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Many datasets have a classes attribute containing the list of class names. I expected the same for Flowers102 but there's no such attribute:
importtorchvisiondataset=torchvision.datasets.Flowers102(root="datasets", download=True)
dataset.classes# AttributeError: 'Flowers102' object has no attribute 'classes'
PyTorch version: 2.4.0.post101
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: macOS 14.7 (x86_64)
GCC version: Could not collect
Clang version: 16.0.0 (clang-1600.0.26.6)
CMake version: version 3.30.0
Libc version: N/A
Python version: 3.12.7 | packaged by conda-forge | (main, Oct 4 2024, 15:55:29) [Clang 17.0.6 ] (64-bit runtime)
Python platform: macOS-14.7-x86_64-i386-64bit
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
🐛 Describe the bug
Many datasets have a
classes
attribute containing the list of class names. I expected the same forFlowers102
but there's no such attribute:I submitted PR #8838 to fix this.
Versions
PyTorch version: 2.4.0.post101
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: macOS 14.7 (x86_64)
GCC version: Could not collect
Clang version: 16.0.0 (clang-1600.0.26.6)
CMake version: version 3.30.0
Libc version: N/A
Python version: 3.12.7 | packaged by conda-forge | (main, Oct 4 2024, 15:55:29) [Clang 17.0.6 ] (64-bit runtime)
Python platform: macOS-14.7-x86_64-i386-64bit
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:
Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.4.0.post101
[pip3] torch-tb-profiler==0.4.3
[pip3] torchmetrics==1.5.1
[pip3] torchvision==0.19.1a0
[conda] libopenvino-pytorch-frontend 2024.4.0 h4398f7a_0 conda-forge
[conda] libtorch 2.4.0 cpu_mkl_h3542c91_101 conda-forge
[conda] mkl 2023.2.0 h54c2260_50500 conda-forge
[conda] numpy 1.26.4 py312he3a82b2_0 conda-forge
[conda] pytorch 2.4.0 cpu_mkl_py312h0c6306f_101 conda-forge
[conda] torch-tb-profiler 0.4.3 pypi_0 pypi
[conda] torchmetrics 1.5.1 pyhe5570ce_0 conda-forge
[conda] torchvision 0.19.1 cpu_py312h2009d5a_0 conda-forge
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