Duplicated class name in ImageNet dataset.

🐛 Bug

There’re two classes in ImageNet dataset that share the same class name, but not the content (134 and 517). So I’ve met key errors when using class_to_idx

To Reproduce

Steps to reproduce the behavior:

1. Create dataset with data = datasets.ImageNet(root, 'train')
2. Try get data.class_to_idx[134]
3. Get key errors.

Expected behavior

No key error should be met here.

Environment

Please copy and paste the output from our environment collection script (or fill out the checklist below manually).

You can get the script and run it with:

wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py

PyTorch version: 1.8.1+cu102                                                                                                                                                    
Is debug build: False                                                                                                                                                           
CUDA used to build PyTorch: 10.2                                                                                                                                                
ROCM used to build PyTorch: N/A                                                                                                                                                 
                                                                                                                                                                                
OS: Ubuntu 18.04.5 LTS (x86_64)                                                                                                                                                 
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0                                                                                                                                
Clang version: Could not collect                                                                                                                                                
CMake version: Could not collect                                                                                                                                                
Libc version: glibc-2.10                                                                                                                                                        
                                                                                                                                                                                
Python version: 3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0] (64-bit runtime)                                                                                            
Python platform: Linux-4.15.0-147-generic-x86_64-with-debian-buster-sid                                                                                                         
Is CUDA available: True                                                                                                                                                         
CUDA runtime version: Could not collect                                                                                                                                         
GPU models and configuration:                                                                                                                                                   
GPU 0: GeForce RTX 2080 Ti                                                                                                                                                      
GPU 1: GeForce RTX 2080 Ti                                                                                                                                                      
                                                                                                                                                                                
Nvidia driver version: 460.67                                                                                                                                                   
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.3.1                                                                                                                      
HIP runtime version: N/A                                                                                                                                                        
MIOpen runtime version: N/A                                                                                                                                                     
                                                                                                                                                                                
Versions of relevant libraries:                                                                                                                                                 
[pip3] numpy==1.17.2                                                                                                                                                            
[pip3] numpydoc==0.9.1                                                                                                                                                          
[pip3] torch==1.8.1+cu102                                                                                                                                                       
[pip3] torchattacks==2.14.2                                                                                                                                                     
[pip3] torchaudio==0.8.1                                                                                                                                                        
[pip3] torchsummary==1.5.1                                                                                                                                                      
[pip3] torchvision==0.9.1+cu102                                                                                                                                                 
[conda] blas                      1.0                         mkl    https://repo.anaconda.com/pkgs/main                                                                        
[conda] cudatoolkit               10.0.130                      0    defaults                                                                                                   
[conda] mkl                       2019.4                      243    https://repo.anaconda.com/pkgs/main                                                                        
[conda] mkl-service               2.3.0            py37he904b0f_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] mkl_fft                   1.0.14           py37ha843d7b_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] mkl_random                1.1.0            py37hd6b4f25_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] numpy                     1.17.2           py37haad9e8e_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] numpy-base                1.17.2           py37hde5b4d6_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] numpydoc                  0.9.1                      py_0    https://repo.anaconda.com/pkgs/main                                                                        
[conda] torch                     1.8.1+cu102              pypi_0    pypi                                                                                                       
[conda] torchattacks              2.14.2                   pypi_0    pypi                                                                                                       
[conda] torchaudio                0.8.1                    pypi_0    pypi                                                                                                       
[conda] torchsummary              1.5.1                    pypi_0    pypi                                                                                                       
[conda] torchvision               0.9.1+cu102              pypi_0    pypi

Additional context

cc @pmeier

[huihui-v]

@pmeier Thanks for replying!

I was just trying to create some new datasets following the torchvision official implementation, with similar APIs but some modifications for my own usage (my case here is to use a subset of ImageNet for some quick testing). Anyway, I just got it done with the classes attribute right now, and it works just fine!

I think it’s better to add some postfix when the duplication class names were met, for example, crane_1 and crane_2. But It’s not a big problem, so I’ll close this issue now.

Thank you for your attention!

[pmeier]

I think it’s better to add some postfix when the duplication class names were met, for example, crane_1 and crane_2. But It’s not a big problem, so I’ll close this issue now.

WordNet actually describes a taxonomy of objects. So in my example above, grass snake is a valid category for n01729977 and n01735189. Thus, we cannot simply enumerate these into grass_snake_1 and grass_snake_2, because they are the same category.