EfficientNet B0-B7 models return invalid hash value

[JBethay]

🐛 Describe the bug

All EfficientNet B0-B7 models are returning an invalid hash when downloaded, tested locally and in Google Colab. The below examples are for B2, but this is happening for B0-B7. I am not experiencing this issue with EfficientNetV2.

Torchvision version: 0.16.0+cu118

import torchvision

effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
effnetb2_transforms = effnetb2_weights.transforms()
effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)

Downloading: "https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth" to /root/.cache/torch/hub/checkpoints/efficientnet_b2_rwightman-bcdf34b7.pth
100%|██████████| 35.2M/35.2M [00:00<00:00, 106MB/s]

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

[<ipython-input-25-a793cdb90f74>](https://localhost:8080/#) in <cell line: 5>()
      3 effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
      4 effnetb2_transforms = effnetb2_weights.transforms()
----> 5 effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights)

6 frames

[/usr/local/lib/python3.10/dist-packages/torch/hub.py](https://localhost:8080/#) in download_url_to_file(url, dst, hash_prefix, progress)
    661             digest = sha256.hexdigest()
    662             if digest[:len(hash_prefix)] != hash_prefix:
--> 663                 raise RuntimeError(f'invalid hash value (expected "{hash_prefix}", got "{digest}")')
    664         shutil.move(f.name, dst)
    665     finally:

RuntimeError: invalid hash value (expected "bcdf34b7", got "c35c147384e385a5bab5a8eabdabbe5a3df0487ee4a554108626ae474a5bf755")

Versions

Collecting environment information...
PyTorch version: 2.1.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.27.7
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.120+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.30GHz
CPU family: 6
Model: 63
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4599.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 45 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.1.0+cu118
[pip3] torchaudio==2.1.0+cu118
[pip3] torchdata==0.7.0
[pip3] torchinfo==1.8.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.16.0+cu118
[pip3] triton==2.1.0
[conda] Could not collect

[NicolasHug]

Thanks for the report and sorry for the trouble.
We've fixed the issue and the fix will be available in the next bugfix release (within a few weeks). Meanwhile, you can work around the issue following #7744 (comment).

[YingzheQin]

I am facing exactly the same issue about the 'invalid hash value', the only difference here is efficientnet_b1 is fine on my Google Colab.

[YingzheQin]

Thanks for the report and sorry for the trouble. We've fixed the issue and the fix will be available in the next bugfix release (within a few weeks). Meanwhile, you can work around the issue following #7744 (comment).

It happened again.

[PHChenGit]

Same here.

Thanks for the report and sorry for the trouble. We've fixed the issue and the fix will be available in the next bugfix release (within a few weeks). Meanwhile, you can work around the issue following #7744 (comment).

It happened again.