104 lines
2.4 KiB
YAML
104 lines
2.4 KiB
YAML
Global:
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use_gpu: True
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epoch_num: 10
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec/r45_abinet/
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save_epoch_step: 1
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# evaluation is run every 2000 iterations
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eval_batch_step: [0, 2000]
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cal_metric_during_train: True
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pretrained_model: ./pretrain_models/abinet_vl_pretrained
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checkpoints:
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save_inference_dir: ./output/rec/r45_abinet/infer
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use_visualdl: False
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infer_img: doc/imgs_words_en/word_10.png
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# for data or label process
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character_dict_path:
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character_type: en
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max_text_length: &max_text_length 25
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infer_mode: False
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use_space_char: False
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save_res_path: ./output/rec/predicts_abinet.txt
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.99
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clip_norm: 20.0
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lr:
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name: Piecewise
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decay_epochs: [6]
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values: [0.0001, 0.00001]
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regularizer:
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name: 'L2'
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factor: 0.
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Architecture:
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model_type: rec
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algorithm: ABINet
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in_channels: 3
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Transform:
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Backbone:
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name: ResNet45
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Head:
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name: ABINetHead
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use_lang: True
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iter_size: 3
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max_length: *max_text_length
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image_size: [ &h 32, &w 128 ] # [ h, w ]
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Loss:
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name: CELoss
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ignore_index: &ignore_index 100 # Must be greater than the number of character classes
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PostProcess:
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name: ABINetLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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name: LMDBDataSet
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data_dir: ./train_data/data_lmdb_release/training/
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transforms:
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- DecodeImage: # load image
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img_mode: RGB
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channel_first: False
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- ABINetRecAug:
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- ABINetLabelEncode: # Class handling label
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ignore_index: *ignore_index
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- ABINetRecResizeImg:
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image_shape: [3, *h, *w]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 96
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drop_last: True
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num_workers: 4
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Eval:
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dataset:
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name: LMDBDataSet
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data_dir: ./train_data/data_lmdb_release/evaluation/
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transforms:
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- DecodeImage: # load image
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img_mode: RGB
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channel_first: False
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- ABINetLabelEncode: # Class handling label
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ignore_index: *ignore_index
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- ABINetRecResizeImg:
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image_shape: [3, *h, *w]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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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: 256
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num_workers: 4
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use_shared_memory: False
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