search

SoccerNet / sn-spotting

Repository containing all necessary codes to get started on the SoccerNet Action Spotting challenge. This repository also contains several benchmark methods.
61 stars 8 forks source link

Possible OOM issue in training for NetVLAD++ (Linux Ubuntu 18.04) #14

Closed mumulmaulana closed 6 months ago

mumulmaulana commented 1 year ago

Hello! Thank you so much for providing this open repository for everyone!

I would like to ask about my issue running training for the Benchmark model. I tried training for the NetVLAD++ benchmark model in my system and ran into this issue: Screenshot from 2023-08-04 11-37-33

This is the parameters for the training:

2023-08-04 11:16:14,511 [MainThread ] [INFO ] Parameters: 2023-08-04 11:16:14,511 [MainThread ] [INFO ] SoccerNet_path : /media/ogatalab/SSD-PGU3C/SoccerNet 2023-08-04 11:16:14,511 [MainThread ] [INFO ] features : ResNET_TF2.npy 2023-08-04 11:16:14,511 [MainThread ] [INFO ] max_epochs : 1000 2023-08-04 11:16:14,511 [MainThread ] [INFO ] load_weights : None 2023-08-04 11:16:14,511 [MainThread ] [INFO ] model_name : NetVLAD++ 2023-08-04 11:16:14,511 [MainThread ] [INFO ] test_only : False 2023-08-04 11:16:14,511 [MainThread ] [INFO ] split_train : ['train'] 2023-08-04 11:16:14,511 [MainThread ] [INFO ] split_valid : ['valid'] 2023-08-04 11:16:14,511 [MainThread ] [INFO ] split_test : ['test', 'challenge'] 2023-08-04 11:16:14,511 [MainThread ] [INFO ] version : 2 2023-08-04 11:16:14,511 [MainThread ] [INFO ] feature_dim : None 2023-08-04 11:16:14,511 [MainThread ] [INFO ] evaluation_frequency : 10 2023-08-04 11:16:14,511 [MainThread ] [INFO ] framerate : 2 2023-08-04 11:16:14,511 [MainThread ] [INFO ] window_size : 15 2023-08-04 11:16:14,511 [MainThread ] [INFO ] pool : NetVLAD++ 2023-08-04 11:16:14,511 [MainThread ] [INFO ] vocab_size : 64 2023-08-04 11:16:14,511 [MainThread ] [INFO ] NMS_window : 30 2023-08-04 11:16:14,511 [MainThread ] [INFO ] NMS_threshold : 0.0 2023-08-04 11:16:14,511 [MainThread ] [INFO ] batch_size : 256 2023-08-04 11:16:14,511 [MainThread ] [INFO ] LR : 0.001 2023-08-04 11:16:14,511 [MainThread ] [INFO ] LRe : 1e-06 2023-08-04 11:16:14,511 [MainThread ] [INFO ] patience : 10 2023-08-04 11:16:14,511 [MainThread ] [INFO ] GPU : 0 2023-08-04 11:16:14,511 [MainThread ] [INFO ] max_num_worker : 4 2023-08-04 11:16:14,511 [MainThread ] [INFO ] seed : 0 2023-08-04 11:16:14,511 [MainThread ] [INFO ] loglevel : INFO

I have tried to reduce the batch_size and max_num_workers as well, but the problem persists. Also, not sure if it is needed, but it seems it only happens after it runs the test() procedure.

I found no trouble training with PCA512 features, so I figure there must be some resource issues. When I look at the System Monitor and GPU usage, this is the reading of the result. Screenshot from 2023-08-03 16-18-17

I have tried increasing the swap file to more than 64GB, but it only results in my system freezing and crashing. Are you familiar with this issue? Is there any way for me to bypass this? Or, if it is no trouble, can you tell me the recommended specification to run this training? Thank you!

My system specs (RAM 16GB, SSD 2TB):

  1. CPU

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 6 On-line CPU(s) list: 0-5 Thread(s) per core: 1 Core(s) per socket: 6 Socket(s): 1 NUMA node(s): 1 Vendor ID: AuthenticAMD CPU family: 23 Model: 113 Model name: AMD Ryzen 5 3500 6-Core Processor Stepping: 0 CPU MHz: 3266.777 CPU max MHz: 3600.0000 CPU min MHz: 2200.0000 BogoMIPS: 7186.49 Virtualization: AMD-V L1d cache: 32K L1i cache: 32K L2 cache: 512K L3 cache: 8192K NUMA node0 CPU(s): 0-5

  1. GPU (NVIDIA GeForce RTX 3060 Ti)

+-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.182.03 Driver Version: 470.182.03 CUDA Version: 11.4 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA GeForce ... Off | 00000000:06:00.0 On | N/A | | 30% 51C P2 57W / 200W | 2270MiB / 7973MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | 0 N/A N/A 1067 G /usr/bin/gnome-shell 87MiB | | 0 N/A N/A 1431 G /usr/lib/xorg/Xorg 146MiB | | 0 N/A N/A 1546 G /usr/bin/gnome-shell 46MiB | | 0 N/A N/A 2130 G ...RendererForSitePerProcess 87MiB | | 0 N/A N/A 2754 G /usr/bin/nvidia-settings 0MiB | | 0 N/A N/A 5116 C python 1809MiB | | 0 N/A N/A 23313 G ...464382835143902905,262144 77MiB | +-----------------------------------------------------------------------------+

SilvioGiancola commented 1 year ago

Hi @mumulmaulana , Your issue most probably originates from your RAM; you don't have enough to pre-load all the ResNET features with dimension 2048. I ran my experiments on a workstation with 256GB of RAM, which could also accommodate the Baidu features of even larger dimensions (~8k AFAIK). The PCA512 version are reduced to a dimension of 512 with PCA, which consumes less RAM for fairly similar performances.

A solution for you would be to update the dataset class to load the features when sampling them in __getitem__, instead of pre-loading them all on __init__.

I hope that helps!

mumulmaulana commented 1 year ago

Thanks for responding! I will try the workaround first and get back with the result!