IndexError: tuple index out of range

[kinvwww]

/home/z/anaconda3/envs/agent_detection/bin/python3.7 /home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py /home/z/zky/Agent-Attention-master/downstream/detection/mmdet/utils/setup_env.py:39: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting OMP_NUM_THREADS environment variable for each process ' /home/z/zky/Agent-Attention-master/downstream/detection/mmdet/utils/setup_env.py:49: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting MKL_NUM_THREADS environment variable for each process ' 2023-12-31 21:14:42,606 - mmdet - INFO - Environment info:

sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True GPU 0,1,2,3: NVIDIA TITAN X (Pascal) CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.1, V10.1.24 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.12.1 PyTorch compiling details: PyTorch built with:

• GCC 9.3
• 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 v2.6.0 (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= -fabi-version=11 -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=1.12.1, 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: 0.13.1 OpenCV: 4.6.0 MMCV: 1.6.1 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMDetection: 2.25.2+daeda61

2023-12-31 21:14:43,557 - mmdet - INFO - Distributed training: False 2023-12-31 21:14:44,477 - mmdet - INFO - Config: model = dict( type='RetinaNet', backbone=dict( type='AgentPVT', img_size=224, patch_size=4, in_chans=3, num_classes=6, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.2, depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], agent_sr_ratios='1111', num_stages=4, agent_num=[9, 16, 49, 49], downstream_agent_shapes=[(12, 12), (16, 16), (28, 28), (28, 28)], kernel_size=3, attn_type='AAAA', scale=-0.5, init_cfg=dict(type='Pretrained', checkpoint=None)), neck=dict( type='FPN', in_channels=[64, 128, 320, 512], out_channels=256, start_level=1, add_extra_convs='on_input', num_outs=5), bbox_head=dict( type='RetinaHead', num_classes=6, in_channels=256, stacked_convs=4, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', octave_base_scale=4, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), 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='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), 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=0.5), max_per_img=100)) dataset_type = 'CocoDataset' data_root = '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/' 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( train_dataloader=dict( samples_per_gpu=2, workers_per_gpu=10, pin_memory=True), train=dict( type='CocoDataset', ann_file= '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_train2017.json', img_prefix= '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_val2017.json', img_prefix= '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_val2017.json', img_prefix= '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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='AdamW', lr=0.0001, 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')]) 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) pretrained = None lr = 0.0001 work_dir = './work_dirs/agent_pvt_m_rtn_1x_12-16-28-28' auto_resume = False gpu_ids = [0]

2023-12-31 21:14:44,477 - mmdet - INFO - Set random seed to 2100000934, deterministic: False Traceback (most recent call last): File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg return obj_cls(**args) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 300, in init for j in range(depths[i])]) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 300, in for j in range(depths[i])]) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 217, in init agent_num=agent_num, downstream_agent_shape=downstream_agent_shape, kernel_size=kernel_size, scale=scale) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 129, in init print('Agent Attention sr{} v{} n{} k{} scale{} reso{}'.format(sr_ratio, agent_num, kernel_size, scale, window_size)) IndexError: tuple index out of range

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg return obj_cls(*args) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/detectors/retinanet.py", line 19, in init test_cfg, pretrained, init_cfg) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/detectors/single_stage.py", line 32, in init self.backbone = build_backbone(backbone) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/builder.py", line 20, in build_backbone return BACKBONES.build(cfg) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 237, in build return self.build_func(args, **kwargs, registry=self) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') IndexError: AgentPVT: tuple index out of range

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py", line 244, in main() File "/home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py", line 215, in main test_cfg=cfg.get('test_cfg')) File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/builder.py", line 59, in build_detector cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 237, in build return self.build_func(*args, **kwargs, registry=self) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') IndexError: RetinaNet: AgentPVT: tuple index out of range

Process finished with exit code 1

[YTianZHU]

@kinvwww Hi! Sorry for the bug, you can simply fix it by deleting the line

print('Agent Attention sr{} v{} n{} k{} scale{} reso{}'.format(sr_ratio, agent_num, kernel_size, scale, window_size)) in File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 129.

Same for agent_swin.py, and for segmentation models. Or you can wait we merge the fix tomorrow. Thanks for pointing out the bug.