你好我把默认的预训练模型resnet50 修改为了18 配置如下 Global: use_gpu: true epoch_num: 200 log_smooth_window: 20 print_batch_step: 100 save_model_dir: ../output/ch_db_mv3_ocr/ save_epoch_step: 1200

evaluation is run every 5000 iterations after the 4000th iteration

eval_batch_step: [3000, 2000] cal_metric_during_train: False pretrained_model: ../pretrain_models/ResNet18_vd_pretrained checkpoints: save_inference_dir: use_visualdl: False infer_img: doc/imgs_en/img_10.jpg save_res_path: ../output/det_db/predicts_db.txt

Architecture: name: DistillationModel algorithm: Distillation model_type: det Models: Student: return_all_feats: false model_type: det algorithm: DB Backbone: name: ResNet_vd in_channels: 3 layers: 18 Neck: name: LKPAN out_channels: 256 Head: name: DBHead kernel_list: [7,2,2] k: 50 Student2: return_all_feats: false model_type: det algorithm: DB Backbone: name: ResNet_vd in_channels: 3 layers: 18 Neck: name: LKPAN out_channels: 256 Head: name: DBHead kernel_list: [7,2,2] k: 50

Loss: name: CombinedLoss loss_config_list:

DistillationDMLLoss: model_name_pairs:
- ["Student", "Student2"] maps_name: "thrink_maps" weight: 1.0
act: None
model_name_pairs: ["Student", "Student2"] key: maps
DistillationDBLoss: weight: 1.0 model_name_list: ["Student", "Student2"]
key: maps
name: DBLoss balance_loss: true main_loss_type: DiceLoss alpha: 5 beta: 10 ohem_ratio: 3

Optimizer: name: Adam beta1: 0.9 beta2: 0.999 lr: name: Cosine learning_rate: 0.001 warmup_epoch: 2 regularizer: name: 'L2' factor: 0

PostProcess: name: DistillationDBPostProcess model_name: ["Student", "Student2"] key: head_out thresh: 0.3 box_thresh: 0.6 max_candidates: 1000 unclip_ratio: 1.5

Metric: name: DistillationMetric base_metric_name: DetMetric main_indicator: hmean key: "Student"

Train: dataset: name: SimpleDataSet data_dir: ../train_data/ocr_det_training/ label_file_list: - ../train_data/ocr_train_label.txt ratio_list: [1.0] transforms: - DecodeImage: # load image img_mode: BGR channel_first: False - DetLabelEncode: # Class handling label - CopyPaste: - IaaAugment: augmenter_args: - { 'type': Fliplr, 'args': { 'p': 0.5 } } - { 'type': Affine, 'args': { 'rotate': [-10, 10] } } - { 'type': Resize, 'args': { 'size': [0.5, 3] } } - EastRandomCropData: size: [960, 960] max_tries: 50 keep_ratio: true - MakeBorderMap: shrink_ratio: 0.4 thresh_min: 0.3 thresh_max: 0.7 - MakeShrinkMap: shrink_ratio: 0.4 min_text_size: 8 - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - ToCHWImage: - KeepKeys: keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list loader: shuffle: True drop_last: False batch_size_per_card: 4 num_workers: 4

Eval: dataset: name: SimpleDataSet data_dir: ../train_data/ocr_det_val/ label_file_list: - ../train_data/ocr_test_label.txt transforms: - DecodeImage: # load image img_mode: BGR channel_first: False - DetLabelEncode: # Class handling label - DetResizeForTest:

image_shape: [736, 1280]

  - NormalizeImage:
      scale: 1./255.
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]
      order: 'hwc'
  - ToCHWImage:
  - KeepKeys:
      keep_keys: ['image', 'shape', 'polys', 'ignore_tags']

loader: shuffle: False drop_last: False batch_size_per_card: 1 # must be 1 num_workers: 4 但是运行时弹出警告模型应该对不上这个是我配置哪里没改全吗还是什么原因