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108 lines
2.4 KiB
YAML
108 lines
2.4 KiB
YAML
8 months ago
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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/det_ct/
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save_epoch_step: 10
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# evaluation is run every 2000 iterations
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eval_batch_step: [0,1000]
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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/ResNet18_vd_pretrained.pdparams
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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: doc/imgs_en/img623.jpg
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save_res_path: ./output/det_ct/predicts_ct.txt
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Architecture:
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model_type: det
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algorithm: CT
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Transform:
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Backbone:
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name: ResNet_vd
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layers: 18
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Neck:
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name: CTFPN
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Head:
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name: CT_Head
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in_channels: 512
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hidden_dim: 128
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num_classes: 3
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Loss:
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name: CTLoss
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Optimizer:
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name: Adam
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lr: #PolynomialDecay
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name: Linear
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learning_rate: 0.001
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end_lr: 0.
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epochs: 600
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step_each_epoch: 1254
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power: 0.9
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PostProcess:
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name: CTPostProcess
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box_type: poly
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Metric:
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name: CTMetric
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main_indicator: f_score
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/total_text/train
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label_file_list:
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- ./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:
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img_mode: RGB
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channel_first: False
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- CTLabelEncode: # Class handling label
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- RandomScale:
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- MakeShrink:
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- GroupRandomHorizontalFlip:
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- GroupRandomRotate:
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- GroupRandomCropPadding:
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- MakeCentripetalShift:
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- ColorJitter:
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brightness: 0.125
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saturation: 0.5
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- ToCHWImage:
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- NormalizeImage:
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- KeepKeys:
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keep_keys: ['image', 'gt_kernel', 'training_mask', 'gt_instance', 'gt_kernel_instance', 'training_mask_distance', 'gt_distance'] # the order of the dataloader list
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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: 4
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num_workers: 8
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/total_text/test
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label_file_list:
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- ./train_data/total_text/test/test.txt
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ratio_list: [1.0]
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transforms:
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- DecodeImage:
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img_mode: RGB
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channel_first: False
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- CTLabelEncode: # Class handling label
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- ScaleAlignedShort:
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- NormalizeImage:
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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'] # the order of the dataloader list
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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
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num_workers: 2
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