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Published 20 hours ago • verified publisher icon open-mmlab

[Bug] The model and loaded state dict do not match exactly

Open fudemin1 opened this issue 1 year ago • 2 comments

Prerequisite

Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

Branch

master branch https://github.com/open-mmlab/mmrotate

Environment

sys.platform: linux Python: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0] CUDA available: True GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.58 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.12.0 PyTorch compiling details: PyTorch built with:

  • GCC 9.3
  • C++ Version: 201402
  • Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 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 11.3
  • 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 8.3.2 (built against CUDA 11.5)
  • Magma 2.5.2
  • Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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 -Werror=cast-function-type -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: 0.13.0 OpenCV: 4.7.0 MMCV: 1.7.1 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMRotate: 0.3.4+

Reproduces the problem - code sample

     2023-06-08 16:37:35,773 - mmrotate - INFO - Distributed training: False

2023-06-08 16:37:36,659 - mmrotate - INFO - Config: dataset_type = 'DOTADataset' data_root = 'data/ships/' 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='RResize', img_scale=(1024, 1024)), dict( type='RRandomFlip', flip_ratio=[0.25, 0.25, 0.25], direction=['horizontal', 'vertical', 'diagonal'], version='le90'), dict( type='PolyRandomRotate', rotate_ratio=0.5, angles_range=180, auto_bound=False, rect_classes=[9, 11], version='le90'), 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=(1024, 1024), flip=False, transforms=[ dict(type='RResize'), 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']) ]) ] data = dict( samples_per_gpu=8, workers_per_gpu=2, train=dict( type='DOTADataset', ann_file='/home/fu/work/m/mmrotate-main/data/ships/trainval/annfiles/', img_prefix='/home/fu/work/m/mmrotate-main/data/ships/trainval/images/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='RResize', img_scale=(1024, 1024)), dict( type='RRandomFlip', flip_ratio=[0.25, 0.25, 0.25], direction=['horizontal', 'vertical', 'diagonal'], version='le90'), dict( type='PolyRandomRotate', rotate_ratio=0.5, angles_range=180, auto_bound=False, rect_classes=[9, 11], version='le90'), 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']) ], version='le90'), val=dict( type='DOTADataset', ann_file='/home/fu/work/m/mmrotate-main/data/ships/trainval/annfiles/', img_prefix='/home/fu/work/m/mmrotate-main/data/ships/trainval/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1024, 1024), flip=False, transforms=[ dict(type='RResize'), 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']) ]) ], version='le90'), test=dict( type='DOTADataset', ann_file='/home/fu/work/m/mmrotate-main/data/ships/test/images/', img_prefix='/home/fu/work/m/mmrotate-main/data/ships/test/images/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1024, 1024), flip=False, transforms=[ dict(type='RResize'), 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']) ]) ], version='le90')) evaluation = dict(interval=1, metric='mAP') optimizer = dict( type='Adam', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.3333333333333333, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) 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' angle_version = 'le90' model = dict( type='ReDet', backbone=dict( type='ReResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch', pretrained= '/home/fu/work/m/mmrotate-main/checkpint/redet_re50_fpn_1x_dota_ms_rr_le90-fc9217b5.pth' ), neck=dict( type='ReFPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RotatedRPNHead', in_channels=256, feat_channels=256, version='le90', anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='RoITransRoIHead', version='le90', num_stages=2, stage_loss_weights=[1, 1], bbox_roi_extractor=[ dict( type='SingleRoIExtractor', roi_layer=dict( type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), dict( type='RotatedSingleRoIExtractor', roi_layer=dict( type='RiRoIAlignRotated', out_size=7, num_samples=2, num_orientations=8, clockwise=True), out_channels=256, featmap_strides=[4, 8, 16, 32]) ], bbox_head=[ dict( type='RotatedShared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=133, bbox_coder=dict( type='DeltaXYWHAHBBoxCoder', angle_range='le90', norm_factor=2, edge_swap=True, target_means=[0.0, 0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2, 1]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='RotatedShared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=133, bbox_coder=dict( type='DeltaXYWHAOBBoxCoder', angle_range='le90', norm_factor=None, edge_swap=True, proj_xy=True, target_means=[0.0, 0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1, 0.5]), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) ]), train_cfg=dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1, gpu_assign_thr=200), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=2000, max_per_img=2000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=[ dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1, iou_calculator=dict(type='BboxOverlaps2D')), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1, iou_calculator=dict(type='RBboxOverlaps2D')), sampler=dict( type='RRandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False) ]), test_cfg=dict( rpn=dict( nms_pre=2000, max_per_img=2000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( nms_pre=2000, min_bbox_size=0, score_thr=0.05, nms=dict(iou_thr=0.1), max_per_img=2000))) work_dir = '/home/fu/work/m/mmrotate-main/work_dir' auto_resume = False gpu_ids = range(0, 1)

Reproduces the problem - command or script

parser.add_argument('--config', default='/home/fu/work/m/mmrotate-main/config/redet_re50_refpn_1x_dota_ms_rr_le90.py',help='train config file path') parser.add_argument('--work-dir', default='/home/fu/work/m/mmrotate-main/work_dir',help='the dir to save logs and models')

Reproduces the problem - error message

unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, backbone.bn1.batch_norm_[8].weight, backbone.bn1.batch_norm_[8].bias, backbone.bn1.batch_norm_[8].running_mean, backbone.bn1.batch_norm_[8].running_var, backbone.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv1.weights, backbone.layer1.0.conv1.filter, backbone.layer1.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn1.indices_8, backbone.layer1.0.bn1.batch_norm_[8].weight, backbone.layer1.0.bn1.batch_norm_[8].bias, backbone.layer1.0.bn1.batch_norm_[8].running_mean, backbone.layer1.0.bn1.batch_norm_[8].running_var, backbone.layer1.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv2.weights, backbone.layer1.0.conv2.filter, backbone.layer1.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn2.indices_8, backbone.layer1.0.bn2.batch_norm_[8].weight, backbone.layer1.0.bn2.batch_norm_[8].bias, backbone.layer1.0.bn2.batch_norm_[8].running_mean, backbone.layer1.0.bn2.batch_norm_[8].running_var, backbone.layer1.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv3.weights, backbone.layer1.0.conv3.filter, backbone.layer1.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn3.indices_8, backbone.layer1.0.bn3.batch_norm_[8].weight, backbone.layer1.0.bn3.batch_norm_[8].bias, backbone.layer1.0.bn3.batch_norm_[8].running_mean, backbone.layer1.0.bn3.batch_norm_[8].running_var, backbone.layer1.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.0.downsample.0.weights, backbone.layer1.0.downsample.0.filter, backbone.layer1.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.downsample.1.indices_8, backbone.layer1.0.downsample.1.batch_norm_[8].weight, backbone.layer1.0.downsample.1.batch_norm_[8].bias, backbone.layer1.0.downsample.1.batch_norm_[8].running_mean, backbone.layer1.0.downsample.1.batch_norm_[8].running_var, backbone.layer1.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv1.weights, backbone.layer1.1.conv1.filter, backbone.layer1.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn1.indices_8, backbone.layer1.1.bn1.batch_norm_[8].weight, backbone.layer1.1.bn1.batch_norm_[8].bias, backbone.layer1.1.bn1.batch_norm_[8].running_mean, backbone.layer1.1.bn1.batch_norm_[8].running_var, backbone.layer1.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv2.weights, backbone.layer1.1.conv2.filter, backbone.layer1.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn2.indices_8, backbone.layer1.1.bn2.batch_norm_[8].weight, backbone.layer1.1.bn2.batch_norm_[8].bias, backbone.layer1.1.bn2.batch_norm_[8].running_mean, backbone.layer1.1.bn2.batch_norm_[8].running_var, backbone.layer1.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv3.weights, backbone.layer1.1.conv3.filter, backbone.layer1.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn3.indices_8, backbone.layer1.1.bn3.batch_norm_[8].weight, backbone.layer1.1.bn3.batch_norm_[8].bias, backbone.layer1.1.bn3.batch_norm_[8].running_mean, backbone.layer1.1.bn3.batch_norm_[8].running_var, backbone.layer1.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv1.weights, backbone.layer1.2.conv1.filter, backbone.layer1.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn1.indices_8, backbone.layer1.2.bn1.batch_norm_[8].weight, backbone.layer1.2.bn1.batch_norm_[8].bias, backbone.layer1.2.bn1.batch_norm_[8].running_mean, backbone.layer1.2.bn1.batch_norm_[8].running_var, backbone.layer1.2.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv2.weights, backbone.layer1.2.conv2.filter, backbone.layer1.2.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn2.indices_8, backbone.layer1.2.bn2.batch_norm_[8].weight, backbone.layer1.2.bn2.batch_norm_[8].bias, backbone.layer1.2.bn2.batch_norm_[8].running_mean, backbone.layer1.2.bn2.batch_norm_[8].running_var, backbone.layer1.2.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv3.weights, backbone.layer1.2.conv3.filter, backbone.layer1.2.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn3.indices_8, backbone.layer1.2.bn3.batch_norm_[8].weight, backbone.layer1.2.bn3.batch_norm_[8].bias, backbone.layer1.2.bn3.batch_norm_[8].running_mean, backbone.layer1.2.bn3.batch_norm_[8].running_var, backbone.layer1.2.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv1.weights, backbone.layer2.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn1.indices_8, backbone.layer2.0.bn1.batch_norm_[8].weight, backbone.layer2.0.bn1.batch_norm_[8].bias, backbone.layer2.0.bn1.batch_norm_[8].running_mean, backbone.layer2.0.bn1.batch_norm_[8].running_var, backbone.layer2.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv2.weights, backbone.layer2.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn2.indices_8, backbone.layer2.0.bn2.batch_norm_[8].weight, backbone.layer2.0.bn2.batch_norm_[8].bias, backbone.layer2.0.bn2.batch_norm_[8].running_mean, backbone.layer2.0.bn2.batch_norm_[8].running_var, backbone.layer2.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv3.weights, backbone.layer2.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn3.indices_8, backbone.layer2.0.bn3.batch_norm_[8].weight, backbone.layer2.0.bn3.batch_norm_[8].bias, backbone.layer2.0.bn3.batch_norm_[8].running_mean, backbone.layer2.0.bn3.batch_norm_[8].running_var, backbone.layer2.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.0.downsample.0.weights, backbone.layer2.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.downsample.1.indices_8, backbone.layer2.0.downsample.1.batch_norm_[8].weight, backbone.layer2.0.downsample.1.batch_norm_[8].bias, backbone.layer2.0.downsample.1.batch_norm_[8].running_mean, backbone.layer2.0.downsample.1.batch_norm_[8].running_var, backbone.layer2.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer2.1.conv1.weights, backbone.layer2.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.1.bn1.indices_8, backbone.layer2.1.bn1.batch_norm_[8].weight, backbone.layer2.1.bn1.batch_norm_[8].bias, backbone.layer2.1.bn1.batch_norm_[8].running_mean, backbone.layer2.1.bn1.batch_norm_[8].running_var, backbone.layer2.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.1.conv2.weights, backbone.layer2.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.1.bn2.indices_8, backbone.layer2.1.bn2.batch_norm_[8].weight, backbone.layer2.1.bn2.batch_norm_[8].bias, backbone.layer2.1.bn2.batch_norm_[8].running_mean, backbone.layer2.1.bn2.batch_norm_[8].running_var, backbone.layer2.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.1.conv3.weights, backbone.layer2.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.1.bn3.indices_8, backbone.layer2.1.bn3.batch_norm_[8].weight, backbone.layer2.1.bn3.batch_norm_[8].bias, backbone.layer2.1.bn3.batch_norm_[8].running_mean, backbone.layer2.1.bn3.batch_norm_[8].running_var, backbone.layer2.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv1.weights, backbone.layer2.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn1.indices_8, backbone.layer2.2.bn1.batch_norm_[8].weight, backbone.layer2.2.bn1.batch_norm_[8].bias, backbone.layer2.2.bn1.batch_norm_[8].running_mean, backbone.layer2.2.bn1.batch_norm_[8].running_var, backbone.layer2.2.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv2.weights, backbone.layer2.2.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn2.indices_8, backbone.layer2.2.bn2.batch_norm_[8].weight, backbone.layer2.2.bn2.batch_norm_[8].bias, backbone.layer2.2.bn2.batch_norm_[8].running_mean, backbone.layer2.2.bn2.batch_norm_[8].running_var, backbone.layer2.2.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv3.weights, backbone.layer2.2.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn3.indices_8, backbone.layer2.2.bn3.batch_norm_[8].weight, backbone.layer2.2.bn3.batch_norm_[8].bias, backbone.layer2.2.bn3.batch_norm_[8].running_mean, backbone.layer2.2.bn3.batch_norm_[8].running_var, backbone.layer2.2.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.3.conv1.weights, backbone.layer2.3.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.3.bn1.indices_8, backbone.layer2.3.bn1.batch_norm_[8].weight, backbone.layer2.3.bn1.batch_norm_[8].bias, backbone.layer2.3.bn1.batch_norm_[8].running_mean, backbone.layer2.3.bn1.batch_norm_[8].running_var, backbone.layer2.3.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.3.conv2.weights, backbone.layer2.3.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.3.bn2.indices_8, backbone.layer2.3.bn2.batch_norm_[8].weight, backbone.layer2.3.bn2.batch_norm_[8].bias, backbone.layer2.3.bn2.batch_norm_[8].running_mean, backbone.layer2.3.bn2.batch_norm_[8].running_var, 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Additional information

加载预训练模型失败，模型的key比网络的key多 backbone. ，

fudemin1 avatar Jun 08 '23 08:06 fudemin1

i also meet this problem

flymmmfly avatar Jun 09 '23 11:06 flymmmfly

有没有换一个rtmdet的backbone试验一下？我使用的时候好像没有出这个问题。

jsxyhelu avatar Sep 03 '23 13:09 jsxyhelu