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pytorch / vision

Datasets, Transforms and Models specific to Computer Vision
https://pytorch.org/vision
BSD 3-Clause "New" or "Revised" License
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Video classification dataset cache can't be loaded in reference training code #8726

Open Met4physics opened 1 week ago

Met4physics commented 1 week ago

🐛 Describe the bug

In this reference, I got this when I try to load cached dataset.

Traceback (most recent call last):                                                                                                                                                        
  File "/home/limaohua/traning_free/video/train.py", line 447, in <module>                                                                                                                
    main(args)                                                                                                                                                                            
  File "/home/limaohua/traning_free/video/train.py", line 204, in main                                                                                                                    
    dataset_test, _ = torch.load(cache_path, weights_only=True)                                                                                                                           
  File "/home/limaohua/miniconda3/envs/py310/lib/python3.10/site-packages/torch/serialization.py", line 1359, in load                                                                     
    raise pickle.UnpicklingError(_get_wo_message(str(e))) from None                                                                                                                       
_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, do those steps only if you trust the source of the checkpoint.           
        (1) Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.                                                                                                                                                                                    
        (2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.                                                            
        WeightsUnpickler error: Unsupported global: GLOBAL datasets.KineticsWithVideoId was not an allowed global by default. Please use `torch.serialization.add_safe_globals([KineticsWithVideoId])` to allowlist this global if you trust this class/function.                                                                                                                   

Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.

This issue is solved by removing weights_only=True.

Versions

Collecting environment information...
PyTorch version: 2.5.0
Is debug build: False
CUDA used to build PyTorch: 12.4
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: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 545.23.06
cuDNN version: Could not collect
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, 57 bits virtual
CPU(s):                          224
On-line CPU(s) list:             0-223
Thread(s) per core:              2
Core(s) per socket:              56
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           143
Model name:                      Intel(R) Xeon(R) Platinum 8480C
Stepping:                        6
Frequency boost:                 enabled
CPU MHz:                         875.639
CPU max MHz:                     2001.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4000.00
Virtualization:                  VT-x
L1d cache:                       5.3 MiB
L1i cache:                       3.5 MiB
L2 cache:                        224 MiB
L3 cache:                        210 MiB
NUMA node0 CPU(s):               0-55,112-167
NUMA node1 CPU(s):               56-111,168-223
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Not affected
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:   Not affected
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single cdp_l2 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.5.0
[pip3] torchaudio==2.5.0
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.20.0
[pip3] triton==3.1.0
[conda] blas                      1.0                         mkl    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
[conda] cuda-cudart               12.4.127                      0    nvidia
[conda] cuda-cupti                12.4.127                      0    nvidia
[conda] cuda-libraries            12.4.1                        0    nvidia
[conda] cuda-nvrtc                12.4.127                      0    nvidia
[conda] cuda-nvtx                 12.4.127                      0    nvidia
[conda] cuda-opencl               12.6.77                       0    nvidia
[conda] cuda-runtime              12.4.1                        0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libcublas                 12.4.5.8                      0    nvidia
[conda] libcufft                  11.2.1.3                      0    nvidia
[conda] libcurand                 10.3.7.77                     0    nvidia
[conda] libcusolver               11.6.1.9                      0    nvidia
[conda] libcusparse               12.3.1.170                    0    nvidia
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] libnvjitlink              12.4.127                      0    nvidia
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py310h5eee18b_1  
[conda] mkl_fft                   1.3.10          py310h5eee18b_0  
[conda] mkl_random                1.2.7           py310h1128e8f_0  
[conda] numpy                     1.26.4          py310h5f9d8c6_0  
[conda] numpy-base                1.26.4          py310hb5e798b_0  
[conda] pytorch                   2.5.0           py3.10_cuda12.4_cudnn9.1.0_0    pytorch
[conda] pytorch-cuda              12.4                 hc786d27_7    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.5.0               py310_cu124    pytorch
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchtriton               3.1.0                     py310    pytorch
[conda] torchvision               0.20.0              py310_cu124    pytorch
Met4physics commented 1 week ago

A PR #8727 is submitted to solve this problem.