- 系统环境/System Environment： ubuntu20.04
- 版本号/Version：Paddle： PaddleOCR： 问题相关组件/Related components： paddle 1.0.2 paddle-bfloat 0.1.2 paddle2onnx 0.9.7 paddlefsl 1.1.0 paddlenlp 2.4.1 paddleocr 2.6.1.2 paddlepaddle-gpu 2.4.1.post116 pandas 1.1.5 pandocfilters 1.5.0
- 运行指令/Command Code：
命令：
# 单卡训练 python3 tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml_zzszyfp.yml \ -o Architecture.Models.Student.pretrained=./pretrain_models/ResNet50_vd_ssld_pretrained \ Architecture.Models.Student2.pretrained=./pretrain_models/ResNet50_vd_ssld_pretraine配置文件ch_PP-OCRv3_det_dml_zzszyfp： 只是把epoch_num和eval_batch_step 改小了 120次。1200太久了
Global: use_gpu: true #epoch_num: 1200 epoch_num: 120 log_smooth_window: 20 print_batch_step: 2 save_model_dir: /home/DiskA/zncsPython/picture_ocr/zzszyfp_v1/model/det/output/ch_db_mv3/ #save_epoch_step: 1200 save_epoch_step: 1200 # evaluation is run every 5000 iterations after the 4000th iteration # eval_batch_step: [3000, 2000] eval_batch_step: [120, 120] 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
- ["Student", "Student2"]
maps_name: "thrink_maps"
weight: 1.0
act: None
- 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: /home/DiskA/zncsPython/picture_ocr/zzszyfp_v1/split_data/det label_file_list:
- /home/DiskA/zncsPython/picture_ocr/zzszyfp_v1/split_data/det/train.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
batch_size_per_card: 2 num_workers: 4
Eval: dataset: name: SimpleDataSet data_dir: /home/DiskA/zncsPython/picture_ocr/zzszyfp_v1/split_data/det label_file_list:
- /home/DiskA/zncsPython/picture_ocr/zzszyfp_v1/split_data/det/test.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
- 完整报错/Complete Error Message：
- 模型不生成best_accuracy
