add config
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LDOUBLEV
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Global:
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use_gpu: true
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: ./output/ch_db_mv3/
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save_epoch_step: 1200
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [3000, 2000]
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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_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: doc/imgs_en/img_10.jpg
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save_res_path: ./output/det_db/predicts_db.txt
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Architecture:
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name: DistillationModel
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algorithm: Distillation
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Models:
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Student:
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pretrained:
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freeze_params: false
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return_all_feats: false
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model_type: det
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algorithm: DB
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: large
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disable_se: True
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Neck:
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name: DBFPN
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out_channels: 96
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Head:
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name: DBHead
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k: 50
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Student2:
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pretrained:
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freeze_params: false
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return_all_feats: false
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model_type: det
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algorithm: DB
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: large
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disable_se: True
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Neck:
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name: DBFPN
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out_channels: 96
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Head:
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name: DBHead
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k: 50
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Teacher:
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model_type: det
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algorithm: DB
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Transform:
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Backbone:
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name: ResNet
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layers: 18
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Neck:
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name: DBFPN
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out_channels: 256
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Head:
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name: DBHead
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k: 50
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Loss:
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name: CombinedLoss
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loss_config_list:
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- DistillationDilaDBLoss:
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weight: 1.0
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model_name_list: ["Student", "Student2", "Teacher"]
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key: maps
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balance_loss: true
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main_loss_type: DiceLoss
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alpha: 5
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beta: 10
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ohem_ratio: 3
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- DistillationDMLLoss:
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maps_name: ["thrink_maps"]
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weight: 1.0
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act: "softmax"
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model_name_pairs: ["Student", "Student2"]
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key: maps
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- DistillationDBLoss:
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model_name_list: ["Student", "Teacher"]
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key: maps
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name: DBLoss
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balance_loss: true
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main_loss_type: DiceLoss
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alpha: 5
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beta: 10
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ohem_ratio: 3
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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: 2
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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: DistillationCTDBPostProcessCLabelDecode
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model_name: ["Student", "Student2"]
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key: head_out
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thresh: 0.3
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box_thresh: 0.6
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max_candidates: 1000
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unclip_ratio: 1.5
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Metric:
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name: DistillationMetric
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base_metric_name: DetMetric
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main_indicator: hmean
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key: "Student"
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Train:
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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/train_icdar2015_label.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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- DetLabelEncode: # Class handling label
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- IaaAugment:
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augmenter_args:
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- { 'type': Fliplr, 'args': { 'p': 0.5 } }
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- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
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- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
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- EastRandomCropData:
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size: [960, 960]
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max_tries: 50
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keep_ratio: true
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- MakeBorderMap:
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shrink_ratio: 0.4
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thresh_min: 0.3
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thresh_max: 0.7
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- MakeShrinkMap:
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shrink_ratio: 0.4
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min_text_size: 8
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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', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
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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: 8
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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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# image_shape: [736, 1280]
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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
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@ -132,6 +132,96 @@ class DistillationCTCLoss(CTCLoss):
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return loss_dict
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class DistillationDBLoss(DBLoss):
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def __init__(self,
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model_name_list=[],
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balance_loss=True,
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main_loss_type='DiceLoss',
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alpha=5,
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beta=10,
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ohem_ratio=3,
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eps=1e-6,
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name="db_loss",
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**kwargs):
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super().__init__()
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self.model_name_list = model_name_list
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self.name = name
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self.key = None
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def forward(self, preicts, batch):
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loss_dict = {}
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for idx, model_name in enumerate(self.model_name_list):
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out = predicts[model_name]
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if self.key is not None:
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out = out[self.key]
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loss = super().forward(out, batch)
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if isinstance(loss, dict):
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for key in loss.keys():
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if key == "loss":
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continue
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name = "{}_{}_{}".format(self.name, model_name, key)
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loss_dict[name] = loss[key]
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else:
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loss_dict["{}_{}".format(self.name, model_name)] = loss
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loss_dict = _sum_loss(loss_dict)
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return loss_dict
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class DistillationDilaDBLoss(DBLoss):
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def __init__(self,
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model_name_pairs=[],
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balance_loss=True,
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main_loss_type='DiceLoss',
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alpha=5,
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beta=10,
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ohem_ratio=3,
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eps=1e-6,
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name="dila_dbloss"):
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super().__init__()
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self.model_name_pairs = model_name_pairs
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self.name = name
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def forward(self, predicts, batch):
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loss_dict = dict()
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for idx, pair in enumerate(self.model_name_pairs):
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stu_outs = predicts[pair[0]]
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tch_outs = predicts[pair[1]]
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if self.key is not None:
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stu_preds = stu_outs[self.key]
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tch_preds = tch_outs[self.key]
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stu_shrink_maps = stu_preds[:, 0, :, :]
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stu_binary_maps = stu_preds[:, 2, :, :]
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# dilation to teacher prediction
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dilation_w = np.array([[1, 1], [1, 1]])
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th_shrink_maps = tch_preds[:, 0, :, :]
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th_shrink_maps = th_shrink_maps.numpy() > 0.3 # thresh = 0.3
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dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
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for i in range(th_shrink_maps.shape[0]):
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dilate_maps[i] = cv2.dilate(
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th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
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th_shrink_maps = paddle.to_tensor(dilate_maps)
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label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
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1:]
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# calculate the shrink map loss
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bce_loss = self.alpha * self.bce_loss(
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stu_shrink_maps, th_shrink_maps, label_shrink_mask)
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loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
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label_shrink_mask)
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# k = f"{self.name}_{pair[0]}_{pair[1]}"
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k = "{}_{}_{}".format(self.name, pair[0], pair[1])
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loss_dict[k] = bce_loss + loss_binary_maps
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loss_dict = _sum_loss(loss_dict)
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return loss
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class DistillationDistanceLoss(DistanceLoss):
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"""
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"""
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@ -79,7 +79,7 @@ class BaseModel(nn.Layer):
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x = self.neck(x)
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y["neck_out"] = x
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x = self.head(x, targets=data)
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if type(x) is dict:
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if isinstance(x, dict):
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y.update(x)
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else:
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y["head_out"] = x
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