在进行知识蒸馏时，加载预训练模型出错，提示ppocr warning: the pretrained params backbone.* not in model

[bianliuyang]

请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem

• 系统环境/System Environment：ubuntu 20.0.4
• 版本号/Version：PaddleOCR-release-2.6 config配置如下：` Global: use_gpu: true epoch_num: 1200 log_smooth_window: 20 print_batch_step: 2 save_model_dir: ./output/ch_db_mv3/ 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/MobileNetV3_large_x0_5_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: 50 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: 50 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/icdar2015/text_localization/ label_file_list:

• ./train_data/icdar2015/text_localization/train_icdar2015_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: 8 num_workers: 4

Eval: dataset: name: SimpleDataSet data_dir: ./train_data/icdar2015/text_localization/ label_file_list:

• ./train_data/icdar2015/text_localization/test_icdar2015_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: 2`

执行指令：python3 tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml \ -o Architecture.Models.Student.pretrained=./pretrain_models/ResNet50_vd_ssld_pretrained \ Architecture.Models.Student2.pretrained=./pretrain_models/ResNet50_vd_ssld_pretrained \ Global.save_model_dir=./output/

终端提示：ppocr warning: the pretrained params backbone.* not in model

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