Repository: pytorch
Issue: _pickle.UnpicklingError: invalid load key, '\x00'. during torch load
Labels: module: serialization,triaged

### 🐛 Describe the bug

I try to convert a pytorch bin file to onnx format with the following script:

import torch
import sys

sys.path.insert(0, '/media/user/SSD1TB/arcoreAI/YOLOPv2-ncnn')

device = torch.device('cpu')
model = torch.load('yolopv2.bin', map_location=device)['model'].float()
torch.onnx.export(model, torch.zeros((1, 3, 640, 640)), 'yolopv2.onnx', opset_version=12)


But it fails with the following error:

user@user-Precision-7510:/media/user/SSD1TB/arcoreAI/YOLOPv2-ncnn/models(main)$ python3 ./exportonnx.py 
/media/user/SSD1TB/arcoreAI/YOLOPv2-ncnn/models/./exportonnx.py:7: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  model = torch.load('yolopv2.bin', map_location=device)['model'].float()
Traceback (most recent call last):
  File "/media/user/SSD1TB/arcoreAI/YOLOPv2-ncnn/models/./exportonnx.py", line 7, in <module>
    model = torch.load('yolopv2.bin', map_location=device)['model'].float()
  File "/home/user/.local/lib/python3.10/site-packages/torch/serialization.py", line 1114, in load
    return _legacy_load(
  File "/home/user/.local/lib/python3.10/site-packages/torch/serialization.py", line 1338, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '\x00'.



Model is from here:
https://github.com/FeiGeChuanShu/YOLOPv2-ncnn/tree/main/models

Is this a bug or am I doing something wrong?
Regards

### Versions

user@user-Precision-7510:/media/user/SSD1TB/arcoreAI/ncnn(master)$ wget https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
--2024-08-08 13:13:12--  https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Resolving [raw.githubusercontent.com](http://raw.githubusercontent.com/) ([raw.githubusercontent.com](http://raw.githubusercontent.com/))... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to [raw.githubusercontent.com](http://raw.githubusercontent.com/) ([raw.githubusercontent.com](http://raw.githubusercontent.com/))|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23357 (23K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py      100%[===================>]  22,81K  --.-KB/s    in 0,03s   

2024-08-08 13:13:13 (837 KB/s) - ‘collect_env.py’ saved [23357/23357]

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 17.0.6 (++20231209124227+6009708b4367-1~exp1~20231209124336.77)
CMake version: version 3.28.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Quadro M1000M
Nvidia driver version: 535.183.01
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
Address sizes:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i7-6820HQ CPU @ 2.70GHz
CPU family:                         6
Model:                              94
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           3
CPU max MHz:                        3600,0000
CPU min MHz:                        800,0000
BogoMIPS:                           5399.81
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 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi md_clear flush_l1d arch_capabilities
Virtualisation:                     VT-x
L1d cache:                          128 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           1 MiB (4 instances)
L3 cache:                           8 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-7
Vulnerability Gather data sampling: Vulnerable: No microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Mitigation; Microcode
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] onnx==1.13.0
[pip3] onnx-tool==0.8.1
[pip3] torch==2.4.0
[pip3] torchaudio==2.4.0
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] Could not collect

cc @mruberry @mikaylagawarecki