[Bug] There is a problem with the setting of optim_wrapper and param_scheduler, which always leads to the best results in the top 100 epochs. #1874
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描述该错误
When I use Swin-large _ 16xb64 _ in1K-384px.py, I use auto_scale_lr, and the default training value is epochs=300, but the best result is always in the top 100 epochs, and the accuracy fluctuates seriously. How to set optim_wrapper and param_scheduler reasonably?
model = dict(
backbone=dict(
arch='large',
img_size=384,
stage_cfgs=dict(block_cfgs=dict(window_size=12)),
type='SwinTransformer'),
optim_wrapper = dict(
clip_grad=dict(max_norm=5.0),
optimizer=dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=0.001,
type='AdamW',
weight_decay=0.05),
paramwise_cfg=dict(
bias_decay_mult=0.0,
custom_keys=dict({
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0)
}),
flat_decay_mult=0.0,
norm_decay_mult=0.0))
param_scheduler = [
dict(
by_epoch=True,
convert_to_iter_based=True,
end=20,
start_factor=0.001,
type='LinearLR'),
dict(begin=20, by_epoch=True, eta_min=1e-05, type='CosineAnnealingLR'),
]
test_dataloader = dict(
batch_size=20,
train_dataloader = dict(
batch_size=16,
环境信息
Python: 3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 1050081049
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda-11.6
NVCC: Cuda compilation tools, release 11.6, V11.6.124
GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.0.1
PyTorch compiling details: PyTorch built with:
GCC 9.3
C++ Version: 201703
Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.7
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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
CuDNN 8.5
Magma 2.6.1
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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, USE_ROCM=OFF,
TorchVision: 0.15.2
OpenCV: 4.7.0
MMEngine: 0.10.3
其他信息
Since I use a single gpu and the batch-size setting is small, I use auto-scale-lr. But acc vibrates greatly, and I think I will miss the best result.
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