lmmlzn commented 1 year ago

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.

fatal: Not a git repository (or any parent up to mount point /wolf) Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set). fatal: Not a git repository (or any parent up to mount point /wolf) Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set). 2022-10-26 18:26:19,305 - davarocr - INFO - Environment info:

sys.platform: linux Python: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0] CUDA available: True GPU 0,1: NVIDIA TITAN X (Pascal) CUDA_HOME: /usr/local/cuda-9.0 NVCC: Cuda compilation tools, release 9.0, V9.0.176 GCC: gcc (Ubuntu 5.5.0-12ubuntu1~14.04) 5.5.0 20171010 PyTorch: 1.5.0 PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
OpenMP 201511 (a.k.a. OpenMP 4.5)
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=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.6.0 OpenCV: 4.6.0 MMCV: 1.3.4 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 DAVAROCR: 0.6.0+

2022-10-26 18:26:19,909 - davarocr - INFO - Distributed training: True 2022-10-26 18:26:20,500 - davarocr - INFO - Config: model = dict( type='LGPMA', pretrained='/wolf/ly/liu/DAVAR-Lab-OCR-main/resnet50-19c8e357.pth', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[4, 8, 16], ratios=[0.05, 0.1, 0.2, 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='LGPMARoIHead', 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]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=2, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2]), 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)), mask_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), mask_head=dict( type='LPMAMaskHead', num_convs=4, in_channels=256, conv_out_channels=256, num_classes=2, loss_mask=dict( type='CrossEntropyLoss', use_mask=True, loss_weight=1.0), loss_lpma=dict(type='L1Loss', loss_weight=1.0))), global_seg_head=dict( type='GPMAMaskHead', in_channels=256, conv_out_channels=256, num_classes=1, loss_mask=dict(type='DiceLoss', loss_weight=1), loss_reg=dict( type='SmoothL1Loss', beta=0.1, loss_weight=0.01, reduction='sum')), 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), 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_post=2000, nms=dict(type='nms', iou_threshold=0.5), 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=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), mask_size=28, pos_weight=-1, debug=False)), test_cfg=dict( rpn=dict( nms_pre=2000, nms_post=2000, max_per_img=2000, nms=dict(type='nms', iou_threshold=0.5), min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.1), max_per_img=1000, mask_thr_binary=0.5), postprocess=dict(type='PostLGPMA', refine_bboxes=False))) train_cfg = None test_cfg = None dataset_type = 'TableRcgDataset' data_root = '' 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='DavarLoadImageFromFile'), dict( type='DavarLoadTableAnnotations', with_bbox=True, with_enlarge_bbox=True, with_label=True, with_poly_mask=True, with_empty_bbox=True), dict( type='DavarResize', img_scale=[(360, 480), (960, 1080)], keep_ratio=True, multiscale_mode='range'), 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='GPMADataGeneration'), dict(type='DavarDefaultFormatBundle'), dict( type='DavarCollect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']) ] val_pipeline = [ dict(type='DavarLoadImageFromFile'), dict( type='MultiScaleFlipAug', scale_factor=1.5, flip=False, transforms=[ dict(type='DavarResize', keep_ratio=True), 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='DavarDefaultFormatBundle'), dict(type='DavarCollect', keys=['img']) ]) ] test_pipeline = [ dict(type='DavarLoadImageFromFile'), dict( type='MultiScaleFlipAug', scale_factor=1.5, flip=False, transforms=[ dict(type='DavarResize', keep_ratio=True), 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='DavarDefaultFormatBundle'), dict(type='DavarCollect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='TableRcgDataset', ann_file= '/wolf/ly/liu/DAVAR-Lab-OCR-main/demo/table_recognition/datalist/datalist_train_detection_pubtabnet.json', img_prefix='/wolf/ly/liu_pubtabnet/Images/train/', pipeline=[ dict(type='DavarLoadImageFromFile'), dict( type='DavarLoadTableAnnotations', with_bbox=True, with_enlarge_bbox=True, with_label=True, with_poly_mask=True, with_empty_bbox=True), dict( type='DavarResize', img_scale=[(360, 480), (960, 1080)], keep_ratio=True, multiscale_mode='range'), 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='GPMADataGeneration'), dict(type='DavarDefaultFormatBundle'), dict( type='DavarCollect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg' ]) ]), val=dict( type='TableRcgDataset', ann_file='path/to/validation.json', img_prefix='path/to/PubTabNet', pipeline=[ dict(type='DavarLoadImageFromFile'), dict( type='MultiScaleFlipAug', scale_factor=1.5, flip=False, transforms=[ dict(type='DavarResize', keep_ratio=True), 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='DavarDefaultFormatBundle'), dict(type='DavarCollect', keys=['img']) ]) ]), test=dict( type='TableRcgDataset', ann_file='path/to/PubTabNet_2.0.0_val.jsonl', img_prefix='path/to/PubTabNet/Images/val/', pipeline=[ dict(type='DavarLoadImageFromFile'), dict( type='MultiScaleFlipAug', scale_factor=1.5, flip=False, transforms=[ dict(type='DavarResize', keep_ratio=True), 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='DavarDefaultFormatBundle'), dict(type='DavarCollect', keys=['img']) ]) ], samples_per_gpu=1)) optimizer = dict(type='SGD', lr=0.01, momentum=0.9, 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=1000, warmup_ratio=0.3333333333333333, step=[6, 10]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict( interval=1, filename_tmpl='checkpoint/maskrcnn-lgpma-pub-e{}.pth') log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = '/home/jinac/liu/DAVAR-Lab-OCR-main/work_dir' load_from = None resume_from = None workflow = [('train', 1)] evaluation_metric = 'TEDS' evaluation = dict( eval_func_params=dict(ENLARGE_ANN_BBOXES=True, IOU_CONSTRAINT=0.5), metric='TEDS', by_epoch=True, interval=1, eval_mode='general', save_best='TEDS', rule='greater') gpu_ids = range(0, 2)

2022-10-26 18:26:21,052 - mmdet - INFO - load model from: /wolf/ly/liu/DAVAR-Lab-OCR-main/resnet50-19c8e357.pth 2022-10-26 18:26:21,052 - mmdet - INFO - Use load_from_local loader 2022-10-26 18:26:21,267 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/pipelines/gpma_data.py", line 50, in init lib = ctl.load_library(lib_name, lib_dir) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/numpy/ctypeslib.py", line 163, in load_library raise OSError("no file with expected extension") OSError: no file with expected extension

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/table_rcg_dataset.py", line 48, in init super().init(ann_file, pipeline, data_root, img_prefix, seg_prefix, proposal_file, test_mode, File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_common/datasets/davar_custom.py", line 135, in init self.pipeline = Compose(pipeline) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmdet/datasets/pipelines/compose.py", line 22, in init transform = build_from_cfg(transform, PIPELINES) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 54, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') OSError: GPMADataGeneration: no file with expected extension

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/wolf/ly/liu/DAVAR-Lab-OCR-main/tools/train.py", line 252, in main() File "/wolf/ly/liu/DAVAR-Lab-OCR-main/tools/train.py", line 227, in main datasets = [davar_build_dataset(cfg.data.train)] File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_common/datasets/builder.py", line 229, in davar_build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 54, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') OSError: TableRcgDataset: GPMADataGeneration: no file with expected extension Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/pipelines/gpma_data.py", line 50, in init lib = ctl.load_library(lib_name, lib_dir) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/numpy/ctypeslib.py", line 163, in load_library raise OSError("no file with expected extension") OSError: no file with expected extension

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/table_rcg_dataset.py", line 48, in init super().init(ann_file, pipeline, data_root, img_prefix, seg_prefix, proposal_file, test_mode, File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_common/datasets/davar_custom.py", line 135, in init self.pipeline = Compose(pipeline) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmdet/datasets/pipelines/compose.py", line 22, in init transform = build_from_cfg(transform, PIPELINES) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 54, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') OSError: GPMADataGeneration: no file with expected extension

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/wolf/ly/liu/DAVAR-Lab-OCR-main/tools/train.py", line 252, in main() File "/wolf/ly/liu/DAVAR-Lab-OCR-main/tools/train.py", line 227, in main datasets = [davar_build_dataset(cfg.data.train)] File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_common/datasets/builder.py", line 229, in davar_build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 54, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') OSError: TableRcgDataset: GPMADataGeneration: no file with expected extension Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/runpy.py", line 192, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/torch/distributed/launch.py", line 263, in main() File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/torch/distributed/launch.py", line 258, in main raise subprocess.CalledProcessError(returncode=process.returncode, subprocess.CalledProcessError: Command '['/home/jinac/anaconda3/envs/lgpma/bin/python', '-u', '/wolf/ly/liu/DAVAR-Lab-OCR-main/tools/train.py', '--local_rank=1', './configs/lgpma_pub.py', '--no-validate', '--launcher', 'pytorch']' returned non-zero exit status 1.

lmmlzn commented 1 year ago

求救

qiaoliang6 commented 1 year ago

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lmmlzn commented 1 year ago

@qiaoliang6 g++ -shared -o ./davarocr/davar_table/datasets/pipelines/lib/gpma_data.so -fPIC ./davarocr/davar_table/datasets/pipelines/lib/gpma_data.cpp pkg-config --cflags --libs opencv

但是我用了这个以后有gpma_data.so文件了，还是报了错

错误： Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/pipelines/gpma_data.py", line 50, in init lib = ctl.load_library(lib_name, lib_dir) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/numpy/ctypeslib.py", line 163, in load_library raise OSError("no file with expected extension") OSError: no file with expected extension

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 51, in build_from_cfg return obj_cls(**args) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_table/datasets/table_rcg_dataset.py", line 48, in init super().init(ann_file, pipeline, data_root, img_prefix, seg_prefix, proposal_file, test_mode, File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/davarocr-0.6.0-py3.8.egg/davarocr/davar_common/datasets/davar_custom.py", line 135, in init self.pipeline = Compose(pipeline) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmdet/datasets/pipelines/compose.py", line 22, in init transform = build_from_cfg(transform, PIPELINES) File "/home/jinac/anaconda3/envs/lgpma/lib/python3.8/site-packages/mmcv/utils/registry.py", line 54, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') OSError: GPMADataGeneration: no file with expected extension

qiaoliang6 commented 1 year ago

可以试一下在配置config中手动指定一下中用到这个.so文件的绝对路径，GPMADataGeneration模块中修改的lib_name和lib_dir

hikopensource / DAVAR-Lab-OCR

OSError: no file with expected extension ( I don't know how to solve it.) #129

TorchVision: 0.6.0 OpenCV: 4.6.0 MMCV: 1.3.4 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.1 DAVAROCR: 0.6.0+

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