执行export_model.py转换成功后，目录下没有.pdmodel文件 #14295

JesuisTong · 2024-11-29T09:14:43Z

JesuisTong
Nov 29, 2024

根据模型导出与预测中说的应该是如下文件

inference/en_PP-OCRv3_rec/
    ├── inference.pdiparams         # 识别inference模型的参数文件
    ├── inference.pdiparams.info    # 识别inference模型的参数信息，可忽略
    └── inference.pdmodel           # 识别inference模型的program文件

但是我生成的是

inference/en_PP-OCRv3_rec/
    ├── inference.pdiparams         # 识别inference模型的参数文件
    ├── inference.json   
    └── inference.yml

并且使用我生成的文件执行 predict_rec.py 失败会报错。

以下是我的 YAML配置文件

Global:
  debug: false
  use_gpu: true
  epoch_num: 800
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/v3_en_mobile
  save_epoch_step: 10
  eval_batch_step: [0, 2000]
  cal_metric_during_train: true
  pretrained_model: ./pretrain_models/en_PP-OCRv3_rec_train/best_accuracy
  checkpoints: # ./output/v3_en_mobile/_checkpoints
  save_inference_dir: ./output/_inference/v3_en_mobile/
  use_visualdl: false
  infer_img: ./train_data/rec/train
  infer_list: ./train_data/rec/infer_list.txt
  character_dict_path: ppocr/utils/en_dict.txt
  max_text_length: &max_text_length 25
  infer_mode: false
  use_space_char: false
  distributed: true
  save_res_path: ./output/rec/predicts_ppocrv3_en_v2.txt


Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Cosine
    learning_rate: 0.0003
    warmup_epoch: 5
  regularizer:
    name: L2
    factor: 3.0e-05


Architecture:
  model_type: rec
  algorithm: SVTR_LCNet
  Transform:
  Backbone:
    name: MobileNetV1Enhance
    scale: 0.5
    last_conv_stride: [1, 2]
    last_pool_type: avg
    last_pool_kernel_size: [2, 2]
  Head:
    name: MultiHead
    head_list:
      - CTCHead:
          Neck:
            name: svtr
            dims: 64
            depth: 2
            hidden_dims: 120
            use_guide: True
          Head:
            fc_decay: 0.00001
      - SARHead:
          enc_dim: 512
          max_text_length: *max_text_length

Loss:
  name: MultiLoss
  loss_config_list:
    - CTCLoss:
    - SARLoss:

PostProcess:  
  name: CTCLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc
  ignore_space: False

Train:
  dataset:
    name: SimpleDataSet
    data_dir: ./
    ext_op_transform_idx: 1
    label_file_list:
    - ./train_data/rec/rec_gt_train_resized.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - RecConAug:
        prob: 0.6
        ext_data_num: 2
        image_shape: [32, 92, 3]
        max_text_length: *max_text_length
    - RecAug:
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 32, 92]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: true
    batch_size_per_card: 108
    drop_last: true
    num_workers: 4
Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./
    label_file_list:
    - ./train_data/rec/rec_gt_test.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 32, 92]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: false
    drop_last: false
    batch_size_per_card: 12
    num_workers: 4