something questions about hyper parameters and experiment results

[007invictus]

When i train diving-48 datasets with clip_base,i change videos_per_gps=4 because my machine limit(v100 32g). And i use 14 gpus to train a model so the batchsize is 56,close to 64.But finally i got top1_accuracy=85.3.This is my training logs,could you give me some advise? May be i should change some hyper parameters?

2023-05-31 00:40:47,429 - mmaction - INFO - Environment info:

sys.platform: linux
Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0]
CUDA available: True
GPU 0,1: Tesla V100-PCIE-32GB
CUDA_HOME: /share/soft/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (GCC) 7.5.0
PyTorch: 1.10.0+cu102
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX512
• CUDA Runtime 10.2
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70
• CuDNN 7.6.5
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.0+cu102
OpenCV: 4.7.0
MMCV: 1.4.0
MMCV Compiler: n/a
MMCV CUDA Compiler: n/a
MMAction2: 0.15.0+unknown

2023-05-31 00:40:47,429 - mmaction - INFO - Distributed training: True
2023-05-31 00:40:47,986 - mmaction - INFO - Config: model = dict(
type='Recognizer3D',
backbone=dict(
type='ViT_CLIP',
input_resolution=224,
patch_size=16,
num_frames=32,
width=768,
layers=12,
heads=12,
drop_path_rate=0.2,
adapter_scale=0.5,
num_tadapter=1,
pretrained='openaiclip'),
cls_head=dict(
type='I3DHead',
in_channels=768,
num_classes=48,
spatial_type='avg',
dropout_ratio=0.5),
test_cfg=dict(average_clips='prob', max_testing_views=4))
checkpoint_config = dict(interval=10)
log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
dataset_type = 'VideoDataset'
data_root = '/share/group/datanlpr_ai/wtyuan/diving48/videos'
data_root_val = '/share/group/datanlpr_ai/wtyuan/diving48//videos'
ann_file_train = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_train_list_videos.txt'
ann_file_val = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt'
ann_file_test = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt'
img_norm_cfg = dict(
mean=[122.769, 116.74, 104.04], std=[68.493, 66.63, 70.321], to_bgr=False)
train_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 224)),
dict(type='ThreeCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
data = dict(
videos_per_gpu=4,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1, workers_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1, workers_per_gpu=1),
train=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_train_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48/videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]),
val=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48//videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]),
test=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48//videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 224)),
dict(type='ThreeCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]))
evaluation = dict(
interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
optimizer = dict(
type='AdamW',
lr=0.0003,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
class_embedding=dict(decay_mult=0.0),
positional_embedding=dict(decay_mult=0.0),
ln_1=dict(decay_mult=0.0),
ln_2=dict(decay_mult=0.0),
ln_pre=dict(decay_mult=0.0),
ln_post=dict(decay_mult=0.0))))
lr_config = dict(
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2.5)
total_epochs = 50
work_dir = 'work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig'
find_unused_parameters = True
fp16 = None
optimizer_config = dict(
type='DistOptimizerHook',
update_interval=1,
grad_clip=None,
coalesce=True,
bucket_size_mb=-1,
use_fp16=True)
gpu_ids = range(0, 14)
omnisource = False
module_hooks = []

2023-05-31 00:40:49,149 - mmaction - INFO - load model from: openaiclip
2023-05-31 00:40:53,108 - mmaction - INFO - Missing keys: ['temporal_embedding', 'transformer.resblocks.0.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.0.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.0.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.0.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.0.S_Adapter.D_fc1.weight', 'transformer.resblocks.0.S_Adapter.D_fc1.bias', 'transformer.resblocks.0.S_Adapter.D_fc2.weight', 'transformer.resblocks.0.S_Adapter.D_fc2.bias', 'transformer.resblocks.0.T_Adapter.D_fc1.weight', 'transformer.resblocks.0.T_Adapter.D_fc1.bias', 'transformer.resblocks.0.T_Adapter.D_fc2.weight', 'transformer.resblocks.0.T_Adapter.D_fc2.bias', 'transformer.resblocks.1.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.1.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.1.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.1.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.1.S_Adapter.D_fc1.weight', 'transformer.resblocks.1.S_Adapter.D_fc1.bias', 'transformer.resblocks.1.S_Adapter.D_fc2.weight', 'transformer.resblocks.1.S_Adapter.D_fc2.bias', 'transformer.resblocks.1.T_Adapter.D_fc1.weight', 'transformer.resblocks.1.T_Adapter.D_fc1.bias', 'transformer.resblocks.1.T_Adapter.D_fc2.weight', 'transformer.resblocks.1.T_Adapter.D_fc2.bias', 'transformer.resblocks.2.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.2.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.2.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.2.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.2.S_Adapter.D_fc1.weight', 'transformer.resblocks.2.S_Adapter.D_fc1.bias', 'transformer.resblocks.2.S_Adapter.D_fc2.weight', 'transformer.resblocks.2.S_Adapter.D_fc2.bias', 'transformer.resblocks.2.T_Adapter.D_fc1.weight', 'transformer.resblocks.2.T_Adapter.D_fc1.bias', 'transformer.resblocks.2.T_Adapter.D_fc2.weight', 'transformer.resblocks.2.T_Adapter.D_fc2.bias', 'transformer.resblocks.3.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.3.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.3.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.3.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.3.S_Adapter.D_fc1.weight', 'transformer.resblocks.3.S_Adapter.D_fc1.bias', 'transformer.resblocks.3.S_Adapter.D_fc2.weight', 'transformer.resblocks.3.S_Adapter.D_fc2.bias', 'transformer.resblocks.3.T_Adapter.D_fc1.weight', 'transformer.resblocks.3.T_Adapter.D_fc1.bias', 'transformer.resblocks.3.T_Adapter.D_fc2.weight', 'transformer.resblocks.3.T_Adapter.D_fc2.bias', 'transformer.resblocks.4.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.4.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.4.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.4.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.4.S_Adapter.D_fc1.weight', 'transformer.resblocks.4.S_Adapter.D_fc1.bias', 'transformer.resblocks.4.S_Adapter.D_fc2.weight', 'transformer.resblocks.4.S_Adapter.D_fc2.bias', 'transformer.resblocks.4.T_Adapter.D_fc1.weight', 'transformer.resblocks.4.T_Adapter.D_fc1.bias', 'transformer.resblocks.4.T_Adapter.D_fc2.weight', 'transformer.resblocks.4.T_Adapter.D_fc2.bias', 'transformer.resblocks.5.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.5.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.5.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.5.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.5.S_Adapter.D_fc1.weight', 'transformer.resblocks.5.S_Adapter.D_fc1.bias', 'transformer.resblocks.5.S_Adapter.D_fc2.weight', 'transformer.resblocks.5.S_Adapter.D_fc2.bias', 'transformer.resblocks.5.T_Adapter.D_fc1.weight', 'transformer.resblocks.5.T_Adapter.D_fc1.bias', 'transformer.resblocks.5.T_Adapter.D_fc2.weight', 'transformer.resblocks.5.T_Adapter.D_fc2.bias', 'transformer.resblocks.6.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.6.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.6.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.6.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.6.S_Adapter.D_fc1.weight', 'transformer.resblocks.6.S_Adapter.D_fc1.bias', 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'transformer.resblocks.11.S_Adapter.D_fc1.bias', 'transformer.resblocks.11.S_Adapter.D_fc2.weight', 'transformer.resblocks.11.S_Adapter.D_fc2.bias', 'transformer.resblocks.11.T_Adapter.D_fc1.weight', 'transformer.resblocks.11.T_Adapter.D_fc1.bias', 'transformer.resblocks.11.T_Adapter.D_fc2.weight', 'transformer.resblocks.11.T_Adapter.D_fc2.bias']
2023-05-31 00:40:53,108 - mmaction - INFO - Unexpected keys: []
2023-05-31 00:40:53,109 - mmaction - INFO - => loaded successfully 'openaiclip'
2023-05-31 00:40:53,763 - mmaction - INFO - backbone.class_embedding: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.positional_embedding: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.temporal_embedding: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.conv1.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.ln_pre.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.ln_pre.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.in_proj_weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.in_proj_bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.out_proj.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.out_proj.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_1.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_1.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_fc.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_fc.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_proj.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_proj.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_2.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_2.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.T_Adapter.D_fc1.bias: True
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2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.in_proj_weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.in_proj_bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.out_proj.weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.out_proj.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.ln_1.weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.ln_1.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_fc.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_fc.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.ln_2.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.ln_2.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.in_proj_weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.in_proj_bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.out_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.out_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_1.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_1.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_fc.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_fc.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_2.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_2.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.in_proj_weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.in_proj_bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.out_proj.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.out_proj.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_1.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_1.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_fc.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_fc.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_proj.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_proj.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_2.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_2.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.ln_post.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.ln_post.bias: True
2023-05-31 00:40:53,773 - mmaction - INFO - cls_head.fc_cls.weight: True
2023-05-31 00:40:53,773 - mmaction - INFO - cls_head.fc_cls.bias: True
2023-05-31 00:40:53,774 - mmaction - INFO - Number of total parameters: 96512304, tunable parameters: 10714416
2023-05-31 00:40:58,009 - mmaction - INFO - Start running, host: ai_zhuxinxin@g28, work_dir: /share/home/ai_zhuxinxin/wtyuan/git/aim3/aim3/work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig
2023-05-31 00:40:58,009 - mmaction - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(ABOVE_NORMAL) DistOptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) DistSamplerSeedHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook

after_train_iter:
(ABOVE_NORMAL) DistOptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(NORMAL ) DistEvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch:
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2023-05-31 00:40:58,009 - mmaction - INFO - workflow: [('train', 1)], max: 50 epochs
2023-05-31 00:40:58,009 - mmaction - INFO - Checkpoints will be saved to /share/home/ai_zhuxinxin/wtyuan/git/aim3/aim3/work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig by HardDiskBackend.
2023-05-31 00:41:34,264 - mmaction - INFO - Epoch [1][20/269] lr: 3.763e-05, eta: 6:45:30, time: 1.812, data_time: 0.949, memory: 20587, top1_acc: 0.0455, top5_acc: 0.1830, loss_cls: 3.7788, loss: 3.7788
''''''''''''''''''''''''''''''''''''''''''''''
2023-05-31 04:07:05,175 - mmaction - INFO - Epoch [50][260/269] lr: 2.960e-07, eta: 0:00:07, time: 0.761, data_time: 0.001, memory: 20590, top1_acc: 0.9313, top5_acc: 0.9839, loss_cls: 0.2157, loss: 0.2157
2023-05-31 04:07:11,845 - mmaction - INFO - Saving checkpoint at 50 epochs
2023-05-31 04:08:10,086 - mmaction - INFO - Evaluating top_k_accuracy ...
2023-05-31 04:08:10,093 - mmaction - INFO -
top1_acc 0.8594
top5_acc 0.9888
2023-05-31 04:08:10,093 - mmaction - INFO - Evaluating mean_class_accuracy ...
2023-05-31 04:08:10,094 - mmaction - INFO -
mean_acc 0.7878
2023-05-31 04:08:10,095 - mmaction - INFO - Epoch(val) [50][141] top1_acc: 0.8594, top5_acc: 0.9888, mean_class_accuracy: 0.7878
2023-05-31 04:09:08,388 - mmaction - INFO - Testing results of the last checkpoint
2023-05-31 04:09:08,388 - mmaction - INFO - top1_acc: 0.8533
2023-05-31 04:09:08,388 - mmaction - INFO - top5_acc: 0.9873
2023-05-31 04:09:08,388 - mmaction - INFO - mean_class_accuracy: 0.7874

[taoyang1122]

Hi, I remember that clip_base should be able to fit into a 32G GPU. If it doesn't, you could increase 'update_interval' to 2/4/8. This is for gradient accumulation. update_interval=2 means that the batchsize is 8/2=4, and gradients are accumulated 2 steps for backward pass. So it is equivalent to batchsize=8, but may slow down the training a little bit.

If you use 14 gpus, you may need to scale up the learning rate by 14/8 times.