavior leads to an unexpected result in Linux, where using a relative path starting with ~ to specify the dataset location creates a directory named '~' in the program running directory, instead of expanding to the user directory.

To Reproduce

You can use the following code to reproduce this problem.

from torchvision.datasets import Kitti

if __name__ == "__main__":
    dataset_train = Kitti("~/data", train=True, download=True)

After running this program, you can see the '~' folder in current folder.

Expected behavior

The root parameter should be expanded to use the user directory when a relative path starting with ~ is provided. This operation should be handled by the parent class VisionDataset.

Additional context

Here is the relevant code snippet from the Kitti class:

class Kitti(VisionDataset):
    ...
    def __init__(
        self,
        root: str,
        train: bool = True,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        transforms: Optional[Callable] = None,
        download: bool = False,
    ):
        super().__init__(
            root,
            transform=transform,
            target_transform=target_transform,
            transforms=transforms,
        )
        self.images = []
        self.targets = []
        self.root = root  # This line should be removed
        self.train = train
        self._location = "training" if self.train else "testing"
        ...

According to my preliminary inspection, I did not find similar problems on other datasets. But I'm not that confident in this conclusion.

Suggested Solution

The line self.root = root (line 69) in the Kitti class should be removed. The root parameter should be handled by the parent class VisionDataset, which already performs the expanduser operation.

Versions

Collecting environment information...
Collecting environment information...
PyTorch version: 2.1.1
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:40:35) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.89.02
cuDNN version: Probably one of the following:
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             48
On-line CPU(s) list:                0-47
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz
Stepping:                           7
CPU MHz:                            1000.055
CPU max MHz:                        3500.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           4800.00
Virtualization:                     VT-x
L1d cache:                          768 KiB
L1i cache:                          768 KiB
L2 cache:                           24 MiB
L3 cache:                           33 MiB
NUMA node0 CPU(s):                  0-11,24-35
NUMA node1 CPU(s):                  12-23,36-47
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: Split huge pages
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] mypy==1.7.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] torch==2.1.1
[pip3] torchvision==0.16.1
[pip3] triton==2.1.0
[conda] blas                      2.116                       mkl    conda-forge
[conda] blas-devel                3.9.0            16_linux64_mkl    conda-forge
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] liblapacke                3.9.0            16_linux64_mkl    conda-forge
[conda] mkl                       2022.1.0           hc2b9512_224    defaults
[conda] mkl-devel                 2022.1.0           h66538d2_224    defaults
[conda] mkl-include               2022.1.0           h06a4308_224    defaults
[conda] numpy                     1.26.2          py311h64a7726_0    conda-forge
[conda] pytorch                   2.1.1           py3.11_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchtriton               2.1.0                     py311    pytorch
[conda] torchvision               0.16.1              py311_cu121    pytorch

[fang-d]

I've submitted a pull request that implements the suggested solution. You can find the PR here. I hope to make some contribution to this project, even if this contribution only solves a small problem.