使用SLANet模型进行训练，每次读完配置文件就停止了，然后就报错退出程序了：(0xC0000409)

[yygc2002]

请提出你的问题 Please ask your question

环境配置没问题 Running verify PaddlePaddle program ... W0314 21:36:45.958832 23256 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.6, Driver API Version: 12.0, Runtime API Version: 11.2 W0314 21:36:46.247545 23256 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. PaddlePaddle works well on 1 GPU. PaddlePaddle works well on 1 GPUs. PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.

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[yygc2002]

配置文件： Global: use_gpu: true epoch_num: 100 log_smooth_window: 20 print_batch_step: 20 save_model_dir: ./output/SLANet save_epoch_step: 400

evaluation is run every 1000 iterations after the 0th iteration

eval_batch_step: [0, 1000] cal_metric_during_train: True pretrained_model: D:\50697\python_pycharm\PaddleOCR-release-2.6\pretrain_models\en_ppstructure_mobile_v2.0_SLANet_train\best_accuracy.pdparams checkpoints: save_inference_dir: ./output/SLANet/infer use_visualdl: False infer_img: ppstructure/docs/table/table.jpg

for data or label process

character_dict_path: ../ppocr/utils/dict/table_structure_dict.txt character_type: en max_text_length: &max_text_length 500 box_format: &box_format 'xyxy' # 'xywh', 'xyxy', 'xyxyxyxy' infer_mode: False use_sync_bn: True save_res_path: 'output/infer'

Optimizer: name: Adam beta1: 0.9 beta2: 0.999 clip_norm: 5.0 lr: name: Piecewise learning_rate: 0.001 decay_epochs : [40, 50] values : [0.001, 0.0001, 0.00005] regularizer: name: 'L2' factor: 0.00000

Architecture: model_type: table algorithm: SLANet Backbone: name: PPLCNet scale: 1.0 pretrained: true use_ssld: true Neck: name: CSPPAN out_channels: 96 Head: name: SLAHead hidden_size: 256 max_text_length: *max_text_length loc_reg_num: &loc_reg_num 4

Loss: name: SLALoss structure_weight: 1.0 loc_weight: 2.0 loc_loss: smooth_l1

PostProcess: name: TableLabelDecode merge_no_span_structure: &merge_no_span_structure True

Metric: name: TableMetric main_indicator: acc compute_bbox_metric: False loc_reg_num: loc_reg_num box_format: box_format

Train: dataset: name: PubTabDataSet data_dir: D:\50697\python_pycharm\data\pubtabnet\train\ label_file_list: [D:\50697\python_pycharm\data\pubtabnet\PubTabNet_2.0.0_train.jsonl] transforms:

• DecodeImage: img_mode: BGR channel_first: False
• TableLabelEncode: learn_empty_box: False merge_no_span_structure: merge_no_span_structure replace_empty_cell_token: False loc_reg_num: loc_reg_num max_text_length: *max_text_length
• TableBoxEncode: in_box_format: box_format out_box_format: box_format
• ResizeTableImage: max_len: 488
• NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc'
• PaddingTableImage: size: [488, 488]
• ToCHWImage:
• KeepKeys: keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: True batch_size_per_card: 48 drop_last: True num_workers: 1

Eval: dataset: name: PubTabDataSet data_dir: D:\50697\python_pycharm\data\pubtabnet\val\ label_file_list: [D:\50697\python_pycharm\data\pubtabnet\PubTabNet_2.0.0_val.jsonl] transforms:

• DecodeImage: # load image img_mode: BGR channel_first: False
• TableLabelEncode: learn_empty_box: False merge_no_span_structure: merge_no_span_structure replace_empty_cell_token: False loc_reg_num: loc_reg_num max_text_length: *max_text_length
• TableBoxEncode: in_box_format: box_format out_box_format: box_format
• ResizeTableImage: max_len: 488
• NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc'
• PaddingTableImage: size: [488, 488]
• ToCHWImage:
• KeepKeys: keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ] loader: shuffle: False drop_last: False batch_size_per_card: 48 num_workers: 1

[yygc2002]

数据集就是pubtabnet

[zmxdream]

你好，请到PaddleOCR下提issue

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