109 lines
2.7 KiB
YAML
Executable File
109 lines
2.7 KiB
YAML
Executable File
Global:
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use_gpu: true
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epoch_num: 5000
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: ./output/sast_r50_vd_ic15/
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save_epoch_step: 1000
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [4000, 5000]
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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
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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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save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
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Architecture:
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model_type: det
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algorithm: SAST
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Transform:
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Backbone:
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name: ResNet_SAST
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layers: 50
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Neck:
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name: SASTFPN
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with_cab: True
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Head:
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name: SASTHead
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Loss:
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name: SASTLoss
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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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# name: Cosine
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learning_rate: 0.001
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# warmup_epoch: 0
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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: SASTPostProcess
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score_thresh: 0.5
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sample_pts_num: 2
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nms_thresh: 0.2
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expand_scale: 1.0
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shrink_ratio_of_width: 0.3
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Metric:
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name: DetMetric
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main_indicator: hmean
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/
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label_file_list: [./train_data/icdar2013/train_label_json.txt, ./train_data/icdar2015/train_label_json.txt, ./train_data/icdar17_mlt_latin/train_label_json.txt, ./train_data/coco_text_icdar_4pts/train_label_json.txt]
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ratio_list: [0.1, 0.45, 0.3, 0.15]
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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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- DetLabelEncode: # Class handling label
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- SASTProcessTrain:
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image_shape: [512, 512]
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min_crop_side_ratio: 0.3
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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: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
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loader:
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shuffle: True
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drop_last: False
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batch_size_per_card: 4
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num_workers: 4
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/icdar2015/text_localization/
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label_file_list:
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- ./train_data/icdar2015/text_localization/test_icdar2015_label.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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- DetLabelEncode: # Class handling label
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- DetResizeForTest:
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resize_long: 1536
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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', 'ignore_tags']
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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 |