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collection.Iterable is deprecated in Python 3.9+ #1117
Comments
This indeed a bug. On 3.9 it raises a warning, on 3.10 it fails with an ImportError. |
@BloodAxe sure. Thank you for confirming this quickly. I will send a PR tonight |
this PR has been merged? |
@skyprince999 sorry. I have loss interests in using yolonas because of the hops I have jumped through to getting started. I tried yolov8 and it’s good enough for my use case |
@BloodAxe Hi, |
so you need to change it to |
@DoManhQuang Can you please elaborate what error do you get for |
Yes, I imported |
How to solve this problem on Kaggle, where we do not have access to the library to edit it? |
@ferdzo Hi, you can try my way: 2/ change
3/ pip install -r ./super-gradients/requirements.dev.txt 4/ pip install ./super-gradients |
It is fixed in master already: #1178 |
Well, I've fixed it on my computer and on Colab, because I can access the library and edit it, too much gymnastics to fix it on Kaggle, thanks anyway! |
Fixed in 3.1.2 |
🐛 Describe the bug
Any code path that touch this particular line of code (for example
trainer.qat
)super-gradients/src/super_gradients/training/utils/utils.py
Line 595 in 7907c48
On Python 3.9+, this will have this error
See this SO answer: https://stackoverflow.com/a/72032097
Versions
Collecting environment information...
PyTorch version: 1.11.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.6 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-2.27
Python version: 3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.4.0-143-generic-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to:
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.105.17
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
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
CPU(s): 16
On-line CPU(s) list: 0-15
Thread(s) per core: 1
Core(s) per socket: 1
Socket(s): 16
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) Platinum 8260M CPU @ 2.40GHz
Stepping: 0
CPU MHz: 2400.000
BogoMIPS: 4800.00
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 36608K
NUMA node0 CPU(s): 0-15
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch invpcid_single kaiser fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid rdseed adx smap xsaveopt arat
Versions of relevant libraries:
[pip3] numpy==1.23.0
[pip3] pytorch-quantization==2.1.2
[pip3] torch==1.11.0
[pip3] torchmetrics==0.8.0
[pip3] torchvision==0.12.0
[conda] numpy 1.23.0 pypi_0 pypi
[conda] pytorch-quantization 2.1.2 pypi_0 pypi
[conda] torch 1.11.0 pypi_0 pypi
[conda] torchmetrics 0.8.0 pypi_0 pypi
[conda] torchvision 0.12.0 pypi_0 pypi
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