将LD的骨干网更改为shufflenetV2后的问题

[cape-zck]

您好，这边我尝试将Student的骨干更换为shufflenetv2后利用ResNet50作为Teacher去蒸馏，采用的检测Head为GFL，结果似乎还不如Shufflenetv2作为骨干GFL从头到尾训练，请问作者有做过类似骨干异构蒸馏的实验么。

[HikariTJU]

可以把log发出来看下吗

[cape-zck]

可以把log发出来看下吗

好的，这边我想了想会不会和我shufflenetv2学生网络设置的FPN融合层数有问题呢，刚接触这方面的工作，可能有些地方理解不周到，还想请作者您多多指教
`2022-11-25 11:53:00,267 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18) [GCC 10.3.0]
CUDA available: True
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.2, V11.2.152
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.12.0
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
• 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-unused-parameter -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=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.2.2
OpenCV: 4.6.0
MMCV: 1.6.2
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.25.3+e71b499

2022-11-25 11:53:01,841 - mmdet - INFO - Distributed training: False
2022-11-25 11:53:03,406 - mmdet - INFO - Config:
dataset_type = 'CocoDataset'
data_root = '/home/studio-lab-user/mmdetection/data/minicoco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=16,
workers_per_gpu=1,
train=dict(
type='CocoDataset',
ann_file=
'/home/studio-lab-user/mmdetection/data/minicoco/annotations/train.json',
img_prefix='/home/studio-lab-user/mmdetection/data/minicoco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='CocoDataset',
ann_file=
'/home/studio-lab-user/mmdetection/data/minicoco/annotations/instances_val2017.json',
img_prefix='/home/studio-lab-user/mmdetection/data/minicoco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file=
'/home/studio-lab-user/mmdetection/data/minicoco/annotations/instances_val2017.json',
img_prefix='/home/studio-lab-user/mmdetection/data/minicoco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
teacher_ckpt = '/mmdetection/tools/work_dirs/gfl_r50_fpn_1x_coco/minicocor50.pth'
shufflenetv2_pretrain = 'https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth'
model = dict(
type='KnowledgeDistillationSingleStageDetector',
teacher_config='../configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py',
teacher_ckpt=
'/mmdetection/tools/work_dirs/gfl_r50_fpn_1x_coco/minicocor50.pth',
backbone=dict(
widen_factor=1.0,
type='ShuffleNetV2',
out_indices=(0, 1, 2, 3),
norm_eval=True,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth',
prefix='backbone.')),
neck=dict(
type='FPN',
in_channels=[116, 232, 464, 1024],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='LDHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
loss_ld=dict(
type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10),
reg_max=16,
loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
train_cfg=dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
train_dataset = dict(
ann_file=
'/home/studio-lab-user/mmdetection/data/minicoco/annotations/train.json',
img_prefix='/home/studio-lab-user/mmdetection/data/minicoco/train2017/')
work_dir = './work_dirs/ld_shufflenetv2_gflv1_r50_fpn_minicoco_1x'
auto_resume = False
gpu_ids = [0]
`

[HikariTJU]

samples per gpu太大了， 你改成2 然后调整一下LR再训练

[cape-zck]

samples per gpu太大了， 你改成2 然后调整一下LR再训练

好的，谢谢