RuntimeError: The model and loaded state dict do not match exactly #9205

vansin · 2022-11-02T06:30:31Z

vansin
Nov 2, 2022
Collaborator

checkpoint is download form https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth

root@project-10489-instance-20306-ov-ide-674f4b55ff-2bjl6:~/mmdetection# python tools/test.py configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py /project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth
11/02 14:26:48 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.7.5 (default, Dec  9 2021, 17:04:37) [GCC 8.4.0]
    CUDA available: True
    numpy_random_seed: 1077139524
    GPU 0: GeForce RTX 2080 Ti
    CUDA_HOME: /usr/local/cuda
    NVCC: Cuda compilation tools, release 11.1, V11.1.105
    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
    PyTorch: 1.10.0+cu111
    PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 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: AVX2
  - CUDA Runtime 11.1
  - 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;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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+cu111
    OpenCV: 4.5.5
    MMEngine: 0.2.0

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: None
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

11/02 14:26:49 - mmengine - INFO - Config:
default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth'
resume = False
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=300,
    val_interval=10,
    dynamic_intervals=[(280, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='CosineAnnealingLR',
        eta_min=0.0002,
        begin=150,
        end=300,
        T_max=150,
        by_epoch=True,
        convert_to_iter_based=True)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='CachedMosaic',
        img_scale=(640, 640),
        pad_val=114.0,
        max_cached_images=20,
        random_pop=False),
    dict(
        type='RandomResize',
        scale=(1280, 1280),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(640, 640)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='CachedMixUp',
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=10,
        random_pop=False,
        pad_val=(114, 114, 114),
        prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=32,
    num_workers=10,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=None,
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='CachedMosaic',
                img_scale=(640, 640),
                pad_val=114.0,
                max_cached_images=20,
                random_pop=False),
            dict(
                type='RandomResize',
                scale=(1280, 1280),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(640, 640)),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='CachedMixUp',
                img_scale=(640, 640),
                ratio_range=(1.0, 1.0),
                max_cached_images=10,
                random_pop=False,
                pad_val=(114, 114, 114),
                prob=0.5),
            dict(type='PackDetInputs')
        ]),
    pin_memory=True)
val_dataloader = dict(
    batch_size=5,
    num_workers=10,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
test_dataloader = dict(
    batch_size=5,
    num_workers=10,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/instances_val2017.json',
    metric='bbox',
    format_only=False)
test_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/instances_val2017.json',
    metric='bbox',
    format_only=False)
model = dict(
    type='RTMDet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        batch_augments=None),
    backbone=dict(
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=0.167,
        widen_factor=0.375,
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU'),
        init_cfg=dict(
            type='Pretrained',
            prefix='backbone.',
            checkpoint=
            'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth'
        )),
    neck=dict(
        type='CSPNeXtPAFPN',
        in_channels=[96, 192, 384],
        out_channels=96,
        num_csp_blocks=1,
        expand_ratio=0.5,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU')),
    bbox_head=dict(
        type='RTMDetSepBNHead',
        num_classes=80,
        in_channels=96,
        stacked_convs=2,
        feat_channels=96,
        anchor_generator=dict(
            type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        with_objectness=False,
        exp_on_reg=False,
        share_conv=True,
        pred_kernel_size=1,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU')),
    train_cfg=dict(
        assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
        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_pipeline_stage2 = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='RandomResize',
        scale=(640, 640),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(640, 640)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(type='PackDetInputs')
]
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.004
interval = 10
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type='PipelineSwitchHook',
        switch_epoch=280,
        switch_pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='RandomResize',
                scale=(640, 640),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(640, 640)),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(type='PackDetInputs')
        ])
]
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth'
launcher = 'none'
work_dir = './work_dirs/rtmdet_tiny_8xb32-300e_coco'

Result has been saved to /project/mmdetection/work_dirs/rtmdet_tiny_8xb32-300e_coco/modules_statistic_results.json
11/02 14:26:55 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
local loads checkpoint from path: /project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth
Traceback (most recent call last):
  File "tools/test.py", line 105, in <module>
    main()
  File "tools/test.py", line 101, in main
    runner.test()
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1703, in test
    self.load_or_resume()
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1600, in load_or_resume
    self.load_checkpoint(self._load_from)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 2009, in load_checkpoint
    self.call_hook('after_load_checkpoint', checkpoint=checkpoint)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1721, in call_hook
    getattr(hook, fn_name)(self, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/hooks/ema_hook.py", line 192, in after_load_checkpoint
    strict=self.strict_load)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/checkpoint.py", line 108, in load_state_dict
    raise RuntimeError(err_msg)
RuntimeError: The model and loaded state dict do not match exactly

unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std

root@project-10489-instance-20306-ov-ide-674f4b55ff-2bjl6:~/mmdetection# python tools/test.py configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py /project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth
11/02 14:27:16 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.7.5 (default, Dec  9 2021, 17:04:37) [GCC 8.4.0]
    CUDA available: True
    numpy_random_seed: 65295392
    GPU 0: GeForce RTX 2080 Ti
    CUDA_HOME: /usr/local/cuda
    NVCC: Cuda compilation tools, release 11.1, V11.1.105
    GCC: x86_64-linux-gnu-gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
    PyTorch: 1.10.0+cu111
    PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 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: AVX2
  - CUDA Runtime 11.1
  - 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;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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+cu111
    OpenCV: 4.5.5
    MMEngine: 0.2.0

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: None
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

11/02 14:27:17 - mmengine - INFO - Config:
default_scope = 'mmdet'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='DetLocalVisualizer',
    vis_backends=[dict(type='LocalVisBackend')],
    name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth'
resume = False
train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=300,
    val_interval=10,
    dynamic_intervals=[(280, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='CosineAnnealingLR',
        eta_min=0.0002,
        begin=150,
        end=300,
        T_max=150,
        by_epoch=True,
        convert_to_iter_based=True)
]
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
file_client_args = dict(backend='disk')
train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='CachedMosaic',
        img_scale=(640, 640),
        pad_val=114.0,
        max_cached_images=20,
        random_pop=False),
    dict(
        type='RandomResize',
        scale=(1280, 1280),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(640, 640)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='CachedMixUp',
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=10,
        random_pop=False,
        pad_val=(114, 114, 114),
        prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='Resize', scale=(640, 640), keep_ratio=True),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    batch_size=32,
    num_workers=10,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=None,
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_train2017.json',
        data_prefix=dict(img='train2017/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='CachedMosaic',
                img_scale=(640, 640),
                pad_val=114.0,
                max_cached_images=20,
                random_pop=False),
            dict(
                type='RandomResize',
                scale=(1280, 1280),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(640, 640)),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='CachedMixUp',
                img_scale=(640, 640),
                ratio_range=(1.0, 1.0),
                max_cached_images=10,
                random_pop=False,
                pad_val=(114, 114, 114),
                prob=0.5),
            dict(type='PackDetInputs')
        ]),
    pin_memory=True)
val_dataloader = dict(
    batch_size=5,
    num_workers=10,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
test_dataloader = dict(
    batch_size=5,
    num_workers=10,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='CocoDataset',
        data_root='data/coco/',
        ann_file='annotations/instances_val2017.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='Resize', scale=(640, 640), keep_ratio=True),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(
                type='PackDetInputs',
                meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
val_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/instances_val2017.json',
    metric='bbox',
    format_only=False)
test_evaluator = dict(
    type='CocoMetric',
    ann_file='data/coco/annotations/instances_val2017.json',
    metric='bbox',
    format_only=False)
model = dict(
    type='RTMDet',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        batch_augments=None),
    backbone=dict(
        type='CSPNeXt',
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=0.167,
        widen_factor=0.375,
        channel_attention=True,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU'),
        init_cfg=dict(
            type='Pretrained',
            prefix='backbone.',
            checkpoint=
            'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
        )),
    neck=dict(
        type='CSPNeXtPAFPN',
        in_channels=[96, 192, 384],
        out_channels=96,
        num_csp_blocks=1,
        expand_ratio=0.5,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU')),
    bbox_head=dict(
        type='RTMDetSepBNHead',
        num_classes=80,
        in_channels=96,
        stacked_convs=2,
        feat_channels=96,
        anchor_generator=dict(
            type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        loss_cls=dict(
            type='QualityFocalLoss',
            use_sigmoid=True,
            beta=2.0,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        with_objectness=False,
        exp_on_reg=False,
        share_conv=True,
        pred_kernel_size=1,
        norm_cfg=dict(type='SyncBN'),
        act_cfg=dict(type='SiLU')),
    train_cfg=dict(
        assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
        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_pipeline_stage2 = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='RandomResize',
        scale=(640, 640),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    dict(type='RandomCrop', crop_size=(640, 640)),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(type='PackDetInputs')
]
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.004
interval = 10
custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type='PipelineSwitchHook',
        switch_epoch=280,
        switch_pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk')),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='RandomResize',
                scale=(640, 640),
                ratio_range=(0.5, 2.0),
                keep_ratio=True),
            dict(type='RandomCrop', crop_size=(640, 640)),
            dict(type='YOLOXHSVRandomAug'),
            dict(type='RandomFlip', prob=0.5),
            dict(
                type='Pad', size=(640, 640),
                pad_val=dict(img=(114, 114, 114))),
            dict(type='PackDetInputs')
        ])
]
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
launcher = 'none'
work_dir = './work_dirs/rtmdet_tiny_8xb32-300e_coco'

Result has been saved to /project/mmdetection/work_dirs/rtmdet_tiny_8xb32-300e_coco/modules_statistic_results.json
11/02 14:27:23 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
local loads checkpoint from path: /project/mmdetection/rtmdet_tiny_8xb32-300e_coco.pth
Traceback (most recent call last):
  File "tools/test.py", line 105, in <module>
    main()
  File "tools/test.py", line 101, in main
    runner.test()
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1703, in test
    self.load_or_resume()
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1600, in load_or_resume
    self.load_checkpoint(self._load_from)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 2009, in load_checkpoint
    self.call_hook('after_load_checkpoint', checkpoint=checkpoint)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/runner.py", line 1721, in call_hook
    getattr(hook, fn_name)(self, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/hooks/ema_hook.py", line 192, in after_load_checkpoint
    strict=self.strict_load)
  File "/usr/local/lib/python3.7/dist-packages/mmengine/runner/checkpoint.py", line 108, in load_state_dict
    raise RuntimeError(err_msg)
RuntimeError: The model and loaded state dict do not match exactly

unexpected key in source state_dict: data_preprocessor.mean, data_preprocessor.std

WdBlink · 2023-08-31T02:51:28Z

WdBlink
Aug 31, 2023

I have the same issue

0 replies

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RuntimeError: The model and loaded state dict do not match exactly #9205

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RuntimeError: The model and loaded state dict do not match exactly #9205

vansin Nov 2, 2022 Collaborator

Replies: 1 comment

WdBlink Aug 31, 2023

vansin
Nov 2, 2022
Collaborator

WdBlink
Aug 31, 2023