115 lines
3.0 KiB
YAML
115 lines
3.0 KiB
YAML
Global:
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use_gpu: True
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epoch_num: 600
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/pgnet_r50_vd_totaltext/
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save_epoch_step: 10
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# evaluation is run every 1000 iterationss after the 0th iteration
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eval_batch_step: [ 0, 1000 ]
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cal_metric_during_train: False
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img:
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valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
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save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
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character_dict_path: ppocr/utils/ic15_dict.txt
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character_type: EN
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max_text_length: 50 # the max length in seq
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max_text_nums: 30 # the max seq nums in a pic
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tcl_len: 64
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Architecture:
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model_type: e2e
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algorithm: PGNet
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Transform:
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Backbone:
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name: ResNet
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layers: 50
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Neck:
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name: PGFPN
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Head:
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name: PGHead
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Loss:
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name: PGLoss
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tcl_bs: 64
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max_text_length: 50 # the same as Global: max_text_length
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max_text_nums: 30 # the same as Global：max_text_nums
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pad_num: 36 # the length of dict for pad
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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learning_rate: 0.001
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regularizer:
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name: 'L2'
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factor: 0
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PostProcess:
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name: PGPostProcess
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score_thresh: 0.5
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mode: fast # fast or slow two ways
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Metric:
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name: E2EMetric
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mode: A # two ways for eval, A: label from txt, B: label from gt_mat
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gt_mat_dir: ./train_data/total_text/gt # the dir of gt_mat
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character_dict_path: ppocr/utils/ic15_dict.txt
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main_indicator: f_score_e2e
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Train:
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dataset:
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name: PGDataSet
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data_dir: ./train_data/total_text/train
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label_file_list: [./train_data/total_text/train/train.txt]
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ratio_list: [1.0]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- E2ELabelEncodeTrain:
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- PGProcessTrain:
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batch_size: 14 # same as loader: batch_size_per_card
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min_crop_size: 24
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min_text_size: 4
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max_text_size: 512
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- KeepKeys:
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keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
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loader:
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shuffle: True
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drop_last: True
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batch_size_per_card: 14
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num_workers: 16
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Eval:
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dataset:
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name: PGDataSet
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data_dir: ./train_data/total_text/test
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label_file_list: [./train_data/total_text/test/test.txt]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- E2ELabelEncodeTest:
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- E2EResizeForTest:
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max_side_len: 768
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- NormalizeImage:
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scale: 1./255.
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mean: [ 0.485, 0.456, 0.406 ]
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std: [ 0.229, 0.224, 0.225 ]
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order: 'hwc'
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- ToCHWImage:
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- KeepKeys:
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keep_keys: [ 'image', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 1 # must be 1
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num_workers: 2
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