RuntimeError: `self._train_loop` should not be None when calling train method. Please provide `train_dataloader`, `train_cfg`, `optimizer` and `param_scheduler` arguments when initializing runner.

[wdzwdxy]

在使用train.py 运行配置文件时
报错RuntimeError: self._train_loop should not be None when calling train method. Please provide train_dataloader, train_cfg, optimizer and param_scheduler arguments when initializing runner.

报错部分全览

System environment: sys.platform: win32 Python: 3.10.14 | packaged by Anaconda, Inc. | (main, Mar 21 2024, 16:20:14) [MSC v.1916 64 bit (AMD64)] CUDA available: True MUSA available: False numpy_random_seed: 895890165 GPU 0: NVIDIA GeForce RTX 3050 Laptop GPU CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0 NVCC: Cuda compilation tools, release 12.0, V12.0.76 MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.39.33523 版 GCC: n/a PyTorch: 2.2.2+cu121 PyTorch compiling details: PyTorch built with:

• C++ Version: 201703

• MSVC 192930151

• Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications

• Intel(R) MKL-DNN v3.3.2 (Git Hash 2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)

• OpenMP 2019

• LAPACK is enabled (usually provided by MKL)

• CPU capability usage: AVX2

• CUDA Runtime 12.1

• NVCC architecture flags: -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_90,code=sm_90

• CuDNN 8.8.1 (built against CUDA 12.0)

• Magma 2.5.4

• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.8.1, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /Zc:__cplusplus /bigobj /FS /utf-8 -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE /wd4624 /wd4068 /wd4067 /wd4267 /wd4661 /wd4717 /wd4244 /wd4804 /wd4273, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.2.2, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

  TorchVision: 0.17.2+cu121 OpenCV: 4.9.0 MMEngine: 0.10.3

Runtime environment: dist_cfg: {'backend': 'nccl'} seed: 895890165 Distributed launcher: none Distributed training: False GPU number: 1

05/09 13:46:54 - mmengine - INFO - Config: checkpoint_config = dict(interval=1) classes = ('CultivatedLand', ) custom_hooks = [ dict(type='NumClassCheckHook'), ] data = dict( samples_per_gpu=1, test=dict( ann_file='out_shp/train/512-512/annotations/test.json', classes=('CultivatedLand', ), img_prefix='out_shp/train/512-512/test/', pipeline=[ dict(type='LoadImageFromFile'), dict( flip=False, img_scale=( 800, 800, ), transforms=[ dict(keep_ratio=True, type='Resize'), dict(type='RandomFlip'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='Normalize'), dict(size_divisor=32, type='Pad'), dict(keys=[ 'img', ], type='ImageToTensor'), dict(keys=[ 'img', ], type='Collect'), ], type='MultiScaleFlipAug'), ], type='CocoDataset'), train=dict( ann_file='out_shp/train/512-512/annotations/train.json', classes=('CultivatedLand', ), img_prefix='out_shp/train/512-512/train/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(img_scale=( 600, 600, ), keep_ratio=True, type='Resize'), dict(flip_ratio=0.5, type='RandomFlip'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='Normalize'), dict(size_divisor=32, type='Pad'), dict(type='DefaultFormatBundle'), dict( keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_masks', ], type='Collect'), ], type='CocoDataset'), val=dict( ann_file='out_shp/train/512-512/annotations/test.json', classes=('CultivatedLand', ), img_prefix='out_shp/train/512-512/test/', pipeline=[ dict(type='LoadImageFromFile'), dict( flip=False, img_scale=( 600, 600, ), transforms=[ dict(keep_ratio=True, type='Resize'), dict(type='RandomFlip'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='Normalize'), dict(size_divisor=32, type='Pad'), dict(keys=[ 'img', ], type='ImageToTensor'), dict(keys=[ 'img', ], type='Collect'), ], type='MultiScaleFlipAug'), ], type='CocoDataset'), workers_per_gpu=1) data_root = 'out_shp/train/512-512' data_test = 'out_shp/train/512-512/' dataset_type = 'CocoDataset' default_scope = 'mmdet' dist_params = dict(backend='nccl') evaluation = dict(metric=[ 'bbox', 'segm', ]) filter_empty_gt = False gpu_ids = range(0, 8) img_norm_cfg = dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True) launcher = 'none' load_from = 'out_shp/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth' log_config = dict( hooks=[ dict(type='TextLoggerHook'), ], interval=50) log_level = 'INFO' lr_config = dict( policy='step', step=[ 8, 11, ], warmup='linear', warmup_iters=500, warmup_ratio=0.001) model = dict( backbone=dict( base_width=4, dcn=dict(deform_groups=1, fallback_on_stride=False, type='DCN'), depth=101, frozen_stages=1, groups=64, init_cfg=dict( checkpoint='open-mmlab://resnext101_64x4d', type='Pretrained'), norm_cfg=dict(requires_grad=True, type='BN'), norm_eval=True, num_stages=4, out_indices=( 0, 1, 2, 3, ), stage_with_dcn=( False, True, True, True, ), style='pytorch', type='ResNeXt'), neck=dict( in_channels=[ 256, 512, 1024, 2048, ], num_outs=5, out_channels=256, type='FPN'), roi_head=dict( bbox_head=[ dict( bbox_coder=dict( target_means=[ 0.0, 0.0, 0.0, 0.0, ], target_stds=[ 0.1, 0.1, 0.2, 0.2, ], type='DeltaXYWHBBoxCoder'), fc_out_channels=1024, in_channels=256, loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'), loss_cls=dict( loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), num_classes=1, reg_class_agnostic=True, roi_feat_size=7, type='Shared2FCBBoxHead'), dict( bbox_coder=dict( target_means=[ 0.0, 0.0, 0.0, 0.0, ], target_stds=[ 0.05, 0.05, 0.1, 0.1, ], type='DeltaXYWHBBoxCoder'), fc_out_channels=1024, in_channels=256, loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'), loss_cls=dict( loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), num_classes=1, reg_class_agnostic=True, roi_feat_size=7, type='Shared2FCBBoxHead'), dict( bbox_coder=dict( target_means=[ 0.0, 0.0, 0.0, 0.0, ], target_stds=[ 0.033, 0.033, 0.067, 0.067, ], type='DeltaXYWHBBoxCoder'), fc_out_channels=1024, in_channels=256, loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'), loss_cls=dict( loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), num_classes=1, reg_class_agnostic=True, roi_feat_size=7, type='Shared2FCBBoxHead'), ], bbox_roi_extractor=dict( featmap_strides=[ 4, 8, 16, 32, ], out_channels=256, roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'), type='SingleRoIExtractor'), interleaved=True, mask_head=[ dict( conv_out_channels=256, in_channels=256, loss_mask=dict( loss_weight=1.0, type='CrossEntropyLoss', use_mask=True), num_classes=1, num_convs=4, type='HTCMaskHead', with_conv_res=False), dict( conv_out_channels=256, in_channels=256, loss_mask=dict( loss_weight=1.0, type='CrossEntropyLoss', use_mask=True), num_classes=1, num_convs=4, type='HTCMaskHead'), dict( conv_out_channels=256, in_channels=256, loss_mask=dict( loss_weight=1.0, type='CrossEntropyLoss', use_mask=True), num_classes=1, num_convs=4, type='HTCMaskHead'), ], mask_info_flow=True, mask_roi_extractor=dict( featmap_strides=[ 4, 8, 16, 32, ], out_channels=256, roi_layer=dict(output_size=14, sampling_ratio=0, type='RoIAlign'), type='SingleRoIExtractor'), num_stages=3, stage_loss_weights=[ 1, 0.5, 0.25, ], type='HybridTaskCascadeRoIHead'), rpn_head=dict( anchor_generator=dict( ratios=[ 0.5, 1.0, 2.0, ], scales=[ 8, ], strides=[ 4, 8, 16, 32, 64, ], type='AnchorGenerator'), bbox_coder=dict( target_means=[ 0.0, 0.0, 0.0, 0.0, ], target_stds=[ 1.0, 1.0, 1.0, 1.0, ], type='DeltaXYWHBBoxCoder'), feat_channels=256, in_channels=256, loss_bbox=dict( beta=0.1111111111111111, loss_weight=1.0, type='SmoothL1Loss'), loss_cls=dict( loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True), type='RPNHead'), test_cfg=dict( rcnn=dict( mask_thr_binary=0.5, max_per_img=100, nms=dict(iou_threshold=0.5, type='soft_nms'), score_thr=0.001), rpn=dict( max_per_img=1000, min_bbox_size=0, nms=dict(iou_threshold=0.7, type='nms'), nms_pre=1000)), train_cfg=dict( rcnn=[ dict( assigner=dict( ignore_iof_thr=-1, min_pos_iou=0.5, neg_iou_thr=0.5, pos_iou_thr=0.5, type='MaxIoUAssigner'), debug=False, mask_size=28, mask_thr_binary=0.5, pos_weight=-1, sampler=dict( add_gt_as_proposals=True, neg_pos_ub=-1, num=512, pos_fraction=0.25, type='RandomSampler')), dict( assigner=dict( ignore_iof_thr=-1, min_pos_iou=0.6, neg_iou_thr=0.6, pos_iou_thr=0.6, type='MaxIoUAssigner'), debug=False, mask_size=28, mask_thr_binary=0.5, pos_weight=-1, sampler=dict( add_gt_as_proposals=True, neg_pos_ub=-1, num=512, pos_fraction=0.25, type='RandomSampler')), dict( assigner=dict( ignore_iof_thr=-1, min_pos_iou=0.7, neg_iou_thr=0.7, pos_iou_thr=0.7, type='MaxIoUAssigner'), debug=False, mask_size=28, mask_thr_binary=0.5, pos_weight=-1, sampler=dict( add_gt_as_proposals=True, neg_pos_ub=-1, num=512, pos_fraction=0.25, type='RandomSampler')), ], rpn=dict( allowed_border=0, assigner=dict( ignore_iof_thr=-1, min_pos_iou=0.3, neg_iou_thr=0.3, pos_iou_thr=0.7, type='MaxIoUAssigner'), debug=False, pos_weight=-1, sampler=dict( add_gt_as_proposals=False, neg_pos_ub=-1, num=256, pos_fraction=0.5, type='RandomSampler')), rpn_proposal=dict( max_per_img=2000, min_bbox_size=0, nms=dict(iou_threshold=0.7, type='nms'), nms_pre=2000)), type='HybridTaskCascade') optimizer = dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001) optimizer_config = dict(grad_clip=None) resume_from = None root_path = 'out_shp/train/512-512/' runner = dict(max_epochs=12, type='EpochBasedRunner') test_pipeline = [ dict(type='LoadImageFromFile'), dict( flip=False, img_scale=( 800, 800, ), transforms=[ dict(keep_ratio=True, type='Resize'), dict(type='RandomFlip'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='Normalize'), dict(size_divisor=32, type='Pad'), dict(keys=[ 'img', ], type='ImageToTensor'), dict(keys=[ 'img', ], type='Collect'), ], type='MultiScaleFlipAug'), ] test_root = 'out_shp/train/512-512/' train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(img_scale=( 600, 600, ), keep_ratio=True, type='Resize'), dict(flip_ratio=0.5, type='RandomFlip'), dict( mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], to_rgb=True, type='Normalize'), dict(size_divisor=32, type='Pad'), dict(type='DefaultFormatBundle'), dict(keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_masks', ], type='Collect'), ] work_dir = 'out_shp/train/512-512/' workflow = [ ( 'train', 1, ), ]

05/09 13:46:58 - 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. 05/09 13:46:58 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook

before_train: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook

before_train_epoch: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
(NORMAL ) NumClassCheckHook

before_train_iter: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook

after_train_iter: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_train_epoch: (NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

before_val: (VERY_HIGH ) RuntimeInfoHook

before_val_epoch: (NORMAL ) IterTimerHook
(NORMAL ) NumClassCheckHook

before_val_iter: (NORMAL ) IterTimerHook

after_val_iter: (NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_val_epoch: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook

after_val: (VERY_HIGH ) RuntimeInfoHook

after_train: (VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook

before_test: (VERY_HIGH ) RuntimeInfoHook

before_test_epoch: (NORMAL ) IterTimerHook

before_test_iter: (NORMAL ) IterTimerHook

after_test_iter: (NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_test_epoch: (VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook

after_test: (VERY_HIGH ) RuntimeInfoHook

after_run: (BELOW_NORMAL) LoggerHook

Traceback (most recent call last): File "D:\codae\mmdetection\tools\train.py", line 121, in main() File "D:\codae\mmdetection\tools\train.py", line 117, in main runner.train() File "C:\Users\gang.conda\envs\pytorch\lib\site-packages\mmengine\runner\runner.py", line 1722, in train raise RuntimeError( RuntimeError: self._train_loop should not be None when calling train method. Please provide train_dataloader, train_cfg, optimizer and param_scheduler arguments when initializing runner.

进程已结束，退出代码为 1

[wdzwdxy]

报错信息为 train_dataloader、train_cfg、optimizer 和 param_scheduler 参数没有提供 但是在配置文件中有数据加载，优化器这些内容

[yurujaja]

same issues here

[zzzcccxx]

报错信息为 train_dataloader、train_cfg、optimizer 和 param_scheduler 参数没有提供 但是在配置文件中有数据加载，优化器这些内容

那请问最后的解决办法是什么呢？在哪里添加这些配置呢？

[ysh-0130]

请问解决了吗？

[corgok]

I just started with the Tutorial.ipynb and noticed that it is written on UBUNTU. Could it be a windows thing? Edit: probably not. I just solved it by moving the script into the base folder of the project and setting the cfg.work_dir relative from there. and that is how it is set up in the tutorial. https://colab.research.google.com/github/open-mmlab/mmocr/blob/dev-1.x/demo/tutorial.ipynb#scrollTo=67OJ6oAvN6NA